rdd_.ipynb

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      "source": [
        "**Transformations : ** map, flatMap, filter, groupBy, groupByKey, reduceByKey, union, sortByKey, join, coealesce, repartition, mapValues, aggregateByKey etc. </br>\n",
        "**Actions : ** first, take, collect, reduce, countByKey, saveAsTextFile, count, foreach etc."
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        "from pyspark import SparkContext"
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        "sc = SparkContext()"
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              "<div class=\"ansiout\"><span class=\"ansi-red-fg\">---------------------------------------------------------------------------</span>\n",
              "<span class=\"ansi-red-fg\">ValueError</span>                                Traceback (most recent call last)\n",
              "<span class=\"ansi-green-fg\">&lt;command-3352225748674864&gt;</span> in <span class=\"ansi-cyan-fg\">&lt;module&gt;</span>\n",
              "<span class=\"ansi-green-fg\">----&gt; 1</span><span class=\"ansi-red-fg\"> </span>sc <span class=\"ansi-blue-fg\">=</span> SparkContext<span class=\"ansi-blue-fg\">(</span><span class=\"ansi-blue-fg\">)</span>\n",
              "\n",
              "<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/context.py</span> in <span class=\"ansi-cyan-fg\">__init__</span><span class=\"ansi-blue-fg\">(self, master, appName, sparkHome, pyFiles, environment, batchSize, serializer, conf, gateway, jsc, profiler_cls)</span>\n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    143</span>                 &#34; is not allowed as it is a security risk.&#34;)\n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    144</span> \n",
              "<span class=\"ansi-green-fg\">--&gt; 145</span><span class=\"ansi-red-fg\">         </span>SparkContext<span class=\"ansi-blue-fg\">.</span>_ensure_initialized<span class=\"ansi-blue-fg\">(</span>self<span class=\"ansi-blue-fg\">,</span> gateway<span class=\"ansi-blue-fg\">=</span>gateway<span class=\"ansi-blue-fg\">,</span> conf<span class=\"ansi-blue-fg\">=</span>conf<span class=\"ansi-blue-fg\">)</span>\n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    146</span>         <span class=\"ansi-green-fg\">try</span><span class=\"ansi-blue-fg\">:</span>\n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    147</span>             self._do_init(master, appName, sparkHome, pyFiles, environment, batchSize, serializer,\n",
              "\n",
              "<span class=\"ansi-green-fg\">/databricks/spark/python/pyspark/context.py</span> in <span class=\"ansi-cyan-fg\">_ensure_initialized</span><span class=\"ansi-blue-fg\">(cls, instance, gateway, conf)</span>\n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    348</span> \n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    349</span>                     <span class=\"ansi-red-fg\"># Raise error if there is already a running Spark context</span>\n",
              "<span class=\"ansi-green-fg\">--&gt; 350</span><span class=\"ansi-red-fg\">                     raise ValueError(\n",
              "</span><span class=\"ansi-green-intense-fg ansi-bold\">    351</span>                         <span class=\"ansi-blue-fg\">&#34;Cannot run multiple SparkContexts at once; &#34;</span>\n",
              "<span class=\"ansi-green-intense-fg ansi-bold\">    352</span>                         <span class=\"ansi-blue-fg\">&#34;existing SparkContext(app=%s, master=%s)&#34;</span>\n",
              "\n",
              "<span class=\"ansi-red-fg\">ValueError</span>: Cannot run multiple SparkContexts at once; existing SparkContext(app=Databricks Shell, master=spark://driver-proxy:7077) created by __init__ at /databricks/python_shell/scripts/PythonShellImpl.py:1887 </div>"
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              "\n",
              "        <div>\n",
              "            <p><b>SparkContext</b></p>\n",
              "\n",
              "            <p><a href=\"/?o=4211239421640757#setting/sparkui/0626-152828-bop61/driver-1338187949812988801\">Spark UI</a></p>\n",
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              "<div class=\"ansiout\">Out[2]: /FileStore/tables/healthcare_dataset_stroke_data.csv MapPartitionsRDD[1] at textFile at NativeMethodAccessorImpl.java:0</div>"
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              "<div class=\"ansiout\">Out[6]: 5111</div>"
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              "<div class=\"ansiout\">Out[7]: &#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;</div>"
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      "source": [
        "rdd_text.take(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[8]: [&#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;,\n",
              " &#39;9046,Male,67,0,1,Yes,Private,Urban,228.69,36.6,formerly smoked,1&#39;,\n",
              " &#39;51676,Female,61,0,0,Yes,Self-employed,Rural,202.21,N/A,never smoked,1&#39;,\n",
              " &#39;31112,Male,80,0,1,Yes,Private,Rural,105.92,32.5,never smoked,1&#39;,\n",
              " &#39;60182,Female,49,0,0,Yes,Private,Urban,171.23,34.4,smokes,1&#39;]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[8]: [&#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;,\n &#39;9046,Male,67,0,1,Yes,Private,Urban,228.69,36.6,formerly smoked,1&#39;,\n &#39;51676,Female,61,0,0,Yes,Self-employed,Rural,202.21,N/A,never smoked,1&#39;,\n &#39;31112,Male,80,0,1,Yes,Private,Rural,105.92,32.5,never smoked,1&#39;,\n &#39;60182,Female,49,0,0,Yes,Private,Urban,171.23,34.4,smokes,1&#39;]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f6b100d2-950c-4a62-bf28-218bd9638932"
        },
        "id": "xXifxopq_5yU",
        "outputId": "b48eef36-26f3-403e-bfb0-8bd840bc92cf"
      },
      "source": [
        "for line in rdd_text.take(5):\n",
        "  print(line)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke\n",
              "9046,Male,67,0,1,Yes,Private,Urban,228.69,36.6,formerly smoked,1\n",
              "51676,Female,61,0,0,Yes,Self-employed,Rural,202.21,N/A,never smoked,1\n",
              "31112,Male,80,0,1,Yes,Private,Rural,105.92,32.5,never smoked,1\n",
              "60182,Female,49,0,0,Yes,Private,Urban,171.23,34.4,smokes,1\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke\n9046,Male,67,0,1,Yes,Private,Urban,228.69,36.6,formerly smoked,1\n51676,Female,61,0,0,Yes,Self-employed,Rural,202.21,N/A,never smoked,1\n31112,Male,80,0,1,Yes,Private,Rural,105.92,32.5,never smoked,1\n60182,Female,49,0,0,Yes,Private,Urban,171.23,34.4,smokes,1\n</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "3c36eb5d-7d5f-4c3e-ba85-00ef90b24021"
        },
        "id": "U_iL9Yiy_5yW"
      },
      "source": [
        "**Removing duplicates records**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "ba15f945-b10f-48ee-b477-1fcf1ce700e8"
        },
        "id": "mqClU4kc_5yq",
        "outputId": "720e41ca-8397-4013-d543-d44186ad8674"
      },
      "source": [
        "rdd_text_unique = rdd_text.distinct()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "8764fec2-6ec8-4e55-83dc-168f5eae6e3c"
        },
        "id": "2eSucQjc_5ys",
        "outputId": "a4a3ba2a-417b-4fb6-a98e-a63b30515882"
      },
      "source": [
        "rdd_text_unique.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[11]: 5111</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[11]: 5111</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "a8f91f3f-239e-48db-b0b5-6e26e89cfdf4"
        },
        "id": "KHe5bY3G_5yt"
      },
      "source": [
        "**Filtering only data where stroke = '1' and saving that data as CSV**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "e1454278-3080-42bf-9b64-e39e1cd711ec"
        },
        "id": "A4V7Q95h_5yu",
        "outputId": "92ecc2bb-e91a-4710-8616-4d57824579ab"
      },
      "source": [
        "positive_stroke_data = rdd_text.map(lambda line: line.split(',')).filter(lambda x: x[-1]=='1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f6c13202-1a10-43dd-801c-5394c253e98f"
        },
        "id": "NmAvf-vq_5yv",
        "outputId": "966c72ce-2e51-4dc4-9fda-433e439ae8fb"
      },
      "source": [
        "positive_stroke_data.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[33]: 249</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[33]: 249</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "2788653d-cad8-4ff6-982d-ee1ed1da6d41"
        },
        "id": "nzFnZi0H_5yx",
        "outputId": "1e2ef6d6-bcc0-4f0c-a681-c65277773e60"
      },
      "source": [
        "positive_stroke_data.first()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[34]: [&#39;9046&#39;,\n",
              " &#39;Male&#39;,\n",
              " &#39;67&#39;,\n",
              " &#39;0&#39;,\n",
              " &#39;1&#39;,\n",
              " &#39;Yes&#39;,\n",
              " &#39;Private&#39;,\n",
              " &#39;Urban&#39;,\n",
              " &#39;228.69&#39;,\n",
              " &#39;36.6&#39;,\n",
              " &#39;formerly smoked&#39;,\n",
              " &#39;1&#39;]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[34]: [&#39;9046&#39;,\n &#39;Male&#39;,\n &#39;67&#39;,\n &#39;0&#39;,\n &#39;1&#39;,\n &#39;Yes&#39;,\n &#39;Private&#39;,\n &#39;Urban&#39;,\n &#39;228.69&#39;,\n &#39;36.6&#39;,\n &#39;formerly smoked&#39;,\n &#39;1&#39;]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "427fb0de-cda2-4893-9c5f-9b4824dcdd2c"
        },
        "id": "MOxIM9Gb_5yy",
        "outputId": "380040cb-ca66-43bd-a57d-c4da02d32054"
      },
      "source": [
        "#retreiving header\n",
        "header = sc.parallelize([rdd_text.map(lambda line: line.split()).first()])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "5759e0bc-ec51-4221-8cae-af720bb95dd8"
        },
        "id": "lhGeHU4v_5yz",
        "outputId": "d9499883-c757-4855-9e7f-edc458a988d4"
      },
      "source": [
        "header.collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[69]: [[&#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;]]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[69]: [[&#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;]]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "41eadd78-1c3a-4f47-be17-1e43a53705ef"
        },
        "id": "Mc4XHQlK_5y1",
        "outputId": "c5cebb5e-fb9b-4822-c96f-74fcfc7612f1"
      },
      "source": [
        "#combining header and data having stroke = '1'\n",
        "positive_stroke_data_with_header = header.union(positive_stroke_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f085022a-2987-4713-80aa-8e80c302f1a4"
        },
        "id": "7dfch0Gq_5y3",
        "outputId": "7e646c1e-c85a-48e4-d672-934ccd2f7550"
      },
      "source": [
        "positive_stroke_data_with_header.count()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[71]: 250</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[71]: 250</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "453d93e5-79d5-46a1-8163-97958817d5f1"
        },
        "id": "dKJwvoSP_5y4",
        "outputId": "451eba1e-7c5c-442a-cbe3-7a4e5d55c0fd"
      },
      "source": [
        "positive_stroke_data_with_header.take(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[72]: [[&#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;],\n",
              " [&#39;9046&#39;,\n",
              "  &#39;Male&#39;,\n",
              "  &#39;67&#39;,\n",
              "  &#39;0&#39;,\n",
              "  &#39;1&#39;,\n",
              "  &#39;Yes&#39;,\n",
              "  &#39;Private&#39;,\n",
              "  &#39;Urban&#39;,\n",
              "  &#39;228.69&#39;,\n",
              "  &#39;36.6&#39;,\n",
              "  &#39;formerly smoked&#39;,\n",
              "  &#39;1&#39;]]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[72]: [[&#39;id,gender,age,hypertension,heart_disease,ever_married,work_type,Residence_type,avg_glucose_level,bmi,smoking_status,stroke&#39;],\n [&#39;9046&#39;,\n  &#39;Male&#39;,\n  &#39;67&#39;,\n  &#39;0&#39;,\n  &#39;1&#39;,\n  &#39;Yes&#39;,\n  &#39;Private&#39;,\n  &#39;Urban&#39;,\n  &#39;228.69&#39;,\n  &#39;36.6&#39;,\n  &#39;formerly smoked&#39;,\n  &#39;1&#39;]]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "0b894e27-0e53-42f2-a9c3-14a3a764e9cc"
        },
        "id": "JGZODGrJ_5y5",
        "outputId": "4a754d04-f6aa-4b7d-c158-9697f83931eb"
      },
      "source": [
        "#Saving as csv file to Databricks File System (DBFS)\n",
        "positive_stroke_data_with_header.saveAsTextFile('/FileStore/savedData/positive_stroke_data.csv')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "4fcfd59e-d5eb-41b8-b616-67bad10fd5e5"
        },
        "id": "W5lpfpUs_5y6",
        "outputId": "928c907f-5a0b-4fe8-dd35-793eeb2ce450"
      },
      "source": [
        "from pyspark import SQLContext\n",
        "sqlCon = SQLContext(sc)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f18cc097-296a-4e49-bad3-8b6f77ee32e9"
        },
        "id": "ZE4O8-EQ_5y7",
        "outputId": "935aec08-00c3-489b-9ee4-62badd1f9e5c"
      },
      "source": [
        "sqlCon.read.csv('/FileStore/savedData/positive_stroke_data.csv').tail(1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[79]: [Row(_c0=&#34;[&#39;id&#34;, _c1=&#39;gender&#39;, _c2=&#39;age&#39;, _c3=&#39;hypertension&#39;, _c4=&#39;heart_disease&#39;, _c5=&#39;ever_married&#39;, _c6=&#39;work_type&#39;, _c7=&#39;Residence_type&#39;, _c8=&#39;avg_glucose_level&#39;, _c9=&#39;bmi&#39;, _c10=&#39;smoking_status&#39;, _c11=&#34;stroke&#39;]&#34;)]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[79]: [Row(_c0=&#34;[&#39;id&#34;, _c1=&#39;gender&#39;, _c2=&#39;age&#39;, _c3=&#39;hypertension&#39;, _c4=&#39;heart_disease&#39;, _c5=&#39;ever_married&#39;, _c6=&#39;work_type&#39;, _c7=&#39;Residence_type&#39;, _c8=&#39;avg_glucose_level&#39;, _c9=&#39;bmi&#39;, _c10=&#39;smoking_status&#39;, _c11=&#34;stroke&#39;]&#34;)]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "de7faa09-251e-4c48-a4a4-2bc4851987f7"
        },
        "id": "kzc2g80l_5y8"
      },
      "source": [
        "**Finding different values of Residence_type and their count in original data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "96eab255-16e1-4293-b8e6-1523f79a0482"
        },
        "id": "iXfnlA2R_5y9",
        "outputId": "b6d513d3-120b-44f0-e35b-a4e1a1e885d3"
      },
      "source": [
        "def split_lines(line):\n",
        "  return line.split(',')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "34fa1938-716f-4889-b35f-02fa0bc611fa"
        },
        "id": "rkkAtK4b_5y-",
        "outputId": "89336fee-4ec5-40b4-a212-a4d5d12ee9f3"
      },
      "source": [
        "rdd_text.map(split_lines).map(lambda values: (values[7], 1)).reduceByKey(lambda a,b : a+b).collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[4]: [(&#39;Residence_type&#39;, 1), (&#39;Rural&#39;, 2514), (&#39;Urban&#39;, 2596)]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[4]: [(&#39;Residence_type&#39;, 1), (&#39;Rural&#39;, 2514), (&#39;Urban&#39;, 2596)]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "476d71e1-f3c5-4443-82f8-f91e9ea5482c"
        },
        "id": "hGzuotiw_5y_"
      },
      "source": [
        "**Find out the number of persons having stroke for each gender**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "676bdb53-eb62-4594-b9d0-51a9a7b0e485"
        },
        "id": "iIbDzJq-_5y_",
        "outputId": "ea21c9be-ea33-4f77-be8e-8d6a69f5c0b5"
      },
      "source": [
        "#1st method (reduceByKey() and sortByKey() are transformations, hence collect() is required to get output)\n",
        "rdd_text.map(split_lines).map(lambda x: ((x[1], x[-1]), 1)).reduceByKey(lambda a,b : a+b).sortByKey().collect()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[20]: [((&#39;Female&#39;, &#39;0&#39;), 2853),\n",
              " ((&#39;Female&#39;, &#39;1&#39;), 141),\n",
              " ((&#39;Male&#39;, &#39;0&#39;), 2007),\n",
              " ((&#39;Male&#39;, &#39;1&#39;), 108),\n",
              " ((&#39;Other&#39;, &#39;0&#39;), 1),\n",
              " ((&#39;gender&#39;, &#39;stroke&#39;), 1)]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[20]: [((&#39;Female&#39;, &#39;0&#39;), 2853),\n ((&#39;Female&#39;, &#39;1&#39;), 141),\n ((&#39;Male&#39;, &#39;0&#39;), 2007),\n ((&#39;Male&#39;, &#39;1&#39;), 108),\n ((&#39;Other&#39;, &#39;0&#39;), 1),\n ((&#39;gender&#39;, &#39;stroke&#39;), 1)]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "2567bc67-604b-462d-be82-904cb6f23708"
        },
        "id": "UzQKmY11_5zB",
        "outputId": "827036b8-2ac2-4eb3-9d36-81dc7ec6cc75"
      },
      "source": [
        "#2nd method (countByKey() is action, hence collect() will not be used but sortByKey() is a transformation. hence it can't be used after countByKey()). It returns hash map.\n",
        "rdd_text.map(split_lines).map(lambda x: ((x[1], x[-1]), 1)).sortByKey().countByKey()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[27]: defaultdict(int,\n",
              "            {(&#39;Female&#39;, &#39;0&#39;): 2853,\n",
              "             (&#39;Female&#39;, &#39;1&#39;): 141,\n",
              "             (&#39;Male&#39;, &#39;0&#39;): 2007,\n",
              "             (&#39;Male&#39;, &#39;1&#39;): 108,\n",
              "             (&#39;Other&#39;, &#39;0&#39;): 1,\n",
              "             (&#39;gender&#39;, &#39;stroke&#39;): 1})</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[27]: defaultdict(int,\n            {(&#39;Female&#39;, &#39;0&#39;): 2853,\n             (&#39;Female&#39;, &#39;1&#39;): 141,\n             (&#39;Male&#39;, &#39;0&#39;): 2007,\n             (&#39;Male&#39;, &#39;1&#39;): 108,\n             (&#39;Other&#39;, &#39;0&#39;): 1,\n             (&#39;gender&#39;, &#39;stroke&#39;): 1})</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "04f1d557-62af-420c-b100-be7330dca073"
        },
        "id": "-n85mUy1_5zC"
      },
      "source": [
        "**Grouping data on the basis of gender**</br>\n",
        "groupBy generates data in the form of key and value pair. Key will be the value on which basis grouping is being done and whole data related to that key will be in value of tuple. To retrive the values, they need to be converted into list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "5b171b42-913e-4463-a0e5-d496a810ff2b"
        },
        "id": "O8d1_1lI_5zD",
        "outputId": "dcce3bae-f126-4f69-aa9e-447dd3d73f06"
      },
      "source": [
        "rdd_grouped = rdd_text.map(lambda line: line.split(',')).groupBy(lambda x: x[1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\"></div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "7e4fa609-c44e-44d2-af49-8dfef6cec3f4"
        },
        "id": "AH7_eK3L_5zE",
        "outputId": "9f0aac96-0762-4500-875b-05a728085073"
      },
      "source": [
        "rdd_grouped.take(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[10]: [(&#39;gender&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc89676d0&gt;),\n",
              " (&#39;Male&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc8967820&gt;)]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[10]: [(&#39;gender&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc89676d0&gt;),\n (&#39;Male&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc8967820&gt;)]</div>",
              "removedWidgets": [],
              "addedWidgets": {},
              "metadata": {},
              "type": "html",
              "arguments": {}
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f2f4c7ef-4a7a-4685-9adb-4103aaa3b027"
        },
        "id": "HGSnt1Ip_5zF",
        "outputId": "6011593c-d809-4cbf-f065-825e0a3c14a6"
      },
      "source": [
        "print([(key, list(val)) for (key, val) in rdd_grouped.take(4)])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
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              "*** WARNING: skipped 461243 bytes of output ***\n",
              "\n",
              "\n",
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&#39;81.13&#39;, &#39;22.8&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;15383&#39;, &#39;Female&#39;, &#39;29&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;118.44&#39;, &#39;24.8&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;19828&#39;, &#39;Female&#39;, &#39;56\n*** WARNING: skipped 461243 bytes of output ***\n\n\n</div>",
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "1d210377-a297-418e-a78e-3589f8564cad"
        },
        "id": "DpyHjaOx_5zH",
        "outputId": "883cc3c1-ce15-4086-a4c5-75118aacc520"
      },
      "source": [
        "for key, val in rdd_grouped.collect():\n",
        "  print((key, list(val)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "    color: #555;\n",
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              "*** WARNING: skipped 460948 bytes of output ***\n",
              "\n",
              "\n",
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&#39;Urban&#39;, &#39;89.06&#39;, &#39;27.7&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;54294&#39;, &#39;Female&#39;, &#39;65&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Govt_job&#39;, &#39;Urban&#39;, &#39;79.39&#39;, &#39;31.5&#39;, &#39;formerly smoked&#39;, &#39;0&#39;], [&#39;46767&#39;, &#39;Female&#39;, &#39;8&#39;, &#39;0&#39;, &#39;0&#39;, &#39;No&#39;, &#39;children&#39;, &#39;Rural&#39;, &#39;67.84&#39;, &#39;24&#39;, &#39;Unknown&#39;, &#39;0&#39;], [&#39;12911&#39;, &#39;Female&#39;, &#39;51&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Private&#39;, &#39;Rural&#39;, &#39;81.73&#39;, &#39;27.4&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;47499&#39;, &#39;Female&#39;, &#39;48&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Govt_job&#39;, &#39;Rural&#39;, &#39;77.55&#39;, &#39;26.2&#39;, &#39;Unknown&#39;, &#39;0&#39;], [&#39;8790&#39;, &#39;Female&#39;, &#39;17&#39;, &#39;0&#39;, &#39;0&#39;, &#39;No&#39;, &#39;Private&#39;, &#39;Urban&#39;, 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&#39;Urban&#39;, &#39;55.96&#39;, &#39;34.8&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;21953&#39;, &#39;Female&#39;, &#39;33&#39;, &#39;0&#39;, &#39;0&#39;, &#39;No&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;84.4&#39;, &#39;N/A&#39;, &#39;smokes&#39;, &#39;0&#39;], [&#39;52234&#39;, &#39;Female&#39;, &#39;72&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Govt_job&#39;, &#39;Urban&#39;, &#39;104.05&#39;, &#39;33.5&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;46461&#39;, &#39;Female&#39;, &#39;52&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;62.54&#39;, &#39;35&#39;, &#39;smokes&#39;, &#39;0&#39;], [&#39;5068&#39;, &#39;Female&#39;, &#39;28&#39;, &#39;0&#39;, &#39;0&#39;, &#39;No&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;76.81&#39;, &#39;28.3&#39;, &#39;smokes&#39;, &#39;0&#39;], [&#39;65277&#39;, &#39;Female&#39;, &#39;78&#39;, &#39;1&#39;, &#39;0&#39;, &#39;No&#39;, &#39;Self-employed&#39;, &#39;Rural&#39;, &#39;198.12&#39;, &#39;29.1&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;52679&#39;, &#39;Female&#39;, &#39;82&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Self-employed&#39;, &#39;Rural&#39;, &#39;78&#39;, &#39;31.3&#39;, &#39;formerly smoked&#39;, &#39;0&#39;], [&#39;46797&#39;, &#39;Female&#39;, &#39;31&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Private&#39;, &#39;Rural&#39;, &#39;75.82&#39;, &#39;29.1&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;63898&#39;, &#39;Female&#39;, &#39;53&#39;, &#39;1&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;240.86&#39;, &#39;31.9&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;13155&#39;, &#39;Female&#39;, &#39;67&#39;, &#39;1&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Govt_job&#39;, &#39;Rural&#39;, &#39;263.56&#39;, &#39;26.3&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;27125&#39;, &#39;Female&#39;, &#39;17&#39;, &#39;0&#39;, &#39;0&#39;, &#39;No&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;81.13&#39;, &#39;22.8&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;15383&#39;, &#39;Female&#39;, &#39;29&#39;, &#39;0&#39;, &#39;0&#39;, &#39;Yes&#39;, &#39;Private&#39;, &#39;Urban&#39;, &#39;118.44&#39;, &#39;24.8&#39;, &#39;never smoked&#39;, &#39;0&#39;], [&#39;19828&#39;, &#39;Female&#39;, &#39;56&#39;\n*** WARNING: skipped 460948 bytes of output ***\n\n\n(&#39;gender&#39;, [[&#39;id&#39;, &#39;gender&#39;, &#39;age&#39;, &#39;hypertension&#39;, &#39;heart_disease&#39;, &#39;ever_married&#39;, &#39;work_type&#39;, &#39;Residence_type&#39;, &#39;avg_glucose_level&#39;, &#39;bmi&#39;, &#39;smoking_status&#39;, &#39;stroke&#39;]])\n(&#39;Other&#39;, [[&#39;56156&#39;, &#39;Other&#39;, &#39;26&#39;, &#39;0&#39;, &#39;0&#39;, &#39;No&#39;, &#39;Private&#39;, &#39;Rural&#39;, &#39;143.33&#39;, &#39;22.4&#39;, &#39;formerly smoked&#39;, &#39;0&#39;]])\n</div>",
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          }
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      "cell_type": "markdown",
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          "showTitle": false,
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          "nuid": "f8a67869-9ded-4ebb-94d3-d98814353c93"
        },
        "id": "rHHr7s4x_5zJ"
      },
      "source": [
        "**Finding no of persons of each gender**</br>\n",
        "groupByKey generates data in the form of key and value pair. Key will be the value on which basis grouping is being done and whole data related to that key will be in value of tuple. To retrive the values, they need to be converted into list."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "c19f9e65-2595-49b7-9f91-73d0817c2ca0"
        },
        "id": "aO7ekD0W_5zL",
        "outputId": "f906215f-1f79-45e0-8821-216d38ffcf45"
      },
      "source": [
        "rdd_groupedByKey = rdd_text.map(lambda line: line.split(',')).map(lambda x: (x[1],1)).groupByKey()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\"></div>"
            ]
          },
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    {
      "cell_type": "code",
      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "2953d2e7-e785-4003-bc8d-77cec6907639"
        },
        "id": "sofsdI8s_5zM",
        "outputId": "68b47953-0f13-4c26-8df3-5d83dc1f7df7"
      },
      "source": [
        "rdd_groupedByKey.take(2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[11]: [(&#39;gender&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc88ba1c0&gt;),\n",
              " (&#39;Male&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc88baa00&gt;)]</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">Out[11]: [(&#39;gender&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc88ba1c0&gt;),\n (&#39;Male&#39;, &lt;pyspark.resultiterable.ResultIterable at 0x7f7fc88baa00&gt;)]</div>",
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    {
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      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "f3ef08ed-f57d-4767-8bb1-39159ad5b914"
        },
        "id": "g-_A-R80_5zO",
        "outputId": "49dcc89e-19fd-43b4-f8a5-1d908335248d"
      },
      "source": [
        "print([x[0], list(x[1])] for x in rdd_groupedByKey.collect())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
              "    unicode-bidi: embed;\n",
              "    white-space: pre-wrap;\n",
              "    word-wrap: break-word;\n",
              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">&lt;generator object &lt;genexpr&gt; at 0x7f7fc892a7b0&gt;\n",
              "</div>"
            ]
          },
          "metadata": {
            "tags": [],
            "application/vnd.databricks.v1+output": {
              "datasetInfos": [],
              "data": "<div class=\"ansiout\">&lt;generator object &lt;genexpr&gt; at 0x7f7fc892a7b0&gt;\n</div>",
              "removedWidgets": [],
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      "metadata": {
        "application/vnd.databricks.v1+cell": {
          "title": "",
          "showTitle": false,
          "inputWidgets": {},
          "nuid": "1a81f58e-0d4e-40f4-a0bf-2a5ea0b81b4a"
        },
        "id": "dd0IwMBM_5zP",
        "outputId": "27eee1fb-865a-440d-d836-9da945e8968f"
      },
      "source": [
        "for x in rdd_groupedByKey.collect():\n",
        "  print((x[0], list(x[1])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<style scoped>\n",
              "  .ansiout {\n",
              "    display: block;\n",
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              "    word-break: break-all;\n",
              "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
              "    font-size: 13px;\n",
              "    color: #555;\n",
              "    margin-left: 4px;\n",
              "    line-height: 19px;\n",
              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">(&#39;gender&#39;, [1])\n",
              "(&#39;Male&#39;, [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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              "<div class=\"ansiout\">Out[18]: [&#39;id&#39;,\n",
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              " &#39;age&#39;,\n",
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              " &#39;heart_disease&#39;,\n",
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              " &#39;avg_glucose_level&#39;,\n",
              " &#39;bmi&#39;,\n",
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              " &#39;stroke&#39;,\n",
              " &#39;9046&#39;,\n",
              " &#39;Male&#39;,\n",
              " &#39;67&#39;,\n",
              " &#39;0&#39;,\n",
              " &#39;1&#39;,\n",
              " &#39;Yes&#39;,\n",
              " &#39;Private&#39;,\n",
              " &#39;Urban&#39;]</div>"
            ]
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              "data": "<div class=\"ansiout\">Out[18]: [&#39;id&#39;,\n &#39;gender&#39;,\n &#39;age&#39;,\n &#39;hypertension&#39;,\n &#39;heart_disease&#39;,\n &#39;ever_married&#39;,\n &#39;work_type&#39;,\n &#39;Residence_type&#39;,\n &#39;avg_glucose_level&#39;,\n &#39;bmi&#39;,\n &#39;smoking_status&#39;,\n &#39;stroke&#39;,\n &#39;9046&#39;,\n &#39;Male&#39;,\n &#39;67&#39;,\n &#39;0&#39;,\n &#39;1&#39;,\n &#39;Yes&#39;,\n &#39;Private&#39;,\n &#39;Urban&#39;]</div>",
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          "showTitle": false,
          "inputWidgets": {},
          "nuid": "a5428fe2-0aa2-4f60-809e-8fdaa4b5da2e"
        },
        "id": "A1_4oXpa_5zT",
        "outputId": "2446c834-d296-48cf-a221-f7e81914c128"
      },
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        "rdd_text.map(split_lines).take(2)"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "  .ansiout {\n",
              "    display: block;\n",
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              "  }\n",
              "</style>\n",
              "<div class=\"ansiout\">Out[20]: [[&#39;id&#39;,\n",
              "  &#39;gender&#39;,\n",
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              "  &#39;hypertension&#39;,\n",
              "  &#39;heart_disease&#39;,\n",
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              "  &#39;36.6&#39;,\n",
              "  &#39;formerly smoked&#39;,\n",
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              "data": "<div class=\"ansiout\">Out[20]: [[&#39;id&#39;,\n  &#39;gender&#39;,\n  &#39;age&#39;,\n  &#39;hypertension&#39;,\n  &#39;heart_disease&#39;,\n  &#39;ever_married&#39;,\n  &#39;work_type&#39;,\n  &#39;Residence_type&#39;,\n  &#39;avg_glucose_level&#39;,\n  &#39;bmi&#39;,\n  &#39;smoking_status&#39;,\n  &#39;stroke&#39;],\n [&#39;9046&#39;,\n  &#39;Male&#39;,\n  &#39;67&#39;,\n  &#39;0&#39;,\n  &#39;1&#39;,\n  &#39;Yes&#39;,\n  &#39;Private&#39;,\n  &#39;Urban&#39;,\n  &#39;228.69&#39;,\n  &#39;36.6&#39;,\n  &#39;formerly smoked&#39;,\n  &#39;1&#39;]]</div>",
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      ],
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}