membership_db_interact.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import re\n",
    "import datetime\n",
    "import itertools\n",
    "from datetimerange import DateTimeRange\n",
    "import pickle\n",
    "import json\n",
    "import sqlalchemy\n",
    "from sqlalchemy.sql import text\n",
    "import matplotlib.ticker as mtick"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('/home/candela/Documents/greeneHill/membershipReportsCIVI')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Historical analysis of trial conversion success"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = '''\n",
    "WITH trial_conversions AS (\n",
    "select cast(JSON_EXTRACT(latest_trial2, '$.start_dt') as date) trial_date, cast(start_dt as date) status_date, \n",
    "datediff(cast(start_dt as date), cast(JSON_EXTRACT(latest_trial2, '$.start_dt') as date)) as date_difference,\n",
    "email, type_clean\n",
    "from mem_type_0130\n",
    "where (type_clean not like '%%trial%%' AND type_clean not like '%%bushwick%%' AND type_clean not like '%%park%%')\n",
    "AND cast(JSON_EXTRACT(latest_trial2, '$.start_dt')as date) is not null),\n",
    "all_trials AS (\n",
    "select cast(start_dt as date) status_date, email, type_clean\n",
    "from mem_type_0130\n",
    "where type_clean like '%%trial%%'),\n",
    "conversion_agg AS (\n",
    "select date_format(trial_date, '%%Y-%%m') as month, count(distinct email) as converted_cnt\n",
    "from trial_conversions \n",
    "group by 1),\n",
    "all_agg AS (\n",
    "select date_format(status_date, '%%Y-%%m') as month, count(distinct email) as trials_cnt\n",
    "from all_trials\n",
    "group by 1)\n",
    "select all_agg.month, sum(converted_cnt) converted_cnt, sum(trials_cnt) trials_cnt\n",
    "from conversion_agg\n",
    "RIGHT JOIN all_agg\n",
    "ON conversion_agg.month = all_agg.month\n",
    "group by 1\n",
    "order by 1 desc;\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "user = 'root'\n",
    "password = 'baeldung'\n",
    "host = '172.17.0.2'\n",
    "port = 3306\n",
    "database = 'membership'\n",
    "\n",
    "url=\"mysql+pymysql://{0}:{1}@{2}:{3}/{4}\".format(user, password, host, port, database)\n",
    "\n",
    "engine = sqlalchemy.create_engine(url)\n",
    "#, connect_args={'options': '-csearch_path=schema_name'}, isolation_level=\"AUTOCOMMIT\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "OperationalError",
     "evalue": "(pymysql.err.OperationalError) (2003, \"Can't connect to MySQL server on '172.17.0.2' (timed out)\")\n(Background on this error at: https://sqlalche.me/e/14/e3q8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mtimeout\u001b[0m                                   Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/pymysql/connections.py:616\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self, sock)\u001b[0m\n\u001b[1;32m    615\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 616\u001b[0m     sock \u001b[39m=\u001b[39m socket\u001b[39m.\u001b[39;49mcreate_connection(\n\u001b[1;32m    617\u001b[0m         (\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mhost, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mport), \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect_timeout, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m    618\u001b[0m     )\n\u001b[1;32m    619\u001b[0m     \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/socket.py:808\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m    807\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 808\u001b[0m     \u001b[39mraise\u001b[39;00m err\n\u001b[1;32m    809\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    810\u001b[0m     \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/socket.py:796\u001b[0m, in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m    795\u001b[0m     sock\u001b[39m.\u001b[39mbind(source_address)\n\u001b[0;32m--> 796\u001b[0m sock\u001b[39m.\u001b[39;49mconnect(sa)\n\u001b[1;32m    797\u001b[0m \u001b[39m# Break explicitly a reference cycle\u001b[39;00m\n",
      "\u001b[0;31mtimeout\u001b[0m: timed out",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:3280\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m   3279\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3280\u001b[0m     \u001b[39mreturn\u001b[39;00m fn()\n\u001b[1;32m   3281\u001b[0m \u001b[39mexcept\u001b[39;00m dialect\u001b[39m.\u001b[39mdbapi\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:310\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    303\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m    304\u001b[0m \n\u001b[1;32m    305\u001b[0m \u001b[39mThe connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    308\u001b[0m \n\u001b[1;32m    309\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m--> 310\u001b[0m \u001b[39mreturn\u001b[39;00m _ConnectionFairy\u001b[39m.\u001b[39;49m_checkout(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:868\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m    867\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m fairy:\n\u001b[0;32m--> 868\u001b[0m     fairy \u001b[39m=\u001b[39m _ConnectionRecord\u001b[39m.\u001b[39;49mcheckout(pool)\n\u001b[1;32m    870\u001b[0m     fairy\u001b[39m.\u001b[39m_pool \u001b[39m=\u001b[39m pool\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:476\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m    475\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheckout\u001b[39m(\u001b[39mcls\u001b[39m, pool):\n\u001b[0;32m--> 476\u001b[0m     rec \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_do_get()\n\u001b[1;32m    477\u001b[0m     \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/impl.py:146\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    145\u001b[0m         \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[0;32m--> 146\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dec_overflow()\n\u001b[1;32m    147\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/impl.py:143\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    142\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection()\n\u001b[1;32m    144\u001b[0m \u001b[39mexcept\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:256\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 256\u001b[0m \u001b[39mreturn\u001b[39;00m _ConnectionRecord(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:371\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m    370\u001b[0m \u001b[39mif\u001b[39;00m connect:\n\u001b[0;32m--> 371\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__connect()\n\u001b[1;32m    372\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalize_callback \u001b[39m=\u001b[39m deque()\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:666\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    665\u001b[0m     \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[0;32m--> 666\u001b[0m         pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mError on connect(): \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, e)\n\u001b[1;32m    667\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    668\u001b[0m     \u001b[39m# in SQLAlchemy 1.4 the first_connect event is not used by\u001b[39;00m\n\u001b[1;32m    669\u001b[0m     \u001b[39m# the engine, so this will usually not be set\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:661\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    660\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstarttime \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 661\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdbapi_connection \u001b[39m=\u001b[39m connection \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_invoke_creator(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    662\u001b[0m pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mCreated new connection \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m, connection)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/create.py:590\u001b[0m, in \u001b[0;36mcreate_engine.<locals>.connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m    589\u001b[0m             \u001b[39mreturn\u001b[39;00m connection\n\u001b[0;32m--> 590\u001b[0m \u001b[39mreturn\u001b[39;00m dialect\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/default.py:597\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39mcargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcparams):\n\u001b[1;32m    596\u001b[0m     \u001b[39m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 597\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdbapi\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/pymysql/connections.py:352\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, user, password, host, database, unix_socket, port, charset, sql_mode, read_default_file, conv, use_unicode, client_flag, cursorclass, init_command, connect_timeout, read_default_group, autocommit, local_infile, max_allowed_packet, defer_connect, auth_plugin_map, read_timeout, write_timeout, bind_address, binary_prefix, program_name, server_public_key, ssl, ssl_ca, ssl_cert, ssl_disabled, ssl_key, ssl_verify_cert, ssl_verify_identity, compress, named_pipe, passwd, db)\u001b[0m\n\u001b[1;32m    351\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 352\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/pymysql/connections.py:668\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self, sock)\u001b[0m\n\u001b[1;32m    667\u001b[0m         \u001b[39mprint\u001b[39m(exc\u001b[39m.\u001b[39mtraceback)\n\u001b[0;32m--> 668\u001b[0m     \u001b[39mraise\u001b[39;00m exc\n\u001b[1;32m    670\u001b[0m \u001b[39m# If e is neither DatabaseError or IOError, It's a bug.\u001b[39;00m\n\u001b[1;32m    671\u001b[0m \u001b[39m# But raising AssertionError hides original error.\u001b[39;00m\n\u001b[1;32m    672\u001b[0m \u001b[39m# So just reraise it.\u001b[39;00m\n",
      "\u001b[0;31mOperationalError\u001b[0m: (2003, \"Can't connect to MySQL server on '172.17.0.2' (timed out)\")",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mOperationalError\u001b[0m                          Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mwith\u001b[39;00m engine\u001b[39m.\u001b[39;49mconnect() \u001b[39mas\u001b[39;00m conn:\n\u001b[1;32m      2\u001b[0m     df \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_sql(sql, con \u001b[39m=\u001b[39m conn)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:3234\u001b[0m, in \u001b[0;36mEngine.connect\u001b[0;34m(self, close_with_result)\u001b[0m\n\u001b[1;32m   3219\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m, close_with_result\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m):\n\u001b[1;32m   3220\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Return a new :class:`_engine.Connection` object.\u001b[39;00m\n\u001b[1;32m   3221\u001b[0m \n\u001b[1;32m   3222\u001b[0m \u001b[39m    The :class:`_engine.Connection` object is a facade that uses a DBAPI\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3231\u001b[0m \n\u001b[1;32m   3232\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3234\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_connection_cls(\u001b[39mself\u001b[39;49m, close_with_result\u001b[39m=\u001b[39;49mclose_with_result)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:96\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, engine, connection, close_with_result, _branch_from, _execution_options, _dispatch, _has_events, _allow_revalidate)\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_has_events \u001b[39m=\u001b[39m _branch_from\u001b[39m.\u001b[39m_has_events\n\u001b[1;32m     92\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     93\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dbapi_connection \u001b[39m=\u001b[39m (\n\u001b[1;32m     94\u001b[0m         connection\n\u001b[1;32m     95\u001b[0m         \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m---> 96\u001b[0m         \u001b[39melse\u001b[39;00m engine\u001b[39m.\u001b[39;49mraw_connection()\n\u001b[1;32m     97\u001b[0m     )\n\u001b[1;32m     99\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transaction \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_nested_transaction \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    100\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__savepoint_seq \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:3313\u001b[0m, in \u001b[0;36mEngine.raw_connection\u001b[0;34m(self, _connection)\u001b[0m\n\u001b[1;32m   3291\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mraw_connection\u001b[39m(\u001b[39mself\u001b[39m, _connection\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m   3292\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Return a \"raw\" DBAPI connection from the connection pool.\u001b[39;00m\n\u001b[1;32m   3293\u001b[0m \n\u001b[1;32m   3294\u001b[0m \u001b[39m    The returned object is a proxied version of the DBAPI\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3311\u001b[0m \n\u001b[1;32m   3312\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3313\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wrap_pool_connect(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpool\u001b[39m.\u001b[39;49mconnect, _connection)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:3283\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m   3281\u001b[0m \u001b[39mexcept\u001b[39;00m dialect\u001b[39m.\u001b[39mdbapi\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   3282\u001b[0m     \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 3283\u001b[0m         Connection\u001b[39m.\u001b[39;49m_handle_dbapi_exception_noconnection(\n\u001b[1;32m   3284\u001b[0m             e, dialect, \u001b[39mself\u001b[39;49m\n\u001b[1;32m   3285\u001b[0m         )\n\u001b[1;32m   3286\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   3287\u001b[0m         util\u001b[39m.\u001b[39mraise_(\n\u001b[1;32m   3288\u001b[0m             sys\u001b[39m.\u001b[39mexc_info()[\u001b[39m1\u001b[39m], with_traceback\u001b[39m=\u001b[39msys\u001b[39m.\u001b[39mexc_info()[\u001b[39m2\u001b[39m]\n\u001b[1;32m   3289\u001b[0m         )\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:2117\u001b[0m, in \u001b[0;36mConnection._handle_dbapi_exception_noconnection\u001b[0;34m(cls, e, dialect, engine)\u001b[0m\n\u001b[1;32m   2115\u001b[0m     util\u001b[39m.\u001b[39mraise_(newraise, with_traceback\u001b[39m=\u001b[39mexc_info[\u001b[39m2\u001b[39m], from_\u001b[39m=\u001b[39me)\n\u001b[1;32m   2116\u001b[0m \u001b[39melif\u001b[39;00m should_wrap:\n\u001b[0;32m-> 2117\u001b[0m     util\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m   2118\u001b[0m         sqlalchemy_exception, with_traceback\u001b[39m=\u001b[39;49mexc_info[\u001b[39m2\u001b[39;49m], from_\u001b[39m=\u001b[39;49me\n\u001b[1;32m   2119\u001b[0m     )\n\u001b[1;32m   2120\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   2121\u001b[0m     util\u001b[39m.\u001b[39mraise_(exc_info[\u001b[39m1\u001b[39m], with_traceback\u001b[39m=\u001b[39mexc_info[\u001b[39m2\u001b[39m])\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[39m.\u001b[39m__cause__ \u001b[39m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[39mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/base.py:3280\u001b[0m, in \u001b[0;36mEngine._wrap_pool_connect\u001b[0;34m(self, fn, connection)\u001b[0m\n\u001b[1;32m   3278\u001b[0m dialect \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdialect\n\u001b[1;32m   3279\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3280\u001b[0m     \u001b[39mreturn\u001b[39;00m fn()\n\u001b[1;32m   3281\u001b[0m \u001b[39mexcept\u001b[39;00m dialect\u001b[39m.\u001b[39mdbapi\u001b[39m.\u001b[39mError \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m   3282\u001b[0m     \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:310\u001b[0m, in \u001b[0;36mPool.connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    303\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Return a DBAPI connection from the pool.\u001b[39;00m\n\u001b[1;32m    304\u001b[0m \n\u001b[1;32m    305\u001b[0m \u001b[39m    The connection is instrumented such that when its\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    308\u001b[0m \n\u001b[1;32m    309\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 310\u001b[0m     \u001b[39mreturn\u001b[39;00m _ConnectionFairy\u001b[39m.\u001b[39;49m_checkout(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:868\u001b[0m, in \u001b[0;36m_ConnectionFairy._checkout\u001b[0;34m(cls, pool, threadconns, fairy)\u001b[0m\n\u001b[1;32m    865\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m    866\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_checkout\u001b[39m(\u001b[39mcls\u001b[39m, pool, threadconns\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, fairy\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    867\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m fairy:\n\u001b[0;32m--> 868\u001b[0m         fairy \u001b[39m=\u001b[39m _ConnectionRecord\u001b[39m.\u001b[39;49mcheckout(pool)\n\u001b[1;32m    870\u001b[0m         fairy\u001b[39m.\u001b[39m_pool \u001b[39m=\u001b[39m pool\n\u001b[1;32m    871\u001b[0m         fairy\u001b[39m.\u001b[39m_counter \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:476\u001b[0m, in \u001b[0;36m_ConnectionRecord.checkout\u001b[0;34m(cls, pool)\u001b[0m\n\u001b[1;32m    474\u001b[0m \u001b[39m@classmethod\u001b[39m\n\u001b[1;32m    475\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mcheckout\u001b[39m(\u001b[39mcls\u001b[39m, pool):\n\u001b[0;32m--> 476\u001b[0m     rec \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_do_get()\n\u001b[1;32m    477\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    478\u001b[0m         dbapi_connection \u001b[39m=\u001b[39m rec\u001b[39m.\u001b[39mget_connection()\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/impl.py:146\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    144\u001b[0m     \u001b[39mexcept\u001b[39;00m:\n\u001b[1;32m    145\u001b[0m         \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[0;32m--> 146\u001b[0m             \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dec_overflow()\n\u001b[1;32m    147\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_do_get()\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m  \u001b[39m# remove potential circular references\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     75\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m compat\u001b[39m.\u001b[39mpy3k \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info[\u001b[39m1\u001b[39m]:\n\u001b[1;32m     76\u001b[0m         \u001b[39m# emulate Py3K's behavior of telling us when an exception\u001b[39;00m\n\u001b[1;32m     77\u001b[0m         \u001b[39m# occurs in an exception handler.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[39m.\u001b[39m__cause__ \u001b[39m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[39mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/impl.py:143\u001b[0m, in \u001b[0;36mQueuePool._do_get\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_inc_overflow():\n\u001b[1;32m    142\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 143\u001b[0m         \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_create_connection()\n\u001b[1;32m    144\u001b[0m     \u001b[39mexcept\u001b[39;00m:\n\u001b[1;32m    145\u001b[0m         \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:256\u001b[0m, in \u001b[0;36mPool._create_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    253\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_create_connection\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    254\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Called by subclasses to create a new ConnectionRecord.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 256\u001b[0m     \u001b[39mreturn\u001b[39;00m _ConnectionRecord(\u001b[39mself\u001b[39;49m)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:371\u001b[0m, in \u001b[0;36m_ConnectionRecord.__init__\u001b[0;34m(self, pool, connect)\u001b[0m\n\u001b[1;32m    369\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__pool \u001b[39m=\u001b[39m pool\n\u001b[1;32m    370\u001b[0m \u001b[39mif\u001b[39;00m connect:\n\u001b[0;32m--> 371\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m__connect()\n\u001b[1;32m    372\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfinalize_callback \u001b[39m=\u001b[39m deque()\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:666\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    664\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m    665\u001b[0m     \u001b[39mwith\u001b[39;00m util\u001b[39m.\u001b[39msafe_reraise():\n\u001b[0;32m--> 666\u001b[0m         pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mError on connect(): \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m, e)\n\u001b[1;32m    667\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    668\u001b[0m     \u001b[39m# in SQLAlchemy 1.4 the first_connect event is not used by\u001b[39;00m\n\u001b[1;32m    669\u001b[0m     \u001b[39m# the engine, so this will usually not be set\u001b[39;00m\n\u001b[1;32m    670\u001b[0m     \u001b[39mif\u001b[39;00m pool\u001b[39m.\u001b[39mdispatch\u001b[39m.\u001b[39mfirst_connect:\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/langhelpers.py:70\u001b[0m, in \u001b[0;36msafe_reraise.__exit__\u001b[0;34m(self, type_, value, traceback)\u001b[0m\n\u001b[1;32m     68\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m  \u001b[39m# remove potential circular references\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mwarn_only:\n\u001b[0;32m---> 70\u001b[0m         compat\u001b[39m.\u001b[39;49mraise_(\n\u001b[1;32m     71\u001b[0m             exc_value,\n\u001b[1;32m     72\u001b[0m             with_traceback\u001b[39m=\u001b[39;49mexc_tb,\n\u001b[1;32m     73\u001b[0m         )\n\u001b[1;32m     74\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     75\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m compat\u001b[39m.\u001b[39mpy3k \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_exc_info[\u001b[39m1\u001b[39m]:\n\u001b[1;32m     76\u001b[0m         \u001b[39m# emulate Py3K's behavior of telling us when an exception\u001b[39;00m\n\u001b[1;32m     77\u001b[0m         \u001b[39m# occurs in an exception handler.\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/util/compat.py:208\u001b[0m, in \u001b[0;36mraise_\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    205\u001b[0m     exception\u001b[39m.\u001b[39m__cause__ \u001b[39m=\u001b[39m replace_context\n\u001b[1;32m    207\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 208\u001b[0m     \u001b[39mraise\u001b[39;00m exception\n\u001b[1;32m    209\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[1;32m    210\u001b[0m     \u001b[39m# credit to\u001b[39;00m\n\u001b[1;32m    211\u001b[0m     \u001b[39m# https://cosmicpercolator.com/2016/01/13/exception-leaks-in-python-2-and-3/\u001b[39;00m\n\u001b[1;32m    212\u001b[0m     \u001b[39m# as the __traceback__ object creates a cycle\u001b[39;00m\n\u001b[1;32m    213\u001b[0m     \u001b[39mdel\u001b[39;00m exception, replace_context, from_, with_traceback\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/pool/base.py:661\u001b[0m, in \u001b[0;36m_ConnectionRecord.__connect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    659\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    660\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstarttime \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[0;32m--> 661\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdbapi_connection \u001b[39m=\u001b[39m connection \u001b[39m=\u001b[39m pool\u001b[39m.\u001b[39;49m_invoke_creator(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    662\u001b[0m     pool\u001b[39m.\u001b[39mlogger\u001b[39m.\u001b[39mdebug(\u001b[39m\"\u001b[39m\u001b[39mCreated new connection \u001b[39m\u001b[39m%r\u001b[39;00m\u001b[39m\"\u001b[39m, connection)\n\u001b[1;32m    663\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfresh \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/create.py:590\u001b[0m, in \u001b[0;36mcreate_engine.<locals>.connect\u001b[0;34m(connection_record)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[39mif\u001b[39;00m connection \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    589\u001b[0m             \u001b[39mreturn\u001b[39;00m connection\n\u001b[0;32m--> 590\u001b[0m \u001b[39mreturn\u001b[39;00m dialect\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/sqlalchemy/engine/default.py:597\u001b[0m, in \u001b[0;36mDefaultDialect.connect\u001b[0;34m(self, *cargs, **cparams)\u001b[0m\n\u001b[1;32m    595\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mconnect\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39m*\u001b[39mcargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcparams):\n\u001b[1;32m    596\u001b[0m     \u001b[39m# inherits the docstring from interfaces.Dialect.connect\u001b[39;00m\n\u001b[0;32m--> 597\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdbapi\u001b[39m.\u001b[39;49mconnect(\u001b[39m*\u001b[39;49mcargs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcparams)\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/pymysql/connections.py:352\u001b[0m, in \u001b[0;36mConnection.__init__\u001b[0;34m(self, user, password, host, database, unix_socket, port, charset, sql_mode, read_default_file, conv, use_unicode, client_flag, cursorclass, init_command, connect_timeout, read_default_group, autocommit, local_infile, max_allowed_packet, defer_connect, auth_plugin_map, read_timeout, write_timeout, bind_address, binary_prefix, program_name, server_public_key, ssl, ssl_ca, ssl_cert, ssl_disabled, ssl_key, ssl_verify_cert, ssl_verify_identity, compress, named_pipe, passwd, db)\u001b[0m\n\u001b[1;32m    350\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sock \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m    351\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 352\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnect()\n",
      "File \u001b[0;32m~/anaconda3/envs/base_38/lib/python3.8/site-packages/pymysql/connections.py:668\u001b[0m, in \u001b[0;36mConnection.connect\u001b[0;34m(self, sock)\u001b[0m\n\u001b[1;32m    666\u001b[0m     \u001b[39mif\u001b[39;00m DEBUG:\n\u001b[1;32m    667\u001b[0m         \u001b[39mprint\u001b[39m(exc\u001b[39m.\u001b[39mtraceback)\n\u001b[0;32m--> 668\u001b[0m     \u001b[39mraise\u001b[39;00m exc\n\u001b[1;32m    670\u001b[0m \u001b[39m# If e is neither DatabaseError or IOError, It's a bug.\u001b[39;00m\n\u001b[1;32m    671\u001b[0m \u001b[39m# But raising AssertionError hides original error.\u001b[39;00m\n\u001b[1;32m    672\u001b[0m \u001b[39m# So just reraise it.\u001b[39;00m\n\u001b[1;32m    673\u001b[0m \u001b[39mraise\u001b[39;00m\n",
      "\u001b[0;31mOperationalError\u001b[0m: (pymysql.err.OperationalError) (2003, \"Can't connect to MySQL server on '172.17.0.2' (timed out)\")\n(Background on this error at: https://sqlalche.me/e/14/e3q8)"
     ]
    }
   ],
   "source": [
    "\n",
    "with engine.connect() as conn:\n",
    "    df = pd.read_sql(sql, con = conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TODO\n",
    "#convert month field to datetime\n",
    "#replace NaN with 0\n",
    "#plot the data as two vertical bars, then later the conversion % as a time series line plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "month             object\n",
      "converted_cnt    float64\n",
      "trials_cnt       float64\n",
      "dtype: object\n"
     ]
    },
    {
     "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>converted_cnt</th>\n",
       "      <th>trials_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2021-05</td>\n",
       "      <td>5.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2021-04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2021-03</td>\n",
       "      <td>7.0</td>\n",
       "      <td>49.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2021-02</td>\n",
       "      <td>6.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2021-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  converted_cnt  trials_cnt\n",
       "32  2021-05            5.0        49.0\n",
       "33  2021-04            5.0        46.0\n",
       "34  2021-03            7.0        49.0\n",
       "35  2021-02            6.0        40.0\n",
       "36  2021-01            NaN         2.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.dtypes) \n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "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>converted_cnt</th>\n",
       "      <th>trials_cnt</th>\n",
       "      <th>month_norm</th>\n",
       "      <th>conversion_ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>0.054054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>0.050847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>0.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-09</td>\n",
       "      <td>7.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023-08</td>\n",
       "      <td>5.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>0.074627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023-07</td>\n",
       "      <td>4.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>0.173913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023-06</td>\n",
       "      <td>3.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>0.063830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023-05</td>\n",
       "      <td>2.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>0.046512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>0.135135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-03</td>\n",
       "      <td>4.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>0.085106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-02</td>\n",
       "      <td>7.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>0.142857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023-01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>0.104167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2022-12</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2022-11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>2022-11-01</td>\n",
       "      <td>0.093750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2022-10</td>\n",
       "      <td>8.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>2022-10-01</td>\n",
       "      <td>0.148148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2022-09</td>\n",
       "      <td>3.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2022-09-01</td>\n",
       "      <td>0.062500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2022-08</td>\n",
       "      <td>2.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2022-08-01</td>\n",
       "      <td>0.060606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2022-07</td>\n",
       "      <td>7.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>0.241379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2022-06</td>\n",
       "      <td>4.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2022-06-01</td>\n",
       "      <td>0.129032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2022-05</td>\n",
       "      <td>7.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>2022-05-01</td>\n",
       "      <td>0.162791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2022-04</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>0.065217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2022-03</td>\n",
       "      <td>6.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>0.130435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2022-02</td>\n",
       "      <td>7.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>0.132075</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      month  converted_cnt  trials_cnt month_norm  conversion_ratio\n",
       "0   2024-01            0.0        96.0 2024-01-01          0.000000\n",
       "1   2023-12            2.0        37.0 2023-12-01          0.054054\n",
       "2   2023-11            3.0        59.0 2023-11-01          0.050847\n",
       "3   2023-10            8.0        88.0 2023-10-01          0.090909\n",
       "4   2023-09            7.0        63.0 2023-09-01          0.111111\n",
       "5   2023-08            5.0        67.0 2023-08-01          0.074627\n",
       "6   2023-07            4.0        23.0 2023-07-01          0.173913\n",
       "7   2023-06            3.0        47.0 2023-06-01          0.063830\n",
       "8   2023-05            2.0        43.0 2023-05-01          0.046512\n",
       "9   2023-04            5.0        37.0 2023-04-01          0.135135\n",
       "10  2023-03            4.0        47.0 2023-03-01          0.085106\n",
       "11  2023-02            7.0        49.0 2023-02-01          0.142857\n",
       "12  2023-01            5.0        48.0 2023-01-01          0.104167\n",
       "13  2022-12            2.0        19.0 2022-12-01          0.105263\n",
       "14  2022-11            3.0        32.0 2022-11-01          0.093750\n",
       "15  2022-10            8.0        54.0 2022-10-01          0.148148\n",
       "16  2022-09            3.0        48.0 2022-09-01          0.062500\n",
       "17  2022-08            2.0        33.0 2022-08-01          0.060606\n",
       "18  2022-07            7.0        29.0 2022-07-01          0.241379\n",
       "19  2022-06            4.0        31.0 2022-06-01          0.129032\n",
       "20  2022-05            7.0        43.0 2022-05-01          0.162791\n",
       "21  2022-04            3.0        46.0 2022-04-01          0.065217\n",
       "22  2022-03            6.0        46.0 2022-03-01          0.130435\n",
       "23  2022-02            7.0        53.0 2022-02-01          0.132075"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.fillna(0)\n",
    "df['month_norm'] = pd.to_datetime(df['month']).dt.normalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['conversion_ratio'] = df['converted_cnt']/df['trials_cnt']\n",
    "fig,ax1 = plt.subplots()\n",
    "df.sort_values('month', ascending = True).plot(x = \"month\", y = [\"converted_cnt\",\"trials_cnt\"], figsize = (10,6), kind = \"bar\", ax=ax1)\n",
    "ax1.set_ylabel(\"count of trials\")\n",
    "ax1.set_xlabel(\"calendar month\")\n",
    "ax1.legend(bbox_to_anchor = (1,.93),labels = ['number converted','trials'])\n",
    "ax1.set_facecolor(\"lightgrey\")\n",
    "\n",
    "ax2=ax1.twinx()\n",
    "df.sort_values('month', ascending = True).plot(x = \"month\", y = \"conversion_ratio\", figsize = (14,8), kind = \"line\", marker = \"*\", ax=ax2, color = \"green\")\n",
    "ax2.set_ylabel(\"conversion rate\")\n",
    "ax2.set_ylim(bottom = 0, top = 0.3)\n",
    "ax2.legend(labels = ['conversion ratio'])\n",
    "ax1.set_xticklabels(df['month'].sort_values(ascending = True),rotation = 45)\n",
    "ax1.set_title(\"Conversion rate of trials vs. Volume \\n (Provide sufficient lag for trial start date to possible conversion)\", fontsize = 16)\n",
    "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter(xmax=1.0))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tracking shopping habits of trial members"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This involves importing a csv resultset from the mysql db, as sqlaclhemy doesn't process some of the data types properly. \n",
    "<br>**NOTE** the query (stored in membership_queries_dev.sql) also requires an updated *shopping activity log* report to be imported into the mysql db."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this cannot be compiled by sqlalchemy, will require a csv import after querying in dBeaver\n",
    "sql = '''\n",
    "select date_add(trial_start_dt, interval  -WEEKDAY(trial_start_dt)-1 day) FirstDayOfWeek, avg(trips) avg_trips_while_trial, \n",
    "count(distinct email) trial_members, sum(case when tsh.trips > (SELECT avg(tsh_m.trips) from trialShoppingHabits tsh_m WHERE date_add(tsh_m.trial_start_dt, interval  -WEEKDAY(tsh_m.trial_start_dt)-1 day) = date_add(tsh.trial_start_dt, interval  -WEEKDAY(tsh.trial_start_dt)-1 day) AND tsh_m.relative_trial_period = 'in trial') THEN 1 ELSE 0 end) num_over_avg\n",
    "-- (select max(mini.type_clean) from mem_type mini where mini.email = mt.email AND mini.type_clean not like '%trial%') \n",
    "from trialShoppingHabits tsh\n",
    "where relative_trial_period = 'in trial'\n",
    "group by 1\n",
    "order by 1 asc;\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#unnecessary bc I can't execute this query properly in sqlalchemy\n",
    "#with engine.connect() as conn:\n",
    "#    shop = pd.read_sql(sql, con = conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/candela/Documents/greeneHill/membershipReportsCIVI'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['problematic_activityCalc',\n",
       " 'mt_ms_join-1691949177932.csv',\n",
       " 'trials_shopping-1690132262897.csv',\n",
       " 'target_set_trial_members-1691340912095.csv',\n",
       " 'active_mems-1692142273116.csv',\n",
       " 'trials_shopping_0925-1695695630675.csv',\n",
       " 'mt_ms_join_WHERE-1691974925006.csv',\n",
       " 'trials_shopping_2024-01-03_diagnostic.csv',\n",
       " 'mem_type_202307041745.csv',\n",
       " 'mt_ms_1015_raw-1697386951565.csv',\n",
       " 'mt_ms_1003.csv',\n",
       " 'target_set_trial_members.csv',\n",
       " 'initialEnrollmentInspection-1700838282779.csv',\n",
       " 'trials_shopping_0925.csv',\n",
       " 'bushwick_psfc_202307041227.ods',\n",
       " 'mt_ms_1001.csv',\n",
       " 'trial_members_tsh_QA.csv',\n",
       " 'trialsToTarget_0925.ods',\n",
       " 'target_trial_members.csv',\n",
       " 'trialsToTarget_0925.csv',\n",
       " 'mt_ms_1001-1696182552098.csv',\n",
       " '_WITH_others_AS_select_from_mem_type_where_type_clean_in_bushwic_202307041216.csv',\n",
       " 'trials_shopping-1690242118896.csv',\n",
       " 'trials_shopping_2024-01-01.csv',\n",
       " 'initialEnrollmentInspection.csv',\n",
       " 'trialsToTarget_0925_emailOnly.csv',\n",
       " 'trials_shopping_avg.csv',\n",
       " 'trials_shopping-1690125122169.csv',\n",
       " 'mt_ms_1002.csv',\n",
       " 'mt_ms_join_WHERE.csv',\n",
       " 'trials_shopping.csv',\n",
       " 'target_trial_members-1691365090521.xlsx',\n",
       " 'target_trial_members-1691365090521.csv',\n",
       " 'trialsToTarget_20231203.csv',\n",
       " 'trials_shopping_2024-01-03_small.csv',\n",
       " 'mt_ms_1015_raw.csv',\n",
       " 'trials_shopping_2024-01-03_prod-1704333125787.csv',\n",
       " 'trials_shopping-1690124599459.csv',\n",
       " 'mt_ms_1015.csv',\n",
       " '_WITH_others_AS_select_from_mem_type_where_type_clean_in_bushwic_202307041227.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir('./membership_mysql_extracts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "shop_avg = pd.read_csv('./membership_mysql_extracts/trials_shopping_2024-01-03_prod-1704333125787.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2023-10-01'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "min(shop_avg['FirstDayOfWeek'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "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>FirstDayOfWeek</th>\n",
       "      <th>avg_trips_while_trial</th>\n",
       "      <th>trial_members</th>\n",
       "      <th>above_avg_trial_members</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023-12-03</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>11</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023-12-10</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023-12-17</td>\n",
       "      <td>0.7143</td>\n",
       "      <td>7</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2023-12-24</td>\n",
       "      <td>0.2500</td>\n",
       "      <td>8</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FirstDayOfWeek  avg_trips_while_trial  trial_members  \\\n",
       "9      2023-12-03                 1.0000             11   \n",
       "10     2023-12-10                 0.0000              4   \n",
       "11     2023-12-17                 0.7143              7   \n",
       "12     2023-12-24                 0.2500              8   \n",
       "13     2023-12-31                 1.0000              1   \n",
       "\n",
       "    above_avg_trial_members  \n",
       "9                       3.0  \n",
       "10                      NaN  \n",
       "11                      4.0  \n",
       "12                      2.0  \n",
       "13                      NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shop_avg.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f503351f2b0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#field names: FirstDayOfWeek\tavg_trips_while_trial\ttrial_members\tabove_avg_trial_members\n",
    "fig,ax1 = plt.subplots(figsize = (10,7))\n",
    "shop_avg.sort_values('FirstDayOfWeek', ascending = True).plot(x = \"FirstDayOfWeek\", y = [\"trial_members\",\"above_avg_trial_members\"], figsize = (10,6), kind = \"bar\", ax=ax1)\n",
    "ax1.set_ylabel('# of trial members')\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(shop_avg['FirstDayOfWeek'],shop_avg['avg_trips_while_trial'], marker = '*', color = 'brown')\n",
    "ax2.set_ylabel('avg trips in-trial')\n",
    "ax1.set_title('Average Trips During Trial by Enrollment Period Cohort', fontsize = 16)\n",
    "ax2.legend(bbox_to_anchor = (1,.89), labels = ['average trips while in-trial'])\n",
    "ax1.legend(bbox_to_anchor = (.883,1))\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QA the low number of active members"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "user = 'root'\n",
    "password = 'baeldung'\n",
    "host = '172.17.0.2'\n",
    "port = 3306\n",
    "database = 'membership'\n",
    "\n",
    "url=\"mysql+pymysql://{0}:{1}@{2}:{3}/{4}\".format(user, password, host, port, database)\n",
    "\n",
    "engine = sqlalchemy.create_engine(url)\n",
    "\n",
    "\n",
    "sql = '''\n",
    "SELECT *\n",
    "FROM stack_jobII sj\n",
    "WHERE DATE('2023-12-03') BETWEEN start_dt AND lead_date\n",
    "AND activity_calc IN ('winback','initial enrollment','technical activation','technical reactivation','technical re-activation');\n",
    "'''\n",
    "\n",
    "with engine.connect() as conn:\n",
    "    df = pd.read_sql(sql, con = conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# query CIVI active member report\n",
    "current_mem = pd.read_csv('./membership_mysql_extracts/currentMembersReport_20240121.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "current_mem.columns = [i.replace(' ','_') for i in current_mem.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "cur2 = current_mem.loc[current_mem['Membership_Type'] != 'Zucchini Plan',:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = cur2.merge(df, how = 'left', left_on = 'Email', right_on = 'mt_email')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Contact_Name', 'First_Name', 'Last_Name', 'Membership_Type',\n",
       "       'Start_Date', 'End_Date', 'Join_Date', 'Status', 'Email', 'Phone',\n",
       "       'Financial_Type', 'Contribution_Status', 'Payment_Type',\n",
       "       'Date_Received', 'Receipt_Date', 'Fee_Amount', 'Amount',\n",
       "       'Auto-Renew_Subscription_Status', 'Admin_Fee_Paid_Amount', 'Donated?'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cur2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mt_email', 'start_dt', 'lead_date', 'activity', 'mem_type', 'type_raw',\n",
       "       'mt_cancel_flag', 'activity_calc', 'text_status_indicator', 'row_num',\n",
       "       'total_rows'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "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>Contact_Name</th>\n",
       "      <th>First_Name</th>\n",
       "      <th>Last_Name</th>\n",
       "      <th>Membership_Type</th>\n",
       "      <th>Email</th>\n",
       "      <th>Start_Date</th>\n",
       "      <th>End_Date</th>\n",
       "      <th>Join_Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Akinyode, Becky</td>\n",
       "      <td>Becky</td>\n",
       "      <td>Akinyode</td>\n",
       "      <td>Apple Plan</td>\n",
       "      <td>oba1620@gmail.com</td>\n",
       "      <td>2019-06-10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-02-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albert, Nathan</td>\n",
       "      <td>Nathan</td>\n",
       "      <td>Albert</td>\n",
       "      <td>Carrot Plan</td>\n",
       "      <td>nathanealbert@gmail.com</td>\n",
       "      <td>2014-03-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014-03-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alcouloumre, Aaron</td>\n",
       "      <td>Aaron</td>\n",
       "      <td>Alcouloumre</td>\n",
       "      <td>Lettuce Plan</td>\n",
       "      <td>aaron1215@me.com</td>\n",
       "      <td>2024-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024-01-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amiel, Travis</td>\n",
       "      <td>Travis</td>\n",
       "      <td>Amiel</td>\n",
       "      <td>Lettuce Plan</td>\n",
       "      <td>travisamiel@gmail.com</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Anderson, Luki</td>\n",
       "      <td>Luki</td>\n",
       "      <td>Anderson</td>\n",
       "      <td>Lettuce Plan</td>\n",
       "      <td>lukianderson@gmail.com</td>\n",
       "      <td>2023-10-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011-12-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Askew, Joy</td>\n",
       "      <td>Joy</td>\n",
       "      <td>Askew</td>\n",
       "      <td>Lettuce Plan</td>\n",
       "      <td>joyaskew@gmail.com</td>\n",
       "      <td>2022-02-09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019-04-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Augenbraun, Susan</td>\n",
       "      <td>Susan</td>\n",
       "      <td>Augenbraun</td>\n",
       "      <td>Lettuce Plan</td>\n",
       "      <td>slaugenbraun@gmail.com</td>\n",
       "      <td>2018-08-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018-08-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Contact_Name First_Name    Last_Name Membership_Type  \\\n",
       "0     Akinyode, Becky      Becky     Akinyode      Apple Plan   \n",
       "1      Albert, Nathan     Nathan       Albert     Carrot Plan   \n",
       "2  Alcouloumre, Aaron      Aaron  Alcouloumre    Lettuce Plan   \n",
       "4       Amiel, Travis     Travis        Amiel    Lettuce Plan   \n",
       "6      Anderson, Luki       Luki     Anderson    Lettuce Plan   \n",
       "8          Askew, Joy        Joy        Askew    Lettuce Plan   \n",
       "9   Augenbraun, Susan      Susan   Augenbraun    Lettuce Plan   \n",
       "\n",
       "                     Email  Start_Date End_Date   Join_Date  \n",
       "0        oba1620@gmail.com  2019-06-10      NaN  2018-02-18  \n",
       "1  nathanealbert@gmail.com  2014-03-17      NaN  2014-03-17  \n",
       "2         aaron1215@me.com  2024-01-08      NaN  2024-01-08  \n",
       "4    travisamiel@gmail.com  2020-02-15      NaN  2020-02-15  \n",
       "6   lukianderson@gmail.com  2023-10-05      NaN  2011-12-31  \n",
       "8       joyaskew@gmail.com  2022-02-09      NaN  2019-04-14  \n",
       "9   slaugenbraun@gmail.com  2018-08-19      NaN  2018-08-19  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.loc[merged['mem_type'].isna(),['Contact_Name','First_Name','Last_Name','Membership_Type','Email','Start_Date','End_Date','Join_Date']][0:7]"
   ]
  }
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