FUR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Focus on FUR and FCS Transactions  \n",
    "\n",
    "by Fred Etter - November, 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data\n",
    "\n",
    "df = pd.read_csv('sends.csv', low_memory=False)\n",
    "df_march = pd.read_csv('march.csv', low_memory=False)\n",
    "df_april = pd.read_csv('april.csv', low_memory=False)\n",
    "df_may = pd.read_csv('may.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new dataframs for fcs and fur transactions - JUST FOR MARCH\n",
    "df_fcs = df_march.loc[df_march['ACTIVITY_TYPE'] == 'FCS']\n",
    "df_fur = df_march.loc[df_march['ACTIVITY_TYPE'] == 'FUR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe that captures all transactions for all 3 months\n",
    "df_t1_all = pd.concat([df_march, df_april], sort=False)\n",
    "df_t_all = pd.concat([df_t1_all, df_may], sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4935216, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all transactions has almost 5 million rows\n",
    "df_t_all.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build new dataframe for all months for fcs and fur\n",
    "df_t_all_fcs = df_t_all.loc[df_t_all['ACTIVITY_TYPE'] == 'FCS']\n",
    "df_t_all_fur = df_t_all.loc[df_t_all['ACTIVITY_TYPE'] == 'FUR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26971, 21)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>ACTIVITY_DATETIME</th>\n",
       "      <th>ACTIVITY_ID</th>\n",
       "      <th>ACTIVITY_STATUS</th>\n",
       "      <th>ACTIVITY_DATA_1</th>\n",
       "      <th>ACTIVITY_DATA_2</th>\n",
       "      <th>ACTIVITY_DATA_3</th>\n",
       "      <th>ACTIVITY_DATA_4</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "      <th>ACTIVITY_DATA_6</th>\n",
       "      <th>ACTIVITY_DATA_7</th>\n",
       "      <th>ACTIVITY_DATA_8</th>\n",
       "      <th>ACTIVITY_DATA_9</th>\n",
       "      <th>ACTIVITY_DATA_10</th>\n",
       "      <th>ACTIVITY_DATA_26</th>\n",
       "      <th>ACTIVITY_DATA_27</th>\n",
       "      <th>ACTIVITY_DATA_28</th>\n",
       "      <th>ACTIVITY_DATA_29</th>\n",
       "      <th>ACTIVITY_DATA_30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>215444</th>\n",
       "      <td>Yv9rfwUIFTVkwMmtusGc0w==</td>\n",
       "      <td>GBR</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-05-18 08:50:00</td>\n",
       "      <td>64120</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.55</td>\n",
       "      <td>25.00</td>\n",
       "      <td>29.30</td>\n",
       "      <td>44.35</td>\n",
       "      <td>43.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214137</th>\n",
       "      <td>wzLa72CjbOO%2FgO2vkuoghg==</td>\n",
       "      <td>CAN</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-05-09 19:53:14</td>\n",
       "      <td>28592</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00</td>\n",
       "      <td>CAD</td>\n",
       "      <td>MasterCard</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.94</td>\n",
       "      <td>56.32</td>\n",
       "      <td>67.01</td>\n",
       "      <td>100.00</td>\n",
       "      <td>99.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287938</th>\n",
       "      <td>FSAnHMk3kI%2BZkArzVWMSmA==</td>\n",
       "      <td>US</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-04-04 11:09:19</td>\n",
       "      <td>19536</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.00</td>\n",
       "      <td>28.14</td>\n",
       "      <td>32.83</td>\n",
       "      <td>46.96</td>\n",
       "      <td>46.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290985</th>\n",
       "      <td>jjgctUuQZxm9awglrPTZ7Q==</td>\n",
       "      <td>GBR</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-04-12 02:24:15</td>\n",
       "      <td>28592</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.00</td>\n",
       "      <td>GBP</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.21</td>\n",
       "      <td>25.00</td>\n",
       "      <td>29.40</td>\n",
       "      <td>41.58</td>\n",
       "      <td>41.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313986</th>\n",
       "      <td>JxaOuXgGl9mBKFlzN0740Q==</td>\n",
       "      <td>US</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-03-18 22:55:02</td>\n",
       "      <td>28592</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.00</td>\n",
       "      <td>28.23</td>\n",
       "      <td>32.55</td>\n",
       "      <td>46.79</td>\n",
       "      <td>45.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      SUPPORTER_ID COUNTRY ACTIVITY_TYPE    ACTIVITY_DATETIME  \\\n",
       "215444    Yv9rfwUIFTVkwMmtusGc0w==     GBR           FCS  2017-05-18 08:50:00   \n",
       "214137  wzLa72CjbOO%2FgO2vkuoghg==     CAN           FCS  2017-05-09 19:53:14   \n",
       "287938  FSAnHMk3kI%2BZkArzVWMSmA==      US           FCS  2017-04-04 11:09:19   \n",
       "290985    jjgctUuQZxm9awglrPTZ7Q==     GBR           FCS  2017-04-12 02:24:15   \n",
       "313986    JxaOuXgGl9mBKFlzN0740Q==      US           FCS  2017-03-18 22:55:02   \n",
       "\n",
       "        ACTIVITY_ID ACTIVITY_STATUS ACTIVITY_DATA_1 ACTIVITY_DATA_2  \\\n",
       "215444        64120         success               0               0   \n",
       "214137        28592         success               0               0   \n",
       "287938        19536         success               0               0   \n",
       "290985        28592         success               0               0   \n",
       "313986        28592         success               0               0   \n",
       "\n",
       "       ACTIVITY_DATA_3 ACTIVITY_DATA_4 ACTIVITY_DATA_5 ACTIVITY_DATA_6  \\\n",
       "215444             NaN           25.00             GBP          PayPal   \n",
       "214137             NaN          100.00             CAD      MasterCard   \n",
       "287938             NaN           35.00             USD          PayPal   \n",
       "290985             NaN           25.00             GBP          PayPal   \n",
       "313986             NaN           35.00             USD          PayPal   \n",
       "\n",
       "       ACTIVITY_DATA_7  ACTIVITY_DATA_8 ACTIVITY_DATA_9 ACTIVITY_DATA_10  \\\n",
       "215444               S              NaN             NaN              NaN   \n",
       "214137               S              NaN             NaN              NaN   \n",
       "287938               S              NaN             NaN              NaN   \n",
       "290985               S              NaN             NaN              NaN   \n",
       "313986               S              NaN             NaN              NaN   \n",
       "\n",
       "        ACTIVITY_DATA_26  ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "215444             32.55             25.00             29.30   \n",
       "214137             72.94             56.32             67.01   \n",
       "287938             35.00             28.14             32.83   \n",
       "290985             31.21             25.00             29.40   \n",
       "313986             35.00             28.23             32.55   \n",
       "\n",
       "        ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "215444             44.35             43.76  \n",
       "214137            100.00             99.35  \n",
       "287938             46.96             46.25  \n",
       "290985             41.58             41.66  \n",
       "313986             46.79             45.51  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at sample data for all fcs transactions\n",
    "\n",
    "pd.set_option('display.max_columns', None)\n",
    "print(df_t_all_fcs.shape)\n",
    "df_t_all_fcs.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# getting unique supporter_ids for all fcs transactions\n",
    "df_dd = df_t_all_fcs.drop_duplicates(subset='SUPPORTER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22433, 21)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the number of unique supporters who made a FCS transaction - 22433\n",
    "df_dd.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1002034, 21)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t_all.drop_duplicates(subset='SUPPORTER_ID').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new dataframe that has all unique supporters who made at least 1 FCS transaction\n",
    "df1 = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of FCS transactions by unique supporter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column that counts number of fcs transaction for each supporter\n",
    "df1['num_fcs'] = df_t_all_fcs.SUPPORTER_ID.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>b4AXry1copIqJI2ZMf1z3w==</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>Al3hZ1v0e24=</th>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>KgBB52buWZ%2B%2FgO2vkuoghg==</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              num_fcs\n",
       "b4AXry1copIqJI2ZMf1z3w==            1\n",
       "q%2FSxC32YJzeZkArzVWMSmA==          1\n",
       "Al3hZ1v0e24=                        1\n",
       "OhscLjclYoQqJI2ZMf1z3w==            1\n",
       "KgBB52buWZ%2B%2FgO2vkuoghg==        1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sample(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### FUR or not ?  (1 or 0):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the column 'fur' where it equals 1 if the supporter also contributed monthly (has a FUR transaction)\n",
    "df1['fur'] = np.where(df1.index.isin(df_t_all_fur.SUPPORTER_ID), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 2)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display number of rows, columns for supporters who made a FCS and FUR transaction\n",
    "df1.loc[df1['fur'] == 1].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fa1rLIqTJOAqJI2ZMf1z3w==</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gva6hUIAKHdkwMmtusGc0w==</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aLBjdFDD64%2BZkArzVWMSmA==</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BytBfCnwrKOZkArzVWMSmA==</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3wqiKKMT4iZkArzVWMSmA==</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "                            num_fcs  fur\n",
       "fa1rLIqTJOAqJI2ZMf1z3w==         33    0\n",
       "Gva6hUIAKHdkwMmtusGc0w==         27    0\n",
       "aLBjdFDD64%2BZkArzVWMSmA==       27    0\n",
       "BytBfCnwrKOZkArzVWMSmA==         11    0\n",
       "C3wqiKKMT4iZkArzVWMSmA==         11    0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22433, 2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show number of unique supporters who made at least 1 FCS transaction\n",
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(174, 21)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show number of supporters who made at least 1 FUR transaction\n",
    "df_t_all_fur.drop_duplicates(subset='SUPPORTER_ID').shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(188, 21)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show number  of FUR transactions\n",
    "df_t_all_fur.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are **4935216** total transactions.\n",
    "\n",
    "There are **1002034** supporters involved in all transactions.\n",
    "\n",
    "There are **26971** FCS transactions total.\n",
    "\n",
    "There are **22433** supporters who made a FCS transaction.\n",
    "\n",
    "There are **188** FUR transactions total.\n",
    "    - 187 are ACTIVITY_ID 24029 (24029 has no other ACTIVITY_TYPE)\n",
    "    - 1 is ACTIVITY_ID 15348 (15348 also only has the 1 FUR ACTIVITY_TYPE)\n",
    "\n",
    "There are **174** supporters who made a FUR transaction.\n",
    "\n",
    "There are **21** FUR supporters who also made a FCS contribution.  \n",
    "\n",
    "All FUR transactions are in **GBP** currency."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graph shows the number of FUR transactions, number of supporters who made those transactions, and the number of supporters who made both a FCS and FUR transaction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'FUR transactions vs FUR unique supporters vs FUR and FCS supporters')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "actions = [188, 174, 21]\n",
    "index = ['FUR_transactions', 'FUR_Unique_supporters', 'FUR_and_FCS_supporters']\n",
    "df = pd.DataFrame({'actions': actions}, index=index)\n",
    "ax = df.plot.bar(rot=0, figsize=(12, 6))\n",
    "ax.set_axisbelow(True)\n",
    "ax.minorticks_on()\n",
    "ax.grid(which='major', linestyle='-', linewidth='0.5', color='red')\n",
    "plt.xlabel(\"Transactions\")\n",
    "plt.ylabel('Occurrences')\n",
    "plt.title(\"FUR transactions vs FUR unique supporters vs FUR and FCS supporters\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Maximum FCS transaction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add new column to df1 datafram that captures the maximum FCS amount for each supporter\n",
    "df1['max_fcs'] = df_t_all_fcs.groupby(['SUPPORTER_ID'])['ACTIVITY_DATA_26'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fa1rLIqTJOAqJI2ZMf1z3w==</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gva6hUIAKHdkwMmtusGc0w==</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aLBjdFDD64%2BZkArzVWMSmA==</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>55.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BytBfCnwrKOZkArzVWMSmA==</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3wqiKKMT4iZkArzVWMSmA==</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            num_fcs  fur  max_fcs\n",
       "fa1rLIqTJOAqJI2ZMf1z3w==         33    0     2.31\n",
       "Gva6hUIAKHdkwMmtusGc0w==         27    0     1.00\n",
       "aLBjdFDD64%2BZkArzVWMSmA==       27    0    55.00\n",
       "BytBfCnwrKOZkArzVWMSmA==         11    0    35.00\n",
       "C3wqiKKMT4iZkArzVWMSmA==         11    0     1.00"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SPGrYS%2FlkdY=</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>23.13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                num_fcs  fur  max_fcs\n",
       "SPGrYS%2FlkdY=        3    0    23.13"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check one supporter to verify the MAX funcion above\n",
    "df1.loc[df1.index == 'SPGrYS%2FlkdY=']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>SUPPORTER_ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>ACTIVITY_DATETIME</th>\n",
       "      <th>ACTIVITY_ID</th>\n",
       "      <th>ACTIVITY_STATUS</th>\n",
       "      <th>ACTIVITY_DATA_1</th>\n",
       "      <th>ACTIVITY_DATA_2</th>\n",
       "      <th>ACTIVITY_DATA_3</th>\n",
       "      <th>ACTIVITY_DATA_4</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "      <th>ACTIVITY_DATA_6</th>\n",
       "      <th>ACTIVITY_DATA_7</th>\n",
       "      <th>ACTIVITY_DATA_8</th>\n",
       "      <th>ACTIVITY_DATA_9</th>\n",
       "      <th>ACTIVITY_DATA_10</th>\n",
       "      <th>ACTIVITY_DATA_26</th>\n",
       "      <th>ACTIVITY_DATA_27</th>\n",
       "      <th>ACTIVITY_DATA_28</th>\n",
       "      <th>ACTIVITY_DATA_29</th>\n",
       "      <th>ACTIVITY_DATA_30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>PET</td>\n",
       "      <td>2017-03-16 21:01:54</td>\n",
       "      <td>37410</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>PET</td>\n",
       "      <td>2017-03-13 11:33:07</td>\n",
       "      <td>37410</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295764</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-03-17 19:21:40</td>\n",
       "      <td>28592</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>30.00</td>\n",
       "      <td>CAD</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.44</td>\n",
       "      <td>18.10</td>\n",
       "      <td>20.92</td>\n",
       "      <td>30.0</td>\n",
       "      <td>29.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295765</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-03-19 16:01:36</td>\n",
       "      <td>28592</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.00</td>\n",
       "      <td>CAD</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.13</td>\n",
       "      <td>18.54</td>\n",
       "      <td>21.68</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>554</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>PET</td>\n",
       "      <td>2017-04-09 21:12:40</td>\n",
       "      <td>37410</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>555</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>PET</td>\n",
       "      <td>2017-04-28 21:49:26</td>\n",
       "      <td>37410</td>\n",
       "      <td>P</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203362</th>\n",
       "      <td>SPGrYS%2FlkdY=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>FCS</td>\n",
       "      <td>2017-05-18 20:29:20</td>\n",
       "      <td>28592</td>\n",
       "      <td>success</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.00</td>\n",
       "      <td>CAD</td>\n",
       "      <td>PayPal</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.79</td>\n",
       "      <td>17.59</td>\n",
       "      <td>20.51</td>\n",
       "      <td>31.0</td>\n",
       "      <td>30.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SUPPORTER_ID COUNTRY ACTIVITY_TYPE    ACTIVITY_DATETIME  \\\n",
       "502     SPGrYS%2FlkdY=     CAN           PET  2017-03-16 21:01:54   \n",
       "503     SPGrYS%2FlkdY=     CAN           PET  2017-03-13 11:33:07   \n",
       "295764  SPGrYS%2FlkdY=     CAN           FCS  2017-03-17 19:21:40   \n",
       "295765  SPGrYS%2FlkdY=     CAN           FCS  2017-03-19 16:01:36   \n",
       "554     SPGrYS%2FlkdY=     CAN           PET  2017-04-09 21:12:40   \n",
       "555     SPGrYS%2FlkdY=     CAN           PET  2017-04-28 21:49:26   \n",
       "203362  SPGrYS%2FlkdY=     CAN           FCS  2017-05-18 20:29:20   \n",
       "\n",
       "        ACTIVITY_ID ACTIVITY_STATUS ACTIVITY_DATA_1 ACTIVITY_DATA_2  \\\n",
       "502           37410               P             NaN             NaN   \n",
       "503           37410               P             NaN             NaN   \n",
       "295764        28592         success               0               0   \n",
       "295765        28592         success               0               0   \n",
       "554           37410               P             NaN             NaN   \n",
       "555           37410               P             NaN             NaN   \n",
       "203362        28592         success               0               0   \n",
       "\n",
       "       ACTIVITY_DATA_3 ACTIVITY_DATA_4 ACTIVITY_DATA_5 ACTIVITY_DATA_6  \\\n",
       "502                NaN             NaN             NaN             NaN   \n",
       "503                NaN             NaN             NaN             NaN   \n",
       "295764             NaN           30.00             CAD          PayPal   \n",
       "295765             NaN           31.00             CAD          PayPal   \n",
       "554                NaN             NaN             NaN             NaN   \n",
       "555                NaN             NaN             NaN             NaN   \n",
       "203362             NaN           31.00             CAD          PayPal   \n",
       "\n",
       "       ACTIVITY_DATA_7  ACTIVITY_DATA_8 ACTIVITY_DATA_9 ACTIVITY_DATA_10  \\\n",
       "502                NaN              NaN             NaN              NaN   \n",
       "503                NaN              NaN             NaN              NaN   \n",
       "295764               S              NaN             NaN              NaN   \n",
       "295765               S              NaN             NaN              NaN   \n",
       "554                NaN              NaN             NaN              NaN   \n",
       "555                NaN              NaN             NaN              NaN   \n",
       "203362               S              NaN             NaN              NaN   \n",
       "\n",
       "        ACTIVITY_DATA_26  ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "502                  NaN               NaN               NaN   \n",
       "503                  NaN               NaN               NaN   \n",
       "295764             22.44             18.10             20.92   \n",
       "295765             23.13             18.54             21.68   \n",
       "554                  NaN               NaN               NaN   \n",
       "555                  NaN               NaN               NaN   \n",
       "203362             22.79             17.59             20.51   \n",
       "\n",
       "        ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "502                  NaN               NaN  \n",
       "503                  NaN               NaN  \n",
       "295764              30.0             29.13  \n",
       "295765              31.0             30.43  \n",
       "554                  NaN               NaN  \n",
       "555                  NaN               NaN  \n",
       "203362              31.0             30.75  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check from cell above - note 3rd and 4th row\n",
    "df_t_all.loc[df_t_all['SUPPORTER_ID'] == 'SPGrYS%2FlkdY=']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total number of FCS transactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create another column to represent the total FCS money spent per supporter in USD amount\n",
    "df1['total_fcs'] = df_t_all_fcs.groupby(['SUPPORTER_ID'])['ACTIVITY_DATA_26'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>fa1rLIqTJOAqJI2ZMf1z3w==</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>61.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gva6hUIAKHdkwMmtusGc0w==</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aLBjdFDD64%2BZkArzVWMSmA==</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>465.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BytBfCnwrKOZkArzVWMSmA==</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>60.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C3wqiKKMT4iZkArzVWMSmA==</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            num_fcs  fur  max_fcs  total_fcs\n",
       "fa1rLIqTJOAqJI2ZMf1z3w==         33    0     2.31      61.05\n",
       "Gva6hUIAKHdkwMmtusGc0w==         27    0     1.00      27.00\n",
       "aLBjdFDD64%2BZkArzVWMSmA==       27    0    55.00     465.00\n",
       "BytBfCnwrKOZkArzVWMSmA==         11    0    35.00      60.00\n",
       "C3wqiKKMT4iZkArzVWMSmA==         11    0     1.00      11.00"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### creating the 'currency' column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a column for currency type\n",
    "df1 = df1.merge(df_dd[['SUPPORTER_ID', 'ACTIVITY_DATA_5']], left_on=df1.index, right_on='SUPPORTER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>61.05</td>\n",
       "      <td>fa1rLIqTJOAqJI2ZMf1z3w==</td>\n",
       "      <td>AUD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
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       "      <td>27.00</td>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>55.00</td>\n",
       "      <td>465.00</td>\n",
       "      <td>aLBjdFDD64%2BZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_fcs  fur  max_fcs  total_fcs                SUPPORTER_ID  \\\n",
       "0       33    0     2.31      61.05    fa1rLIqTJOAqJI2ZMf1z3w==   \n",
       "1       27    0     1.00      27.00    Gva6hUIAKHdkwMmtusGc0w==   \n",
       "2       27    0    55.00     465.00  aLBjdFDD64%2BZkArzVWMSmA==   \n",
       "3       11    0    35.00      60.00    BytBfCnwrKOZkArzVWMSmA==   \n",
       "4       11    0     1.00      11.00    C3wqiKKMT4iZkArzVWMSmA==   \n",
       "\n",
       "  ACTIVITY_DATA_5  \n",
       "0             AUD  \n",
       "1             USD  \n",
       "2             USD  \n",
       "3             USD  \n",
       "4             USD  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of Ps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_fur = df_march.loc[df_march['ACTIVITY_TYPE'] == 'FUR']\n",
    "df_p = df_t_all.loc[df_t_all['ACTIVITY_STATUS'] == 'P']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(776634, 21)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows of all transactions with a 'P'\n",
    "df_p.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# group by supporters to get a count for number of Ps per supporter\n",
    "df_p = df_p.groupby(['SUPPORTER_ID']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "                                  COUNTRY  ACTIVITY_TYPE  ACTIVITY_DATETIME  \\\n",
       "SUPPORTER_ID                                                                  \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==          1              1                  1   \n",
       "%2B%2B%2Fq5UZ3Q34UHgIT5m4sfA==          1              1                  1   \n",
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       "%2B%2B%2Fq5UZ3Q369awglrPTZ7Q==          1              1                  1   \n",
       "\n",
       "                                  ACTIVITY_ID  ACTIVITY_STATUS  \\\n",
       "SUPPORTER_ID                                                     \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==              1                1   \n",
       "%2B%2B%2Fq5UZ3Q34UHgIT5m4sfA==              1                1   \n",
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       "\n",
       "                                  ACTIVITY_DATA_1  ACTIVITY_DATA_2  \\\n",
       "SUPPORTER_ID                                                         \n",
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       "%2B%2B%2Fq5UZ3Q34UHgIT5m4sfA==                  0                0   \n",
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       "%2B%2B%2Fq5UZ3Q369awglrPTZ7Q==                  0                0   \n",
       "\n",
       "                                  ACTIVITY_DATA_3  ACTIVITY_DATA_4  \\\n",
       "SUPPORTER_ID                                                         \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                  0                0   \n",
       "%2B%2B%2Fq5UZ3Q34UHgIT5m4sfA==                  0                0   \n",
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       "%2B%2B%2Fq5UZ3Q369awglrPTZ7Q==                  0                0   \n",
       "\n",
       "                                  ACTIVITY_DATA_5  ACTIVITY_DATA_6  \\\n",
       "SUPPORTER_ID                                                         \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                  0                0   \n",
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       "\n",
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       "SUPPORTER_ID                                                         \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                  0                0   \n",
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       "\n",
       "                                  ACTIVITY_DATA_9  ACTIVITY_DATA_10  \\\n",
       "SUPPORTER_ID                                                          \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                  0                 0   \n",
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       "\n",
       "                                  ACTIVITY_DATA_26  ACTIVITY_DATA_27  \\\n",
       "SUPPORTER_ID                                                           \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                   0                 0   \n",
       "%2B%2B%2Fq5UZ3Q34UHgIT5m4sfA==                   0                 0   \n",
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       "\n",
       "                                  ACTIVITY_DATA_28  ACTIVITY_DATA_29  \\\n",
       "SUPPORTER_ID                                                           \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                   0                 0   \n",
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       "\n",
       "                                  ACTIVITY_DATA_30  \n",
       "SUPPORTER_ID                                        \n",
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      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset index of df_p dataframe\n",
    "df_p = df_p.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>%2B%2B%2Fq5UZ3Q369awglrPTZ7Q==</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SUPPORTER_ID  COUNTRY  ACTIVITY_TYPE  \\\n",
       "0    %2B%2B%2BtGpmdCBuZkArzVWMSmA==        1              1   \n",
       "1    %2B%2B%2Fq5UZ3Q34UHgIT5m4sfA==        1              1   \n",
       "2    %2B%2B%2Fq5UZ3Q35kwMmtusGc0w==        1              1   \n",
       "3  %2B%2B%2Fq5UZ3Q36%2FgO2vkuoghg==        1              1   \n",
       "4    %2B%2B%2Fq5UZ3Q369awglrPTZ7Q==        1              1   \n",
       "\n",
       "   ACTIVITY_DATETIME  ACTIVITY_ID  ACTIVITY_STATUS  ACTIVITY_DATA_1  \\\n",
       "0                  1            1                1                1   \n",
       "1                  1            1                1                0   \n",
       "2                  1            1                1                0   \n",
       "3                  1            1                1                0   \n",
       "4                  1            1                1                0   \n",
       "\n",
       "   ACTIVITY_DATA_2  ACTIVITY_DATA_3  ACTIVITY_DATA_4  ACTIVITY_DATA_5  \\\n",
       "0                1                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                0   \n",
       "4                0                0                0                0   \n",
       "\n",
       "   ACTIVITY_DATA_6  ACTIVITY_DATA_7  ACTIVITY_DATA_8  ACTIVITY_DATA_9  \\\n",
       "0                0                0                0                0   \n",
       "1                0                0                0                0   \n",
       "2                0                0                0                0   \n",
       "3                0                0                0                0   \n",
       "4                0                0                0                0   \n",
       "\n",
       "   ACTIVITY_DATA_10  ACTIVITY_DATA_26  ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "0                 0                 0                 0                 0   \n",
       "1                 0                 0                 0                 0   \n",
       "2                 0                 0                 0                 0   \n",
       "3                 0                 0                 0                 0   \n",
       "4                 0                 0                 0                 0   \n",
       "\n",
       "   ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "0                 0                 0  \n",
       "1                 0                 0  \n",
       "2                 0                 0  \n",
       "3                 0                 0  \n",
       "4                 0                 0  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_p.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge df1 with df_p to get the number of Ps corresponding with the supporter\n",
    "df1 = df1.merge(df_p[['SUPPORTER_ID', 'ACTIVITY_STATUS']], left_on='SUPPORTER_ID', right_on='SUPPORTER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "      <th>ACTIVITY_STATUS</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>27</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
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       "      <td>465.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_fcs  fur  max_fcs  total_fcs                SUPPORTER_ID  \\\n",
       "0       27    0      1.0       27.0    Gva6hUIAKHdkwMmtusGc0w==   \n",
       "1       27    0     55.0      465.0  aLBjdFDD64%2BZkArzVWMSmA==   \n",
       "2       11    0     35.0       60.0    BytBfCnwrKOZkArzVWMSmA==   \n",
       "3       11    0      1.0       11.0    C3wqiKKMT4iZkArzVWMSmA==   \n",
       "4       10    0      0.0        0.0  ZyaXRfHOj%2BiZkArzVWMSmA==   \n",
       "\n",
       "  ACTIVITY_DATA_5  ACTIVITY_STATUS  \n",
       "0             USD              108  \n",
       "1             USD                4  \n",
       "2             USD                4  \n",
       "3             USD               19  \n",
       "4             USD                2  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the new column equal to the ACTIVITY_STATUS column (which is the number of Ps)\n",
    "df1['num_p'] = df1.ACTIVITY_STATUS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "      <th>ACTIVITY_STATUS</th>\n",
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       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4</td>\n",
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       "      <th>2</th>\n",
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       "      <td>4</td>\n",
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       "      <th>3</th>\n",
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       "      <td>11.0</td>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_fcs  fur  max_fcs  total_fcs                SUPPORTER_ID  \\\n",
       "0       27    0      1.0       27.0    Gva6hUIAKHdkwMmtusGc0w==   \n",
       "1       27    0     55.0      465.0  aLBjdFDD64%2BZkArzVWMSmA==   \n",
       "2       11    0     35.0       60.0    BytBfCnwrKOZkArzVWMSmA==   \n",
       "3       11    0      1.0       11.0    C3wqiKKMT4iZkArzVWMSmA==   \n",
       "4       10    0      0.0        0.0  ZyaXRfHOj%2BiZkArzVWMSmA==   \n",
       "\n",
       "  ACTIVITY_DATA_5  ACTIVITY_STATUS  num_p  \n",
       "0             USD              108    108  \n",
       "1             USD                4      4  \n",
       "2             USD                4      4  \n",
       "3             USD               19     19  \n",
       "4             USD                2      2  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some columns\n",
    "df1 = df1.rename(columns={'ACTIVITY_DATA_5_x': 'currency', 'SUPPORTER_ID': 'supp_id'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>supp_id</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "      <th>ACTIVITY_STATUS</th>\n",
       "      <th>num_p</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5048</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>P8NWEGEP6i2ZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11938</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
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       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2374</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>biKZPjSU7b2ZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>10752</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>gO%2FPqL1hEriZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>pdIT%2FPxG9P%2BZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       num_fcs  fur  max_fcs  total_fcs                       supp_id  \\\n",
       "5048         1    0     35.0       35.0      P8NWEGEP6i2ZkArzVWMSmA==   \n",
       "11938        1    0    100.0      100.0      JS7Ij9ub5QWZkArzVWMSmA==   \n",
       "2374         2    0      5.0       10.0      biKZPjSU7b2ZkArzVWMSmA==   \n",
       "10752        1    0     55.0       55.0    gO%2FPqL1hEriZkArzVWMSmA==   \n",
       "446          3    0    100.0      190.0  pdIT%2FPxG9P%2BZkArzVWMSmA==   \n",
       "\n",
       "      ACTIVITY_DATA_5  ACTIVITY_STATUS  num_p  \n",
       "5048              USD                1      1  \n",
       "11938             USD                1      1  \n",
       "2374              USD                3      3  \n",
       "10752             USD                1      1  \n",
       "446               USD                3      3  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop some unneeded columns\n",
    "df1.drop(columns=['ACTIVITY_STATUS'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some columns\n",
    "df1 = df1.rename(columns={'ACTIVITY_DATA_5': 'currency'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>USD</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
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       "      <td>11.0</td>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
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       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   num_fcs  fur  max_fcs  total_fcs                     supp_id currency  \\\n",
       "0       27    0      1.0       27.0    Gva6hUIAKHdkwMmtusGc0w==      USD   \n",
       "1       27    0     55.0      465.0  aLBjdFDD64%2BZkArzVWMSmA==      USD   \n",
       "2       11    0     35.0       60.0    BytBfCnwrKOZkArzVWMSmA==      USD   \n",
       "3       11    0      1.0       11.0    C3wqiKKMT4iZkArzVWMSmA==      USD   \n",
       "4       10    0      0.0        0.0  ZyaXRfHOj%2BiZkArzVWMSmA==      USD   \n",
       "\n",
       "   num_p  \n",
       "0    108  \n",
       "1      4  \n",
       "2      4  \n",
       "3     19  \n",
       "4      2  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reordering the columns\n",
    "df1 = df1[['supp_id', 'num_fcs', 'fur', 'max_fcs', 'total_fcs', 'currency', 'num_p']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aLBjdFDD64%2BZkArzVWMSmA==</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  fur  max_fcs  total_fcs currency  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27    0      1.0       27.0      USD   \n",
       "1  aLBjdFDD64%2BZkArzVWMSmA==       27    0     55.0      465.0      USD   \n",
       "2    BytBfCnwrKOZkArzVWMSmA==       11    0     35.0       60.0      USD   \n",
       "3    C3wqiKKMT4iZkArzVWMSmA==       11    0      1.0       11.0      USD   \n",
       "4  ZyaXRfHOj%2BiZkArzVWMSmA==       10    0      0.0        0.0      USD   \n",
       "\n",
       "   num_p  \n",
       "0    108  \n",
       "1      4  \n",
       "2      4  \n",
       "3     19  \n",
       "4      2  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Total transactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new dataframe for all transactions\n",
    "df_tt = df_t_all.groupby(['SUPPORTER_ID']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the index\n",
    "df_tt.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge df1 with all transactions to get the total transactions\n",
    "df1 = df1.merge(df_tt[['SUPPORTER_ID', 'ACTIVITY_ID']], left_on='supp_id', right_on='SUPPORTER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>ACTIVITY_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aLBjdFDD64%2BZkArzVWMSmA==</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>aLBjdFDD64%2BZkArzVWMSmA==</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  fur  max_fcs  total_fcs currency  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27    0      1.0       27.0      USD   \n",
       "1  aLBjdFDD64%2BZkArzVWMSmA==       27    0     55.0      465.0      USD   \n",
       "2    BytBfCnwrKOZkArzVWMSmA==       11    0     35.0       60.0      USD   \n",
       "3    C3wqiKKMT4iZkArzVWMSmA==       11    0      1.0       11.0      USD   \n",
       "4  ZyaXRfHOj%2BiZkArzVWMSmA==       10    0      0.0        0.0      USD   \n",
       "\n",
       "   num_p                SUPPORTER_ID  ACTIVITY_ID  \n",
       "0    108    Gva6hUIAKHdkwMmtusGc0w==          158  \n",
       "1      4  aLBjdFDD64%2BZkArzVWMSmA==           34  \n",
       "2      4    BytBfCnwrKOZkArzVWMSmA==           29  \n",
       "3     19    C3wqiKKMT4iZkArzVWMSmA==           38  \n",
       "4      2  ZyaXRfHOj%2BiZkArzVWMSmA==           22  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some columns\n",
    "df1 = df1.rename(columns={'ACTIVITY_ID': 'total_trans'})\n",
    "\n",
    "# drop some columns\n",
    "df1.drop(columns=['SUPPORTER_ID'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
       "      <th>total_trans</th>\n",
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       "      <th>0</th>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "      <td>158</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aLBjdFDD64%2BZkArzVWMSmA==</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>465.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
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       "      <th>2</th>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  fur  max_fcs  total_fcs currency  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27    0      1.0       27.0      USD   \n",
       "1  aLBjdFDD64%2BZkArzVWMSmA==       27    0     55.0      465.0      USD   \n",
       "2    BytBfCnwrKOZkArzVWMSmA==       11    0     35.0       60.0      USD   \n",
       "3    C3wqiKKMT4iZkArzVWMSmA==       11    0      1.0       11.0      USD   \n",
       "4  ZyaXRfHOj%2BiZkArzVWMSmA==       10    0      0.0        0.0      USD   \n",
       "\n",
       "   num_p  total_trans  \n",
       "0    108          158  \n",
       "1      4           34  \n",
       "2      4           29  \n",
       "3     19           38  \n",
       "4      2           22  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reordering the columns\n",
    "df1 = df1[['supp_id', 'num_fcs', 'total_trans', 'fur', 'max_fcs', 'total_fcs', 'currency', 'num_p']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>total_trans</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
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       "      <td>USD</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C3wqiKKMT4iZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  total_trans  fur  max_fcs  total_fcs  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27          158    0      1.0       27.0   \n",
       "1  aLBjdFDD64%2BZkArzVWMSmA==       27           34    0     55.0      465.0   \n",
       "2    BytBfCnwrKOZkArzVWMSmA==       11           29    0     35.0       60.0   \n",
       "3    C3wqiKKMT4iZkArzVWMSmA==       11           38    0      1.0       11.0   \n",
       "4  ZyaXRfHOj%2BiZkArzVWMSmA==       10           22    0      0.0        0.0   \n",
       "\n",
       "  currency  num_p  \n",
       "0      USD    108  \n",
       "1      USD      4  \n",
       "2      USD      4  \n",
       "3      USD     19  \n",
       "4      USD      2  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of 'formsub' transactions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new df for only 'formsub' actions\n",
    "df_fs = df_t_all.loc[df_t_all['ACTIVITY_DATA_3'] == 'formsub']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>ACTIVITY_DATETIME</th>\n",
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       "      <td>R</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "          SUPPORTER_ID COUNTRY ACTIVITY_TYPE    ACTIVITY_DATETIME  \\\n",
       "315531    BDoflNnPHog=      CA           EBC  2017-03-07 09:37:06   \n",
       "315541  kudFmD%2ByF7w=     CAN           EBC  2017-03-06 15:07:12   \n",
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       "315561    nEgT0q6l4MY=     CAN           EBC  2017-03-06 16:28:24   \n",
       "315566    nEgT0q6l4MY=     CAN           EBC  2017-03-20 12:40:05   \n",
       "\n",
       "        ACTIVITY_ID ACTIVITY_STATUS ACTIVITY_DATA_1 ACTIVITY_DATA_2  \\\n",
       "315531       154395               R            open           click   \n",
       "315541       154395               R            open           click   \n",
       "315556       159229               R            open           click   \n",
       "315561       154395               R            open           click   \n",
       "315566       159229               R            open           click   \n",
       "\n",
       "       ACTIVITY_DATA_3 ACTIVITY_DATA_4 ACTIVITY_DATA_5 ACTIVITY_DATA_6  \\\n",
       "315531         formsub             NaN             NaN             NaN   \n",
       "315541         formsub             NaN             NaN             NaN   \n",
       "315556         formsub             NaN             NaN             NaN   \n",
       "315561         formsub             NaN             NaN             NaN   \n",
       "315566         formsub             NaN             NaN             NaN   \n",
       "\n",
       "       ACTIVITY_DATA_7  ACTIVITY_DATA_8 ACTIVITY_DATA_9 ACTIVITY_DATA_10  \\\n",
       "315531             NaN              NaN          131397              NaN   \n",
       "315541             NaN              NaN          131397              NaN   \n",
       "315556             NaN              NaN          136256              NaN   \n",
       "315561             NaN              NaN          131397              NaN   \n",
       "315566             NaN              NaN          136256              NaN   \n",
       "\n",
       "        ACTIVITY_DATA_26  ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "315531               NaN               NaN               NaN   \n",
       "315541               NaN               NaN               NaN   \n",
       "315556               NaN               NaN               NaN   \n",
       "315561               NaN               NaN               NaN   \n",
       "315566               NaN               NaN               NaN   \n",
       "\n",
       "        ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "315531               NaN               NaN  \n",
       "315541               NaN               NaN  \n",
       "315556               NaN               NaN  \n",
       "315561               NaN               NaN  \n",
       "315566               NaN               NaN  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a count of formsubs by supporter\n",
    "df_fs = df_fs.groupby(['SUPPORTER_ID']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
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       "                                COUNTRY  ACTIVITY_TYPE  ACTIVITY_DATETIME  \\\n",
       "SUPPORTER_ID                                                                \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==        1              1                  1   \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==        1              1                  1   \n",
       "%2B%2B1FhQXXKX2ZkArzVWMSmA==          1              1                  1   \n",
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       "%2B%2B7DPGIjppsqJI2ZMf1z3w==          1              1                  1   \n",
       "\n",
       "                                ACTIVITY_ID  ACTIVITY_STATUS  ACTIVITY_DATA_1  \\\n",
       "SUPPORTER_ID                                                                    \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==            1                1                1   \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==            1                1                1   \n",
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       "%2B%2B7DPGIjppsqJI2ZMf1z3w==              1                1                1   \n",
       "\n",
       "                                ACTIVITY_DATA_2  ACTIVITY_DATA_3  \\\n",
       "SUPPORTER_ID                                                       \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                1                1   \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==                1                1   \n",
       "%2B%2B1FhQXXKX2ZkArzVWMSmA==                  1                1   \n",
       "%2B%2B637G3QaaOZkArzVWMSmA==                  1                1   \n",
       "%2B%2B7DPGIjppsqJI2ZMf1z3w==                  1                1   \n",
       "\n",
       "                                ACTIVITY_DATA_4  ACTIVITY_DATA_5  \\\n",
       "SUPPORTER_ID                                                       \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                0                0   \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==                0                0   \n",
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       "%2B%2B7DPGIjppsqJI2ZMf1z3w==                  0                0   \n",
       "\n",
       "                                ACTIVITY_DATA_6  ACTIVITY_DATA_7  \\\n",
       "SUPPORTER_ID                                                       \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                0                0   \n",
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       "\n",
       "                                ACTIVITY_DATA_8  ACTIVITY_DATA_9  \\\n",
       "SUPPORTER_ID                                                       \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                0                1   \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==                0                1   \n",
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       "\n",
       "                                ACTIVITY_DATA_10  ACTIVITY_DATA_26  \\\n",
       "SUPPORTER_ID                                                         \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                 0                 0   \n",
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       "\n",
       "                                ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "SUPPORTER_ID                                                         \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                 0                 0   \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==                 0                 0   \n",
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       "\n",
       "                                ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "SUPPORTER_ID                                                        \n",
       "%2B%2B%2BtGpmdCBuZkArzVWMSmA==                 0                 0  \n",
       "%2B%2B0EBm3qo%2BcUHgIT5m4sfA==                 0                 0  \n",
       "%2B%2B1FhQXXKX2ZkArzVWMSmA==                   0                 0  \n",
       "%2B%2B637G3QaaOZkArzVWMSmA==                   0                 0  \n",
       "%2B%2B7DPGIjppsqJI2ZMf1z3w==                   0                 0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_fs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset the index\n",
    "df_fs.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge df1 with df_fs to get the number of 'formsub' transactions by supporter\n",
    "df1 = df1.merge(df_fs[['SUPPORTER_ID', 'ACTIVITY_DATA_3']], left_on='supp_id', right_on='SUPPORTER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>total_trans</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>ACTIVITY_DATA_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>27</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26D%2B9fXgFTeZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>62.71</td>\n",
       "      <td>GBP</td>\n",
       "      <td>5</td>\n",
       "      <td>26D%2B9fXgFTeZkArzVWMSmA==</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O%2FSV5ONZIxiZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>131.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>O%2FSV5ONZIxiZkArzVWMSmA==</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  total_trans  fur  max_fcs  total_fcs  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27          158    0      1.0      27.00   \n",
       "1    BytBfCnwrKOZkArzVWMSmA==       11           29    0     35.0      60.00   \n",
       "2  ZyaXRfHOj%2BiZkArzVWMSmA==       10           22    0      0.0       0.00   \n",
       "3  26D%2B9fXgFTeZkArzVWMSmA==        8           32    0     12.4      62.71   \n",
       "4  O%2FSV5ONZIxiZkArzVWMSmA==        8           10    0     25.0     131.00   \n",
       "\n",
       "  currency  num_p                SUPPORTER_ID  ACTIVITY_DATA_3  \n",
       "0      USD    108    Gva6hUIAKHdkwMmtusGc0w==                3  \n",
       "1      USD      4    BytBfCnwrKOZkArzVWMSmA==                1  \n",
       "2      USD      2  ZyaXRfHOj%2BiZkArzVWMSmA==                7  \n",
       "3      GBP      5  26D%2B9fXgFTeZkArzVWMSmA==                2  \n",
       "4      USD      1  O%2FSV5ONZIxiZkArzVWMSmA==                1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop some columns\n",
    "df1.drop(columns=['SUPPORTER_ID'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some columns\n",
    "df1 = df1.rename(columns={'ACTIVITY_DATA_3': 'num_fs'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>total_trans</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
       "      <th>num_fs</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>27</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>27.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>11</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26D%2B9fXgFTeZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>62.71</td>\n",
       "      <td>GBP</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O%2FSV5ONZIxiZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>131.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  total_trans  fur  max_fcs  total_fcs  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27          158    0      1.0      27.00   \n",
       "1    BytBfCnwrKOZkArzVWMSmA==       11           29    0     35.0      60.00   \n",
       "2  ZyaXRfHOj%2BiZkArzVWMSmA==       10           22    0      0.0       0.00   \n",
       "3  26D%2B9fXgFTeZkArzVWMSmA==        8           32    0     12.4      62.71   \n",
       "4  O%2FSV5ONZIxiZkArzVWMSmA==        8           10    0     25.0     131.00   \n",
       "\n",
       "  currency  num_p  num_fs  \n",
       "0      USD    108       3  \n",
       "1      USD      4       1  \n",
       "2      USD      2       7  \n",
       "3      GBP      5       2  \n",
       "4      USD      1       1  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of clicks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create new df with just transactions with a click\n",
    "df_c = df_t_all.loc[df_t_all['ACTIVITY_DATA_2'] == 'click']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUPPORTER_ID</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
       "      <th>ACTIVITY_DATETIME</th>\n",
       "      <th>ACTIVITY_ID</th>\n",
       "      <th>ACTIVITY_STATUS</th>\n",
       "      <th>ACTIVITY_DATA_1</th>\n",
       "      <th>ACTIVITY_DATA_2</th>\n",
       "      <th>ACTIVITY_DATA_3</th>\n",
       "      <th>ACTIVITY_DATA_4</th>\n",
       "      <th>ACTIVITY_DATA_5</th>\n",
       "      <th>ACTIVITY_DATA_6</th>\n",
       "      <th>ACTIVITY_DATA_7</th>\n",
       "      <th>ACTIVITY_DATA_8</th>\n",
       "      <th>ACTIVITY_DATA_9</th>\n",
       "      <th>ACTIVITY_DATA_10</th>\n",
       "      <th>ACTIVITY_DATA_26</th>\n",
       "      <th>ACTIVITY_DATA_27</th>\n",
       "      <th>ACTIVITY_DATA_28</th>\n",
       "      <th>ACTIVITY_DATA_29</th>\n",
       "      <th>ACTIVITY_DATA_30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>315531</th>\n",
       "      <td>BDoflNnPHog=</td>\n",
       "      <td>CA</td>\n",
       "      <td>EBC</td>\n",
       "      <td>2017-03-07 09:37:06</td>\n",
       "      <td>154395</td>\n",
       "      <td>R</td>\n",
       "      <td>open</td>\n",
       "      <td>click</td>\n",
       "      <td>formsub</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131397</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>kudFmD%2ByF7w=</td>\n",
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       "      <td>2017-03-06 15:07:12</td>\n",
       "      <td>154395</td>\n",
       "      <td>R</td>\n",
       "      <td>open</td>\n",
       "      <td>click</td>\n",
       "      <td>formsub</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131397</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2017-03-13 12:37:17</td>\n",
       "      <td>158828</td>\n",
       "      <td>R</td>\n",
       "      <td>open</td>\n",
       "      <td>click</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>135857</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>315554</th>\n",
       "      <td>ALJUUgR5fOs=</td>\n",
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       "      <td>EBC</td>\n",
       "      <td>2017-03-06 15:10:05</td>\n",
       "      <td>154395</td>\n",
       "      <td>R</td>\n",
       "      <td>open</td>\n",
       "      <td>click</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>131397</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>315556</th>\n",
       "      <td>ALJUUgR5fOs=</td>\n",
       "      <td>CAN</td>\n",
       "      <td>EBC</td>\n",
       "      <td>2017-03-20 10:07:03</td>\n",
       "      <td>159229</td>\n",
       "      <td>R</td>\n",
       "      <td>open</td>\n",
       "      <td>click</td>\n",
       "      <td>formsub</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>136256</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          SUPPORTER_ID COUNTRY ACTIVITY_TYPE    ACTIVITY_DATETIME  \\\n",
       "315531    BDoflNnPHog=      CA           EBC  2017-03-07 09:37:06   \n",
       "315541  kudFmD%2ByF7w=     CAN           EBC  2017-03-06 15:07:12   \n",
       "315545  kudFmD%2ByF7w=     CAN           EBC  2017-03-13 12:37:17   \n",
       "315554    ALJUUgR5fOs=     CAN           EBC  2017-03-06 15:10:05   \n",
       "315556    ALJUUgR5fOs=     CAN           EBC  2017-03-20 10:07:03   \n",
       "\n",
       "        ACTIVITY_ID ACTIVITY_STATUS ACTIVITY_DATA_1 ACTIVITY_DATA_2  \\\n",
       "315531       154395               R            open           click   \n",
       "315541       154395               R            open           click   \n",
       "315545       158828               R            open           click   \n",
       "315554       154395               R            open           click   \n",
       "315556       159229               R            open           click   \n",
       "\n",
       "       ACTIVITY_DATA_3 ACTIVITY_DATA_4 ACTIVITY_DATA_5 ACTIVITY_DATA_6  \\\n",
       "315531         formsub             NaN             NaN             NaN   \n",
       "315541         formsub             NaN             NaN             NaN   \n",
       "315545             NaN             NaN             NaN             NaN   \n",
       "315554             NaN             NaN             NaN             NaN   \n",
       "315556         formsub             NaN             NaN             NaN   \n",
       "\n",
       "       ACTIVITY_DATA_7  ACTIVITY_DATA_8 ACTIVITY_DATA_9 ACTIVITY_DATA_10  \\\n",
       "315531             NaN              NaN          131397              NaN   \n",
       "315541             NaN              NaN          131397              NaN   \n",
       "315545             NaN              NaN          135857              NaN   \n",
       "315554             NaN              NaN          131397              NaN   \n",
       "315556             NaN              NaN          136256              NaN   \n",
       "\n",
       "        ACTIVITY_DATA_26  ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "315531               NaN               NaN               NaN   \n",
       "315541               NaN               NaN               NaN   \n",
       "315545               NaN               NaN               NaN   \n",
       "315554               NaN               NaN               NaN   \n",
       "315556               NaN               NaN               NaN   \n",
       "\n",
       "        ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "315531               NaN               NaN  \n",
       "315541               NaN               NaN  \n",
       "315545               NaN               NaN  \n",
       "315554               NaN               NaN  \n",
       "315556               NaN               NaN  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "# groupby supporter_id to ge a count by supporter\n",
    "df_c = df_c.groupby(['SUPPORTER_ID']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>ACTIVITY_TYPE</th>\n",
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      "text/plain": [
       "                              COUNTRY  ACTIVITY_TYPE  ACTIVITY_DATETIME  \\\n",
       "SUPPORTER_ID                                                              \n",
       "S4kIJRdHHDGZkArzVWMSmA==            2              2                  2   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==        1              1                  1   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==          1              1                  1   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==        1              1                  1   \n",
       "QNYjA1RG4Wk=                        2              2                  2   \n",
       "\n",
       "                              ACTIVITY_ID  ACTIVITY_STATUS  ACTIVITY_DATA_1  \\\n",
       "SUPPORTER_ID                                                                  \n",
       "S4kIJRdHHDGZkArzVWMSmA==                2                2                2   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==            1                1                1   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==              1                1                1   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==            1                1                1   \n",
       "QNYjA1RG4Wk=                            2                2                2   \n",
       "\n",
       "                              ACTIVITY_DATA_2  ACTIVITY_DATA_3  \\\n",
       "SUPPORTER_ID                                                     \n",
       "S4kIJRdHHDGZkArzVWMSmA==                    2                2   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                1                1   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                  1                1   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                1                1   \n",
       "QNYjA1RG4Wk=                                2                1   \n",
       "\n",
       "                              ACTIVITY_DATA_4  ACTIVITY_DATA_5  \\\n",
       "SUPPORTER_ID                                                     \n",
       "S4kIJRdHHDGZkArzVWMSmA==                    0                0   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                0                0   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                  0                0   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                0                0   \n",
       "QNYjA1RG4Wk=                                0                0   \n",
       "\n",
       "                              ACTIVITY_DATA_6  ACTIVITY_DATA_7  \\\n",
       "SUPPORTER_ID                                                     \n",
       "S4kIJRdHHDGZkArzVWMSmA==                    0                0   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                0                0   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                  0                0   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                0                0   \n",
       "QNYjA1RG4Wk=                                0                0   \n",
       "\n",
       "                              ACTIVITY_DATA_8  ACTIVITY_DATA_9  \\\n",
       "SUPPORTER_ID                                                     \n",
       "S4kIJRdHHDGZkArzVWMSmA==                    0                2   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                0                1   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                  0                1   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                0                1   \n",
       "QNYjA1RG4Wk=                                0                2   \n",
       "\n",
       "                              ACTIVITY_DATA_10  ACTIVITY_DATA_26  \\\n",
       "SUPPORTER_ID                                                       \n",
       "S4kIJRdHHDGZkArzVWMSmA==                     0                 0   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                 0                 0   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                   0                 0   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                 0                 0   \n",
       "QNYjA1RG4Wk=                                 0                 0   \n",
       "\n",
       "                              ACTIVITY_DATA_27  ACTIVITY_DATA_28  \\\n",
       "SUPPORTER_ID                                                       \n",
       "S4kIJRdHHDGZkArzVWMSmA==                     0                 0   \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                 0                 0   \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                   0                 0   \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                 0                 0   \n",
       "QNYjA1RG4Wk=                                 0                 0   \n",
       "\n",
       "                              ACTIVITY_DATA_29  ACTIVITY_DATA_30  \n",
       "SUPPORTER_ID                                                      \n",
       "S4kIJRdHHDGZkArzVWMSmA==                     0                 0  \n",
       "OIpTkli%2FAhm15xl1A%2FXc2A==                 0                 0  \n",
       "0znU%2B6TJdrWZkArzVWMSmA==                   0                 0  \n",
       "9It8km%2FJPV2%2FgO2vkuoghg==                 0                 0  \n",
       "QNYjA1RG4Wk=                                 0                 0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reset index\n",
    "df_c.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merge df1 with df_c to get a datafram with number of clicks for each supporter who made a FCS transaction\n",
    "df1 = df1.merge(df_c[['SUPPORTER_ID', 'ACTIVITY_DATA_2']], left_on='supp_id', right_on='SUPPORTER_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>62.71</td>\n",
       "      <td>GBP</td>\n",
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       "      <td>1</td>\n",
       "      <td>O%2FSV5ONZIxiZkArzVWMSmA==</td>\n",
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      ],
      "text/plain": [
       "                      supp_id  num_fcs  total_trans  fur  max_fcs  total_fcs  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27          158    0      1.0      27.00   \n",
       "1    BytBfCnwrKOZkArzVWMSmA==       11           29    0     35.0      60.00   \n",
       "2  ZyaXRfHOj%2BiZkArzVWMSmA==       10           22    0      0.0       0.00   \n",
       "3  26D%2B9fXgFTeZkArzVWMSmA==        8           32    0     12.4      62.71   \n",
       "4  O%2FSV5ONZIxiZkArzVWMSmA==        8           10    0     25.0     131.00   \n",
       "\n",
       "  currency  num_p  num_fs                SUPPORTER_ID  ACTIVITY_DATA_2  \n",
       "0      USD    108       3    Gva6hUIAKHdkwMmtusGc0w==                6  \n",
       "1      USD      4       1    BytBfCnwrKOZkArzVWMSmA==                1  \n",
       "2      USD      2       7  ZyaXRfHOj%2BiZkArzVWMSmA==                8  \n",
       "3      GBP      5       2  26D%2B9fXgFTeZkArzVWMSmA==                3  \n",
       "4      USD      1       1  O%2FSV5ONZIxiZkArzVWMSmA==                1  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop some columns\n",
    "df1.drop(columns=['SUPPORTER_ID'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renaming some columns\n",
    "df1 = df1.rename(columns={'ACTIVITY_DATA_2': 'num_c'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26D%2B9fXgFTeZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>12.4</td>\n",
       "      <td>62.71</td>\n",
       "      <td>GBP</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O%2FSV5ONZIxiZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>131.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      supp_id  num_fcs  total_trans  fur  max_fcs  total_fcs  \\\n",
       "0    Gva6hUIAKHdkwMmtusGc0w==       27          158    0      1.0      27.00   \n",
       "1    BytBfCnwrKOZkArzVWMSmA==       11           29    0     35.0      60.00   \n",
       "2  ZyaXRfHOj%2BiZkArzVWMSmA==       10           22    0      0.0       0.00   \n",
       "3  26D%2B9fXgFTeZkArzVWMSmA==        8           32    0     12.4      62.71   \n",
       "4  O%2FSV5ONZIxiZkArzVWMSmA==        8           10    0     25.0     131.00   \n",
       "\n",
       "  currency  num_p  num_fs  num_c  \n",
       "0      USD    108       3      6  \n",
       "1      USD      4       1      1  \n",
       "2      USD      2       7      8  \n",
       "3      GBP      5       2      3  \n",
       "4      USD      1       1      1  "
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 21)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This is the lone FUR supporter who does not have the 24029 ACTIVITY_ID (this person only has 1 transaction)\n",
    "df_t_all.loc[df_t_all['ACTIVITY_ID'] == 15348].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion  \n",
    "\n",
    "I wanted to use the dataframe below to perform machine learning / predictive analytics to try to find correlations between supporters who made FCS donations and those who made FUR donations.\n",
    "\n",
    "However, there are only **21** supporters who made a FCS and FUR donation (from Cell number 15 above).  This is 21 supporters out of a total of **22433** (from cell 17) who made a FCS transaction.  This seems like too little data to run a ML algorithm.\n",
    "\n",
    "Regardless, the framework / code is here to add to the dataframe below by creating more columns as needed.  See the dataframe below: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>supp_id</th>\n",
       "      <th>num_fcs</th>\n",
       "      <th>total_trans</th>\n",
       "      <th>fur</th>\n",
       "      <th>max_fcs</th>\n",
       "      <th>total_fcs</th>\n",
       "      <th>currency</th>\n",
       "      <th>num_p</th>\n",
       "      <th>num_fs</th>\n",
       "      <th>num_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Gva6hUIAKHdkwMmtusGc0w==</td>\n",
       "      <td>27</td>\n",
       "      <td>158</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>108</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BytBfCnwrKOZkArzVWMSmA==</td>\n",
       "      <td>11</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>60.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ZyaXRfHOj%2BiZkArzVWMSmA==</td>\n",
       "      <td>10</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26D%2B9fXgFTeZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>12.40</td>\n",
       "      <td>62.71</td>\n",
       "      <td>GBP</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>O%2FSV5ONZIxiZkArzVWMSmA==</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>131.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>IvP0sjovDmcUHgIT5m4sfA==</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2.24</td>\n",
       "      <td>8.24</td>\n",
       "      <td>USD</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>aSenO6UEKN%2BzFWMrHGgntg==</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>11.24</td>\n",
       "      <td>76.16</td>\n",
       "      <td>EUR</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Qgs8fytFBaiZkArzVWMSmA==</td>\n",
       "      <td>6</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nzyNvreYKcCha0eWUOvjsw==</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>460.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2VPzadxilIkUHgIT5m4sfA==</td>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>175.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>ETECe8xac7CZkArzVWMSmA==</td>\n",
       "      <td>6</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>30.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>pYmmH5UlB3SZkArzVWMSmA==</td>\n",
       "      <td>6</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>32.39</td>\n",
       "      <td>150.73</td>\n",
       "      <td>GBP</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PZFjRIJL8ZSZkArzVWMSmA==</td>\n",
       "      <td>5</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>175.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18dU0kP4BT%2BZkArzVWMSmA==</td>\n",
       "      <td>5</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>78.69</td>\n",
       "      <td>318.03</td>\n",
       "      <td>EUR</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>z%2BtTOQEh4qqZkArzVWMSmA==</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>100.00</td>\n",
       "      <td>380.00</td>\n",
       "      <td>USD</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       supp_id  num_fcs  total_trans  fur  max_fcs  total_fcs  \\\n",
       "0     Gva6hUIAKHdkwMmtusGc0w==       27          158    0     1.00      27.00   \n",
       "1     BytBfCnwrKOZkArzVWMSmA==       11           29    0    35.00      60.00   \n",
       "2   ZyaXRfHOj%2BiZkArzVWMSmA==       10           22    0     0.00       0.00   \n",
       "3   26D%2B9fXgFTeZkArzVWMSmA==        8           32    0    12.40      62.71   \n",
       "4   O%2FSV5ONZIxiZkArzVWMSmA==        8           10    0    25.00     131.00   \n",
       "5     IvP0sjovDmcUHgIT5m4sfA==        7           22    0     2.24       8.24   \n",
       "6   aSenO6UEKN%2BzFWMrHGgntg==        7           28    0    11.24      76.16   \n",
       "7     Qgs8fytFBaiZkArzVWMSmA==        6           45    0     0.00       0.00   \n",
       "8     nzyNvreYKcCha0eWUOvjsw==        6           22    0   100.00     460.00   \n",
       "9     2VPzadxilIkUHgIT5m4sfA==        6           18    0    35.00     175.00   \n",
       "10    ETECe8xac7CZkArzVWMSmA==        6           21    0     5.00      30.00   \n",
       "11    pYmmH5UlB3SZkArzVWMSmA==        6           29    0    32.39     150.73   \n",
       "12    PZFjRIJL8ZSZkArzVWMSmA==        5           17    0    35.00     175.00   \n",
       "13  18dU0kP4BT%2BZkArzVWMSmA==        5           16    0    78.69     318.03   \n",
       "14  z%2BtTOQEh4qqZkArzVWMSmA==        5           19    0   100.00     380.00   \n",
       "\n",
       "   currency  num_p  num_fs  num_c  \n",
       "0       USD    108       3      6  \n",
       "1       USD      4       1      1  \n",
       "2       USD      2       7      8  \n",
       "3       GBP      5       2      3  \n",
       "4       USD      1       1      1  \n",
       "5       USD      3       5      5  \n",
       "6       EUR      5       7      8  \n",
       "7       USD      4       4      8  \n",
       "8       USD      5       5      6  \n",
       "9       USD      1       4      4  \n",
       "10      USD     12       1      1  \n",
       "11      GBP      7       7      8  \n",
       "12      USD      7       1      1  \n",
       "13      EUR      1       3      3  \n",
       "14      USD      2       4      4  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataframe has all unique supporters who made a FCS transaction.  The columns are as follows:\n",
    "\n",
    "  - supp_id = SUPPORTER_ID who made a FCS transaction\n",
    "  - num_fcs = number of FCS transactions total\n",
    "  - fur = if supporter made at least 1 FUR transaction, a 1 is placed, otherwise 0\n",
    "  - max_fcs = maximum FCS contribution amount in USD\n",
    "  - total_fcs = total FCS contribution amount in USD\n",
    "  - total_trans = total number of transactions made by that supporter\n",
    "  - currency = currency of donation\n",
    "  - num_p = number of 'P' transactions by the supporter\n",
    "  - num_fs = number of 'formsub' transactions by the supporter\n",
    "  - num_c = number of 'click' transactions by the supporter"
   ]
  }
 ],
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