Historical Donor Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis\n",
    "\n",
    "\n",
    "**Who**\n",
    "All individual donors who gave to the c3 recorded in NGP, minus monthly donors and Indiegogo donors. We’re working with 1,561 donors in this analysis.\n",
    "\n",
    "**Approach**\n",
    "This is what I would call an “exploratory data analysis.” My goal here was to simply understand or donor data and start asking questions. Like every good analysis, I wound up with more questions than I was able to answer.   \n",
    "\n",
    "**I started my analysis by asking the questions:**  \n",
    "How many of our donors give below \\\\$100? Below \\\\$500? Below \\\\$1,000?  \n",
    "What’s the average lifetime value of our donors?  \n",
    "How many of our donors give once and then never again?  \n",
    "Who loves us the most (donors with the most gifts)?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import datetime\n",
    "datetime.datetime.strptime\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>Contact Name</th>\n",
       "      <th>Count</th>\n",
       "      <th>Contribution ID</th>\n",
       "      <th>Received On</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Designation</th>\n",
       "      <th>Contribution Type</th>\n",
       "      <th>Cycle</th>\n",
       "      <th>Period</th>\n",
       "      <th>Contribution.Note</th>\n",
       "      <th>Source Code Path</th>\n",
       "      <th>Contact ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4112</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNW16CGKCY8</td>\n",
       "      <td>2011-07-08</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>New Era Colorado Foundation</td>\n",
       "      <td>Monetary Contribution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNX0EGHGYZ0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNW16CGJP70</td>\n",
       "      <td>2013-10-11</td>\n",
       "      <td>100.0</td>\n",
       "      <td>New Era Colorado Foundation</td>\n",
       "      <td>Monetary Contribution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNX0EGHVH14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4716</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNW16E3PPR3</td>\n",
       "      <td>2015-12-22</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>New Era Colorado Foundation</td>\n",
       "      <td>Monetary Contribution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNX0EGJ1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNW16CGFH74</td>\n",
       "      <td>2008-02-07</td>\n",
       "      <td>21.0</td>\n",
       "      <td>New Era Colorado Foundation</td>\n",
       "      <td>Monetary Contribution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNX0EGH3992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1932</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNW16CGDHA2</td>\n",
       "      <td>2007-03-21</td>\n",
       "      <td>50.0</td>\n",
       "      <td>New Era Colorado Foundation</td>\n",
       "      <td>Monetary Contribution</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VNX0EGH2VW2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Contact Name  Count Contribution ID Received On  Amount  \\\n",
       "0           4112    NaN     VNW16CGKCY8  2011-07-08  2000.0   \n",
       "1           1966    NaN     VNW16CGJP70  2013-10-11   100.0   \n",
       "2           4716    NaN     VNW16E3PPR3  2015-12-22  2500.0   \n",
       "3           4793    NaN     VNW16CGFH74  2008-02-07    21.0   \n",
       "4           1932    NaN     VNW16CGDHA2  2007-03-21    50.0   \n",
       "\n",
       "                   Designation      Contribution Type  Cycle  Period  \\\n",
       "0  New Era Colorado Foundation  Monetary Contribution    NaN     NaN   \n",
       "1  New Era Colorado Foundation  Monetary Contribution    NaN     NaN   \n",
       "2  New Era Colorado Foundation  Monetary Contribution    NaN     NaN   \n",
       "3  New Era Colorado Foundation  Monetary Contribution    NaN     NaN   \n",
       "4  New Era Colorado Foundation  Monetary Contribution    NaN     NaN   \n",
       "\n",
       "  Contribution.Note  Source Code Path   Contact ID  \n",
       "0               NaN               NaN  VNX0EGHGYZ0  \n",
       "1               NaN               NaN  VNX0EGHVH14  \n",
       "2               NaN               NaN  VNX0EGJ1973  \n",
       "3               NaN               NaN  VNX0EGH3992  \n",
       "4               NaN               NaN  VNX0EGH2VW2  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/historical_data.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Liftime Value"
   ]
  },
  {
   "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>lifetime_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4421</th>\n",
       "      <td>301000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5780</th>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>673</th>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6974</th>\n",
       "      <td>35500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6980</th>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>813</th>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>26100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5906</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6093</th>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3521</th>\n",
       "      <td>23000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3475</th>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3887</th>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3304</th>\n",
       "      <td>16000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7135</th>\n",
       "      <td>12000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               lifetime_amount\n",
       " Contact Name                 \n",
       "4421                  301000.0\n",
       "5780                   50000.0\n",
       "673                    50000.0\n",
       "6974                   35500.0\n",
       "6980                   35000.0\n",
       "813                    35000.0\n",
       "1456                   26100.0\n",
       "5906                   25000.0\n",
       "3058                   25000.0\n",
       "6093                   25000.0\n",
       "3521                   23000.0\n",
       "3475                   18000.0\n",
       "3887                   16000.0\n",
       "3304                   16000.0\n",
       "7135                   12000.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime = data.groupby([' Contact Name'])['Amount'].sum().sort_values(ascending = False).to_frame()\n",
    "donors_by_lifetime.columns = ['lifetime_amount']\n",
    "donors_by_lifetime.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>lifetime_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1561.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>736.985951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8180.588119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>25.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>105.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>301000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       lifetime_amount\n",
       "count      1561.000000\n",
       "mean        736.985951\n",
       "std        8180.588119\n",
       "min           0.000000\n",
       "25%          25.000000\n",
       "50%          50.000000\n",
       "75%         105.000000\n",
       "max      301000.000000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Lifetime Values\n",
    "5.8% of donors have a lifetime value of \\\\$1,000 or more and make up 86.0% of our total revenue. This means that 94.2% of our donors are small dollar donors, and make up 14% of our total revenue. Given the 80/20 rule, these numbers make sense.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifetime_amount    0.057655\n",
       "dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] > 1000)].count()/len(donors_by_lifetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8599927069330386"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] > 1000)].lifetime_amount.sum()/donors_by_lifetime.lifetime_amount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifetime_amount    0.942345\n",
       "dtype: float64"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] <= 1000)].count()/len(donors_by_lifetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14000729306696116"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] <= 1000)].lifetime_amount.sum()/donors_by_lifetime.lifetime_amount.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "donors_100_1000 = donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] > 100) & (donors_by_lifetime[\"lifetime_amount\"] <= 1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4664f14048>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4_dims = (11.7, 8.27)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "\n",
    "sns.distplot(donors_100_1000['lifetime_amount'], bins = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "donors_900_10000 = donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] > 900) & (donors_by_lifetime[\"lifetime_amount\"] <= 10000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4662c245f8>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a4_dims = (11.7, 8.27)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "\n",
    "sns.distplot(donors_900_10000['lifetime_amount'], bins = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quick Check on Lifetime Amounts  \n",
    "\n",
    "7.5% of donors have a lifetime value of \\\\$1,000 or more and make up 88.3\\% of our total revenue.\n",
    "\n",
    "This means that 92.5\\% of our donors are small dollar donors, and make up 16\\% of our total revenue. Given the 80/20 rule, these numbers make sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lifetime_amount    0.074952\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] >= 1000)].count()/len(donors_by_lifetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.88346208882523"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_major_donors = donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] >= 1000)].lifetime_amount.sum()\n",
    "total = donors_by_lifetime.lifetime_amount.sum()\n",
    "total_major_donors/total"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for Repeat Donors  \n",
    "\n",
    "How many of our donors only give once? How many gifts does someone give before they lapse?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>number_of_gifts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4543</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               number_of_gifts\n",
       " Contact Name                 \n",
       "1365                        12\n",
       "530                         12\n",
       "4543                        11\n",
       "482                         11\n",
       "417                         10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "by_gifts = data.groupby([' Contact Name']).size().sort_values(ascending = False).to_frame()\n",
    "by_gifts.columns = ['number_of_gifts']\n",
    "by_gifts.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Repeat Donors\n",
    "76\\% of our donors have given only once. This isn't our exact retention rate, because it doesn't take into account time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7616912235746316"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "only_once = by_gifts.loc[by_gifts['number_of_gifts'] == 1]\n",
    "len(only_once)/len(by_gifts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "repeat_donors = by_gifts.loc[by_gifts['number_of_gifts'] != 1]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at some summary statistics. 372 of our donors have given more than once. The average number a gift someone gives is 3, with a median of 2. Most of the data fall within the range of 2 to 4 gifts. I wonder how these stats would change when we separate out small dollar donors from major donors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>number_of_gifts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>372.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.201613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.801376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       number_of_gifts\n",
       "count       372.000000\n",
       "mean          3.201613\n",
       "std           1.801376\n",
       "min           2.000000\n",
       "25%           2.000000\n",
       "50%           2.000000\n",
       "75%           4.000000\n",
       "max          12.000000"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repeat_donors.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(repeat_donors.number_of_gifts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences between small and major donors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# separate out major and small donors into two dataframes\n",
    "major = donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] >= 1000)]\n",
    "small = donors_by_lifetime.loc[(donors_by_lifetime[\"lifetime_amount\"] < 1000)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# locate the donors in the dataframe with individual transcations\n",
    "i1 = data[\" Contact Name\"]\n",
    "i2 = major.index\n",
    "major = data[i1.isin(i2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# locate the donors in the dataframe with individual transcations\n",
    "i1 = data[\" Contact Name\"]\n",
    "i2 = small.index\n",
    "small = data[i1.isin(i2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>number_of_gifts</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contact Name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1365</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3454</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5848</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6290</th>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3818</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3058</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7542</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               number_of_gifts\n",
       " Contact Name                 \n",
       "1365                        12\n",
       "530                         12\n",
       "482                         11\n",
       "3454                        10\n",
       "5848                         9\n",
       "6290                         9\n",
       "2357                         8\n",
       "3818                         8\n",
       "3058                         8\n",
       "7542                         7"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# major gifts by number of gifts\n",
    "major_by_gifts = major.groupby([' Contact Name']).size().sort_values(ascending = False).to_frame()\n",
    "major_by_gifts.columns = ['number_of_gifts']\n",
    "major_by_gifts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4655d2b860>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.distplot(major_by_gifts[\"number_of_gifts\"], bins = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#small gifts by number of gifts\n",
    "small_by_gifts = small.groupby([' Contact Name']).size().sort_values(ascending = False).to_frame()\n",
    "small_by_gifts.columns = ['number_of_gifts']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "small_by_gifts[\"type\"] = \"small\"\n",
    "major_by_gifts[\"type\"] = \"major\"\n",
    "all_gifts = pd.concat([small_by_gifts, major_by_gifts])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see below that most small dollar donors only give a few number of times (primarily only one gift), while major donors have a larger spread, ranging primarily from 1 to 5 gifts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"type\", y=\"number_of_gifts\", kind=\"violin\", data=all_gifts);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1440x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.catplot(x=\"type\", y=\"number_of_gifts\", kind=\"boxen\", data=all_gifts);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we see that we have 1,444 small dollar donors and 117 major donors. Small dollar donors on average give 1.3 gifts, while major donors on average give 3.5 gifts. We clearly see that most small dollar donors usually give only once. This tells me that there is room for imporvement in our retention rate for small dollar donors."
   ]
  },
  {
   "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>number_of_gifts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1444.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.360111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.935458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       number_of_gifts\n",
       "count      1444.000000\n",
       "mean          1.360111\n",
       "std           0.935458\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           1.000000\n",
       "75%           1.000000\n",
       "max          11.000000"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gifts.loc[all_gifts[\"type\"] == \"small\"].describe()"
   ]
  },
  {
   "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>number_of_gifts</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>117.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.614431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       number_of_gifts\n",
       "count       117.000000\n",
       "mean          3.555556\n",
       "std           2.614431\n",
       "min           1.000000\n",
       "25%           1.000000\n",
       "50%           3.000000\n",
       "75%           5.000000\n",
       "max          12.000000"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_gifts.loc[all_gifts[\"type\"] == \"major\"].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Retention Rate  for Small Dollar Donors\n",
    "\n",
    "The retention rate is what percentage of your donors give two years in a row. It is useful for determine how well you keep your donors, and can provide a launching for point fo improving donor communications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert column to datetime\n",
    "small['Received On'] = pd.to_datetime(small['Received On'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# this is a horrible workaround for something I presume is easy\n",
    "# Create separate dataframes and add a yes if the donation was received in a given year, per donor\n",
    "small_2007 = small[small['Received On'].apply(lambda x: x.year == 2007)]\n",
    "small_2007[\"2007\"] = \"yes\"\n",
    "small_2008 = small[small['Received On'].apply(lambda x: x.year == 2008)]\n",
    "small_2008[\"2008\"] = \"yes\"\n",
    "small_2009 = small[small['Received On'].apply(lambda x: x.year == 2009)]\n",
    "small_2009[\"2009\"] = \"yes\"\n",
    "small_2010 = small[small['Received On'].apply(lambda x: x.year == 2010)]\n",
    "small_2010[\"2010\"] = \"yes\"\n",
    "small_2011 = small[small['Received On'].apply(lambda x: x.year == 2011)]\n",
    "small_2011[\"2011\"] = \"yes\"\n",
    "small_2012 = small[small['Received On'].apply(lambda x: x.year == 2012)]\n",
    "small_2012[\"2012\"] = \"yes\"\n",
    "small_2013 = small[small['Received On'].apply(lambda x: x.year == 2013)]\n",
    "small_2013[\"2013\"] = \"yes\"\n",
    "small_2014 = small[small['Received On'].apply(lambda x: x.year == 2014)]\n",
    "small_2014[\"2014\"] = \"yes\"\n",
    "small_2015 = small[small['Received On'].apply(lambda x: x.year == 2015)]\n",
    "small_2015[\"2015\"] = \"yes\"\n",
    "small_2016 = small[small['Received On'].apply(lambda x: x.year == 2016)]\n",
    "small_2016[\"2016\"] = \"yes\"\n",
    "small_2017 = small[small['Received On'].apply(lambda x: x.year == 2017)]\n",
    "small_2017[\"2017\"] = \"yes\"\n",
    "small_2018 = small[small['Received On'].apply(lambda x: x.year == 2018)]\n",
    "small_2018[\"2018\"] = \"yes\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame(columns=[' Contact Name','Count','Contribution ID','Recevied On',\n",
    "                           'Amount','Designation','Contribution Type', 'Cycle',\n",
    "                          'Period', 'Contribution.Note', 'Source Code Path', 'Contact ID', \n",
    "                          '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015',\n",
    "                          '2016', '2017', '2018'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.concat([data, small_2007, small_2008, small_2009, small_2010, \n",
    "           small_2011, small_2012, small_2013, small_2014,\n",
    "          small_2015, small_2016, small_2017, small_2018])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = data.groupby([' Contact Name']).count().iloc[:, [1,3,4,5,6,7,8,9,10,11,12,13,14]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the retention rate\n",
    "retention_2009 = df.loc[(df[\"2008\"] >= 1) & (df[\"2009\"] >= 1)].Amount.count()/len(small_2008)*100\n",
    "retention_2010 = df.loc[(df[\"2009\"] >= 1) & (df[\"2010\"] >= 1)].Amount.count()/len(small_2009)\n",
    "retention_2011 = df.loc[(df[\"2010\"] >= 1) & (df[\"2011\"] >= 1)].Amount.count()/len(small_2010)\n",
    "retention_2012 = df.loc[(df[\"2011\"] >= 1) & (df[\"2012\"] >= 1)].Amount.count()/len(small_2011)\n",
    "retention_2013 = df.loc[(df[\"2012\"] >= 1) & (df[\"2013\"] >= 1)].Amount.count()/len(small_2012)\n",
    "retention_2014 = df.loc[(df[\"2013\"] >= 1) & (df[\"2014\"] >= 1)].Amount.count()/len(small_2013)\n",
    "retention_2015 = df.loc[(df[\"2014\"] >= 1) & (df[\"2015\"] >= 1)].Amount.count()/len(small_2014)\n",
    "retention_2016 = df.loc[(df[\"2015\"] >= 1) & (df[\"2016\"] >= 1)].Amount.count()/len(small_2015)\n",
    "retention_2017 = df.loc[(df[\"2016\"] >= 1) & (df[\"2017\"] >= 1)].Amount.count()/len(small_2016)\n",
    "retention_2018 = df.loc[(df[\"2017\"] >= 1) & (df[\"2018\"] >= 1)].Amount.count()/len(small_2017)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The retention rate for 2009 was : 0.0%\n",
      "The retention rate for 2010 was : 5.9%\n",
      "The retention rate for 2011 was : 6.4%\n",
      "The retention rate for 2012 was : 10.8%\n",
      "The retention rate for 2013 was : 11.8%\n",
      "The retention rate for 2014 was : 8.5%\n",
      "The retention rate for 2015 was : 7.7%\n",
      "The retention rate for 2016 was : 16.9%\n",
      "The retention rate for 2017 was : 15.2%\n",
      "The retention rate for 2018 was : 8.9%\n"
     ]
    }
   ],
   "source": [
    "# print the retention rates\n",
    "print(\"The retention rate for 2009 was :\", \"{:.1%}\".format(retention_2009))\n",
    "print(\"The retention rate for 2010 was :\", \"{:.1%}\".format(retention_2010))\n",
    "print(\"The retention rate for 2011 was :\", \"{:.1%}\".format(retention_2011))\n",
    "print(\"The retention rate for 2012 was :\", \"{:.1%}\".format(retention_2012))\n",
    "print(\"The retention rate for 2013 was :\", \"{:.1%}\".format(retention_2013))\n",
    "print(\"The retention rate for 2014 was :\", \"{:.1%}\".format(retention_2014))\n",
    "print(\"The retention rate for 2015 was :\", \"{:.1%}\".format(retention_2015))\n",
    "print(\"The retention rate for 2016 was :\", \"{:.1%}\".format(retention_2016))\n",
    "print(\"The retention rate for 2017 was :\", \"{:.1%}\".format(retention_2017))\n",
    "print(\"The retention rate for 2018 was :\", \"{:.1%}\".format(retention_2018))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>Year</th>\n",
       "      <th>Retention_Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>5.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011</td>\n",
       "      <td>6.382979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012</td>\n",
       "      <td>10.810811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013</td>\n",
       "      <td>11.818182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Retention_Rate\n",
       "0  2009        0.000000\n",
       "1  2010        5.882353\n",
       "2  2011        6.382979\n",
       "3  2012       10.810811\n",
       "4  2013       11.818182"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [[\"2009\", retention_2009*100], [\"2010\", retention_2010*100], [\"2011\",retention_2011*100], \n",
    "        [\"2012\", retention_2012*100], [\"2013\", retention_2013*100], [\"2014\", retention_2014*100],\n",
    "    [\"2015\",retention_2015*100], [\"2016\", retention_2016*100], [\"2017\",retention_2017*100],\n",
    "    [\"2018\",retention_2018*100]]\n",
    "plot_df = pd.DataFrame(data, columns = [\"Year\", \"Retention_Rate\"])\n",
    "plot_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f465556ef98>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.xlabel('xlabel', fontsize=18)\n",
    "plt.ylabel('ylabel', fontsize=16)\n",
    "plt.tick_params(axis='both', which='major', labelsize=20)\n",
    "plt.tick_params(axis='both', which='minor', labelsize=20)\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.barplot(x = \"Year\", y = \"Retention_Rate\", data = plot_df, palette=\"Blues_d\", saturation=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Received On\n",
       "2001.0      1\n",
       "2007.0     50\n",
       "2008.0     64\n",
       "2009.0     17\n",
       "2010.0     47\n",
       "2011.0    148\n",
       "2012.0    110\n",
       "2013.0    435\n",
       "2014.0    352\n",
       "2015.0    189\n",
       "2016.0    269\n",
       "2017.0    169\n",
       "2018.0    112\n",
       "Name: Amount, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "small.groupby(small[\"Received On\"].dt.year).Amount.count()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}