volunteer_plus_12_13.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "This notebook answers the following question:  \n",
    "\n",
    "- For those who haven’t donated, what is the probability of a gift in the following year of their first instance of standard volunteerism?  \n",
    "\n",
    "- What is the probabillity of a donation in the first year of volunteerism ?\n",
    "\n",
    "by Fred Etter - December, 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\n",
    "from sklearn import linear_model\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn import preprocessing\n",
    "import sklearn\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "from datetime import datetime\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn import metrics\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read in the data\n",
    "df = pd.read_csv('file1_12_3.csv', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>ContactId</th>\n",
       "      <th>Year</th>\n",
       "      <th>PledgeTotal</th>\n",
       "      <th>VolType</th>\n",
       "      <th>VolunteerActivityCnt</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>874ddbce-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2007</td>\n",
       "      <td>52.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>9a4ddbce-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2007</td>\n",
       "      <td>80.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>5e4edbce-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2007</td>\n",
       "      <td>10000.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>c150dbce-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2007</td>\n",
       "      <td>120.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>0b53dbce-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2007</td>\n",
       "      <td>500.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ContactId  Year  PledgeTotal VolType  \\\n",
       "0  874ddbce-11cd-e111-941f-00259073dc22  2007        52.00     NaN   \n",
       "1  9a4ddbce-11cd-e111-941f-00259073dc22  2007        80.00     NaN   \n",
       "2  5e4edbce-11cd-e111-941f-00259073dc22  2007     10000.00     NaN   \n",
       "3  c150dbce-11cd-e111-941f-00259073dc22  2007       120.00     NaN   \n",
       "4  0b53dbce-11cd-e111-941f-00259073dc22  2007       500.16     NaN   \n",
       "\n",
       "   VolunteerActivityCnt  BirthYear  Gender  \n",
       "0                   NaN     1977.0  female  \n",
       "1                   NaN     1968.0  female  \n",
       "2                   NaN     1958.0    male  \n",
       "3                   NaN        NaN    male  \n",
       "4                   NaN     1947.0  female  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the first 5 lines of the data\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(385722, 7)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show the number of rows and columns of the original data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop Council, Committe, Board members\n",
    "df = df.drop(df[df.VolType == 'Council, Committe or Board'].index).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dataframe that only has volunteers of 'Standard' type:\n",
    "df_gender = df.copy()\n",
    "df_gender = df_gender.fillna(0)\n",
    "df_gender = df_gender.drop(df_gender[df_gender.VolType == 0].index).fillna(0)\n",
    "df_std_only = df_gender.drop(df_gender[df_gender.VolType == 'Council, Committe or Board'].index).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a series that captures the earliest year that someone volunteered\n",
    "a = df_std_only.groupby('ContactId')['Year'].transform('min')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column and populate it:\n",
    "df_std_only['min_year'] = a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ContactId</th>\n",
       "      <th>Year</th>\n",
       "      <th>PledgeTotal</th>\n",
       "      <th>VolType</th>\n",
       "      <th>VolunteerActivityCnt</th>\n",
       "      <th>BirthYear</th>\n",
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       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>Standard</td>\n",
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       "      <td>2015</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1948.0</td>\n",
       "      <td>male</td>\n",
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       "      <td>2016</td>\n",
       "      <td>390.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>377432</td>\n",
       "      <td>5fbf99cf-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2012</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2012</td>\n",
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       "    <tr>\n",
       "      <td>382691</td>\n",
       "      <td>6f435cd9-7aa7-e211-a0e0-4040184c1c1a</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
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       "      <td>214e9364-55cc-e211-a0e0-4040184c1c1a</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>2012</td>\n",
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      ],
      "text/plain": [
       "                                   ContactId  Year  PledgeTotal   VolType  \\\n",
       "383751  2e4315d2-11cd-e111-941f-00259073dc22  2012        500.0  Standard   \n",
       "378277  3cada565-0d9e-e211-a0e0-4040184c1c1a  2012          0.0  Standard   \n",
       "377541  2c4315d2-11cd-e111-941f-00259073dc22  2011        725.0  Standard   \n",
       "380057  97b755d0-11cd-e111-941f-00259073dc22  2010          0.0  Standard   \n",
       "379152  3540dbce-11cd-e111-941f-00259073dc22  2015       1300.0  Standard   \n",
       "385063  b2ea7dc9-5406-e611-828d-26d4160798d6  2016        390.0  Standard   \n",
       "380352  c2dc8191-a231-e711-b10c-005056975e92  2017          0.0  Standard   \n",
       "377432  5fbf99cf-11cd-e111-941f-00259073dc22  2012         52.0  Standard   \n",
       "382691  6f435cd9-7aa7-e211-a0e0-4040184c1c1a  2013          0.0  Standard   \n",
       "380407  214e9364-55cc-e211-a0e0-4040184c1c1a  2012          0.0  Standard   \n",
       "\n",
       "        VolunteerActivityCnt  BirthYear  Gender  min_year  \n",
       "383751                   1.0        0.0  female      2012  \n",
       "378277                   1.0        0.0  female      2012  \n",
       "377541                   1.0     1950.0    male      2011  \n",
       "380057                   1.0        0.0  female      2010  \n",
       "379152                   1.0     1948.0    male      2010  \n",
       "385063                   3.0        0.0    male      2016  \n",
       "380352                   1.0        0.0  female      2017  \n",
       "377432                   1.0        0.0  female      2012  \n",
       "382691                   1.0        0.0  female      2012  \n",
       "380407                   1.0        0.0  female      2012  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display a random sample of 10 rows of the dataframe\n",
    "df_std_only.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows where Year does not equal min_year\n",
    "df_std_only_t = df_std_only.drop(df_std_only[df_std_only.Year != df_std_only.min_year].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop rows where there was a pledge\n",
    "df_init = df_std_only_t.drop(df_std_only_t[df_std_only_t.PledgeTotal != 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4089, 8)"
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of rows and columns of the dataframe\n",
    "df_init.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2010</td>\n",
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       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>375987</td>\n",
       "      <td>9937814c-5f24-e311-95aa-4040184c1c1a</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   ContactId  Year  PledgeTotal   VolType  \\\n",
       "379941  443d15d2-11cd-e111-941f-00259073dc22  2011          0.0  Standard   \n",
       "384710  d7db941b-2fec-4148-a344-377cf1e7a10e  2016          0.0  Standard   \n",
       "377997  5e3d15d2-11cd-e111-941f-00259073dc22  2011          0.0  Standard   \n",
       "378635  7f6cf402-8524-e311-95aa-4040184c1c1a  2013          0.0  Standard   \n",
       "383066  84615108-eb25-e311-a975-4040184c1c1a  2013          0.0  Standard   \n",
       "376201  f63e15d2-11cd-e111-941f-00259073dc22  2011          0.0  Standard   \n",
       "375947  7565c18a-2d20-e511-9cbe-26d4160798d6  2010          0.0  Standard   \n",
       "378028  c8cdd312-67d7-e211-a0e0-4040184c1c1a  2012          0.0  Standard   \n",
       "381040  e44cb1d1-11cd-e111-941f-00259073dc22  2011          0.0  Standard   \n",
       "375987  9937814c-5f24-e311-95aa-4040184c1c1a  2013          0.0  Standard   \n",
       "\n",
       "        VolunteerActivityCnt  BirthYear  Gender  min_year  \n",
       "379941                   1.0        0.0    male      2011  \n",
       "384710                   2.0        0.0  female      2016  \n",
       "377997                   1.0        0.0  female      2011  \n",
       "378635                   1.0        0.0    male      2013  \n",
       "383066                   1.0        0.0  female      2013  \n",
       "376201                   1.0        0.0    male      2011  \n",
       "375947                   1.0        0.0  female      2010  \n",
       "378028                   1.0        0.0  female      2012  \n",
       "381040                   1.0        0.0  female      2011  \n",
       "375987                   1.0        0.0  female      2013  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show a random sample of 10 rows of the dataframe\n",
    "df_init.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above dataframe shows the volunteers who volunteered and have never donated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new column that add 1 year to the 'min_year' column\n",
    "df_init['y2'] = df_init.min_year + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ContactId</th>\n",
       "      <th>Year</th>\n",
       "      <th>PledgeTotal</th>\n",
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       "      <td>375651</td>\n",
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       "      <td>2015</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>375652</td>\n",
       "      <td>e34cb1d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
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       "      <td>2011</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>375653</td>\n",
       "      <td>29f9f78a-6681-e411-9b7f-26d4160798d6</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>375655</td>\n",
       "      <td>34aaf5d1-d3d1-e211-a0e0-4040184c1c1a</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2012</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>375658</td>\n",
       "      <td>9350b1d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2011</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   ContactId  Year  PledgeTotal   VolType  \\\n",
       "375651  4c58f4ca-3545-e511-92f8-26d4160798d6  2015          0.0  Standard   \n",
       "375652  e34cb1d1-11cd-e111-941f-00259073dc22  2011          0.0  Standard   \n",
       "375653  29f9f78a-6681-e411-9b7f-26d4160798d6  2018          0.0  Standard   \n",
       "375655  34aaf5d1-d3d1-e211-a0e0-4040184c1c1a  2012          0.0  Standard   \n",
       "375658  9350b1d1-11cd-e111-941f-00259073dc22  2011          0.0  Standard   \n",
       "\n",
       "        VolunteerActivityCnt  BirthYear  Gender  min_year    y2  \n",
       "375651                   1.0        0.0       0      2015  2016  \n",
       "375652                   1.0        0.0  female      2011  2012  \n",
       "375653                   1.0     1955.0  female      2018  2019  \n",
       "375655                   1.0        0.0  female      2012  2013  \n",
       "375658                   1.0        0.0  female      2011  2012  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the first 5 rows of the dataframe\n",
    "df_init.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new dataframe to capture the pledge amount for the year after they volunteered\n",
    "new_df = pd.merge(df_init, df, how='left', left_on=['ContactId','y2'], right_on = ['ContactId', 'Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4089, 15)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the number of rows and columns of dataframe\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>Year_x</th>\n",
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       "      <th>Year_y</th>\n",
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       "      <td>1302</td>\n",
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       "      <td>2011</td>\n",
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       "      <td>16b25678-c4ce-e211-a0e0-4040184c1c1a</td>\n",
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       "      <td>2013.0</td>\n",
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       "      <td>2726</td>\n",
       "      <td>c25ab1d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>2013</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2013</td>\n",
       "      <td>2014</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",
       "      <td>413</td>\n",
       "      <td>cd4c3f6d-fec7-4574-b9e3-62349d523e71</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2012</td>\n",
       "      <td>2013</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 ContactId  Year_x  PledgeTotal_x VolType_x  \\\n",
       "1302  35b062d1-11cd-e111-941f-00259073dc22    2011            0.0  Standard   \n",
       "2596  16b25678-c4ce-e211-a0e0-4040184c1c1a    2012            0.0  Standard   \n",
       "2726  c25ab1d1-11cd-e111-941f-00259073dc22    2011            0.0  Standard   \n",
       "1849  fa2698d2-e3f2-e211-a0e0-4040184c1c1a    2013            0.0  Standard   \n",
       "413   cd4c3f6d-fec7-4574-b9e3-62349d523e71    2012            0.0  Standard   \n",
       "\n",
       "      VolunteerActivityCnt_x  BirthYear_x Gender_x  min_year    y2  Year_y  \\\n",
       "1302                     1.0          0.0   female      2011  2012     NaN   \n",
       "2596                     1.0          0.0   female      2012  2013  2013.0   \n",
       "2726                     1.0          0.0   female      2011  2012     NaN   \n",
       "1849                     1.0       1963.0   female      2013  2014     NaN   \n",
       "413                      1.0          0.0     male      2012  2013  2013.0   \n",
       "\n",
       "      PledgeTotal_y VolType_y  VolunteerActivityCnt_y  BirthYear_y Gender_y  \n",
       "1302            NaN       NaN                     NaN          NaN      NaN  \n",
       "2596            0.0  Standard                     2.0          0.0   female  \n",
       "2726            NaN       NaN                     NaN          NaN      NaN  \n",
       "1849            NaN       NaN                     NaN          NaN      NaN  \n",
       "413            52.0  Standard                     1.0          0.0     male  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# view a random sample of 5 rows\n",
    "new_df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fill NaNs with 0\n",
    "new_df = new_df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop all rows with 0 in pledge total\n",
    "new_df = new_df.drop(new_df[new_df.PledgeTotal_y == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 15)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# new number of rows which is the number of people who donated the year after they volunteered\n",
    "new_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "237.19337254901964"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# capture the mean donation\n",
    "new_df.PledgeTotal_y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06236243580337491"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the probability of those who donated the year after they volunteered\n",
    "255/4089"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, **6.2%** of volunteers who had never donated, donated the following year. In other words, there is a **6.2%** chance that a volunteer who has never donated will make a donation in the year following there first volunteer activity.\n",
    "\n",
    "There average donation for this group was **237** USD."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now, check about the case for 5 years later:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### All of the following steps are same as above except adding 5 years instead of 1 #######\n",
    "df_init['y5'] = df_init.min_year + 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df_5 = pd.merge(df_init, df, how='left', left_on=['ContactId','y5'], right_on = ['ContactId', 'Year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>2012</td>\n",
       "      <td>2016</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",
       "      <td>2</td>\n",
       "      <td>29f9f78a-6681-e411-9b7f-26d4160798d6</td>\n",
       "      <td>2018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1955.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2018</td>\n",
       "      <td>2019</td>\n",
       "      <td>2023</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",
       "      <td>3</td>\n",
       "      <td>34aaf5d1-d3d1-e211-a0e0-4040184c1c1a</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2012</td>\n",
       "      <td>2013</td>\n",
       "      <td>2017</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",
       "      <td>4</td>\n",
       "      <td>9350b1d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2011</td>\n",
       "      <td>2012</td>\n",
       "      <td>2016</td>\n",
       "      <td>2016.0</td>\n",
       "      <td>54.6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              ContactId  Year_x  PledgeTotal_x VolType_x  \\\n",
       "0  4c58f4ca-3545-e511-92f8-26d4160798d6    2015            0.0  Standard   \n",
       "1  e34cb1d1-11cd-e111-941f-00259073dc22    2011            0.0  Standard   \n",
       "2  29f9f78a-6681-e411-9b7f-26d4160798d6    2018            0.0  Standard   \n",
       "3  34aaf5d1-d3d1-e211-a0e0-4040184c1c1a    2012            0.0  Standard   \n",
       "4  9350b1d1-11cd-e111-941f-00259073dc22    2011            0.0  Standard   \n",
       "\n",
       "   VolunteerActivityCnt_x  BirthYear_x Gender_x  min_year    y2    y5  Year_y  \\\n",
       "0                     1.0          0.0        0      2015  2016  2020     NaN   \n",
       "1                     1.0          0.0   female      2011  2012  2016     NaN   \n",
       "2                     1.0       1955.0   female      2018  2019  2023     NaN   \n",
       "3                     1.0          0.0   female      2012  2013  2017     NaN   \n",
       "4                     1.0          0.0   female      2011  2012  2016  2016.0   \n",
       "\n",
       "   PledgeTotal_y VolType_y  VolunteerActivityCnt_y  BirthYear_y Gender_y  \n",
       "0            NaN       NaN                     NaN          NaN      NaN  \n",
       "1            NaN       NaN                     NaN          NaN      NaN  \n",
       "2            NaN       NaN                     NaN          NaN      NaN  \n",
       "3            NaN       NaN                     NaN          NaN      NaN  \n",
       "4           54.6         0                     0.0          0.0   female  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4089, 16)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df_5 = new_df_5.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df_5 = new_df_5.drop(new_df_5[new_df_5.PledgeTotal_y == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(192, 16)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "267.56671875"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df_5.PledgeTotal_y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.046863558701488894"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "192/4097"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion:  \n",
    "\n",
    "After the first year of volunteering, those volunteers gave a gift **6.2%** of the time the following year and **4.7%** of the time in the 5th year after volunteering.  \n",
    "\n",
    "The average gift 1 year later was **237 USD** and it was **268 USD** 5 years later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Addendum:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check about the case for the current year of volunteerism:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6147, 8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_std_only_t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_pledge_y0 = df_std_only_t.drop(df_std_only_t[df_std_only_t.PledgeTotal == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>ContactId</th>\n",
       "      <th>Year</th>\n",
       "      <th>PledgeTotal</th>\n",
       "      <th>VolType</th>\n",
       "      <th>VolunteerActivityCnt</th>\n",
       "      <th>BirthYear</th>\n",
       "      <th>Gender</th>\n",
       "      <th>min_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>377656</td>\n",
       "      <td>d859b7d0-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2010</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>383521</td>\n",
       "      <td>a1b862d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2015</td>\n",
       "      <td>752.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1986.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>377609</td>\n",
       "      <td>f33ab1d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2010</td>\n",
       "      <td>50.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>381220</td>\n",
       "      <td>2f47b1d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2017</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>384852</td>\n",
       "      <td>0b33061a-e4ac-e611-8226-26d4160798d6</td>\n",
       "      <td>2017</td>\n",
       "      <td>600.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>377549</td>\n",
       "      <td>3a8703d1-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2011</td>\n",
       "      <td>1820.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>379165</td>\n",
       "      <td>36bb99cf-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2015</td>\n",
       "      <td>260.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1975.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>381630</td>\n",
       "      <td>f0c599cf-11cd-e111-941f-00259073dc22</td>\n",
       "      <td>2013</td>\n",
       "      <td>312.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>female</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>385564</td>\n",
       "      <td>bcb85611-c533-e611-a946-26d4160798d6</td>\n",
       "      <td>2016</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>381174</td>\n",
       "      <td>6ebe30d0-1481-4b68-a934-22b3f3872b7d</td>\n",
       "      <td>2018</td>\n",
       "      <td>104.0</td>\n",
       "      <td>Standard</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>male</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   ContactId  Year  PledgeTotal   VolType  \\\n",
       "377656  d859b7d0-11cd-e111-941f-00259073dc22  2010         72.0  Standard   \n",
       "383521  a1b862d1-11cd-e111-941f-00259073dc22  2015        752.0  Standard   \n",
       "377609  f33ab1d1-11cd-e111-941f-00259073dc22  2010         50.0  Standard   \n",
       "381220  2f47b1d1-11cd-e111-941f-00259073dc22  2017         26.0  Standard   \n",
       "384852  0b33061a-e4ac-e611-8226-26d4160798d6  2017        600.0  Standard   \n",
       "377549  3a8703d1-11cd-e111-941f-00259073dc22  2011       1820.0  Standard   \n",
       "379165  36bb99cf-11cd-e111-941f-00259073dc22  2015        260.0  Standard   \n",
       "381630  f0c599cf-11cd-e111-941f-00259073dc22  2013        312.0  Standard   \n",
       "385564  bcb85611-c533-e611-a946-26d4160798d6  2016       1038.0  Standard   \n",
       "381174  6ebe30d0-1481-4b68-a934-22b3f3872b7d  2018        104.0  Standard   \n",
       "\n",
       "        VolunteerActivityCnt  BirthYear  Gender  min_year  \n",
       "377656                   1.0        0.0  female      2010  \n",
       "383521                   1.0     1986.0    male      2015  \n",
       "377609                   1.0        0.0    male      2010  \n",
       "381220                   1.0     1979.0  female      2017  \n",
       "384852                   2.0     1965.0    male      2017  \n",
       "377549                   1.0        0.0    male      2011  \n",
       "379165                   1.0     1975.0  female      2015  \n",
       "381630                   1.0        0.0  female      2013  \n",
       "385564                   9.0     1954.0    male      2016  \n",
       "381174                   1.0        0.0    male      2018  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pledge_y0.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2058, 8)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pledge_y0.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.33479746217667156"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2058/6147"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, **33.5%** of the Standard volunteers donate in their first year of volunteerism."
   ]
  }
 ],
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