Helix.ipynb

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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jnunez03/Pandas_forDataAnalysis/blob/master/Helix.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
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        "outputId": "d91415e1-fd0a-42e4-b72e-c2a6dbb30175",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from fbprophet import Prophet \n",
        "\n",
        "\n",
        "# re-run new query from ATOM/BQ.\n",
        "\n",
        "q = \"\"\"\n",
        "SELECT\n",
        "regi_id, \n",
        "product_code, \n",
        "mIN(case when is_sub = true then date(update_time) else null end) as NL_Sub_date,\n",
        "max(case when is_sub = False then date(update_time) else null end) as NL_Unsub_date\n",
        "FROM `nyt-messaging-prd.helix_email.email_user_subscriptions`\n",
        "WHERE _PARTITIONTIME >= '2016-01-01 00:00:00' AND _PARTITIONTIME < '2019-09-25 00:00:00'\n",
        "and product_code = 'CK'\n",
        "group by 1,2\n",
        "\"\"\"\n",
        "\n",
        "df = pd.read_gbq(q,\n",
        "    project_id = 'nyt-bigquery-beta-workspace',\n",
        "    dialect='standard')"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:google.auth.compute_engine._metadata:Compute Engine Metadata server unavailable.\n",
            "INFO:pandas_gbq.gbq:Requesting query... \n",
            "INFO:pandas_gbq.gbq:ok.\n",
            "Query running...\n",
            "INFO:pandas_gbq.gbq:Job ID: d538592b-690f-4429-bbcc-c53fb8ae5501\n",
            "Query running...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 7.2 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 9.79 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 11.15 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 13.1 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 14.22 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:Got 5616651 rows.\n",
            "\n",
            "INFO:pandas_gbq.gbq:Total time taken 329.92 s.\n",
            "Finished at 2019-09-25 15:50:35.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9caZ1PlmsFFx",
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          "height": 1000
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      },
      "source": [
        "# Now Run This Segment\n",
        "#df.head(n=5)\n",
        "\n",
        "#df['f0_'].str.len() # tells me how long each row is. \n",
        "df.head(n=40)\n",
        "\n",
        "\n"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>regi_id</th>\n",
              "      <th>product_code</th>\n",
              "      <th>NL_Sub_date</th>\n",
              "      <th>NL_Unsub_date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>95966264</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-26</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>89529746</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-12-29</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>91032993</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-03-21</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>95902137</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-24</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>94469074</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-07-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>68184151</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-12-13</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>88689902</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-07-10</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>93899961</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-07-16</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>96013511</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-26</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>83790175</td>\n",
              "      <td>CK</td>\n",
              "      <td>2017-12-09</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>72029801</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-07-26</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>94598993</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-07-30</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>82952373</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-15</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>57059756</td>\n",
              "      <td>CK</td>\n",
              "      <td>2017-07-03</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>89104396</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-12-03</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>88094884</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-10-08</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>79627351</td>\n",
              "      <td>CK</td>\n",
              "      <td>2017-03-21</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>81978426</td>\n",
              "      <td>CK</td>\n",
              "      <td>2017-08-25</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>85007368</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-02-24</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>67823962</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-06-20</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>96037199</td>\n",
              "      <td>CK</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>96032380</td>\n",
              "      <td>CK</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>96054800</td>\n",
              "      <td>CK</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>96060917</td>\n",
              "      <td>CK</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>96068601</td>\n",
              "      <td>CK</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>96089405</td>\n",
              "      <td>CK</td>\n",
              "      <td>None</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>96035866</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>96030250</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>96041306</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>96043257</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>96066350</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>96061887</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>96066324</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>88890124</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-11-20</td>\n",
              "      <td>2018-11-30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>74847492</td>\n",
              "      <td>CK</td>\n",
              "      <td>2019-08-27</td>\n",
              "      <td>2019-09-09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>83873368</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>2018-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>70003589</td>\n",
              "      <td>CK</td>\n",
              "      <td>2014-10-15</td>\n",
              "      <td>2018-08-26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>87249841</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-08-21</td>\n",
              "      <td>2018-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>87345321</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>2018-08-27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>87344274</td>\n",
              "      <td>CK</td>\n",
              "      <td>2018-08-26</td>\n",
              "      <td>2018-08-27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     regi_id product_code NL_Sub_date NL_Unsub_date\n",
              "0   95966264           CK  2019-08-26    2019-08-27\n",
              "1   89529746           CK  2018-12-29    2019-08-27\n",
              "2   91032993           CK  2019-03-21    2019-08-27\n",
              "3   95902137           CK  2019-08-24    2019-08-27\n",
              "4   94469074           CK  2019-07-27    2019-08-27\n",
              "5   68184151           CK  2018-12-13    2019-08-27\n",
              "6   88689902           CK  2019-07-10    2019-08-27\n",
              "7   93899961           CK  2019-07-16    2019-08-27\n",
              "8   96013511           CK  2019-08-26    2019-08-27\n",
              "9   83790175           CK  2017-12-09    2019-08-27\n",
              "10  72029801           CK  2019-07-26    2019-08-27\n",
              "11  94598993           CK  2019-07-30    2019-08-27\n",
              "12  82952373           CK  2019-08-15    2019-08-27\n",
              "13  57059756           CK  2017-07-03    2019-08-27\n",
              "14  89104396           CK  2018-12-03    2019-08-27\n",
              "15  88094884           CK  2018-10-08    2019-08-27\n",
              "16  79627351           CK  2017-03-21    2019-08-27\n",
              "17  81978426           CK  2017-08-25    2019-08-27\n",
              "18  85007368           CK  2018-02-24    2019-08-27\n",
              "19  67823962           CK  2019-06-20    2019-08-27\n",
              "20  96037199           CK        None    2019-08-27\n",
              "21  96032380           CK        None    2019-08-27\n",
              "22  96054800           CK        None    2019-08-27\n",
              "23  96060917           CK        None    2019-08-27\n",
              "24  96068601           CK        None    2019-08-27\n",
              "25  96089405           CK        None    2019-08-27\n",
              "26  96035866           CK  2019-08-27    2019-08-27\n",
              "27  96030250           CK  2019-08-27    2019-08-27\n",
              "28  96041306           CK  2019-08-27    2019-08-27\n",
              "29  96043257           CK  2019-08-27    2019-08-27\n",
              "30  96066350           CK  2019-08-27    2019-08-27\n",
              "31  96061887           CK  2019-08-27    2019-08-27\n",
              "32  96066324           CK  2019-08-27    2019-08-27\n",
              "33  88890124           CK  2018-11-20    2018-11-30\n",
              "34  74847492           CK  2019-08-27    2019-09-09\n",
              "35  83873368           CK  2018-08-27    2018-08-27\n",
              "36  70003589           CK  2014-10-15    2018-08-26\n",
              "37  87249841           CK  2018-08-21    2018-08-27\n",
              "38  87345321           CK  2018-08-27    2018-08-27\n",
              "39  87344274           CK  2018-08-26    2018-08-27"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8-P9zeOZY1v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#from google.colab import files\n",
        "#df.to_csv('XWdata.csv') \n",
        "#files.download('XWdata.csv')\n",
        "\n",
        "#df.dtypes  \n",
        "\n",
        "\n",
        "# Drop values where we see 'None' \n",
        "df1 = df[df.NL_Sub_date != 'None']\n",
        "\n",
        "df2 = df1[df1.NL_Unsub_date != 'None']\n",
        "\n",
        "\n",
        "\n",
        "#df['NL_Sub_date'] = pd.to_datetime(df['NL_Sub_date'])\n",
        "\n",
        "\n",
        "\n",
        "# Value_counts \n",
        "#print('Sub date Values...', df['NL_Sub_date'].value_counts())\n",
        "#print('\\n')\n",
        "#print('Unsub date Values...', df['NL_Unsub_date'].value_counts())\n",
        "\n",
        "#df['NL_Sub_date'] = pd.to_datetime(df['NL_Sub_date'])\n",
        "#df['NL_Unsub_date'] = pd.to_datetime(df['NL_Unsub_date'])\n",
        "#df[diff] = "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xtb4jBBIZZEP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Subscribed before January 2017\n",
        "\n",
        "#df2.head(n=10)\n",
        "\n",
        "\n",
        "df2['NL_Sub_date'] = pd.to_datetime(df2['NL_Sub_date'], errors='coerce')\n",
        "df2['NL_Unsub_date'] = pd.to_datetime(df2['NL_Unsub_date'], errors='coerce')\n",
        "# Changes out of bounds values to NaT  the -errors='coerce'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cHk3vBx56tzs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "fea16e2f-801e-44d5-8317-676b8ea5c664"
      },
      "source": [
        "# Subscribed before January 2017\n",
        "\n",
        "df3 = df2[df2.NL_Sub_date < '2017-01-01']\n",
        "df3.head(n=15)\n",
        "df3.length"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-73-3d07828011ff>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mdf3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNL_Sub_date\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;34m'2017-01-01'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdf3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlength\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5065\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5066\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5067\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5069\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'length'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N9PPWhr97KX8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 153
        },
        "outputId": "ead40c96-f3d1-4410-a8f7-7286adbb0147"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from pandas import Series, DataFrame\n",
        "from pylab import *\n",
        "import statsmodels.api as sm\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "from matplotlib.font_manager import FontProperties\n",
        "from scipy import stats\n",
        "from scipy.stats import norm\n",
        "\n",
        "#plt.style.use('fivethirtyeight')\n",
        "\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "\n",
        "   # PLOTS\n",
        "  \n",
        "#df3.tail(n=15)\n",
        "#df3.NL_Unsub_date.value_counts().plot(color='teal')\n",
        "#df3.NL_Sub_date.value_counts().plot(color='teal')\n",
        "\n",
        "#df3.head(n=10)\n",
        "\n",
        "df3['Diff'] = (df3['NL_Unsub_date'] - df3['NL_Sub_date']).dt.days\n",
        "\n",
        "#sb.distplot(df3['Diff'])\n",
        "\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
            "\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mjhBlnWE-3ul",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "5fda6fbe-a470-45aa-886e-6c2d665c6593"
      },
      "source": [
        "df3.head()\n",
        "#df3.Diff.value_counts()\n",
        "#df3.dropna()\n",
        "#df3.Diff.head()\n",
        "\n"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>regi_id</th>\n",
              "      <th>product_code</th>\n",
              "      <th>NL_Sub_date</th>\n",
              "      <th>NL_Unsub_date</th>\n",
              "      <th>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>70003589</td>\n",
              "      <td>CK</td>\n",
              "      <td>2014-10-15</td>\n",
              "      <td>2018-08-26</td>\n",
              "      <td>1411.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>67301490</td>\n",
              "      <td>CK</td>\n",
              "      <td>2016-11-14</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>651.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>70024087</td>\n",
              "      <td>CK</td>\n",
              "      <td>2014-10-18</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>1409.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>4929013</td>\n",
              "      <td>CK</td>\n",
              "      <td>2015-09-07</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>1085.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>75942092</td>\n",
              "      <td>CK</td>\n",
              "      <td>2016-06-25</td>\n",
              "      <td>2018-08-27</td>\n",
              "      <td>793.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     regi_id product_code NL_Sub_date NL_Unsub_date    Diff\n",
              "36  70003589           CK  2014-10-15    2018-08-26  1411.0\n",
              "40  67301490           CK  2016-11-14    2018-08-27   651.0\n",
              "48  70024087           CK  2014-10-18    2018-08-27  1409.0\n",
              "63   4929013           CK  2015-09-07    2018-08-27  1085.0\n",
              "85  75942092           CK  2016-06-25    2018-08-27   793.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62TIl9RPAq-y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "ca57d1bf-1617-4db6-929e-f8740d9a7ace"
      },
      "source": [
        "\n",
        "df3 = df3.dropna()\n",
        "#df3['Diff'] = df3['Diff'].astype('int')\n",
        "df4 = df3[df3['NL_Unsub_date'] != '2018-12-20']\n",
        "df4['NL_Unsub_date'].value_counts().describe()"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count     600.000000\n",
              "mean      137.895000\n",
              "std       379.068673\n",
              "min        13.000000\n",
              "25%        71.000000\n",
              "50%       109.500000\n",
              "75%       146.000000\n",
              "max      8290.000000\n",
              "Name: NL_Unsub_date, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7pwjHZlBB8p",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "e6908785-4f12-48be-de42-750237b1408f"
      },
      "source": [
        "#df3.dtypes\n",
        "df3.describe()\n",
        "#sb.distplot(df3['Diff'])\n",
        "#sb.distplot(df3['Diff'])\n",
        "\n"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>regi_id</th>\n",
              "      <th>Diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.111470e+05</td>\n",
              "      <td>111147.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>6.254517e+07</td>\n",
              "      <td>1131.843702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.790429e+07</td>\n",
              "      <td>321.851284</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.430000e+03</td>\n",
              "      <td>397.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.792981e+07</td>\n",
              "      <td>862.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.023802e+07</td>\n",
              "      <td>1106.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7.396816e+07</td>\n",
              "      <td>1406.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.837963e+07</td>\n",
              "      <td>2040.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            regi_id           Diff\n",
              "count  1.111470e+05  111147.000000\n",
              "mean   6.254517e+07    1131.843702\n",
              "std    1.790429e+07     321.851284\n",
              "min    1.430000e+03     397.000000\n",
              "25%    5.792981e+07     862.000000\n",
              "50%    7.023802e+07    1106.000000\n",
              "75%    7.396816e+07    1406.000000\n",
              "max    7.837963e+07    2040.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzQTyjgyZZLT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Account for day '2018-12-20'\n",
        "\n",
        "df4 = df3[df3['NL_Unsub_date'] != '2018-12-20']\n",
        "\n",
        "#df3.describe()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6emd8wzqZZOL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "19f42918-f32d-471f-b5ca-6dbd98d6692c"
      },
      "source": [
        "#df4.describe()\n",
        "\n",
        "sb.distplot(df4['Diff'])"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3dc65e2a58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wr19Peno6NpuNOXPmkJSU1OAxn376aY4fP05AQADx8fHcf//9BAR0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\nC/8fWIeQv6JR8k8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ll3DJtlqAqWD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "f8917fd9-3571-4cb6-ecf6-be0cdbe82838"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from fbprophet import Prophet \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from pandas import Series, DataFrame\n",
        "from pylab import *\n",
        "import statsmodels.api as sm\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "from matplotlib.font_manager import FontProperties\n",
        "from scipy import stats\n",
        "from scipy.stats import norm\n",
        "\n",
        "c = \"\"\"\n",
        "SELECT\n",
        "--EXTRACT(YEAR FROM NL_Sub_date) as subyear,\n",
        "--EXTRACT(MONTH FROM NL_Sub_date) as submonth,\n",
        "diff,\n",
        "COUNT(distinct regi_id) as totals, \n",
        "count(distinct case when NL_Unsub_date is not null then regi_id ELSE NULL END) as drop\n",
        "FROM (\n",
        "SELECT \n",
        "       regi_id,\n",
        "       NL_Sub_date,\n",
        "       NL_Unsub_date,\n",
        "       DATE_DIFF(NL_Unsub_date, NL_Sub_date, Month) as Diff\n",
        "      --Count(distinct case when NL_Sub_date is not null THEN regi_id ELSE NULL END) as SubbedThisMonth,\n",
        "      --Count(distinct case when NL_Unsub_date is not null THEN regi_id ELSE NULL END) as UnsubbedThisMonth\n",
        "      FROM (\n",
        "              SELECT\n",
        "              regi_id, \n",
        "              product_code, \n",
        "              mIN(case when is_sub = true then date(update_time) else null end) as NL_Sub_date,\n",
        "              max(case when is_sub = False then date(update_time) else null end) as NL_Unsub_date\n",
        "              FROM `nyt-messaging-prd.helix_email.email_user_subscriptions`\n",
        "              WHERE _PARTITIONTIME >= '2016-01-01 00:00:00' AND _PARTITIONTIME < '2019-09-25 00:00:00'\n",
        "              and product_code = 'CK'\n",
        "              group by 1,2\n",
        "           ) \n",
        "      WHERE \n",
        "              NL_Sub_date < '2017-01-01'-- >= '2018-08-01'\n",
        "      GROUP BY 1,2,3,4 \n",
        "      )\n",
        "GROUP BY 1\n",
        "\"\"\"\n",
        "\n",
        "dff = pd.read_gbq(c,\n",
        "    project_id = 'nyt-bigquery-beta-workspace',\n",
        "    dialect='standard')"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:google.auth.compute_engine._metadata:Compute Engine Metadata server unavailable.\n",
            "INFO:pandas_gbq.gbq:Requesting query... \n",
            "INFO:pandas_gbq.gbq:ok.\n",
            "Query running...\n",
            "INFO:pandas_gbq.gbq:Job ID: b2331ac2-e5b6-43ad-8483-0f7688cbc36f\n",
            "Query running...\n",
            "INFO:pandas_gbq.gbq:Got 52 rows.\n",
            "\n",
            "INFO:pandas_gbq.gbq:Total time taken 7.41 s.\n",
            "Finished at 2019-09-25 14:35:56.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "21dot4vR0dza",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "f0f9fef8-51fd-46a6-aaf0-0b1c5dcabe37"
      },
      "source": [
        "dff.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>diff</th>\n",
              "      <th>totals</th>\n",
              "      <th>drop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16</td>\n",
              "      <td>627</td>\n",
              "      <td>627</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>22</td>\n",
              "      <td>1148</td>\n",
              "      <td>1148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>3484</td>\n",
              "      <td>3484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>35</td>\n",
              "      <td>3907</td>\n",
              "      <td>3907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>55</td>\n",
              "      <td>1055</td>\n",
              "      <td>1055</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  diff  totals  drop\n",
              "0   16     627   627\n",
              "1   22    1148  1148\n",
              "2   28    3484  3484\n",
              "3   35    3907  3907\n",
              "4   55    1055  1055"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cIM3hCtC0rlE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 751
        },
        "outputId": "3ecd2139-eb27-4c3d-9807-fcbd710926a9"
      },
      "source": [
        "sb.jointplot(x=\"diff\", y=\"totals\", data=dff, color=\"k\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-145d09d77d8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"diff\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"totals\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"k\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mjointplot\u001b[0;34m(x, y, data, kind, stat_func, color, height, ratio, space, dropna, xlim, ylim, joint_kws, marginal_kws, annot_kws, **kwargs)\u001b[0m\n\u001b[1;32m   2292\u001b[0m         \u001b[0mmarginal_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"kde\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2293\u001b[0m         \u001b[0mmarginal_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetdefault\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"color\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2294\u001b[0;31m         \u001b[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_marginals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mmarginal_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2295\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2296\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hex\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/axisgrid.py\u001b[0m in \u001b[0;36mplot_marginals\u001b[0;34m(self, func, **kwargs)\u001b[0m\n\u001b[1;32m   1807\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"vertical\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1808\u001b[0m         \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0max_marg_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1809\u001b[0;31m         \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1810\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1811\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"vertical\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mdistplot\u001b[0;34m(a, bins, hist, kde, rug, fit, hist_kws, kde_kws, rug_kws, fit_kws, color, vertical, norm_hist, axlabel, label, ax)\u001b[0m\n\u001b[1;32m    223\u001b[0m         \u001b[0mhist_color\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist_kws\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"color\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         ax.hist(a, bins, orientation=orientation,\n\u001b[0;32m--> 225\u001b[0;31m                 color=hist_color, **hist_kws)\n\u001b[0m\u001b[1;32m    226\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mhist_color\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m             \u001b[0mhist_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"color\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhist_color\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mhist\u001b[0;34m(self, x, bins, range, density, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, normed, **kwargs)\u001b[0m\n\u001b[1;32m   6563\u001b[0m                     \u001b[0;34m\"color kwarg must have one color per data set. %d data \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6564\u001b[0m                     \"sets and %d colors were provided\" % (nx, len(color)))\n\u001b[0;32m-> 6565\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merror_message\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6566\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6567\u001b[0m         \u001b[0;31m# If bins are not specified either explicitly or via range,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: color kwarg must have one color per data set. 51 data sets and 1 colors were provided"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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XV1WN5wHnA1/sZeekbrqV5F977bUnBNzxV6yG4Uw3upSWtD5DWzQ4Ip5Fu5hjC+3AvD4z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\nZJKkohlkkqSiGWSSpKIZZJKkov1/EF0agdO+Pg8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RBH2cCo71kSB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#dff1 = dff[dff['diff'] != 'None']\n",
        "#dff1.dtypes\n",
        "dff1 = dff[pd.to_numeric(dff['diff'], errors='coerce').notnull()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYtxxnuX7MK6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dDtwveN2S7_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dff1.plot.bar(x=\"diff\", y=\"totals\", rot=90)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cALTaiNR3dxw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "outputId": "9e61a417-2639-4e5f-9d16-d6785b12f9d4"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(15,10))\n",
        "dff1.groupby(['diff']).sum()['totals'].plot.bar(ax=ax)\n",
        "plt.title('Tenure before Unsubscription for Users who Subbed Before Jan 2017', fontsize = 20, alpha=.85, weight = 'bold')\n",
        "plt.xlabel('Months before Unsubscribing', fontsize = 12)\n",
        "plt.ylabel('\\n Counts \\n     ', rotation = 0)\n",
        "\n",
        "plt.annotate(\n",
        "# Label and coordinate\n",
        "'2 years Out!', xy=(10, 3570), xytext=(5, 4000),\n",
        " \n",
        "# Custom arrow\n",
        "arrowprops=dict(facecolor='black', shrink=0.05)\n",
        ")\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(5, 4000, '2 years Out!')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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K1/2K3Pyp5gNUhMdi/X+42L7UDg5eSwtfClBe1leBfWtN1yJiIrkWHPJB9ufItcCtLLRt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\n4VGSJEmSVMlrHiVJkhaB121KWtZY8yh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fzPm+qPpj076Y82jUH3MeL/pztjlfjx1voOfr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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sJKI1yNC3dD6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "34e99d34-4051-4f87-a774-83bc71e2b40f"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from fbprophet import Prophet \n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sb\n",
        "from pandas import Series, DataFrame\n",
        "from pylab import *\n",
        "import statsmodels.api as sm\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "from matplotlib.font_manager import FontProperties\n",
        "from scipy import stats\n",
        "from scipy.stats import norm\n",
        "\n",
        "g = \"\"\"\n",
        "SELECT\n",
        "--EXTRACT(YEAR FROM NL_Sub_date) as subyear,\n",
        "--EXTRACT(MONTH FROM NL_Sub_date) as submonth,\n",
        "diff,\n",
        "COUNT(distinct regi_id) as totals, \n",
        "count(distinct case when NL_Unsub_date is not null then regi_id ELSE NULL END) as drop\n",
        "FROM (\n",
        "SELECT \n",
        "       regi_id,\n",
        "       NL_Sub_date,\n",
        "       NL_Unsub_date,\n",
        "       DATE_DIFF(NL_Unsub_date, NL_Sub_date, Month) as Diff\n",
        "      --Count(distinct case when NL_Sub_date is not null THEN regi_id ELSE NULL END) as SubbedThisMonth,\n",
        "      --Count(distinct case when NL_Unsub_date is not null THEN regi_id ELSE NULL END) as UnsubbedThisMonth\n",
        "      FROM (\n",
        "              SELECT\n",
        "              regi_id, \n",
        "              product_code, \n",
        "              mIN(case when is_sub = true then date(update_time) else null end) as NL_Sub_date,\n",
        "              max(case when is_sub = False then date(update_time) else null end) as NL_Unsub_date\n",
        "              FROM `nyt-messaging-prd.helix_email.email_user_subscriptions`\n",
        "              WHERE _PARTITIONTIME >= '2016-01-01 00:00:00' AND _PARTITIONTIME < '2019-09-25 00:00:00'\n",
        "              and product_code = 'CK'\n",
        "              group by 1,2\n",
        "           ) \n",
        "      WHERE \n",
        "              NL_Unsub_date not in ('2017-08-26','2017-08-25','2018-12-20')    -- >= '2018-08-01'\n",
        "      GROUP BY 1,2,3,4 \n",
        "      )\n",
        "GROUP BY 1\n",
        "\"\"\"\n",
        "\n",
        "bh = pd.read_gbq(g,\n",
        "    project_id = 'nyt-bigquery-beta-workspace',\n",
        "    dialect='standard')"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:google.auth.compute_engine._metadata:Compute Engine Metadata server unavailable.\n",
            "INFO:pandas_gbq.gbq:Requesting query... \n",
            "INFO:pandas_gbq.gbq:ok.\n",
            "Query running...\n",
            "INFO:pandas_gbq.gbq:Job ID: c6668628-003d-4612-a8ed-3bd70c71e6f8\n",
            "Query running...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 7.31 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:  Elapsed 10.19 s. Waiting...\n",
            "INFO:pandas_gbq.gbq:Got 93 rows.\n",
            "\n",
            "INFO:pandas_gbq.gbq:Total time taken 10.83 s.\n",
            "Finished at 2019-09-25 15:11:26.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LB2vQRUA7NXp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bh1 = bh[pd.to_numeric(bh['diff'], errors='coerce').notnull()]\n",
        "\n",
        "bh1 = bh1[bh1['diff'] > 0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HT4JBi2g7SfU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 649
        },
        "outputId": "24e9dbf0-7715-4ac1-aec2-96d6d9099593"
      },
      "source": [
        "fig1, ax1 = plt.subplots(figsize=(15,10))\n",
        "bh1.groupby(['diff']).sum()['totals'].plot.bar(ax=ax1)\n",
        "plt.title('Tenure before Unsubscription for Users who Subbed Before Jan 2017', fontsize = 20, alpha=.85, weight = 'bold')\n",
        "plt.xlabel('Months before Unsubscribing', fontsize = 12)\n",
        "plt.ylabel('\\n Counts \\n \\n      ', rotation = 0)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, '\\n Counts \\n \\n      ')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xpl9YP/sh33j6uyS92czPLZ9t3Z6elftwVi57LdL2mgXaxU4RLkrye7OazykY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\nSZIkteL/A4h0N70YRtmmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}