From 789ee59de81b7ce0980757215418758834fbb274 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-student-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Fri, 27 Oct 2023 22:22:46 +0000
Subject: [PATCH 1/9] test commit

---
 data_upload.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/data_upload.py b/data_upload.py
index 59a1a91..f1fb440 100644
--- a/data_upload.py
+++ b/data_upload.py
@@ -12,7 +12,7 @@
                 username="abeburton@me.com",
                 password="js9@5x#H9@wp2#Y")
 # results is a generator
-results = client.get_all("m6dm-c72p")
+results = client.get_all("m6dm-c72p", year=)
 # select all indices of the generator to make a list of dicts
 result_list = list(itertools.islice(results, 299602996))
 

From 7c0b35a9234377912a2d8af4aad2cee515100154 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Fri, 10 Nov 2023 16:38:35 +0000
Subject: [PATCH 2/9] eda first draft

---
 eda_2021.ipynb | 165 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 165 insertions(+)
 create mode 100644 eda_2021.ipynb

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
new file mode 100644
index 0000000..8bfd069
--- /dev/null
+++ b/eda_2021.ipynb
@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import SparkSession\n",
+    "from pyspark.sql import functions as F\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "spark = SparkSession.builder.appName('2021EDA').getOrCreate()\n",
+    "\n",
+    "#change configuration settings on Spark \n",
+    "conf = spark.sparkContext._conf.setAll([('spark.executor.memory', '4g'), ('spark.app.name', 'Spark Updated Conf'), ('spark.executor.cores', '4'), ('spark.cores.max', '4'), ('spark.driver.memory','4g')])\n",
+    "\n",
+    "#print spark configuration settings\n",
+    "spark.sparkContext.getConf().getAll()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2021\", inferSchema=True, header=True)\n",
+    "# figure out how to read in shp file msca-bdp-student-gcs/bdp-rideshare-project/neighborhoods/shp files\n",
+    "df_weather = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2020-01-01 to 2022-08-31.csv\", inferSchema=True, header=True)\n",
+    "df_2021.printSchema()\n",
+    "df_weather.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8138c57a-26d6-44c4-b765-c7b137277044",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#display number of records by partition\n",
+    "def displaypartitions(df):\n",
+    "    #number of records by partition\n",
+    "    num = df.rdd.getNumPartitions()\n",
+    "    print(\"Partitions:\", num)\n",
+    "    df.withColumn(\"partitionId\", F.spark_partition_id())\\\n",
+    "        .groupBy(\"partitionId\")\\\n",
+    "        .count()\\\n",
+    "        .orderBy(F.asc(\"count\"))\\\n",
+    "        .show(num)\n",
+    "\n",
+    "df_2021.rdd.getNumPartitions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e70c86dd-041c-4967-b726-c058e32a76b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "displaypartitions(df_2021)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe004162-5b22-4a11-9fad-665fa5cdecc0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2021 = df_2021.repartition(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f34f9ec5-1a72-42ed-8bbe-3b54683a8bf4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "displaypartitions(df_2021)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "22a6039e-9848-4717-98b6-bc915540357b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2021.describe().show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c78e4618-8383-4df2-862b-4cb9dbeb20ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Find the number of missing values for each column\n",
+    "from pyspark.sql.functions import isnan, when, count, col\n",
+    "df_2021.select([count(when(df_2021[c].isNull(), c)).alias(c) for c in df_2021.columns]).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2dd6ea75-5417-4d27-92bb-4d9a24808545",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# number of observations with all the data in each column\n",
+    "df_2021.dropna().count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Working with just data that contains full information and check for dupes\n",
+    "df_2021 = df_2021.dropna()\n",
+    "df_2021 = df_2021.dropDuplicates()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PySpark",
+   "language": "python",
+   "name": "pyspark"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 3140a1b5412405084bb63379fc7423483a385a63 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Fri, 10 Nov 2023 17:56:48 +0000
Subject: [PATCH 3/9] eda update 2

---
 eda_2021.ipynb | 724 +++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 703 insertions(+), 21 deletions(-)

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 8bfd069..9c9fdf0 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
    "metadata": {},
    "outputs": [],
@@ -16,10 +16,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('spark.stage.maxConsecutiveAttempts', '10'),\n",
+       " ('spark.dynamicAllocation.minExecutors', '1'),\n",
+       " ('spark.eventLog.enabled', 'true'),\n",
+       " ('spark.submit.pyFiles',\n",
+       "  '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n",
+       " ('spark.kryoserializer.buffer.max', '2000M'),\n",
+       " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n",
+       " ('spark.dataproc.sql.local.rank.pushdown.enabled', 'true'),\n",
+       " ('spark.driver.maxResultSize', '0'),\n",
+       " ('spark.yarn.unmanagedAM.enabled', 'true'),\n",
+       " ('spark.sql.autoBroadcastJoinThreshold', '43m'),\n",
+       " ('spark.ui.filters',\n",
+       "  'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1699633504496_0001'),\n",
+       " ('spark.metrics.namespace',\n",
+       "  'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n",
+       " ('spark.executor.memory', '4g'),\n",
+       " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n",
+       " ('spark.hadoop.hive.execution.engine', 'mr'),\n",
+       " ('spark.executor.id', 'driver'),\n",
+       " ('spark.app.startTime', '1699634010505'),\n",
+       " ('spark.driver.port', '35733'),\n",
+       " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n",
+       " ('spark.dynamicAllocation.maxExecutors', '10000'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m'),\n",
+       " ('spark.yarn.dist.pyFiles',\n",
+       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.dataproc.metrics.listener.metrics.collector.hostname',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m'),\n",
+       " ('spark.yarn.am.attemptFailuresValidityInterval', '1h'),\n",
+       " ('spark.app.name', 'Spark Updated Conf'),\n",
+       " ('spark.sql.catalogImplementation', 'hive'),\n",
+       " ('spark.executorEnv.OPENBLAS_NUM_THREADS', '1'),\n",
+       " ('spark.yarn.secondary.jars',\n",
+       "  'com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,com.typesafe_config-1.4.2.jar,org.rocksdb_rocksdbjni-6.29.5.jar,com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,com.github.universal-automata_liblevenshtein-3.0.0.jar,com.google.cloud_google-cloud-storage-2.16.0.jar,com.navigamez_greex-1.0.jar,com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,it.unimi.dsi_fastutil-7.0.12.jar,org.projectlombok_lombok-1.16.8.jar,com.google.guava_guava-31.1-jre.jar,com.google.guava_failureaccess-1.0.1.jar,com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,com.google.errorprone_error_prone_annotations-2.16.jar,com.google.j2objc_j2objc-annotations-1.3.jar,com.google.http-client_google-http-client-1.42.3.jar,io.opencensus_opencensus-contrib-http-util-0.31.1.jar,com.google.http-client_google-http-client-jackson2-1.42.3.jar,com.google.http-client_google-http-client-gson-1.42.3.jar,com.google.api-client_google-api-client-2.1.1.jar,commons-codec_commons-codec-1.15.jar,com.google.oauth-client_google-oauth-client-1.34.1.jar,com.google.http-client_google-http-client-apache-v2-1.42.3.jar,com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,com.google.code.gson_gson-2.10.jar,com.google.cloud_google-cloud-core-2.9.0.jar,com.google.auto.value_auto-value-annotations-1.10.1.jar,com.google.cloud_google-cloud-core-http-2.9.0.jar,com.google.http-client_google-http-client-appengine-1.42.3.jar,com.google.api_gax-httpjson-0.105.1.jar,com.google.cloud_google-cloud-core-grpc-2.9.0.jar,io.grpc_grpc-core-1.51.0.jar,com.google.api_gax-2.20.1.jar,com.google.api_gax-grpc-2.20.1.jar,io.grpc_grpc-alts-1.51.0.jar,io.grpc_grpc-grpclb-1.51.0.jar,org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,io.grpc_grpc-protobuf-1.51.0.jar,com.google.auth_google-auth-library-credentials-1.13.0.jar,com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,com.google.api_api-common-2.2.2.jar,javax.annotation_javax.annotation-api-1.3.2.jar,io.opencensus_opencensus-api-0.31.1.jar,io.grpc_grpc-context-1.51.0.jar,com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,com.google.protobuf_protobuf-java-3.21.10.jar,com.google.protobuf_protobuf-java-util-3.21.10.jar,com.google.api.grpc_proto-google-common-protos-2.11.0.jar,org.threeten_threetenbp-1.6.4.jar,com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,com.fasterxml.jackson.core_jackson-core-2.14.1.jar,com.google.code.findbugs_jsr305-3.0.2.jar,io.grpc_grpc-api-1.51.0.jar,io.grpc_grpc-auth-1.51.0.jar,io.grpc_grpc-stub-1.51.0.jar,org.checkerframework_checker-qual-3.28.0.jar,com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,io.grpc_grpc-protobuf-lite-1.51.0.jar,com.google.android_annotations-4.1.1.4.jar,org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,io.grpc_grpc-netty-shaded-1.51.0.jar,io.perfmark_perfmark-api-0.26.0.jar,io.grpc_grpc-googleapis-1.51.0.jar,io.grpc_grpc-xds-1.51.0.jar,io.opencensus_opencensus-proto-0.2.0.jar,io.grpc_grpc-services-1.51.0.jar,com.google.re2j_re2j-1.6.jar,dk.brics.automaton_automaton-1.11-8.jar,org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.repl.local.jars',\n",
+       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.driver.appUIAddress',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:37817'),\n",
+       " ('spark.driver.host',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal'),\n",
+       " ('spark.sql.cbo.enabled', 'true'),\n",
+       " ('spark.yarn.historyServer.address',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m:18080'),\n",
+       " ('spark.executorEnv.PYTHONPATH',\n",
+       "  '/usr/lib/spark/python/lib/py4j-0.10.9-src.zip:/usr/lib/spark/python/:<CPS>{{PWD}}/pyspark.zip<CPS>{{PWD}}/py4j-0.10.9-src.zip<CPS>{{PWD}}/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar<CPS>{{PWD}}/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar<CPS>{{PWD}}/com.typesafe_config-1.4.2.jar<CPS>{{PWD}}/org.rocksdb_rocksdbjni-6.29.5.jar<CPS>{{PWD}}/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar<CPS>{{PWD}}/com.github.universal-automata_liblevenshtein-3.0.0.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-storage-2.16.0.jar<CPS>{{PWD}}/com.navigamez_greex-1.0.jar<CPS>{{PWD}}/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar<CPS>{{PWD}}/it.unimi.dsi_fastutil-7.0.12.jar<CPS>{{PWD}}/org.projectlombok_lombok-1.16.8.jar<CPS>{{PWD}}/com.google.guava_guava-31.1-jre.jar<CPS>{{PWD}}/com.google.guava_failureaccess-1.0.1.jar<CPS>{{PWD}}/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar<CPS>{{PWD}}/com.google.errorprone_error_prone_annotations-2.16.jar<CPS>{{PWD}}/com.google.j2objc_j2objc-annotations-1.3.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-1.42.3.jar<CPS>{{PWD}}/io.opencensus_opencensus-contrib-http-util-0.31.1.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-jackson2-1.42.3.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-gson-1.42.3.jar<CPS>{{PWD}}/com.google.api-client_google-api-client-2.1.1.jar<CPS>{{PWD}}/commons-codec_commons-codec-1.15.jar<CPS>{{PWD}}/com.google.oauth-client_google-oauth-client-1.34.1.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-apache-v2-1.42.3.jar<CPS>{{PWD}}/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar<CPS>{{PWD}}/com.google.code.gson_gson-2.10.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-2.9.0.jar<CPS>{{PWD}}/com.google.auto.value_auto-value-annotations-1.10.1.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-http-2.9.0.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-appengine-1.42.3.jar<CPS>{{PWD}}/com.google.api_gax-httpjson-0.105.1.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-grpc-2.9.0.jar<CPS>{{PWD}}/io.grpc_grpc-core-1.51.0.jar<CPS>{{PWD}}/com.google.api_gax-2.20.1.jar<CPS>{{PWD}}/com.google.api_gax-grpc-2.20.1.jar<CPS>{{PWD}}/io.grpc_grpc-alts-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-grpclb-1.51.0.jar<CPS>{{PWD}}/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar<CPS>{{PWD}}/io.grpc_grpc-protobuf-1.51.0.jar<CPS>{{PWD}}/com.google.auth_google-auth-library-credentials-1.13.0.jar<CPS>{{PWD}}/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar<CPS>{{PWD}}/com.google.api_api-common-2.2.2.jar<CPS>{{PWD}}/javax.annotation_javax.annotation-api-1.3.2.jar<CPS>{{PWD}}/io.opencensus_opencensus-api-0.31.1.jar<CPS>{{PWD}}/io.grpc_grpc-context-1.51.0.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar<CPS>{{PWD}}/com.google.protobuf_protobuf-java-3.21.10.jar<CPS>{{PWD}}/com.google.protobuf_protobuf-java-util-3.21.10.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-common-protos-2.11.0.jar<CPS>{{PWD}}/org.threeten_threetenbp-1.6.4.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.fasterxml.jackson.core_jackson-core-2.14.1.jar<CPS>{{PWD}}/com.google.code.findbugs_jsr305-3.0.2.jar<CPS>{{PWD}}/io.grpc_grpc-api-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-auth-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-stub-1.51.0.jar<CPS>{{PWD}}/org.checkerframework_checker-qual-3.28.0.jar<CPS>{{PWD}}/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar<CPS>{{PWD}}/io.grpc_grpc-protobuf-lite-1.51.0.jar<CPS>{{PWD}}/com.google.android_annotations-4.1.1.4.jar<CPS>{{PWD}}/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar<CPS>{{PWD}}/io.grpc_grpc-netty-shaded-1.51.0.jar<CPS>{{PWD}}/io.perfmark_perfmark-api-0.26.0.jar<CPS>{{PWD}}/io.grpc_grpc-googleapis-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-xds-1.51.0.jar<CPS>{{PWD}}/io.opencensus_opencensus-proto-0.2.0.jar<CPS>{{PWD}}/io.grpc_grpc-services-1.51.0.jar<CPS>{{PWD}}/com.google.re2j_re2j-1.6.jar<CPS>{{PWD}}/dk.brics.automaton_automaton-1.11-8.jar<CPS>{{PWD}}/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.yarn.dist.jars',\n",
+       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.dataproc.sql.parquet.enableFooterCache', 'true'),\n",
+       " ('spark.driver.memory', '4g'),\n",
+       " ('spark.sql.warehouse.dir', 'file:/spark-warehouse'),\n",
+       " ('spark.yarn.executor.failuresValidityInterval', '1h'),\n",
+       " ('spark.yarn.am.memory', '640m'),\n",
+       " ('spark.cores.max', '4'),\n",
+       " ('spark.executor.cores', '4'),\n",
+       " ('spark.jars.packages',\n",
+       "  'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n",
+       " ('spark.executor.instances', '2'),\n",
+       " ('spark.dataproc.listeners',\n",
+       "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
+       " ('spark.serializer.objectStreamReset', '100'),\n",
+       " ('spark.submit.deployMode', 'client'),\n",
+       " ('spark.ui.proxyBase', '/proxy/application_1699633504496_0001'),\n",
+       " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
+       " ('spark.shuffle.service.enabled', 'true'),\n",
+       " ('spark.scheduler.mode', 'FAIR'),\n",
+       " ('spark.history.fs.logDirectory',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/64cc95a2-795c-4a54-be08-11a36626a92f/spark-job-history'),\n",
+       " ('spark.sql.adaptive.enabled', 'true'),\n",
+       " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
+       " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n",
+       " ('spark.app.id', 'application_1699633504496_0001'),\n",
+       " ('spark.master', 'yarn'),\n",
+       " ('spark.ui.port', '0'),\n",
+       " ('spark.rpc.message.maxSize', '512'),\n",
+       " ('spark.rdd.compress', 'True'),\n",
+       " ('spark.task.maxFailures', '10'),\n",
+       " ('spark.yarn.isPython', 'true'),\n",
+       " ('spark.dynamicAllocation.enabled', 'true'),\n",
+       " ('spark.eventLog.dir',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/64cc95a2-795c-4a54-be08-11a36626a92f/spark-job-history'),\n",
+       " ('spark.ui.showConsoleProgress', 'true')]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "spark = SparkSession.builder.appName('2021EDA').getOrCreate()\n",
     "\n",
@@ -32,10 +127,89 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 180:>                                                        (0 + 1) / 1]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- Trip ID: string (nullable = true)\n",
+      " |-- Trip Start Timestamp: string (nullable = true)\n",
+      " |-- Trip End Timestamp: string (nullable = true)\n",
+      " |-- Trip Seconds: integer (nullable = true)\n",
+      " |-- Trip Miles: double (nullable = true)\n",
+      " |-- Pickup Census Tract: long (nullable = true)\n",
+      " |-- Dropoff Census Tract: long (nullable = true)\n",
+      " |-- Pickup Community Area: integer (nullable = true)\n",
+      " |-- Dropoff Community Area: integer (nullable = true)\n",
+      " |-- Fare: double (nullable = true)\n",
+      " |-- Tip: integer (nullable = true)\n",
+      " |-- Additional Charges: string (nullable = true)\n",
+      " |-- Trip Total: double (nullable = true)\n",
+      " |-- Shared Trip Authorized: boolean (nullable = true)\n",
+      " |-- Trips Pooled: integer (nullable = true)\n",
+      " |-- Pickup Centroid Latitude: double (nullable = true)\n",
+      " |-- Pickup Centroid Longitude: double (nullable = true)\n",
+      " |-- Pickup Centroid Location: string (nullable = true)\n",
+      " |-- Dropoff Centroid Latitude: double (nullable = true)\n",
+      " |-- Dropoff Centroid Longitude: double (nullable = true)\n",
+      " |-- Dropoff Centroid Location: string (nullable = true)\n",
+      "\n",
+      "root\n",
+      " |-- name: string (nullable = true)\n",
+      " |-- datetime: string (nullable = true)\n",
+      " |-- tempmax: double (nullable = true)\n",
+      " |-- tempmin: double (nullable = true)\n",
+      " |-- temp: double (nullable = true)\n",
+      " |-- feelslikemax: double (nullable = true)\n",
+      " |-- feelslikemin: double (nullable = true)\n",
+      " |-- feelslike: double (nullable = true)\n",
+      " |-- dew: double (nullable = true)\n",
+      " |-- humidity: double (nullable = true)\n",
+      " |-- precip: double (nullable = true)\n",
+      " |-- precipprob: integer (nullable = true)\n",
+      " |-- precipcover: double (nullable = true)\n",
+      " |-- preciptype: string (nullable = true)\n",
+      " |-- snow: double (nullable = true)\n",
+      " |-- snowdepth: double (nullable = true)\n",
+      " |-- windgust: double (nullable = true)\n",
+      " |-- windspeed: double (nullable = true)\n",
+      " |-- winddir: double (nullable = true)\n",
+      " |-- sealevelpressure: double (nullable = true)\n",
+      " |-- cloudcover: double (nullable = true)\n",
+      " |-- visibility: double (nullable = true)\n",
+      " |-- solarradiation: double (nullable = true)\n",
+      " |-- solarenergy: double (nullable = true)\n",
+      " |-- uvindex: integer (nullable = true)\n",
+      " |-- severerisk: integer (nullable = true)\n",
+      " |-- sunrise: string (nullable = true)\n",
+      " |-- sunset: string (nullable = true)\n",
+      " |-- moonphase: double (nullable = true)\n",
+      " |-- conditions: string (nullable = true)\n",
+      " |-- description: string (nullable = true)\n",
+      " |-- icon: string (nullable = true)\n",
+      " |-- stations: string (nullable = true)\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2021\", inferSchema=True, header=True)\n",
     "# figure out how to read in shp file msca-bdp-student-gcs/bdp-rideshare-project/neighborhoods/shp files\n",
@@ -46,10 +220,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "8138c57a-26d6-44c4-b765-c7b137277044",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#display number of records by partition\n",
     "def displaypartitions(df):\n",
@@ -67,17 +252,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "e70c86dd-041c-4967-b726-c058e32a76b7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Partitions: 6\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 4:=================================================>         (5 + 1) / 6]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----------+------+\n",
+      "|partitionId| count|\n",
+      "+-----------+------+\n",
+      "|          5| 69847|\n",
+      "|          4|523726|\n",
+      "|          3|527064|\n",
+      "|          1|527581|\n",
+      "|          2|528719|\n",
+      "|          0|531719|\n",
+      "+-----------+------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "displaypartitions(df_2021)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "fe004162-5b22-4a11-9fad-665fa5cdecc0",
    "metadata": {},
    "outputs": [],
@@ -87,30 +311,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "f34f9ec5-1a72-42ed-8bbe-3b54683a8bf4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 7:=================================================>         (5 + 1) / 6]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Partitions: 10\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 13:=====================================================>(199 + 1) / 200]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----------+------+\n",
+      "|partitionId| count|\n",
+      "+-----------+------+\n",
+      "|          5|270864|\n",
+      "|          0|270865|\n",
+      "|          6|270865|\n",
+      "|          7|270865|\n",
+      "|          1|270866|\n",
+      "|          4|270866|\n",
+      "|          8|270866|\n",
+      "|          9|270866|\n",
+      "|          2|270866|\n",
+      "|          3|270867|\n",
+      "+-----------+------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "displaypartitions(df_2021)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "22a6039e-9848-4717-98b6-bc915540357b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 16:===================================================>     (9 + 1) / 10]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------+----------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+------------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|summary|         Trip ID|Trip Start Timestamp|  Trip End Timestamp|     Trip Seconds|       Trip Miles| Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|              Fare|               Tip|Additional Charges|        Trip Total|        Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+----------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+------------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|  count|         2708656|             2708652|             2708652|          2708652|          2708649|              983470|              975128|              2479944|               2447038|           2708100|           2708100|           2708099|           2708098|             2708651|                 2482320|                  2482320|                 2482320|                  2449465|                   2449465|                  2449465|\n",
+      "|   mean|            null|                null|                null|942.2844891111889|6.688875228942609|1.703138547767500...|1.703138094929255...|    29.80978683389625|     29.28494816999164|13.057426793692995|0.4912218898858979|3.3111284709689235|16.859785314286004|  1.0000036918746638|       41.87740670463228|       -87.67185372619028|                    null|        41.87714315855053|         -87.6694694706963|                     null|\n",
+      "| stddev|            null|                null|                null|630.3003334457269|7.608257555036324|  339740.56288590573|  335430.99940793717|   22.278028203042062|     21.90521219841838| 9.839634058047901|1.6231638297086177| 2.236087560001195|11.125341127829076|0.001921421972618...|     0.07804151182131637|      0.06527591183128632|                    null|      0.07698901524173858|       0.06276134276291706|                     null|\n",
+      "|    min|  \"error\" : true|01/01/2021 01:00:...|01/01/2021 01:00:...|                5|              0.0|         17031010100|         17031010100|                    1|                     1|               0.0|                 0|                 0|               0.0|                   1|           41.6502216756|            -87.913624596|    POINT (-87.530712...|            41.6502216756|             -87.913624596|     POINT (-87.529950...|\n",
+      "|    max|               }|01/26/2021 12:45:...|01/26/2021 12:45:...|            64707|            661.2|         17031980100|         17031980100|                   77|                    77|             840.0|               100|                 {|            845.85|                   2|           42.0212235931|           -87.5307124836|    POINT (-87.913624...|            42.0212235931|             -87.529950466|     POINT (-87.913624...|\n",
+      "+-------+----------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+------------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "df_2021.describe().show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "c78e4618-8383-4df2-862b-4cb9dbeb20ab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 22:==================================>                      (6 + 4) / 10]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+---+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|Trip ID|Trip Start Timestamp|Trip End Timestamp|Trip Seconds|Trip Miles|Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|Fare|Tip|Additional Charges|Trip Total|Shared Trip Authorized|Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+---+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|      0|                   4|                 4|           4|         7|            1725186|             1733528|               228712|                261618| 556|556|               557|       558|                     5|           5|                  226336|                   226336|                  226336|                   259191|                    259191|                   259191|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+---+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "#Find the number of missing values for each column\n",
     "from pyspark.sql.functions import isnan, when, count, col\n",
@@ -119,26 +451,376 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "2dd6ea75-5417-4d27-92bb-4d9a24808545",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "921081"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# number of observations with all the data in each column\n",
-    "df_2021.dropna().count()"
+    "df_2021.dropna(how='any').count()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Working with just data that contains full information and check for dupes\n",
-    "df_2021 = df_2021.dropna()\n",
+    "df_2021 = df_2021.dropna(how='any')\n",
     "df_2021 = df_2021.dropDuplicates()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6a52d857-bc07-47a7-8cc9-71478bf10e08",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop columns unlikely to be useful for analysis for speed of computation and rename columns to remove spacing for ease of code writing\n",
+    "spark.conf.set(\"spark.sql.legacy.timeParserPolicy\", \"LEGACY\")\n",
+    "\n",
+    "df_2021 = df_2021.drop('Trip Seconds','Trips Pooled','Additional Charges','Shared Trip Authorized')\n",
+    "df_2021 = df_2021.withColumnRenamed(\"Trip ID\",\"ID\").withColumnRenamed(\"Trip Start Timestamp\",\"start_timestamp\").withColumnRenamed(\"Trip End Timestamp\",\"end_timestamp\").withColumnRenamed(\"Trip Miles\",\\\n",
+    "                \"miles\").withColumnRenamed(\"Pickup Census Tract\",\"pickup_tract\").withColumnRenamed(\"Dropoff Census Tract\",\"dropoff_tract\").withColumnRenamed(\"Pickup Community Area\",\"pickup_area\"\\\n",
+    "                ).withColumnRenamed(\"Dropoff Community Area\",\"dropoff_area\").withColumnRenamed(\"Trip Total\",\"total\").withColumnRenamed(\"Pickup Centroid Latitude\",\"pickup_lat\").withColumnRenamed(\\\n",
+    "                \"Pickup Centroid Longitude\",\"pickup_lon\").withColumnRenamed(\"Pickup Centroid Location\",\"pickup_location\").withColumnRenamed(\"Dropoff Centroid Latitude\",\"dropoff_lat\").withColumnRenamed(\\\n",
+    "                \"Dropoff Centroid Longitude\",\"dropoff_lon\").withColumnRenamed(\"Dropoff Centroid Location\",\"dropoff_location\")\n",
+    "# fix datatypes\n",
+    "df_2021 = df_2021.withColumn('start_timestamp', F.to_timestamp(df_2021['start_timestamp'], 'MM/DD/YYYY HH:mm:ss AM/PM')).withColumn('end_timestamp', F.to_timestamp(df_2021['end_timestamp'], 'MM/DD/YYYY HH:mm:ss AM/PM'))\n",
+    "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"MM/dd/yyyy\"))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "17ebe569-82bb-49b0-aaaf-ac5062bede35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2021 = df_2021.withColumn('month', F.month(df_2021.start_timestamp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "88ed9106-1b80-472e-8638-ece1d3d5d25e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 46:=============================================>           (8 + 2) / 10]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+\n",
+      "|                  ID|    start_timestamp|      end_timestamp|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|     pickup_location|  dropoff_lat|   dropoff_lon|    dropoff_location|\n",
+      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+\n",
+      "|960742b2998c7908e...|2021-01-04 01:45:00|2021-01-04 02:00:00|  5.9| 17031063000|  17031830600|          6|           1|15.0|  0|16.23|41.9363101308|-87.6515625922|POINT (-87.651562...|42.0016981937|-87.6735740325|POINT (-87.673574...|\n",
+      "|97d44c73c92c79cc4...|2021-01-04 04:30:00|2021-01-04 05:00:00|  7.3| 17031081600|  17031835900|          8|          38|22.5|  0|25.48|41.8920726347|-87.6288741572|POINT (-87.628874...|41.8201666062| -87.621499206|POINT (-87.621499...|\n",
+      "|d460b58d94cbf8f53...|2021-01-04 01:00:00|2021-01-04 01:15:00|  7.6| 17031980000|  17031110200|         76|          11|17.5|  0|23.73|41.9790708201|-87.9030396611|POINT (-87.903039...|41.9800778511|-87.7734709274|POINT (-87.773470...|\n",
+      "|ac48c66511eecd2b1...|2021-01-04 04:45:00|2021-01-04 05:00:00|  6.3| 17031330100|  17031070700|         33|           7|20.0|  0|24.85| 41.859349715|-87.6173580061|POINT (-87.617358...|41.9292725315|-87.6738072384|POINT (-87.673807...|\n",
+      "|21c328431f2e2928c...|2021-01-01 10:15:00|2021-01-01 10:30:00|  1.3| 17031081700|  17031081401|          8|           8|10.0|  1| 14.1|41.8920421365|-87.6318639497|POINT (-87.631863...|41.8950334495|-87.6197106717|POINT (-87.619710...|\n",
+      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "df_2021.show(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "be4d107a-96c5-4a2b-96eb-4e408f6a8f42",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------+----------+-------+-------+----+------------+------------+---------+----+--------+------+----------+-----------+----------+----+---------+--------+---------+-------+----------------+----------+----------+--------------+-----------+-------+----------+-------------------+-------------------+---------+--------------------+--------------------+-----------------+--------------------+\n",
+      "|   name|  datetime|tempmax|tempmin|temp|feelslikemax|feelslikemin|feelslike| dew|humidity|precip|precipprob|precipcover|preciptype|snow|snowdepth|windgust|windspeed|winddir|sealevelpressure|cloudcover|visibility|solarradiation|solarenergy|uvindex|severerisk|            sunrise|             sunset|moonphase|          conditions|         description|             icon|            stations|\n",
+      "+-------+----------+-------+-------+----+------------+------------+---------+----+--------+------+----------+-----------+----------+----+---------+--------+---------+-------+----------------+----------+----------+--------------+-----------+-------+----------+-------------------+-------------------+---------+--------------------+--------------------+-----------------+--------------------+\n",
+      "|chicago|2020-01-01|   43.0|   22.2|32.9|        35.2|        12.8|     24.5|24.1|    71.1|   0.0|         0|        0.0|      null| 0.0|      0.4|    31.8|     19.6|  207.9|          1007.3|      32.0|       9.9|          98.7|        8.5|      4|      null|2020-01-01T07:18:18|2020-01-01T16:29:47|     0.21|    Partially cloudy|Partly cloudy thr...|partly-cloudy-day|72534014819,KORD,...|\n",
+      "|chicago|2020-01-02|   48.0|   37.3|42.8|        43.2|        29.0|     36.3|32.9|    68.0|   0.0|         0|        0.0|      null| 0.0|      0.1|    30.5|     18.4|  214.1|          1002.4|      43.2|       9.9|          85.5|        7.4|      4|      null|2020-01-02T07:18:24|2020-01-02T16:30:39|     0.25|    Partially cloudy|Partly cloudy thr...|partly-cloudy-day|72534014819,KORD,...|\n",
+      "|chicago|2020-01-03|   41.5|   34.5|37.3|        38.1|        28.4|     32.7|30.4|    76.0|   0.0|         0|        0.0|      null| 0.0|      0.0|    null|      8.7|  335.7|          1009.8|      91.4|       9.7|          27.3|        2.4|      1|      null|2020-01-03T07:18:26|2020-01-03T16:31:33|     0.27|            Overcast|Cloudy skies thro...|           cloudy|72534014819,KORD,...|\n",
+      "|chicago|2020-01-04|   34.5|   28.0|31.6|        28.3|        18.6|     23.5|24.0|    73.4| 0.015|       100|       12.5| rain,snow| 0.1|      0.0|    20.8|     13.8|  313.1|          1016.5|      89.8|       8.4|          20.5|        1.8|      2|      null|2020-01-04T07:18:27|2020-01-04T16:32:28|     0.31|Snow, Rain, Parti...|Partly cloudy thr...|             snow|72534014819,KORD,...|\n",
+      "|chicago|2020-01-05|   42.9|   25.0|33.5|        35.5|        13.9|     24.1|25.3|    72.4|   0.0|         0|        0.0|      null| 0.0|      0.0|    36.9|     21.2|  238.8|          1016.2|      73.0|       9.8|          62.1|        5.5|      4|      null|2020-01-05T07:18:25|2020-01-05T16:33:24|     0.34|    Partially cloudy|Partly cloudy thr...|partly-cloudy-day|72534014819,KORD,...|\n",
+      "+-------+----------+-------+-------+----+------------+------------+---------+----+--------+------+----------+-----------+----------+----+---------+--------+---------+-------+----------------+----------+----------+--------------+-----------+-------+----------+-------------------+-------------------+---------+--------------------+--------------------+-----------------+--------------------+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"yyyy-mm-dd\"))\n",
+    "df_weather.show(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "2ae6d6b2-9001-4ded-ba3a-225861cbe451",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hp_census_tracts_2010_2020 = [17031411000,17031410900,17031410100,17031411100,17031410800,17031410200,17031410700,17031411200,17031836200,17031410600,17031836300,17031410500,\n",
+    "                    17031410300,17031410400,17031410600,17031410800,17031411300,17031411400]\n",
+    "df_hp = df_2021.filter((df_2021.pickup_tract.isin(hp_census_tracts_2010_2020)) & (df_2021.dropoff_tract.isin(hp_census_tracts_2010_2020)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "67a9c9c1-dd4e-41b6-9d29-b475a1189268",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 61:================================================>         (5 + 1) / 6]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "|                  ID|    start_timestamp|      end_timestamp|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|     pickup_location|  dropoff_lat|   dropoff_lon|    dropoff_location|month|\n",
+      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "|edb44ac5f0ca61fcd...|2021-01-17 04:15:00|2021-01-17 04:15:00|  1.4| 17031410800|  17031836200|         41|          41| 5.0|  0|  8.1|41.7979652088|-87.5896070309|POINT (-87.589607...|41.7904693995|-87.6012851221|POINT (-87.601285...|    1|\n",
+      "|a6ff5154d2a4e8b45...|2021-01-22 03:00:00|2021-01-22 03:00:00|  0.9| 17031836200|  17031410600|         41|          41| 5.0|  0|  8.1|41.7904693995|-87.6012851221|POINT (-87.601285...|41.7979711911|-87.5989445134|POINT (-87.598944...|    1|\n",
+      "|b0a0ccd90ca248d40...|2021-01-21 02:45:00|2021-01-21 03:00:00|  1.5| 17031836200|  17031410100|         41|          41| 7.5|  0| 10.6|41.7904693995|-87.6012851221|POINT (-87.601285...|41.8012268363|-87.5853031602|POINT (-87.585303...|    1|\n",
+      "|75d9b7289cd03afa9...|2021-01-04 01:30:00|2021-01-04 01:30:00|  0.9| 17031410700|  17031836200|         41|          41| 2.5|  0| 6.38|41.7980417164|-87.5941966274|POINT (-87.594196...|41.7904693995|-87.6012851221|POINT (-87.601285...|    1|\n",
+      "|24aa15d3c6be5ad06...|2021-01-22 07:15:00|2021-01-22 07:15:00|  0.9| 17031836200|  17031411000|         41|          41| 7.5|  1| 11.6|41.7904693995|-87.6012851221|POINT (-87.601285...|41.7905062613|-87.5831437169|POINT (-87.583143...|    1|\n",
+      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "only showing top 5 rows\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "df_hp.show(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "2c54ef92-e61e-4827-ad6c-bb8b9405e701",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>month</th>\n",
+       "      <th>count(ID)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1825</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   month  count(ID)\n",
+       "0      1       1825"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hp.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "b1ed924a-e358-4ead-852e-6026d23fb8ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "921081"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_2021.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "024ff08a-42d2-4a8f-8189-c35d6af0186a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1825"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hp.count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "2fda94cd-990c-436b-93b0-979f7e3c8ad3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+\n",
+      "|month|\n",
+      "+-----+\n",
+      "|    1|\n",
+      "+-----+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+\n",
+      "|month|\n",
+      "+-----+\n",
+      "|    1|\n",
+      "+-----+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_2021.select('month').distinct().show()\n",
+    "df_hp.select('month').distinct().show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "765edf2e-10ad-4fda-870d-8e9a488cc7ff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

From 1d68da73b09bc73a170b71543871acdd6ec848a3 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Fri, 10 Nov 2023 23:32:06 +0000
Subject: [PATCH 4/9] eda post group meeting

---
 eda_2021.ipynb | 523 ++++++++++++++++++++++++++++---------------------
 1 file changed, 295 insertions(+), 228 deletions(-)

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 9c9fdf0..452e6af 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 57,
    "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 58,
    "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
    "metadata": {},
    "outputs": [
@@ -110,7 +110,7 @@
        " ('spark.ui.showConsoleProgress', 'true')]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 88,
    "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
    "metadata": {},
    "outputs": [
@@ -135,7 +135,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 180:>                                                        (0 + 1) / 1]\r"
+      "                                                                                \r"
      ]
     },
     {
@@ -154,7 +154,7 @@
       " |-- Dropoff Community Area: integer (nullable = true)\n",
       " |-- Fare: double (nullable = true)\n",
       " |-- Tip: integer (nullable = true)\n",
-      " |-- Additional Charges: string (nullable = true)\n",
+      " |-- Additional Charges: double (nullable = true)\n",
       " |-- Trip Total: double (nullable = true)\n",
       " |-- Shared Trip Authorized: boolean (nullable = true)\n",
       " |-- Trips Pooled: integer (nullable = true)\n",
@@ -201,17 +201,10 @@
       " |-- stations: string (nullable = true)\n",
       "\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                \r"
-     ]
     }
    ],
    "source": [
-    "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2021\", inferSchema=True, header=True)\n",
+    "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2022\", inferSchema=True, header=True)\n",
     "# figure out how to read in shp file msca-bdp-student-gcs/bdp-rideshare-project/neighborhoods/shp files\n",
     "df_weather = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2020-01-01 to 2022-08-31.csv\", inferSchema=True, header=True)\n",
     "df_2021.printSchema()\n",
@@ -220,17 +213,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 89,
    "id": "8138c57a-26d6-44c4-b765-c7b137277044",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "6"
+       "135"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -252,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 90,
    "id": "e70c86dd-041c-4967-b726-c058e32a76b7",
    "metadata": {},
    "outputs": [
@@ -260,14 +253,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Partitions: 6\n"
+      "Partitions: 135\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 4:=================================================>         (5 + 1) / 6]\r"
+      "[Stage 321:====================================================>(134 + 1) / 135]\r"
      ]
     },
     {
@@ -277,12 +270,141 @@
       "+-----------+------+\n",
       "|partitionId| count|\n",
       "+-----------+------+\n",
-      "|          5| 69847|\n",
-      "|          4|523726|\n",
-      "|          3|527064|\n",
-      "|          1|527581|\n",
-      "|          2|528719|\n",
-      "|          0|531719|\n",
+      "|        134|337327|\n",
+      "|         22|499083|\n",
+      "|        123|504285|\n",
+      "|        126|504809|\n",
+      "|        115|504841|\n",
+      "|         95|505099|\n",
+      "|         87|505483|\n",
+      "|         10|505553|\n",
+      "|        129|505717|\n",
+      "|        109|505821|\n",
+      "|         30|506415|\n",
+      "|         12|506815|\n",
+      "|        112|506840|\n",
+      "|        118|506913|\n",
+      "|          8|507499|\n",
+      "|         17|507526|\n",
+      "|         40|507585|\n",
+      "|         53|507693|\n",
+      "|         90|507836|\n",
+      "|         27|507856|\n",
+      "|         71|508094|\n",
+      "|        100|508435|\n",
+      "|        103|508829|\n",
+      "|         74|508860|\n",
+      "|         92|509111|\n",
+      "|        106|509156|\n",
+      "|         50|509316|\n",
+      "|         45|509490|\n",
+      "|         25|509572|\n",
+      "|         79|509585|\n",
+      "|         48|509604|\n",
+      "|         89|509853|\n",
+      "|         32|510003|\n",
+      "|         66|510055|\n",
+      "|         58|510094|\n",
+      "|         82|510167|\n",
+      "|         37|510177|\n",
+      "|         61|510194|\n",
+      "|          6|510259|\n",
+      "|         35|510618|\n",
+      "|         19|510748|\n",
+      "|          4|510798|\n",
+      "|         68|510849|\n",
+      "|         77|510867|\n",
+      "|         97|511038|\n",
+      "|         98|511105|\n",
+      "|         15|511323|\n",
+      "|          5|511378|\n",
+      "|         94|511724|\n",
+      "|         42|512086|\n",
+      "|          3|512151|\n",
+      "|         47|512355|\n",
+      "|         84|512447|\n",
+      "|         24|512577|\n",
+      "|        128|512747|\n",
+      "|         14|512800|\n",
+      "|         76|512944|\n",
+      "|        102|513121|\n",
+      "|         56|513164|\n",
+      "|         69|513214|\n",
+      "|         33|513364|\n",
+      "|         63|513382|\n",
+      "|        101|513611|\n",
+      "|        117|513692|\n",
+      "|         55|513736|\n",
+      "|        104|513776|\n",
+      "|          1|513780|\n",
+      "|         20|513856|\n",
+      "|         21|513928|\n",
+      "|         29|513958|\n",
+      "|        122|514102|\n",
+      "|         51|514119|\n",
+      "|         60|514176|\n",
+      "|         28|514184|\n",
+      "|        125|514312|\n",
+      "|         13|514332|\n",
+      "|        105|514426|\n",
+      "|         73|514504|\n",
+      "|         81|514680|\n",
+      "|         88|514692|\n",
+      "|        108|514720|\n",
+      "|         64|514837|\n",
+      "|        114|514879|\n",
+      "|         43|514882|\n",
+      "|         38|514932|\n",
+      "|         16|514954|\n",
+      "|         91|514997|\n",
+      "|         39|515034|\n",
+      "|        130|515082|\n",
+      "|          7|515187|\n",
+      "|         57|515299|\n",
+      "|        107|515369|\n",
+      "|         70|515379|\n",
+      "|         52|515427|\n",
+      "|          2|515744|\n",
+      "|         86|515803|\n",
+      "|         49|516126|\n",
+      "|        127|516242|\n",
+      "|         85|516262|\n",
+      "|         54|516281|\n",
+      "|        116|516302|\n",
+      "|        111|516334|\n",
+      "|         44|516357|\n",
+      "|         93|516403|\n",
+      "|         65|516579|\n",
+      "|        110|516601|\n",
+      "|         34|516674|\n",
+      "|         23|516689|\n",
+      "|         67|516905|\n",
+      "|        124|516979|\n",
+      "|        133|517062|\n",
+      "|         96|517079|\n",
+      "|         72|517573|\n",
+      "|         36|517673|\n",
+      "|         26|517765|\n",
+      "|         99|517784|\n",
+      "|         41|517946|\n",
+      "|          9|518089|\n",
+      "|        113|518207|\n",
+      "|         83|518265|\n",
+      "|          0|518352|\n",
+      "|         78|518388|\n",
+      "|         46|518399|\n",
+      "|        121|518588|\n",
+      "|         11|518723|\n",
+      "|         75|518753|\n",
+      "|         31|518838|\n",
+      "|         59|518855|\n",
+      "|         80|518857|\n",
+      "|         18|519062|\n",
+      "|        131|519379|\n",
+      "|         62|519942|\n",
+      "|        119|521049|\n",
+      "|        120|521694|\n",
+      "|        132|527685|\n",
       "+-----------+------+\n",
       "\n"
      ]
@@ -301,17 +423,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 67,
    "id": "fe004162-5b22-4a11-9fad-665fa5cdecc0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_2021 = df_2021.repartition(10)"
+    "# df_2021 = df_2021.repartition(10)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 68,
    "id": "f34f9ec5-1a72-42ed-8bbe-3b54683a8bf4",
    "metadata": {},
    "outputs": [
@@ -319,7 +441,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 7:=================================================>         (5 + 1) / 6]\r"
+      "[Stage 250:===============================================>         (5 + 1) / 6]\r"
      ]
     },
     {
@@ -333,7 +455,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 13:=====================================================>(199 + 1) / 200]\r"
+      "[Stage 256:=================================================>   (188 + 6) / 200]\r"
      ]
     },
     {
@@ -344,13 +466,13 @@
       "|partitionId| count|\n",
       "+-----------+------+\n",
       "|          5|270864|\n",
-      "|          0|270865|\n",
-      "|          6|270865|\n",
       "|          7|270865|\n",
+      "|          6|270865|\n",
+      "|          0|270865|\n",
+      "|          9|270866|\n",
       "|          1|270866|\n",
       "|          4|270866|\n",
       "|          8|270866|\n",
-      "|          9|270866|\n",
       "|          2|270866|\n",
       "|          3|270867|\n",
       "+-----------+------+\n",
@@ -412,7 +534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 91,
    "id": "c78e4618-8383-4df2-862b-4cb9dbeb20ab",
    "metadata": {},
    "outputs": [
@@ -420,18 +542,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 22:==================================>                      (6 + 4) / 10]\r"
+      "[Stage 324:===================================================> (132 + 3) / 135]\r"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+---+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
-      "|Trip ID|Trip Start Timestamp|Trip End Timestamp|Trip Seconds|Trip Miles|Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|Fare|Tip|Additional Charges|Trip Total|Shared Trip Authorized|Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
-      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+---+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
-      "|      0|                   4|                 4|           4|         7|            1725186|             1733528|               228712|                261618| 556|556|               557|       558|                     5|           5|                  226336|                   226336|                  226336|                   259191|                    259191|                   259191|\n",
-      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+---+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+------+------+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|Trip ID|Trip Start Timestamp|Trip End Timestamp|Trip Seconds|Trip Miles|Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|  Fare|   Tip|Additional Charges|Trip Total|Shared Trip Authorized|Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+------+------+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|      0|                   0|                 0|        2140|       525|           29231644|            29365970|              5801676|               6049435|113627|113627|            113627|    113627|                     0|           0|                 5605172|                  5605172|                 5605172|                  5872849|                   5872849|                  5872849|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+------+------+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
       "\n"
      ]
     },
@@ -480,19 +602,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 93,
    "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "# Working with just data that contains full information and check for dupes\n",
-    "df_2021 = df_2021.dropna(how='any')\n",
-    "df_2021 = df_2021.dropDuplicates()"
+    "df_2021 = df_2021.dropna(how='any', subset=['Trip Start Timestamp','Trip End Timestamp','Fare','Dropoff Community Area','Pickup Community Area'])\n",
+    "df_2021 = df_2021.dropDuplicates()\n",
+    "# df_2021.count()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 94,
    "id": "6a52d857-bc07-47a7-8cc9-71478bf10e08",
    "metadata": {},
    "outputs": [],
@@ -500,113 +631,47 @@
     "# Drop columns unlikely to be useful for analysis for speed of computation and rename columns to remove spacing for ease of code writing\n",
     "spark.conf.set(\"spark.sql.legacy.timeParserPolicy\", \"LEGACY\")\n",
     "\n",
-    "df_2021 = df_2021.drop('Trip Seconds','Trips Pooled','Additional Charges','Shared Trip Authorized')\n",
+    "df_2021 = df_2021.drop('Trips Pooled','Additional Charges','Shared Trip Authorized','Pickup Centroid Location','Dropoff Centroid Location')\n",
     "df_2021 = df_2021.withColumnRenamed(\"Trip ID\",\"ID\").withColumnRenamed(\"Trip Start Timestamp\",\"start_timestamp\").withColumnRenamed(\"Trip End Timestamp\",\"end_timestamp\").withColumnRenamed(\"Trip Miles\",\\\n",
     "                \"miles\").withColumnRenamed(\"Pickup Census Tract\",\"pickup_tract\").withColumnRenamed(\"Dropoff Census Tract\",\"dropoff_tract\").withColumnRenamed(\"Pickup Community Area\",\"pickup_area\"\\\n",
     "                ).withColumnRenamed(\"Dropoff Community Area\",\"dropoff_area\").withColumnRenamed(\"Trip Total\",\"total\").withColumnRenamed(\"Pickup Centroid Latitude\",\"pickup_lat\").withColumnRenamed(\\\n",
     "                \"Pickup Centroid Longitude\",\"pickup_lon\").withColumnRenamed(\"Pickup Centroid Location\",\"pickup_location\").withColumnRenamed(\"Dropoff Centroid Latitude\",\"dropoff_lat\").withColumnRenamed(\\\n",
     "                \"Dropoff Centroid Longitude\",\"dropoff_lon\").withColumnRenamed(\"Dropoff Centroid Location\",\"dropoff_location\")\n",
     "# fix datatypes\n",
-    "df_2021 = df_2021.withColumn('start_timestamp', F.to_timestamp(df_2021['start_timestamp'], 'MM/DD/YYYY HH:mm:ss AM/PM')).withColumn('end_timestamp', F.to_timestamp(df_2021['end_timestamp'], 'MM/DD/YYYY HH:mm:ss AM/PM'))\n",
-    "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"MM/dd/yyyy\"))\n"
+    "df_2021 = df_2021.withColumn('start_timestamp', F.to_timestamp(df_2021['start_timestamp'], 'MM/dd/yyyy hh:mm:ss a')).withColumn('end_timestamp', F.to_timestamp(df_2021['end_timestamp'], 'MM/dd/yyyy hh:mm:ss a'))\n",
+    "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"yyyy-MM-dd\"))\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 101,
    "id": "17ebe569-82bb-49b0-aaaf-ac5062bede35",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_2021 = df_2021.withColumn('month', F.month(df_2021.start_timestamp))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "88ed9106-1b80-472e-8638-ece1d3d5d25e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 46:=============================================>           (8 + 2) / 10]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+\n",
-      "|                  ID|    start_timestamp|      end_timestamp|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|     pickup_location|  dropoff_lat|   dropoff_lon|    dropoff_location|\n",
-      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+\n",
-      "|960742b2998c7908e...|2021-01-04 01:45:00|2021-01-04 02:00:00|  5.9| 17031063000|  17031830600|          6|           1|15.0|  0|16.23|41.9363101308|-87.6515625922|POINT (-87.651562...|42.0016981937|-87.6735740325|POINT (-87.673574...|\n",
-      "|97d44c73c92c79cc4...|2021-01-04 04:30:00|2021-01-04 05:00:00|  7.3| 17031081600|  17031835900|          8|          38|22.5|  0|25.48|41.8920726347|-87.6288741572|POINT (-87.628874...|41.8201666062| -87.621499206|POINT (-87.621499...|\n",
-      "|d460b58d94cbf8f53...|2021-01-04 01:00:00|2021-01-04 01:15:00|  7.6| 17031980000|  17031110200|         76|          11|17.5|  0|23.73|41.9790708201|-87.9030396611|POINT (-87.903039...|41.9800778511|-87.7734709274|POINT (-87.773470...|\n",
-      "|ac48c66511eecd2b1...|2021-01-04 04:45:00|2021-01-04 05:00:00|  6.3| 17031330100|  17031070700|         33|           7|20.0|  0|24.85| 41.859349715|-87.6173580061|POINT (-87.617358...|41.9292725315|-87.6738072384|POINT (-87.673807...|\n",
-      "|21c328431f2e2928c...|2021-01-01 10:15:00|2021-01-01 10:30:00|  1.3| 17031081700|  17031081401|          8|           8|10.0|  1| 14.1|41.8920421365|-87.6318639497|POINT (-87.631863...|41.8950334495|-87.6197106717|POINT (-87.619710...|\n",
-      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+\n",
-      "only showing top 5 rows\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                \r"
-     ]
-    }
-   ],
-   "source": [
-    "df_2021.show(5)"
+    "# add the month column\n",
+    "df_2021 = df_2021.withColumn('month', F.month(df_2021.start_timestamp))\n",
+    "df_2021 = df_2021.withColumn('hour', F.hour(df_2021.start_timestamp))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "be4d107a-96c5-4a2b-96eb-4e408f6a8f42",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+-------+----------+-------+-------+----+------------+------------+---------+----+--------+------+----------+-----------+----------+----+---------+--------+---------+-------+----------------+----------+----------+--------------+-----------+-------+----------+-------------------+-------------------+---------+--------------------+--------------------+-----------------+--------------------+\n",
-      "|   name|  datetime|tempmax|tempmin|temp|feelslikemax|feelslikemin|feelslike| dew|humidity|precip|precipprob|precipcover|preciptype|snow|snowdepth|windgust|windspeed|winddir|sealevelpressure|cloudcover|visibility|solarradiation|solarenergy|uvindex|severerisk|            sunrise|             sunset|moonphase|          conditions|         description|             icon|            stations|\n",
-      "+-------+----------+-------+-------+----+------------+------------+---------+----+--------+------+----------+-----------+----------+----+---------+--------+---------+-------+----------------+----------+----------+--------------+-----------+-------+----------+-------------------+-------------------+---------+--------------------+--------------------+-----------------+--------------------+\n",
-      "|chicago|2020-01-01|   43.0|   22.2|32.9|        35.2|        12.8|     24.5|24.1|    71.1|   0.0|         0|        0.0|      null| 0.0|      0.4|    31.8|     19.6|  207.9|          1007.3|      32.0|       9.9|          98.7|        8.5|      4|      null|2020-01-01T07:18:18|2020-01-01T16:29:47|     0.21|    Partially cloudy|Partly cloudy thr...|partly-cloudy-day|72534014819,KORD,...|\n",
-      "|chicago|2020-01-02|   48.0|   37.3|42.8|        43.2|        29.0|     36.3|32.9|    68.0|   0.0|         0|        0.0|      null| 0.0|      0.1|    30.5|     18.4|  214.1|          1002.4|      43.2|       9.9|          85.5|        7.4|      4|      null|2020-01-02T07:18:24|2020-01-02T16:30:39|     0.25|    Partially cloudy|Partly cloudy thr...|partly-cloudy-day|72534014819,KORD,...|\n",
-      "|chicago|2020-01-03|   41.5|   34.5|37.3|        38.1|        28.4|     32.7|30.4|    76.0|   0.0|         0|        0.0|      null| 0.0|      0.0|    null|      8.7|  335.7|          1009.8|      91.4|       9.7|          27.3|        2.4|      1|      null|2020-01-03T07:18:26|2020-01-03T16:31:33|     0.27|            Overcast|Cloudy skies thro...|           cloudy|72534014819,KORD,...|\n",
-      "|chicago|2020-01-04|   34.5|   28.0|31.6|        28.3|        18.6|     23.5|24.0|    73.4| 0.015|       100|       12.5| rain,snow| 0.1|      0.0|    20.8|     13.8|  313.1|          1016.5|      89.8|       8.4|          20.5|        1.8|      2|      null|2020-01-04T07:18:27|2020-01-04T16:32:28|     0.31|Snow, Rain, Parti...|Partly cloudy thr...|             snow|72534014819,KORD,...|\n",
-      "|chicago|2020-01-05|   42.9|   25.0|33.5|        35.5|        13.9|     24.1|25.3|    72.4|   0.0|         0|        0.0|      null| 0.0|      0.0|    36.9|     21.2|  238.8|          1016.2|      73.0|       9.8|          62.1|        5.5|      4|      null|2020-01-05T07:18:25|2020-01-05T16:33:24|     0.34|    Partially cloudy|Partly cloudy thr...|partly-cloudy-day|72534014819,KORD,...|\n",
-      "+-------+----------+-------+-------+----+------------+------------+---------+----+--------+------+----------+-----------+----------+----+---------+--------+---------+-------+----------------+----------+----------+--------------+-----------+-------+----------+-------------------+-------------------+---------+--------------------+--------------------+-----------------+--------------------+\n",
-      "only showing top 5 rows\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"yyyy-mm-dd\"))\n",
-    "df_weather.show(5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 96,
    "id": "2ae6d6b2-9001-4ded-ba3a-225861cbe451",
    "metadata": {},
    "outputs": [],
    "source": [
-    "hp_census_tracts_2010_2020 = [17031411000,17031410900,17031410100,17031411100,17031410800,17031410200,17031410700,17031411200,17031836200,17031410600,17031836300,17031410500,\n",
-    "                    17031410300,17031410400,17031410600,17031410800,17031411300,17031411400]\n",
-    "df_hp = df_2021.filter((df_2021.pickup_tract.isin(hp_census_tracts_2010_2020)) & (df_2021.dropoff_tract.isin(hp_census_tracts_2010_2020)))"
+    "# get rides that occurred within hyde park\n",
+    "# add kenwood and woodlawn to this list - only if the other location is hyde park                    \n",
+    "df_hp = df_2021.filter((df_2021.pickup_area == 41) & (df_2021.dropoff_area == 41))\n",
+    "df_kw = df_2021.filter(((df_2021.pickup_area == 41) & (df_2021.dropoff_area == 42)) | ((df_2021.pickup_area == 42) & (df_2021.dropoff_area == 41)))\n",
+    "df_wl = df_2021.filter(((df_2021.pickup_area == 41) & (df_2021.dropoff_area == 39)) | ((df_2021.pickup_area == 39) & (df_2021.dropoff_area == 41)))\n",
+    "df_area = df_hp.union(df_kw).union(df_wl)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 97,
    "id": "67a9c9c1-dd4e-41b6-9d29-b475a1189268",
    "metadata": {},
    "outputs": [
@@ -614,22 +679,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 61:================================================>         (5 + 1) / 6]\r"
+      "[Stage 339:>                                                        (0 + 1) / 1]35]\r"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
-      "|                  ID|    start_timestamp|      end_timestamp|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|     pickup_location|  dropoff_lat|   dropoff_lon|    dropoff_location|month|\n",
-      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
-      "|edb44ac5f0ca61fcd...|2021-01-17 04:15:00|2021-01-17 04:15:00|  1.4| 17031410800|  17031836200|         41|          41| 5.0|  0|  8.1|41.7979652088|-87.5896070309|POINT (-87.589607...|41.7904693995|-87.6012851221|POINT (-87.601285...|    1|\n",
-      "|a6ff5154d2a4e8b45...|2021-01-22 03:00:00|2021-01-22 03:00:00|  0.9| 17031836200|  17031410600|         41|          41| 5.0|  0|  8.1|41.7904693995|-87.6012851221|POINT (-87.601285...|41.7979711911|-87.5989445134|POINT (-87.598944...|    1|\n",
-      "|b0a0ccd90ca248d40...|2021-01-21 02:45:00|2021-01-21 03:00:00|  1.5| 17031836200|  17031410100|         41|          41| 7.5|  0| 10.6|41.7904693995|-87.6012851221|POINT (-87.601285...|41.8012268363|-87.5853031602|POINT (-87.585303...|    1|\n",
-      "|75d9b7289cd03afa9...|2021-01-04 01:30:00|2021-01-04 01:30:00|  0.9| 17031410700|  17031836200|         41|          41| 2.5|  0| 6.38|41.7980417164|-87.5941966274|POINT (-87.594196...|41.7904693995|-87.6012851221|POINT (-87.601285...|    1|\n",
-      "|24aa15d3c6be5ad06...|2021-01-22 07:15:00|2021-01-22 07:15:00|  0.9| 17031836200|  17031411000|         41|          41| 7.5|  1| 11.6|41.7904693995|-87.6012851221|POINT (-87.601285...|41.7905062613|-87.5831437169|POINT (-87.583143...|    1|\n",
-      "+--------------------+-------------------+-------------------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----------------+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "|                  ID|    start_timestamp|      end_timestamp|Trip Seconds|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|            total|   pickup_lat|    pickup_lon|     pickup_location|  dropoff_lat|   dropoff_lon|    dropoff_location|month|\n",
+      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----------------+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "|c958c47a865cd140f...|2022-01-05 13:15:00|2022-01-05 13:15:00|         311|  0.9|        null|         null|         41|          41| 7.5|  0|             8.52| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
+      "|2dc3081bcb17fcdfa...|2022-01-05 18:15:00|2022-01-05 18:15:00|         160|  0.6|        null|         null|         41|          41| 5.0|  0|             6.02| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
+      "|be3cb691f48c0f939...|2022-01-05 19:45:00|2022-01-05 19:45:00|         237|  1.2| 17031410800|  17031410500|         41|          41| 7.5|  0|             8.52|41.7979652088|-87.5896070309|POINT (-87.589607...|41.7978270187|-87.6037457654|POINT (-87.603745...|    1|\n",
+      "|9d757b875335e7a68...|2022-01-05 21:45:00|2022-01-05 21:45:00|         225|  1.3|        null|         null|         41|          41| 5.0|  0|7.359999999999999| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
+      "|6884006612e9514b9...|2022-01-06 11:00:00|2022-01-06 11:00:00|         288|  1.2|        null|         null|         41|          41| 5.0|  0|             7.46| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
+      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----------------+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
       "only showing top 5 rows\n",
       "\n"
      ]
@@ -643,12 +708,12 @@
     }
    ],
    "source": [
-    "df_hp.show(5)"
+    "df_area.show(5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 98,
    "id": "2c54ef92-e61e-4827-ad6c-bb8b9405e701",
    "metadata": {},
    "outputs": [
@@ -656,117 +721,96 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                \r"
+      "                                                                                35]\r"
      ]
     },
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>month</th>\n",
-       "      <th>count(ID)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1825</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "   month  count(ID)\n",
-       "0      1       1825"
+       "<AxesSubplot:xlabel='month'>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_hp.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "b1ed924a-e358-4ead-852e-6026d23fb8ad",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                \r"
-     ]
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "921081"
+       "<Figure size 640x480 with 1 Axes>"
       ]
      },
-     "execution_count": 28,
      "metadata": {},
-     "output_type": "execute_result"
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "df_2021.count()"
+    "df_area.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas().plot(x=\"month\",y=\"count(ID)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8e9f1724-7b0f-4a80-8a3d-aa101a7fbe19",
+   "metadata": {},
+   "source": [
+    "The code after this point is what remains to be done and is untested. If we all finish the things written below we should be done with the EDA"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "id": "024ff08a-42d2-4a8f-8189-c35d6af0186a",
+   "execution_count": null,
+   "id": "f1638b38-1606-428c-aac8-5f48bc36a3d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# verify this is the correct time period for your given year\n",
+    "df_area_program = df_area.filter((df_area.Fare <= 15.0) & (df_area.hour >= 17) & (df_area.hour < 4))\n",
+    "# do the same kind of monthly plot here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "a1b60972-6242-4259-8209-87d9fe9e750f",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                \r"
+      "[Stage 369:=================================================>     (19 + 2) / 21]\r"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "1825"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------------------------+\n",
+      "|approx_count_distinct(ID)|\n",
+      "+-------------------------+\n",
+      "|                   858074|\n",
+      "+-------------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
     }
    ],
    "source": [
-    "df_hp.count()"
+    "from pyspark.sql.functions import approxCountDistinct\n",
+    "\n",
+    "df_area.select(approxCountDistinct(\"ID\", rsd = 0.05)).show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 79,
    "id": "2fda94cd-990c-436b-93b0-979f7e3c8ad3",
    "metadata": {},
    "outputs": [
@@ -774,7 +818,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                \r"
+      "[Stage 283:========================================>                (5 + 2) / 7]\r"
      ]
     },
     {
@@ -795,32 +839,55 @@
      "text": [
       "                                                                                \r"
      ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+-----+\n",
-      "|month|\n",
-      "+-----+\n",
-      "|    1|\n",
-      "+-----+\n",
-      "\n"
-     ]
     }
    ],
    "source": [
-    "df_2021.select('month').distinct().show()\n",
-    "df_hp.select('month').distinct().show()\n"
+    "# basic plots for all rides (not just in the program area)\n",
+    "df_2021.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas().plot(x=\"month\",y=\"count(ID)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90914451-ac49-44f6-98d5-4eb6b5f8dbf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_2021.groupby(\"pickup_area\").agg({'ID':'count'}).orderBy(F.col('pickup_area').asc()).toPandas().plot(x=\"pickup_area\",y=\"count(ID)\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "765edf2e-10ad-4fda-870d-8e9a488cc7ff",
+   "id": "5758417d-edcd-448a-9535-16f5b9c1528a",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "df_2021.groupby(\"dropoff_area\").agg({'ID':'count'}).orderBy(F.col('dropoff_area').asc()).toPandas().plot(x=\"pickup_area\",y=\"count(ID)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8cd74633-8919-4376-8b5f-a512aa3ee28a",
+   "metadata": {},
+   "source": [
+    "# Next Steps\n",
+    "Filter the in-area dataframe to only include rides with a fare under 15, and rides within the timeframe for the given year.\n",
+    "\n",
+    "plot rides by hour\n",
+    "\n",
+    "\n",
+    "## we still don't know how to do these, if you figure it out pls share\n",
+    "the geospatial ipynb (notebook 4.8) from ashish shows some ways to work with this kind of data in pyspark\n",
+    "\n",
+    "Plot Cloropleths (for all of chicago)\n",
+    "\n",
+    "heat map of dropoff location and pickup location (for in-program rides)\n",
+    "\n",
+    "for 2021 - think about how to show the september to october switch - vline when program starts\n",
+    "\n",
+    "add vertical lines at and key shifts in the program policy"
+   ]
   }
  ],
  "metadata": {

From f171b761308164d4577b6eb65b723ae773c50f5d Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Mon, 13 Nov 2023 03:43:36 +0000
Subject: [PATCH 5/9] added graphs and program area logic

---
 eda_2021.ipynb | 687 +++++++++++++++++++++++++++++++------------------
 1 file changed, 440 insertions(+), 247 deletions(-)

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 452e6af..0449657 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 2,
    "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 3,
    "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
    "metadata": {},
    "outputs": [
@@ -28,6 +28,9 @@
        " ('spark.eventLog.enabled', 'true'),\n",
        " ('spark.submit.pyFiles',\n",
        "  '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1699830118479_0001'),\n",
+       " ('spark.app.id', 'application_1699830118479_0001'),\n",
        " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n",
        " ('spark.kryoserializer.buffer.max', '2000M'),\n",
        " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n",
@@ -35,18 +38,19 @@
        " ('spark.driver.maxResultSize', '0'),\n",
        " ('spark.yarn.unmanagedAM.enabled', 'true'),\n",
        " ('spark.sql.autoBroadcastJoinThreshold', '43m'),\n",
+       " ('spark.eventLog.dir',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/f1303a69-431a-4ba9-87a2-c0c185ed3a99/spark-job-history'),\n",
+       " ('spark.driver.appUIAddress',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:38871'),\n",
        " ('spark.ui.filters',\n",
        "  'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n",
-       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1699633504496_0001'),\n",
+       " ('spark.ui.proxyBase', '/proxy/application_1699830118479_0001'),\n",
        " ('spark.metrics.namespace',\n",
        "  'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n",
        " ('spark.executor.memory', '4g'),\n",
        " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n",
        " ('spark.hadoop.hive.execution.engine', 'mr'),\n",
        " ('spark.executor.id', 'driver'),\n",
-       " ('spark.app.startTime', '1699634010505'),\n",
-       " ('spark.driver.port', '35733'),\n",
        " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n",
        " ('spark.dynamicAllocation.maxExecutors', '10000'),\n",
        " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
@@ -63,8 +67,6 @@
        "  'com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,com.typesafe_config-1.4.2.jar,org.rocksdb_rocksdbjni-6.29.5.jar,com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,com.github.universal-automata_liblevenshtein-3.0.0.jar,com.google.cloud_google-cloud-storage-2.16.0.jar,com.navigamez_greex-1.0.jar,com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,it.unimi.dsi_fastutil-7.0.12.jar,org.projectlombok_lombok-1.16.8.jar,com.google.guava_guava-31.1-jre.jar,com.google.guava_failureaccess-1.0.1.jar,com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,com.google.errorprone_error_prone_annotations-2.16.jar,com.google.j2objc_j2objc-annotations-1.3.jar,com.google.http-client_google-http-client-1.42.3.jar,io.opencensus_opencensus-contrib-http-util-0.31.1.jar,com.google.http-client_google-http-client-jackson2-1.42.3.jar,com.google.http-client_google-http-client-gson-1.42.3.jar,com.google.api-client_google-api-client-2.1.1.jar,commons-codec_commons-codec-1.15.jar,com.google.oauth-client_google-oauth-client-1.34.1.jar,com.google.http-client_google-http-client-apache-v2-1.42.3.jar,com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,com.google.code.gson_gson-2.10.jar,com.google.cloud_google-cloud-core-2.9.0.jar,com.google.auto.value_auto-value-annotations-1.10.1.jar,com.google.cloud_google-cloud-core-http-2.9.0.jar,com.google.http-client_google-http-client-appengine-1.42.3.jar,com.google.api_gax-httpjson-0.105.1.jar,com.google.cloud_google-cloud-core-grpc-2.9.0.jar,io.grpc_grpc-core-1.51.0.jar,com.google.api_gax-2.20.1.jar,com.google.api_gax-grpc-2.20.1.jar,io.grpc_grpc-alts-1.51.0.jar,io.grpc_grpc-grpclb-1.51.0.jar,org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,io.grpc_grpc-protobuf-1.51.0.jar,com.google.auth_google-auth-library-credentials-1.13.0.jar,com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,com.google.api_api-common-2.2.2.jar,javax.annotation_javax.annotation-api-1.3.2.jar,io.opencensus_opencensus-api-0.31.1.jar,io.grpc_grpc-context-1.51.0.jar,com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,com.google.protobuf_protobuf-java-3.21.10.jar,com.google.protobuf_protobuf-java-util-3.21.10.jar,com.google.api.grpc_proto-google-common-protos-2.11.0.jar,org.threeten_threetenbp-1.6.4.jar,com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,com.fasterxml.jackson.core_jackson-core-2.14.1.jar,com.google.code.findbugs_jsr305-3.0.2.jar,io.grpc_grpc-api-1.51.0.jar,io.grpc_grpc-auth-1.51.0.jar,io.grpc_grpc-stub-1.51.0.jar,org.checkerframework_checker-qual-3.28.0.jar,com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,io.grpc_grpc-protobuf-lite-1.51.0.jar,com.google.android_annotations-4.1.1.4.jar,org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,io.grpc_grpc-netty-shaded-1.51.0.jar,io.perfmark_perfmark-api-0.26.0.jar,io.grpc_grpc-googleapis-1.51.0.jar,io.grpc_grpc-xds-1.51.0.jar,io.opencensus_opencensus-proto-0.2.0.jar,io.grpc_grpc-services-1.51.0.jar,com.google.re2j_re2j-1.6.jar,dk.brics.automaton_automaton-1.11-8.jar,org.slf4j_slf4j-api-1.7.16.jar'),\n",
        " ('spark.repl.local.jars',\n",
        "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
-       " ('spark.driver.appUIAddress',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:37817'),\n",
        " ('spark.driver.host',\n",
        "  'hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal'),\n",
        " ('spark.sql.cbo.enabled', 'true'),\n",
@@ -88,29 +90,27 @@
        "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
        " ('spark.serializer.objectStreamReset', '100'),\n",
        " ('spark.submit.deployMode', 'client'),\n",
-       " ('spark.ui.proxyBase', '/proxy/application_1699633504496_0001'),\n",
        " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
        " ('spark.shuffle.service.enabled', 'true'),\n",
-       " ('spark.scheduler.mode', 'FAIR'),\n",
        " ('spark.history.fs.logDirectory',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/64cc95a2-795c-4a54-be08-11a36626a92f/spark-job-history'),\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/f1303a69-431a-4ba9-87a2-c0c185ed3a99/spark-job-history'),\n",
+       " ('spark.app.startTime', '1699830435306'),\n",
+       " ('spark.scheduler.mode', 'FAIR'),\n",
        " ('spark.sql.adaptive.enabled', 'true'),\n",
        " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
        " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n",
-       " ('spark.app.id', 'application_1699633504496_0001'),\n",
        " ('spark.master', 'yarn'),\n",
        " ('spark.ui.port', '0'),\n",
        " ('spark.rpc.message.maxSize', '512'),\n",
        " ('spark.rdd.compress', 'True'),\n",
+       " ('spark.driver.port', '43209'),\n",
        " ('spark.task.maxFailures', '10'),\n",
        " ('spark.yarn.isPython', 'true'),\n",
        " ('spark.dynamicAllocation.enabled', 'true'),\n",
-       " ('spark.eventLog.dir',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/64cc95a2-795c-4a54-be08-11a36626a92f/spark-job-history'),\n",
        " ('spark.ui.showConsoleProgress', 'true')]"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 4,
    "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
    "metadata": {},
    "outputs": [
@@ -192,8 +192,8 @@
       " |-- solarenergy: double (nullable = true)\n",
       " |-- uvindex: integer (nullable = true)\n",
       " |-- severerisk: integer (nullable = true)\n",
-      " |-- sunrise: string (nullable = true)\n",
-      " |-- sunset: string (nullable = true)\n",
+      " |-- sunrise: timestamp (nullable = true)\n",
+      " |-- sunset: timestamp (nullable = true)\n",
       " |-- moonphase: double (nullable = true)\n",
       " |-- conditions: string (nullable = true)\n",
       " |-- description: string (nullable = true)\n",
@@ -204,7 +204,7 @@
     }
    ],
    "source": [
-    "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2022\", inferSchema=True, header=True)\n",
+    "df_2021 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2021\", inferSchema=True, header=True)\n",
     "# figure out how to read in shp file msca-bdp-student-gcs/bdp-rideshare-project/neighborhoods/shp files\n",
     "df_weather = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2020-01-01 to 2022-08-31.csv\", inferSchema=True, header=True)\n",
     "df_2021.printSchema()\n",
@@ -213,17 +213,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 5,
    "id": "8138c57a-26d6-44c4-b765-c7b137277044",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "135"
+       "99"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -245,7 +245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 6,
    "id": "e70c86dd-041c-4967-b726-c058e32a76b7",
    "metadata": {},
    "outputs": [
@@ -253,14 +253,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Partitions: 135\n"
+      "Partitions: 99\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 321:====================================================>(134 + 1) / 135]\r"
+      "[Stage 10:=====================>                                  (7 + 11) / 18]\r"
      ]
     },
     {
@@ -270,141 +270,105 @@
       "+-----------+------+\n",
       "|partitionId| count|\n",
       "+-----------+------+\n",
-      "|        134|337327|\n",
-      "|         22|499083|\n",
-      "|        123|504285|\n",
-      "|        126|504809|\n",
-      "|        115|504841|\n",
-      "|         95|505099|\n",
-      "|         87|505483|\n",
-      "|         10|505553|\n",
-      "|        129|505717|\n",
-      "|        109|505821|\n",
-      "|         30|506415|\n",
-      "|         12|506815|\n",
-      "|        112|506840|\n",
-      "|        118|506913|\n",
-      "|          8|507499|\n",
-      "|         17|507526|\n",
-      "|         40|507585|\n",
-      "|         53|507693|\n",
-      "|         90|507836|\n",
-      "|         27|507856|\n",
-      "|         71|508094|\n",
-      "|        100|508435|\n",
-      "|        103|508829|\n",
-      "|         74|508860|\n",
-      "|         92|509111|\n",
-      "|        106|509156|\n",
-      "|         50|509316|\n",
-      "|         45|509490|\n",
-      "|         25|509572|\n",
-      "|         79|509585|\n",
-      "|         48|509604|\n",
-      "|         89|509853|\n",
-      "|         32|510003|\n",
-      "|         66|510055|\n",
-      "|         58|510094|\n",
-      "|         82|510167|\n",
-      "|         37|510177|\n",
-      "|         61|510194|\n",
-      "|          6|510259|\n",
-      "|         35|510618|\n",
-      "|         19|510748|\n",
-      "|          4|510798|\n",
-      "|         68|510849|\n",
-      "|         77|510867|\n",
-      "|         97|511038|\n",
-      "|         98|511105|\n",
-      "|         15|511323|\n",
-      "|          5|511378|\n",
-      "|         94|511724|\n",
-      "|         42|512086|\n",
-      "|          3|512151|\n",
-      "|         47|512355|\n",
-      "|         84|512447|\n",
-      "|         24|512577|\n",
-      "|        128|512747|\n",
-      "|         14|512800|\n",
-      "|         76|512944|\n",
-      "|        102|513121|\n",
-      "|         56|513164|\n",
-      "|         69|513214|\n",
-      "|         33|513364|\n",
-      "|         63|513382|\n",
-      "|        101|513611|\n",
-      "|        117|513692|\n",
-      "|         55|513736|\n",
-      "|        104|513776|\n",
-      "|          1|513780|\n",
-      "|         20|513856|\n",
-      "|         21|513928|\n",
-      "|         29|513958|\n",
-      "|        122|514102|\n",
-      "|         51|514119|\n",
-      "|         60|514176|\n",
-      "|         28|514184|\n",
-      "|        125|514312|\n",
-      "|         13|514332|\n",
-      "|        105|514426|\n",
-      "|         73|514504|\n",
-      "|         81|514680|\n",
-      "|         88|514692|\n",
-      "|        108|514720|\n",
-      "|         64|514837|\n",
-      "|        114|514879|\n",
-      "|         43|514882|\n",
-      "|         38|514932|\n",
-      "|         16|514954|\n",
-      "|         91|514997|\n",
-      "|         39|515034|\n",
-      "|        130|515082|\n",
-      "|          7|515187|\n",
-      "|         57|515299|\n",
-      "|        107|515369|\n",
-      "|         70|515379|\n",
-      "|         52|515427|\n",
-      "|          2|515744|\n",
-      "|         86|515803|\n",
-      "|         49|516126|\n",
-      "|        127|516242|\n",
-      "|         85|516262|\n",
-      "|         54|516281|\n",
-      "|        116|516302|\n",
-      "|        111|516334|\n",
-      "|         44|516357|\n",
-      "|         93|516403|\n",
-      "|         65|516579|\n",
-      "|        110|516601|\n",
-      "|         34|516674|\n",
-      "|         23|516689|\n",
-      "|         67|516905|\n",
-      "|        124|516979|\n",
-      "|        133|517062|\n",
-      "|         96|517079|\n",
-      "|         72|517573|\n",
-      "|         36|517673|\n",
-      "|         26|517765|\n",
-      "|         99|517784|\n",
-      "|         41|517946|\n",
-      "|          9|518089|\n",
-      "|        113|518207|\n",
-      "|         83|518265|\n",
-      "|          0|518352|\n",
-      "|         78|518388|\n",
-      "|         46|518399|\n",
-      "|        121|518588|\n",
-      "|         11|518723|\n",
-      "|         75|518753|\n",
-      "|         31|518838|\n",
-      "|         59|518855|\n",
-      "|         80|518857|\n",
-      "|         18|519062|\n",
-      "|        131|519379|\n",
-      "|         62|519942|\n",
-      "|        119|521049|\n",
-      "|        120|521694|\n",
-      "|        132|527685|\n",
+      "|         98|247115|\n",
+      "|         78|503687|\n",
+      "|         92|504130|\n",
+      "|         85|507725|\n",
+      "|         69|508327|\n",
+      "|         83|508472|\n",
+      "|         80|508483|\n",
+      "|         89|508726|\n",
+      "|         90|509264|\n",
+      "|         71|509326|\n",
+      "|         73|509865|\n",
+      "|         76|509943|\n",
+      "|         82|510114|\n",
+      "|         67|511481|\n",
+      "|         94|512490|\n",
+      "|         75|512592|\n",
+      "|         91|512824|\n",
+      "|         41|513552|\n",
+      "|         95|513946|\n",
+      "|         45|514289|\n",
+      "|         84|514945|\n",
+      "|         77|515161|\n",
+      "|         53|515174|\n",
+      "|         70|515754|\n",
+      "|         49|515909|\n",
+      "|         63|515916|\n",
+      "|         93|516070|\n",
+      "|         51|516100|\n",
+      "|         74|516196|\n",
+      "|         65|516213|\n",
+      "|         66|516473|\n",
+      "|         43|516499|\n",
+      "|         81|516882|\n",
+      "|         64|517161|\n",
+      "|         61|517254|\n",
+      "|         87|517327|\n",
+      "|         39|517434|\n",
+      "|         47|517452|\n",
+      "|         88|517511|\n",
+      "|         34|517740|\n",
+      "|         72|518066|\n",
+      "|         79|518108|\n",
+      "|         62|518477|\n",
+      "|         86|518712|\n",
+      "|         68|519004|\n",
+      "|         60|519038|\n",
+      "|         59|519275|\n",
+      "|         57|519389|\n",
+      "|         58|519453|\n",
+      "|         56|519487|\n",
+      "|         55|519652|\n",
+      "|         37|519668|\n",
+      "|         97|520203|\n",
+      "|         30|520475|\n",
+      "|         32|521062|\n",
+      "|         15|521106|\n",
+      "|         25|521140|\n",
+      "|          7|521697|\n",
+      "|         50|522011|\n",
+      "|         36|522062|\n",
+      "|         10|522587|\n",
+      "|         27|522685|\n",
+      "|         44|522782|\n",
+      "|         13|522890|\n",
+      "|         22|523188|\n",
+      "|         38|523228|\n",
+      "|         54|523277|\n",
+      "|         35|523391|\n",
+      "|          4|523726|\n",
+      "|         20|523791|\n",
+      "|         46|524033|\n",
+      "|         52|524158|\n",
+      "|         48|524472|\n",
+      "|         28|524492|\n",
+      "|         23|524657|\n",
+      "|         17|524732|\n",
+      "|         42|525331|\n",
+      "|         40|525413|\n",
+      "|          5|525704|\n",
+      "|         18|525715|\n",
+      "|         12|525737|\n",
+      "|          6|525965|\n",
+      "|         96|525968|\n",
+      "|         29|526221|\n",
+      "|         24|526334|\n",
+      "|          9|526393|\n",
+      "|         33|526581|\n",
+      "|          3|527064|\n",
+      "|         31|527425|\n",
+      "|          8|527569|\n",
+      "|          1|527581|\n",
+      "|         11|527982|\n",
+      "|         21|528139|\n",
+      "|         19|528662|\n",
+      "|          2|528719|\n",
+      "|         16|529072|\n",
+      "|         26|529191|\n",
+      "|         14|529695|\n",
+      "|          0|531719|\n",
       "+-----------+------+\n",
       "\n"
      ]
@@ -493,7 +457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "22a6039e-9848-4717-98b6-bc915540357b",
    "metadata": {},
    "outputs": [
@@ -501,22 +465,52 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 16:===================================================>     (9 + 1) / 10]\r"
+      "                                                                                \r"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+-------+----------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+------------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
-      "|summary|         Trip ID|Trip Start Timestamp|  Trip End Timestamp|     Trip Seconds|       Trip Miles| Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|              Fare|               Tip|Additional Charges|        Trip Total|        Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
-      "+-------+----------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+------------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
-      "|  count|         2708656|             2708652|             2708652|          2708652|          2708649|              983470|              975128|              2479944|               2447038|           2708100|           2708100|           2708099|           2708098|             2708651|                 2482320|                  2482320|                 2482320|                  2449465|                   2449465|                  2449465|\n",
-      "|   mean|            null|                null|                null|942.2844891111889|6.688875228942609|1.703138547767500...|1.703138094929255...|    29.80978683389625|     29.28494816999164|13.057426793692995|0.4912218898858979|3.3111284709689235|16.859785314286004|  1.0000036918746638|       41.87740670463228|       -87.67185372619028|                    null|        41.87714315855053|         -87.6694694706963|                     null|\n",
-      "| stddev|            null|                null|                null|630.3003334457269|7.608257555036324|  339740.56288590573|  335430.99940793717|   22.278028203042062|     21.90521219841838| 9.839634058047901|1.6231638297086177| 2.236087560001195|11.125341127829076|0.001921421972618...|     0.07804151182131637|      0.06527591183128632|                    null|      0.07698901524173858|       0.06276134276291706|                     null|\n",
-      "|    min|  \"error\" : true|01/01/2021 01:00:...|01/01/2021 01:00:...|                5|              0.0|         17031010100|         17031010100|                    1|                     1|               0.0|                 0|                 0|               0.0|                   1|           41.6502216756|            -87.913624596|    POINT (-87.530712...|            41.6502216756|             -87.913624596|     POINT (-87.529950...|\n",
-      "|    max|               }|01/26/2021 12:45:...|01/26/2021 12:45:...|            64707|            661.2|         17031980100|         17031980100|                   77|                    77|             840.0|               100|                 {|            845.85|                   2|           42.0212235931|           -87.5307124836|    POINT (-87.913624...|            42.0212235931|             -87.529950466|     POINT (-87.913624...|\n",
-      "+-------+----------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+------------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "+-------+--------------------+--------------------+--------------------+------------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+-----------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|summary|             Trip ID|Trip Start Timestamp|  Trip End Timestamp|      Trip Seconds|       Trip Miles| Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|              Fare|               Tip|Additional Charges|       Trip Total|        Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+--------------------+--------------------+--------------------+------------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+-----------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|  count|            51169876|            51169876|            51169876|          51167860|         51169381|            24950744|            24856643|             46825937|              46549520|          51131684|          51131684|          51131672|         51131672|            51169876|                46938298|                 46938298|                46938298|                 46655261|                  46655261|                 46655261|\n",
+      "|   mean|                null|                null|                null|1062.3170379023081| 6.87116612569546|1.703137163919982E10|1.703138276734825...|    27.41452385672496|    27.986973979538348|18.573343927025757|0.9571861939849272|3.9098466187522347|23.44038132328375|  1.0000076998427747|      41.888136449399795|       -87.67150697522486|                    null|       41.888351420915896|        -87.67341022424851|                     null|\n",
+      "| stddev|                null|                null|                null| 746.3118566300686|7.690577780647859|  341605.83209225716|   346356.5636468578|   21.719718245721253|     22.05215203756523|14.096824060781321|  2.52196794530865|3.7664613472774318|16.32324595465821|0.002809841832379336|     0.06967997334778019|      0.06741844400108002|                    null|      0.06945844779950419|        0.0713396752319024|                     null|\n",
+      "|    min|0000007dc1d97f4db...|01/01/2021 01:00:...|01/01/2021 01:00:...|                 0|              0.0|         17031010100|         17031010100|                    1|                     1|               0.0|                 0|               0.0|              0.0|                   1|           41.6502216756|            -87.913624596|    POINT (-87.529950...|            41.6502216756|             -87.913624596|     POINT (-87.529950...|\n",
+      "|    max|ffffffb2ca3b007da...|12/31/2021 12:45:...|12/31/2021 12:45:...|             85068|            859.7|         17031980100|         17031980100|                   77|                    77|            6145.0|               474|            794.96|          6197.97|                   3|           42.0212235931|            -87.529950466|    POINT (-87.913624...|            42.0212235931|             -87.529950466|     POINT (-87.913624...|\n",
+      "+-------+--------------------+--------------------+--------------------+------------------+-----------------+--------------------+--------------------+---------------------+----------------------+------------------+------------------+------------------+-----------------+--------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_2021.describe().show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "c78e4618-8383-4df2-862b-4cb9dbeb20ab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 14:======================================================> (97 + 2) / 99]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+-----+-----+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|Trip ID|Trip Start Timestamp|Trip End Timestamp|Trip Seconds|Trip Miles|Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area| Fare|  Tip|Additional Charges|Trip Total|Shared Trip Authorized|Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+-----+-----+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|      0|                   0|                 0|        2016|       495|           26219132|            26313233|              4343939|               4620356|38192|38192|             38204|     38204|                     0|           0|                 4231578|                  4231578|                 4231578|                  4514615|                   4514615|                  4514615|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+-----+-----+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
       "\n"
      ]
     },
@@ -529,31 +523,34 @@
     }
    ],
    "source": [
-    "df_2021.describe().show()"
+    "#Find the number of missing values for each column\n",
+    "from pyspark.sql.functions import isnan, when, count, col\n",
+    "df_2021.select([count(when(df_2021[c].isNull(), c)).alias(c) for c in df_2021.columns]).show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
-   "id": "c78e4618-8383-4df2-862b-4cb9dbeb20ab",
+   "execution_count": 10,
+   "id": "90aa424e-4efa-47af-9710-0e5d50216fc8",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 324:===================================================> (132 + 3) / 135]\r"
+      "23/11/12 23:22:47 WARN org.apache.spark.sql.catalyst.util.package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n",
+      "[Stage 17:=======================================================>(98 + 1) / 99]\r"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+------+------+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
-      "|Trip ID|Trip Start Timestamp|Trip End Timestamp|Trip Seconds|Trip Miles|Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|  Fare|   Tip|Additional Charges|Trip Total|Shared Trip Authorized|Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
-      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+------+------+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
-      "|      0|                   0|                 0|        2140|       525|           29231644|            29365970|              5801676|               6049435|113627|113627|            113627|    113627|                     0|           0|                 5605172|                  5605172|                 5605172|                  5872849|                   5872849|                  5872849|\n",
-      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+------+------+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "+------------------------------+\n",
+      "|approx_count_distinct(Trip ID)|\n",
+      "+------------------------------+\n",
+      "|                      51541076|\n",
+      "+------------------------------+\n",
       "\n"
      ]
     },
@@ -566,14 +563,15 @@
     }
    ],
    "source": [
-    "#Find the number of missing values for each column\n",
-    "from pyspark.sql.functions import isnan, when, count, col\n",
-    "df_2021.select([count(when(df_2021[c].isNull(), c)).alias(c) for c in df_2021.columns]).show()"
+    "#Approximate number of 2021 trips\n",
+    "from pyspark.sql.functions import approxCountDistinct\n",
+    "\n",
+    "df_2021.select(approxCountDistinct(\"Trip ID\", rsd = 0.01)).show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "2dd6ea75-5417-4d27-92bb-4d9a24808545",
    "metadata": {},
    "outputs": [
@@ -587,10 +585,10 @@
     {
      "data": {
       "text/plain": [
-       "921081"
+       "23351507"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -602,18 +600,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 12,
    "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                \r"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Working with just data that contains full information and check for dupes\n",
     "df_2021 = df_2021.dropna(how='any', subset=['Trip Start Timestamp','Trip End Timestamp','Fare','Dropoff Community Area','Pickup Community Area'])\n",
@@ -623,13 +613,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 14,
    "id": "6a52d857-bc07-47a7-8cc9-71478bf10e08",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Drop columns unlikely to be useful for analysis for speed of computation and rename columns to remove spacing for ease of code writing\n",
-    "spark.conf.set(\"spark.sql.legacy.timeParserPolicy\", \"LEGACY\")\n",
+    "#spark.conf.set(\"spark.sql.legacy.timeParserPolicy\", \"LEGACY\")\n",
     "\n",
     "df_2021 = df_2021.drop('Trips Pooled','Additional Charges','Shared Trip Authorized','Pickup Centroid Location','Dropoff Centroid Location')\n",
     "df_2021 = df_2021.withColumnRenamed(\"Trip ID\",\"ID\").withColumnRenamed(\"Trip Start Timestamp\",\"start_timestamp\").withColumnRenamed(\"Trip End Timestamp\",\"end_timestamp\").withColumnRenamed(\"Trip Miles\",\\\n",
@@ -644,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 15,
    "id": "17ebe569-82bb-49b0-aaaf-ac5062bede35",
    "metadata": {},
    "outputs": [],
@@ -656,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 16,
    "id": "2ae6d6b2-9001-4ded-ba3a-225861cbe451",
    "metadata": {},
    "outputs": [],
@@ -671,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 17,
    "id": "67a9c9c1-dd4e-41b6-9d29-b475a1189268",
    "metadata": {},
    "outputs": [
@@ -679,22 +669,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 339:>                                                        (0 + 1) / 1]35]\r"
+      "[Stage 29:>                                                         (0 + 1) / 1]\r"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----------------+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
-      "|                  ID|    start_timestamp|      end_timestamp|Trip Seconds|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|            total|   pickup_lat|    pickup_lon|     pickup_location|  dropoff_lat|   dropoff_lon|    dropoff_location|month|\n",
-      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----------------+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
-      "|c958c47a865cd140f...|2022-01-05 13:15:00|2022-01-05 13:15:00|         311|  0.9|        null|         null|         41|          41| 7.5|  0|             8.52| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
-      "|2dc3081bcb17fcdfa...|2022-01-05 18:15:00|2022-01-05 18:15:00|         160|  0.6|        null|         null|         41|          41| 5.0|  0|             6.02| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
-      "|be3cb691f48c0f939...|2022-01-05 19:45:00|2022-01-05 19:45:00|         237|  1.2| 17031410800|  17031410500|         41|          41| 7.5|  0|             8.52|41.7979652088|-87.5896070309|POINT (-87.589607...|41.7978270187|-87.6037457654|POINT (-87.603745...|    1|\n",
-      "|9d757b875335e7a68...|2022-01-05 21:45:00|2022-01-05 21:45:00|         225|  1.3|        null|         null|         41|          41| 5.0|  0|7.359999999999999| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
-      "|6884006612e9514b9...|2022-01-06 11:00:00|2022-01-06 11:00:00|         288|  1.2|        null|         null|         41|          41| 5.0|  0|             7.46| 41.794090253| -87.592310855|POINT (-87.592310...| 41.794090253| -87.592310855|POINT (-87.592310...|    1|\n",
-      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----------------+-------------+--------------+--------------------+-------------+--------------+--------------------+-----+\n",
+      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----+------------+-------------+------------+-------------+-----+----+\n",
+      "|                  ID|    start_timestamp|      end_timestamp|Trip Seconds|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|  pickup_lat|   pickup_lon| dropoff_lat|  dropoff_lon|month|hour|\n",
+      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----+------------+-------------+------------+-------------+-----+----+\n",
+      "|18ebf670b1e3b6edc...|2021-01-01 03:00:00|2021-01-01 03:00:00|         403|  0.9|        null|         null|         41|          41| 5.0|  0| 9.01|41.794090253|-87.592310855|41.794090253|-87.592310855|    1|   3|\n",
+      "|e58f4477036633ebe...|2021-01-02 19:00:00|2021-01-02 19:00:00|         209|  0.7|        null|         null|         41|          41| 2.5|  0|  6.7|41.794090253|-87.592310855|41.794090253|-87.592310855|    1|  19|\n",
+      "|a78e291761784f090...|2021-01-05 18:00:00|2021-01-05 18:00:00|         264|  0.9|        null|         null|         41|          41|10.0|  0| 13.1|41.794090253|-87.592310855|41.794090253|-87.592310855|    1|  18|\n",
+      "|fdceca63330b53269...|2021-01-06 06:30:00|2021-01-06 06:30:00|         327|  1.3|        null|         null|         41|          41| 5.0|  0| 8.47|41.794090253|-87.592310855|41.794090253|-87.592310855|    1|   6|\n",
+      "|cc1ecd62fba13ba8a...|2021-01-01 10:15:00|2021-01-01 10:15:00|         185|  0.7|        null|         null|         41|          41|10.0|  0| 13.5|41.794090253|-87.592310855|41.794090253|-87.592310855|    1|  10|\n",
+      "+--------------------+-------------------+-------------------+------------+-----+------------+-------------+-----------+------------+----+---+-----+------------+-------------+------------+-------------+-----+----+\n",
       "only showing top 5 rows\n",
       "\n"
      ]
@@ -713,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 28,
    "id": "2c54ef92-e61e-4827-ad6c-bb8b9405e701",
    "metadata": {},
    "outputs": [
@@ -721,22 +711,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                35]\r"
+      "                                                                                \r"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:xlabel='month'>"
+       "<matplotlib.lines.Line2D at 0x7fce32d57460>"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -746,32 +736,66 @@
     }
    ],
    "source": [
-    "df_area.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas().plot(x=\"month\",y=\"count(ID)\")"
+    "df_count_plot = df_area.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")\n",
+    "ax = df_count_plot.plot(x=\"month\", y=\"count(ID)\")\n",
+    "ax.axvline(x=9, color='g', linestyle='--', label='Program Starts, Fall Quarter Begins')\n",
+    "ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n"
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "8e9f1724-7b0f-4a80-8a3d-aa101a7fbe19",
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "a4969a36-7c79-422e-a1b1-cf2017208b55",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "The code after this point is what remains to be done and is untested. If we all finish the things written below we should be done with the EDA"
+    "# rides per hour:\n",
+    "rides_per_hour = df_area.groupBy(\"hour\").count().orderBy(\"hour\")\n",
+    "\n",
+    "# Convert the result to Pandas for local plotting\n",
+    "rides_per_hour_pd = rides_per_hour.toPandas()\n",
+    "\n",
+    "# Plot the data\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(rides_per_hour_pd[\"hour\"], rides_per_hour_pd[\"count\"], color=\"skyblue\")\n",
+    "plt.xlabel(\"Hour of the Day\")\n",
+    "plt.ylabel(\"Number of Rides\")\n",
+    "plt.title(\"Number of Rides Per Hour\")\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "f1638b38-1606-428c-aac8-5f48bc36a3d2",
    "metadata": {},
    "outputs": [],
    "source": [
     "# verify this is the correct time period for your given year\n",
-    "df_area_program = df_area.filter((df_area.Fare <= 15.0) & (df_area.hour >= 17) & (df_area.hour < 4))\n",
-    "# do the same kind of monthly plot here"
+    "df_area_program = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 17) | (df_area.hour < 4)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 25,
    "id": "a1b60972-6242-4259-8209-87d9fe9e750f",
    "metadata": {},
    "outputs": [
@@ -779,7 +803,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 369:=================================================>     (19 + 2) / 21]\r"
+      "[Stage 117:==================================================>    (36 + 3) / 39]\r"
      ]
     },
     {
@@ -789,7 +813,7 @@
       "+-------------------------+\n",
       "|approx_count_distinct(ID)|\n",
       "+-------------------------+\n",
-      "|                   858074|\n",
+      "|                   123125|\n",
       "+-------------------------+\n",
       "\n"
      ]
@@ -805,40 +829,118 @@
    "source": [
     "from pyspark.sql.functions import approxCountDistinct\n",
     "\n",
-    "df_area.select(approxCountDistinct(\"ID\", rsd = 0.05)).show()"
+    "df_area_program.select(approxCountDistinct(\"ID\", rsd = 0.05)).show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "id": "2fda94cd-990c-436b-93b0-979f7e3c8ad3",
+   "execution_count": 34,
+   "id": "4170f6ac-afca-44c9-9e2e-7e78670de3d1",
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 283:========================================>                (5 + 2) / 7]\r"
+      "Exception in thread \"serve-DataFrame\" java.net.SocketTimeoutException: Accept timed out\n",
+      "\tat java.net.PlainSocketImpl.socketAccept(Native Method)\n",
+      "\tat java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)\n",
+      "\tat java.net.ServerSocket.implAccept(ServerSocket.java:560)\n",
+      "\tat java.net.ServerSocket.accept(ServerSocket.java:528)\n",
+      "\tat org.apache.spark.security.SocketAuthServer$$anon$1.run(SocketAuthServer.scala:64)\n",
+      "                                                                                \r"
      ]
     },
     {
-     "name": "stdout",
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "df_count_plot_program = df_area_program.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")\n",
+    "ax = df_count_plot_program.plot(x=\"month\", y=\"count(ID)\")\n",
+    "ax.axvline(x=9, color='g', linestyle='--', label='Program Starts, Fall Quarter Begins')\n",
+    "ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "d266e90c-ae57-41cb-99a5-3185b61e60ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "+-----+\n",
-      "|month|\n",
-      "+-----+\n",
-      "|    1|\n",
-      "+-----+\n",
-      "\n"
+      "                                                                                \r"
      ]
     },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "\n",
+    "df_area_program_pd = df_area_program.toPandas()\n",
+    "\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "sns.histplot(df_area_program_pd['hour'], bins=24, kde=False, color='skyblue')\n",
+    "plt.title('Distribution of Pickup Hours for Filtered Rides')\n",
+    "plt.xlabel('Hour of Day')\n",
+    "plt.ylabel('Number of Rides')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "2fda94cd-990c-436b-93b0-979f7e3c8ad3",
+   "metadata": {},
+   "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
       "                                                                                \r"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='month'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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iaX5LS0vY2dnhzp07sLKyglTaIHJavaLT6XDnzh3Y2dnB0rJB/LUlIgPtSizd7PBJVwc9icLWCjMHB+HDX89h8R9XMbKtJ1zlXDLBVDWI3/wSiQQeHh5ISUnBzZs3xS6HqkkqlcLHx8eg7Q2IqGHIKypB3NU7ACqfv/JXL7TzwobjqUhIy8X83Zfwzei2tVQh1VSDCCxA6Z44AQEBHBYyY9bW1uwdI6In+vNSNoo1Ovi72CPQ7clXBz2JVCrB3BEhePa7w9h8Jh2vdPZBh2ZOtVgpVVeDCSxA6f+hc4VUIqL6Z5d+7yAPg3th23g3wksdvbHxRBo+2ZaE7dOegYWUPbmmhv+7SkREZq1ArUHM5dLhoMFPuTqoIh8MaAm5jSUuZKiw4USqMcsjI2FgISIis3bgcjbUGh18ne0Q7FHxXjSVcXaQ4YOBLQEAX/9+GfcKOH3A1DCwEBGRWdv9yNVBNZmU/0onH7TykEP5oARf771srPLISBhYiIjIbD0o1uLPS9kAnr5Y3NNYWkgxd0QIgNIdnxNvKWtcHxkPAwsREZmt2CvZeFCiRdNGtghtqqjx+Tr5OeHZtp4QBOCT385Dp+MK26aCgYWIiMxW2WJxQ0LdjbZG06whrWBvbYEzqbmIPn3LKOekmmNgISIis1RUosUfF7MAAIMNWCzuadzkNni3bwAAYMGeS1AVlRjt3FR9DCxERGSWDl7NQUGxFh4KG7T1amTUc0/o5gd/F3vk5Bdj0b6rRj03VQ8DCxERmaXdDxeLG9zaA1IjL/RmbSnFnOGlE3B/PHoDlzPzjHp+MhwDCxERmR21Rot9D4eDanp1UEV6BLpgYIgbtDoBc35LgiBwAq6YGFiIiMjsHE7OQV6RBq6OMrTzaVxrrzN7aDBkllIcvX4XOx/26JA4GFiIiMjs7NIvFudu9OGgR3k72eHtXi0AAF/svIjCYk2tvRZVrkaBJTIyEhKJBDNmzKiwzebNm9G/f3+4uLhALpcjIiICv//+e7k2a9euhUQieexWVFRUk/KIiKgeKtbosDfpYWAx4tVBFXmjpz+8GtsiQ1mEZQeSa/316MmqHVji4+OxcuVKhIWFVdouLi4O/fv3x65du3Dq1Cn07t0bw4cPx5kzZ8q1k8vlyMjIKHfjzspERPRXR6/fhapIgyYO1ujYzKnWX8/GygKfDAsGAKyKS0FKTkGtvyY9rlqBJT8/H2PGjMGqVavQuHHlY4eLFi3Chx9+iI4dOyIgIADz5s1DQEAAtm/fXq6dRCKBu7t7uRsREdFflV0dNDDEHRa1OBz0qP7BbugZ6IJirQ6fbU+qk9ek8qoVWKZOnYqhQ4eiX79+Bj9Xp9MhLy8PTk7lU3F+fj58fX3h5eWFYcOGPdYD81dqtRoqlarcjYiI6jeNVoffk8pWt6394aAyEokEnw4PhpWFBAcu39EvWEd1x+DAEhUVhdOnTyMyMrJaL7hw4UIUFBRg1KhR+mNBQUFYu3YtfvvtN2zcuBE2Njbo1q0brl6teLGeyMhIKBQK/c3b27ta9RARkfk4nnIP9wtL0NjOCp39an846FH+Lg6Y9Iw/AGDu9gsoKtHW6es3dAYFlrS0NEyfPh3r16+v1vySjRs3Ys6cOdi0aRNcXV31x7t06YJXX30Vbdq0Qffu3fHf//4XgYGB+Pbbbys816xZs6BUKvW3tLQ0g+shIiLzsuuR4SBLi7q/0HVanxZwk8uQeq8Qq+Ku1/nrN2QG/bRPnTqF7OxstG/fHpaWlrC0tERsbCyWLFkCS0tLaLUVp81NmzZh0qRJ+O9///vUoSSpVIqOHTtW2sMik8kgl8vL3YiIqP7S6gT9cFBdXB30JPYyS3w0pBUAYFlMMtJzH4hSR0NkUGDp27cvEhMTkZCQoL916NABY8aMQUJCAiwsLJ74vI0bN2L8+PHYsGEDhg4d+tTXEQQBCQkJ8PAQ5y8kERGZnvgb95CTXwyFrRW6NncWrY4RbTzRyc8JRSU6fLHzgmh1NDSWhjR2dHRE69atyx2zt7eHs7Oz/visWbOQnp6OdevWASgNK6+99hoWL16MLl26IDOzNB3b2tpCoVAAAObOnYsuXbogICAAKpUKS5YsQUJCApYtW1bjN0hERPVD2dVB/YPdYCXCcFAZiUSCuSNCMOzbQ9iVmInDyTno1qKJaPU0FEb/iWdkZCA1NVV/f8WKFdBoNJg6dSo8PDz0t+nTp+vb5Obm4vXXX0erVq0wYMAApKenIy4uDp06dTJ2eUREZIZ0OgG7z5ddHST+shetPOQY28UXAPDpb0ko0epErqj+kwj1ZDcnlUoFhUIBpVLJ+SxERPXMyRv38ML3R+FoY4mTs/tBZvnkKQh1SfmgBH2+jsHdgmLMHtoKk7v7i12SWarq9zf3EiIiIpNXtvFg/1ZuJhFWAEBha4WZg4IAAIv2X0W2itvJ1CYGFiIiMmk6nYA958W9OqgiL7T3QhvvRshXazB/zyWxy6k1Oflq/PekuMuHMLAQEZFJS7iViwxlEeytLdA9wLQmt0qlEnw2IgQSCbD5dDpO3rgndklGpdMJ2HA8FX2+jsGHv57DqZvivT8GFiIiMmllVwf1beUGGyvTGA56VBvvRhjdoXS19U+2JUGrqxdTQ3HhtgrPf38EH21JhKpIg9ZN5aIOxxl0WTMREVFdEgQBuxJN5+qgivxjYEvsSszAhQwVNp5IxasPryAyRwVqDRbtv4L/HL4BrU6Ag8wSfx8QiNcimtXZZpNPwh4WIiIyWYnpSqTnPoCtlQV6Bro+/QkicXaQ4e8DWgIAvt57GfcLikWuyHCCULqScL9vYrHqYAq0OgFDQz2w//2emNDNT9SwAjCwEBGRCSvrXekT5Apba9MbDnrUmM4+CHJ3RG5hCb7ae1nscgxy634hpqw7iTd+OoUMZRG8nWyxZkJHLBvTDu4Kw/cOrA0MLEREZJIEQcDu86XzVwab8HBQGUsLKeaOCAEAbDyRivPpSpEreroSrQ7fx15D/2/isP9iNqwsJHindwvsndETvVuaVo8WAwsREZmkCxkq3LxbCJml1OS+PCvS2d8ZI9t6QhCAT7adh86EJ+CevHEPw5Ycwvzdl/CgRIvOfk7YPb07PhjY0iR7szjploiITNLuh8NBvVq6wF5mPl9XHw1phf0XsnA6NRebz6TjhfZeYpdUzv2CYizYcwlR8aXrqjjZW+P/hrTCc+2aQiIRd55KZdjDQkREJqf06qDS4aAhJrZY3NO4yW3wbt8AAMD83RehKioRuaJSgiDgl5Np6PtNrD6svNzJG3+83xPPt/cy6bACMLAQEZEJupKVj+s5BbC2lKJPkHkMBz1qQjc/+LvYIye/GIv3XxW7HCRn52H0ymP4x6/ncK+gGC3dHPHrmxGIfC4Mje2txS6vShhYiIjI5JT1rvQIcIGjjZXI1RjO2lKKOcNLJ+CuPXIDV7LyRKnjQbEWX/1+CYMXH8SJlHuwtbLArMFB2PHuM+jQzEmUmqqLgYWIiEzO/4aDTP/qoIr0CHTBwBA3aHUCPt2WBEGo2wm4By5nY8CiWCw7cA0lWgH9Wrlh3/s98EbP5rCyML+vf/OZxURERA3C1aw8XM3Oh5WFBH1buYldTo3MHhqMmMt3cPT6XexKzMTQsNqfj5OpLMJnO5L0a9h4KmwwZ0QIBoSYb/gD2MNCREQmZvfDnZmfadEEClvzGw56lLeTHd7q1RwA8PnOCygs1tTaa2m0OvznUAr6LozBrsRMWEgleL2HP/a939PswwrAHhYiIjIxZcNBg83s6qCKvNmzOX49dQu37j/AsgPJ+MfAIKO/RkJaLv5vSyKSbqsAAO18GuGLv4WilYfc6K8lFvawEBGRybh+Jx+XMvNgKZVgQLB5DweVsbGywMfDggEAq+JScCOnwGjnVj4owcdbz+Nv3x1G0m0VFLZWiHwuFL++2bVehRWAgYWIiExI2XBQRHNnNLIzj8ttq2JAsBt6BLqgWKvDZzsu1Ph8giBgW0I6+i6MxU/HbkIQgOfCm+KPv/fEy518IBV5o8LawMBCREQmo2zvIHNbLO5pJBIJPh0eDCsLCf68lI0/LmZV+1wpOQV47T8nMD0qATn5avi72GPDlM74ZnRbNHGQGbFq08I5LEREZBJS7xbifLoKUgnqzXDQo5q7OGDiM35YEXsdn+24gG4tmsDGqup79qg1Wnwfcx3LYpJRrNFBZinFO71b4PWe/pBZmt7eP8bGwEJERCahrHeli78znOtpT8G0PgHYeiYdN+8W4oeD1/FOn4AqPe9wcg4+3noe1x/Of+ke0ASfP9savs72tVmuSeGQEBERmYRdD+ev1Jerg57EQWaJj4a0AgAsPZCM9NwHlba/k6fGjKgzGPPDcVzPKYCrowxLXwnHuomdGlRYARhYiIjIBNy6X4izabmQSICBIfVvOOhRI9p4opOfE4pKdJi38+IT2+h0AtYfu4k+C2OwNeE2JBJgfNdm2P/3nhgW5mnyGxXWBgYWIiIS3Z6HvSsdmznB1dFG5Gpql0QiwdwRIZBKgJ2JGTicnFPu8aTbSjy3/Ahmbz2PvCINQpsqsG1qN8wZEQK5Ge6rZCwMLEREJLqyy5mHtDb/FVmropWHHK9FNAMAfPpbEkq0OuSrNfjXjgsY/u0hJKTlwkFmibkjQrB1ajeEeTUStV5TwEm3REQkqkxlEU7dvA+gfs9f+av3+gXit7O3kZydj5m/nsORa3eRqSoCAAwL88DHw4LhJq/fvU2GYA8LERGJquzqoA6+jRvUF7TCzgozB7UEAGw+k45MVRF8nOzw48ROWPpKuwb1WVQFe1iIiEhUuxPr/9VBFXmxvTe2JdxG/I17eLNnc0zt3cKgtVkaEgYWIiISTbaqCPE37wEABjWQ+SuPkkol+HFiJ2h1AoPKUzCwEBGRaH5PyoQgAG29G6FpI1uxyxGFlYUUzCpPxzksREQkml0Ph4OGhDa83hUyDAMLERGJIidfjeMpdwEAg1s3vPkrZBgGFiIiEsXepCzoBCC0qQLeTnZil0MmjoGFiIhEUXY582AOB1EVMLAQEVGdu19QjCPXOBxEVcfAQkREdW7fhSxodQJaecjh16Rh7TpM1cPAQkREdW7Xw+GghrJ3ENVcjQJLZGQkJBIJZsyYUWm72NhYtG/fHjY2NvD398f333//WJvo6GgEBwdDJpMhODgYW7ZsqUlpRERkopSFJfodihvi6rZUPdUOLPHx8Vi5ciXCwsIqbZeSkoIhQ4age/fuOHPmDD766CO8++67iI6O1rc5evQoRo8ejbFjx+Ls2bMYO3YsRo0ahePHj1e3PCIiMlH7L2ahRCugpZsjWrg6iF0OmYlqBZb8/HyMGTMGq1atQuPGjStt+/3338PHxweLFi1Cq1atMHnyZEycOBFff/21vs2iRYvQv39/zJo1C0FBQZg1axb69u2LRYsWVac8IiIyYbsSeXUQGa5agWXq1KkYOnQo+vXr99S2R48exYABA8odGzhwIE6ePImSkpJK2xw5cqTC86rVaqhUqnI3IiIybaqiEhy8WjocNITDQWQAgwNLVFQUTp8+jcjIyCq1z8zMhJubW7ljbm5u0Gg0yMnJqbRNZmZmheeNjIyEQqHQ37y9vQ18J0REVNf+vJiNYq0OzV3sEcDhIDKAQYElLS0N06dPx/r162FjY1Pl50kkknL3BUF47PiT2vz12KNmzZoFpVKpv6WlpVW5HiIiEkfZcNCQUI9Kf8cT/ZVBuzWfOnUK2dnZaN++vf6YVqtFXFwcli5dCrVaDQuL8ltOuru7P9ZTkp2dDUtLSzg7O1fa5q+9Lo+SyWSQyWSGlE9ERCLKV2sQc+UOAC4WR4YzqIelb9++SExMREJCgv7WoUMHjBkzBgkJCY+FFQCIiIjAvn37yh3bu3cvOnToACsrq0rbdO3a1dD3Q0REJurApWwUa3Ro5myHVh6OYpdDZsagHhZHR0e0bt263DF7e3s4Ozvrj8+aNQvp6elYt24dAODNN9/E0qVL8f7772PKlCk4evQoVq9ejY0bN+rPMX36dPTo0QMLFizAyJEjsW3bNuzfvx+HDh2q6fsjIiIT8b+9gzgcRIYz+kq3GRkZSE1N1d/38/PDrl27EBMTg7Zt2+Jf//oXlixZgueff17fpmvXroiKisKaNWsQFhaGtWvXYtOmTejcubOxyyMiIhEUFmtw4FLpcNAQDgdRNUiEshmwZk6lUkGhUECpVEIul4tdDhERPWJ3Ygbe+vk0vBrb4uCHvdnDQnpV/f7mXkJERFTrdp0vvbCCVwdRdTGwEBFRrSoq0eLPi1kAgMHc7JCqiYGFiIhqVdyVOygo1sJTYYO23o3ELofMFAMLERHVqt0Ph4N4dRDVBAMLERHVGrVGi/0XSoeDhnCzQ6oBBhYiIqo1h67mIE+tgZtchnDvxmKXQ2aMgYWIiGrNrsSHw0GtPSCVcjiIqo+BhYiIakWxRod9F8oCC4eDqGYYWIiIqFYcuZYDVZEGTRxk6NDMSexyyMwxsBARUa3Y/XA4aFBrN1hwOIhqiIGFiIiMrkSrw+8Ph4O4dxAZAwMLEREZ3fHr95BbWAIne2t08uNwENUcAwsRERndrvMZAICBIW6wtOBXDdUc/xYREZFRaXUCfj//v8uZiYyBgYWIiIzqRMo93C0ohsLWChHNncUuh+oJBhYiIjKq3Q+HgwYEu8GKw0FkJPybRERERqPTCfrNDoeEcjiIjIeBhYiIjObkzfu4k6eGo40lurVoInY5VI8wsBARkdHsSiwdDuof7AZrS37FkPHwbxMRERmFTidgz3kuFke1g4GFiIiM4kxaLjJVRXCQWeKZAA4HkXExsBARkVHsfjgc1LeVK2ysLESuhuobBhYiIqoxQfjf1UFcLI5qAwMLERHV2LlbSqTnPoCdtQV6tXQRuxyqhxhYiIioxsr2DuodxOEgqh0MLEREVCOCIGB3Iq8OotrFwEJERDWSdFuF1HuFsLGScjiIag0DCxER1UjZ3kG9Al1hL7MUuRqqrxhYiIio2gRBwK6Hw0GDQ91FrobqMwYWIiKqtstZeUjJKYC1pRR9W7mJXQ7VYwwsRERUbbvOlQ4H9Qx0gQOHg6gWMbAQEVG17SrbO4jDQVTLGFiIiKharmblITk7H1YWEg4HUa1jYCEiomopm2zbPcAFchsrkauh+o6BhYiIDHbzbgHWH78JABjcmsNBVPsYWIiIyCC37hfilVXHcSdPjRauDhgSytVtqfYxsBARUZVlKovwyqrjSM99AL8m9tgwuTMXi6M6YVBgWb58OcLCwiCXyyGXyxEREYHdu3dX2H78+PGQSCSP3UJCQvRt1q5d+8Q2RUVF1X9XRERkdNl5RXhl1TGk3iuEt5MtNkzpDFe5jdhlUQNhUCz28vLC/Pnz0aJFCwDAjz/+iJEjR+LMmTPlQkiZxYsXY/78+fr7Go0Gbdq0wYsvvliunVwux+XLl8sds7HhPwIiIlNxN1+NMauO43pOAZo2ssWGyV3gobAVuyxqQAwKLMOHDy93/4svvsDy5ctx7NixJwYWhUIBhUKhv79161bcv38fEyZMKNdOIpHA3Z2TtoiITFFuYTFeXX0CV7Pz4SaXYcOUzvB2shO7LGpgqj2HRavVIioqCgUFBYiIiKjSc1avXo1+/frB19e33PH8/Hz4+vrCy8sLw4YNw5kzZ6pbFhERGZHyQQnGrj6BixkqNHGQYcOULvB1the7LGqADJ4plZiYiIiICBQVFcHBwQFbtmxBcHDwU5+XkZGB3bt3Y8OGDeWOBwUFYe3atQgNDYVKpcLixYvRrVs3nD17FgEBARWeT61WQ61W6++rVCpD3woREVUiX63B+DUnkJiuhJO9NTZM6YzmLg5il0UNlEQQBMGQJxQXFyM1NRW5ubmIjo7GDz/8gNjY2KeGlsjISCxcuBC3b9+GtbV1he10Oh3atWuHHj16YMmSJRW2mzNnDubOnfvYcaVSCblcXvU3REREjyks1mDcf04g/sZ9KGytsHFKFwR78ncrGZ9KpYJCoXjq97fBgeWv+vXrh+bNm2PFihUVthEEAYGBgRg2bBj+/e9/P/WcU6ZMwa1btyq9AulJPSze3t4MLERENVRUosXEtfE4cu0uHGWW+HlKZ4R5NRK7LKqnqhpYanzxvCAI5YLDk8TGxiI5ORmTJk2q0vkSEhIQGhpaaTuZTAaZTGZQrUREVDm1Ros3fjqFI9fuwt7aAmsndmJYIZNgUGD56KOPMHjwYHh7eyMvLw9RUVGIiYnBnj17AACzZs1Ceno61q1bV+55q1evRufOndG6devHzjl37lx06dIFAQEBUKlUWLJkCRISErBs2bIavC0iooodTs7Bset3MekZPzSyq3iIuqEp1ugw9efTiL1yB7ZWFvjP+I5o79tY7LKIABgYWLKysjB27FhkZGRAoVAgLCwMe/bsQf/+/QGUTqxNTU0t9xylUono6GgsXrz4iefMzc3F66+/jszMTCgUCoSHhyMuLg6dOnWq5lsiIqpYlqoIU9adRGGxFtGnbuHbV8LR3tdJ7LJEp9HqMD3qDPZfzIbMUoofxnVAZ39nscsi0qvxHBZTUdUxMCJq2N7blIAtZ9L19y2kEvxjYEu83t0fUqlExMrEo9UJmLEpAdvP3oa1hRQrX2uPXi1dxS6LGoiqfn9zLyEiajBO3riHLWfSIZEAG6Z0xog2ntDqBMzffQkTf4zH3fzK5+PVRzqdgA9/PYftZ2/DUirBd2PaMayQSWJgIaIGQasTMGd7EgBgVHtvdG3eBItfaov5z4VCZilFzOU7GLLkII5fvytypXVHpxPwf1sTEX36FiykEnz7cjj6BbuJXRbREzGwEFGD8N+TaTifroKjzBL/GNQSQOm2IC918sG2d7qhuYs9slRqvLzqGL794yq0unoxWl4hQSgNcBtPpEEqAb4Z1QaDQz3ELouoQgwsRFTvKR+U4KvfSzdYndE/EE0cyi+JEOQux/Zpz+D5dl7QCcDCfVfw2n+OIzuvfu4aLwgCvth5EeuO3gQAfPlCG4xs21Tkqogqx8BCRPXeov1XcK+gGC1cHfBahO8T29hZW2LhqDb4+sU2sLWywOHkuxiy+BAOJ+fUcbW1SxAEfL33Mn44lAIAmPe3ULzQ3kvkqoiejoGFiOq1K1l5+p6ET4cHw8qi8l97L7T3wm/vdENLN0fk5Kvx6urj+Gbv5XozRLTkj2QsO3ANADB3RAhe6ewjckVEVcPAQkT1liAImLs9CVqdgAHBbuge4FKl5wW4OWLr1G54qaM3BAFY8mcyXll1DFkq8x4iWh5zDf/efwUA8H9DWmFc12biFkRkAAYWIqq3fk/KxOHku7C2lGL20KfvKv8oW2sLzH8+DItfagt7awscT7mHwYsPIuZydi1VW7t+OHgdC/ZcAgD8Y2BLTOnhL3JFRIZhYCGieqmoRIt/7bgIAHijhz98nO2qdZ6RbZti+7RnEOwhx72CYoxfE48Fey6hRKszZrm16qejN/D5ztLP4t2+AZjau4XIFREZjoGFiOqllXHXkZ77AB4KG7zVq3mNzuXv4oDNb3fF2C6lE3aXx1zDSyuP4XbuA2OUWquiTqTi422l68+82bM53usXIHJFRNXDwEJE9U567gN8F5MMAPhoSCvYWdd4Y3rYWFngX8+2xndj2sFRZolTN+9jyJKD2H8hq8bnri2bT9/CrC2JAICJ3fwwc1BLSCQNc/sBMn8MLERU78zbdRFFJTp08nPCsDDjLoY2JNQDO9/tjjAvBXILSzB53Ul8vuMCijWmNUS0/extfPDLWQgC8GoXH3w8rBXDCpk1BhYiqleOXruLnecyIJWUXsZcG1/SPs52+OXNCEzs5gcA+OFQCl5ccRRp9wqN/lrVsed8JmZsSoBOAEZ38MZnI1ozrJDZY2AhonpDo9Vh7sP9gl7p7IMQT0WtvZbM0gKfDA/GyrHtIbexxNm0XAxZchB7zmfU2mtWxR8XszBt42lodQKeC2+Kec+FNthdqKl+YWAhonpj44lUXMrMg8LWCn/v37JOXnNAiDt2Te+OcJ9GyCvS4M31p/HptvNQa7R18vqPirtyB2+tP40SrYChYR748oUwWDCsUD3BwEJE9cL9gmJ8vbd0UbQPBgSisb11nb22V2M7/PeNCLzRs3Rtkx+P3sTzy4/gRk5BndVw5FoOpqw7iWKtDgND3LBodFtYPmVVXyJzwr/NRFQvLNx3GcoHJQhyd8TLnep+uXkrCylmDW6FNeM7orGdFc6nqzDs20PYfvZ2rb92/I17mLT2JNQaHfoEueLbl9s9dQsCInPDv9FEZPaSbiux4XgqAGDOiBBRexZ6B7li1/Tu6NisMfLVGkzbeAazNieiqKR2hojOpN7HhDXxeFCiRfeAJvhuTDtYW/JXO9U//FtNRGZNEATM/e0CdAIwNMwDXfydxS4JHgpbbJzSBe/0bgGJpHRuzbPLDiM5O9+or3M+XYnX/nMC+WoNuvg7YeXYDrCxsjDqaxCZCgYWIjJrO85l4MSNe7CxkuKjIa3ELkfP0kKKDwa2xLqJndDEwRqXMvMwYukhbD59yyjnv5ihwqurjyOvSIMOvo2xelxH2FozrFD9xcBCRGarsFiDebtK98h5u1cLNG1kK3JFj+se4IJd73ZHhL8zCou1eP+/Z/HBL2dRWKyp9jmvZuXh1R+OI7ewBG28G2HNhI6wl9V8NV8iU8bAQkRma3nMNWQoi+DV2Bavm/Duw65yG6yf3Bnv9QuEVAL8euoWRi49jCtZeQaf6/qdfLzyw3HcLShGiKcc6yZ0gqONVS1UTWRaGFiIyCyl3i3EirjrAIDZQ1uZ/NwNC6kE0/sF4OfJXeDqKMPV7HyMWHoIm+JTIQhClc6RercQr6w6jjt5agS5O2L9pM5Q2DGsUMPAwEJEZunznaX793Rr4YyBIe5il1NlEc2dsWt6d3QPaIKiEh1mRifivU0JyFdXPkR0634hXl51DJmqIrRwdcD6yZ3rdK0ZIrExsBCR2Tl49Q72XsiChVSCT4eHmN0+OU0cZPhxQid8OKglLKQSbE24jRHfHsKF26onts9UFuGVVceRnvsAfk3ssWFyZzRxkNVx1UTiYmAhIrNSotVh7vYLAIDXInwR6OYockXVI5VK8HavFoh6vQs8FDa4nlOAZ787jPXHbpYbIsrOK8Irq44h9V4hvJ1ssWFKZ7jKbUSsnEgcDCxEZFbWHb2J5Ox8ONlbY0a/QLHLqbGOzZyw893u6BPkimKNDrO3nsc7G89AVVSCu/lqvPrDcVzPKYCnwgYbJneBh8L0roQiqgu8Do6IzEZOvhqL9pXuF/ThwJZQ2NaPCadO9tb44bUOWH0oBQv2XMLOcxlIvKWEnbUFrmTlw00uw4YpXeDtZCd2qUSiYWAhIrPx1Z7LyFNr0LqpHC928Ba7HKOSSiWY0sMf7Zs1xrQNZ5B6rxBA6XyXnyd3QbMm9iJXSCQuDgkRkVk4dysX/z2VBgCYMzwEFlLzmmhbVe18GmPXu90xsq0ngtwd8fPkzmjh6iB2WUSiYw8LEZk8nU7AnN+SIAjA38KbokMzJ7FLqlUKOyssfilc7DKITAp7WIjI5G1NSMfp1FzYWVvgn4ODxC6HiETAwEJEJi1frUHk7ksAgGl9AuDGS3qJGiQGFiIyad/+eRV38tRo5myHic80E7scIhIJAwsRmazrd/Lxn0MpAICPhwVDZmna+wURUe1hYCEik/X5zoso0Qro1dIFfYJcxS6HiETEwEJEJunPS1n481I2rCwk+HhYsNntF0RExsXAQkQmR63R4l87LgIAJnbzQ3MXrkNC1NAZFFiWL1+OsLAwyOVyyOVyREREYPfu3RW2j4mJgUQieex26dKlcu2io6MRHBwMmUyG4OBgbNmypXrvhojqhTWHbyAlpwAujjK806eF2OUQkQkwKLB4eXlh/vz5OHnyJE6ePIk+ffpg5MiRSEpKqvR5ly9fRkZGhv4WEBCgf+zo0aMYPXo0xo4di7Nnz2Ls2LEYNWoUjh8/Xr13RERmLUtVhG//uAoAmDkoCI429WO/ICKqGYnw6D7m1eDk5ISvvvoKkyZNeuyxmJgY9O7dG/fv30ejRo2e+PzRo0dDpVKV66kZNGgQGjdujI0bN1a5DpVKBYVCAaVSCblcbvD7ICLT8P6mBGw+k4623o2w+a2ukNbTJfiJqFRVv7+rPYdFq9UiKioKBQUFiIiIqLRteHg4PDw80LdvXxw4cKDcY0ePHsWAAQPKHRs4cCCOHDlS6TnVajVUKlW5GxGZt1M372PzmXQAwNwRIQwrRKRncGBJTEyEg4MDZDIZ3nzzTWzZsgXBwcFPbOvh4YGVK1ciOjoamzdvRsuWLdG3b1/ExcXp22RmZsLNza3c89zc3JCZmVlpHZGRkVAoFPqbt3f92rmVqKEp2y8IAEZ18EIb70biFkREJsXgzQ9btmyJhIQE5ObmIjo6GuPGjUNsbOwTQ0vLli3RsmVL/f2IiAikpaXh66+/Ro8ePfTH/3q5oiAIT72EcdasWXj//ff191UqFUMLkRn75VQaEtOVcJRZ4h8DuV8QEZVncGCxtrZGixals/Y7dOiA+Ph4LF68GCtWrKjS87t06YL169fr77u7uz/Wm5Kdnf1Yr8tfyWQyyGQyA6snIlOkfFCCL/dcBgBM7xcAF0f+2yai8mq8DosgCFCr1VVuf+bMGXh4eOjvR0REYN++feXa7N27F127dq1paURkJhbvv4q7BcVo7mKPcV2biV0OEZkgg3pYPvroIwwePBje3t7Iy8tDVFQUYmJisGfPHgClwzTp6elYt24dAGDRokVo1qwZQkJCUFxcjPXr1yM6OhrR0dH6c06fPh09evTAggULMHLkSGzbtg379+/HoUOHjPg2ichUXc3Kw7qjNwAAnw4PgZUF17MkoscZFFiysrIwduxYZGRkQKFQICwsDHv27EH//v0BABkZGUhNTdW3Ly4uxgcffID09HTY2toiJCQEO3fuxJAhQ/RtunbtiqioKMyePRsff/wxmjdvjk2bNqFz585GeotEZKoEQcDc7Reg0QnoH+yGHoEuYpdERCaqxuuwmAquw0Jkfn5PysQbP52CtaUU+9/rCR9nO7FLIqI6VuvrsBAR1URRiRb/2nEBAPB6d3+GFSKqFAMLEYliVdx13Lr/AO5yG7zdu7nY5RCRiWNgIaI6dzv3Ab6LuQYAmDUkCHbWBq+wQEQNDAMLEdW5yN2X8KBEi47NGmNEG0+xyyEiM8DAQkR16vj1u9h+9jakEmDOiJCnrmpNRAQwsBBRHdJodfj04X5BL3fyQYinQuSKiMhcMLAQUZ3ZGJ+GS5l5UNha4e8DWj79CUREDzGwEFGdyC0sxsK9pfsFvd8/EE721iJXRETmhIGFiOrEN/uuILewBC3dHDGms4/Y5RCRmWFgIaJadzFDhfXHbgIAPh0RDEvuF0REBuJvDSKqVYIgYM5vSdAJwNBQD3Rt3kTskojIDDGwEFGt2pmYgeMp92BjJcWsIUFil0NEZoqBhYhqzYNiLebtvAgAeLNnc3g15n5BRFQ9DCxEVGuWx17DbWURmjayxZs9uV8QEVUfAwsR1Yq0e4X4PrZ0v6DZQ1vBxspC5IqIyJwxsBBRrfhi50UUa3To2twZg1q7i10OEZk5BhYiMrpDV3OwJykTFlIJPh3O/YKIqOYYWIjIqEq0OszdXrpf0Nguvmjp7ihyRURUH1iKXQARma8HxVpk5xUhS6VGdl4RslVqJKTl4mp2PhrbWeG9foFil0hE9QQDCxGVIwgC8tUaZOepkf1IEMnOK9Ify8orwh2VGnlqTYXn+WBgSyjsrOqwciKqzxhYiBoIQRCgeqAp3yPylwBSdqywWFvl89paWcBVLoObow1c5DK4OsoQ6OaI0R28a/HdEFFDw8BCZOZ0OgH3C4tLw0eeGtmqovL/zVMjS1WEO3lqqDW6Kp/XQWYJ14cBxNXRBq6OMrjJbeAql8Gl7M+OMjjILDmplohqHQMLkRk5m5aLX0/dQpaqCFl5atxRFeFOvholWqHK51DYWpWGEPnDICL/SyB5+JidNX89EJHp4G8kIjORkJaLl1YeRVHJk3tJnOyt4epYvvejNHzYwO1hKHFxlHEBNyIySwwsRGbgRk4BJq6NR1GJDp39nDAszAOu+t4QG7g4yGBtyVUKiKj+YmAhMnF389UYt+YE7hUUo3VTOf4zviPsZfynS0QNC/+XjMiEPSjWYuKPJ3HzbiG8GtsyrBBRg8XAQmSiNFodpm08jbNpuWhkZ4UfJ3aCq6ON2GUREYmCgYXIBAmCgE9+S8L+i9mQWUqxelwHNHdxELssIiLRMLAQmaDvYq5hw/FUSCTA4pfC0d7XSeySiIhExcBCZGKiT93CV79fBgDMGR6CQa3dRa6IiEh8DCxEJuTg1TuYGX0OAPBGD3+M69pM3IKIiEwEAwuRiUi6rcRb609DoxMwoo0nZg4KErskIiKTwcBCZAJu3S/EhDXxyFdr0MXfCV+9GAaplPvzEBGVYWAhEpmysATj18QjO0+Nlm6OWDG2A2SWXD6fiOhRDCxEIioq0WLKTyeRnJ0Pd7kN1kzoCIWtldhlERGZHAYWIpHodAL+/stZnEi5B0eZJdZO7AjPRrZil0VEZJIYWIhEMm/XRew8lwErCwlWjG2PIHe52CUREZksgwLL8uXLERYWBrlcDrlcjoiICOzevbvC9ps3b0b//v3h4uKib//777+Xa7N27VpIJJLHbkVFRdV7R0RmYPWhFPxwKAUA8PWLbdC1RRORKyIiMm0GBRYvLy/Mnz8fJ0+exMmTJ9GnTx+MHDkSSUlJT2wfFxeH/v37Y9euXTh16hR69+6N4cOH48yZM+XayeVyZGRklLvZ2HDPFKqfdp7LwOc7LwAA/jk4CCPbNhW5IiIi0ycRBEGoyQmcnJzw1VdfYdKkSVVqHxISgtGjR+OTTz4BUNrDMmPGDOTm5takDKhUKigUCiiVSsjl7Fon03Qi5R5eXX0cxRodXovwxdwRIZBIePkyETVcVf3+rvYcFq1Wi6ioKBQUFCAiIqJKz9HpdMjLy4OTU/l9UfLz8+Hr6wsvLy8MGzbssR6YJ1Gr1VCpVOVuRKbsalYeJv8Yj2KNDgOC3fDpcIYVIqKqMjiwJCYmwsHBATKZDG+++Sa2bNmC4ODgKj134cKFKCgowKhRo/THgoKCsHbtWvz222/YuHEjbGxs0K1bN1y9erXSc0VGRkKhUOhv3t7ehr4VojqTpSrC+DXxUBVp0M6nEZa8HA4LLgxHRFRlBg8JFRcXIzU1Fbm5uYiOjsYPP/yA2NjYp4aWjRs3YvLkydi2bRv69etXYTudTod27dqhR48eWLJkSYXt1Go11Gq1/r5KpYK3tzeHhMjk5BWVYNSKY7iYoYJ/E3v8+lZXONlbi10WEZFJqOqQkKWhJ7a2tkaLFi0AAB06dEB8fDwWL16MFStWVPicTZs2YdKkSfjll18qDSsAIJVK0bFjx6f2sMhkMshkMkPLJ6pTJVod3v75NC5mqNDEwRprJ3RiWCEiqoYar8MiCEK5no6/2rhxI8aPH48NGzZg6NChVTpfQkICPDw8aloakagEQcDM6HM4eDUHdtYW+M/4jvBxthO7LCIis2RQD8tHH32EwYMHw9vbG3l5eYiKikJMTAz27NkDAJg1axbS09Oxbt06AKVh5bXXXsPixYvRpUsXZGZmAgBsbW2hUCgAAHPnzkWXLl0QEBAAlUqFJUuWICEhAcuWLTPm+ySqc9/su4LNp9NhIZVg2SvtEObVSOySiIjMlkGBJSsrC2PHjkVGRgYUCgXCwsKwZ88e9O/fHwCQkZGB1NRUffsVK1ZAo9Fg6tSpmDp1qv74uHHjsHbtWgBAbm4uXn/9dWRmZkKhUCA8PBxxcXHo1KmTEd4ekTh+Pn4T3/6ZDACY97fW6B3kKnJFRETmrcbrsJgKrsNCpuKPi1mYsu4kdAIwvW8A3usfKHZJREQmq9bXYSGixyWk5eKdDWegE4BRHbwwo1+A2CUREdULDCxERnIjpwCT1sbjQYkWPQNd8MXfQrkwHBGRkTCwEBnB3Xw1xq85gbsFxWjdVI7vxrSDlQX/eRERGQt/oxLV0INiLSb+eBI37hbCq7Et/jO+I+xlBi9xRERElWBgIaoBjVaHaRtP42xaLhrZWeHHiZ3g6sidxomIjI2BhaiaBEHAJ78lYf/FbMgspVg9rgOauziIXRYRUb3EwEJUTd/FXMOG46mQSIDFL7VFe1+npz+JiIiqhYGFqBo2n76Fr36/DAD4dFgwBrXmVhJERLWJgYXIQAev3sGHv54DALzRwx/ju/mJXBERUf3HwEJkgKTbSry1/jQ0OgHD23hi5qAgsUsiImoQGFiIqig99wEmrIlHvlqDLv5O+PrFMEilXBiOiKgucLEIqhPHr9/F70lZCHRzQKiXAoFujma1sJqysATj/nMC2XlqtHRzxIqxHSCztBC7LCKiBoOBhWpdUYkWUzecQU6+Wn9MZilFiKccYV6NEOalQJiXAv5NHEyyx6KoRIspP51EcnY+3OU2WDOhIxS2VmKXRUTUoDCwUK379dQt5OSr0cRBhkA3ByTeUiJPrcHp1FycTs3Vt7O3tkDrpgq08W6E0KYKtPFqBG8nW1H349HpBPz9l7M4kXIPjjJLrJ3YEZ6NbEWrh4iooWJgoVql0eqwMu46AOCd3s0xvpsfdDoBN+4W4Nwt5cNbLs7fVqKgWIvjKfdwPOWe/vmN7KwQ2lTxsBemtDfGXW5TZyFm3q6L2HkuA1YWEqwY2x5B7hVvfU5ERLWHgYVq1a7zmUi9Vwgne2uM7ugDAJBKJfB3cYC/iwOeDW8KoDTYJN/J1weYxFtKXMzIQ25hCQ5ezcHBqzn6c7o4yhDWVFFuOMnZQWb02lcfSsEPh1IAAF+/2AZdWzQx+msQEVHVMLBQrREEActjrgEAxndtBlvriiepWlpIEeQuR5C7HKM6eAMA1BotrmTm4+zDAHP2Vi6uZufjTp4af1zKxh+XsvXPb9rIFmFeCoR6lQ4ltW6qqNE8k12JGfh85wUAwD8HB2Fk26bVPhcREdUcAwvVmtgrd3AxQwU7awu8FuFr8PNllhYIfRhCyjwo1uJChhJn05RITC8NMdfvFCA99wHScx9g9/lMfVu/JvalIebhvJgQTznsrJ/+V/5Eyj3M2JQAQQBei/DFGz38Da6diIiMi4GFak1Z78rLnXzQyM7aKOe0tbZAe1+ncvv2qIpKcD5dicSyOTHpuUi79wApOQVIySnAtoTbAACpBAhwdXzYC6NAqFcjtPJwLHd5cnJ2HqasO4lijQ4Dgt3w6fAQUSf9EhFRKQYWqhWnbt7H8ZR7sLKQYHL32l26Xm5jha7Nm6Br8//NMblXUIzEdCXOpeXi7C0lEtNzkaVS43JWHi5n5eHXU7cAAFYWErR0d0SYV2kPzHcHrkH5oATtfBphycvhsDDBy6yJiBoiBhaqFd/HlvauPNu2KTwUdX8ZsJO9NXoGuqBnoIv+WJaqSD+pt+y/9wtLcD5dhfPpKn07vyb2+GFcR9hYcWE4IiJTwcBCRnc1Kw/7LmRBIgHe6Gk68z/c5DboH2yD/sFuAEonBd+6/0A/jHQuTYlirQ7/HtUWTvbGGcIiIiLjYGAho/s+tnTdlQHBbmjh6ihyNRWTSCTwdrKDt5MdhoZ5iF0OERFVwnw2cyGzcDv3AbYlpAMA3uzZXORqiIiovmBgIaP64WAKNDoBEf7OCPdpLHY5RERUTzCwkNHcLyjGxhOpAIC3erF3hYiIjIeBhYzmx6M38KBEixBPOboHcBl7IiIyHgYWMorCYg3WHrkBoLR3hYutERGRMTGwkFFEnUhDbmEJfJ3tMLg1r7ghIiLjYmChGivW6PDDwdJLmd/o0ZyrwxIRkdExsFCN/Xb2Nm4ri+DiKMNz7birMRERGR8DC9WITifol+Gf9Iwfl7MnIqJawcBCNbL/YhaSs/PhaGOJMZ19xC6HiIjqKQYWqjZBEPBdTGnvyqtdfOFoYyVyRUREVF8xsFC1HU+5h4S0XFhbSjGhWzOxyyEionqMgYWqrWzuyovtveDqaCNyNUREVJ8xsFC1XLitQszlO5BKgNd7+ItdDhER1XMGBZbly5cjLCwMcrkccrkcERER2L17d6XPiY2NRfv27WFjYwN/f398//33j7WJjo5GcHAwZDIZgoODsWXLFsPeBdW5st6VoWGe8HW2F7kaIiKq7wwKLF5eXpg/fz5OnjyJkydPok+fPhg5ciSSkpKe2D4lJQVDhgxB9+7dcebMGXz00Ud49913ER0drW9z9OhRjB49GmPHjsXZs2cxduxYjBo1CsePH6/ZO6Nak3q3EDvO3QYAvNmTvStERFT7JIIgCDU5gZOTE7766itMmjTpscdmzpyJ3377DRcvXtQfe/PNN3H27FkcPXoUADB69GioVKpyPTWDBg1C48aNsXHjxirXoVKpoFAooFQqIZfLa/CO6Glmb03E+mOp6Bnogh8ndhK7HCIiMmNV/f6u9hwWrVaLqKgoFBQUICIi4oltjh49igEDBpQ7NnDgQJw8eRIlJSWVtjly5Eh1S6NadCdPjf+evAWgdJNDIiKiumBp6BMSExMRERGBoqIiODg4YMuWLQgODn5i28zMTLi5uZU75ubmBo1Gg5ycHHh4eFTYJjMzs9I61Go11Gq1/r5KpTL0rVA1rDmcgmKNDuE+jdDZz0nscoiIqIEwuIelZcuWSEhIwLFjx/DWW29h3LhxuHDhQoXtJZLyG+GVjUA9evxJbf567K8iIyOhUCj0N29vb0PfChlIVVSCn47eBAC81bP5U39GRERExmJwYLG2tkaLFi3QoUMHREZGok2bNli8ePET27q7uz/WU5KdnQ1LS0s4OztX2uavvS5/NWvWLCiVSv0tLS3N0LdCBtpwPBV5ag1auDqgX6vKfz5ERETGVON1WARBKDc086iIiAjs27ev3LG9e/eiQ4cOsLKyqrRN165dK31dmUymv7y67Ea1p6hEi9WHUgAAb/ZsDqmUvStERFR3DJrD8tFHH2Hw4MHw9vZGXl4eoqKiEBMTgz179gAo7fVIT0/HunXrAJReEbR06VK8//77mDJlCo4ePYrVq1eXu/pn+vTp6NGjBxYsWICRI0di27Zt2L9/Pw4dOmTEt0k1tfl0Ou7kqeGpsMGINp5il0NERA2MQYElKysLY8eORUZGBhQKBcLCwrBnzx70798fAJCRkYHU1FR9ez8/P+zatQvvvfceli1bBk9PTyxZsgTPP/+8vk3Xrl0RFRWF2bNn4+OPP0bz5s2xadMmdO7c2UhvkWpKqxOwIq50objJ3f1hbckFkomIqG7VeB0WU8F1WGrPjnO38c6GM2hkZ4Uj/+wDO2uDLy4jIiJ6olpfh4UaBkEQ9Mvwj4toxrBCRESiYGChSh1KzsH5dBVsrSwwrmszscshIqIGioGFKrU8prR35aVO3nCytxa5GiIiaqgYWKhCCWm5OHLtLiylEkzuzk0OiYhIPAwsVKHvH/aujGzbFE0b2YpcDRERNWQMLPREydn5+P1C6QrEb/Zk7woREYmLgYWeaGXcNQgC0D/YDQFujmKXQ0REDRwDCz0mQ/kAW86kAwDe6tVc5GqIiIgYWOgJVh9MQYlWQGc/J7TzaSx2OURERAwsVF5uYTE2nCjdXoG9K0REZCoYWKicdUdvorBYi1YecvQMdBG7HCIiIgAMLPSIwmIN1hxOAVDauyKRSESuiIiIqBQDC+n9Nz4N9wtL4ONkhyGt3cUuh4iISI+BhQAAJVodVh0s7V15vYc/LC34V4OIiEwHv5UIALDj3G2k5z5AEwdrvNDeS+xyiIiIymFgIeh0gn6Twwnd/GBjZSFyRUREROUxsBAOXM7Glax8OMgs8WoXX7HLISIiegwDC+l7V8Z08YHC1krkaoiIiB7HwNLAxd+4h5M378PaQopJ3fzELoeIiOiJGFgauLLelefbe8FVbiNyNURERE/GwNKAXcxQ4c9L2ZBKgDd6+ItdDhERUYUYWBqwFbGlvSuDQz3QrIm9yNUQERFVjIGlgUq7V4jt5zIAAG/15CaHRERk2hhYGqhVB69DqxPQPaAJWjdViF0OERFRpRhYGqCcfDU2xacBKN3kkIiIyNQxsDRAPx65AbVGhzbejRDh7yx2OURERE/FwNLA5Ks1+PHIDQClc1ckEom4BREREVUBA0sDs/F4KlRFGvi72GNAsJvY5RAREVUJA0sDotZo8cOh6wCAN3s2h1TK3hUiIjIPDCwNyNYz6chSqeEut8GzbZuKXQ4REVGVMbA0EFqdgBWxpb0rk7v7wdqSP3oiIjIf/NZqIPYmZeJ6TgEUtlZ4qZOP2OUQEREZhIGlARAEAcsfLsM/LsIXDjJLkSsiIiIyDANLA3Dk2l2cu6WEjZUU47o2E7scIiIigzGwNADLY0p7V17q6ANnB5nI1RARERmOgaWeO3crF4eSc2AhlWBydz+xyyEiIqoWBpZ67vuHc1dGtvGEV2M7kashIiKqHgaWeuz6nXzsPp8JAHijJzc5JCIi82VQYImMjETHjh3h6OgIV1dXPPvss7h8+XKlzxk/fjwkEsljt5CQEH2btWvXPrFNUVFR9d4VAQBWHbwOQQD6tXJFS3dHscshIiKqNoMCS2xsLKZOnYpjx45h37590Gg0GDBgAAoKCip8zuLFi5GRkaG/paWlwcnJCS+++GK5dnK5vFy7jIwM2NjYVO9dGdHqQyn4YucFHLx6B0UlWrHLqbIsVRGiT6UDAN7qxd4VIiIybwYtyLFnz55y99esWQNXV1ecOnUKPXr0eOJzFAoFFAqF/v7WrVtx//59TJgwoVw7iUQCd3d3Q8qpE/+NT8PlrDysOpgCGyspuvg7o2egC3oGusCvib3J7nb8n0MpKNbq0KmZE9r7OoldDhERUY3UaAUxpVIJAHByqvoX4urVq9GvXz/4+vqWO56fnw9fX19otVq0bdsW//rXvxAeHl6T8mpMEAS82zcAsVeyEXvlDrJUasRcvoOYy3cAAF6NbdHjYXjp2twZjjZWotZbRllYgvXHbgJg7woREdUPEkEQhOo8URAEjBw5Evfv38fBgwer9JyMjAx4e3tjw4YNGDVqlP74sWPHkJycjNDQUKhUKixevBi7du3C2bNnERAQ8MRzqdVqqNVq/X2VSgVvb28olUrI5fLqvKVKCYKAK1n5iL2SjbgrOTiRcg/FWp3+cUupBO18G+t7X4I95KLthrzsQDK++v0ygtwdsXt6d5PtBSIiIlKpVFAoFE/9/q52YJk6dSp27tyJQ4cOwcvLq0rPiYyMxMKFC3H79m1YW1tX2E6n06Fdu3bo0aMHlixZ8sQ2c+bMwdy5cx87XluB5a8KizU4fv0eYq/cQdyVO7ieU34eTxMHa3QPKA0vzwQ0QZM6WrCtqESLbvP/xN2CYiwa3RbPhnNXZiIiMl21GlimTZuGrVu3Ii4uDn5+VVuMTBAEBAYGYtiwYfj3v//91PZTpkzBrVu3sHv37ic+Xtc9LE+TercQsVdLw8uR5BwUFJefoBvaVIEegU3QM9AV4T6NYGVRO1eU/3T0Bj7elgSvxraI+aAXLGvpdYiIiIyhqoHFoDksgiBg2rRp2LJlC2JiYqocVoDSK4ySk5MxadKkKr1OQkICQkNDK2wjk8kgk5nOMvM+znYY6+yLsV18UazR4XTqfX3vS9JtFRLTlUhMV2LZgWtwlFmiawtn9Ah0QY8AF3g7GWdBN41WhxVx1wEAr/fwZ1ghIqJ6w6DAMnXqVGzYsAHbtm2Do6MjMjNLFyVTKBSwtbUFAMyaNQvp6elYt25dueeuXr0anTt3RuvWrR8779y5c9GlSxcEBARApVJhyZIlSEhIwLJly6r7vkRlbVl6NVEXf2fMHBSE7LwiHLqag9grd3Dwag7uFRTj96Qs/J6UBQDwd7FHz0AX9Ah0QRc/Z9haW1TrdXcmZuDW/QdwtrfGi+29jfmWiIiIRGVQYFm+fDkAoFevXuWOr1mzBuPHjwdQOrE2NTW13ONKpRLR0dFYvHjxE8+bm5uL119/HZmZmVAoFAgPD0dcXBw6depkSHkmy9XRBs+188Jz7byg0wk4f1uJuCt3EHvlDk6n5uL6nQJcv1OANYdvwNpSis5+TvrJuy1cHao0aVYQBP0mhxO6Nat26CEiIjJF1Z50a2qqOgZmalRFJTiSnIPYKzmIu3IH6bkPyj3uobDR9750a9EECtsnXzp94FI2JqyNh721BY78sy8UdqZxiTUREVFlamUOCxmf3MYKg1p7YFBrDwiCgGt38hF7pXT46Pj1u8hQFiEqPg1R8WmwkErQ1ruRPsCENlXA4uGl02W9K2O6+DKsEBFRvcMeFhNWVKLF8ZR7+uGj5Oz8co83trPCMwEuCHB1wDf7rsDaQoqDM3vDTS7+lgZERERVwR6WesDGykI/l+VjAOm5DxD38MqjQ1dzcL+wBNvP3ta3f65dU4YVIiKqlxhYzEjTRrZ4uZMPXu7kgxKtDglpufrelwK1BlN7txC7RCIiolrBISEiIiISTVW/v7myGBEREZk8BhYiIiIyeQwsREREZPIYWIiIiMjkMbAQERGRyWNgISIiIpPHwEJEREQmj4GFiIiITB4DCxEREZk8BhYiIiIyeQwsREREZPIYWIiIiMjkMbAQERGRyWNgISIiIpNnKXYBxiIIAoDSbaqJiIjIPJR9b5d9j1ek3gSWvLw8AIC3t7fIlRAREZGh8vLyoFAoKnxcIjwt0pgJnU6H27dvw9HRERKJROxy6pRKpYK3tzfS0tIgl8vFLsds8XM0Dn6OxsHP0Tj4ORpHbX6OgiAgLy8Pnp6ekEornqlSb3pYpFIpvLy8xC5DVHK5nP8gjYCfo3HwczQOfo7Gwc/ROGrrc6ysZ6UMJ90SERGRyWNgISIiIpPHwFIPyGQyfPrpp5DJZGKXYtb4ORoHP0fj4OdoHPwcjcMUPsd6M+mWiIiI6i/2sBAREZHJY2AhIiIik8fAQkRERCaPgcWMRUZGomPHjnB0dISrqyueffZZXL58WeyyzFpkZCQkEglmzJghdilmKT09Ha+++iqcnZ1hZ2eHtm3b4tSpU2KXZVY0Gg1mz54NPz8/2Nrawt/fH5999hl0Op3YpZm0uLg4DB8+HJ6enpBIJNi6dWu5xwVBwJw5c+Dp6QlbW1v06tULSUlJ4hRrwir7HEtKSjBz5kyEhobC3t4enp6eeO2113D79u06qY2BxYzFxsZi6tSpOHbsGPbt2weNRoMBAwagoKBA7NLMUnx8PFauXImwsDCxSzFL9+/fR7du3WBlZYXdu3fjwoULWLhwIRo1aiR2aWZlwYIF+P7777F06VJcvHgRX375Jb766it8++23Ypdm0goKCtCmTRssXbr0iY9/+eWX+Oabb7B06VLEx8fD3d0d/fv312/rQqUq+xwLCwtx+vRpfPzxxzh9+jQ2b96MK1euYMSIEXVTnED1RnZ2tgBAiI2NFbsUs5OXlycEBAQI+/btE3r27ClMnz5d7JLMzsyZM4VnnnlG7DLM3tChQ4WJEyeWO/bcc88Jr776qkgVmR8AwpYtW/T3dTqd4O7uLsyfP19/rKioSFAoFML3338vQoXm4a+f45OcOHFCACDcvHmz1uthD0s9olQqAQBOTk4iV2J+pk6diqFDh6Jfv35il2K2fvvtN3To0AEvvvgiXF1dER4ejlWrVoldltl55pln8Mcff+DKlSsAgLNnz+LQoUMYMmSIyJWZr5SUFGRmZmLAgAH6YzKZDD179sSRI0dErMz8KZVKSCSSOulJrTd7CTV0giDg/fffxzPPPIPWrVuLXY5ZiYqKwunTpxEfHy92KWbt+vXrWL58Od5//3189NFHOHHiBN59913IZDK89tprYpdnNmbOnAmlUomgoCBYWFhAq9Xiiy++wMsvvyx2aWYrMzMTAODm5lbuuJubG27evClGSfVCUVER/vnPf+KVV16pk32aGFjqiXfeeQfnzp3DoUOHxC7FrKSlpWH69OnYu3cvbGxsxC7HrOl0OnTo0AHz5s0DAISHhyMpKQnLly9nYDHApk2bsH79emzYsAEhISFISEjAjBkz4OnpiXHjxoldnlmTSCTl7guC8NgxqpqSkhK89NJL0Ol0+O677+rkNRlY6oFp06bht99+Q1xcXIPfsdpQp06dQnZ2Ntq3b68/ptVqERcXh6VLl0KtVsPCwkLECs2Hh4cHgoODyx1r1aoVoqOjRarIPP3jH//AP//5T7z00ksAgNDQUNy8eRORkZEMLNXk7u4OoLSnxcPDQ388Ozv7sV4XerqSkhKMGjUKKSkp+PPPP+tsF2zOYTFjgiDgnXfewebNm/Hnn3/Cz89P7JLMTt++fZGYmIiEhAT9rUOHDhgzZgwSEhIYVgzQrVu3xy6rv3LlCnx9fUWqyDwVFhZCKi3/q9nCwoKXNdeAn58f3N3dsW/fPv2x4uJixMbGomvXriJWZn7KwsrVq1exf/9+ODs719lrs4fFjE2dOhUbNmzAtm3b4OjoqB+nVSgUsLW1Fbk68+Do6PjYnB97e3s4OztzLpCB3nvvPXTt2hXz5s3DqFGjcOLECaxcuRIrV64UuzSzMnz4cHzxxRfw8fFBSEgIzpw5g2+++QYTJ04UuzSTlp+fj+TkZP39lJQUJCQkwMnJCT4+PpgxYwbmzZuHgIAABAQEYN68ebCzs8Mrr7wiYtWmp7LP0dPTEy+88AJOnz6NHTt2QKvV6r93nJycYG1tXbvF1fp1SFRrADzxtmbNGrFLM2u8rLn6tm/fLrRu3VqQyWRCUFCQsHLlSrFLMjsqlUqYPn264OPjI9jY2Aj+/v7C//3f/wlqtVrs0kzagQMHnvj7cNy4cYIglF7a/Omnnwru7u6CTCYTevToISQmJopbtAmq7HNMSUmp8HvnwIEDtV4bd2smIiIik8c5LERERGTyGFiIiIjI5DGwEBERkcljYCEiIiKTx8BCREREJo+BhYiIiEweAwsRERGZPAYWIiIiMnkMLERUb82ZMwdt27YVuwwiMgIGFiKqFyQSCbZu3Sp2GURUSxhYiIiIyOQxsBCRUfXq1QvTpk3DjBkz0LhxY7i5uWHlypUoKCjAhAkT4OjoiObNm2P37t3658TGxqJTp06QyWTw8PDAP//5T2g0mnLnfPfdd/Hhhx/CyckJ7u7umDNnjv7xZs2aAQD+9re/QSKR6O+X+emnn9CsWTMoFAq89NJLyMvLq82PgIhqAQMLERndjz/+iCZNmuDEiROYNm0a3nrrLbz44ovo2rUrTp8+jYEDB2Ls2LEoLCxEeno6hgwZgo4dO+Ls2bNYvnw5Vq9ejc8///yxc9rb2+P48eP48ssv8dlnn2Hfvn0AgPj4eADAmjVrkJGRob8PANeuXcPWrVuxY8cO7NixA7GxsZg/f37dfRhEZBTcrZmIjKpXr17QarU4ePAgAECr1UKhUOC5557DunXrAACZmZnw8PDA0aNHsX37dkRHR+PixYuQSCQAgO+++w4zZ86EUqmEVCp97JwA0KlTJ/Tp00cfPiQSCbZs2YJnn31W32bOnDn46quvkJmZCUdHRwDAhx9+iLi4OBw7dqwuPg4iMhL2sBCR0YWFhen/bGFhAWdnZ4SGhuqPubm5AQCys7Nx8eJFRERE6MMKAHTr1g35+fm4devWE88JAB4eHsjOzn5qLc2aNdOHFUOeR0SmhYGFiIzOysqq3H2JRFLuWFk40el0EAShXFgBgLKO30ePP+mcOp2uWrVU5XlEZFoYWIhIVMHBwThy5AgeHZ0+cuQIHB0d0bRp0yqfx8rKClqttjZKJCITwMBCRKJ6++23kZaWhmnTpuHSpUvYtm0bPv30U7z//vuQSqv+K6pZs2b4448/kJmZifv379dixUQkBgYWIhJV06ZNsWvXLpw4cQJt2rTBm2++iUmTJmH27NkGnWfhwoXYt28fvL29ER4eXkvVEpFYeJUQERERmTz2sBAREZHJY2AhIiIik8fAQkRERCaPgYWIiIhMHgMLERERmTwGFiIiIjJ5DCxERERk8hhYiIiIyOQxsBAREZHJY2AhIiIik8fAQkRERCaPgYWIiIhM3v8DKeQAG3P2ZMYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -848,22 +950,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "90914451-ac49-44f6-98d5-4eb6b5f8dbf6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
-    "df_2021.groupby(\"pickup_area\").agg({'ID':'count'}).orderBy(F.col('pickup_area').asc()).toPandas().plot(x=\"pickup_area\",y=\"count(ID)\")"
+    "df_total = df_2021.groupby(\"pickup_area\").agg({'ID':'count'}).orderBy(F.col('pickup_area').asc()).toPandas()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
+   "id": "748f2fd1-b2d6-47bb-8315-b4034acbbcee",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "ax = df_total.plot(x=\"pickup_area\",y=\"count(ID)\", kind='bar')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
    "id": "5758417d-edcd-448a-9535-16f5b9c1528a",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='dropoff_area'>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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gAQAAXEPQAAAAriFoAAAA1xA0AACAawgaAADANUEFjUcffVQejyfgVadOHbf6BgAAqrnwYCucffbZWrhwofNzWFhYSDsEAABOHEEHjfDwcO5iAACA4xL0MxpZWVlKSUlRRkaGrr32Wm3durXU9fPz85WbmxvwAgAAJ4eggsYFF1ygadOmad68eXr99deVnZ2ttm3b6scffyyxTmZmphITE51XampquTsNAACqB4+ZWVkr5+Xl6YwzztD999+vu+++u9h18vPzlZ+f7/ycm5ur1NRU5eTkKCEhoaybrhTpD84O+Hn7uO6V1BMAACpWbm6uEhMTg75+B/2MxtFiY2PVrFkzZWVllbiOz+eTz+crz2YAAEA1Va55NPLz87VhwwbVrVs3VP0BAAAnkKCCxr333qt//etf2rZtm7788kv17dtXubm5Gjx4sFv9AwAA1VhQb5385z//0Z///Gf98MMPqlWrlv70pz9p2bJlSktLc6t/AACgGgsqaMyYMcOtfgAAgBMQ33UCAABcQ9AAAACuIWgAAADXEDQAAIBrCBoAAMA1BA0AAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuIagAQAAXEPQAAAAriFoAAAA1xA0AACAawgaAADANQQNAADgGoIGAABwDUEDAAC4hqABAABcQ9AAAACuIWgAAADXEDQAAIBrCBoAAMA1BA0AAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuKZcQSMzM1Mej0cjRowIUXcAAMCJpMxB46uvvtKkSZPUvHnzUPYHAACcQMoUNA4ePKgBAwbo9ddfV82aNUtdNz8/X7m5uQEvAABwcihT0Bg2bJi6d++uTp06HXPdzMxMJSYmOq/U1NSybBIAAFRDQQeNGTNmaNWqVcrMzDyu9UeOHKmcnBzntWvXrqA7CQAAqqfwYFbetWuXhg8frvnz5ysqKuq46vh8Pvl8vjJ1DgAAVG9BBY2VK1dq3759atmypVNWUFCgxYsX66WXXlJ+fr7CwsJC3kkAAFA9BRU0Lr30Uq1duzag7Prrr1fjxo31wAMPEDIAAECAoIJGfHy8mjZtGlAWGxurpKSkIuUAAADMDAoAAFwT1B2N4ixatCgE3QAAACci7mgAAADXEDQAAIBrCBoAAMA1BA0AAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuIagAQAAXEPQAAAAriFoAAAA1xA0AACAawgaAADANeGV3QGgOkl/cLbz7+3juldiTwCgeuCOBgAAcA1BAwAAuIagAQAAXMMzGkAVxLMgAE4U3NEAAACuIWgAAADXEDQAAIBrCBoAAMA1BA0AAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuCaooDFx4kQ1b95cCQkJSkhIUJs2bTR37ly3+gYAAKq5oIJGvXr1NG7cOK1YsUIrVqzQJZdcol69emndunVu9Q8AAFRj4cGs3LNnz4Cfn3zySU2cOFHLli3T2WefHdKOAQCA6i+ooHG0goICzZw5U3l5eWrTpk2J6+Xn5ys/P9/5OTc3t6ybBAAA1UzQQWPt2rVq06aNfv31V8XFxen9999XkyZNSlw/MzNTY8aMKVcn3ZD+4OyAn7eP615JPQEA4MQV9KdOzjzzTK1evVrLli3TrbfeqsGDB2v9+vUlrj9y5Ejl5OQ4r127dpWrwwAAoPoI+o5GZGSkGjRoIElq1aqVvvrqK40fP16vvfZasev7fD75fL7y9RIAAFRL5Z5Hw8wCnsEAAADwC+qOxqhRo9StWzelpqbqwIEDmjFjhhYtWqSPP/7Yrf4BAIBqLKigsXfvXl133XXas2ePEhMT1bx5c3388cfq3LmzW/0DAADVWFBBY/LkyW71AwAAnID4rhMAAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuIagAQAAXEPQAAAAriFoAAAA1xA0AACAawgaAADANQQNAADgGoIGAABwDUEDAAC4JryyOwCEQvqDswN+3j6ueyX1BABwNO5oAAAA1xA0AACAawgaAADANQQNAADgGoIGAABwDZ86KcbRn2Dg0wsAAJQddzQAAIBrCBoAAMA1BA0AAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuIagAQAAXBNU0MjMzFTr1q0VHx+v5ORk9e7dWxs3bnSrbwAAoJoLKmj861//0rBhw7Rs2TItWLBAv/32my677DLl5eW51T8AAFCNhQez8scffxzw85QpU5ScnKyVK1fqoosuCmnHAABA9RdU0PijnJwcSdIpp5xS4jr5+fnKz893fs7NzS3PJgEAQDVS5odBzUx333232rdvr6ZNm5a4XmZmphITE51XampqWTcJAACqmTIHjdtvv13ffPONpk+fXup6I0eOVE5OjvPatWtXWTcJAACqmTK9dXLHHXfogw8+0OLFi1WvXr1S1/X5fPL5fGXqHAAAqN6CChpmpjvuuEPvv/++Fi1apIyMDLf6BQAATgBBBY1hw4bp7bff1j//+U/Fx8crOztbkpSYmKjo6GhXOggAAKqvoJ7RmDhxonJyctShQwfVrVvXeb3zzjtu9Q8AAFRjQb91AgAAcLz4rhMAAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuIagAQAAXEPQAAAAriFoAAAA1xA0AACAa8IruwMAAKBypD84O+Dn7eO6h3wb3NEAAACuIWgAAADXEDQAAIBrCBoAAMA1BA0AAOAaggYAAHANQQMAALiGeTRQ5VTE57oBABWDOxoAAMA1BA0AAOAaggYAAHANQQMAALiGoAEAAFxD0AAAAK4haAAAANcQNAAAgGsIGgAAwDUEDQAA4BqCBgAAcA1BAwAAuIagAQAAXEPQAAAAriFoAAAA1xA0AACAa4IOGosXL1bPnj2VkpIij8ejWbNmudAtAABwIgg6aOTl5emcc87RSy+95EZ/AADACSQ82ArdunVTt27djnv9/Px85efnOz/n5uYGu0kAAFBNuf6MRmZmphITE51Xamqq25sEAABVhOtBY+TIkcrJyXFeu3btcnuTAACgigj6rZNg+Xw++Xy+kLSV/uBs59/bx3UPSZsAAMA9fLwVAAC4hqABAABcE/RbJwcPHtTmzZudn7dt26bVq1frlFNOUf369UPaOQAAUL0FHTRWrFihjh07Oj/ffffdkqTBgwdr6tSpIesYAACo/oIOGh06dJCZudEXAMdw9APREg9FA6j6eEYDAAC4hqABAABcQ9AAAACuIWgAAADXEDQAAIBrCBoAAMA1BA0AAOAaggYAAHCN69/eClQ2vvUXACoPQQNApSEEAlVXqP4+eesEAAC4hqABAABcQ9AAAACuIWgAAADXEDQAAIBr+NQJAJykjv5UgcQnf+AO7mgAAADXVNodjaaj58nri5FEisbJif9Nlg1zbwDVywn/1gknJQAAKg9vnQAAANecMHc0uHNxcuA4A0D1csIEDQSvsi/alb39UDvR9gcAQoG3TgAAgGsIGgAAwDVV8q0TbkFXPxwzVDbGIFA1VcmgAQBAdUPYLR5BAwCASnIyhBOCBnCCq44nMmZNBU4cBI0TREkXE07Yla86XuhLc6LtDwB3ETQAIAQI9ScPwnZwCBpAMTiRABWjsv/WKnv7JwPm0QAAAK7hjgaAkOB/hihJdRwbvBUWOgQN4ARRHU/mAE58BA2ctLgwgzEQPP6nj2ARNAC4igsTEFrVLSATNFxW2oCoboOlrE6W/QQqAn9P1fN3UB37HCoEjWqmIgbryfwHAaDsquq5g7tqlXtsCBoAUEmq8gWwqoYGVD9lChqvvPKKnnnmGe3Zs0dnn322XnzxRV144YWh7hsAl50sF5NQ7mdZ2jpZfs8IrRNl3AQdNN555x2NGDFCr7zyitq1a6fXXntN3bp10/r161W/fn03+njSqcqDqyr3DcCJg0B34gg6aDz//PO68cYbNXToUEnSiy++qHnz5mnixInKzMwMeQerCwY4TiSM5+r5O6iOfcaJL6igcfjwYa1cuVIPPvhgQPlll12mpUuXFlsnPz9f+fn5zs85OTmSpML8Q05Zbm5uQJ2SloWyztHlFVWnOu5ndexzWeqwn+xnVdvPpqPnOf/+dkyXcvX5eNoKRZ9PlmNTVffz6OMsHd+4CWY//cvMTEGxIHz//fcmyb744ouA8ieffNIaNWpUbJ3Ro0ebJF68ePHixYvXCfDatWtXMNHByvQwqMfjCfjZzIqU+Y0cOVJ3332383NhYaH279+vpKQkHThwQKmpqdq1a5cSEhKcdXJzc4stL21ZKOtU9vbZT/bzZNnPyt4++8l+niz7GYq2zEwHDhxQSkqKghFU0Dj11FMVFham7OzsgPJ9+/apdu3axdbx+Xzy+XwBZTVq1JD0f4ElISGhyI6XVl5RdSp7+xVVp7K3X1F1Knv7FVWnsrd/svS5LHUqe/sVVaeyt19RdSp7+5XR58TExGLrliaor4mPjIxUy5YttWDBgoDyBQsWqG3btkFvHAAAnNiCfuvk7rvv1nXXXadWrVqpTZs2mjRpknbu3KlbbrnFjf4BAIBqLOigcc011+jHH3/UY489pj179qhp06aaM2eO0tLSgt64z+fT6NGji7y1UlJ5RdWp7O2zn+znybKflb199pP9PFn2M9TbD4bHgv6cCgAAwPEJ6hkNAACAYBA0AACAawgaAADANQQNAADgmhMqaPBcKwAAVUuZpiCvqnw+n9asWaOzzjrL9W3t2bNHEydO1Oeff649e/YoLCxMGRkZ6t27t4YMGaKwsDDX+wAAQFVXJT7e+ssvv2jOnDl666239N577wUsW716tV566SXde++9aty4sb777jtdc801KigoUOPGjVW/fn1n3fHjx2vgwIFKSkqS9PtX2v/000964403tHTpUqWmpmrEiBFKTU3Vm2++qYkTJ2rnzp0qKCjQzTffrEcffbRI3yZMmKAVK1aoe/fuuvrqq/W3v/1NjzzyiHbu3KlatWopIyNDy5cv14ABA3T48GHNmzdPZ511lubNm6f4+HhXf29VVV5ent5++20tXbpU2dnZ8ng8ql27ttq1a6c///nPio2NLbbe119/rXfffVdPPvlkQHl2drZmzJihQYMG6ZRTTtEPP/ygyZMnKz8/X/369QsIlqeffrrmzZunhg0bSpKOHDmi2bNnKysrSz6fT7169XLmfFmyZIleffVVLV26VOecc44eeOABtWnTpki/PvzwQ61YsUJdu3ZVmzZt9Omnn+rZZ5/VkSNHlJKSorCwsCJh89JLLw3Vr7NaKssY2Lt3r55++mmNGTNGcXFxTvmPP/6oVatWKT8/Xz169Ajq+Ev/Nwa++uor1atXT4MGDVJsbKxz/Hfu3KlDhw7poYce0lVXXVWkXyUd/8LCQvXo0UMxMTHF/oeDMVA5Y0AK/XmAMVBOQX0Fmws2btxoaWlp5vF4TJJdfPHFtnv3bjMzmzt3rkVGRpoki4qKsrlz51qtWrVMksXFxZnH47FzzjnHOnToYB06dDCPx2MRERHWrl0769ixo23dutXq1KljderUsfj4eDv11FMtMTHRHnvsMYuOjrY777zTJk6c6HwjXe3atW3cuHG2Z88eMzN77LHHLD4+3vr06WN16tSxcePGWVJSktWvX98uvfRSq1Wrlj3yyCP2t7/9zS644AIzM9u/f781a9bMLrnkEhsyZIh17drVunXrZkOGDLHXX3/dDh48WOLvYtWqVTZq1Kgi5Xv27LEXXnjBfvzxRzMz++9//2vjxo2zMWPG2Pr16wPWzcjIsE2bNgWUHT582N5//30bNWqUvfLKK04fFi9ebP3797f09HTr1auXLV26tNh+ffDBB/bII484yz/55BPr1q2bderUyQYNGmTXX3+9de3a1bp37279+/e3pKQkq1GjhvXq1ctuvvlmu+mmm6xXr15Wo0YNO+2002zdunUB7e/evdtat25tXq/XJNmgQYPswIEDZmb25ZdfWkJCgkmymjVr2ooVKywjI8Nq1aplp556qkVERNi9995r48ePt/Hjx5sku+uuu2z8+PH25JNPWrNmzSwyMtIaNmxoHo/HatWqZf/5z39s1qxZ5vV67YorrjBJ5vF4zOPx2P3332/5+flO3yZOnGjh4eHWsmVLS0hIsDfffNPi4+OtX79+FhcXZ5IsISHBPB6Pde/e3S644AILCwuzK6+80iZOnBjUGMjOzra7777b2Xe/H374webPn28ffvihK8e/ffv2dt5559k//vGPoI5/ly5dbMKECTZ58uSQjIGzzz7bJFlYWJgzBr788ktLTEx0zg/HOv5hYWF22mmnWWZmpo0fP9727dvnjIGoqCiLiIiw+vXr2+TJk53j/8ADDzjngHPPPddmzJjhjIGSjv/QoUPt2muvNY/HY7GxsVa3bt2QjIHKOgeUdQwUdw64/fbbbfLkyZaSkuLqGGjYsGGxYyDU54GyjgGv12stW7a0QYMGVYvrwLHGQHlUWND45z//WezrggsusNatW9vjjz9uHo/HevbsaRkZGbZjxw5r06aNjRgxwrxer02fPt1q1qxpo0aNsrFjx1pGRob179/fOnfu7GwjPDzcPB6P7d2718zMrr32WuvQoYPl5eVZTEyMbdq0yXr06GE1atSw1157zann8Xhs5MiRdsoppzgD94orrrA6derYzJkzzcxs9erVFhYWZm+++aZFR0fbli1b7L333rMGDRpYQUGBRUREWHZ2tq1bt86SkpLM4/FUyoW2LCfaUF5o4+PjzePxWJ8+fezIkSMB+7lixQrr2rWrtWrVytasWeO8evbsac2bN7exY8eax+OxVq1aWcuWLW3//v3WqVMnGzBggEmyZ555xurVq2dDhw41j8dj9erVs7i4OIuJibH09HRLT083SVanTh1LT0+3+Ph4O/fcc53gGBcXZ+edd57dcMMNdsEFF9i4ceOc4z9lyhRr1qyZeTweS0pKsuHDh9vatWvtrLPOskmTJpmZ2aeffmpRUVH28ssvW7du3ex//ud/bPLkyXbWWWdZZmamdevWzczM5syZY2FhYRYVFXVSXmhLGgNr1qwpdQz4LwILFixwxsDFF19sQ4cOtc2bN5ukYx5//+8pNTXVMjIy7KabbnLGQHx8vK1atcratm1rtWrVco6/fwwMGDDAEhMTLSIiwhkDp59+erHH38ysW7du1qFDB2vcuLGZWbnGQGWfA8oyBkoL2/5jkJeXFzDOQzkG/MctNjY2YAyE+jxQljGwbt06S05ONq/Xa2eeeWa1uA6UNgbKq8KChsfjMa/X61zQ/C//jvmXm5nddtttVr9+fYuLi7N///vf5vV6raCgwMLDw23lypVmZrZ8+XJLT0+3mJgYO3z4sJkVDRoZGRn2ySefmJlZUlKSrVixwpYtW2Zer9dWr14d0Lcvv/zSoqOj7fDhw/bOO+9Yly5dTJIlJyfbqFGjLCsryyIiIuzbb7+1tLQ0+/zzz2379u0WExNju3fvNo/HY4cOHbIOHTpYz549zefzFfkdVMSFtiwn2lBeaKOjo23OnDmWnp5uo0ePLnYMHB1s/P31/9vr9dqvv/5qvXr1snPPPddq1KhhixcvNq/Xa4cPHzav12tffvml3XzzzXbuuefau+++a6eddpqzDUm2ePFiMzNr1KiRffTRR86yxMRE+9///V9LT0+35ORkW7NmjdOvvXv32ubNmy06Otqeeuopa9y4sXm9XvN6vZaZmWm5ublmZhYREWFr1651guu2bdssJibG8vPzLSIiwn744Qfr0KGDXXjhhZaWlhaw/yfLhbakMXD0OaC4MSDJOQf4x0BYWJgtXbrUsrOznfqlHf8/ngOOHgOJiYm2Zs0a++yzz8zr9TrH/+hzQExMjO3du9cZA5KsefPmNmnSJMvNzXWOv5lZTEyMffrppxYTE2NmVq4xUNnngLKMgdLCdlRUlKWkpJR4DgjFGDAzu/nmm+3MM8+05OTkgO2E8jxQljHQrl07u/baa23mzJmWnp4e0Leqeh0obQz497esKixopKSk2Pvvv1+kPD4+3tavX29ff/21M8DMzG6//XbzeDz29ttvO+VxcXG2ZcsWZ51169aZ1+u15s2b2zfffGMRERHm8Xhs3759zja//fZbMzMbOHCg3XjjjbZt2zbzer32l7/8xWnH4/HYqFGjrFmzZgF9S01NtQEDBjhv7Xi9Xvv73/9uw4cPt6ZNm9qYMWOsdu3a1rFjR+vQoYOZmUVHR9trr71mZ5xxRpF9rYgLbVlOtKG80NapU8dmzZpls2bNKvIHduqpp9qwYcMsOTnZtm/f7rxiYmLss88+s9mzZzvH+siRI9a7d2/zer02d+7cYsfA+++/bykpKRYeHu5sQ5ItWbLEzMySk5MD/tdwxRVX2K233mo+n8+6dOli48ePD9j/119/3Ro2bOisv3jxYouJibGoqCiLjY2177//3jwej82ePdtSUlJs5cqVtmjRIqtXr5799NNP5vF4LDc316Kjo23evHlFwubJcqEtaQyceuqpNnnyZHvttdeKHQOTJk0KOAccOXLEwsLCrHHjxvbNN9+Y1+s95vH377//HHD0GLjiiivswQcftO3bt5vH43GOv3//n3vuuYDjb2ZWq1Yt69q1q8XGxlp0dLRz/M1+P79MmjTJ6tWrZ2ZWrjFQ2eeAsoyB0sJ2SkqKjRw5sthzQCjHgJnZpEmTTJJNmDDBKQvleaAsYyA6OtrWrVtnW7duLXEMVLXrQGljwOv1WuvWrZ0xEKwKCxo9e/a0hx9+uEh569atbdq0abZ69WrzeDwBy5KSkiw2NtYZeGvXrg24FbtkyRLLyMiw6dOnW+3atZ0/4GbNmlmLFi0sLi7O3nvvPTMz+/777y09Pd3OOecci4uLs+joaGvfvr3ddNNNJskiIiKcweP30EMPWa1atezGG2+0OnXq2MiRI61+/fr2wgsvWMuWLZ2LQ9u2bW3r1q1mZk6K//vf/15kXyviQluWE20oL7QPPvigJSYm2siRIy0iIsL27Nlj2dnZtnr1amvUqJFFRUXZmDFjAn4vzZo1s3fffbfIGDhy5IjFxcU5tyDNzD766CM7dOiQs84HH3xgPp/Punbtanv27DFJdtFFF9mVV15pNWvWtDlz5jjrrl+/3hITEy0qKsoef/xxi4uLs4EDB5rH47F+/fqZz+ezKVOmBPRt2LBhdsYZZ1ivXr3s/PPPt8GDB1vjxo2tU6dO1qxZM2vYsKH169fPrrnmGmvRooUzBp544glLTU0tcvxPhgttSWOgbdu21qVLF6tZs2axY+DZZ58tcg5o3LixtWvXzurXr29er/eYx9+//5dffnmRMbB+/XpLSkqybt26WWxsrHP8n3zySfN4PBYZGVns8W/YsKH95S9/sfT0dOf4z5071zp16mSxsbF21VVX2datW8s1Bir7HFCWMVBa2B49erTFx8dbWFiYrV692tUxsGzZMqtbt65dcsklrpwHyjIGUlJSbNasWbZo0aJix0BVvA6UNgYWL15sgwcPttjYWIuNjbVgVVjQWLx4sc2dO7dI+dixY61bt2528OBBW7RoUcCyiRMn2uWXX15k4PmNGjXKbrzxRjMz27Vrl82aNctGjRpljz76qPP6+OOPnfV/+uknO//88y0hIcGioqIsMjLS0tLSLDY21hYsWFCk/d9++82eeOIJ69Gjh3N7afr06ZaammpJSUk2cOBAy87ODqgzevRoS0xMtGeeeSbgD6yiLrRlOdGG+kLrf2jW/z90fwCsWbOmXXPNNUV+z/fff79ddtlltn//fps6dWrAskceecRatmwZcAE+2qhRo+zKK6+0sWPHWp06dczj8Vjv3r1tyJAhNmTIkCKBb+jQoc7DwUf/T6JVq1bF3nE7ePCgDR061Jo2bWq33HKLHT582J555hmLiIgIuAuRnp5uq1atcsZATEyM9erVy/WTbFW90BY3BiRZYmKiPfXUU8WOgY4dOxY5/o8++qi9+eabdsUVVxQ7Bv54/MPCwgKO/x/HwObNm61x48YWFhbmHH//sfzjvpd2/P0PqfufTSjvGKjsc0BZxkBp5wCz39/W8P/tuz0GrrrqKissLHTlPFCWMeC/Dlx33XX2wAMPVIvrQGljwC8nJ8d5Gy0Ylf6pkxPRuHHjnIfjQnWhLS1slfdEG+oLrZnZzJkzbfTo0bZ06VJbunSpc8enOEeOHLGcnJwSl//222+2ffv2Ypfl5eXZr7/+ama/v/f54osv2v79+0ts6+DBg/bLL79YYWGhZWdn2+7du51nfILxyy+/WG5urm3atKnInTaz4sdAVbnQnnnmma5faM2KjoGjb3cfrbCwsMQxUFhYaGZFx4C//I/H//nnny/x+PvrHDx40A4dOuQc/9IeePPX+aO8vDznFvIfx4C/TjBjIJRhu6LCVnHnAP8zdGa/H/+//vWvtnXrVlfPA0ePAbOKOw/4zwFmRceAmTvXgWDGQK9evUI2BsqrSsyjcaLatm2bsrOzJUl16tRRRkZGsev99ttvOnTokBISEopdXlBQoP/85z/O576PdujQIYWFhcnn82nlypX6/PPPNWjQINWsWbPEfuXl5cnr9So3N1eFhYU69dRTFREREdS+/frrrzpy5Iiys7OVn5+vxo0bKzz8hJr/LSTcHgOVffzj4+OVlZV1XGMgMjKy2An1SiovS51QthWqOsczBkJ1Dli1apWWLFnCOaAKOZGvA8eLUeGijIyMIoNq165dGj16tP7f//t/Tll4eLgSEhKKXSZJu3fv1pgxY4qUS79PZOOv07JlS7Vs2bLE7fjt37+/2GUl1SmuPCoqSlFRUfr555+VmZkZsKy0Cdh++uknPffcc+rfv7+aNGkSUGfp0qVavny5Ro4cWa46oWyrrHX8E83dd999atOmjb777js9/fTT2rt3rxo0aKAbbrjBmYBu/Pjxys/P14UXXihJatOmTcCyo+tIKra8cePGio2N1Xfffad77rnHaatt27Y688wzi9S58cYbnfLHHnvsuLZ/dJ0/9rlt27YKDw8vdUK9I0eOqE+fPoqOjpYkXXzxxZJ+P4EWV15anVC2Feo6BQUFGjduXMDEgTVq1NAbb7yhrKwspaSkaNCgQUpNTXUmFMzKylLdunU1ePDgYsuHDBniXGBKq7N48WJlZWXp5ZdfdspLq/Prr78W269jbefoOoMHD1bDhg0Dys1MAwYMULt27STJmSBx8+bNSk1N1b333qtrr702YOLEpKQk3XDDDbrzzjurVJ2ytLVmzRplZGTooYce0rXXXhtwHbjjjjt09dVXO387fnfddVex5aXVKa7cfx043jr+MVzadsot5PdIUKrVq1eXePurpGWVXSeYtkqbgG3jxo1Wr14951arf1ko61T29s1Knmju3HPPddaPjIx0yjt16mTnnnuuSbL4+PjjqhPKtkJdRyVMqCfJYmJirEaNGlajRo2Aifb8dY4uL61OKNsKdR3/beijJw6sW7eu1alTxyIjI61u3bqWmJhoCxYscCYULKm8c+fO5vV6LT4+3jZs2BAwCWFl1ymtrYiICIuNjbUNGzbY66+/7kyQmJqaan369LG4uDgbPHhwwMSJtWrVsqioKJs8eXKVqlOWto7+NEmfPn2cOTzM/u9TJw0bNgyYILKk8qpQp7wIGiFW0sRk//znP52HVz0eT5Hy0pa5XSeUbZU2AVvv3r2tc+fO5vF4LCsry1nWpUsX69Gjh61bt67cdULZVlnrlDTRXJs2beyhhx5yPkrtLzcza9OmjbVt29Y6d+58XHVC2Vao65Q0oZ7X67V69eo5c9v4hYeH24gRIwLmvTlWnVC2Feo6pU0c6PF4bOfOndajRw9LTU09ZrnZ7yf/zp07W9++fY+rrYqqU1pbMTExdumll1rfvn2tRYsWzgSJMTExtmPHDnvrrbcsKioqYOLEmJgYGz9+vDVp0qRK1SlLWx6PxxYuXGhdu3a1sLAwZxLIDz/80Fk2fPjwgAkiPR6PzZ8/v0h5Vajz4YcfWkFBgZUVQSPE/Inw6ER79EOX/ldJ5aUtc6uOG235fw9m/zcBW1JSkvPZbb/bbrvNvF6vzZkzx7Kzs8tdJ5RtlbVOSRPNJSQkWFZWlq1du9aSk5MDJqBLSEiw2bNnW+3atY+rTijbCnUds5In1JsxY4Y1atTI7rnnnoDydevW2fLly4tdVlKdULYV6jpHB42jw4i/fNmyZRYWFnbMcv+yOXPmWL169Y6rrYqqU1pbSUlJNnXqVKtXr54lJyc7EyT6J070T0B39MSJSUlJNmvWLIuOjq5SdcrSlv93s3nzZouKinImgQwLCzNJNnz4cMvKyioyQWTt2rVt1KhRtn79+ipVJywszFJSUpzJK4NF0AixkiYm8y97/vnni7wN4a/zx0nLKqpOKNs61gRs06ZNK9JWRESE1a5d25mQpjx1QtlWeeoUN9Gc/6K9fft2i4qKCvgsfEJCgi1atMiioqKOq04o2wp1Hb/iJtRbt26dHThwwAYNGlSk3MxKXBZseVnaCmUdj6f4iQP95du2bTNJxyz3L1uxYoX5fL7jaqui6pTW1sCBA+3qq682n89n/fr1cyZI9E+cOHbsWEtMTAyYOHHgwIHWqlUra9asWZWqU5a2/EFj7NixARNB7tixw6TfZ/j94znF4/HYvffea2lpaQHLqkKdHTt22OjRo4vUOV4EjRAraWIy/7Kbb77ZPB5PsXWKm7SsIuqEsq3SJmBLTk62mJiYIgO1devW1qlTJ6tRo0aRZcHWCWVbZa1T0kRzzZs3t7lz5zoTzR39cbjmzZvbs88+axkZGcdVJ5RthbqOX3ET6h09Q2NJ5aUtq4i2QlHH4yl+4kB/uX+6+WOV+5edfvrpFhERcVxtVVSd0tr6/vvvnec47r77bmeCxP79+5vP5zOPx2NXXnllwMSJ/u9Iadq0aZWqU5a2JNmf/vQni4yMLDIRpMfjsezsbJs/f36R8r1791phYWGxyyqzjl9xdY4HnzoJsfvuu095eXklLvvxxx/Vv3//Yus0aNBAn332WYXXCWVbV155paZPn66rrrqqSFsjRozQq6++ql27dgWUX3nllVqyZIn+/Oc/69VXXy1XnVC2VdY6TzzxhD788EPNnTtXktS0aVNJ0q233qqCggLNnTtXl1xyiVPuX/bZZ5/pkksuOa46oWwr1HX8/HWuvfZatW/fXitXrgz4aF5J5aUtq4i2QlFn+fLlioyMdJbFxMRIkkaPHi1Jmj9/vnw+3zHL/cvmz5+vmJgY9e3bt8rUKa2tlJQU9e3bV5988ok+/vhjmZmWL1+uXbt2qWfPnoqJidGKFSsCytu1a6fMzEzNmzdPH374YZWqE2xbYWFhOu200zRhwgS1atUqYJykpaUpPDxcnTt3LlIeFhYmj8dT7LLKrONXXJ3jwTwaAADANd7K7gAAADhxETQAAIBrCBoAAMA1BA0AAOAaggZQTXXo0EEjRoyo7G5IkiZNmqTU1FR5vV69+OKLJZYBOPnw8VYA5ZKbm6vbb79dzz//vPr06aPExMRiywCcnLijAZyADh8+XGHb2rlzp44cOaLu3burbt26iomJKbYs1CpyHwGUHUEDqAby8vI0aNAgxcXFqW7dunruuecClqenp+uJJ57QkCFDlJiYqJtuukmS9I9//ENnn322fD6f0tPTi633+OOPq3///oqLi1NKSoomTJgQsM7OnTvVq1cvxcXFKSEhQVdffbX27t0rSZo6daqaNWsmSTr99NPl8XiKLdu+fXup+7dlyxb16tVLtWvXVlxcnFq3bq2FCxce1z4uXbpUF110kaKjo5Wamqo777wzYNK8N998U61atVJ8fLzq1Kmj/v37a9++fcfzawcQCkHPJQqgwt16661Wr149mz9/vn3zzTfWo0cPi4uLs+HDh5uZWVpamiUkJNgzzzxjWVlZlpWVZStWrDCv12uPPfaYbdy40aZMmWLR0dE2ZcoUp920tDSLj4+3zMxM27hxo/31r3+1sLAwZ5rhwsJCa9GihbVv395WrFhhy5Yts/POO88uvvhiMzM7dOiQLVy40CTZ8uXLbc+ePXbw4MEiZb/99lup+7d69Wp79dVX7ZtvvrFNmzbZQw89ZFFRUbZjx46Avv5xH7/55huLi4uzF154wTZt2mRffPGFtWjRwoYMGeLUmzx5ss2ZM8e2bNli//73v+1Pf/qTdevWLTQHBsAxETSAKu7AgQMWGRlpM2bMcMp+/PFHi46ODggavXv3Dqj3x69pNzO77777rEmTJs7PaWlp1rVr14B1rrnmGudCPH/+fAsLC7OdO3c6y9etW+eECDOzr7/+2iTZtm3bnHWKKwtWkyZNbMKECQF9/eM+XnfddXbzzTcHlC1ZssS8Xq/98ssvxba7fPlyk2QHDhwoc98AHD/eOgGquC1btujw4cNq06aNU3bKKafozDPPDFjvj9+psGHDBrVr1y6grF27dsrKylJBQYFTdnS7/p83bNjgtJGamqrU1FRneZMmTVSjRg1nnVDIy8vT/fff77QdFxen7777Tjt37gxY74/7uHLlSk2dOlVxcXHOq0uXLiosLNS2bdskSV9//bV69eqltLQ0xcfHq0OHDpJUpG0A7uBTJ0AVZ8f5dUSxsbFF6nk8njK15a9XXBullZfVfffdp3nz5unZZ59VgwYNFB0drb59+xZ54POP+1hYWKj/+Z//0Z133lmkzfr16ysvL0+XXXaZLrvsMr355puqVauWdu7cqS5duvAwKVBBCBpAFdegQQNFRERo2bJlql+/viTpp59+0qZNm3TxxReXWK9Jkyb6/PPPA8qWLl2qRo0aKSwszClbtmxZwDrLli1T48aNnTZ27typXbt2OXc11q9fr5ycHJ111lkh2T9JWrJkiYYMGaIrr7xSknTw4MFjPkAqSeedd57WrVunBg0aFLt87dq1+uGHHzRu3Din/ytWrAhZvwEcG2+dAFVcXFycbrzxRt1333365JNP9O2332rIkCHyekv/873nnnv0ySef6PHHH9emTZv0xhtv6KWXXtK9994bsN4XX3yhp59+Wps2bdLLL7+smTNnavjw4ZKkTp06qXnz5howYIBWrVql5cuXa9CgQbr44ouLvI1RHg0aNNB7772n1atXa82aNerfv78KCwuPWe+BBx7Qv//9bw0bNkyrV69WVlaWPvjgA91xxx2Sfr+rERkZqQkTJmjr1q364IMP9Pjjj4es3wCOjaABVAPPPPOMLrroIl1xxRXq1KmT2rdvr5YtW5Za57zzztPf//53zZgxQ02bNtUjjzyixx57TEOGDAlY75577tHKlSvVokULPf7443ruuefUpUsXSb+/hTJr1izVrFlTF110kTp16qTTTz9d77zzTkj374UXXlDNmjXVtm1b9ezZU126dNF55513zHrNmzfXv/71L2VlZenCCy9UixYt9PDDD6tu3bqSpFq1amnq1KmaOXOmmjRponHjxunZZ58Nad8BlM5jx/umLYATTnp6ukaMGFFlpjIHcOLhjgYAAHANQQOA684+++yAj6Ae/Xrrrbcqu3sAXMRbJwBct2PHDh05cqTYZbVr11Z8fHwF9whARSFoAAAA1/DWCQAAcA1BAwAAuIagAQAAXEPQAAAAriFoAAAA1xA0AACAawgaAADANf8fW6S1U/pA888AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_2021.groupby(\"dropoff_area\").agg({'ID':'count'}).orderBy(F.col('dropoff_area').asc()).toPandas().plot(x=\"dropoff_area\",y=\"count(ID)\", kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54b6778e-7e08-4087-95f2-911d4458c048",
+   "metadata": {},
    "outputs": [],
    "source": [
-    "df_2021.groupby(\"dropoff_area\").agg({'ID':'count'}).orderBy(F.col('dropoff_area').asc()).toPandas().plot(x=\"pickup_area\",y=\"count(ID)\")"
+    "import folium\n",
+    "from folium.plugins import HeatMap\n",
+    "\n",
+    "#Create a Folium map centered on the mean coordinates\n",
+    "map_center = [df_area_program_pd['Latitude'].mean(), df_area_program_pd['Longitude'].mean()]\n",
+    "mymap = folium.Map(location=map_center, zoom_start=15)\n",
+    "\n",
+    "# Convert the DataFrame to a list of points\n",
+    "heat_data = [[point['Latitude'], point['Longitude']] for index, point in df.iterrows()]\n",
+    "\n",
+    "# Add a heatmap layer to the map\n",
+    "HeatMap(heat_data).add_to(mymap)\n",
+    "\n",
+    "# Save or display the map\n",
+    "mymap.save(\"heatmap.html\")  # Save the map to an HTML file\n",
+    "mymap"
    ]
   },
   {
@@ -872,12 +1070,7 @@
    "metadata": {},
    "source": [
     "# Next Steps\n",
-    "Filter the in-area dataframe to only include rides with a fare under 15, and rides within the timeframe for the given year.\n",
-    "\n",
-    "plot rides by hour\n",
-    "\n",
     "\n",
-    "## we still don't know how to do these, if you figure it out pls share\n",
     "the geospatial ipynb (notebook 4.8) from ashish shows some ways to work with this kind of data in pyspark\n",
     "\n",
     "Plot Cloropleths (for all of chicago)\n",

From 53d7c071a559f5926952fa3bd8b6915ee4ede2e2 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Tue, 14 Nov 2023 16:09:38 +0000
Subject: [PATCH 6/9] eda update labeled graphs

---
 eda_2021.ipynb | 79 ++++++++++++++++++++++----------------------------
 1 file changed, 34 insertions(+), 45 deletions(-)

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 0449657..2c6bef1 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
    "metadata": {},
    "outputs": [
@@ -28,9 +28,6 @@
        " ('spark.eventLog.enabled', 'true'),\n",
        " ('spark.submit.pyFiles',\n",
        "  '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
-       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1699830118479_0001'),\n",
-       " ('spark.app.id', 'application_1699830118479_0001'),\n",
        " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n",
        " ('spark.kryoserializer.buffer.max', '2000M'),\n",
        " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n",
@@ -38,19 +35,19 @@
        " ('spark.driver.maxResultSize', '0'),\n",
        " ('spark.yarn.unmanagedAM.enabled', 'true'),\n",
        " ('spark.sql.autoBroadcastJoinThreshold', '43m'),\n",
-       " ('spark.eventLog.dir',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/f1303a69-431a-4ba9-87a2-c0c185ed3a99/spark-job-history'),\n",
-       " ('spark.driver.appUIAddress',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:38871'),\n",
        " ('spark.ui.filters',\n",
        "  'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n",
-       " ('spark.ui.proxyBase', '/proxy/application_1699830118479_0001'),\n",
+       " ('spark.app.id', 'application_1699975669214_0001'),\n",
+       " ('spark.eventLog.dir',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/0f852a69-184b-4243-a2ca-dd44bcce018a/spark-job-history'),\n",
+       " ('spark.driver.port', '35309'),\n",
        " ('spark.metrics.namespace',\n",
        "  'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n",
        " ('spark.executor.memory', '4g'),\n",
        " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n",
        " ('spark.hadoop.hive.execution.engine', 'mr'),\n",
        " ('spark.executor.id', 'driver'),\n",
+       " ('spark.ui.proxyBase', '/proxy/application_1699975669214_0001'),\n",
        " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n",
        " ('spark.dynamicAllocation.maxExecutors', '10000'),\n",
        " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
@@ -83,18 +80,22 @@
        " ('spark.yarn.am.memory', '640m'),\n",
        " ('spark.cores.max', '4'),\n",
        " ('spark.executor.cores', '4'),\n",
+       " ('spark.app.startTime', '1699975920175'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1699975669214_0001'),\n",
        " ('spark.jars.packages',\n",
        "  'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n",
        " ('spark.executor.instances', '2'),\n",
        " ('spark.dataproc.listeners',\n",
        "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
+       " ('spark.history.fs.logDirectory',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/0f852a69-184b-4243-a2ca-dd44bcce018a/spark-job-history'),\n",
        " ('spark.serializer.objectStreamReset', '100'),\n",
        " ('spark.submit.deployMode', 'client'),\n",
        " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
        " ('spark.shuffle.service.enabled', 'true'),\n",
-       " ('spark.history.fs.logDirectory',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/f1303a69-431a-4ba9-87a2-c0c185ed3a99/spark-job-history'),\n",
-       " ('spark.app.startTime', '1699830435306'),\n",
+       " ('spark.driver.appUIAddress',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:39155'),\n",
        " ('spark.scheduler.mode', 'FAIR'),\n",
        " ('spark.sql.adaptive.enabled', 'true'),\n",
        " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
@@ -103,14 +104,13 @@
        " ('spark.ui.port', '0'),\n",
        " ('spark.rpc.message.maxSize', '512'),\n",
        " ('spark.rdd.compress', 'True'),\n",
-       " ('spark.driver.port', '43209'),\n",
        " ('spark.task.maxFailures', '10'),\n",
        " ('spark.yarn.isPython', 'true'),\n",
        " ('spark.dynamicAllocation.enabled', 'true'),\n",
        " ('spark.ui.showConsoleProgress', 'true')]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
    "metadata": {},
    "outputs": [
@@ -600,7 +600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 19,
    "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
    "metadata": {},
    "outputs": [],
@@ -613,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "id": "6a52d857-bc07-47a7-8cc9-71478bf10e08",
    "metadata": {},
    "outputs": [],
@@ -634,7 +634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 21,
    "id": "17ebe569-82bb-49b0-aaaf-ac5062bede35",
    "metadata": {},
    "outputs": [],
@@ -646,7 +646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 22,
    "id": "2ae6d6b2-9001-4ded-ba3a-225861cbe451",
    "metadata": {},
    "outputs": [],
@@ -703,7 +703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 23,
    "id": "2c54ef92-e61e-4827-ad6c-bb8b9405e701",
    "metadata": {},
    "outputs": [
@@ -716,17 +716,7 @@
     },
     {
      "data": {
-      "text/plain": [
-       "<matplotlib.lines.Line2D at 0x7fce32d57460>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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6dPTt2xfe3t6YOHEili5delcfxtWqJ8QrLy+/5SR81PhVz5p87SSHRESN2dvbfkWFzY6uLQ0Y2vH6QTq18duerSAEsHDdfry74yQUAJ5rIsWOQjTh/+6WlJTAYDDAZDLdsCUoNzcXxcXFCAkJgY+PT5P4C+FJhBAoLy9Hfn4+/P39XfqOUSNTVgZUj7bMz+cSEB6szFqGkKXOa50/P59LQNzEhZJK9H9lKyxVDqx6vAcGxtxdoVNt9e7TeG6dcxLYqfdGYeF97lns3O77+2pc6+oWqidGvN0aTtS4+fv733CSS2pkuF5Zk1Fu47W+nTe3HoelyoH4yAAMaF93S9w81isSQgB//SIb7/xwEgqFAgtGdnDLYqemWOjcgkKhQFhYGEJCQm643hM1fhqNxuW2KBFRY3e+uAJrMpzTn8wd1r7Oi5Df9Y6EAPD8F9lYuf0EFArg2RGeW+yw0KkBlUrFL0siImoQy7ceh9XuQK+oQPRpG1Qvr5HUOxIQAs9/eQAr0k5AAQWeGRHjkcXOXQ0vJyIiorqTU1iO/+1xtubMS6zfwiMpoTX+fn8nAMDbab/ilW+PeOQoVRY6REREjcS/vz+GKofAvdHB6BkVWO+v9/uE1nhxrLPYeWvbr1jigcUOCx0iIqJG4OTFMnz+0zkAwF+GtW+w153UpzWSx8QCAN7c9iuWbvKsYod9dIhIfkolMGDAlX3yWEqFEgMiB0j7dMXr3x2F3SEwKKY5ftMq4PYPqEOT+0ZBAHjx64P4z9ZfoYAC8xLrviO0HFjoEJH8vL2BbdvkzoIagLfGG9smb5M7jUbneH4pvvz5PABg7rAYWXJ4vG8UhAD+/s1BLN96HApF/Yz6amgsp4mIiGS27LtjEAJIjA1F55YG2fL4Q78oPD/aeRvrjS3HsWzzUbe/jcVCh4iISEaHckuw/hfnItUN2TfnZqb0i8JfR3UEAPx7y3Es++6YzBndHRY6RCS/sjKgeXPnVlYmdzZUj8qsZWi+pDmaL2mOMiuvNQAs23wUADCqSxg6htXtwtR36o/3trlS7Hx/TMrRHbGPDhE1Dhcvyp0BNZCL5bzW1fafNWHTwQtQKIC/DI2WOx0Xf7y3DYQAXtpwCK9/fwwKBTBnqPwtTrXFFh0iIiKZLPvO2VJyf9dwtAvxlTmb603t3wYL7+sAAPjXd8fwuhvexmKhQ0REJIN9Z4qw5XA+VEoFnmzELSXT+rfFgpHOYmfZd0fx7+/dq9hhoUNERCSD6n4v47u1QFSwXuZsbu1PA9rimRHOYue1zUexfIv7FDssdIiIiBpYxslC/HDsItRKBWYPaVx9c27miYFt8fQI5xw/SzcdxX+2Hpc5o5phoUNERNSAhBB4ddMRAMCEHhGICPSROaOamz6wHZ4a7ix2lnx7xC2KHY66IiL5KZVA9+5X9sljKRVKdA/vLu03RTt/vYTdJwuhVSkxc1A7udOptRmXc17y7REs+fYIFApnAdRYsdAhIvl5ewN79sidBTUAb4039kxtutdaCIHXLvfN+W3PCIT7e8uc0Z2ZMagdhBBYuukoXkk9AgUUeGJgW7nTuqGmWU4TERHJIO1oATJPF0GnVkotI+5q5uBozLs8k/M/Uw/j7bRfZc7oxljoEBERNYCrW3OSekcixM9L5ozu3qwh0Zh7udh5eeNhrGiExQ4LHSKSX3k50Lq1cysvlzsbqkfltnK0/ldrtP5Xa5Tbmta1/u5QPn45a4K3RoU/N9LbPHdi9pBo/OXyPECLNx7Gyu2Nq9hhHx0ikp8QwOnTV/bJYwkhcNp0WtpvKhyOK605k/q0RnAzncwZ1a0nh0ZDQOBf3x3Dog2HoYACU/u3kTstAGzRISIiqnffHsjDodwSNNOp8adGUgDUtTlD2+PJy3MCvbThEP77wwmZM3JioUNERFSP7A4hrWn1h76tEaDXypxR/ZkzNBqzBzs7Wf9jfeModljoEBER1aNvfjmPoxfM8PNSY8q9ntmaU02hUOAvw9pjViMqdljoEBER1ZMqu0Na8XvqvW1g8NbInFH9UygUmDusvTQZ4q8FZln7Y7EzMhERUT35Ius8Tlwsg7+PBo/3i5I7nQajUCgwL7E9Orc0YFjHUCgUCtlyYaFDRPJTKIDY2Cv75LEUCgVim8dK+57MZnfg3987W3P+1L8tmuma1leuQqHA8E5GudNgoUNEjYCPD3DggNxZUAPw0fjgwPSmca3XZp7FmcJyBDfTYlKfSLnTabLYR4eIiKiOWarseGOLc2XvPw9oCx8t2xXkwkKHiIiojv1vTw7OFVcg1E+H3/Vma46cWOgQkfzKy4FOnZwbl4DwaOW2cnR6sxM6vdnJY5eAqLTZsXyrszVnxqB28NKoZM6oaburQmfx4sVQKBSYM2eOdGzy5MlQKBQuW+/evV0eZ7FYMGvWLAQHB0Ov12Ps2LE4e/asS0xRURGSkpJgMBhgMBiQlJSE4uJil5gzZ85gzJgx0Ov1CA4OxuzZs2G1Wu/mLRGRHIQADh50bk1oWYCmSAiBgwUHcbDgoMcuAbF69xlcKLEg3OCFR3pEyJ1Ok3fHhc6ePXuwcuVKdOnS5bpzI0aMQG5urrRt2LDB5fycOXOwbt06pKSkYMeOHTCbzRg9ejTsdrsUM3HiRGRlZSE1NRWpqanIyspCUlKSdN5ut2PUqFEoKyvDjh07kJKSgrVr12LevHl3+paIiIjuSrm1Cm9tc7bmzBwcDZ2arTlyu6PeUWazGY899hjeeecd/OMf/7juvE6ng9F44yFlJpMJ7777Lj788EMMHToUAPDRRx8hIiIC3333HYYPH45Dhw4hNTUV6enp6NWrFwDgnXfeQUJCAo4cOYKYmBhs2rQJBw8eRE5ODsLDwwEAr776KiZPnoyXXnoJfn5+1722xWKBxWKRfi4pKbmTt09ERHRDH+46jYtmKyICvfFw95Zyp0O4wxadGTNmYNSoUVKhcq1t27YhJCQE7du3x9SpU5Gfny+dy8zMhM1mQ2JionQsPDwccXFx2LlzJwBg165dMBgMUpEDAL1794bBYHCJiYuLk4ocABg+fDgsFgsyMzNvmNfixYulW2EGgwEREWxSJCKiumG2VOHttF8BALMHR0OjYjfYxqDWVyElJQX79u3D4sWLb3h+5MiRWL16NbZs2YJXX30Ve/bsweDBg6WWlLy8PGi1WgQEBLg8LjQ0FHl5eVJMSEjIdc8dEhLiEhMaGupyPiAgAFqtVoq51oIFC2AymaQtJyendm+eiIjoJlb9eBJF5Ta0CdbjgW4t5E6HLqvVraucnBw8+eST2LRpE7y8vG4Y88gjj0j7cXFx6N69OyIjI7F+/XqMHz/+ps8thHCZJfNGM2beSczVdDoddDrdTXMgIiK6E6YKG1Zudy5e+eTQaKjZmtNo1OpKZGZmIj8/H/Hx8VCr1VCr1UhLS8O///1vqNVql87E1cLCwhAZGYljx5zTYBuNRlitVhQVFbnE5efnSy00RqMRFy5cuO65CgoKXGKubbkpKiqCzWa7rqWHiBo5hQKIjHRuHr4sQFOnUCgQaYhEpCHSo5aAeHfHSZRUViE6pBlGdwm//QOowdSq0BkyZAj279+PrKwsaevevTsee+wxZGVlQaW6vnf5pUuXkJOTg7CwMABAfHw8NBoNNm/eLMXk5uYiOzsbffr0AQAkJCTAZDIhIyNDitm9ezdMJpNLTHZ2NnJzc6WYTZs2QafTIT4+vjZvi4jk5uMDnDrl3Hx85M6G6pGPxgen5pzCqTmn4KPxjGtdXG7F/9txEgAwZ2h7qJSeU8B5glrduvL19UVcXJzLMb1ej6CgIMTFxcFsNiM5ORkPPvggwsLCcOrUKSxcuBDBwcF44IEHAAAGgwFTpkzBvHnzEBQUhMDAQMyfPx+dO3eWOjd37NgRI0aMwNSpU7FixQoAwLRp0zB69GjExMQAABITExEbG4ukpCQsWbIEhYWFmD9/PqZOnXrDEVdERET1YeX2EzBbqtDB6IuRcfIvYkmu6vQmokqlwv79+3H//fejffv2mDRpEtq3b49du3bB19dXilu2bBnGjRuHCRMmoG/fvvDx8cHXX3/t0iK0evVqdO7cGYmJiUhMTESXLl3w4YcfurzW+vXr4eXlhb59+2LChAkYN24cli5dWpdviYiI6KYumS1YtfMUAGDusPZQsjWn0VEIT52asgZKSkpgMBhgMpnYCkQkp4oKoH9/5/727YC3t7z5UL2psFWg/yrntd4+eTu8Ne59rV9afxDv/HASnVsY8NXMvh7V76gxq833N5dTJSL5ORzA3r1X9sljOYQDe8/vlfbdWX5JJT7YdRqAszWHRU7jxPFvREREd+DNbb/CUuVAt1b+GBjTXO506CZY6BAREdVSrqkCH+8+AwCYNyyGrTmNGAsdIiKiWlq+5Tisdgd6RgWib7sgudOhW2ChQ0REVAs5heX4317nEkLz2Den0WOhQ0REVAtvbDkGm12gX7tg9GrD1pzGjqOuiKhxCA6WOwNqIME+7nutT10sw9p95wAAfxnWXuZsqCZY6BCR/PR6oKBA7iyoAei1ehQ85b7X+t/fH4PdITAwpjniIwPkTodqgLeuiIiIauB4vhlfZDlbc+ayNcdtsNAhIiKqgX99dxQOAQyLDUWXlv5yp0M1xEKHiORXUQEMHOjcKirkzobqUYWtAgNXDcTAVQNRYXOfa304rwTf/JILAPjLULbmuBP20SEi+TkcQFralX3yWA7hQNrpNGnfXSzbfBQAcF9nI2LDuTaiO2GLDhER0S1knzPh2wMXoFAAc9ia43ZY6BAREd1CdWvO2K7haB/qK3M2VFssdIiIiG7ipzNF+P5wPpQK4Mkh0XKnQ3eAhQ4REdFNvHa5NWf8b1qiTfNmMmdDd4KFDhER0Q3sOVWIH45dhFqpwOzBbM1xVxx1RUSNg4+P3BlQA/HRuMe1fm2TszXn4e4t0SrIPXKm67HQISL56fVAWZncWVAD0Gv1KFvY+K/1zl8vYteJS9CqlJjJ1hy3xltXREREVxFCSK05j/aMQAt/b5kzorvBQoeIiOgqPx6/hL2ni6BTKzFjUDu506G7xEKHiORXWQmMGuXcKivlzobqUWVVJUZ9PAqjPh6FyqrGea03HcwDADwY3xKhfl4yZ0N3i310iEh+djuwYcOVffJYdocdG45tkPYbo5/OFAMA+rQNkjcRqhNs0SEiIrqswmrHodwSAEC3VgEyZ0N1gYUOERHRZdnnTahyCDT31SHcwNtWnoCFDhER0WU/nSkCAHSL8IdCoZA5G6oLLHSIiIguy8opBsDbVp6EhQ4REdFl1R2Ru7XylzUPqjssdIiIiADkmiqQa6qEUgF0aWmQOx2qIxxeTkTy0+sBIeTOghqAXquHeKFxXuusy605HYx+8NHy69FTsEWHiIgIwE9S/xx/WfOgusVCh4iICFeNuGJHZI9yV4XO4sWLoVAoMGfOHOmYEALJyckIDw+Ht7c3Bg4ciAMHDrg8zmKxYNasWQgODoZer8fYsWNx9uxZl5iioiIkJSXBYDDAYDAgKSkJxcXFLjFnzpzBmDFjoNfrERwcjNmzZ8Nqtd7NWyIiOVRWAg8/7Ny4BIRHq6yqxMOfPoyHP324US0BYbM78MtZEwC26HiaOy509uzZg5UrV6JLly4ux1955RW89tprWL58Ofbs2QOj0Yhhw4ahtLRUipkzZw7WrVuHlJQU7NixA2azGaNHj4b9qqnfJ06ciKysLKSmpiI1NRVZWVlISkqSztvtdowaNQplZWXYsWMHUlJSsHbtWsybN+9O3xIRycVuBz77zLlxCQiPZnfY8dnBz/DZwc8a1RIQh3NLYalywOCtQVSQXu50qC6JO1BaWiqio6PF5s2bxYABA8STTz4phBDC4XAIo9EoXn75ZSm2srJSGAwG8fbbbwshhCguLhYajUakpKRIMefOnRNKpVKkpqYKIYQ4ePCgACDS09OlmF27dgkA4vDhw0IIITZs2CCUSqU4d+6cFLNmzRqh0+mEyWSq0fswmUwCQI3jiaiemM1COLsjO/fJY5ktZoFkCCRDmC2N51q/v/OkiHzmG/H7d3fLnQrVQG2+v++oRWfGjBkYNWoUhg4d6nL85MmTyMvLQ2JionRMp9NhwIAB2LlzJwAgMzMTNpvNJSY8PBxxcXFSzK5du2AwGNCrVy8ppnfv3jAYDC4xcXFxCA8Pl2KGDx8Oi8WCzMzMG+ZtsVhQUlLishEREXH+HM9V6/FzKSkp2LdvH/bs2XPdubw859L2oaGhLsdDQ0Nx+vRpKUar1SIgIOC6mOrH5+XlISQk5LrnDwkJcYm59nUCAgKg1WqlmGstXrwYL774Yk3eJhERNSHsiOy5atWik5OTgyeffBIfffQRvLxuvtjZteuDCCFuu2bItTE3ir+TmKstWLAAJpNJ2nJycm6ZExEReb7CMitOXSoHANzT0l/eZKjO1arQyczMRH5+PuLj46FWq6FWq5GWloZ///vfUKvVUgvLtS0q+fn50jmj0Qir1YqioqJbxly4cOG61y8oKHCJufZ1ioqKYLPZrmvpqabT6eDn5+eyERFR05aV4/w+atNcD4OPRuZsqK7VqtAZMmQI9u/fj6ysLGnr3r07HnvsMWRlZaFNmzYwGo3YvHmz9Bir1Yq0tDT06dMHABAfHw+NRuMSk5ubi+zsbCkmISEBJpMJGRkZUszu3bthMplcYrKzs5GbmyvFbNq0CTqdDvHx8XfwURARUVNUPSNytwjetvJEteqj4+vri7i4OJdjer0eQUFB0vE5c+Zg0aJFiI6ORnR0NBYtWgQfHx9MnDgRAGAwGDBlyhTMmzcPQUFBCAwMxPz589G5c2epc3PHjh0xYsQITJ06FStWrAAATJs2DaNHj0ZMTAwAIDExEbGxsUhKSsKSJUtQWFiI+fPnY+rUqWypIXI3Pj6A2XxlnzyWj8YH5gVmab8x4IzInq3OF/N4+umnUVFRgenTp6OoqAi9evXCpk2b4OvrK8UsW7YMarUaEyZMQEVFBYYMGYJVq1ZBpVJJMatXr8bs2bOl0Vljx47F8uXLpfMqlQrr16/H9OnT0bdvX3h7e2PixIlYunRpXb8lIqpvCoVzvSvyeAqFAnpt47nWDoe40qLDQscjKYRouivplZSUwGAwwGQysRWIiKgJOnahFMOWbYe3RoX9yYlQq7gykjuozfc3rygRyc9iASZPdm4Wi9zZUD2yVFkw+YvJmPzFZFiq5L/W1fPndGlpYJHjoXhViUh+VVXA++87t6oqubOhelTlqML7P7+P939+H1UO+a/1TzmcP8fTsdAhIqImizMiez4WOkRE1CSZLVU4csG54HS3CH95k6F6w0KHiIiapF9yiiEE0MLfGyF+N5/tn9wbCx0iImqSOH9O08BCh4iImiQu5Nk0sNAhIqImRwghdUS+h/1zPFqdz4xMRFRrPj5Afv6VffJYPhof5M/Pl/blklNYgUtlVmhUCnQK54SxnoyFDhHJT6EAmjeXOwtqAAqFAs318l/r6vlzYsMN8NKobhNN7oy3roiIqMmR5s/hbSuPx0KHiORnsQAzZjg3LgHh0SxVFsxYPwMz1s+QdQkIjrhqOljoEJH8qqqAN990blwCwqNVOarw5t438ebeN2VbAqLSZsfB8yYAwG844srjsdAhIqIm5cD5EtjsAsHNtGgZ4C13OlTPWOgQEVGTUj1/zj0RAVAoFDJnQ/WNhQ4RETUp7J/TtLDQISKiJiWLK5Y3KSx0iIioybhQUolzxRVQKoAuLf3lTocaAAsdIiJqMqrnz2kf6otmOs6Z2xTwKhOR/Ly9gZMnr+yTx/LWeOPkkyel/YZWPSMyF/JsOljoEJH8lEqgdWu5s6AGoFQo0dq/tWyvzxmRmx7euiIioiahyu7A/rPOiQLZEbnpYKFDRPKzWoGnnnJuVqvc2VA9stqteGrTU3hq01Ow2hv2Wh+5UIoKmx2+OjXaNm/WoK9N8mGhQ0Tys9mApUudm80mdzZUj2x2G5buWoqlu5bCZm/Ya1192+qeVv5QKjlRYFPBQoeIiJoE9s9pmljoEBFRk8ARV00TCx0iIvJ4xeVWnCgoAwDcwxadJoWFDhERebysy+tbRQXrEaDXypsMNSgWOkRE5PHYP6fpYqFDREQejyuWN12cGZmI5OftDWRnX9knj+Wt8Ub2E9nSfkNwOASyzrAjclPFQoeI5KdUAp06yZ0FNQClQolOIQ17rU9cLENJZRW8NErEGH0b9LVJfrx1RUREHu2ny605nVsYoFHxa6+pqdUVf+utt9ClSxf4+fnBz88PCQkJ2Lhxo3R+8uTJUCgULlvv3r1dnsNisWDWrFkIDg6GXq/H2LFjcfbsWZeYoqIiJCUlwWAwwGAwICkpCcXFxS4xZ86cwZgxY6DX6xEcHIzZs2fDyqnjidyT1QokJzs3/jv2aFa7FcnbkpG8LbnBloC40j+Ht62aoloVOi1btsTLL7+MvXv3Yu/evRg8eDDuv/9+HDhwQIoZMWIEcnNzpW3Dhg0uzzFnzhysW7cOKSkp2LFjB8xmM0aPHg273S7FTJw4EVlZWUhNTUVqaiqysrKQlJQknbfb7Rg1ahTKysqwY8cOpKSkYO3atZg3b96dfg5EJCebDXjxRefGJSA8ms1uw4tpL+LFtBcbbAmILI64atrEXQoICBD//e9/hRBCTJo0Sdx///03jS0uLhYajUakpKRIx86dOyeUSqVITU0VQghx8OBBAUCkp6dLMbt27RIAxOHDh4UQQmzYsEEolUpx7tw5KWbNmjVCp9MJk8lU49xNJpMAUKvHEFE9MJuFAJyb2Sx3NlSPzBazQDIEkiHMlvq/1mUWm4h69hsR+cw3Ire4ot5fjxpGbb6/7/hmpd1uR0pKCsrKypCQkCAd37ZtG0JCQtC+fXtMnToV+fn50rnMzEzYbDYkJiZKx8LDwxEXF4edO3cCAHbt2gWDwYBevXpJMb1794bBYHCJiYuLQ3h4uBQzfPhwWCwWZGZm3jRni8WCkpISl42IiDzXL2dNcAggzOAFo8FL7nRIBrUudPbv349mzZpBp9Phz3/+M9atW4fY2FgAwMiRI7F69Wps2bIFr776Kvbs2YPBgwfDYrEAAPLy8qDVahEQ4HqfNDQ0FHl5eVJMSEjIda8bEhLiEhMaGupyPiAgAFqtVoq5kcWLF0v9fgwGAyIiImr79omIyI1IEwVy/pwmq9bDy2NiYpCVlYXi4mKsXbsWkyZNQlpaGmJjY/HII49IcXFxcejevTsiIyOxfv16jB8//qbPKYSAQqGQfr56/25irrVgwQLMnTtX+rmkpITFDhGRB6secdUtgh2Rm6pat+hotVq0a9cO3bt3x+LFi9G1a1e8/vrrN4wNCwtDZGQkjh07BgAwGo2wWq0oKipyicvPz5daaIxGIy5cuHDdcxUUFLjEXNtyU1RUBJvNdl1Lz9V0Op00Yqx6IyIizySE4IzIdPfz6AghpFtT17p06RJycnIQFhYGAIiPj4dGo8HmzZulmNzcXGRnZ6NPnz4AgISEBJhMJmRkZEgxu3fvhslkconJzs5Gbm6uFLNp0ybodDrEx8ff7VsiIiIPcK64AgWlFqiVCsS1MMidDsmkVreuFi5ciJEjRyIiIgKlpaVISUnBtm3bkJqaCrPZjOTkZDz44IMICwvDqVOnsHDhQgQHB+OBBx4AABgMBkyZMgXz5s1DUFAQAgMDMX/+fHTu3BlDhw4FAHTs2BEjRozA1KlTsWLFCgDAtGnTMHr0aMTExAAAEhMTERsbi6SkJCxZsgSFhYWYP38+pk6dylYaInfk5QVU/+fGix1GPZmX2gsZf8yQ9utTdf+c2HA/eGlU9fpa1HjVqtC5cOECkpKSkJubC4PBgC5duiA1NRXDhg1DRUUF9u/fjw8++ADFxcUICwvDoEGD8Mknn8DX98qU28uWLYNarcaECRNQUVGBIUOGYNWqVVCprvwlXL16NWbPni2Nzho7diyWL18unVepVFi/fj2mT5+Ovn37wtvbGxMnTsTSpUvv9vMgIjmoVECPHnJnQQ1ApVShR4uGudZcsZwAQCGEEHInIZeSkhIYDAaYTCa2BBEReZgH3vwRP50pxr8euQfjurWQOx2qQ7X5/uainkQkP6sVqB7U8OSTgFYrbz5Ub6x2K15Pd17rJ3s/Ca2qfq61pcqOA+ecc6WxI3LTxkKHiORnswFPP+3cnz6dhY4Hs9ltePo757We3mN6vRU6B8+XwGp3IFCvRatAn3p5DXIPXMaViIg8TnX/nHsi/G85vxp5PhY6RETkcbKq589hR+Qmj4UOERF5nJ9yLs+I3IozIjd1LHSIiMijFJRakFNYAYUC6BLBiQKbOhY6RETkUapvW0WHNIOfl0beZEh2LHSIiMijcCFPuhqHlxOR/Ly8gK1br+yTx/JSe2HrpK3Sfn2QZkTm/DkEFjpE1BioVMDAgXJnQQ1ApVRhYOuB9fb8dofAz2eLAbAjMjnx1hUREXmMoxdKUW61o5lOjXYhzeROhxoBtugQkfxsNmDlSuf+tGmAhh1IPZXNbsPKTOe1nhY/DRpV3V7r6ttWXSMMUCk5USCx0CGixsBqBWbOdO5PnsxCx4NZ7VbM3Oi81pPvmVwPhQ47IpMr3roiIiKP8VP1jMjsiEyXsdAhIiKPYKqw4Xi+GYBzjSsigIUOERF5iJ8vt+a0CvRBUDOdvMlQo8FCh4iIPEIWb1vRDbDQISIij3ClI7K/vIlQo8JCh4iI3J4Q4qqOyBxxRVdweDkRyU+nA7755so+eSydWodvfvuNtF9XTl0qR3G5DVq1Eh3D/Orsecn9sdAhIvmp1cCoUXJnQQ1ArVRjVPu6v9bVt606tzBAq+bNCrqCfxuIiMjtSQt5sn8OXYMtOkQkP5sNWL3auf/YY5wZ2YPZ7Das3u+81o91fqzOZkb+KedyR2T2z6FrsNAhIvlZrcDjjzv3H36YhY4Hs9qtePxL57V+OPbhOil0Kqx2HMotBcCh5XQ93roiIiK3tv+cCXaHQKifDmEGL7nToUaGhQ4REbm1qxfyVCi4Yjm5YqFDRERuTeqIzNtWdAMsdIiIyG0JIbDvcosOF/KkG2GhQ0REbivXVIn8UgtUSgU6tzTInQ41Qix0iIjIbVUv5NnB6AsfLQcS0/X4t4KI5KfTAf/735V98lg6tQ7/e+h/0v7dkjois38O3QQLHSKSn1rtnD+HPJ5aqcbDneruWl+ZEZkTBdKN8dYVERG5JWuVA/vPmQCwRYdurlaFzltvvYUuXbrAz88Pfn5+SEhIwMaNG6XzQggkJycjPDwc3t7eGDhwIA4cOODyHBaLBbNmzUJwcDD0ej3Gjh2Ls2fPusQUFRUhKSkJBoMBBoMBSUlJKC4udok5c+YMxowZA71ej+DgYMyePRtWq7WWb5+IGoWqKuDTT51bVZXc2VA9qnJU4dMDn+LTA5+iynF31/pwXgksVQ4YvDWICtbXUYbkaWpV6LRs2RIvv/wy9u7di71792Lw4MG4//77pWLmlVdewWuvvYbly5djz549MBqNGDZsGEpLS6XnmDNnDtatW4eUlBTs2LEDZrMZo0ePht1ul2ImTpyIrKwspKamIjU1FVlZWUhKSpLO2+12jBo1CmVlZdixYwdSUlKwdu1azJs3724/DyKSg8UCTJjg3CwWubOhemSpsmDCZxMw4bMJsFTd3bW+ev4cThRINyXuUkBAgPjvf/8rHA6HMBqN4uWXX5bOVVZWCoPBIN5++20hhBDFxcVCo9GIlJQUKebcuXNCqVSK1NRUIYQQBw8eFABEenq6FLNr1y4BQBw+fFgIIcSGDRuEUqkU586dk2LWrFkjdDqdMJlMN821srJSmEwmacvJyREAbvkYImoAZrMQgHMzm+XOhuqR2WIWSIZAMoTZcnfX+sk1+0TkM9+If20+WkfZkbswmUw1/v6+4z46drsdKSkpKCsrQ0JCAk6ePIm8vDwkJiZKMTqdDgMGDMDOnTsBAJmZmbDZbC4x4eHhiIuLk2J27doFg8GAXr16STG9e/eGwWBwiYmLi0N4eLgUM3z4cFgsFmRmZt4058WLF0u3wwwGAyIiIu707RMRkcx+ujy0nP1z6FZqXejs378fzZo1g06nw5///GesW7cOsbGxyMvLAwCEhoa6xIeGhkrn8vLyoNVqERAQcMuYkJCQ6143JCTEJeba1wkICIBWq5VibmTBggUwmUzSlpOTU8t3T0REjcElswWnL5UDALpyRmS6hVoPL4+JiUFWVhaKi4uxdu1aTJo0CWlpadL5a++TCiFue+/02pgbxd9JzLV0Oh10nKODiMjtVU8U2C6kGQzeGnmToUat1i06Wq0W7dq1Q/fu3bF48WJ07doVr7/+OoxGIwBc16KSn58vtb4YjUZYrVYUFRXdMubChQvXvW5BQYFLzLWvU1RUBJvNdl1LDxEReZ4r8+f4y5oHNX53PY+OEAIWiwVRUVEwGo3YvHmzdM5qtSItLQ19+vQBAMTHx0Oj0bjE5ObmIjs7W4pJSEiAyWRCRkaGFLN7926YTCaXmOzsbOTm5koxmzZtgk6nQ3x8/N2+JSIiauR+yrm8kCf759Bt1OrW1cKFCzFy5EhERESgtLQUKSkp2LZtG1JTU6FQKDBnzhwsWrQI0dHRiI6OxqJFi+Dj44OJEycCAAwGA6ZMmYJ58+YhKCgIgYGBmD9/Pjp37oyhQ4cCADp27IgRI0Zg6tSpWLFiBQBg2rRpGD16NGJiYgAAiYmJiI2NRVJSEpYsWYLCwkLMnz8fU6dOhZ+fX11+PkTUELRa4L33ruyTx9KqtHjv/vek/Tthdwj8nHN5okDOiEy3UatC58KFC0hKSkJubi4MBgO6dOmC1NRUDBs2DADw9NNPo6KiAtOnT0dRURF69eqFTZs2wdfXV3qOZcuWQa1WY8KECaioqMCQIUOwatUqqFQqKWb16tWYPXu2NDpr7NixWL58uXRepVJh/fr1mD59Ovr27Qtvb29MnDgRS5cuvasPg4hkotEAkyfLnQU1AI1Kg8n3TL6r5/i1wAyzpQo+WhXahzarm8TIYymEEELuJORSUlICg8EAk8nEliAiIjfxyZ4zeGbtfvRuE4iUaQlyp0MyqM33Nxf1JCL5VVUB337r3B8+3LnIJ3mkKkcVvj3uvNbD2w2HWln7a31lRmTetqLb428TIpKfxQKMHu3cN5tZ6HgwS5UFo9c4r7V5gRlq7V0UOhxxRTXA1cuJiMhtlFbacDTfuX4iR1xRTbDQISIit/HLWROEAFoGeCPE10vudMgNsNAhIiK38dMZ5/w57J9DNcVCh4iI3Ab751BtsdAhIiK3IITgiuVUayx0iIjILZwpLEdhmRValRKx4Zz7jGqGYziJSH5aLVA9+zmXgPBoWpUWy0cul/Zro/q2VacWftCpVbcOJrqMhQ4RyU+jAWbMkDsLagAalQYzet7Zta7uiHwP++dQLfDWFRERuYUsqX8OR1xRzbFFh4jkZ7cDP/zg3L/3XkDF2xKeyu6w44czzmt9b6t7oVLW7FpX2uw4cL4EAEdcUe2w0CEi+VVWAoMGOffNZkCvlzcfqjeVVZUY9L7zWpsXmKHX1uxaHzhvQpVDILiZDi0DvOszRfIwvHVFRESN3pWFPP2hUCjkTYbcCgsdIiJq9K4udIhqg4UOERE1etLSDxHsiEy1w0KHiIgatTxTJc6bKqFUAF1aGuROh9wMCx0iImrUsnKcrTkxRj/odRxDQ7XDQoeIiBo19s+hu8HSmIjkp9EAr7xyZZ88lkalwStDX5H2a4IrltPdYKFDRPLTaoGnnpI7C2oAWpUWT/Wt+bW22R345VwxAM6ITHeGt66IiKjROpJXikqbA35earQJ5kSSVHts0SEi+dntwL59zv3f/IZLQHgwu8OOfbnOa/2bsN/cdgmI6mHlXSP8oVRyokCqPRY6RCS/ykqgZ0/nPpeA8GiVVZXo+V/nta7JEhA/cSFPuku8dUVERI1WFkdc0V1ioUNERI1SUZkVJy6WAQDuaekvbzLktljoEBFRo5R1thgA0CZYjwC9Vt5kyG2x0CEiokapev6ce3jbiu4CCx0iImqUpIU82RGZ7gILHSIianQcDoGs6hFXnBGZ7gKHlxOR/DQa4IUXruyTx9KoNHhhwAvS/s2cuGhGaWUVvDRKdDD6NlR65IFY6BCR/LRaIDlZ7iyoAWhVWiQPTL5t3L7L/XO6tPSHWsWbD3TnavW3Z/HixejRowd8fX0REhKCcePG4ciRIy4xkydPhkKhcNl69+7tEmOxWDBr1iwEBwdDr9dj7NixOHv2rEtMUVERkpKSYDAYYDAYkJSUhOLiYpeYM2fOYMyYMdDr9QgODsbs2bNhtVpr85aIiKgR4orlVFdqVeikpaVhxowZSE9Px+bNm1FVVYXExESUlZW5xI0YMQK5ubnStmHDBpfzc+bMwbp165CSkoIdO3bAbDZj9OjRsNvtUszEiRORlZWF1NRUpKamIisrC0lJSdJ5u92OUaNGoaysDDt27EBKSgrWrl2LefPm3cnnQERycjiAAwecm8MhdzZUjxzCgQP5B3Ag/wAc4ubXWuqIHMGOyHSXxF3Iz88XAERaWpp0bNKkSeL++++/6WOKi4uFRqMRKSkp0rFz584JpVIpUlNThRBCHDx4UAAQ6enpUsyuXbsEAHH48GEhhBAbNmwQSqVSnDt3TopZs2aN0Ol0wmQy1Sh/k8kkANQ4nojqidksBODczGa5s6F6ZLaYBZIhkAxhttz4WpdW2kTUs9+IyGe+EXmmigbOkNxBbb6/7+rGp8lkAgAEBga6HN+2bRtCQkLQvn17TJ06Ffn5+dK5zMxM2Gw2JCYmSsfCw8MRFxeHnTt3AgB27doFg8GAXr16STG9e/eGwWBwiYmLi0N4eLgUM3z4cFgsFmRmZt4wX4vFgpKSEpeNiIgal1/OFsMhgHCDF0L9vOROh9zcHRc6QgjMnTsX/fr1Q1xcnHR85MiRWL16NbZs2YJXX30Ve/bsweDBg2GxWAAAeXl50Gq1CAhwbY4MDQ1FXl6eFBMSEnLda4aEhLjEhIaGupwPCAiAVquVYq61ePFiqc+PwWBARETEnb59IiKqJ1lcyJPq0B2Pupo5cyZ++eUX7Nixw+X4I488Iu3HxcWhe/fuiIyMxPr16zF+/PibPp8QAgqFQvr56v27ibnaggULMHfuXOnnkpISFjtERI0MOyJTXbqjFp1Zs2bhq6++wtatW9GyZctbxoaFhSEyMhLHjh0DABiNRlitVhQVFbnE5efnSy00RqMRFy5cuO65CgoKXGKubbkpKiqCzWa7rqWnmk6ng5+fn8tGRESNhxCChQ7VqVoVOkIIzJw5E59//jm2bNmCqKio2z7m0qVLyMnJQVhYGAAgPj4eGo0GmzdvlmJyc3ORnZ2NPn36AAASEhJgMpmQkZEhxezevRsmk8klJjs7G7m5uVLMpk2boNPpEB8fX5u3RUREjcTZogpcNFugUSnQKdwgdzrkAWp162rGjBn4+OOP8eWXX8LX11dqUTEYDPD29obZbEZycjIefPBBhIWF4dSpU1i4cCGCg4PxwAMPSLFTpkzBvHnzEBQUhMDAQMyfPx+dO3fG0KFDAQAdO3bEiBEjMHXqVKxYsQIAMG3aNIwePRoxMTEAgMTERMTGxiIpKQlLlixBYWEh5s+fj6lTp7KlhojITf10uX9ObJgfvDQqeZMhj1CrQuett94CAAwcONDl+HvvvYfJkydDpVJh//79+OCDD1BcXIywsDAMGjQIn3zyCXx9r0zhvWzZMqjVakyYMAEVFRUYMmQIVq1aBZXqyl/q1atXY/bs2dLorLFjx2L58uXSeZVKhfXr12P69Ono27cvvL29MXHiRCxdurTWHwIRyUyjAebPv7JPHkuj0mB+wnxp/1pcyJPqmkIIIeROQi4lJSUwGAwwmUxsBSIiagTG/edHZOUU4/VH78H997SQOx1qpGrz/c0FRIiIqFGwVNlx8LxzfjPOiEx1hYt6EpH8HA7gzBnnfqtWgJL/B/NUDuHAGZPzWrcytIJSceVaHzhfAqvdgSC9FhGB3nKlSB6GhQ4Rya+iAqgexWk2A3q9vPlQvamwVSDqdee1Ni8wQ6+9cq2vHlZ+s/nQiGqL/20iIqJGgR2RqT6w0CEiokZBatGJ8Jc1D/IsLHSIiEh2+aWVOFdcAYUC6NySEwVS3WGhQ0REssu63JrTPsQXvl6cS4nqDgsdIiKS3U/SiuX+suZBnoeFDhERye5KR2R/eRMhj8Ph5UQkP7UamD79yj55LLVSjendp0v7AFBld+CXsyYAHHFFdY+/UYhIfjod8J//yJ0FNQCdWof/jHK91kcvmFFutcNXp0a75s1kyow8FW9dERGRrH7Kcd626hrhD6WSEwVS3WKLDhHJTwjg4kXnfnAwwFlxPZYQAhfLndc62CcYCoXCZUZkorrGQoeI5FdeDoSEOPe5BIRHK7eVI2Sp81pXLwHBjshUn3jrioiIZGMqt+HXgjIAwD1csZzqAQsdIiKSTdbZYgBA6yAfBOq18iZDHomFDhERyYYLeVJ9Y6FDRESyYUdkqm8sdIiISBZCCGRdXvrhHq5YTvWEhQ4REcni1KUymCps0KmV6GD0kzsd8lAcXk5E8lOrgUmTruyTx1Ir1ZjU1Xmts8+aAQCdWxigVfP/3VQ/+BuFiOSn0wGrVsmdBTUAnVqHVeNWAQD++sV+AOyfQ/WLJTQREcniSkdkjrii+sMWHSKSnxDO2ZEBwMeHS0B4MCEEym3lKLdW4VBeCQC26FD9YqFDRPIrLweaXV61mktAeLRyWzmaLXZe6wjHZwj380eYwVvmrMiT8dYVERHJhq05VN9Y6BARkWxY6FB9Y6FDRESyYUdkqm8sdIiISBZqpQJx4Qa50yAPx0KHiIhkEWP0hbdWJXca5OFY6BARkSy6tGBrDtU/Di8nIvmpVMBDD13ZJ4+lUqrQQjcIReVWdGsVKHc61ASw0CEi+Xl5AZ9+KncW1ACU0MLH/BTUVQ70igqTOx1qAmp162rx4sXo0aMHfH19ERISgnHjxuHIkSMuMUIIJCcnIzw8HN7e3hg4cCAOHDjgEmOxWDBr1iwEBwdDr9dj7NixOHv2rEtMUVERkpKSYDAYYDAYkJSUhOLiYpeYM2fOYMyYMdDr9QgODsbs2bNhtVpr85aIiKgBHcotgbXKAX8fDVoH+cidDjUBtSp00tLSMGPGDKSnp2Pz5s2oqqpCYmIiysrKpJhXXnkFr732GpYvX449e/bAaDRi2LBhKC0tlWLmzJmDdevWISUlBTt27IDZbMbo0aNht9ulmIkTJyIrKwupqalITU1FVlYWkpKSpPN2ux2jRo1CWVkZduzYgZSUFKxduxbz5s27m8+DiIjq0feH8wEA3SL8oeBSH9QQxF3Iz88XAERaWpoQQgiHwyGMRqN4+eWXpZjKykphMBjE22+/LYQQori4WGg0GpGSkiLFnDt3TiiVSpGamiqEEOLgwYMCgEhPT5didu3aJQCIw4cPCyGE2LBhg1AqleLcuXNSzJo1a4ROpxMmk6lG+ZtMJgGgxvFEVE/MZiGcK14598kjnSgwi7bPfS6QDIFkCLOF15ruTG2+v+9q1JXJZAIABAY6O5SdPHkSeXl5SExMlGJ0Oh0GDBiAnTt3AgAyMzNhs9lcYsLDwxEXFyfF7Nq1CwaDAb169ZJievfuDYPB4BITFxeH8PBwKWb48OGwWCzIzMy8Yb4WiwUlJSUuGxER1T8hBBZ8/gusVQ65U6Em5o4LHSEE5s6di379+iEuLg4AkJeXBwAIDQ11iQ0NDZXO5eXlQavVIiAg4JYxISEh171mSEiIS8y1rxMQEACtVivFXGvx4sVSnx+DwYCIiIjavm0iIroD/9ubg/QThfDWcFQdNaw7LnRmzpyJX375BWvWrLnu3LX3XYUQt70Xe23MjeLvJOZqCxYsgMlkkracnJxb5kRERHcvv7QSL60/BACYNbidzNlQU3NHhc6sWbPw1VdfYevWrWjZsqV03Gg0AsB1LSr5+flS64vRaITVakVRUdEtYy5cuHDd6xYUFLjEXPs6RUVFsNls17X0VNPpdPDz83PZiIiofr341UGUVFahcwsDknpHyp0ONTG1KnSEEJg5cyY+//xzbNmyBVFRUS7no6KiYDQasXnzZumY1WpFWloa+vTpAwCIj4+HRqNxicnNzUV2drYUk5CQAJPJhIyMDClm9+7dMJlMLjHZ2dnIzc2VYjZt2gSdTof4+PjavC0iIqonmw9ewPr9uVApFVg8vjPUKk7ITw2rVhMGzpgxAx9//DG+/PJL+Pr6Si0qBoMB3t7eUCgUmDNnDhYtWoTo6GhER0dj0aJF8PHxwcSJE6XYKVOmYN68eQgKCkJgYCDmz5+Pzp07Y+jQoQCAjh07YsSIEZg6dSpWrFgBAJg2bRpGjx6NmJgYAEBiYiJiY2ORlJSEJUuWoLCwEPPnz8fUqVPZUkNE1AiUVtrw/BfZAIA/3huFuBYGlFnLbvMoorpVq0LnrbfeAgAMHDjQ5fh7772HyZMnAwCefvppVFRUYPr06SgqKkKvXr2wadMm+Pr6SvHLli2DWq3GhAkTUFFRgSFDhmDVqlVQXTX1++rVqzF79mxpdNbYsWOxfPly6bxKpcL69esxffp09O3bF97e3pg4cSKWLl1aqw+AiBoBlQq4774r++QRlnx7BHkllYgM8sGcIe0BOJeAuC/6PmmfqL4phBBC7iTkUlJSAoPBAJPJxFYgIqI6lHm6EA+9vQtCAKv/2At92wXLnRJ5kNp8f/NmKRER1SlLlR3Prt0PIYCH41uyyCFZsdAhIqI69fa2EziWb0ZwMy2eG9VR7nSoiWOhQ0TyKysD9HrnVsbOqu7seH4p/rP1OADghTGd4O+jdTlfZi2DfpEe+kV6dkymBlGrzshERPWmvFzuDOguORwCz67dD6vdgcEdQjC6S9gN48ptvNbUcNiiQ0REdWJ1xhnsPV0EvVaF/xsXx9XJqVFgoVMPisqs2Lg/F6YKm9ypEBE1iDxTJf658TAA4KnhMWjh7y1zRkROvHVVD7YdzcdfPvkZKqUC90T4o390c/RvH4wuLf2hUvJ/OETkWYQQeP7LbJgtVejWyh9JCa3lTolIwkKnHiigQNvmevxaUIbM00XIPF2EZd8dhb+PBn3bBWNAdHP0b98cRoOX3KkSEd211Ow8bD54ARqVAi+P78L/0FGjwkKnHozr1gLjurXA2aJy/HDsItKOFODHXy+iuNyG9b/kYv0vzvW5YkJ90b99MPq3b44erQPhpeEsoUTkXkzlNvztqwMAgCcGtEWM0fc2jyBqWCx06lHLAB/8tmcr/LZnK1TZHcjKKcb2owVIO3YRv5wtxpELpThyoRTv/HASXholekUFoX/75hjQPhhtmzdjRz5qOpRKYMCAK/vkNl5OPYSCUgvaNNdj+qB2t41XKpQYEDlA2ieqb1wCQqYlIIrKrNhx/CK2Hy3A9mMFuFBicTkfbvBC//bOW1x92wXD4K1p0PyIiG4n/cQlPLoyHQDwvz8loGdUoMwZUVNRm+9vFjqNYK0rIQSOXCh1Fj1HLyLjVCGsVQ7pvFIBdGsVwE7NRNRoVNrsGPn6Dzh5sQwTe7XCogc6y50SNSEsdGqosRQ616qw2pF+8tLlwqcAvxa4zh7KTs1EJLcl3x7Gf7b+ihBfHb6bNwB+Xmx1pobDQqeGGmuhc61rOzWXVla5nG8f2uxya09z9Ixip2ZyQ2VlQOvWzv1Tp5xLQVCjdSi3BGPe2IEqh8Dbv4vHiDhjjR9bZi1D69dbAwBOPXkKei2vNdVebb6/2RnZDdyuU/PRC2YcvWDGf3ewUzO5sYsX5c6AasDuEHh27S+ocggM7xRaqyKn2sVyXmtqOCx03IxapUT31oHo3joQcxNjbtipOe1oAdKOFuD/wE7NRFS33t95Cj+fNcFXp8bf74+TOx2i22Kh4+YC9FqM6RqOMV3Db9ip+bypEil7cpCyJwdKBXBPhD8GtA9hp2YiqrWzReVYuukIAODZ+zog1I/9A6nxY6HjQRQKBToY/dDB6Idp/dvesFPzvjPF2HemWJqpeXSXMCT1bs1JvojoloQQ+OsX2Si32tGzdSB+26OV3CkR1QgLHQ/mrVVhUEwIBsWEALi+U3NxuQ0fpZ/BR+ln0LN1IH6XEIkRnYzQqjmJFxG5+urn89h2pABalRKLxneGkq3B5CZY6DQh13ZqTj9RiNW7T2PTwQvIOFWIjFOFCG6mw297RuC3PVshnKsPExGcE5z+/euDAIBZg9uhXUgzmTMiqjkWOk2UWqVEv+hg9IsORp6pEmsyzmBNxhnkl1rwxpbj+M/W4xjaMRRJCZHo2zaY/3uj+qVUAt27X9mnRuUf6w/hUpkVMaG++NOAtnf1XEqFEt3Du0v7RPWN8+i4wTw6DcVmd2DTgQv4MP0U0k8USsfbBOvxWO9IPPSbljD4cNQWUVPyw7ECJL2bAYUCWPtEH/ymVYDcKRFxwsCaYqFzc8culOKj9NNYu+8czBbnBIVeGiXu79oCSQmRiGthkDlDIqpvFVY7Ev+VhpzCCkzu0xrJYzvJnRIRABY6NcZC5/bMlip8mXUOH+46jcN5pdLxeyL8kdQ7EqO6hHEmZiIPtWjDIazcfgLhBi9smjsAzXTs7UCNAwudGmKhU3NCCOw9XYQPd53Gxuxc2OzOvzYBPhpM6BGB3/WKRESgj8xZktsqLwdiY537Bw8CPvy7JLfscyaMXb4DDgH8v8ndMbhDaJ08b7mtHLH/cV7rgzMOwkfDa021xyUgqM4pFAr0aB2IHq0DUVAai//tzcHq9NM4b6rEirQTWLn9BAa2b47fJ7RG//bNOREh1Y4QwOnTV/ZJVlV2B55Z+wscAhjTNbzOihzA+Z+m06bT0j5RfWOhQ7XW3FeHGYPa4U/922DL4Xx8mH4aPxy7iK1HCrD1SAEiAr3xWK9ITOgegUC9Vu50iaiW3t1xEgfOl8DgrcHfRsfKnQ7RXWGhQ3dMrVIisZMRiZ2MOHmxDKvTT+N/e3OQU1iBlzcexmubj2J05zD8LiES3SL8ubjoHXA4BMptdpRbqlBmtaPcWoVyqx1lFuef5ZePlVmunLv25zKrHRabHXEtDBjcIQT9ooPh58XRc3Rjpy+V4bXNRwEAz43qiOa+OpkzIro77KPDPjp1qsJqx9c/n8cH6aeQfa5EOt4p3A+/T4jE2K4t4K31vM7LQghU2Owos9hRYbWjzFp1VcFxufiw2lFxVRHi/PlK0VJmrbryWIvzz0qbo85zVSsV6N46AIM7hGBwh5DGscJ9WRnQ7PIkdGYzoNfLm08TJYTA797djR+PX0KftkFY/cdedf53o8xahmaLndfavMAMvZbXmmqPnZFriIVO/RFC4OezJny46zS+/uU8rFXOL2w/LzUeio/A73q3Qpvm7jG7qhACReU2nC0qx9miCpwrqpD2zxZV4HxxBczWqnrtWqJUAHqtGj46FfRaNby1qhv8rIKPTg29VgVvrdrlZyGA9BOXsOVIPk4UlLk8d8sAbwzuEIJBHUKQ0CZInlF0LHQahU/35uCpz36BTq3Et3P6o3Vw3V8HFjpUF1jo1BALnYZRVGbFp5k5+Cj9DM4UlkvH740Oxu96R2JIhxCoVfLNkCqEQGGZVSpcpIKm+Mp+udVe4+fz0argo1VDr1PBW6OCXqeGT3VholVJxYkUc9U5vU593c8+WhV0amWd/c/69KUybD2cjy1HCpB+4pJUhALOuZL6tA3GoMutPS0aahkQFjqyu2i2YOhraSgut+HZkR3w57ucAflmWOhQXWChU0MsdBqWwyGQdqwAH+06jS1H8qUWkDCDFyb2bIVHekYgxNerzl9XCIGLZivOFpVfLl5cW2TOFVWgwnb7QibEV4eWAd5oGeCDFgHeV/b9vWDw1kKvU8FLrXKr5TLKrVX48fglbD2Sj62H85FrqnQ5HxPqi4EdmmNwTAjiIwPqryAtLwd69HDu79nD4eUymL3mJ3z183nEhvnhq5l96+1al9vK0eMd57XeM3UPh5fTHWGhU0MsdOSTU1iOjzPO4JM9OSgsswJw9h0ZEWdEUu9I9IwKrHELhhACBWbL9S0yl/fPFVfctq+LQlFdyPhcLmC8pf0W/t4I9/f2+IkRhRA4nFeKLYfzse1IPjJPF8Fx1W8HPy81+rdvjkExIRgY0xxBzdhJ1VNsPZyPx1ftgVIBfDmjHzq35Mzn1LjVa6Gzfft2LFmyBJmZmcjNzcW6deswbtw46fzkyZPx/vvvuzymV69eSE9Pl362WCyYP38+1qxZg4qKCgwZMgRvvvkmWrZsKcUUFRVh9uzZ+OqrrwAAY8eOxRtvvAF/f38p5syZM5gxYwa2bNkCb29vTJw4EUuXLoVWW7MhzSx05Fdps2Njdi4+3HUa+84US8djQn3xu4RIPNCtBXw0qsuFTPlVxczlIubyLSZL1e0LGaOf11WtMK7FTJi/F3Rqzy5kaqu43Iq0owXYejgfaUcLUFRuk84pFEDXlv7Ovj0xIegU7udWLVl0hdlShcTX0nDeVImp90bhuVEcTk6NX70WOhs3bsSPP/6I3/zmN3jwwQdvWOhcuHAB7733nnRMq9UiMDBQ+vmJJ57A119/jVWrViEoKAjz5s1DYWEhMjMzoVI5v2xGjhyJs2fPYuXKlQCAadOmoXXr1vj6668BAHa7Hffccw+aN2+OV199FZcuXcKkSZMwfvx4vPHGGzV6Lyx0Gpfscyas3n0aX/x0XrqV5KVRwuEArPZbFzJKqZBxbZGpvsUUZvCGVs2Vku+U3SGQlVPs7NtzOB8Hc0tczjf31WFQTHMM7hCCvu2C4cvh624j+asDWLXzFCICvfHtnP7w0XLWEWr8GuzWlUKhuGGhU1xcjC+++OKGjzGZTGjevDk+/PBDPPLIIwCA8+fPIyIiAhs2bMDw4cNx6NAhxMbGIj09Hb169QIApKenIyEhAYcPH0ZMTAw2btyI0aNHIycnB+Hh4QCAlJQUTJ48Gfn5+Td84xaLBRaLRfq5pKQEERERLHQaGVOFDZ/vO4sP009LI4SUCiDM4CxaWlzVEtMywBsRAT4wGrygkbFDc1OTZ6rEtiPOomfH8YsunbU1Kucs2tUjudoE629/G5J9dGTx05kijH9rJ4QAPpzSE/dGN6/312QfHaoLsi8BsW3bNoSEhMDf3x8DBgzASy+9hJCQEABAZmYmbDYbEhMTpfjw8HDExcVh586dGD58OHbt2gWDwSAVOQDQu3dvGAwG7Ny5EzExMdi1axfi4uKkIgcAhg8fDovFgszMTAwaNOi6vBYvXowXX3yxPt4y1SGDtwaP943C5D6tcfSCGT5aFQuZRsZo8MKjPVvh0Z6tYKmyI+NkIbYeLsDWI/k4ebEMO3+9hJ2/XsI/1h9Cq0AfqejpFRV4475OQjjXuKrep3pnrXLg2bX7IQQw/jctGqTIAZx9wQ4WHJT2iepbnRc6I0eOxMMPP4zIyEicPHkSzz//PAYPHozMzEzodDrk5eVBq9UiICDA5XGhoaHIy8sDAOTl5UmF0dVCQkJcYkJDXddfCQgIgFarlWKutWDBAsydO1f6ubpFhxonhUKBGKOv3GnQbejUKtwb3Rz3RjfH38bE4uTFMqlD8+4ThThTWI5VO09h1c5T8Nao0LddEAZd7tsT3lDD1+k6K7f/iiMXShGo1+Kv7JdDHqzOC53q21EAEBcXh+7duyMyMhLr16/H+PHjb/o4IYRL8/aNmrrvJOZqOp0OOh1HihDVp6hgPab0i8KUflEos1Rhx/GL2Ho4H1uP5ONCiQXfHcrHd4fyAQAdjL4Y1CEEQ1vpES9z3k3JrwVm/Pv74wCAF8bEck068mj13ussLCwMkZGROHbsGADAaDTCarWiqKjIpVUnPz8fffr0kWIuXLhw3XMVFBRIrThGoxG7d+92OV9UVASbzXZdSw8RyUOvU2N4JyOGdzI6b1nklkgdmn/KKcbhvFIczivFKmslDl1+zLmicrTghIH1xuEQWPD5fljtDgxo3xxju4bf/kFEbqzeOz1cunQJOTk5CAsLAwDEx8dDo9Fg8+bNUkxubi6ys7OlQichIQEmkwkZGRlSzO7du2EymVxisrOzkZubK8Vs2rQJOp0O8fH8vyFRY6NQKNAp3ICZg6Px+fS+yPzrMPzrkXswtms4DN5XRmkN/9cPmL3mJ2SfM8mYredK2ZODjJOF8NGq8NIDcfKvc0ZUz2rdomM2m3H8+HHp55MnTyIrKwuBgYEIDAxEcnIyHnzwQYSFheHUqVNYuHAhgoOD8cADDwAADAYDpkyZgnnz5iEoKAiBgYGYP38+OnfujKFDhwIAOnbsiBEjRmDq1KlYsWIFAOfw8tGjRyMmJgYAkJiYiNjYWCQlJWHJkiUoLCzE/PnzMXXqVI6gInIDgXotxnVrgXHdWqCqpB2wyHnc7hD46ufz+Orn8+jbLgjT+rdF/+hgfiHXgQsllVi80dl2Ni8xBi0DOOKJPF+tC529e/e6jGiq7tw7adIkvPXWW9i/fz8++OADFBcXIywsDIMGDcInn3wCX98rnUqXLVsGtVqNCRMmSBMGrlq1SppDBwBWr16N2bNnS6Ozxo4di+XLl0vnVSoV1q9fj+nTp6Nv374uEwYSkXtRq1VAZCQA4LPpfbBiTx7W78/Fj8cv4cfjl9DB6Itp/dtgTNdwjr67Cy98eQCllVXo2tKAyX1ay5KDQqFApCFS2ieqb1wCghMGEjVKOYXl+H8/nsQne3KkeXrCDF6Y0i8Kj/ZshWY6TmxXG6nZefjzR5lQKxX4elY/dAzj7zxyX1zrqoZY6BA1fsXlVnyUfhqrdp7GRbNzwk9fLzUe6xWJx/u2Rqhf3S8E62lKKm0Y+moa8kstmD6wLZ4e0UHulIjuCgudGmKhQ+Q+Km12rPvpHN754YQ0Y7ZGpcC4e1pgWv82iA7lnEs389y6/Vi9+wyigvXY+OS9Hr9ALXk+Fjo1xEKHqJGoqAD693fub98OeN98IkGHQ+C7QxewcvsJ7D1dJB0f3CEE0/q3Qa+omq983xTsOVWIh9/eBQBYM7U3EtoGyZpPha0C/Vc5r/X2ydvhreGkkVR7si8BQURUKw4HsHfvlf1bUCoVSOxkRGInIzJPF2Hl9l+x6eAFbLk8P0/XlgZM698WI+KMUDXxFdUtVXY8u/YXAMCjPSJkL3IAwCEc2Ht+r7RPVN9Y6BCR24qPDMCKpO44UWDGf3ecxGeZZ/HzWRNmfLwPrQJ98Md7o/BwfAS8tU3zVs1/tv6KXwvKENxMhwUjO8qdDpEsOE6TiNxem+bNsOiBztj57GDMHtwO/j4anCksx9++PIA+L3+P1zYfxaXLHZmbiqMXSvHWNuecZ3+/vxMMPprbPILIM7HQISKPEdxMh7mJMdj57GC8OLYTIgK9UVRuw7+/P4Y+L2/BX7/Yj1MXy+ROs97ZHQLPrP0FNrvA0I6hGBlnlDslItmw0CEij+OjVWNSn9bYOm8glk/shi4tDbBUOfBR+hkMenUbnvgoEz+dKbr9E7mpj9JP46czxWimU+P/xnVi52xq0thHh4g8llqlxOgu4RjVOQzpJwqxYvuv2HakABuz87AxOw89WwdiWv82GNwhBEoP6bh8vrgCr6QeBgA8MyIGYQaOaqKmjYUOETUOwcH19tQKhQIJbYOQ0DYIR/JKsXL7CXz18zlknCpExqlCtAtphmn3tsH93cKhU7tvx2UhBJ7/IhtlVjviIwPwWK9IuVO6oWCf+rvWRNfiPDqcR4eoScozVeK9H0/i491nUGqpAgCE+OowuW9rPNYr0mVFdbk5HAKF5Vbkl1hQYLYgv6Ty8p/OnwuuOl5mtUOjUmDD7Hs5iSJ5LE4YWEMsdIiopNKGlIwz+H87TiGvpBIAoNeq8GjPVvhDvyi08K+/Wz+VNjsKSi3IL628/KfF+Wd14XL5+EWzFXZHzX5Vq5UKPD86FpNkWrSTqCGw0KkhFjpEVM1a5cDXP5/Hyu0ncORCKQBApVRgTJcwTOvfFrHhNfsdIYRAcbntStFybRFz1c+llVW1yjFIr0VzXx2a++oQ4ut1+U+dy5+hfl7Qc8FT8nAsdGqIhQ5RI1FRAYwc6dzfuPGWS0DUNyEEth0twMq0E9h14pJ0/N7oYEzpFwWDt+aawsX5Z8HlAqbAbIHNXvNfq1q1EiEuBcu1BYzz56BmWmhU7j9QtsJWgZGrndd642MbuQQE3REuAUFE7sXhANLSruzLSKFQYFBMCAbFhGD/WRNWbP8VG/bn4odjF/HDsYs1fh5/H81Ni5cr+17w81I3qeHfDuFA2uk0aZ+ovrHQISK6ic4tDVg+8TfIKSzHuztO4uufz0OnVqK5nxeaN9MhxE93zZ/Ogia4mdatR28ReRIWOkREtxER6IPksZ2QPLaT3KkQUS25/w1fIiIioptgoUNEREQei4UOEREReSz20SGixsHHR+4MqIH4aHitqeGw0CEi+en1QFmZ3FlQA9Br9ShbyGtNDYe3roiIiMhjsdAhIiIij8VCh4jkV1kJjBrl3Cor5c6G6lFlVSVGfTwKoz4ehcoqXmuqf+yjQ0Tys9uBDRuu7JPHsjvs2HBsg7RPVN/YokNEREQei4UOEREReSwWOkREROSxWOgQERGRx2KhQ0RERB6rSY+6EkIAAEpKSmTOhKiJu3pW5JISjrzyYGXWMuDyqPKSkhLYtbzWVHvV39vV3+O3ohA1ifJQZ8+eRUREhNxpEBER0R3IyclBy5YtbxnTpAsdh8OB8+fPw9fXFwqFQu50GlxJSQkiIiKQk5MDPz8/udNxW/wc6wY/x7rBz7Fu8HOsG/X1OQohUFpaivDwcCiVt+6F06RvXSmVyttWgk2Bn58f/yHXAX6OdYOfY93g51g3+DnWjfr4HA0GQ43i2BmZiIiIPBYLHSIiIvJYLHSaMJ1OhxdeeAE6nU7uVNwaP8e6wc+xbvBzrBv8HOtGY/gcm3RnZCIiIvJsbNEhIiIij8VCh4iIiDwWCx0iIiLyWCx0iIiIyGOx0GliFi9ejB49esDX1xchISEYN24cjhw5Indabm/x4sVQKBSYM2eO3Km4nXPnzuF3v/sdgoKC4OPjg3vuuQeZmZlyp+VWqqqq8Ne//hVRUVHw9vZGmzZt8Pe//x0Oh0Pu1Bq17du3Y8yYMQgPD4dCocAXX3zhcl4IgeTkZISHh8Pb2xsDBw7EgQMH5Em2EbvV52iz2fDMM8+gc+fO0Ov1CA8Px+9//3ucP3++wfJjodPEpKWlYcaMGUhPT8fmzZtRVVWFxMRElF29qCLVyp49e7By5Up06dJF7lTcTlFREfr27QuNRoONGzfi4MGDePXVV+Hv7y93am7ln//8J95++20sX74chw4dwiuvvIIlS5bgjTfekDu1Rq2srAxdu3bF8uXLb3j+lVdewWuvvYbly5djz549MBqNGDZsGEpLSxs408btVp9jeXk59u3bh+effx779u3D559/jqNHj2Ls2LENl6CgJi0/P18AEGlpaXKn4pZKS0tFdHS02Lx5sxgwYIB48skn5U7JrTzzzDOiX79+cqfh9kaNGiX+8Ic/uBwbP368+N3vfidTRu4HgFi3bp30s8PhEEajUbz88svSscrKSmEwGMTbb78tQ4bu4drP8UYyMjIEAHH69OkGyYktOk2cyWQCAAQGBsqciXuaMWMGRo0ahaFDh8qdilv66quv0L17dzz88MMICQlBt27d8M4778idltvp168fvv/+exw9ehQA8PPPP2PHjh247777ZM7MfZ08eRJ5eXlITEyUjul0OgwYMAA7d+6UMTP3ZzKZoFAoGqzltkkv6tnUCSEwd+5c9OvXD3FxcXKn43ZSUlKwb98+7NmzR+5U3NaJEyfw1ltvYe7cuVi4cCEyMjIwe/Zs6HQ6/P73v5c7PbfxzDPPwGQyoUOHDlCpVLDb7XjppZfw29/+Vu7U3FZeXh4AIDQ01OV4aGgoTp8+LUdKHqGyshLPPvssJk6c2GCLpbLQacJmzpyJX375BTt27JA7FbeTk5ODJ598Eps2bYKXl5fc6bgth8OB7t27Y9GiRQCAbt264cCBA3jrrbdY6NTCJ598go8++ggff/wxOnXqhKysLMyZMwfh4eGYNGmS3Om5NYVC4fKzEOK6Y1QzNpsNjz76KBwOB958880Ge10WOk3UrFmz8NVXX2H79u1o2bKl3Om4nczMTOTn5yM+Pl46ZrfbsX37dixfvhwWiwUqlUrGDN1DWFgYYmNjXY517NgRa9eulSkj9/TUU0/h2WefxaOPPgoA6Ny5M06fPo3Fixez0LlDRqMRgLNlJywsTDqen59/XSsP3Z7NZsOECRNw8uRJbNmypcFacwCOumpyhBCYOXMmPv/8c2zZsgVRUVFyp+SWhgwZgv379yMrK0vaunfvjsceewxZWVkscmqob9++101vcPToUURGRsqUkXsqLy+HUun661ylUnF4+V2IioqC0WjE5s2bpWNWqxVpaWno06ePjJm5n+oi59ixY/juu+8QFBTUoK/PFp0mZsaMGfj444/x5ZdfwtfXV7oPbTAY4O3tLXN27sPX1/e6fk16vR5BQUHs71QLf/nLX9CnTx8sWrQIEyZMQEZGBlauXImVK1fKnZpbGTNmDF566SW0atUKnTp1wk8//YTXXnsNf/jDH+ROrVEzm804fvy49PPJkyeRlZWFwMBAtGrVCnPmzMGiRYsQHR2N6OhoLFq0CD4+Ppg4caKMWTc+t/ocw8PD8dBDD2Hfvn345ptvYLfbpe+dwMBAaLXa+k+wQcZ2UaMB4Ibbe++9J3dqbo/Dy+/M119/LeLi4oROpxMdOnQQK1eulDslt1NSUiKefPJJ0apVK+Hl5SXatGkjnnvuOWGxWOROrVHbunXrDX8fTpo0SQjhHGL+wgsvCKPRKHQ6nejfv7/Yv3+/vEk3Qrf6HE+ePHnT752tW7c2SH4KIYSo/3KKiIiIqOGxjw4RERF5LBY6RERE5LFY6BAREZHHYqFDREREHouFDhEREXksFjpERETksVjoEBERkcdioUNEREQei4UOEdE1kpOTcc8998idBhHVARY6RNSkKRQKfPHFF3KnQUT1hIUOEREReSwWOkTUKAwcOBCzZs3CnDlzEBAQgNDQUKxcuRJlZWV4/PHH4evri7Zt22Ljxo3SY9LS0tCzZ0/odDqEhYXh2WefRVVVlctzzp49G08//TQCAwNhNBqRnJwsnW/dujUA4IEHHoBCoZB+rvbhhx+idevWMBgMePTRR1FaWlqfHwER1QMWOkTUaLz//vsIDg5GRkYGZs2ahSeeeAIPP/ww+vTpg3379mH48OFISkpCeXk5zp07h/vuuw89evTAzz//jLfeegvvvvsu/vGPf1z3nHq9Hrt378Yrr7yCv//979i8eTMAYM+ePQCA9957D7m5udLPAPDrr7/iiy++wDfffINvvvkGaWlpePnllxvuwyCiOsHVy4moURg4cCDsdjt++OEHAIDdbofBYMD48ePxwQcfAADy8vIQFhaGXbt24euvv8batWtx6NAhKBQKAMCbb76JZ555BiaTCUql8rrnBICePXti8ODBUtGiUCiwbt06jBs3TopJTk7GkiVLkJeXB19fXwDA008/je3btyM9Pb0hPg4iqiNs0SGiRqNLly7SvkqlQlBQEDp37iwdCw0NBQDk5+fj0KFDSEhIkIocAOjbty/MZjPOnj17w+cEgLCwMOTn5982l9atW0tFTm0eR0SNCwsdImo0NBqNy88KhcLlWHVR43A4IIRwKXIAoLqB+urjN3pOh8NxR7nU5HFE1Liw0CEitxQbG4udO3fi6rvvO3fuhK+vL1q0aFHj59FoNLDb7fWRIhE1Aix0iMgtTZ8+HTk5OZg1axYOHz6ML7/8Ei+88ALmzp0LpbLmv9pat26N77//Hnl5eSgqKqrHjIlIDix0iMgttWjRAhs2bEBGRga6du2KP//5z5gyZQr++te/1up5Xn31VWzevBkRERHo1q1bPWVLRHLhqCsiIiLyWGzRISIiIo/FQoeIiIg8FgsdIiIi8lgsdIiIiMhjsdAhIiIij8VCh4iIiDwWCx0iIiLyWCx0iIiIyGOx0CEiIiKPxUKHiIiIPBYLHSIiIvJY/x9IcHzzKKM+FQAAAABJRU5ErkJggg==",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -736,10 +726,13 @@
     }
    ],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "df_count_plot = df_area.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")\n",
     "ax = df_count_plot.plot(x=\"month\", y=\"count(ID)\")\n",
     "ax.axvline(x=9, color='g', linestyle='--', label='Program Starts, Fall Quarter Begins')\n",
-    "ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n"
+    "ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -834,7 +827,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 25,
    "id": "4170f6ac-afca-44c9-9e2e-7e78670de3d1",
    "metadata": {},
    "outputs": [
@@ -842,12 +835,6 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Exception in thread \"serve-DataFrame\" java.net.SocketTimeoutException: Accept timed out\n",
-      "\tat java.net.PlainSocketImpl.socketAccept(Native Method)\n",
-      "\tat java.net.AbstractPlainSocketImpl.accept(AbstractPlainSocketImpl.java:409)\n",
-      "\tat java.net.ServerSocket.implAccept(ServerSocket.java:560)\n",
-      "\tat java.net.ServerSocket.accept(ServerSocket.java:528)\n",
-      "\tat org.apache.spark.security.SocketAuthServer$$anon$1.run(SocketAuthServer.scala:64)\n",
       "                                                                                \r"
      ]
     },
@@ -863,7 +850,6 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
     "df_count_plot_program = df_area_program.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")\n",
     "ax = df_count_plot_program.plot(x=\"month\", y=\"count(ID)\")\n",
     "ax.axvline(x=9, color='g', linestyle='--', label='Program Starts, Fall Quarter Begins')\n",
@@ -874,7 +860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 26,
    "id": "d266e90c-ae57-41cb-99a5-3185b61e60ed",
    "metadata": {},
    "outputs": [
@@ -1050,17 +1036,20 @@
     "from folium.plugins import HeatMap\n",
     "\n",
     "#Create a Folium map centered on the mean coordinates\n",
-    "map_center = [df_area_program_pd['Latitude'].mean(), df_area_program_pd['Longitude'].mean()]\n",
+    "map_center = [df_area_program_pd['dropoff_lat'].mean(), df_area_program_pd['dropoff_lon'].mean()]\n",
     "mymap = folium.Map(location=map_center, zoom_start=15)\n",
     "\n",
     "# Convert the DataFrame to a list of points\n",
-    "heat_data = [[point['Latitude'], point['Longitude']] for index, point in df.iterrows()]\n",
+    "heat_data = [[point['dropoff_lat'], point['dropoff_lon']] for index, point in df_area_program_pd.iterrows()]\n",
+    "heat_data2 = [[point['pickup_lat'], point['pickup_lon']] for index, point in df_area_program_pd.iterrows()]\n",
+    "\n",
     "\n",
     "# Add a heatmap layer to the map\n",
     "HeatMap(heat_data).add_to(mymap)\n",
+    "HeatMap(heat_data2).add_to(mymap)\n",
     "\n",
     "# Save or display the map\n",
-    "mymap.save(\"heatmap.html\")  # Save the map to an HTML file\n",
+    "#mymap.save(\"heatmap.html\")  # Save the map to an HTML file\n",
     "mymap"
    ]
   },

From 298059748c774d66fd866cc38d33db5b4630fd45 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Wed, 15 Nov 2023 22:52:26 +0000
Subject: [PATCH 7/9] more exploratory plots and program filters

---
 eda_2021.ipynb | 403 ++++++++++++++++++++++++++++++++++---------------
 1 file changed, 284 insertions(+), 119 deletions(-)

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 2c6bef1..5466719 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
    "metadata": {},
    "outputs": [
@@ -28,7 +28,10 @@
        " ('spark.eventLog.enabled', 'true'),\n",
        " ('spark.submit.pyFiles',\n",
        "  '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.history.fs.logDirectory',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/ef995586-ab93-4aae-8a58-b7f73789ab77/spark-job-history'),\n",
        " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n",
+       " ('spark.ui.proxyBase', '/proxy/application_1700083397876_0002'),\n",
        " ('spark.kryoserializer.buffer.max', '2000M'),\n",
        " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n",
        " ('spark.dataproc.sql.local.rank.pushdown.enabled', 'true'),\n",
@@ -37,17 +40,16 @@
        " ('spark.sql.autoBroadcastJoinThreshold', '43m'),\n",
        " ('spark.ui.filters',\n",
        "  'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n",
-       " ('spark.app.id', 'application_1699975669214_0001'),\n",
-       " ('spark.eventLog.dir',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/0f852a69-184b-4243-a2ca-dd44bcce018a/spark-job-history'),\n",
-       " ('spark.driver.port', '35309'),\n",
        " ('spark.metrics.namespace',\n",
        "  'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n",
        " ('spark.executor.memory', '4g'),\n",
+       " ('spark.eventLog.dir',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/ef995586-ab93-4aae-8a58-b7f73789ab77/spark-job-history'),\n",
        " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1700083397876_0002'),\n",
        " ('spark.hadoop.hive.execution.engine', 'mr'),\n",
        " ('spark.executor.id', 'driver'),\n",
-       " ('spark.ui.proxyBase', '/proxy/application_1699975669214_0001'),\n",
        " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n",
        " ('spark.dynamicAllocation.maxExecutors', '10000'),\n",
        " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
@@ -64,6 +66,7 @@
        "  'com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,com.typesafe_config-1.4.2.jar,org.rocksdb_rocksdbjni-6.29.5.jar,com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,com.github.universal-automata_liblevenshtein-3.0.0.jar,com.google.cloud_google-cloud-storage-2.16.0.jar,com.navigamez_greex-1.0.jar,com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,it.unimi.dsi_fastutil-7.0.12.jar,org.projectlombok_lombok-1.16.8.jar,com.google.guava_guava-31.1-jre.jar,com.google.guava_failureaccess-1.0.1.jar,com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,com.google.errorprone_error_prone_annotations-2.16.jar,com.google.j2objc_j2objc-annotations-1.3.jar,com.google.http-client_google-http-client-1.42.3.jar,io.opencensus_opencensus-contrib-http-util-0.31.1.jar,com.google.http-client_google-http-client-jackson2-1.42.3.jar,com.google.http-client_google-http-client-gson-1.42.3.jar,com.google.api-client_google-api-client-2.1.1.jar,commons-codec_commons-codec-1.15.jar,com.google.oauth-client_google-oauth-client-1.34.1.jar,com.google.http-client_google-http-client-apache-v2-1.42.3.jar,com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,com.google.code.gson_gson-2.10.jar,com.google.cloud_google-cloud-core-2.9.0.jar,com.google.auto.value_auto-value-annotations-1.10.1.jar,com.google.cloud_google-cloud-core-http-2.9.0.jar,com.google.http-client_google-http-client-appengine-1.42.3.jar,com.google.api_gax-httpjson-0.105.1.jar,com.google.cloud_google-cloud-core-grpc-2.9.0.jar,io.grpc_grpc-core-1.51.0.jar,com.google.api_gax-2.20.1.jar,com.google.api_gax-grpc-2.20.1.jar,io.grpc_grpc-alts-1.51.0.jar,io.grpc_grpc-grpclb-1.51.0.jar,org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,io.grpc_grpc-protobuf-1.51.0.jar,com.google.auth_google-auth-library-credentials-1.13.0.jar,com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,com.google.api_api-common-2.2.2.jar,javax.annotation_javax.annotation-api-1.3.2.jar,io.opencensus_opencensus-api-0.31.1.jar,io.grpc_grpc-context-1.51.0.jar,com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,com.google.protobuf_protobuf-java-3.21.10.jar,com.google.protobuf_protobuf-java-util-3.21.10.jar,com.google.api.grpc_proto-google-common-protos-2.11.0.jar,org.threeten_threetenbp-1.6.4.jar,com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,com.fasterxml.jackson.core_jackson-core-2.14.1.jar,com.google.code.findbugs_jsr305-3.0.2.jar,io.grpc_grpc-api-1.51.0.jar,io.grpc_grpc-auth-1.51.0.jar,io.grpc_grpc-stub-1.51.0.jar,org.checkerframework_checker-qual-3.28.0.jar,com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,io.grpc_grpc-protobuf-lite-1.51.0.jar,com.google.android_annotations-4.1.1.4.jar,org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,io.grpc_grpc-netty-shaded-1.51.0.jar,io.perfmark_perfmark-api-0.26.0.jar,io.grpc_grpc-googleapis-1.51.0.jar,io.grpc_grpc-xds-1.51.0.jar,io.opencensus_opencensus-proto-0.2.0.jar,io.grpc_grpc-services-1.51.0.jar,com.google.re2j_re2j-1.6.jar,dk.brics.automaton_automaton-1.11-8.jar,org.slf4j_slf4j-api-1.7.16.jar'),\n",
        " ('spark.repl.local.jars',\n",
        "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.app.id', 'application_1700083397876_0002'),\n",
        " ('spark.driver.host',\n",
        "  'hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal'),\n",
        " ('spark.sql.cbo.enabled', 'true'),\n",
@@ -80,26 +83,23 @@
        " ('spark.yarn.am.memory', '640m'),\n",
        " ('spark.cores.max', '4'),\n",
        " ('spark.executor.cores', '4'),\n",
-       " ('spark.app.startTime', '1699975920175'),\n",
-       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1699975669214_0001'),\n",
        " ('spark.jars.packages',\n",
        "  'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n",
        " ('spark.executor.instances', '2'),\n",
        " ('spark.dataproc.listeners',\n",
        "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
-       " ('spark.history.fs.logDirectory',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/0f852a69-184b-4243-a2ca-dd44bcce018a/spark-job-history'),\n",
+       " ('spark.driver.appUIAddress',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:42687'),\n",
        " ('spark.serializer.objectStreamReset', '100'),\n",
        " ('spark.submit.deployMode', 'client'),\n",
        " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
        " ('spark.shuffle.service.enabled', 'true'),\n",
-       " ('spark.driver.appUIAddress',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:39155'),\n",
+       " ('spark.driver.port', '35485'),\n",
        " ('spark.scheduler.mode', 'FAIR'),\n",
        " ('spark.sql.adaptive.enabled', 'true'),\n",
        " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
        " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n",
+       " ('spark.app.startTime', '1700085601972'),\n",
        " ('spark.master', 'yarn'),\n",
        " ('spark.ui.port', '0'),\n",
        " ('spark.rpc.message.maxSize', '512'),\n",
@@ -110,7 +110,7 @@
        " ('spark.ui.showConsoleProgress', 'true')]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 4,
    "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
    "metadata": {},
    "outputs": [
@@ -395,66 +395,6 @@
     "# df_2021 = df_2021.repartition(10)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "id": "f34f9ec5-1a72-42ed-8bbe-3b54683a8bf4",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 250:===============================================>         (5 + 1) / 6]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Partitions: 10\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Stage 256:=================================================>   (188 + 6) / 200]\r"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "+-----------+------+\n",
-      "|partitionId| count|\n",
-      "+-----------+------+\n",
-      "|          5|270864|\n",
-      "|          7|270865|\n",
-      "|          6|270865|\n",
-      "|          0|270865|\n",
-      "|          9|270866|\n",
-      "|          1|270866|\n",
-      "|          4|270866|\n",
-      "|          8|270866|\n",
-      "|          2|270866|\n",
-      "|          3|270867|\n",
-      "+-----------+------+\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "                                                                                \r"
-     ]
-    }
-   ],
-   "source": [
-    "displaypartitions(df_2021)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -528,6 +468,46 @@
     "df_2021.select([count(when(df_2021[c].isNull(), c)).alias(c) for c in df_2021.columns]).show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ee6f3669-6fc4-404d-8e91-ba819dec25fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import missingno as msno\n",
+    "%matplotlib inline\n",
+    "msno.matrix(df_2021.sample(fraction=1/10000).toPandas())"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -600,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
    "metadata": {},
    "outputs": [],
@@ -613,7 +593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 6,
    "id": "6a52d857-bc07-47a7-8cc9-71478bf10e08",
    "metadata": {},
    "outputs": [],
@@ -626,7 +606,7 @@
     "                \"miles\").withColumnRenamed(\"Pickup Census Tract\",\"pickup_tract\").withColumnRenamed(\"Dropoff Census Tract\",\"dropoff_tract\").withColumnRenamed(\"Pickup Community Area\",\"pickup_area\"\\\n",
     "                ).withColumnRenamed(\"Dropoff Community Area\",\"dropoff_area\").withColumnRenamed(\"Trip Total\",\"total\").withColumnRenamed(\"Pickup Centroid Latitude\",\"pickup_lat\").withColumnRenamed(\\\n",
     "                \"Pickup Centroid Longitude\",\"pickup_lon\").withColumnRenamed(\"Pickup Centroid Location\",\"pickup_location\").withColumnRenamed(\"Dropoff Centroid Latitude\",\"dropoff_lat\").withColumnRenamed(\\\n",
-    "                \"Dropoff Centroid Longitude\",\"dropoff_lon\").withColumnRenamed(\"Dropoff Centroid Location\",\"dropoff_location\")\n",
+    "                \"Dropoff Centroid Longitude\",\"dropoff_lon\").withColumnRenamed(\"Dropoff Centroid Location\",\"dropoff_location\").withColumnRenamed(\"Trip Seconds\",\"seconds\")\n",
     "# fix datatypes\n",
     "df_2021 = df_2021.withColumn('start_timestamp', F.to_timestamp(df_2021['start_timestamp'], 'MM/dd/yyyy hh:mm:ss a')).withColumn('end_timestamp', F.to_timestamp(df_2021['end_timestamp'], 'MM/dd/yyyy hh:mm:ss a'))\n",
     "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"yyyy-MM-dd\"))\n"
@@ -641,7 +621,215 @@
    "source": [
     "# add the month column\n",
     "df_2021 = df_2021.withColumn('month', F.month(df_2021.start_timestamp))\n",
-    "df_2021 = df_2021.withColumn('hour', F.hour(df_2021.start_timestamp))"
+    "df_2021 = df_2021.withColumn('day_of_month', F.dayofmonth(df_2021.start_timestamp))\n",
+    "df_2021 = df_2021.withColumn('hour', F.hour(df_2021.start_timestamp))\n",
+    "df_2021 = df_2021.withColumn('day', F.dayofweek(df_2021.start_timestamp))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "38ae8fc8-7d8b-4560-a6fb-7d84fc672929",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "sample_df = df_2021.sample(fraction=1/10000).toPandas().loc[:,[\"pickup_area\",\"dropoff_area\",\"total\",\"Fare\",\"Tip\",\"total\",\"miles\",\"seconds\",\"hour\",\"day\",\"month\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "fdd98395-7ec5-413f-975c-db44d2871c46",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pickup_area       int32\n",
+       "dropoff_area      int32\n",
+       "total           float64\n",
+       "Fare            float64\n",
+       "Tip               int32\n",
+       "total           float64\n",
+       "miles           float64\n",
+       "seconds           int32\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sample_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "064352aa-8513-4953-b527-48cc322fdfab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_df = sample_df.dropna()\n",
+    "sample_df = sample_df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "a81f2984-28da-4dbb-9645-7615f81c1b67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_df.head()\n",
+    "sample_df = sample_df.drop(columns='total')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aaa804fd-84c8-4670-a4fe-4027d9fc1510",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_theme(style=\"ticks\")\n",
+    "sns.pairplot(sample_df)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "a973b0bb-3518-4d17-a53e-6201c08fe814",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb1fb265d00>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"miles\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "43beea36-d574-484a-909d-11698c9e43c8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb1f9529130>"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "d1a92dc5-2b9b-45cf-b4d0-e5a6732b281f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb1ef8c3280>"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"Fare\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "42f2cac9-9ba2-4004-b631-64d9c4cc0e4d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7fb1f95422b0>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"Tip\")"
    ]
   },
   {
@@ -783,7 +971,17 @@
    "outputs": [],
    "source": [
     "# verify this is the correct time period for your given year\n",
-    "df_area_program = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 17) | (df_area.hour < 4)))"
+    "df_area_program_1 = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 21) | (df_area.hour < 4)) & (df_area.day >= 3) & ((df_area.month == 10) | ((df_area.month == 11) & (df_area.day_of_month < 11))))\n",
+    "df_area_program_2 = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 17) | (df_area.hour < 4)) & ((df_area.month == 12) | ((df_area.month == 11) & (df_area.day_of_month > 11))))\n",
+    "df_area_program = df_area_program_1.union(df_area_program_2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1b936e9-7aa6-4298-9ea4-aa753e9e1e5c",
+   "metadata": {},
+   "source": [
+    "If I actually do the above filter ^^ the the how will I filter during the pre-program period? I thought the graph was a nice demonstration of the change"
    ]
   },
   {
@@ -1028,47 +1226,14 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "54b6778e-7e08-4087-95f2-911d4458c048",
+   "id": "4876aef0-e741-4302-82d5-24f133f1d762",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import folium\n",
-    "from folium.plugins import HeatMap\n",
-    "\n",
-    "#Create a Folium map centered on the mean coordinates\n",
-    "map_center = [df_area_program_pd['dropoff_lat'].mean(), df_area_program_pd['dropoff_lon'].mean()]\n",
-    "mymap = folium.Map(location=map_center, zoom_start=15)\n",
-    "\n",
-    "# Convert the DataFrame to a list of points\n",
-    "heat_data = [[point['dropoff_lat'], point['dropoff_lon']] for index, point in df_area_program_pd.iterrows()]\n",
-    "heat_data2 = [[point['pickup_lat'], point['pickup_lon']] for index, point in df_area_program_pd.iterrows()]\n",
-    "\n",
-    "\n",
-    "# Add a heatmap layer to the map\n",
-    "HeatMap(heat_data).add_to(mymap)\n",
-    "HeatMap(heat_data2).add_to(mymap)\n",
-    "\n",
-    "# Save or display the map\n",
-    "#mymap.save(\"heatmap.html\")  # Save the map to an HTML file\n",
-    "mymap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8cd74633-8919-4376-8b5f-a512aa3ee28a",
-   "metadata": {},
-   "source": [
-    "# Next Steps\n",
-    "\n",
-    "the geospatial ipynb (notebook 4.8) from ashish shows some ways to work with this kind of data in pyspark\n",
-    "\n",
-    "Plot Cloropleths (for all of chicago)\n",
-    "\n",
-    "heat map of dropoff location and pickup location (for in-program rides)\n",
-    "\n",
-    "for 2021 - think about how to show the september to october switch - vline when program starts\n",
-    "\n",
-    "add vertical lines at and key shifts in the program policy"
+    "# storing data on the bucket\n",
+    "df_area_program.write.option(\"header\", \"true\").csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/program_area_time_rides_2021.csv\")\n",
+    "df_2021.write.option(\"header\", \"true\").csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2021.csv\")\n",
+    "\n"
    ]
   }
  ],

From 3c79176cc1581e54880745aff66bafe7abbd260b Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Thu, 16 Nov 2023 01:36:30 +0000
Subject: [PATCH 8/9] finalized graphs and put clean data in bucket

---
 eda_2021.ipynb | 135 ++++++++++++++++++++++++++++++++-----------------
 1 file changed, 88 insertions(+), 47 deletions(-)

diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 5466719..402be1d 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "201288da-86ac-4db0-a56b-4d75e26e1753",
    "metadata": {},
    "outputs": [],
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "3443992c-4530-48f2-a133-fb1dacf4b84f",
    "metadata": {},
    "outputs": [
@@ -28,10 +28,7 @@
        " ('spark.eventLog.enabled', 'true'),\n",
        " ('spark.submit.pyFiles',\n",
        "  '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
-       " ('spark.history.fs.logDirectory',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/ef995586-ab93-4aae-8a58-b7f73789ab77/spark-job-history'),\n",
        " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n",
-       " ('spark.ui.proxyBase', '/proxy/application_1700083397876_0002'),\n",
        " ('spark.kryoserializer.buffer.max', '2000M'),\n",
        " ('spark.serializer', 'org.apache.spark.serializer.KryoSerializer'),\n",
        " ('spark.dataproc.sql.local.rank.pushdown.enabled', 'true'),\n",
@@ -43,13 +40,10 @@
        " ('spark.metrics.namespace',\n",
        "  'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n",
        " ('spark.executor.memory', '4g'),\n",
-       " ('spark.eventLog.dir',\n",
-       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/ef995586-ab93-4aae-8a58-b7f73789ab77/spark-job-history'),\n",
        " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n",
-       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1700083397876_0002'),\n",
        " ('spark.hadoop.hive.execution.engine', 'mr'),\n",
        " ('spark.executor.id', 'driver'),\n",
+       " ('spark.app.startTime', '1700095565609'),\n",
        " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n",
        " ('spark.dynamicAllocation.maxExecutors', '10000'),\n",
        " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
@@ -66,7 +60,8 @@
        "  'com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,com.typesafe_config-1.4.2.jar,org.rocksdb_rocksdbjni-6.29.5.jar,com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,com.github.universal-automata_liblevenshtein-3.0.0.jar,com.google.cloud_google-cloud-storage-2.16.0.jar,com.navigamez_greex-1.0.jar,com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,it.unimi.dsi_fastutil-7.0.12.jar,org.projectlombok_lombok-1.16.8.jar,com.google.guava_guava-31.1-jre.jar,com.google.guava_failureaccess-1.0.1.jar,com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,com.google.errorprone_error_prone_annotations-2.16.jar,com.google.j2objc_j2objc-annotations-1.3.jar,com.google.http-client_google-http-client-1.42.3.jar,io.opencensus_opencensus-contrib-http-util-0.31.1.jar,com.google.http-client_google-http-client-jackson2-1.42.3.jar,com.google.http-client_google-http-client-gson-1.42.3.jar,com.google.api-client_google-api-client-2.1.1.jar,commons-codec_commons-codec-1.15.jar,com.google.oauth-client_google-oauth-client-1.34.1.jar,com.google.http-client_google-http-client-apache-v2-1.42.3.jar,com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,com.google.code.gson_gson-2.10.jar,com.google.cloud_google-cloud-core-2.9.0.jar,com.google.auto.value_auto-value-annotations-1.10.1.jar,com.google.cloud_google-cloud-core-http-2.9.0.jar,com.google.http-client_google-http-client-appengine-1.42.3.jar,com.google.api_gax-httpjson-0.105.1.jar,com.google.cloud_google-cloud-core-grpc-2.9.0.jar,io.grpc_grpc-core-1.51.0.jar,com.google.api_gax-2.20.1.jar,com.google.api_gax-grpc-2.20.1.jar,io.grpc_grpc-alts-1.51.0.jar,io.grpc_grpc-grpclb-1.51.0.jar,org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,io.grpc_grpc-protobuf-1.51.0.jar,com.google.auth_google-auth-library-credentials-1.13.0.jar,com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,com.google.api_api-common-2.2.2.jar,javax.annotation_javax.annotation-api-1.3.2.jar,io.opencensus_opencensus-api-0.31.1.jar,io.grpc_grpc-context-1.51.0.jar,com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,com.google.protobuf_protobuf-java-3.21.10.jar,com.google.protobuf_protobuf-java-util-3.21.10.jar,com.google.api.grpc_proto-google-common-protos-2.11.0.jar,org.threeten_threetenbp-1.6.4.jar,com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,com.fasterxml.jackson.core_jackson-core-2.14.1.jar,com.google.code.findbugs_jsr305-3.0.2.jar,io.grpc_grpc-api-1.51.0.jar,io.grpc_grpc-auth-1.51.0.jar,io.grpc_grpc-stub-1.51.0.jar,org.checkerframework_checker-qual-3.28.0.jar,com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,io.grpc_grpc-protobuf-lite-1.51.0.jar,com.google.android_annotations-4.1.1.4.jar,org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,io.grpc_grpc-netty-shaded-1.51.0.jar,io.perfmark_perfmark-api-0.26.0.jar,io.grpc_grpc-googleapis-1.51.0.jar,io.grpc_grpc-xds-1.51.0.jar,io.opencensus_opencensus-proto-0.2.0.jar,io.grpc_grpc-services-1.51.0.jar,com.google.re2j_re2j-1.6.jar,dk.brics.automaton_automaton-1.11-8.jar,org.slf4j_slf4j-api-1.7.16.jar'),\n",
        " ('spark.repl.local.jars',\n",
        "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
-       " ('spark.app.id', 'application_1700083397876_0002'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1700094704615_0002'),\n",
        " ('spark.driver.host',\n",
        "  'hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal'),\n",
        " ('spark.sql.cbo.enabled', 'true'),\n",
@@ -83,34 +78,39 @@
        " ('spark.yarn.am.memory', '640m'),\n",
        " ('spark.cores.max', '4'),\n",
        " ('spark.executor.cores', '4'),\n",
+       " ('spark.app.id', 'application_1700094704615_0002'),\n",
        " ('spark.jars.packages',\n",
        "  'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n",
+       " ('spark.history.fs.logDirectory',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/2bfcb1ba-12f1-4fdd-8722-7dc6c9d23e48/spark-job-history'),\n",
        " ('spark.executor.instances', '2'),\n",
        " ('spark.dataproc.listeners',\n",
        "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
-       " ('spark.driver.appUIAddress',\n",
-       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:42687'),\n",
        " ('spark.serializer.objectStreamReset', '100'),\n",
        " ('spark.submit.deployMode', 'client'),\n",
+       " ('spark.driver.port', '35653'),\n",
        " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
+       " ('spark.eventLog.dir',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/2bfcb1ba-12f1-4fdd-8722-7dc6c9d23e48/spark-job-history'),\n",
        " ('spark.shuffle.service.enabled', 'true'),\n",
-       " ('spark.driver.port', '35485'),\n",
        " ('spark.scheduler.mode', 'FAIR'),\n",
        " ('spark.sql.adaptive.enabled', 'true'),\n",
        " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
        " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n",
-       " ('spark.app.startTime', '1700085601972'),\n",
+       " ('spark.ui.proxyBase', '/proxy/application_1700094704615_0002'),\n",
        " ('spark.master', 'yarn'),\n",
        " ('spark.ui.port', '0'),\n",
        " ('spark.rpc.message.maxSize', '512'),\n",
        " ('spark.rdd.compress', 'True'),\n",
+       " ('spark.driver.appUIAddress',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:39485'),\n",
        " ('spark.task.maxFailures', '10'),\n",
        " ('spark.yarn.isPython', 'true'),\n",
        " ('spark.dynamicAllocation.enabled', 'true'),\n",
        " ('spark.ui.showConsoleProgress', 'true')]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "a10a9fef-7517-4947-a7a5-b17db05dbb79",
    "metadata": {},
    "outputs": [
@@ -580,27 +580,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "46e2e9e5-3581-444c-b149-827a5cbc62f5",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Working with just data that contains full information and check for dupes\n",
     "df_2021 = df_2021.dropna(how='any', subset=['Trip Start Timestamp','Trip End Timestamp','Fare','Dropoff Community Area','Pickup Community Area'])\n",
-    "df_2021 = df_2021.dropDuplicates()\n",
-    "# df_2021.count()"
+    "df_2021 = df_2021.dropDuplicates()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "6a52d857-bc07-47a7-8cc9-71478bf10e08",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Drop columns unlikely to be useful for analysis for speed of computation and rename columns to remove spacing for ease of code writing\n",
-    "#spark.conf.set(\"spark.sql.legacy.timeParserPolicy\", \"LEGACY\")\n",
-    "\n",
     "df_2021 = df_2021.drop('Trips Pooled','Additional Charges','Shared Trip Authorized','Pickup Centroid Location','Dropoff Centroid Location')\n",
     "df_2021 = df_2021.withColumnRenamed(\"Trip ID\",\"ID\").withColumnRenamed(\"Trip Start Timestamp\",\"start_timestamp\").withColumnRenamed(\"Trip End Timestamp\",\"end_timestamp\").withColumnRenamed(\"Trip Miles\",\\\n",
     "                \"miles\").withColumnRenamed(\"Pickup Census Tract\",\"pickup_tract\").withColumnRenamed(\"Dropoff Census Tract\",\"dropoff_tract\").withColumnRenamed(\"Pickup Community Area\",\"pickup_area\"\\\n",
@@ -614,21 +611,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 8,
    "id": "17ebe569-82bb-49b0-aaaf-ac5062bede35",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 6:>                                                          (0 + 1) / 1]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
+      "|                  ID|    start_timestamp|      end_timestamp|seconds|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|  dropoff_lat|   dropoff_lon|month|day_of_month|hour|day|\n",
+      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
+      "|76d913bb3f0771ab2...|2021-01-01 00:00:00|2021-01-01 00:15:00|   1039|  5.9|        null|         null|         44|          39|20.0|  0|21.23|41.7402057565|-87.6159695226|41.8089162826|-87.5961833442|    1|           1|   0|  6|\n",
+      "|6ebae7e105b93a085...|2021-01-01 00:15:00|2021-01-01 00:15:00|    285|  2.4| 17031833100|  17031839100|         28|          32| 5.0|  0| 12.1|41.8790669938| -87.657005027|41.8809944707|-87.6327464887|    1|           1|   0|  6|\n",
+      "|ba7d98f8744740a4b...|2021-01-01 00:15:00|2021-01-01 00:30:00|    277|  1.1| 17031842300|  17031842200|         24|           8| 2.5|  0|  5.6|41.8983058696|-87.6536139825|41.9049353016|-87.6499072264|    1|           1|   0|  6|\n",
+      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
+      "only showing top 3 rows\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "# add the month column\n",
     "df_2021 = df_2021.withColumn('month', F.month(df_2021.start_timestamp))\n",
     "df_2021 = df_2021.withColumn('day_of_month', F.dayofmonth(df_2021.start_timestamp))\n",
     "df_2021 = df_2021.withColumn('hour', F.hour(df_2021.start_timestamp))\n",
-    "df_2021 = df_2021.withColumn('day', F.dayofweek(df_2021.start_timestamp))"
+    "df_2021 = df_2021.withColumn('day', F.dayofweek(df_2021.start_timestamp))\n",
+    "df_2021.show(3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "id": "38ae8fc8-7d8b-4560-a6fb-7d84fc672929",
    "metadata": {},
    "outputs": [
@@ -646,7 +674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "id": "fdd98395-7ec5-413f-975c-db44d2871c46",
    "metadata": {},
    "outputs": [
@@ -660,22 +688,25 @@
        "Tip               int32\n",
        "total           float64\n",
        "miles           float64\n",
-       "seconds           int32\n",
+       "seconds         float64\n",
+       "hour              int32\n",
+       "day               int32\n",
+       "month             int32\n",
        "dtype: object"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sample_df.head()"
+    "sample_df.dtypes"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "id": "064352aa-8513-4953-b527-48cc322fdfab",
    "metadata": {},
    "outputs": [],
@@ -686,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "id": "a81f2984-28da-4dbb-9645-7615f81c1b67",
    "metadata": {},
    "outputs": [],
@@ -703,8 +734,9 @@
    "outputs": [],
    "source": [
     "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
     "sns.set_theme(style=\"ticks\")\n",
-    "sns.pairplot(sample_df)\n",
+    "sns.pairplot(sample_df, hue='dropoff_area')\n",
     "plt.show()"
    ]
   },
@@ -834,7 +866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "id": "2ae6d6b2-9001-4ded-ba3a-225861cbe451",
    "metadata": {},
    "outputs": [],
@@ -971,7 +1003,7 @@
    "outputs": [],
    "source": [
     "# verify this is the correct time period for your given year\n",
-    "df_area_program_1 = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 21) | (df_area.hour < 4)) & (df_area.day >= 3) & ((df_area.month == 10) | ((df_area.month == 11) & (df_area.day_of_month < 11))))\n",
+    "df_area_program_1 = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 21) | (df_area.hour < 4)) & ((df_area.day >= 3) & (df_area.day < 6)) & ((df_area.month == 10) | ((df_area.month == 11) & (df_area.day_of_month < 11))))\n",
     "df_area_program_2 = df_area.filter((df_area.Fare <= 15.0) & ((df_area.hour >= 17) | (df_area.hour < 4)) & ((df_area.month == 12) | ((df_area.month == 11) & (df_area.day_of_month > 11))))\n",
     "df_area_program = df_area_program_1.union(df_area_program_2)"
    ]
@@ -994,7 +1026,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Stage 117:==================================================>    (36 + 3) / 39]\r"
+      "[Stage 25:=================================>                     (40 + 10) / 66]\r"
      ]
     },
     {
@@ -1004,7 +1036,7 @@
       "+-------------------------+\n",
       "|approx_count_distinct(ID)|\n",
       "+-------------------------+\n",
-      "|                   123125|\n",
+      "|                    36889|\n",
       "+-------------------------+\n",
       "\n"
      ]
@@ -1013,19 +1045,20 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "                                                                                \r"
+      "23/11/16 01:18:12 WARN org.apache.spark.scheduler.cluster.YarnSchedulerBackend$YarnSchedulerEndpoint: Requesting driver to remove executor 1 for reason Container marked as failed: container_1700094704615_0002_01_000001 on host: hub-msca-bdp-dphub-students-abejburton-sw-w0bj.c.msca-bdp-student-ap.internal. Exit status: -100. Diagnostics: Container released on a *lost* node.\n",
+      "23/11/16 01:18:12 ERROR org.apache.spark.scheduler.cluster.YarnScheduler: Lost executor 1 on hub-msca-bdp-dphub-students-abejburton-sw-w0bj.c.msca-bdp-student-ap.internal: Container marked as failed: container_1700094704615_0002_01_000001 on host: hub-msca-bdp-dphub-students-abejburton-sw-w0bj.c.msca-bdp-student-ap.internal. Exit status: -100. Diagnostics: Container released on a *lost* node.\n"
      ]
     }
    ],
    "source": [
     "from pyspark.sql.functions import approxCountDistinct\n",
     "\n",
-    "df_area_program.select(approxCountDistinct(\"ID\", rsd = 0.05)).show()"
+    "df_area_program.select(approxCountDistinct(\"ID\", rsd = 0.20)).show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "id": "4170f6ac-afca-44c9-9e2e-7e78670de3d1",
    "metadata": {},
    "outputs": [
@@ -1038,7 +1071,7 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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49ttvMX78eCxduhRvvvkmgPwcHolEUiBpuWbNmgCAtLQ02NnZQSaTwcrKqsDzSqVSpKenC/dlMhmkUukr2xWmS5cuL12XnJyM+vXrF/l4IiJDU9z8nrbN60Fag/k9ZLxKHQQ9evQIEydOhIeHB0aPHi0sHzZsmFa7gIAADBw4EMuWLROCIAAFZncAQK1WF1hXWLuilr+4veK0IyKq7DT5PacupeDiDeb3EBVHqYKgjIwMjB49GhYWFli5ciXMzMxe2lYkEqF79+5YsGABcnJyYGFhAalUitzcXOTm5sLc3FxoK5PlH5/WzAhJpVIkJycX2OaLMz9SqVR47Iv9fFU+0OHDh1+6rqhZIiIifdLk92hOY2d+D1HJlTgIys3Nxbhx4/D48WPs2LEDtWrVeuVjNDM8GprAJDExEW5ubsLyxMRE1KhRA/Xq1RPa/f777wVmdK5fv64V3Dg5ORXI/ZHL5bh9+3aBXCF9M4EJ6lS3EW4TERVXkfk9JoBbk9rwaVGf+T1ExVSiIEihUODjjz/G33//ja1btxa4YGFhVCoVDh48iKZNmwo5QF5eXrCyssK+ffuEIEipVGL//v3w9/cXAh5/f3+sWLECJ06cEM4QS05ORkJCAmbNmiU8h5+fH1auXImnT58KQdmhQ4cgl8uFU+sNhbmpBCuC5uq7G0RUSTC/h6jilCgICg8Px9GjR/Hpp58iJycH586dE9Y5OzsjPT0doaGhCAwMhKOjI9LT0/Htt9/i4sWLiIqKEtpKJBKMGzcOkZGRsLGxES6WeOfOHSxevFho5+npic6dO2PmzJkIDQ2FpaUlli5dCgcHB/Tt21doN3DgQGzduhUhISEICQlBamoqvvrqKwQFBfH0eCKqdJjfQ6QbJQqCNKelL1iwoMC6zZs3w9XVFZaWllixYgWePHkCMzMzuLu7Y+3atejUqZNW+5EjR0KtVmPLli14/PgxXFxcsGbNGq2rRQPAokWLMH/+fMyZMwd5eXnw8fFBVFSU1pllUqkUmzZtQkREBD766CNYWFggMDAQU6dOLcnwiIj0Qq3+9/o9F1+e35N/Gnt95vcQlSMT9YsJOyTQJEYXlTxdUnKFHP93JH+2a07AZEhYNoPIKBU3v8enhR3q2zK/h6gkivv9zSryOqaCGolPbwm3ich4ZGbL8eeVB4i/lIKEvx/iWa5CWMf8HiLdYxBERFSBUlKzhNPYmd9DZFgYBBERlaNX5fc42lnBh/k9RAaBQRARURkxv4eocmIQRERUCszvIar8GAQRERXTq/J72rnlH+Zifg9R5cAgSA+szC313QUiKgbm9xBVbQyCdMzC1BzRfQpebJKIDIOQ3/PvFZtT05nfQ1RVMQgiIqOX+W99rjjm9xAZFQZBRGSUUtOf4eT5+4Xm99SyMhfKVDC/h6jqYhCkY3KFHF8eXw4A+MxvAstmEOlB4t00zPjmpNaMD/N7iIwPgyAdU0GNy4+uCbeJSLdS05/hi/XxeJarwGv1pejSzpH5PURGikEQERmNHLkCEevjkZqeg4b1LDFvfEdYVjPTd7eISE9E+u4AEZEuqFRqLN6egOt302FVXYKwYF8GQERGjkEQERmFrQeu4I8LyTAVizBzhDfsavPwF5GxYxBERFXer6duI+Zwfi7eR++1QosmtfXcIyIyBAyCiKhKu5j4GCv+dw4A8F5XFwS0bajfDhGRwWBitB6Yi3laPJEu3H+ciS83noJCqUYHT3t80KOZvrtERAaEQZCOWZiaY0v/pfruBlGVl5ktR/i6eGRk56FpQ2t8MrA1r/1DRFp4OIyIqhyFUoV5m/7EvUeZsLWuhtkjfWAh4W8+ItLGIIiIqhS1Wo1VO//CX9cfw0IiRliwD2pJLfTdLSIyQPxppGNyZR4WnVwDAJjSYQwkYl6nhKg8/Xg8EQfjbsHEBPh0cFs0tq+p7y4RkYFiEKRjKrUKZ5MvCreJqPycupSC9XsuAQBGBrnDu4WdnntERIaMh8OIqEpIup+OBVtPQ60Gevg2wtt+TfTdJSIycAyCiKjSeyLLQfi6OOTIlfBsaosP32kJExOeCUZERWMQRESVmqYo6uP0HDjUsUTo0HYwFfOjjYhejZ8URFRpqVRqLPn2LK7dSYNVdQn+b5QvLKvzYqREVDwMgoio0tp28G+c/Os+TMUm+Gx4O9S3ZVFUIio+BkFEVCkdOX0b3//6DwBgwrut4O5kq+ceEVFlw1PkdczC1BzfD1ip724QVWqXbqQi6vtzAIB3uzRFl3aO+u0QEVVKnAkiokol+XEW5m7IL4r6esv6GPxmc313iYgqKQZBRFRpZD7LQ3h0HDKy5XBuaI1Jg7xYFJWISo2Hw3RMrszD8riNAIAJvsNZNoOomBRKFeZv+hN3H2bCtqYFi6ISUZlxJkjHVGoV4u4mIO5uAstmEBWTWq3G6l0XcO7aI1hIxJgd7AsbFkUlojJiEEREBu+nEzdw4I+bMDEBpn7QBk0cWBSViMqOQRARGbRTl1MQ/VN+0eERgS3g415fzz0ioqqCQRARGayk++lY+FxR1D7+TvruEhFVIQyCiMggPZXlIDw6Hs9ylWjpzKKoRFT+GAQRkcHJzVMiYkM8Hqc9g0OdGpgxjEVRiaj88VOFiAyKSqVG5LcJ+Od2GqyqmyGMRVGJqILwIhs6Zi6WYHO/JcJtItK2/eDfOHk+vyjqjOHesLe11HeXiKiKYhCkYyYmJrAwNdd3N4gM0tEzd7Dj36Ko4/t7woNFUYmoAvFwGBEZhEs3UrFsxzkAQL83nNHVu5F+O0REVR5ngnQsT5mHNae3AwDGtH0fZiybQYSU1Cx8ufEUFEoV2nvUx9BebvruEhEZAc4E6ZhSrULszTjE3oyDkmUziJD1b1FUWZYcTg1qYjKLohKRjjAIIiK9USpVmL/5T9x5kInamqKo5pygJiLdYBBERHqhVquxevcFnP3nEcwlYswa6YPaNavpu1tEZEQYBBGRXuz57Qb2/55fFHXK+23g3MBa310iIiPDIIiIdO70lQeI/jG/KOrw3m5o78GiqESkewyCiEinbibL8PWW01CpgW7ejujb2VnfXSIiI8UgiIh05mlGDsKj4/AsV4GWzrYY18+TRVGJSG94GoaOmYslWPf218JtImORm6fE3PWn8OjpM9jb1kDosHYwM+XvMCLSnxJ9Au3fvx8hISHw9/dHq1atEBQUhO3bt0Ol0r7eTWxsLPr06QMPDw9069YN27ZtK3R70dHRCAgIgIeHB/r164f4+PgCbTIzMxEWFgYfHx+0bt0aH374Ie7du1egXVJSEoKDg9GqVSu0b98eERERyMnJKcnwdMLExARSCytILaz4C5iMhlqtxtLvzuLq7aewrGaG/xvlCysWRSUiPStRELRhwwZIJBJMmzYNq1atQteuXTF37lwsWLBAaHP27FmEhITAzc0Na9euRd++fREREYGYmBitbUVHRyMyMhIffPAB1qxZg0aNGmH06NG4evWqVrspU6bgyJEjmD17NiIjI/Hw4UOMGDFCK8CRyWQYNmwYsrKysGzZMkyfPh179uzBrFmzSvOaEFE5237wKk6cuwexyASfDfeGfR0WRSUi/SvR4bBVq1bBxsZGuO/r64vs7Gxs27YNkyZNgkQiwYoVK+Dm5oYvv/xSaJOcnIylS5eiX79+EIlEkMvlWLlyJYYOHYrg4GAAgLe3N4KCgrBq1SpERkYCAM6fP49jx45hzZo18Pf3BwC4uLigW7du2LVrFwYNGgQA+O677yCTybB7926hf2KxGFOnTsW4cePg5ORUxpep/OQp87Dp3P8AAMNa9WfZDKryjp25g+8O5f+4Gd/fEx7OLIpKRIahRDNBzwdAGs2bN0dubi7S0tIgl8sRFxeH3r17a7UJCgrCo0ePcPnyZQBAQkICMjIyEBgYKLQRi8Xo1asXYmNjoVarAeQfVpNKpfDz8xPa2dvbw8vLC7GxscKy48ePo3379lr969GjByQSiVY7Q6BUq/DL9eP45fpxls2gKu9K0hMsfa4oajcfFkUlIsNR5sToM2fOwNraGrVr10ZSUhLy8vLQpEkTrTbOzvmnwCYmJsLd3R2JiYkAUKCdk5MTsrKy8ODBA9jZ2SExMRGNGzcukDvj7OyM3377TbifmJiIfv36abWRSCRwdHQUnutlunTp8tJ1ycnJqF+f1y8hKo2U1CzM3RgPhVIFX3c7FkUlIoNTplMzLly4gJ07d2LYsGEQi8VIT08HAEilUq12mvua9TKZDBKJBBYWFlrtatasCQBIS0sT2llZWRV4XqlUKmxL0+7F5yysHRHpRn5R1HikZ8rRxKEmprzfhkVRicjglHom6NGjR5g4cSI8PDwwevRorXUvO+vp+eWFtdEcBntVu6KWv7i9V7U7fPjwS9cVNUtERIVTKlX4estp3HmQARupBcKCWRSViAxTqWaCMjIyMHr0aFhYWGDlypUwM8tP7tXM5Lw4+yKTyQD8NyMklUqRm5uL3NzcQttptiOVSoVlL7Z7fubnZe0yMjIKnSEiooqz9seLSLj6EOYSMWYHsygqERmuEgdBubm5GDduHB4/fox169ahVq1awjpHR0eYmZnhxo0bWo+5fv06AAhnaWn+fzFfJzExETVq1EC9evWEdklJScIM0fPbe/6MLycnpwLbksvluH37tkGdGUZU1e397QZ+Ppn0b1FULxZFJSKDVqIgSKFQ4OOPP8bff/+NdevWwcHBQWu9RCKBr68v9u/fr7V87969qFOnDtzc8hMjvby8YGVlhX379gltlEol9u/fD39/f+EQlr+/P2QyGU6cOCG0S05ORkJCgnDKPAD4+fkhLi4OT58+FZYdOnQIcrlcqx0RVZzTVx5g7e4LAIBhvdzQ3sNezz0iIipaiQ7Uh4eH4+jRo/j000+Rk5ODc+fOCeucnZ1haWmJ8ePHY/DgwZg1axaCgoKQkJCAmJgYhIeHQyTKj7kkEgnGjRuHyMhI2NjYwM3NDTExMbhz5w4WL14sbNPT0xOdO3fGzJkzERoaCktLSyxduhQODg7o27ev0G7gwIHYunUrQkJCEBISgtTUVHz11VcICgoyuJkgidgMywMjhNtEVcGtF4qivvMGi6ISkeEzUb94rKkIAQEBhZasAIDNmzfDx8cHQP71fRYvXozExETY2dlhxIgR+OCDD7Taq9VqREdHY9u2bXj8+DFcXFzw6aefwtfXV6tdZmYm5s+fj4MHDyIvLw8+Pj6YPXt2gVmopKQkRERE4MyZM7CwsEBgYCCmTp1a4Ay0ktAkRheVPE1k7NIycjFlaSwePn0Gd6faCB/zOmuCEZFeFff7u0RBkLFhEERUNHmeEjNXnsTft56ivm0NLJzoB2kN1gQjIv0q7vc3z1vVMYVSgW8v/AgAGOTxNkzFfAuoclKr1Vi64yz+vvUUNaqZISzYhwEQEVUqnLPWMYVaiT1Xf8Weq79CoVbquztEpfbdL1dx/Gx+UdQZw9qhQd2CFzYlIjJkDIKIqMRiE+5i+y/5RVHH9fOEZ9M6eu4REVHJMQgiohL5++YTLN1xFgDQt7MzeviyKCoRVU4Mgoio2B48ycbcDaeQp1DBp4UdhvVmUVQiqrwYBBFRsWTn5OGL6DikZeaiiX1NTPmgDcQsikpElRiDICJ6JU1R1FspGbCRmmN2sA+qsSgqEVVyDIKI6JXW/XQRZ/5+CImZGLNG+sDWmkVRiajy4085HZOIzbDozdnCbSJD9/NvN7D3tyQA+UVRmzas9YpHEBFVDgyCdExkIkLDmiwsSZVDwt8PsebHiwCAob2a4/WW/NsloqqDh8OIqFC3UmSYv+VPqFRqBLRtiP4BTfXdJSKicsWZIB1TKBXYeeUAAOCd5m+ybAYZpLSMXIRHxyM7R4EWTWpjwrutYGLCM8GIqGrhN7COKdRK/O/SzwCAt5p1gynfAjIw8jwlvtx4Cg+fZKN+7RqYMawdq8ITUZXETzYiEqjVaizbcQ5Xbj5BjWpmmB3sg5qW5vruFhFRhWAQRESCHb/+g9izd/OLog5th4b1WBSViKouBkFEBAA4cfYeth34GwAwrl9LeLqwKCoRVW0MgogIf996gsjvEgAAffyd0MP3Nf12iIhIBxgEERm5h0+yMXd9flFUbzc7DA9soe8uERHpBIMgIiOWnZOH8H+Loja2l2LqYBZFJSLjwfOzdUwiMsOXXacLt4n0RalSY8HWM7iVkoFaVuaYPdKXRVGJyKjwE0/HRCIRnGu/pu9uEGH9Txdx+soDoShqnVosikpExoWHw4iM0L7fk/DTiRsAgMmDvODiyKKoRGR8OBOkYwqlAvuuHQEA9GoawLIZpHMJVx9i9a4LAIAhPZujgyeLohKRceI3sI4p1EpsPb8LANDd2Z9lM0inbqfIMH/zf0VR3+3CoqhEZLx4OIzISKRn/lcU1a2xDSa868miqERk1BgEERmBPIUSczecwoMn2bCrXR2fDfeGmalY390iItIrBkFEVZxarcay7/8timphirBgXxZFJSICgyCiKu/7w//g2Jm7EIlMEDqMRVGJiDQYBBFVYSfO3cPW/flFUT/s64FWLnX13CMiIsPBIIioivrn9lMs+Ta/KOpbfk3Q8/XGeu4REZFh4fnZOiYRmeH/3pgk3CaqCA+fZuOL9fGQK1Ro27weRga567tLREQGh0GQjolEIrSo66LvblAVlp2Thy+i45GWkYvX6kvxKYuiEhEViofDiKoQTVHUm8kyWFuZY3awD6pbcMaRiKgwnAnSMYVKiV8TTwAAujp1gqmI12qh8rNhz6X8oqimIswe6YO6tarru0tERAaLQZCOKVQKrE/YAQDo3Lg9gyAqN/v/uIkfjycCAD5hUVQiolfi4TCiKuDcPw+xaudfAIDBbzZDp1YOeu4REZHhYxBEVMndeZCBrzblF0Xt3KYB3uvKxHsiouJgEERUieUXRY1DVo4CzV+zwcT3WrEoKhFRMTEIIqqk8hRKzNv0J1JSs1HPpjpmjmBRVCKikmAQRFQJqdVqLI85j0s3UlHdwhRhwT4sikpEVEIMgogqof8duYYjp+9AJDLB9KHt4Ggn1XeXiIgqHZ4ir2NmIlOEdgoRbhOV1Mnz97F53xUAwNi+HvByZVFUIqLS4LewjolFYnjZe+i7G1RJ/XP7KRZriqJ2aoJeLIpKRFRqPBxGVEk8evoMEevjIc9T5hdFfYtFUYmIyoIzQTqmUCnx261TAICOjbx5xWgqlme5CnyxPg5PWRSViKjcMAjSMYVKgW9ObQYA+Db0YhBEr6RUqbFw6xkk3ZfB2tIcs0eyKCoRUXng4TAiA7dx7yWcupwCM1MRZo70Rl0bFkUlIioPDIKIDNjBuJvYHZtfFHXSQC80a2Sj5x4REVUdDIKIDNT5fx5h5Q/5RVHf79EMnVqzKCoRUXliEERkgO48yMC8zX9CqVLDv3UDDOzGoqhEROWNQRCRgZFlyfFFdDyynuXlF0UdwKKoREQVocRB0K1btxAWFoa3334bbm5uCAwMLNAmNDQUrq6uBf4dP368QNvo6GgEBATAw8MD/fr1Q3x8fIE2mZmZCAsLg4+PD1q3bo0PP/wQ9+7dK9AuKSkJwcHBaNWqFdq3b4+IiAjk5OSUdIhEepOnUOLLjaeQnJqFujbV8dlwb0jMeAYhEVFFKPEp8teuXUNsbCw8PT2hUqmgVqsLbdewYUMsXLhQa5mTk5PW/ejoaERGRmLSpElwc3NDTEwMRo8ejZiYGLi6ugrtpkyZgkuXLmH27NmwtLTEsmXLMGLECPz000+wsLAAAMhkMgwbNgz29vZYtmwZnjx5gnnz5iEtLa1AP/TJTGSKSa+PEm4TaRRWFNXaikVRiYgqSom/hQMCAtC1a1cA+TM+Fy9eLLSdhYUFWrVq9dLtyOVyrFy5EkOHDkVwcDAAwNvbG0FBQVi1ahUiIyMBAOfPn8exY8ewZs0a+Pv7AwBcXFzQrVs37Nq1C4MGDQIAfPfdd5DJZNi9ezdsbPLPoBGLxZg6dSrGjRtXIADTF7FIjPYN2+i7G2SAhKKoJsD0Ie3QiEVRiYgqVIkPh4lE5ZNGlJCQgIyMDK3DaWKxGL169UJsbKwwwxQbGwupVAo/Pz+hnb29Pby8vBAbGyssO378ONq3by8EQADQo0cPSCQSrXZEhuj3v/4rijqmjwe8mrEoKhFRRauwxOjbt2+jbdu2cHd3xzvvvINff/1Va31iYv61T5o0aaK13MnJCVlZWXjw4IHQrnHjxgUSQ52dnYVtaNq9ONsjkUjg6Oio1U7flCol/rhzBn/cOQOlSqnv7pABuHbnKRZtzy+KGtixMXp3bPKKRxARUXmokKSU5s2bw8PDA87OzsjIyMC3336L8ePHY+nSpXjzzTcB5OfwSCQSIadHo2bNmgCAtLQ02NnZQSaTwcrKqsBzSKVSpKenC/dlMhmk0oKHD15s96IuXbq8dF1ycjLq169f9GBLKE+lQOTv6wAAm/stgZhlM4za47T/iqK2aVYXo1gUlYhIZyokCBo2bJjW/YCAAAwcOBDLli0TgiAAhZ72qzkM9vy6l50eXJzThtVqNU8vJoP0LFeBL6Lj8USWC0c7K0wb0hZiMa9aQUSkKzo5PUkkEqF79+5YsGABcnJyYGFhAalUitzcXOTm5sLc/L8zYGQyGYD/ZoSkUimSk5MLbPPFmR+pVCo89nkZGRlFJkUfPnz4peuKmiUiKgulSo1F287gxv10WFuaIyzYl0VRiYh0TGc/O188lV4TmLyYr5OYmIgaNWqgXr16QrukpKQCj79+/bpWcOPk5FRgW3K5HLdv3zaYM8OINDb/fBnxl/4tijrCG/VYFJWISOd0EgSpVCocPHgQTZs2FXKAvLy8YGVlhX379gntlEol9u/fD39/f+EQlr+/P2QyGU6cOCG0S05ORkJCgnDKPAD4+fkhLi4OT58+FZYdOnQIcrlcqx2Rvh2Mu4Wdx64DAD4e0BrNXmNRVCIifSjx4bBnz54Jp5zfu3cPmZmZOHDgAID86/w8e/YMoaGhCAwMhKOjI9LT0/Htt9/i4sWLiIqKErYjkUgwbtw4REZGwsbGRrhY4p07d7B48WKhnaenJzp37oyZM2ciNDQUlpaWWLp0KRwcHNC3b1+h3cCBA7F161aEhIQgJCQEqamp+OqrrxAUFMSZIDIYf11/hJU/nAcAvN/dFf5eDfTcIyIi41XiICg1NRUff/yx1jLN/c2bN8PV1RWWlpZYsWIFnjx5AjMzM7i7u2Pt2rXo1KmT1uNGjhwJtVqNLVu24PHjx3BxccGaNWu0rhYNAIsWLcL8+fMxZ84c5OXlwcfHB1FRUVpnlkmlUmzatAkRERH46KOPYGFhgcDAQEydOrWkQySqEPceZWLexvyiqH6tHTCwu+urH0RERBXGRP2yuhckJEYXlTxdUgqVEr/dOgUA6NjIG6Y8Rd4oyLLkmLrsOJIfZ6FZo1qYO64Da4IREVWQ4n5/s3iVjpmKxOjcuL2+u0E6lKdQYd6mU0h+nIW6tarhsxEsikpEZAh4URKiCqRWq7Hyh/O4mJiKauamCAv2RS0ri1c/kIiIKhxngnRMqVLifMplAICnnRuvGF3F7Tx6HYdO3c4vijq0LRrVZ1FUIiJDwSBIx/JUCnx14hsALJtR1f1x4T427csPeEf38UCbZvX03CMiInoeD4cRVYDrd9OwaHsC1Gqgd4fGCGRRVCIig8MgiKicpaY/wxfR8ciVK+HlWhej32ZRVCIiQ8QgiKgc5eQqEB4djyeyHDSsx6KoRESGjJ/OROVEpVJj0fYzuHEvHTUtJQgL9kGNaiyKSkRkqBgEEZWTzfsuI+5iCkzFIswc7gO72jX03SUiIioCgyCicnAo/hZ+OKopitoKzRuzKCoRkaHjKfI6ZioyxUivAcJtqvwuXH+MFf/LL4o6sJsrOrdpqOceERFRcfBbWMdMRWK82bSzvrtB5eT+o0x8ufEUlCo1OrVywPs9WBSViKiy4OEwolLKyJZjzro4ZD7Lg6tjLXw8sDVMTEz03S0iIiomzgTpmEqlwpXH+bkjzW2dIRIxDq2M8hQqfLXpT9x/nIU6taph5khvmLMoKhFRpcIgSMfkqjzMORoJIL9shoXIXM89opLSFEX96/pjVDMXsygqEVElxWkIohLadSxRKIo6bUg7vMaiqERElRKDIKISiLuYjI0/XwIABL/tjrbNWRSViKiyYhBEVEyJd9OwcNsZqNVAz9dfQxCLohIRVWoMgoiKITX9Gb5Yn18UtbVLHYzt48EzwYiIKjkGQUSvkJOrQMT6eKSm56BhPUtMG9qORVGJiKoAfpITFUGlUmPxtwm4fjcd0hoShAX7wpJFUYmIqgSeIq9jpiZiDPbsK9wmw7Zl/xX8cSE5vyjqCG8WRSUiqkIYBOmYqdgUbzXrru9uUDH8euo2/nfkGgBg4oBWcGtcW889IiKi8sTDYUSFuJD4GCv+dw4AMKCrC95gUVQioiqHM0E6plKpcOPpbQBAk1qOLJthgO4/zsS8jaegUKrR0dMe7/dopu8uERFRBWAQpGNyVR4++3U+AJbNMESZ2XKEr4tDRnYeXByt8ckgL4hEPBWeiKgq4jQE0b8UShXmbfoT9x5lwda6GmaN8GFRVCKiKoxBEBHyi6Ku2vnXc0VRfVBLyqKoRERVGYMgIgA/Hk/EwbhbEJkAUwe3RWP7mvruEhERVTAGQWT04i8mY/2e/KKoI99yh7ebnZ57REREusAgiIzajXvpQlHUN9u/hrc6sSgqEZGxYBBERuuJLAdfRMchR65Eq6Z1MLYvi6ISERkTniKvY6YmYvRv0Vu4TfqRI1fgi/XxeJyegwZ1LTF9WDuYsigqEZFRYRCkY6ZiU7znHqjvbhg1lUqNyG8TcP1OGqyqsygqEZGx4k9fMjpbD1zB738lw1RsgpkjvFHflkVRiYiMEWeCdEylVuGeLAUA4CC1g8iEcaguHTl9GzGH84uifvReK7RowqKoRETGikGQjsmVeZhy4AsA/5bNMGXZDF25dCMVUd+fAwC826UpAto66rdDRESkV5yGIKOQ/DgLczfkF0Xt0NIeg99sru8uERGRnjEIoiov81kewqPjkJEtR9OG1vhkUGsWRSUiIgZBVLUplCrM3/Qn7j7MhG1NC8wa6QMLCY8CExERgyCqwtRqNVbvuoBz1x7BQiJG2Chf2LAoKhER/YtBEFVZP524gQN/3ISJCfApi6ISEdELGARRlXTqcgqif7oIABgZ1ALeLVgUlYiItDE5QsdMTcQIcu0q3Kbyl3Q/HQu2nIZaDfTwbYS3/Zz03SUiIjJADIJ0zFRsiiGt+um7G1XWE1kOwqPjkSNXwrOpLT58pyWLohIRUaF4OIyqjNw8JSLWx+Nx2jM41LFE6FAWRSUiopfjTJCOqdQqPM5+CgCwrV6LZTPKiaYo6rU7abCqboawUT6wrC7Rd7eIiMiAMQjSMbkyDxP2zgLAshnlafvBv3Hy/H2Yik3w2XBv2Nta6rtLRERk4DgNQZXekdN3sOPXfwAA4/u3gruTrZ57RERElQGDIKrUni+K2j+gKbp6sygqEREVD4MgqrRSUrPw5cZTUChVaO9RH0N6sigqEREVH4MgqpQ0RVFlWXI4N6iJye97sSgqERGVSImDoFu3biEsLAxvv/023NzcEBgYWGi72NhY9OnTBx4eHujWrRu2bdtWaLvo6GgEBATAw8MD/fr1Q3x8fIE2mZmZCAsLg4+PD1q3bo0PP/wQ9+7dK9AuKSkJwcHBaNWqFdq3b4+IiAjk5OSUdIhk4BRKFeZv/hN3HmSiNouiEhFRKZU4CLp27RpiY2PRqFEjODkVfiXes2fPIiQkBG5ubli7di369u2LiIgIxMTEaLWLjo5GZGQkPvjgA6xZswaNGjXC6NGjcfXqVa12U6ZMwZEjRzB79mxERkbi4cOHGDFihFaAI5PJMGzYMGRlZWHZsmWYPn069uzZg1mzZpV0iGTA1Go11uy6gHP/PIK5RIzZI31Qu2Y1fXeLiIgqoRL/fA4ICEDXrvllH0JDQ3Hx4sUCbVasWAE3Nzd8+eWXAABfX18kJydj6dKl6NevH0QiEeRyOVauXImhQ4ciODgYAODt7Y2goCCsWrUKkZGRAIDz58/j2LFjWLNmDfz9/QEALi4u6NatG3bt2oVBgwYBAL777jvIZDLs3r0bNjY2AACxWIypU6di3LhxLw3YdE1sIkJ3Zz/hNpXMnhM3sP/foqhTP2gDpwbW+u4SERFVUiX+FhaJin6IXC5HXFwcevfurbU8KCgIjx49wuXLlwEACQkJyMjI0DqcJhaL0atXL8TGxkKtVgPIP6wmlUrh5+cntLO3t4eXlxdiY2OFZcePH0f79u2FAAgAevToAYlEotVO38zEZhjVZhBGtRkEM7GZvrtTqfz5XFHU4b1bwNe9vp57RERElVm5J1Lcvn0beXl5aNKkidZyZ2dnAEBiYiLc3d2RmJgIAAXaOTk5ISsrCw8ePICdnR0SExPRuHHjAvWfnJ2d8dtvvwn3ExMT0a+fdk0uiUQCR0dH4bkK06VLl5euS05ORv36/KI1BDeTZViw9TRUaqC7TyP07WwYM3tERFR5lfvxmPT0dACAVCrVWq65r1kvk8kgkUhgYWGh1a5mzZoAgLS0NKGdlZVVgeeRSqXCtjTtXnzOwtrpm1qthiwnA7KcDGG2i4r2VJaD8Og4PMtVoqUzi6ISEVH5qLBTal72JfX88sLaaAKDV7UravmL2yuq3eHDh1+6rqhZotLKVcox6sdpAFg2ozhy85SYu+EUHj19Boc6NTBjWDuYmTKXioiIyq7cv000Mzkvzr7IZDIA/80ISaVS5ObmIjc3t9B2mu1IpVJh2Yvtnp/5eVm7jIyMQmeIyPCpVGos/e4srt5+ml8UNdiXRVGJiKjclHsQ5OjoCDMzM9y4cUNr+fXr1wFAOEtL8/+L+TqJiYmoUaMG6tWrJ7RLSkoqcOjo+vXrWmd8OTk5FdiWXC7H7du3DebMMCqZb3+5ihPn7sFUbIIZw71hX4dFUYmIqPyUexAkkUjg6+uL/fv3ay3fu3cv6tSpAzc3NwCAl5cXrKyssG/fPqGNUqnE/v374e/vLxzC8vf3h0wmw4kTJ4R2ycnJSEhIEE6ZBwA/Pz/ExcXh6dOnwrJDhw5BLpdrtaPK4diZO/juUP71osb394QHi6ISEVE5K3FO0LNnz4RTzu/du4fMzEwcOHAAQP51fmxsbDB+/HgMHjwYs2bNQlBQEBISEhATE4Pw8HDhFHuJRIJx48YhMjISNjY2cHNzQ0xMDO7cuYPFixcLz+fp6YnOnTtj5syZCA0NhaWlJZYuXQoHBwf07dtXaDdw4EBs3boVISEhCAkJQWpqKr766isEBQVxJqiSuZyUiqU7zgEA+r3hjK7ejfTbISIiqpJM1CU8Renu3bsvTRjevHkzfHx8AORf32fx4sVITEyEnZ0dRowYgQ8++ECrvVqtRnR0NLZt24bHjx/DxcUFn376KXx9fbXaZWZmYv78+Th48CDy8vLg4+OD2bNnw8HBQatdUlISIiIicObMGVhYWCAwMBBTp04tcAZacWnGWVTydEnlKHIx9IdPADAxujApqVmYuuw40jPlaO9RH6FD27EmGBERlUhxv79LHAQZEwZBupX1LA+fRp3AnQcZcGpQE1+FdISFOWuCERFRyRT3+5vfMDomNhHB/zVf4TblUwpFUTNgI7XA7JE+DICIiKhC8VtGx8zEZhjvM0zf3TA4a3+8iLOaoqjBLIpKREQVj1MRpHd7TtzAzyeTYGICTHm/DZxZFJWIiHSAM0E6plarkauUAwDMxRKjL/9w+soDrPvxAgBgeG83tPdgrTYiItINzgTpWK5SjqE/fIKhP3wiBEPG6layDF9vyS+K2s3bEX07O+u7S0REZEQYBJFePM3QFEVVwMPJFuP6eRr9rBgREekWgyDSOU1R1IdPn8HetgZmDGdRVCIi0j1+85BOqdVqLPvuLK7eegrLamYIG+ULKxZFJSIiPWAQRDr17S9XcfzcPYhFJpgxvB0cWBSViIj0hEEQ6cyxhLv49pf8oqgh/T3R0rmOnntERETGjEEQ6cTfN59g2Y6zAIB3Ojujuw+LohIRkX7xOkE6JjIRwbeBl3DbGDx4ko2IDfHIU6jg08IOQ3u76btLREREDIJ0TSI2w+QOo/XdDZ3JzslDeHQc0jPlaOJQE1M/aAMxq8ITEZEBMI6pCNILpVKF+VtO43YKi6ISEZHhYRBEFWbdTxeR8PdDSMzEmD3SB7bWLIpKRESGg0GQjuUocvHejnF4b8c45Chy9d2dCvPzbzew97ckAMCU973g3NBavx0iIiJ6AYMgKndn/n6ANbvzi6IO6+2G11va67lHREREBTEIonJ1K+W/oqhd2zmi3xssikpERIaJQRCVm7SMXIRHxyM7R4EWTWojpD+LohIRkeFiEETlQp6nxNwN8Xj4JBv1bWvgs+HeLIpKREQGjd9SVGZqtRrLdpzD37eeokY1M4QF+0Bag0VRiYjIsDEIojL77tA/iD17N78o6rB2aFDXSt9dIiIieiVeuU7HRCYitK7vLtyu7I6fvYvtB/8GAIzr5wnPpiyKSkRElQODIB2TiM0ww2+8vrtRLv6+9QRLvssvitrH3wk9fFkUlYiIKo/KPxVBevHwSTbmrj8lFEUdHthC310iIiIqEQZBVGKaoqhpmbloYl8TU1gUlYiIKiEGQTqWo8jFkP99jCH/+7hSls1QKlX4estp3ErJgI3UHLODfVCNRVGJiKgS4reXHuQq5fruQqlF77mEM/8WRZ3FoqhERFSJcSaIiu3nk0nYc+IGAGDy+15o2rCWnntERERUegyCqFgS/n4oFEUd2qs5OrAoKhERVXIMguiVbqfIMH/Ln1Cp1Aho2xD9A5rqu0tERERlxiCIipSeqV0UdcK7LIpKRERVA4Mgeqk8hRJzN5zCgyfZqF+7BmYMawczU7G+u0VERFQueHaYjolgArc6TYXbhkqtVmPZ9+dw5eYT1LAwxexgH9S0NNd3t4iIiMoNgyAdk5hK8HnAZH1345W+//UfHDtzFyKRCUKHtUPDeiyKSkREVQsPh1EBJ87dw9YD/xZFfaclWrnU1XOPiIiIyh+DINJy9dYTLPk2AQDwtp8T3mz/mn47REREVEEYBOlYjiIXwbs/RfDuTw2ubMbDp9mI2HAKcoUK7dzqYUQQi6ISEVHVxZwgPcjIzdR3FwrIzsnDF9HxSMvIxWv1pZjKoqhERFTFcSaIoFSpsWDrGdxMlqGWlTnCgn1R3cJM390iIiKqUAyCCOv3XMTpKw8gMRVh1kgf1KnFoqhERFT1MQgycvt/T8JPx/OLok563wsujiyKSkRExoE5QUbs7NWHWLUrvyjq4J7N0NHTQc89oldRKpXIy8vTdzeIiPTGzMwMYnH5VC9gEGSk7jzIwPzN+UVR32jTAO91cdF3l6gIarUaKSkpSEtL03dXiIj0ztraGnZ2dmWuZckgSMdEMIFTrUbCbX3IL4oah6wcBdwa2+Cj91qxKKqB0wRAdevWRfXq1fl+EZFRUqvVyM7OxsOHDwEA9evXL9P2GATpmMRUgnndQ/X2/HkKJb7ceAopqdmwq10dnw33ZlFUA6dUKoUAqHbt2vruDhGRXlWrln/yzsOHD1G3bt0yHRpjYrQRUavViPr+HC4n5RdFDQv2ZVHUSkCTA1S9enU994SIyDBoPg/LmiPJIMiIxBy+hqP/FkWdPpRFUSsbHgIjIspXXp+HDIJ0LFchx/g9MzF+z0zkKuQ6e97fzt/Dlv1XAAAf9vVAa1cWRSUiIuPGnCAdU0ONR9lPhNu68M/tp4jcnl8U9S2/Juj5emOdPC8REZEh40xQFffo6TNErI+HXKFC2+b1MDLIXd9dIjIo8fHxWLVq1UvXT5w4EfPmzRPuR0VFoXXr1lptXF1dhX/u7u7o2LEjgoODERMTUyBn4fTp0/Dx8UFmpuHVECQyNhUSBO3cuVPrQ0Hzb+HChVrtYmNj0adPH3h4eKBbt27Ytm1boduLjo5GQEAAPDw80K9fP8THxxdok5mZibCwMPj4+KB169b48MMPce/evYoYXqXxLFeBL9bH4em/RVE/HcyiqEQvOnXqFFavXl3ouosXL+Lo0aMYOXLkK7czZMgQ7NixAxs3bsRnn32GunXr4vPPP8cHH3ygFfC0bdsWTk5OiI6OLrcxEFHpVOjhsHXr1sHK6r/k23r16gm3z549i5CQELz99tsIDQ1FQkICIiIiIJFI8O677wrtoqOjERkZiUmTJsHNzQ0xMTEYPXo0YmJi4OrqKrSbMmUKLl26hNmzZ8PS0hLLli3DiBEj8NNPP8HCwqIih2mQlCo1Fm49g6T7MlhbmWP2SB8WRSUqoc2bN6NTp05an10vU79+fbRq1Uq436tXL/Ts2RNjx47FV199hYiICGFdv379sGDBAoSEhMDMjPslkb5U6OGwFi1aoFWrVsK/5y9qtGLFCri5ueHLL7+Er68vQkJC0L9/fyxduhQqlQoAIJfLsXLlSgwdOhTBwcFo3749FixYgAYNGmhNX58/fx7Hjh3D3LlzERgYiM6dO2P58uW4d+8edu3aVZFDNFgb917Cqcsp+UVRR3ijrg1PrybDcfbsWYwcORJeXl5o3bo13n33XZw8eRIAkJaWhpkzZ8LX1xctW7ZE//798dtvv2k9PiAgAOHh4VrLDhw4AFdXV9y9excAcPfuXbi6uuLHH39EeHg42rVrh44dO2L+/PlQKBQA8g9tLV++HNnZ2cKM9ZAhQwAA2dnZ+OWXX9CjR49Sj9PPzw/du3fH7t27tWaDunXrBplMhtjY2FJvm4jKTi85QXK5HHFxcejdu7fW8qCgIDx69AiXL18GACQkJCAjIwOBgYFCG7FYjF69eiE2NhZqdX5icWxsLKRSKfz8/IR29vb28PLyMsoPmQN/3MTu2EQAwCeDvODayEbPPaKKoFarkZOr0Ns/zf5XUmfOnMGQIUMgl8sRERGBqKgodOnSBffv34dSqcTo0aPx66+/YtKkSYiKioKtrS3GjBmDuLi4Uj3fkiVLIBKJsGTJEgwYMADr169HTEwMAODdd99F//79YWFhgR07dmDHjh34v//7PwD5gdqzZ8/g5eVVqufV6NixI/Ly8oTPNQCQSqVwdnYWAj8i0o8KPRwWGBiIp0+fwt7eHu+99x5GjRoFsViM27dvIy8vD02aNNFq7+zsDABITEyEu7s7EhPzv8hfbOfk5ISsrCw8ePAAdnZ2SExMROPGjQtcN8DZ2bnAL8gXdenS5aXrkpOTy3xJ7heZwAQNpPWF2+Xt/D+PsGrnXwCAD95shk6tWBS1KlKr1Zi+/DdcuflEb31o/poN5k/oWOLrdSxYsACNGjXCpk2bhCu9duzYEQBw+PBh/PXXX1izZg38/f0BAJ06dUJgYCBWrFgBX1/fEvezZcuWmDVrFgCgQ4cO+P3333Hw4EEMGjQIdnZ2sLOzg0gk0jqUBeTnA1WvXh0NGzYs8XM+z87ODgDw+PFjreXNmzfH+fPny7RtIiqbCgmC6tSpg48++gienp4wMTHBkSNHsGTJEjx48ABhYWFIT08HkP9r6Hma+5r1MpkMEomkQE5PzZo1AeRPm9vZ2UEmk2nlHj2/Pc22DIW5qQSLe4ZVyLbvPMjAvE2noFSp0dmrAQZ0ZVFUMizPnj3D+fPnMXny5EIvdX/69GnUqFFDCIAAQCQSoWfPnli9ejWUSmWJL5GvCbA0nJ2d8eeff77ycY8ePUKtWrVK9FyFedmMWa1atQoERkSkWxUSBHXq1AmdOnUS7nfs2BHm5ubYtGkTPvzwQ2H5y35BPr+8sDaaD5VXtStqucbhw4dfuq6oWSJDk56Ziy+i45GVo0Dz11gUtaozMTHB/AkdkStX6q0P5hJxif/GZDIZVCoV6tYt/GKdMpkMtra2BZbb2toiLy8P2dnZhf7gKcqL7c3MzCCXv/pCpbm5uZBIJCV6rsI8ePAAQP6Pw+eZm5sjNze3zNsnotLT2cUSe/bsifXr1+PKlStwcMg/RPPiLI1MJgPw34yQVCpFbm4ucnNzYW5uXqCdZkZIKpUiOTm5wHPKZLICs01VUZ5CiXmb/kRyahbq2VTHzBHekJixKGpVZ2JiAgvzynW9UysrK4hEIqEC9Itq1qxZ6OzI48ePYWZmJtQLkkgkBa6/U96zvjVr1kRGRkaZt3PixAlIJBK0aNFCa3l6ejqsra3LvH0iKj29JEY7OjrCzMwMN27c0Fp+/fp1APk5P8//r8kN0khMTESNGjWE01adnJyQlJRUYNr5+vXrwjYMRa5Cjsn7wzF5f3i5lM1Qq9VYHnMel26korqFKcKCfVgUlQxW9erV0apVK/z4449QKgvOYrVp0wZZWVk4fvy4sEylUuHAgQNo3bq1cChMkwv4vNImGb9sZqhx48Z48uQJsrOzS7VdADh+/DgOHTqEvn37FiiAe/fuXTRuzKu3E+mTzoKgffv2QSwWw83NDRKJBL6+vti/f79Wm71796JOnTpwc3MDAHh5ecHKygr79u0T2iiVSuzfvx/+/v7CVLy/vz9kMhlOnDghtEtOTkZCQoJWboEhUEONu7Jk3JUll0vZjP8duYYjp+/kF0Ud0g6OdlV/5osqtylTpuDmzZsYPnw49u/fj99//x1r167F//73P3Tu3BktW7bEtGnT8P333yM2Nhbjx49HUlISJkyYIGyjR48eOHPmDJYvX46TJ09i7ty5uHDhQqn64+TkBIVCgU2bNuGvv/4Sfpx5eXlBpVJpndVVlOTkZJw7dw5nzpzBgQMH8Nlnn2HcuHHw9PTE9OnTC7S/ePEi2rRpU6o+E1H5qJC59ODgYPj6+sLFJT8x9/Dhw/j+++8xdOhQ4bj4+PHjMXjwYMyaNQtBQUFISEhATEwMwsPDIRLlx2YSiQTjxo1DZGQkbGxshIsl3rlzB4sXLxaez9PTE507d8bMmTMRGhoKS0tLLF26FA4ODujbt29FDNEgnPzrPjbvyy+KOqaPB7yasSgqGb62bdti8+bNWLJkCWbMmAGRSISmTZvik08+gVgsxtq1a/H1119j0aJFwvV7Vq9eDR8fH2Eb7777Lm7fvo1vv/0WGzduRK9evfDxxx8XGmy8yhtvvIH3338fa9asQWpqKtq1a4ctW7agcePGcHV1xYkTJ9C2bdtXbmfLli3YsmULzMzMYG1tDVdXV8yZMwd9+vSBqan2R+1ff/2FtLS0Ml2DiIjKzkRd2ot9FCEiIgInTpxASkoKVCoVXnvtNbz77rsYMmSIViJlbGwsFi9ejMTERNjZ2WHEiBH44IMPtLalVqsRHR2Nbdu24fHjx3BxccGnn35a4FTZzMxMzJ8/HwcPHkReXh58fHwwe/ZsIf+oNDSJ0UUlT5dUjiIXQ3/4BACwud8SWJiW7tDVtTtPEbriJOR5SgR1aoIxfTzKrY9kWHJycpCUlITGjRsb5dXP9WnLli3YtGkTDh06VK4nGsybNw9XrlzB5s2by22bRMbkVZ+Lxf3+rpAgqKow1CDocdozTFkaiyeyXLRpVhezR/pALGYt3KqKQZD+5OTkoFu3bggLC0O3bt3KZZuZmZno3LkzVq5ciXbt2pXLNomMTXkFQfzmrGSe5SrwRXQ8nshy0cjOCtOGtGUARFRBLCws8NVXXxU4E60s7t27h08++YQBEJEBqFzn1xo5pUqNRdvO4Mb9dFhbmiMs2JdFUYkqWIcOHcp1e5oaZUSkfwyCdMwEJqhT3Ua4XRKbfr6M+EspMDMVYeZIFkUlIiIqCwZBOmZuKsGKoLklftzBuFvYdSz/OkqfDGyNZiyKSkREVCZMJqkEzl97hJU/5BdafL9HM/i1bqDnHhEREVV+DIIM3N2HGZi36U8oVWr4t26Agd1YFJWIiKg88HCYjskVcvzfkfwLPc4JmAyJ6csLNMqy5AiPjkfWszw0a1QLEwewKCoREVF5YRCkYyqokfj0lnD7ZfIUKszbdArJj7NQ16Y6Zo7wYVFUIiKicsTDYQZIrVbjm/+dx8XEVFQzzy+Kam3FoqhU+UVFRQmniLu6usLX1xfDhg3D6dOn9d01vXr69Cm+/PJLdO/eHR4eHmjfvj0GDRqEjRs3Cm3u3r2LqKgoPHjwoFyfOz4+HqtWrSrXbT4vNDRU6z3X/AsODi72NgICAhAeHi7cj4qKQuvWrYv12AsXLmDChAnw9fWFh4cHevTogcjISGRmZpZ4LOVh48aNiI2N1clzvfiad+jQARMmTChQfLg8xcfHw9XVtdS1/HSNM0EGaOfR6/j1z9sQmQDTh7ZFIxZFpSrEwsICmzZtAgCkpKRg5cqVGD58OH744QejvH5OXl4ehg4diszMTIwZMwZNmjTB48ePkZCQgKNHj2L48OEA8i+yuHz5cnTu3Bn16tUrt+c/deoU1q9fjw8//LDctvmihg0bYuHChVrLrKysKuz5NA4ePIgpU6bA3d0dYWFhsLW1xYULF7BmzRocPXoUmzdvhrW1dYX343mbN29G586ddVbce8iQIQgMDIRarUZycjKWL1+OUaNG4eeff0b16uV/mZUWLVpgx44dcHJyKvdtVwQGQQbmjwv3sWlfftXqMX080KZZ+X3YERkCkUiEVq1aCfdbtmyJgIAA7NixA2FhYQXaq9Vq5OXlQSJ5ef5cecjNzYW5ue5nXE+dOoV//vkHW7du1bqKdO/evaFSqSrseeVyeYHCrhXFwsJC6z3XhYcPH2LGjBlo2bIlNm3aBDOz/AvLent7o0OHDujfvz/mzp2LBQsW6KQ/OTk5FVL25lXbrV+/vvDat27dGnXq1MHgwYNx6dKlCrlquaWlpc7f67Lg4TADcv1OGhZuS4BaDQR2aIzeHZvou0tEFc7e3h61atXC3bt3AeQfPgkMDERsbCzeeusteHh4CPV/fv31V/Tp0wceHh7o0KED5syZg6ysLK3tXbt2DR988AE8PDzQtWtX7N69G2PHjsWQIUOENprDKX/99RcGDBgADw8PbNmyBQCwcOFCBAUFoXXr1ujUqRMmT56Mhw8faj3HkCFDMHbsWPz444/o1q0bPD09MXbsWKSlpeHevXsIDg5G69at0bt3b8TFxRU5fplMBgCoU6dOgXUiUf5HdHx8PIYOHQoA6N+/v9ZVp7OzsxEeHo4ePXrA09MTAQEBCAsLQ0ZGhta2NIeU1q1bhzfeeAOenp744osvsHz5cmRnZwvb1LxOKSkp+Pjjj/H666/Dw8MDAQEB+PLLL4scS0lpApUuXbqgZcuW6N69OxYvXgy5XF7mbcfExCArKwuTJk0SAiCNZs2aISgoCD///LNweHHnzp1wdXXFkydPtNoGBgYiNDRUuH/27Fl8+OGH6NixI1q1aoW3334bu3fv1nqM5pDQsWPHMHHiRHh5eeHjjz9GQEAA7t27h23btgmv986dO4XH7dy5E0FBQfDw8ECnTp0QGRkJhUKhtd7V1RVnz57FiBEj0KpVK8yfP79Er0uNGjUAQGu7mnENHToUrVq1Qps2bTBlyhSkpqZqtUlJScHYsWPRsmVLdOrUCevWrUN4eDgCAgIKjP35w2Gurq5Yu3Ytli1bhtdffx0+Pj6YMWMGsrOzhTYymQyzZs1Cp06d4OHhAX9/f0yaNKlEYysNzgQZiNT0Z/hifTzkeUp4NauLUW+767tLVAnkKHJfuk5kIoJEbFa8tjDROlOxJG3LKjMzE+np6ahbt66w7OHDh5g7dy7GjRsHOzs71K9fH4cPH8aECRPw5ptvYtKkSbh79y4WLVqEpKQkIXcmJycHI0eOhFQqFX7hR0VFQSaT4bXXXtN63ry8PEydOhXDhg3D5MmTUbNmTQBAamoqxo4di7p16+LJkyfYsGEDhgwZgp9//llr5uTy5ctIT09HaGgoMjIyEBERgVmzZiE5ORl9+vTBiBEjsHr1akycOBFHjx4Vvnxe5ObmBpFIhFmzZmH8+PFo06ZNgVmvFi1aICwsDOHh4Zg3bx6aNPnvB1JOTg6USiUmTZoEGxsbJCcnY9WqVRg/fnyBKvW//PILXnvtNcycORMikQiurq6Qy+XYu3evcIjS0tISADBt2jQ8fPgQs2bNQu3atZGcnIyLFy8W920t4MUvXVNTUzx9+hTW1taYMWMGpFIpbt68iaioKDx69Ajz5s0r9XMB+TNsNWvWfOlsR5cuXbBz506cPn0avXv3LvZ279+/Dy8vLwwaNAgSiQQJCQmYNWsWAKBPnz5abcPCwvDWW29hxYoVMDExgVQqxZgxY+Dl5YWRI0cCABwdHQEAGzZswIIFCzBs2DCEhoYiMTERkZGRUCqVmDp1qtZ2p06digEDBmDs2LGvnF1SqVRQKBTC4bDIyEjUrVtXK6fq7NmzGDJkCPz9/REZGYlnz55hyZIlGDduHL7//nsA+TOyISEhePz4McLDw2FlZYV169bh/v37EItffdLOtm3b0KZNG3z11VdISkrCggULULt2bWFs8+bNw4kTJzBlyhQ4ODjg0aNHOH78+Cu3W1YMgvTAytxS635OrgLh0fF4IsuBo50Vpg1mUVQqnqE/fPLSda3ru2OG33jh/ujd05CrLPwXtludpvg8YLJwf/zeWcjILTxx1KlWI8zrHlrouuLSfCGmpKRg/vz5UCqV6NGjh7A+PT0d69atQ8uWLYVlkyZNgoeHB5YsWSIsq1mzJqZMmYL4+Hj4+Pjghx9+wOPHj7F9+3Y0bNgwf2xubujRo0ehQdCkSZPQs2dPreXPf/kqlUq0bt0afn5+iIuLQ8eOHYV1mZmZWLlyJWrVqgUAuHr1KtavX4/PP/8cgwYNAgDUrVsXQUFB+OOPP9C1a9dCX4tGjRohNDQUCxYswPDhw2FmZoaWLVuiZ8+eGDRoEExNTWFpaQlnZ2cAQNOmTeHh4SE83sbGBnPmzNF6bRs0aID3339fqLL9/Lq1a9eiWrVqwjI7O7sChyiB/ITiyZMno1evXsKyF7/ki+vatWto0aKF1rJt27ahbdu2mD59urDMy8sL1apVQ2hoKMLCwrT6WVIPHjyAvb39S9c7ODgAAJKTk0u03ecDJrVajXbt2uHBgwf47rvvCrw+Xbp0KRDASCQS2Nraar3emZmZWLZsGUaNGoXJk/P3ww4dOkAsFuPrr79GcHCw8HcGAIMGDcKoUaOK1d+FCxdq5WPVrl0bK1eu1AqeFi1aBHd3dyxfvly4DEvTpk0RFBSE2NhY+Pv74/jx47h06ZLwvgGAj48P/Pz8ipVXZWtri0WLFgEA/Pz8cOHCBRw8eFB4fS5cuIDAwED07dtXeExJgtPSYhCkYxam5oju898xaJVKjUXbz+DGvXTUtJRg9kgf1KjGoqhUdWVnZ2t9IdasWRNhYWHo1KmTsKxWrVpaAVBWVhauXLmCadOmaW3rzTffxPTp03H69Gn4+Pjg4sWLcHV1FQIgIP+XdtOmTQvtS2HJqbGxsVi5ciWuXbumdQbRzZs3tYKgZs2aaX0xaYKs119/vcCylJSUQp9fY9iwYejVqxeOHDmCU6dO4Y8//kBERAR++eUXbNq0STgs9jK7d+/Gxo0bcevWLa1DDDdv3tQKgry9vYsdWLi5uWH9+vUQi8Xo0KEDGjVqVKzHFcbR0RGLFy/WWtakSROo1Wps2rQJ33//Pe7evYvc3P9mIO/cuQMXl4q/OGxJr72Wnp6OqKgoHD58GA8ePIBSqQSAQgOB4iY/nz17FtnZ2XjzzTe1Zsx8fX2Rk5ODa9euwdvbu8TbBYChQ4firbfeAgA8efIE27Ztw5gxY7Blyxa4uLjg2bNnSEhIwLRp04SxAEDjxo1Rp04dXLhwAf7+/rhw4QKkUqkQAAH5s4Y+Pj64evXqK/vxYiFiZ2dnHDx4ULjv5uaGXbt2oU6dOujUqZNO3nuAQZDebd53GXEX/y2KOtwHdrULnzInKszmfkteuk5kov3FubbP1y9v+0Ix3xWBEcVuW1IWFhbYunUrTExMUKtWLdSvX7/Al3zt2rW17mdkZECtVsPW1lZruampKaytrZGeng4g/zCajU3Bunq1a9cucDimWrVqBc6O+euvvxASEoIuXbpg9OjRqF27NkxMTPDee+9pfUEDgFSqfdamJu/k+bOeNIe1XnxsYerUqYMBAwZgwIAByMvLQ1hYGHbu3ImjR4+iS5cuL33coUOHMH36dAwYMACTJk2CtbU1Hj16hPHjxxd43hdf16JERkYiMjISS5YswZw5c9C4cWNMnjwZ3bt3L/Y2NMzNzbVmrzQ2btyI+fPnY9SoUfDx8YFUKsWFCxcQHh5erNesKHZ2drh8+fJL19+7dw8ASnymXWhoKM6ePYvx48fD2dkZlpaW+Pbbb7F///4CbQv7WyzM06dPAUBrFuR5L85WleR9tLOz03rt27dvDz8/PyxfvhzLli2DTCaDUqnEvHnzCj0EqXnuovat4ihsf3k+92v27NmoWbMmNmzYgK+//hr169fHmDFj8P777xdr+6XFIEiPfom/hR+O5hdFnTigNZo3ZlFUKhkL0+KfzVRRbUtKJBIV+oX4vBd/nVtZWcHExKRAoqZCoUBaWpqQz1O3bl1cuXKlwPZSU1OFNi97DiA/8drS0hJLliwRAjPNl6UumZmZYfjw4di5cycSExOLDIIOHDiA5s2ba11H59SpU4W2LcmsR926dTFv3jyoVCpcvHgRK1euxKRJk3DgwAGtmbayOHDgAAICAjBlyhRhWXldw8bb2xt//PEHTp8+rTV7oXHkyBGIRCK0adMGAIQzA/Py8rTapaWlCbdzc3MRGxuL6dOnayXab9++vdA+FPf11vxtLl++HHZ2dgXWN2hQfvUiJRIJGjZsiGvXrgH4b98aO3ZsoYdsNbOdmhy5F724T5aWlZUVZs6ciZkzZ+Lq1avYvHkz5syZg6ZNm1bIWWwaTDzRMblCjs+PLManP8/HNz8kAAAGdXdFZy8WRSV6mRo1aqB58+YFfm3/8ssvUCgUwpecu7s7rl69ijt37ghtbt++LXzgv0pOTg7MzMy0vrz27NlTDiN4ubS0tAKzVED+oSzgv7PGNDNNL86QaPr8vJL0+cVf5C8SiURo2bIlPvnkEygUCty6davY236Vsva9KP3790eNGjWwZMmSAq/vP//8g59++gn+/v6oX78+gP9mhG7cuKHV7vHjx8J9uVwOpVKp1efMzEwcOXKk2P0yMzMr8B5qcqFSUlLg4eFR4N/zh13LKjc3F7dv3xa2Wb16dbRq1Qo3btwo9Lk1AZiHhwdkMhn+/PNPYVuZmZmIj48vt75puLq6YsaMGQC034+KwJkgHVNBjcuP8j+QlWpn+LVqiEHdje8CcUQlNWHCBIwfPx6TJ09Gnz59cOfOHSxevBjt27eHj48PAKBfv35YtWoVPvzwQ0ycOBFqtRpRUVGwtbUt1q/yDh06YNOmTfjiiy/QrVs3nD17Fj/++GOFjisuLg4LFy5E37590bJlS5iamuLKlStYvXo17O3t0a1bNwD5+UVisRg//PADxGIxTE1N4eHhgddffx3h4eFYvnw5vLy8cPz4cfzxxx/Ffn4nJycoFAps2rQJrVu3hqWlJerUqYPg4GC8/fbbaNy4MfLy8rBlyxZIpVK4ubkByL+CdZcuXTBhwgR89NFHpRr766+/js2bN2Pr1q147bXXsGfPnnILsjQzWVOmTMGQIUMwZMgQ2Nra4tKlS1i1ahVsbGzwxRdfCO09PT1Rv359fPnll5gyZQoyMzOxZs0arRlEKysreHh4YO3atbCxsYGpqSnWrFkDS0vLQmdJCtOkSRPExcXh5MmTkEqlaNCgAWrVqoWJEydiwYIFSElJgY+PD0QiEe7cuYPDhw8jKiqq1EniycnJOHfuHID8nKDt27cjLS0NAwcOFNpMmzYNw4YNwyeffILevXtDKpUiJSUFv//+O9555x0hAbpFixaYMmUKJk+eDKlUirVr1wozSWU1cOBAdOvWDU2bNoVYLMbu3bthZmZW6CxeeWIQpEdNG9bCxwNbsygqUTF06dIFUVFRWLFiBUJCQiCVSvHWW29pnX1jYWGB9evX4//+7/8wZcoU1KtXD+PHj8cPP/xQrCsU+/v7Y+rUqdi6dSt27twJLy8vrF69WuvMtfLm6emJHj164PDhw9i0aRNyc3NhZ2eHoKAgjBkzRjhl3cbGBmFhYVi3bh1++uknKBQKXL16FQMHDsTdu3exbds2rF+/Hh07dsSiRYvw3nvvFev533jjDbz//vtYs2YNUlNT0a5dO0RHR8PFxQVbtmxBcnIyLCws4O7ujujoaCEv5NmzZwBQIE+rJMaPH4+nT59i2bJlAIAePXpg1qxZ5Xb16h49esDe3h6rVq3CnDlzkJ6eDrVajTZt2mD58uVaOS5mZmZYvnw5Pv/8c3z88cdwdHTEZ599hrlz52ptc9GiRZg9ezZCQ0NhbW2NIUOGIDs7G+vXry9WnyZPnozPP/8cH330EbKysjBv3jy88847GDlyJOrVq4cNGzZg69atMDU1haOjIzp37lxgtqwktmzZIlwDSyqVwsnJCStWrNA69OXl5YXt27cjKioKM2bMQF5eHuzs7ODr6yskxJuYmOCbb75BWFgYwsLCIJVKMXToUFy7dq3YM61F8fLywu7du3H37l2IRCK4uLhg1apVFX7laRO1Wv3yKp5GTnMcXnOhtvKQo8gVTmte2WsBaltZFv0AMno5OTnCqc4VccXZqu7p06fo2rUrRowYgQkTJui7O1VGTEwMFi1ahKNHj5bpVHZd+/TTT3HkyBFs377dKMu0lCe5XI6ePXvC29u7zNd1KqlXfS4W9/ubM0F6xFPhicrfmjVrYGtrK1xwbf369VCpVOjXr5++u1alJCQkYPjw4ZUqAAKAiIgIDB48GGPHjsWOHTvKtQ5bVbdjxw6oVCo0btwYMpkM3377LZKTkyv8DK6KxCCIiKoUsViMVatWISUlBWKxGJ6enti0aZOQAEvlQ9e//MuLubk5YmJi9N2NSsnc3Bxr164VStw0a9YMq1evfuXZnoaMQRARVSnBwcEIDg7WdzeIqpw+ffqU+qrhhopBkB6Yiyu2GjYRERG9GoMgHbMwNceW/kv13Q0iIiKjx4slElUSPJGTiChfeX0eMggiMnCaa4Q8XxiTiMiYaT4Py3INJYCHw3ROrszDopNrAABTOoyBRMzT5KloYrEY1tbWePjwIYD8y9zzAptEZIzUajWys7Px8OFDWFtbQywWl2l7DIJ0TKVW4WzyReE2UXFoiipqAiEiImNmbW1daLHZkmIQRFQJmJiYoH79+qhbt26BKtdERMbEzMyszDNAGgyCiCoRsVhcbjs/EZGxY2I0ERERGSUGQURERGSUGAQRERGRUTJR8wpsL+Xh4QGlUlmuhRfVUONRVioAoE6N2jABT3UmIiIqT8nJyRCLxbhw4UKR7ZgYXQRzc3PI5fJy3aYJTKCU5Z/dY1Kj6gZAycnJAFBlK3dX9fEBVX+MHF/lV9XHyPGVnqmpKSSSV9fp5EyQHnTp0gUAcPjwYT33pOJU9TFW9fEBVX+MHF/lV9XHyPFVPOYEERERkVFiEERERERGiUEQERERGSUGQURERGSUGAQRERGRUWIQREREREaJp8gTERGRUeJMEBERERklBkFERERklBgEERERkVFiEERERERGiUEQERERGSVWkS+mW7duITo6GufPn8e1a9fQpEkT7N27t0C72NhYREZGIjExEXZ2dhg+fDg++OCDV24/Ly8Py5Ytw65du5CRkYGWLVti5syZaNasmVa7R48eYe7cuTh+/DhEIhECAgLw2Wefwdra2qDHmJSUhK1bt+KPP/7A/fv3UatWLbRv3x6TJk1CnTp1hHbx8fEYOnRogcf36tULkZGRBjs+AHB1dS2wzNbWFidPntRaVlHvYUWOb+fOnZgxY0ah6zp27Ijo6GgAFfv+AcUb48mTJ7Fz506cP38ed+7cwQcffICwsLBibV/f+2FFjq+y7INlef/0vQ8CFTtGQ9gPXzU+pVKJ9evXIzY2FtevX4dSqYSLiwsmTJiA9u3bv3L7ut4HGQQV07Vr1xAbGwtPT0+oVCoUdmWBs2fPIiQkBG+//TZCQ0ORkJCAiIgISCQSvPvuu0Vuf968edi9ezdCQ0Ph4OCAdevWYfjw4dizZ4/wAaVQKDBq1Cjk5eXh66+/hkKhwIIFCxASEoJt27bBxMTEYMd48uRJnDp1Cu+99x6aN2+OlJQULF++HAMGDMCePXtQo0aNAq9HkyZNhPu1atUq09gqenwaQ4YMQWBgoHDfzMxMa31FvocVOb7OnTtjx44dWstu3ryJ6dOnw8/Pr0D7inj/gOKN8fjx47hy5QratWuH9PT0Em1f3/thRY6vsuyDZXn/AP3ug0DFjtEQ9sNXjS8nJwerV69Gnz59EBwcDFNTU+zatQsjRozAypUr8cYbbxS5fZ3vg2oqFqVSKdyePn26unfv3gXaBAcHq/v376+1bNasWeoOHTpoPf5FKSkp6ubNm6u3bt0qLMvIyFB7e3urFyxYICz7+eef1S4uLup//vlHWHbmzBm1i4uLOjY2tlTjel5FjjE1NVWtUqm0ll25ckXt4uKi3rlzp7AsLi5O7eLiov7rr79KO4yXqsjxqdVqtYuLi3rdunVFtqnI97Cix/eiZcuWqZs3b65++PChsKwi3z+1unhjfL7NG2+8oZ4zZ06xtm0I+2FFjq+y7IOlHZ9arf99UK2u+DG+SNf74avGp1Ao1GlpaVrLVCqVum/fvurBgwcXuW197IPMCSomkajol0oulyMuLg69e/fWWh4UFIRHjx7h8uXLL33sb7/9BqVSqfVYS0tLBAQEIDY2VlgWGxsLV1dXNG3aVFjm5eUFBwcHrXalVZFjtLGxKRCdu7q6QiwW4+HDh6XvdAlU5PiKqyLfQ12Pb+/evfD19dU6lFLRXjXG4rYpjCHshxU5vsqwDxa3TVno+3O0uG2KS9f74av6LhaLUbNmTa1lJiYmaNas2Sv/zvSxDzIIKie3b99GXl6e1tQjADg7OwMAEhMTX/rYxMRE2NraFjiW6eTkhKSkJKhUKqGdk5NTgcc7OzsXuf3yUpYxFubs2bNQKpWFjmnMmDFo3rw5/Pz8MH/+fOTk5JS+48VUHuNbs2YNWrRogbZt2+KTTz7B/fv3tdbr8z0sz/fvwoULuHnzptZhh+fp4/0rq8qyH5YnQ9sHy4Mh74PlrbLshyqVCmfPni30dX+ePvZB5gSVE81xXalUqrVcc7+o474ymQxWVlYFltesWRN5eXnIzs6GpaXlS9tJpVKd7LxlGeOL8vLy8OWXX6Jx48bo3LmzsNzKygqjRo1Cu3btYG5ujri4OKxfvx43btzA6tWryz6IIpR1fH369EHnzp1ha2uLf/75BytXrsT777+PH3/8UfhlpM/3sDzfv71798Lc3Bzdu3fXWq7P96+sKst+WF4McR8sK0PfB8tbZdkPt2zZgqSkJISHhxfZTh/7IIOgcvayhKxXJWoVtl5dSELdy9qVNZmvJEo7xud98cUXuHbtGrZu3QpT0//+DN3c3ODm5ibcb9++PerWrYvw8HD89ddfaNmyZek7XkylHd/8+fOF2+3atUObNm3wzjvv4Pvvv8fo0aOL3I4u38Oyvn8qlQr79u1D586dYWlpqbXOEN6/sqhM+2FZGfI+WFqVZR8sD5VlPzx16hQWLFiAkSNHol27dq9sr+t9kIfDyonmV8aLv6ZlMhmAgr++nyeVSoV2Lz7WzMwM1atXL7JdRkZGkdsvL2UZ4/OWL1+O//3vf1i8eDE8PDxe2b5nz54AgIsXL5akuyVWXuPTaNasGRo3boxLly4Jy/T5HpbX+OLj4/Hw4UMEBQUVq72u3r+yqiz7YXkw1H2wvBnaPlieKsN++PfffyMkJARdu3bFp59++sr2+tgHGQSVE0dHR5iZmeHGjRtay69fvw4ARR4LdXJyQmpqKtLS0rSWJyYmonHjxkIimpOTU6FTfdevX3/lsdbyUJYxamzbtg1RUVH4v//7P3Tp0qVC+lla5TG+F734C0af72F5jW/Pnj2wsrKCv79/ufdRnyrLflhWhrwPVgRD2gfLk6Hvh7dv38aoUaPg5uaGr7/+ulgzNPrYBxkElROJRAJfX1/s379fa/nevXtRp04dranJF3Xs2BEikUjrsVlZWThy5IjWH7i/vz/++ecfrTf/3LlzuHfvnk52hLKMEQB+/vlnREREYOLEiRgwYECxn/fnn38GgGL9Yi2Lso7vRVeuXMHNmze1+q3P97A8xieXy3Ho0CF0794dEomkWM+rq/evrCrLflgWhr4PljdD2wfLi6Hvh48ePcLIkSNha2uLb775pth91Mc+yJygYnr27Jlw6t29e/eQmZmJAwcOAAC8vb1hY2OD8ePHY/DgwZg1axaCgoKQkJCAmJgYhIeHa51W2K1bN9jb22PTpk0AgHr16mHgwIFYuHAhTE1NYW9vj/Xr1wMAhg0bJjyue/fucHV1xcSJEzF58mQolUp8/fXXaNOmDTp16mTQYzx16hSmT5+Otm3bokOHDjh37pzQ1sbGBo6OjgCAqVOnolGjRnBzcxMS+jZu3IguXbqUeeetyPFFR0fjzp07wnauXbuGVatWwc7OTusihBX5Hlbk+DRiY2Mhk8leOgVfke9fccd47949XLhwQWh/+/Ztoc2bb7750jEawn5YkeOrLPtgacdnCPtgRY9RQ5/74avGV716dYwaNQqpqakIDQ0VZpo1WrVq9dLx6WMfNFEXlnFEBdy9e/elU8ebN2+Gj48PgPw/zsWLFwslCUaMGFGgJEFAQAAcHBywZcsWYZlcLte6VLinp2ehlwp/+PAh5s6dixMnTsDExES4VHh5XAm0IscYFRWF5cuXF7rtvn374quvvgIArF69Gnv27MG9e/eQl5cHBwcHBAUFYcyYMcX+NaGP8R05cgSrV69GUlISsrKyUKtWLfj5+eGTTz5B3bp1tR5bUe9hRf+NAsDEiRORkJAgXKr+RRX5/hV3jEWVFrh69apw2xD3w4ocX2XZB0s7PkPYByt6jBr63A9fNT4HB4ciD7Ma2j7IIIiIiIiMEnOCiIiIyCgxCCIiIiKjxCCIiIiIjBKDICIiIjJKDIKIiIjIKDEIIiIiIqPEIIiIiIiMEoMgIiIiMkoMgoiIXiE2NhZRUVGFrnN1dUV4eLiOe0RE5YFBEBHRK8TGxr605AQRVV4MgoiIiMgoMQgiIoMTFRUFV1dX/P3335g4cSLatGkDb29vzJs3DwqFAjdu3EBwcDBat26NgIAArF27Vuvx9+/fx9SpU9G+fXu4u7ujZ8+eWL9+PVQqldDm7t27cHV1RXR0NDZs2ICAgAC0bt0aAwYM0KqwHhoaim3btgHIP/Sl+Xf37l2t59y9ezd69uwJT09PvPXWWzh69GjFvUBEVC5M9d0BIqKX+eSTT/DWW29h4MCBOHnyJNatWweFQoHff/8d77//PoKDg7Fnzx4sXLgQjRo1Qvfu3fHkyRMMHDgQeXl5+Pjjj+Hg4IBjx45h/vz5uH37Nj7//HOt59i2bRuaNGmCzz77DACwdOlSjBkzBocPH4aVlRVCQkKQnZ2NgwcPYseOHcLjnq9MfuzYMVy4cAETJ05E9erVsW7dOkyYMAEHDhxAw4YNdfJaEVHJMQgiIoM1YMAAjBgxAgDw+uuv4+TJk9i6dSuWL1+Obt26AQC8vb1x7Ngx7NmzB927d8eGDRvw4MEDxMTEoGXLlgCATp06QalU4rvvvsOwYcPQuHFj4Tlq1KiB1atXQywWA8gPbt59910cP34cvXv3hqOjI2xtbQEArVq1KrSfubm52LBhAywtLQEALVq0QKdOnbB//36MGTOmQl4bIio7Hg4jIoPVuXNnrftOTk4wMTGBn5+fsMzU1BSNGjXCvXv3AABxcXFwdnYWAiCNd955B2q1GnFxcQWeQxMAAUCzZs0AQNhecfj4+AgBEADY2tqidu3aJdoGEekegyAiMlg1a9bUum9mZoZq1arB3Ny8wHK5XA4ASEtLQ506dQpsS3P4Ki0tTWu5tbW11n2JRAIgf3anuF7chmY7JdkGEekegyAiqlKsra3x6NGjAssfPnwIAKhVq5auu0REBopBEBFVKe3bt8f169dx6dIlreW7d++GiYkJfHx8SrxNzexQTk5OufSRiAwDE6OJqEoZPnw4du/ejbFjx2LixImwt7fHsWPHsH37dgwaNEgrKbq4XFxcAABr166Fn58fRCIRXF1dheCIiConBkFEVKXY2Njgu+++w6JFi7Bo0SJkZWWhQYMG+PTTT4UzzUoqMDAQCQkJ2L59O1asWAG1Wo3Dhw+jQYMG5dx7ItIlE7VardZ3J4iIiIh0jTlBREREZJQYBBEREZFRYhBERERERolBEBERERklBkFERERklBgEERERkVFiEERERERGiUEQERERGSUGQURERGSUGAQRERGRUWIQREREREbp/wGPyxoX3bnElwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1050,15 +1083,15 @@
    "source": [
     "df_count_plot_program = df_area_program.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")\n",
     "ax = df_count_plot_program.plot(x=\"month\", y=\"count(ID)\")\n",
-    "ax.axvline(x=9, color='g', linestyle='--', label='Program Starts, Fall Quarter Begins')\n",
-    "ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n",
+    "ax.axvline(x=10, color='g', linestyle='--', label='Program Starts, Fall Quarter Begins')\n",
+    "#ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n",
     "plt.legend()\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "id": "d266e90c-ae57-41cb-99a5-3185b61e60ed",
    "metadata": {},
    "outputs": [
@@ -1071,7 +1104,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1225,10 +1258,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "4876aef0-e741-4302-82d5-24f133f1d762",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
    "source": [
     "# storing data on the bucket\n",
     "df_area_program.write.option(\"header\", \"true\").csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/program_area_time_rides_2021.csv\")\n",

From 8196d171e61a40aa5a6b2c02016c57f49dd80f32 Mon Sep 17 00:00:00 2001
From: root
 <root@hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal>
Date: Thu, 16 Nov 2023 02:15:58 +0000
Subject: [PATCH 9/9] final run of 2020 and 2021 eda

---
 eda_2020.ipynb | 1063 ++++++++++++++++++++++++++++++++++++++++++++++++
 eda_2021.ipynb |   14 -
 2 files changed, 1063 insertions(+), 14 deletions(-)
 create mode 100644 eda_2020.ipynb

diff --git a/eda_2020.ipynb b/eda_2020.ipynb
new file mode 100644
index 0000000..270dd6b
--- /dev/null
+++ b/eda_2020.ipynb
@@ -0,0 +1,1063 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4b88859b-b964-408d-bcbe-bd152197646c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pyspark.sql import SparkSession\n",
+    "from pyspark.sql import functions as F\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1db17959-867e-47ab-a531-467a4ac9d953",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('spark.stage.maxConsecutiveAttempts', '10'),\n",
+       " ('spark.dynamicAllocation.minExecutors', '1'),\n",
+       " ('spark.eventLog.enabled', 'true'),\n",
+       " ('spark.submit.pyFiles',\n",
+       "  '/root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,/root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,/root/.ivy2/jars/com.typesafe_config-1.4.2.jar,/root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,/root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,/root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,/root/.ivy2/jars/com.navigamez_greex-1.0.jar,/root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,/root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,/root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,/root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,/root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,/root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,/root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,/root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,/root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,/root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,/root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,/root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,/root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,/root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,/root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,/root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,/root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,/root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,/root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,/root/.ivy2/jars/com.google.api_gax-2.20.1.jar,/root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,/root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,/root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,/root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,/root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,/root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,/root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,/root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,/root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,/root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,/root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,/root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,/root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,/root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,/root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,/root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,/root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,/root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,/root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,/root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,/root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,/root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,/root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,/root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,/root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,/root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,/root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,/root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.app.id', 'application_1700094704615_0003'),\n",
+       " ('spark.dataproc.sql.joinConditionReorder.enabled', 'true'),\n",
+       " ('spark.ui.proxyBase', '/proxy/application_1700094704615_0003'),\n",
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+       " ('spark.driver.port', '38633'),\n",
+       " ('spark.driver.maxResultSize', '0'),\n",
+       " ('spark.yarn.unmanagedAM.enabled', 'true'),\n",
+       " ('spark.sql.autoBroadcastJoinThreshold', '43m'),\n",
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+       "  'org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_URI_BASES',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m:8088/proxy/application_1700094704615_0003'),\n",
+       " ('spark.metrics.namespace',\n",
+       "  'app_name:${spark.app.name}.app_id:${spark.app.id}'),\n",
+       " ('spark.executor.memory', '4g'),\n",
+       " ('spark.dataproc.sql.optimizer.leftsemijoin.conversion.enabled', 'true'),\n",
+       " ('spark.hadoop.hive.execution.engine', 'mr'),\n",
+       " ('spark.executor.id', 'driver'),\n",
+       " ('spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version', '2'),\n",
+       " ('spark.dynamicAllocation.maxExecutors', '10000'),\n",
+       " ('spark.org.apache.hadoop.yarn.server.webproxy.amfilter.AmIpFilter.param.PROXY_HOSTS',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m'),\n",
+       " ('spark.yarn.dist.pyFiles',\n",
+       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.dataproc.metrics.listener.metrics.collector.hostname',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m'),\n",
+       " ('spark.yarn.am.attemptFailuresValidityInterval', '1h'),\n",
+       " ('spark.app.name', 'Spark Updated Conf'),\n",
+       " ('spark.sql.catalogImplementation', 'hive'),\n",
+       " ('spark.app.startTime', '1700098813742'),\n",
+       " ('spark.executorEnv.OPENBLAS_NUM_THREADS', '1'),\n",
+       " ('spark.yarn.secondary.jars',\n",
+       "  'com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,com.typesafe_config-1.4.2.jar,org.rocksdb_rocksdbjni-6.29.5.jar,com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,com.github.universal-automata_liblevenshtein-3.0.0.jar,com.google.cloud_google-cloud-storage-2.16.0.jar,com.navigamez_greex-1.0.jar,com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,it.unimi.dsi_fastutil-7.0.12.jar,org.projectlombok_lombok-1.16.8.jar,com.google.guava_guava-31.1-jre.jar,com.google.guava_failureaccess-1.0.1.jar,com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,com.google.errorprone_error_prone_annotations-2.16.jar,com.google.j2objc_j2objc-annotations-1.3.jar,com.google.http-client_google-http-client-1.42.3.jar,io.opencensus_opencensus-contrib-http-util-0.31.1.jar,com.google.http-client_google-http-client-jackson2-1.42.3.jar,com.google.http-client_google-http-client-gson-1.42.3.jar,com.google.api-client_google-api-client-2.1.1.jar,commons-codec_commons-codec-1.15.jar,com.google.oauth-client_google-oauth-client-1.34.1.jar,com.google.http-client_google-http-client-apache-v2-1.42.3.jar,com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,com.google.code.gson_gson-2.10.jar,com.google.cloud_google-cloud-core-2.9.0.jar,com.google.auto.value_auto-value-annotations-1.10.1.jar,com.google.cloud_google-cloud-core-http-2.9.0.jar,com.google.http-client_google-http-client-appengine-1.42.3.jar,com.google.api_gax-httpjson-0.105.1.jar,com.google.cloud_google-cloud-core-grpc-2.9.0.jar,io.grpc_grpc-core-1.51.0.jar,com.google.api_gax-2.20.1.jar,com.google.api_gax-grpc-2.20.1.jar,io.grpc_grpc-alts-1.51.0.jar,io.grpc_grpc-grpclb-1.51.0.jar,org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,io.grpc_grpc-protobuf-1.51.0.jar,com.google.auth_google-auth-library-credentials-1.13.0.jar,com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,com.google.api_api-common-2.2.2.jar,javax.annotation_javax.annotation-api-1.3.2.jar,io.opencensus_opencensus-api-0.31.1.jar,io.grpc_grpc-context-1.51.0.jar,com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,com.google.protobuf_protobuf-java-3.21.10.jar,com.google.protobuf_protobuf-java-util-3.21.10.jar,com.google.api.grpc_proto-google-common-protos-2.11.0.jar,org.threeten_threetenbp-1.6.4.jar,com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,com.fasterxml.jackson.core_jackson-core-2.14.1.jar,com.google.code.findbugs_jsr305-3.0.2.jar,io.grpc_grpc-api-1.51.0.jar,io.grpc_grpc-auth-1.51.0.jar,io.grpc_grpc-stub-1.51.0.jar,org.checkerframework_checker-qual-3.28.0.jar,com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,io.grpc_grpc-protobuf-lite-1.51.0.jar,com.google.android_annotations-4.1.1.4.jar,org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,io.grpc_grpc-netty-shaded-1.51.0.jar,io.perfmark_perfmark-api-0.26.0.jar,io.grpc_grpc-googleapis-1.51.0.jar,io.grpc_grpc-xds-1.51.0.jar,io.opencensus_opencensus-proto-0.2.0.jar,io.grpc_grpc-services-1.51.0.jar,com.google.re2j_re2j-1.6.jar,dk.brics.automaton_automaton-1.11-8.jar,org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.repl.local.jars',\n",
+       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.driver.host',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal'),\n",
+       " ('spark.sql.cbo.enabled', 'true'),\n",
+       " ('spark.yarn.historyServer.address',\n",
+       "  'hub-msca-bdp-dphub-students-abejburton-m:18080'),\n",
+       " ('spark.driver.appUIAddress',\n",
+       "  'http://hub-msca-bdp-dphub-students-abejburton-m.c.msca-bdp-student-ap.internal:46169'),\n",
+       " ('spark.executorEnv.PYTHONPATH',\n",
+       "  '/usr/lib/spark/python/lib/py4j-0.10.9-src.zip:/usr/lib/spark/python/:<CPS>{{PWD}}/pyspark.zip<CPS>{{PWD}}/py4j-0.10.9-src.zip<CPS>{{PWD}}/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar<CPS>{{PWD}}/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar<CPS>{{PWD}}/com.typesafe_config-1.4.2.jar<CPS>{{PWD}}/org.rocksdb_rocksdbjni-6.29.5.jar<CPS>{{PWD}}/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar<CPS>{{PWD}}/com.github.universal-automata_liblevenshtein-3.0.0.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-storage-2.16.0.jar<CPS>{{PWD}}/com.navigamez_greex-1.0.jar<CPS>{{PWD}}/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar<CPS>{{PWD}}/it.unimi.dsi_fastutil-7.0.12.jar<CPS>{{PWD}}/org.projectlombok_lombok-1.16.8.jar<CPS>{{PWD}}/com.google.guava_guava-31.1-jre.jar<CPS>{{PWD}}/com.google.guava_failureaccess-1.0.1.jar<CPS>{{PWD}}/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar<CPS>{{PWD}}/com.google.errorprone_error_prone_annotations-2.16.jar<CPS>{{PWD}}/com.google.j2objc_j2objc-annotations-1.3.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-1.42.3.jar<CPS>{{PWD}}/io.opencensus_opencensus-contrib-http-util-0.31.1.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-jackson2-1.42.3.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-gson-1.42.3.jar<CPS>{{PWD}}/com.google.api-client_google-api-client-2.1.1.jar<CPS>{{PWD}}/commons-codec_commons-codec-1.15.jar<CPS>{{PWD}}/com.google.oauth-client_google-oauth-client-1.34.1.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-apache-v2-1.42.3.jar<CPS>{{PWD}}/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar<CPS>{{PWD}}/com.google.code.gson_gson-2.10.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-2.9.0.jar<CPS>{{PWD}}/com.google.auto.value_auto-value-annotations-1.10.1.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-http-2.9.0.jar<CPS>{{PWD}}/com.google.http-client_google-http-client-appengine-1.42.3.jar<CPS>{{PWD}}/com.google.api_gax-httpjson-0.105.1.jar<CPS>{{PWD}}/com.google.cloud_google-cloud-core-grpc-2.9.0.jar<CPS>{{PWD}}/io.grpc_grpc-core-1.51.0.jar<CPS>{{PWD}}/com.google.api_gax-2.20.1.jar<CPS>{{PWD}}/com.google.api_gax-grpc-2.20.1.jar<CPS>{{PWD}}/io.grpc_grpc-alts-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-grpclb-1.51.0.jar<CPS>{{PWD}}/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar<CPS>{{PWD}}/io.grpc_grpc-protobuf-1.51.0.jar<CPS>{{PWD}}/com.google.auth_google-auth-library-credentials-1.13.0.jar<CPS>{{PWD}}/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar<CPS>{{PWD}}/com.google.api_api-common-2.2.2.jar<CPS>{{PWD}}/javax.annotation_javax.annotation-api-1.3.2.jar<CPS>{{PWD}}/io.opencensus_opencensus-api-0.31.1.jar<CPS>{{PWD}}/io.grpc_grpc-context-1.51.0.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar<CPS>{{PWD}}/com.google.protobuf_protobuf-java-3.21.10.jar<CPS>{{PWD}}/com.google.protobuf_protobuf-java-util-3.21.10.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-common-protos-2.11.0.jar<CPS>{{PWD}}/org.threeten_threetenbp-1.6.4.jar<CPS>{{PWD}}/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar<CPS>{{PWD}}/com.fasterxml.jackson.core_jackson-core-2.14.1.jar<CPS>{{PWD}}/com.google.code.findbugs_jsr305-3.0.2.jar<CPS>{{PWD}}/io.grpc_grpc-api-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-auth-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-stub-1.51.0.jar<CPS>{{PWD}}/org.checkerframework_checker-qual-3.28.0.jar<CPS>{{PWD}}/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar<CPS>{{PWD}}/io.grpc_grpc-protobuf-lite-1.51.0.jar<CPS>{{PWD}}/com.google.android_annotations-4.1.1.4.jar<CPS>{{PWD}}/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar<CPS>{{PWD}}/io.grpc_grpc-netty-shaded-1.51.0.jar<CPS>{{PWD}}/io.perfmark_perfmark-api-0.26.0.jar<CPS>{{PWD}}/io.grpc_grpc-googleapis-1.51.0.jar<CPS>{{PWD}}/io.grpc_grpc-xds-1.51.0.jar<CPS>{{PWD}}/io.opencensus_opencensus-proto-0.2.0.jar<CPS>{{PWD}}/io.grpc_grpc-services-1.51.0.jar<CPS>{{PWD}}/com.google.re2j_re2j-1.6.jar<CPS>{{PWD}}/dk.brics.automaton_automaton-1.11-8.jar<CPS>{{PWD}}/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.yarn.dist.jars',\n",
+       "  'file:///root/.ivy2/jars/com.johnsnowlabs.nlp_spark-nlp_2.12-4.4.0.jar,file:///root/.ivy2/jars/graphframes_graphframes-0.8.2-spark3.1-s_2.12.jar,file:///root/.ivy2/jars/com.typesafe_config-1.4.2.jar,file:///root/.ivy2/jars/org.rocksdb_rocksdbjni-6.29.5.jar,file:///root/.ivy2/jars/com.amazonaws_aws-java-sdk-bundle-1.11.828.jar,file:///root/.ivy2/jars/com.github.universal-automata_liblevenshtein-3.0.0.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-storage-2.16.0.jar,file:///root/.ivy2/jars/com.navigamez_greex-1.0.jar,file:///root/.ivy2/jars/com.johnsnowlabs.nlp_tensorflow-cpu_2.12-0.4.4.jar,file:///root/.ivy2/jars/it.unimi.dsi_fastutil-7.0.12.jar,file:///root/.ivy2/jars/org.projectlombok_lombok-1.16.8.jar,file:///root/.ivy2/jars/com.google.guava_guava-31.1-jre.jar,file:///root/.ivy2/jars/com.google.guava_failureaccess-1.0.1.jar,file:///root/.ivy2/jars/com.google.guava_listenablefuture-9999.0-empty-to-avoid-conflict-with-guava.jar,file:///root/.ivy2/jars/com.google.errorprone_error_prone_annotations-2.16.jar,file:///root/.ivy2/jars/com.google.j2objc_j2objc-annotations-1.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-1.42.3.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-contrib-http-util-0.31.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-jackson2-1.42.3.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-gson-1.42.3.jar,file:///root/.ivy2/jars/com.google.api-client_google-api-client-2.1.1.jar,file:///root/.ivy2/jars/commons-codec_commons-codec-1.15.jar,file:///root/.ivy2/jars/com.google.oauth-client_google-oauth-client-1.34.1.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-apache-v2-1.42.3.jar,file:///root/.ivy2/jars/com.google.apis_google-api-services-storage-v1-rev20220705-2.0.0.jar,file:///root/.ivy2/jars/com.google.code.gson_gson-2.10.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-2.9.0.jar,file:///root/.ivy2/jars/com.google.auto.value_auto-value-annotations-1.10.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-http-2.9.0.jar,file:///root/.ivy2/jars/com.google.http-client_google-http-client-appengine-1.42.3.jar,file:///root/.ivy2/jars/com.google.api_gax-httpjson-0.105.1.jar,file:///root/.ivy2/jars/com.google.cloud_google-cloud-core-grpc-2.9.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-core-1.51.0.jar,file:///root/.ivy2/jars/com.google.api_gax-2.20.1.jar,file:///root/.ivy2/jars/com.google.api_gax-grpc-2.20.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-alts-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-grpclb-1.51.0.jar,file:///root/.ivy2/jars/org.conscrypt_conscrypt-openjdk-uber-2.5.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-1.51.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-credentials-1.13.0.jar,file:///root/.ivy2/jars/com.google.auth_google-auth-library-oauth2-http-1.13.0.jar,file:///root/.ivy2/jars/com.google.api_api-common-2.2.2.jar,file:///root/.ivy2/jars/javax.annotation_javax.annotation-api-1.3.2.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-api-0.31.1.jar,file:///root/.ivy2/jars/io.grpc_grpc-context-1.51.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-3.21.10.jar,file:///root/.ivy2/jars/com.google.protobuf_protobuf-java-util-3.21.10.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-common-protos-2.11.0.jar,file:///root/.ivy2/jars/org.threeten_threetenbp-1.6.4.jar,file:///root/.ivy2/jars/com.google.api.grpc_proto-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.google.api.grpc_gapic-google-cloud-storage-v2-2.16.0-alpha.jar,file:///root/.ivy2/jars/com.fasterxml.jackson.core_jackson-core-2.14.1.jar,file:///root/.ivy2/jars/com.google.code.findbugs_jsr305-3.0.2.jar,file:///root/.ivy2/jars/io.grpc_grpc-api-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-auth-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-stub-1.51.0.jar,file:///root/.ivy2/jars/org.checkerframework_checker-qual-3.28.0.jar,file:///root/.ivy2/jars/com.google.api.grpc_grpc-google-iam-v1-1.6.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-protobuf-lite-1.51.0.jar,file:///root/.ivy2/jars/com.google.android_annotations-4.1.1.4.jar,file:///root/.ivy2/jars/org.codehaus.mojo_animal-sniffer-annotations-1.22.jar,file:///root/.ivy2/jars/io.grpc_grpc-netty-shaded-1.51.0.jar,file:///root/.ivy2/jars/io.perfmark_perfmark-api-0.26.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-googleapis-1.51.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-xds-1.51.0.jar,file:///root/.ivy2/jars/io.opencensus_opencensus-proto-0.2.0.jar,file:///root/.ivy2/jars/io.grpc_grpc-services-1.51.0.jar,file:///root/.ivy2/jars/com.google.re2j_re2j-1.6.jar,file:///root/.ivy2/jars/dk.brics.automaton_automaton-1.11-8.jar,file:///root/.ivy2/jars/org.slf4j_slf4j-api-1.7.16.jar'),\n",
+       " ('spark.dataproc.sql.parquet.enableFooterCache', 'true'),\n",
+       " ('spark.driver.memory', '4g'),\n",
+       " ('spark.sql.warehouse.dir', 'file:/spark-warehouse'),\n",
+       " ('spark.yarn.executor.failuresValidityInterval', '1h'),\n",
+       " ('spark.yarn.am.memory', '640m'),\n",
+       " ('spark.cores.max', '4'),\n",
+       " ('spark.executor.cores', '4'),\n",
+       " ('spark.jars.packages',\n",
+       "  'com.johnsnowlabs.nlp:spark-nlp_2.12:4.4.0,graphframes:graphframes:0.8.2-spark3.1-s_2.12'),\n",
+       " ('spark.history.fs.logDirectory',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/2bfcb1ba-12f1-4fdd-8722-7dc6c9d23e48/spark-job-history'),\n",
+       " ('spark.executor.instances', '2'),\n",
+       " ('spark.dataproc.listeners',\n",
+       "  'com.google.cloud.spark.performance.DataprocMetricsListener'),\n",
+       " ('spark.serializer.objectStreamReset', '100'),\n",
+       " ('spark.submit.deployMode', 'client'),\n",
+       " ('spark.sql.cbo.joinReorder.enabled', 'true'),\n",
+       " ('spark.eventLog.dir',\n",
+       "  'gs://dataproc-temp-us-central1-635155370842-uzamlpgc/2bfcb1ba-12f1-4fdd-8722-7dc6c9d23e48/spark-job-history'),\n",
+       " ('spark.shuffle.service.enabled', 'true'),\n",
+       " ('spark.scheduler.mode', 'FAIR'),\n",
+       " ('spark.sql.adaptive.enabled', 'true'),\n",
+       " ('spark.yarn.jars', 'local:/usr/lib/spark/jars/*'),\n",
+       " ('spark.scheduler.minRegisteredResourcesRatio', '0.0'),\n",
+       " ('spark.master', 'yarn'),\n",
+       " ('spark.ui.port', '0'),\n",
+       " ('spark.rpc.message.maxSize', '512'),\n",
+       " ('spark.rdd.compress', 'True'),\n",
+       " ('spark.task.maxFailures', '10'),\n",
+       " ('spark.yarn.isPython', 'true'),\n",
+       " ('spark.dynamicAllocation.enabled', 'true'),\n",
+       " ('spark.ui.showConsoleProgress', 'true')]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "spark = SparkSession.builder.appName('2020EDA').getOrCreate()\n",
+    "\n",
+    "#change configuration settings on Spark \n",
+    "conf = spark.sparkContext._conf.setAll([('spark.executor.memory', '4g'), ('spark.app.name', 'Spark Updated Conf'), ('spark.executor.cores', '4'), ('spark.cores.max', '4'), ('spark.driver.memory','4g')])\n",
+    "\n",
+    "#print spark configuration settings\n",
+    "spark.sparkContext.getConf().getAll()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "80de887c-c8cc-4f15-81b7-ca42292edfd1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "root\n",
+      " |-- Trip ID: string (nullable = true)\n",
+      " |-- Trip Start Timestamp: string (nullable = true)\n",
+      " |-- Trip End Timestamp: string (nullable = true)\n",
+      " |-- Trip Seconds: integer (nullable = true)\n",
+      " |-- Trip Miles: double (nullable = true)\n",
+      " |-- Pickup Census Tract: long (nullable = true)\n",
+      " |-- Dropoff Census Tract: long (nullable = true)\n",
+      " |-- Pickup Community Area: integer (nullable = true)\n",
+      " |-- Dropoff Community Area: integer (nullable = true)\n",
+      " |-- Fare: double (nullable = true)\n",
+      " |-- Tip: integer (nullable = true)\n",
+      " |-- Additional Charges: double (nullable = true)\n",
+      " |-- Trip Total: double (nullable = true)\n",
+      " |-- Shared Trip Authorized: boolean (nullable = true)\n",
+      " |-- Trips Pooled: integer (nullable = true)\n",
+      " |-- Pickup Centroid Latitude: double (nullable = true)\n",
+      " |-- Pickup Centroid Longitude: double (nullable = true)\n",
+      " |-- Pickup Centroid Location: string (nullable = true)\n",
+      " |-- Dropoff Centroid Latitude: double (nullable = true)\n",
+      " |-- Dropoff Centroid Longitude: double (nullable = true)\n",
+      " |-- Dropoff Centroid Location: string (nullable = true)\n",
+      "\n",
+      "root\n",
+      " |-- name: string (nullable = true)\n",
+      " |-- datetime: string (nullable = true)\n",
+      " |-- tempmax: double (nullable = true)\n",
+      " |-- tempmin: double (nullable = true)\n",
+      " |-- temp: double (nullable = true)\n",
+      " |-- feelslikemax: double (nullable = true)\n",
+      " |-- feelslikemin: double (nullable = true)\n",
+      " |-- feelslike: double (nullable = true)\n",
+      " |-- dew: double (nullable = true)\n",
+      " |-- humidity: double (nullable = true)\n",
+      " |-- precip: double (nullable = true)\n",
+      " |-- precipprob: integer (nullable = true)\n",
+      " |-- precipcover: double (nullable = true)\n",
+      " |-- preciptype: string (nullable = true)\n",
+      " |-- snow: double (nullable = true)\n",
+      " |-- snowdepth: double (nullable = true)\n",
+      " |-- windgust: double (nullable = true)\n",
+      " |-- windspeed: double (nullable = true)\n",
+      " |-- winddir: double (nullable = true)\n",
+      " |-- sealevelpressure: double (nullable = true)\n",
+      " |-- cloudcover: double (nullable = true)\n",
+      " |-- visibility: double (nullable = true)\n",
+      " |-- solarradiation: double (nullable = true)\n",
+      " |-- solarenergy: double (nullable = true)\n",
+      " |-- uvindex: integer (nullable = true)\n",
+      " |-- severerisk: integer (nullable = true)\n",
+      " |-- sunrise: timestamp (nullable = true)\n",
+      " |-- sunset: timestamp (nullable = true)\n",
+      " |-- moonphase: double (nullable = true)\n",
+      " |-- conditions: string (nullable = true)\n",
+      " |-- description: string (nullable = true)\n",
+      " |-- icon: string (nullable = true)\n",
+      " |-- stations: string (nullable = true)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_2020 = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/2020\", inferSchema=True, header=True)\n",
+    "# figure out how to read in shp file msca-bdp-student-gcs/bdp-rideshare-project/neighborhoods/shp files\n",
+    "df_weather = spark.read.csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/weather/chicago 2020-01-01 to 2022-08-31.csv\", inferSchema=True, header=True)\n",
+    "df_2020.printSchema()\n",
+    "df_weather.printSchema()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "02922865-1064-4da9-8547-e5904e260a71",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "97"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#display number of records by partition\n",
+    "def displaypartitions(df):\n",
+    "    #number of records by partition\n",
+    "    num = df.rdd.getNumPartitions()\n",
+    "    print(\"Partitions:\", num)\n",
+    "    df.withColumn(\"partitionId\", F.spark_partition_id())\\\n",
+    "        .groupBy(\"partitionId\")\\\n",
+    "        .count()\\\n",
+    "        .orderBy(F.asc(\"count\"))\\\n",
+    "        .show(num)\n",
+    "\n",
+    "df_2020.rdd.getNumPartitions()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "2717e023-5f4e-4393-a57f-d81674eefae6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Partitions: 97\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----------+------+\n",
+      "|partitionId| count|\n",
+      "+-----------+------+\n",
+      "|         96|258032|\n",
+      "|         26|500083|\n",
+      "|          5|501403|\n",
+      "|         25|501617|\n",
+      "|          9|501838|\n",
+      "|         13|501919|\n",
+      "|         30|501968|\n",
+      "|         17|502424|\n",
+      "|         42|502478|\n",
+      "|         21|502487|\n",
+      "|         34|502608|\n",
+      "|         38|505369|\n",
+      "|         29|505648|\n",
+      "|         24|505937|\n",
+      "|          8|506529|\n",
+      "|          4|507322|\n",
+      "|         39|507425|\n",
+      "|         32|507539|\n",
+      "|         10|508044|\n",
+      "|          1|508091|\n",
+      "|         35|508308|\n",
+      "|         20|508847|\n",
+      "|         28|508849|\n",
+      "|          7|508943|\n",
+      "|         33|509077|\n",
+      "|          0|509157|\n",
+      "|         12|509264|\n",
+      "|         14|509338|\n",
+      "|         22|509490|\n",
+      "|         16|509594|\n",
+      "|         37|509717|\n",
+      "|         19|509765|\n",
+      "|         27|509816|\n",
+      "|         41|510016|\n",
+      "|         18|510197|\n",
+      "|         23|510233|\n",
+      "|         15|510280|\n",
+      "|         11|510321|\n",
+      "|         40|510615|\n",
+      "|          2|511375|\n",
+      "|         36|511411|\n",
+      "|          6|511524|\n",
+      "|          3|511528|\n",
+      "|         31|513062|\n",
+      "|         43|516927|\n",
+      "|         79|517478|\n",
+      "|         74|518098|\n",
+      "|         81|518307|\n",
+      "|         71|519141|\n",
+      "|         84|519158|\n",
+      "|         69|519657|\n",
+      "|         76|520374|\n",
+      "|         77|520851|\n",
+      "|         63|521166|\n",
+      "|         72|521485|\n",
+      "|         66|521582|\n",
+      "|         60|521583|\n",
+      "|         82|521898|\n",
+      "|         83|522349|\n",
+      "|         68|523313|\n",
+      "|         65|523712|\n",
+      "|         56|523812|\n",
+      "|         62|523882|\n",
+      "|         86|524026|\n",
+      "|         75|524419|\n",
+      "|         57|524580|\n",
+      "|         78|524679|\n",
+      "|         59|524726|\n",
+      "|         73|525173|\n",
+      "|         87|525497|\n",
+      "|         70|525678|\n",
+      "|         67|525913|\n",
+      "|         64|526308|\n",
+      "|         80|526397|\n",
+      "|         92|527091|\n",
+      "|         61|527305|\n",
+      "|         55|528823|\n",
+      "|         91|528997|\n",
+      "|         93|529058|\n",
+      "|         58|529125|\n",
+      "|         85|529157|\n",
+      "|         53|529171|\n",
+      "|         54|529329|\n",
+      "|         94|529819|\n",
+      "|         88|530841|\n",
+      "|         89|533123|\n",
+      "|         44|533431|\n",
+      "|         90|533636|\n",
+      "|         95|535170|\n",
+      "|         52|535269|\n",
+      "|         50|537569|\n",
+      "|         51|537856|\n",
+      "|         48|538147|\n",
+      "|         49|538619|\n",
+      "|         47|538848|\n",
+      "|         45|539321|\n",
+      "|         46|539781|\n",
+      "+-----------+------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "displaypartitions(df_2020)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "dfbb2a35-c27e-4ea1-9645-62252deb4ae8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------+--------------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+-----------------+------------------+------------------+------------------+------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|summary|             Trip ID|Trip Start Timestamp|  Trip End Timestamp|     Trip Seconds|       Trip Miles| Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|             Fare|               Tip|Additional Charges|        Trip Total|      Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+--------------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+-----------------+------------------+------------------+------------------+------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|  count|            50011143|            50011143|            50011143|         50009609|         50011143|            25552144|            25398361|             46235118|              45840972|         50008963|          50008963|          50008963|          50008963|          50011143|                46263229|                 46263229|                46263229|                 45868779|                  45868779|                 45868779|\n",
+      "|   mean|1.373583619993279...|                null|                null|981.6210416682122|6.464524796004038|1.703137217611449...|1.703137874885807E10|   27.884638360823477|    28.103252893503218|11.98546143618295|0.5381202365663931| 3.653324201719032|  16.1769058744832|1.0598493419756474|       41.88332258320719|       -87.66831634171737|                    null|        41.88384125938399|        -87.66921480888618|                     null|\n",
+      "| stddev|1.942540584448027...|                null|                null|665.9193598074514|7.136882245055214|  335400.77230484586|  338354.06991829333|    21.37707505859244|    21.493822677956796|9.248252506760796|1.6621638065659219|2.0097223238234836|10.711679809453972|0.3174307548516801|     0.07161681186613116|     0.061545577440721574|                    null|      0.07130685256487491|       0.06370229215885044|                     null|\n",
+      "|    min|0000019aa70ee91e2...|01/01/2020 01:00:...|01/01/2020 01:00:...|                0|              0.0|         17031010100|         17031010100|                    1|                     1|              0.0|                 0|               0.0|               0.0|                 1|           41.6502216756|            -87.913624596|    POINT (-87.529950...|            41.6502216756|             -87.913624596|     POINT (-87.529950...|\n",
+      "|    max|ffffffa50e88bdb8d...|12/31/2020 12:45:...|12/31/2020 12:45:...|            67195|            856.1|         17031980100|         17031980100|                   77|                    77|           1182.5|               500|             257.1|           1196.03|               128|           42.0212235931|            -87.529950466|    POINT (-87.913624...|            42.0212235931|             -87.529950466|     POINT (-87.913624...|\n",
+      "+-------+--------------------+--------------------+--------------------+-----------------+-----------------+--------------------+--------------------+---------------------+----------------------+-----------------+------------------+------------------+------------------+------------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "df_2020.describe().show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "e5c42630-ef1e-41dc-ab97-610dc79cbbdf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Stage 14:=======================================================>(96 + 1) / 97]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+----+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|Trip ID|Trip Start Timestamp|Trip End Timestamp|Trip Seconds|Trip Miles|Pickup Census Tract|Dropoff Census Tract|Pickup Community Area|Dropoff Community Area|Fare| Tip|Additional Charges|Trip Total|Shared Trip Authorized|Trips Pooled|Pickup Centroid Latitude|Pickup Centroid Longitude|Pickup Centroid Location|Dropoff Centroid Latitude|Dropoff Centroid Longitude|Dropoff Centroid Location|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+----+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "|      0|                   0|                 0|        1534|         0|           24458999|            24612782|              3776025|               4170171|2180|2180|              2180|      2180|                     0|           0|                 3747914|                  3747914|                 3747914|                  4142364|                   4142364|                  4142364|\n",
+      "+-------+--------------------+------------------+------------+----------+-------------------+--------------------+---------------------+----------------------+----+----+------------------+----------+----------------------+------------+------------------------+-------------------------+------------------------+-------------------------+--------------------------+-------------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "#Find the number of missing values for each column\n",
+    "from pyspark.sql.functions import isnan, when, count, col\n",
+    "df_2020.select([count(when(df_2020[c].isNull(), c)).alias(c) for c in df_2020.columns]).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "3385292a-c03a-4f9c-b9f9-3dca1c60fad6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 2500x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import missingno as msno\n",
+    "%matplotlib inline\n",
+    "msno.matrix(df_2020.sample(fraction=1/10000).toPandas())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7a9eb890-7b83-4d8e-b61a-3e4b31a9cc2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "23/11/16 01:59:12 WARN org.apache.spark.sql.catalyst.util.package: Truncated the string representation of a plan since it was too large. This behavior can be adjusted by setting 'spark.sql.debug.maxToStringFields'.\n",
+      "[Stage 18:=======================================================>(96 + 1) / 97]\r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------------------------------+\n",
+      "|approx_count_distinct(Trip ID)|\n",
+      "+------------------------------+\n",
+      "|                      50256700|\n",
+      "+------------------------------+\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "#Approximate number of 2020 trips\n",
+    "from pyspark.sql.functions import approxCountDistinct\n",
+    "\n",
+    "df_2020.select(approxCountDistinct(\"Trip ID\", rsd = 0.01)).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ff6789dc-c0dc-4c33-a9af-eefef922c78f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "23700840"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# number of observations with all the data in each column\n",
+    "df_2020.dropna(how='any').count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "1490666d-50f9-4276-9d79-68f5dce4e643",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Working with just data that contains full information and check for dupes\n",
+    "df_2020 = df_2020.dropna(how='any', subset=['Trip Start Timestamp','Trip End Timestamp','Fare','Dropoff Community Area','Pickup Community Area'])\n",
+    "df_2020 = df_2020.dropDuplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0c978f1a-3510-4c65-8dab-8b9e5d6f4678",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Drop columns unlikely to be useful for analysis for speed of computation and rename columns to remove spacing for ease of code writing\n",
+    "df_2020 = df_2020.drop('Trips Pooled','Additional Charges','Shared Trip Authorized','Pickup Centroid Location','Dropoff Centroid Location')\n",
+    "df_2020 = df_2020.withColumnRenamed(\"Trip ID\",\"ID\").withColumnRenamed(\"Trip Start Timestamp\",\"start_timestamp\").withColumnRenamed(\"Trip End Timestamp\",\"end_timestamp\").withColumnRenamed(\"Trip Miles\",\\\n",
+    "                \"miles\").withColumnRenamed(\"Pickup Census Tract\",\"pickup_tract\").withColumnRenamed(\"Dropoff Census Tract\",\"dropoff_tract\").withColumnRenamed(\"Pickup Community Area\",\"pickup_area\"\\\n",
+    "                ).withColumnRenamed(\"Dropoff Community Area\",\"dropoff_area\").withColumnRenamed(\"Trip Total\",\"total\").withColumnRenamed(\"Pickup Centroid Latitude\",\"pickup_lat\").withColumnRenamed(\\\n",
+    "                \"Pickup Centroid Longitude\",\"pickup_lon\").withColumnRenamed(\"Pickup Centroid Location\",\"pickup_location\").withColumnRenamed(\"Dropoff Centroid Latitude\",\"dropoff_lat\").withColumnRenamed(\\\n",
+    "                \"Dropoff Centroid Longitude\",\"dropoff_lon\").withColumnRenamed(\"Dropoff Centroid Location\",\"dropoff_location\").withColumnRenamed(\"Trip Seconds\",\"seconds\")\n",
+    "# fix datatypes\n",
+    "df_2020 = df_2020.withColumn('start_timestamp', F.to_timestamp(df_2020['start_timestamp'], 'MM/dd/yyyy hh:mm:ss a')).withColumn('end_timestamp', F.to_timestamp(df_2020['end_timestamp'], 'MM/dd/yyyy hh:mm:ss a'))\n",
+    "df_weather = df_weather.withColumn('datetime',F.to_date(df_weather['datetime'], \"yyyy-MM-dd\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "05db6ff5-2681-4c75-aea8-78f22b428ec7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
+      "|                  ID|    start_timestamp|      end_timestamp|seconds|miles|pickup_tract|dropoff_tract|pickup_area|dropoff_area|Fare|Tip|total|   pickup_lat|    pickup_lon|  dropoff_lat|   dropoff_lon|month|day_of_month|hour|day|\n",
+      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
+      "|084b474ff41bde283...|2020-01-04 20:15:00|2020-01-04 20:30:00|    823|  5.1| 17031320100|  17031832300|         32|          22|10.0|  3|15.55|41.8849871918|-87.6209929134|41.9192250505| -87.671445766|    1|           4|  20|  7|\n",
+      "|0ebadbb0bf0b38c85...|2020-01-04 20:15:00|2020-01-04 20:45:00|   1589| 14.5| 17031980000|  17031830800|         76|           4|20.0|  6|33.55|41.9790708201|-87.9030396611|41.9651417087|-87.6765780714|    1|           4|  20|  7|\n",
+      "|18454ee6e067572b0...|2020-01-04 20:15:00|2020-01-04 20:30:00|    840|  3.0| 17031241400|  17031062700|         24|           6| 7.5|  3|13.05| 41.906025969|-87.6753116216|41.9360865352|-87.6661106945|    1|           4|  20|  7|\n",
+      "+--------------------+-------------------+-------------------+-------+-----+------------+-------------+-----------+------------+----+---+-----+-------------+--------------+-------------+--------------+-----+------------+----+---+\n",
+      "only showing top 3 rows\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# add the time columns\n",
+    "df_2020 = df_2020.withColumn('month', F.month(df_2020.start_timestamp))\n",
+    "df_2020 = df_2020.withColumn('day_of_month', F.dayofmonth(df_2020.start_timestamp))\n",
+    "df_2020 = df_2020.withColumn('hour', F.hour(df_2020.start_timestamp))\n",
+    "df_2020 = df_2020.withColumn('day', F.dayofweek(df_2020.start_timestamp))\n",
+    "df_2020.show(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a32a480c-c001-4aae-b8c5-3fa4813c5c95",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "sample_df = df_2020.sample(fraction=1/10000).toPandas().loc[:,[\"pickup_area\",\"dropoff_area\",\"total\",\"Fare\",\"Tip\",\"total\",\"miles\",\"seconds\",\"hour\",\"day\",\"month\"]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "98414276-76a3-411c-bf8d-9ca20cb7d759",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_df = sample_df.dropna()\n",
+    "sample_df = sample_df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "d5f24493-9647-46ad-a90e-44be51562aeb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_df = sample_df.drop(columns='total')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "82c05217-105d-43ce-b4cb-3b90d3427e7f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7ff5400cc880>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.displot(sample_df, x=\"miles\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "79b1eac2-49cc-4094-bdd3-dd0eaf6efa9b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7ff5400cc4c0>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1be85789-9a81-452f-8224-003e7aca29fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7ff505b21940>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"Fare\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "95a5282a-42a6-42a2-89e0-6673b3bac0d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<seaborn.axisgrid.FacetGrid at 0x7ff5057592b0>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.displot(sample_df, x=\"Tip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "71c6969f-e508-4538-94af-2986577aa782",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get rides that occurred within hyde park\n",
+    "# add kenwood and woodlawn to this list - only if the other location is hyde park                    \n",
+    "df_hp = df_2020.filter((df_2020.pickup_area == 41) & (df_2020.dropoff_area == 41))\n",
+    "df_kw = df_2020.filter(((df_2020.pickup_area == 41) & (df_2020.dropoff_area == 42)) | ((df_2020.pickup_area == 42) & (df_2020.dropoff_area == 41)))\n",
+    "df_wl = df_2020.filter(((df_2020.pickup_area == 41) & (df_2020.dropoff_area == 39)) | ((df_2020.pickup_area == 39) & (df_2020.dropoff_area == 41)))\n",
+    "df_area = df_hp.union(df_kw).union(df_wl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "7a3c96fb-cd42-4baa-b073-1922137bf52f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "df_count_plot = df_area.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas() #.plot(x=\"month\",y=\"count(ID)\")\n",
+    "ax = df_count_plot.plot(x=\"month\", y=\"count(ID)\")\n",
+    "ax.axvline(x=9, color='g', linestyle='--', label='Fall Quarter Begins')\n",
+    "ax.axvline(x=6, color='r', linestyle='--', label='Spring Quarter Ends')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "5d208e27-4020-485c-a6b5-3e3067297251",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# rides per hour:\n",
+    "rides_per_hour = df_area.groupBy(\"hour\").count().orderBy(\"hour\")\n",
+    "\n",
+    "# Convert the result to Pandas for local plotting\n",
+    "rides_per_hour_pd = rides_per_hour.toPandas()\n",
+    "\n",
+    "# Plot the data\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(rides_per_hour_pd[\"hour\"], rides_per_hour_pd[\"count\"], color=\"skyblue\")\n",
+    "plt.xlabel(\"Hour of the Day\")\n",
+    "plt.ylabel(\"Number of Rides\")\n",
+    "plt.title(\"Number of Rides Per Hour\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "26bfe18d-e655-495c-a6a6-c84fa766d139",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='month'>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# basic plots for all rides (not just in the program area)\n",
+    "df_2020.groupby(\"month\").agg({'ID':'count'}).orderBy(F.col('month').asc()).toPandas().plot(x=\"month\",y=\"count(ID)\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a3b6345b-d4e2-429a-ab7f-76b94beb6cfb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "df_total = df_2020.groupby(\"pickup_area\").agg({'ID':'count'}).orderBy(F.col('pickup_area').asc()).toPandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "76a51e78-713e-4bd7-a97e-cec7fc8d9d10",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1000x1000 with 0 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "plt.figure(figsize=(10, 10))\n",
+    "ax = df_total.plot(x=\"pickup_area\",y=\"count(ID)\", kind='bar')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "10c0f3ad-746f-4bf8-8685-adb91818139f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='dropoff_area'>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df_2020.groupby(\"dropoff_area\").agg({'ID':'count'}).orderBy(F.col('dropoff_area').asc()).toPandas().plot(x=\"dropoff_area\",y=\"count(ID)\", kind='bar')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9893253c-1680-4f9b-b391-510465e5363c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "                                                                                \r"
+     ]
+    }
+   ],
+   "source": [
+    "df_area.write.option(\"header\", \"true\").csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/program_area_2020.csv\")\n",
+    "df_2020.write.option(\"header\", \"true\").csv(\"gs://msca-bdp-student-gcs/bdp-rideshare-project/rideshare/processed_data/rides_2020.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f0f2ab9-9331-4219-ac20-0d83c8807902",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PySpark",
+   "language": "python",
+   "name": "pyspark"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.15"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/eda_2021.ipynb b/eda_2021.ipynb
index 402be1d..41271eb 100644
--- a/eda_2021.ipynb
+++ b/eda_2021.ipynb
@@ -726,20 +726,6 @@
     "sample_df = sample_df.drop(columns='total')"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "aaa804fd-84c8-4670-a4fe-4027d9fc1510",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.set_theme(style=\"ticks\")\n",
-    "sns.pairplot(sample_df, hue='dropoff_area')\n",
-    "plt.show()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 24,