membership_graphics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graphics for Membership Levels\n",
    "### This can include plotting total active members, leave, temporary leave, revoked/suspended, etc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "from container_credentials import return_credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Connection to the 172.17.0.2 for user root created successfully.\n"
     ]
    }
   ],
   "source": [
    "#connect to the database instance\n",
    "cred_dict = return_credentials()\n",
    "\n",
    "user = cred_dict['user'] \n",
    "password = cred_dict['pass'] \n",
    "host = cred_dict['host'] \n",
    "port = cred_dict['port'] \n",
    "database = cred_dict['database']\n",
    "\n",
    "'''user = 'root'\n",
    "password = 'baeldung'\n",
    "host = '172.17.0.2'\n",
    "port = 3306\n",
    "database = 'membership'''\n",
    "\n",
    "def get_connection():\n",
    "\treturn sqlalchemy.create_engine(\n",
    "\t\turl=\"mysql+pymysql://{0}:{1}@{2}:{3}/{4}\".format(\n",
    "\t\t\tuser, password, host, port, database\n",
    "\t\t)\n",
    "\t)\n",
    "\n",
    "if __name__ == '__main__':\n",
    "\n",
    "\ttry:\n",
    "\t\n",
    "\t\t# GET THE CONNECTION OBJECT (ENGINE) FOR THE DATABASE\n",
    "\t\tengine = get_connection()\n",
    "\t\tprint(\n",
    "\t\t\tf\"Connection to the {host} for user {user} created successfully.\")\n",
    "\texcept Exception as ex:\n",
    "\t\tprint(\"Connection could not be made due to the following error: \\n\", ex)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''\n",
    "SELECT calendar_date, \n",
    "SUM(CASE WHEN activity_calc IN ('winback','initial enrollment','technical activation','technical reactivation','technical re-activation') THEN 1 ELSE 0 END) AS active_cnts, \n",
    "SUM(CASE WHEN activity_calc IN ('parental leave','medical leave','care giving leave') THEN 1 ELSE 0 END) AS temporary_inactivte, \n",
    "SUM(CASE WHEN activity_calc IN ('cancelled','deactivated','deactive','suspended') THEN 1 ELSE 0 END) revoked,\n",
    "SUM(CASE WHEN activity_calc IN ('general leave') THEN 1 ELSE 0 END) general_leave, \n",
    "SUM(CASE WHEN activity_calc IN ('winback') THEN 1 ELSE 0 END) winbacks\n",
    "FROM stack_job2 sj\n",
    "INNER JOIN calendar cal ON calendar_date between start_dt AND lead_date \n",
    "GROUP BY 1 \n",
    "ORDER BY 1 desc\n",
    "'''\n",
    "from sqlalchemy import text\n",
    "\n",
    "with engine.connect() as conn:\n",
    "    df = pd.read_sql(query, con = conn)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.assign(calendar_date = pd.to_datetime(df['calendar_date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calendar_date</th>\n",
       "      <th>active_cnts</th>\n",
       "      <th>temporary_inactivte</th>\n",
       "      <th>revoked</th>\n",
       "      <th>general_leave</th>\n",
       "      <th>winbacks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1822</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1822.000000</td>\n",
       "      <td>1822.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2022-06-29 12:00:00</td>\n",
       "      <td>296.536773</td>\n",
       "      <td>9.183864</td>\n",
       "      <td>303.214050</td>\n",
       "      <td>184.796926</td>\n",
       "      <td>34.207464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2020-01-01 00:00:00</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2021-03-31 06:00:00</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>196.250000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2022-06-29 12:00:00</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>311.000000</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2023-09-27 18:00:00</td>\n",
       "      <td>309.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>38.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2024-12-26 00:00:00</td>\n",
       "      <td>337.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>494.000000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>46.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18.878714</td>\n",
       "      <td>3.085582</td>\n",
       "      <td>119.743329</td>\n",
       "      <td>67.150347</td>\n",
       "      <td>5.566313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             calendar_date  active_cnts  temporary_inactivte      revoked  \\\n",
       "count                 1822  1822.000000          1822.000000  1822.000000   \n",
       "mean   2022-06-29 12:00:00   296.536773             9.183864   303.214050   \n",
       "min    2020-01-01 00:00:00   260.000000             3.000000    93.000000   \n",
       "25%    2021-03-31 06:00:00   282.000000             7.000000   196.250000   \n",
       "50%    2022-06-29 12:00:00   300.000000             9.000000   311.000000   \n",
       "75%    2023-09-27 18:00:00   309.000000            10.000000   410.000000   \n",
       "max    2024-12-26 00:00:00   337.000000            18.000000   494.000000   \n",
       "std                    NaN    18.878714             3.085582   119.743329   \n",
       "\n",
       "       general_leave     winbacks  \n",
       "count    1822.000000  1822.000000  \n",
       "mean      184.796926    34.207464  \n",
       "min        47.000000    20.000000  \n",
       "25%       130.000000    31.000000  \n",
       "50%       197.000000    34.000000  \n",
       "75%       241.000000    38.000000  \n",
       "max       296.000000    46.000000  \n",
       "std        67.150347     5.566313  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#describe will tell me the high watermark for members\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#subtract winbacks from active_cnts in order to plot the regions on the stacked bar chart\n",
    "df=df.assign(active_non_win = df['active_cnts'] - df['winbacks'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#weekly snapshots is sufficient (intervals set to 7)\n",
    "df2 = df.sort_values('calendar_date').iloc[::7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'calendar_date': Timestamp('2024-12-26 00:00:00'), 'active_cnts': 319.0, 'temporary_inactivte': 17.0, 'revoked': 494.0, 'general_leave': 296.0, 'winbacks': 42.0, 'active_non_win': 277.0}\n",
      "{'calendar_date': Timestamp('2024-12-19 00:00:00'), 'active_cnts': 319.0, 'temporary_inactivte': 18.0, 'revoked': 494.0, 'general_leave': 295.0, 'winbacks': 41.0, 'active_non_win': 278.0}\n",
      "{'calendar_date': Timestamp('2024-12-10 00:00:00'), 'active_cnts': 327.0, 'temporary_inactivte': 18.0, 'revoked': 488.0, 'general_leave': 291.0, 'winbacks': 44.0, 'active_non_win': 283.0}\n",
      "{'calendar_date': Timestamp('2024-11-30 00:00:00'), 'active_cnts': 325.0, 'temporary_inactivte': 18.0, 'revoked': 488.0, 'general_leave': 289.0, 'winbacks': 45.0, 'active_non_win': 280.0}\n",
      "{'calendar_date': Timestamp('2024-11-20 00:00:00'), 'active_cnts': 321.0, 'temporary_inactivte': 18.0, 'revoked': 490.0, 'general_leave': 287.0, 'winbacks': 44.0, 'active_non_win': 277.0}\n",
      "{'calendar_date': Timestamp('2024-11-11 00:00:00'), 'active_cnts': 312.0, 'temporary_inactivte': 17.0, 'revoked': 490.0, 'general_leave': 288.0, 'winbacks': 42.0, 'active_non_win': 270.0}\n",
      "{'calendar_date': Timestamp('2024-11-04 00:00:00'), 'active_cnts': 315.0, 'temporary_inactivte': 15.0, 'revoked': 487.0, 'general_leave': 284.0, 'winbacks': 43.0, 'active_non_win': 272.0}\n"
     ]
    }
   ],
   "source": [
    "#pull stats on the last measurement date\n",
    "print(df.iloc[0].to_dict())\n",
    "print(df.iloc[7].to_dict())\n",
    "print(df.iloc[16].to_dict())\n",
    "print(df.iloc[26].to_dict())\n",
    "print(df.iloc[36].to_dict())\n",
    "print(df.iloc[45].to_dict())\n",
    "print(df.iloc[52].to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import timedelta\n",
    "graph_values = df.iloc[0,:]\n",
    "\n",
    "#print the max/high-water-mark of membership\n",
    "max_value = df['active_cnts'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.ticker as ticker \n",
    "#the two ahlines coincide w/the high water mark of member-owners and the most recent level (graph_values)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "ax.bar(df2['calendar_date'],df2['active_non_win'],bottom=df2['winbacks'], width=7, edgecolor = 'black')\n",
    "ax.bar(df2['calendar_date'],df2['winbacks'], width=7, edgecolor = 'black')\n",
    "ax.set_title(\"Active Accounts by Week w/Winback* Callout\")\n",
    "ax.tick_params(axis='x', labelrotation = 40)\n",
    "ax.axhline(y=max_value,linestyle = '-', color = 'pink')\n",
    "ax.axhline(y=graph_values['active_cnts'],linestyle = '--', color = 'grey')\n",
    "ax.set_ylabel('Number of Member-Owners')\n",
    "ax.legend(['winbacks', 'all other members'])\n",
    "#create 'artists' for the legend\n",
    "blue_patch = mpatches.Patch(color='blue', label='all other members')\n",
    "orange_patch = mpatches.Patch(color='orange', label='winbacks')\n",
    "ax.legend(handles=[blue_patch,orange_patch], loc = 'center right')\n",
    "ax.set_xlabel(\"Date\")\n",
    "ax.text(datetime(2021,8,25),330,f'highwater mark {int(max_value)} members',fontsize=8,color='pink')\n",
    "\n",
    "ax.annotate(f'{int(graph_values['active_cnts'])} total members\\n date {datetime.strftime(graph_values['calendar_date'],'%Y-%m-%d')}', xy=(graph_values['calendar_date'] - timedelta(days = 9), graph_values['active_cnts']+9), xycoords='data',\n",
    "            xytext=(-150, 22), color='grey',textcoords='offset points',arrowprops=dict(facecolor='grey',width=0.25))\n",
    "space = 75\n",
    "ax.xaxis.set_major_locator(ticker.MultipleLocator(space)) \n",
    "\n",
    "#centering xtick labels (middle of text to coincide w/tick)\n",
    "for label in ax.get_xticklabels(which='major'):\n",
    "    label.set(horizontalalignment='center')\n",
    "\n",
    "plt.figtext(0.05,-.08,'*Winbacks are reactivations after general leave, suspensions, etc')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trial tracking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''\n",
    "select date_format(start_dt, \"%Y-%m\") as month, count(distinct email) as count\n",
    "from consolidated_mem_type\n",
    "where type_clean like '%trial%'\n",
    "AND start_dt > DATE(\"2019-06-01\")\n",
    "group by 1\n",
    "order by 1 desc'''\n",
    "\n",
    "from sqlalchemy import text\n",
    "\n",
    "with engine.connect() as conn:\n",
    "    df3 = pd.read_sql(text(query), con = conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-11</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-10</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-09</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-08</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-07</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     month  count\n",
       "0  2024-11     45\n",
       "1  2024-10     43\n",
       "2  2024-09     77\n",
       "3  2024-08     42\n",
       "4  2024-07     42"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = df3.assign(calendar_mo = pd.to_datetime(df3['month'], format = \"%Y-%m\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.ticker as ticker \n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,6))\n",
    "ax.bar(df3['calendar_mo'],df3['count'], width = 12,edgecolor = 'black')\n",
    "ax.set_title(\"Trial Starts by Month\")\n",
    "ax.set_ylabel('Number of trial starts')\n",
    "ax.set_xlabel(\"Date\")\n",
    "\n",
    "'''\n",
    "space = len(df3['calendar_mo'])\n",
    "ax.set_xticklabels(df3['month'].sort_values(ascending = True))\n",
    "ax.xaxis.set_major_locator(ticker.LinearLocator(space)) \n",
    "'''\n",
    "plt.xticks(rotation=45, ha='left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export for time series analytics\n",
    "df3.to_csv('/home/mofongo/Documents/ghfc/membershipReportsCIVI/trial_timeseries.csv', index = False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py12",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}