Canada_Charity_Overhead_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "345f64ee",
   "metadata": {},
   "source": [
    "<h1>Data Loading and Cleaning</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a6646d5",
   "metadata": {},
   "source": [
    "Import required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb0eedd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statsmodels import robust"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb968569",
   "metadata": {},
   "source": [
    "Data based on [2019 List of Charities](https://open.canada.ca/data/en/dataset/d4287672-3253-4bb8-84c7-4e515ea3fddf/resource/8d011273-894e-4834-8d22-60075c1c2beb?inner_span=True).\n",
    "\n",
    "List of selected columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d778b90a",
   "metadata": {},
   "outputs": [],
   "source": [
    "id_cols = ['BN', 'Category', 'Province', 'Country']\n",
    "fin_cols = ['BN', 'Section Used', '5000', '5050', '5100']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "efea02e1",
   "metadata": {},
   "source": [
    "Read CSV files: charity identification and financial data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb4166f6",
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106693120RR0001</td>\n",
       "      <td>11</td>\n",
       "      <td>AB</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108197633RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>NB</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108219650RR0001</td>\n",
       "      <td>110</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118799154RR0001</td>\n",
       "      <td>210</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119033702RR0001</td>\n",
       "      <td>200</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BN  Category Province Country\n",
       "0  106693120RR0001        11       AB      CA\n",
       "1  108197633RR0001        30       NB      CA\n",
       "2  108219650RR0001       110       ON      CA\n",
       "3  118799154RR0001       210       ON      CA\n",
       "4  119033702RR0001       200       ON      CA"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ident = pd.read_csv('2019_ident.csv', usecols=id_cols)\n",
    "ident.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3783b1",
   "metadata": {},
   "source": [
    "\"Section Used\" refers to the filing document used, with [Schedule 6](https://www.canada.ca/en/revenue-agency/services/forms-publications/publications/t4033/t4033-completing-registered-charity-information-return.html#_Toc412013150) being a more detailed format used for charities that exceed certain criteria for revenue and property.\n",
    "\n",
    "Column numbers of the financial CSV refer to Lines of the T3010 Registered Charity Information Return tax form, with amounts in Canadian Dollars:\n",
    "\n",
    "* 5000: \"Total charitable programs expenditures included in line 4950\"\n",
    "* 5050: \"Total gifts to qualified donees excluding enduring property and specified gifts\"\n",
    "* 5100: \"Total expenditures\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "67edf23d",
   "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>BN</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106693120RR0001</td>\n",
       "      <td>6</td>\n",
       "      <td>94123.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108197633RR0001</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108219650RR0001</td>\n",
       "      <td>6</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>34600.0</td>\n",
       "      <td>37545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118799154RR0001</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119033702RR0001</td>\n",
       "      <td>D</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3119.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BN Section Used     5000     5050     5100\n",
       "0  106693120RR0001            6  94123.0      NaN  94123.0\n",
       "1  108197633RR0001            D      NaN      NaN      NaN\n",
       "2  108219650RR0001            6   1698.0  34600.0  37545.0\n",
       "3  118799154RR0001            D      NaN      NaN      NaN\n",
       "4  119033702RR0001            D   2859.0      NaN   3119.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finance = pd.read_csv('2019_financial.csv', usecols=fin_cols)\n",
    "finance.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aafc873",
   "metadata": {},
   "source": [
    "Join finance and identification data on charity identifiers BN found in both dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f853c27e",
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106693120RR0001</td>\n",
       "      <td>11</td>\n",
       "      <td>AB</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>94123.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94123.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>108197633RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>NB</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108219650RR0001</td>\n",
       "      <td>110</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>34600.0</td>\n",
       "      <td>37545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>118799154RR0001</td>\n",
       "      <td>210</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119033702RR0001</td>\n",
       "      <td>200</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>2859.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3119.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                BN  Category Province Country Section Used     5000     5050  \\\n",
       "0  106693120RR0001        11       AB      CA            6  94123.0      NaN   \n",
       "1  108197633RR0001        30       NB      CA            D      NaN      NaN   \n",
       "2  108219650RR0001       110       ON      CA            6   1698.0  34600.0   \n",
       "3  118799154RR0001       210       ON      CA            D      NaN      NaN   \n",
       "4  119033702RR0001       200       ON      CA            D   2859.0      NaN   \n",
       "\n",
       "      5100  \n",
       "0  94123.0  \n",
       "1      NaN  \n",
       "2  37545.0  \n",
       "3      NaN  \n",
       "4   3119.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.merge(ident, finance, on='BN')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ca4adf7",
   "metadata": {},
   "source": [
    "Vast majority of charities are in Canada. Limiting analysis to such entities simplifies the province visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f3d1c52d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    83496\n",
       "US       24\n",
       "GB        1\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9af20485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(83496, 8)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Country'] == 'CA']\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0c1740",
   "metadata": {},
   "source": [
    "Overhead based on comparing total expenditure to program spending and qualified gifts, compare Lines 5000 and 5050 to 5100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d4fd484",
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108219650RR0001</td>\n",
       "      <td>110</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>1698.0</td>\n",
       "      <td>34600.0</td>\n",
       "      <td>37545.0</td>\n",
       "      <td>3.321348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>132410671RR0097</td>\n",
       "      <td>1</td>\n",
       "      <td>QC</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>91.0</td>\n",
       "      <td>19718.0</td>\n",
       "      <td>20890.0</td>\n",
       "      <td>5.174725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>806138780RR0001</td>\n",
       "      <td>50</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>22484.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26388.0</td>\n",
       "      <td>14.794604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>817213697RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>6806.0</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>8663.0</td>\n",
       "      <td>2.966640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>834522435RR0001</td>\n",
       "      <td>1</td>\n",
       "      <td>MB</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>56958.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>59958.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BN  Category Province Country Section Used     5000     5050  \\\n",
       "2   108219650RR0001       110       ON      CA            6   1698.0  34600.0   \n",
       "7   132410671RR0097         1       QC      CA            D     91.0  19718.0   \n",
       "15  806138780RR0001        50       ON      CA            D  22484.0      0.0   \n",
       "18  817213697RR0001        30       ON      CA            6   6806.0   1600.0   \n",
       "22  834522435RR0001         1       MB      CA            6  56958.0   3000.0   \n",
       "\n",
       "       5100   Overhead  \n",
       "2   37545.0   3.321348  \n",
       "7   20890.0   5.174725  \n",
       "15  26388.0  14.794604  \n",
       "18   8663.0   2.966640  \n",
       "22  59958.0   0.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overhead = (1 - ((df['5000'] + df ['5050'])/df['5100'])) * 100\n",
    "df = df.assign(Overhead = overhead)\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "df.dropna(subset = ['Overhead'], inplace = True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "24e32496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    22599.000000\n",
       "mean         7.705262\n",
       "std        313.638092\n",
       "min     -32223.000000\n",
       "25%          1.100606\n",
       "50%          7.572503\n",
       "75%         21.724067\n",
       "max        289.363999\n",
       "Name: Overhead, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Overhead'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef124ae2",
   "metadata": {},
   "source": [
    "Note presence of negative overhead values. These seem to be due to errors in inputting tax form information, such as with repeated entries for program spending and qualified gifts. May be best to drop such rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d0f167ec",
   "metadata": {
    "scrolled": false
   },
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>119256600RR0001</td>\n",
       "      <td>60</td>\n",
       "      <td>BC</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>7375.0</td>\n",
       "      <td>7375.0</td>\n",
       "      <td>8349.0</td>\n",
       "      <td>-76.667864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>897120333RR0001</td>\n",
       "      <td>180</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>3228.0</td>\n",
       "      <td>-50.898389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>861098895RR0001</td>\n",
       "      <td>100</td>\n",
       "      <td>SK</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>24372.0</td>\n",
       "      <td>22090.0</td>\n",
       "      <td>24454.0</td>\n",
       "      <td>-89.997546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>389</th>\n",
       "      <td>118850502RR0001</td>\n",
       "      <td>200</td>\n",
       "      <td>BC</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>4020.0</td>\n",
       "      <td>32163.0</td>\n",
       "      <td>32163.0</td>\n",
       "      <td>-12.498834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>416</th>\n",
       "      <td>139138465RR0001</td>\n",
       "      <td>1</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>195.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>-100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  BN  Category Province Country Section Used     5000  \\\n",
       "140  119256600RR0001        60       BC      CA            D   7375.0   \n",
       "192  897120333RR0001       180       YT      CA            D   3228.0   \n",
       "340  861098895RR0001       100       SK      CA            D  24372.0   \n",
       "389  118850502RR0001       200       BC      CA            D   4020.0   \n",
       "416  139138465RR0001         1       ON      CA            6    195.0   \n",
       "\n",
       "        5050     5100    Overhead  \n",
       "140   7375.0   8349.0  -76.667864  \n",
       "192   1643.0   3228.0  -50.898389  \n",
       "340  22090.0  24454.0  -89.997546  \n",
       "389  32163.0  32163.0  -12.498834  \n",
       "416    195.0    195.0 -100.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Overhead'] < 0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7160c2df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21475, 9)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Overhead'] >= 0]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ffc70ef5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Overhead Percentage'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Overhead'].plot.box(ylabel='Overhead Percentage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "69e848fb",
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44057</th>\n",
       "      <td>839603305RR0001</td>\n",
       "      <td>210</td>\n",
       "      <td>AB</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>7418.0</td>\n",
       "      <td>68000.0</td>\n",
       "      <td>-39827.0</td>\n",
       "      <td>289.363999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BN  Category Province Country Section Used    5000  \\\n",
       "44057  839603305RR0001       210       AB      CA            6  7418.0   \n",
       "\n",
       "          5050     5100    Overhead  \n",
       "44057  68000.0 -39827.0  289.363999  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Overhead'] > 100]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05301380",
   "metadata": {},
   "source": [
    "Outlier with maximum overhead value above 100% clearly has wrong entries: negative total expenses with a high gift expense. Need to drop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aaa524d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21474, 9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df['Overhead'] <= 100]\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37f9be02",
   "metadata": {},
   "source": [
    "<h1>Overhead by Section Used</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d05e23b",
   "metadata": {},
   "source": [
    "Minimal difference in the mean overhead for both types of organizations, but large when medians are considered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c58acc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Section Used   Overhead\n",
      "0            6  18.899589\n",
      "1            D  16.424937\n"
     ]
    },
    {
     "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>Section Used</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>10.110869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>D</td>\n",
       "      <td>3.619474</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Section Used   Overhead\n",
       "0            6  10.110869\n",
       "1            D   3.619474"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sections = df.groupby('Section Used', as_index=False).mean()\n",
    "print(sections[['Section Used','Overhead']])\n",
    "\n",
    "sections = df.groupby('Section Used', as_index=False).median()\n",
    "sections[['Section Used','Overhead']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfdcc629",
   "metadata": {},
   "source": [
    "Due to the long right tails of the distributions (see [Appendix](#SUd)), the median and the median absolute deviation (MAD) will be used to compare the two categories. Schedule 6 charities had higher median overhead percentages and deviation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "58b68ec2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Section Used'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "serr = df.groupby('Section Used')['Overhead'].apply(robust.mad)\n",
    "\n",
    "sections.plot.bar(\n",
    "    'Section Used', \n",
    "    'Overhead', \n",
    "    yerr = serr, \n",
    "    figsize=(13,7.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e05e7a60",
   "metadata": {},
   "source": [
    "<h1>Overhead by Province of Charity</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145edbf5",
   "metadata": {},
   "source": [
    "Most provinces similar in distribution of overhead percentages, except for Nunavut at the low end and Yukon at the high one. This may simply be due to a smaller count of charities compared to other provinces (see [Appendix](#Poi))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a98da6a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Province'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "provinces = df.groupby('Province', as_index=False).median()\n",
    "perr = df.groupby('Province')['Overhead'].apply(robust.mad)\n",
    "\n",
    "provinces.plot.bar(\n",
    "    'Province', \n",
    "    'Overhead', \n",
    "    yerr = perr,\n",
    "    figsize=(13,7.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bfd1463",
   "metadata": {},
   "source": [
    "<h1>Overhead by Category of Charity</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8596aaa2",
   "metadata": {},
   "source": [
    "Charities were categorized numerically according to their field of work, e.g. 0001 corresponding to \"Organizations Relieving Poverty\". Medians and variances were roughly similar except for some categories:\n",
    "* Religious organizations (Categories 30 - 90) had the lowest median overheads. Of particular note are charities that are Christian (30) and non-Abrahamic (60, \"Other Religions).\n",
    "* Of the main group of charities with median overheads below 20%, \"Core Health Care\" (Category 100) was the highest being just under that threshold.\n",
    "* Categories 155, 175, and 215 had unusual deviations due to the smaller number of entities involved (see [Appendix](#Coi))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dfa682dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Category'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = df.groupby('Category', as_index=False).median()\n",
    "cerr = df.groupby('Category')['Overhead'].apply(robust.mad)\n",
    "\n",
    "categories.plot.bar(\n",
    "    'Category', \n",
    "    'Overhead', \n",
    "    yerr = cerr, \n",
    "    figsize=(13,7.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de93b5db",
   "metadata": {},
   "source": [
    "<h1>Appendix</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a92b755c",
   "metadata": {},
   "source": [
    "<h4>Section Used distributions</h4>\n",
    "<a id='SUd'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a8f95b5",
   "metadata": {},
   "source": [
    "Similar shape for both distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6adb8b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Section Used\n",
       "6    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "D    AxesSubplot(0.125,0.125;0.775x0.755)\n",
       "Name: Overhead, dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "section = df.groupby('Section Used')['Overhead']\n",
    "\n",
    "section.plot.hist(\n",
    "    by='Overhead', \n",
    "    title='''Distribution of Canadian Charity Overhead Percentage by Section Used''', \n",
    "    legend=1,\n",
    "    figsize=(13,7.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79145fc6",
   "metadata": {},
   "source": [
    "<h4>Provinces of interest</h4>\n",
    "<a id='Poi'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "be8a9783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Province'].value_counts().plot.bar(figsize=(13,7.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793ca74a",
   "metadata": {},
   "source": [
    "**Nunavut**: Note how all double-digit percentage overheads are found in Schedule 6 organizations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6d49776",
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1481</th>\n",
       "      <td>810143925RR0001</td>\n",
       "      <td>1</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>914413.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1072933.0</td>\n",
       "      <td>14.774455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>897090536RR0001</td>\n",
       "      <td>200</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>658962.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>836139.0</td>\n",
       "      <td>21.189898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14868</th>\n",
       "      <td>861547230RR0001</td>\n",
       "      <td>160</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>1691491.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1691491.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29023</th>\n",
       "      <td>764942280RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>5675.0</td>\n",
       "      <td>7068.0</td>\n",
       "      <td>12836.0</td>\n",
       "      <td>0.724525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44680</th>\n",
       "      <td>840564223RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>16976.0</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>22406.0</td>\n",
       "      <td>2.142283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51531</th>\n",
       "      <td>866896228RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>158157.0</td>\n",
       "      <td>25264.0</td>\n",
       "      <td>186198.0</td>\n",
       "      <td>1.491423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58840</th>\n",
       "      <td>816624142RR0001</td>\n",
       "      <td>1</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>140206.0</td>\n",
       "      <td>16231.0</td>\n",
       "      <td>182176.0</td>\n",
       "      <td>14.128645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63862</th>\n",
       "      <td>887332591RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>28551.0</td>\n",
       "      <td>6051.0</td>\n",
       "      <td>36105.0</td>\n",
       "      <td>4.162858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70611</th>\n",
       "      <td>130402019RR0022</td>\n",
       "      <td>30</td>\n",
       "      <td>NU</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>45437.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45437.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BN  Category Province Country Section Used       5000  \\\n",
       "1481   810143925RR0001         1       NU      CA            6   914413.0   \n",
       "2019   897090536RR0001       200       NU      CA            6   658962.0   \n",
       "14868  861547230RR0001       160       NU      CA            6  1691491.0   \n",
       "29023  764942280RR0001        30       NU      CA            D     5675.0   \n",
       "44680  840564223RR0001        30       NU      CA            D    16976.0   \n",
       "51531  866896228RR0001        30       NU      CA            6   158157.0   \n",
       "58840  816624142RR0001         1       NU      CA            6   140206.0   \n",
       "63862  887332591RR0001        30       NU      CA            D    28551.0   \n",
       "70611  130402019RR0022        30       NU      CA            D    45437.0   \n",
       "\n",
       "          5050       5100   Overhead  \n",
       "1481       0.0  1072933.0  14.774455  \n",
       "2019       0.0   836139.0  21.189898  \n",
       "14868      0.0  1691491.0   0.000000  \n",
       "29023   7068.0    12836.0   0.724525  \n",
       "44680   4950.0    22406.0   2.142283  \n",
       "51531  25264.0   186198.0   1.491423  \n",
       "58840  16231.0   182176.0  14.128645  \n",
       "63862   6051.0    36105.0   4.162858  \n",
       "70611      0.0    45437.0   0.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Province'] == 'NU']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c3e98ec",
   "metadata": {},
   "source": [
    "**Yukon**: Decent proportion of charities with double-digit overhead percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "daba28e0",
   "metadata": {
    "scrolled": true
   },
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2059</th>\n",
       "      <td>123034464RR0001</td>\n",
       "      <td>1</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>594067.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>730292.0</td>\n",
       "      <td>18.653498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>890308448RR0001</td>\n",
       "      <td>60</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>12431.0</td>\n",
       "      <td>19255.0</td>\n",
       "      <td>31686.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13993</th>\n",
       "      <td>890660947RR0001</td>\n",
       "      <td>160</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>40.257946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27522</th>\n",
       "      <td>131494726RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>106087.0</td>\n",
       "      <td>14840.0</td>\n",
       "      <td>224487.0</td>\n",
       "      <td>46.131847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27528</th>\n",
       "      <td>764962882RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>20720.0</td>\n",
       "      <td>9122.0</td>\n",
       "      <td>29968.0</td>\n",
       "      <td>0.420448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28301</th>\n",
       "      <td>119307916RR0001</td>\n",
       "      <td>15</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>212278.0</td>\n",
       "      <td>27299.0</td>\n",
       "      <td>319906.0</td>\n",
       "      <td>25.110189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29882</th>\n",
       "      <td>119208031RR0003</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>2870.0</td>\n",
       "      <td>2870.0</td>\n",
       "      <td>68200.0</td>\n",
       "      <td>91.583578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39376</th>\n",
       "      <td>118913318RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>5100.0</td>\n",
       "      <td>22422.0</td>\n",
       "      <td>54.508964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40701</th>\n",
       "      <td>108206798RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>322228.0</td>\n",
       "      <td>36530.0</td>\n",
       "      <td>454558.0</td>\n",
       "      <td>21.075418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40920</th>\n",
       "      <td>118804509RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>642321.0</td>\n",
       "      <td>70102.0</td>\n",
       "      <td>852408.0</td>\n",
       "      <td>16.422300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43426</th>\n",
       "      <td>131161010RR0001</td>\n",
       "      <td>180</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>381375.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>514929.0</td>\n",
       "      <td>25.936391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47388</th>\n",
       "      <td>779125467RR0001</td>\n",
       "      <td>210</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>0.0</td>\n",
       "      <td>61765.0</td>\n",
       "      <td>71649.0</td>\n",
       "      <td>13.795029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50348</th>\n",
       "      <td>119208031RR0002</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>522958.0</td>\n",
       "      <td>32862.0</td>\n",
       "      <td>667181.0</td>\n",
       "      <td>16.691273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51282</th>\n",
       "      <td>107387227RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>167463.0</td>\n",
       "      <td>56048.0</td>\n",
       "      <td>226969.0</td>\n",
       "      <td>1.523556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53061</th>\n",
       "      <td>107444093RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>71797.0</td>\n",
       "      <td>5375.0</td>\n",
       "      <td>105673.0</td>\n",
       "      <td>26.970939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64366</th>\n",
       "      <td>118841055RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>608491.0</td>\n",
       "      <td>9712.0</td>\n",
       "      <td>645052.0</td>\n",
       "      <td>4.162300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65549</th>\n",
       "      <td>837846583RR0001</td>\n",
       "      <td>40</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>15349.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16934.0</td>\n",
       "      <td>9.359868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65554</th>\n",
       "      <td>815784079RR0001</td>\n",
       "      <td>12</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>74497.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>77426.0</td>\n",
       "      <td>3.782967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71663</th>\n",
       "      <td>118824846RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>196389.0</td>\n",
       "      <td>7200.0</td>\n",
       "      <td>228959.0</td>\n",
       "      <td>11.080586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72077</th>\n",
       "      <td>118841055RR0010</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>146542.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>203833.0</td>\n",
       "      <td>27.984183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75729</th>\n",
       "      <td>107396905RR0062</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>172794.0</td>\n",
       "      <td>27286.0</td>\n",
       "      <td>200080.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77207</th>\n",
       "      <td>890904121RR0001</td>\n",
       "      <td>30</td>\n",
       "      <td>YT</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>52028.0</td>\n",
       "      <td>11043.0</td>\n",
       "      <td>63303.0</td>\n",
       "      <td>0.366491</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BN  Category Province Country Section Used      5000  \\\n",
       "2059   123034464RR0001         1       YT      CA            6  594067.0   \n",
       "3074   890308448RR0001        60       YT      CA            D   12431.0   \n",
       "13993  890660947RR0001       160       YT      CA            D    1297.0   \n",
       "27522  131494726RR0001        30       YT      CA            6  106087.0   \n",
       "27528  764962882RR0001        30       YT      CA            D   20720.0   \n",
       "28301  119307916RR0001        15       YT      CA            6  212278.0   \n",
       "29882  119208031RR0003        30       YT      CA            D    2870.0   \n",
       "39376  118913318RR0001        30       YT      CA            D    5100.0   \n",
       "40701  108206798RR0001        30       YT      CA            6  322228.0   \n",
       "40920  118804509RR0001        30       YT      CA            6  642321.0   \n",
       "43426  131161010RR0001       180       YT      CA            6  381375.0   \n",
       "47388  779125467RR0001       210       YT      CA            D       0.0   \n",
       "50348  119208031RR0002        30       YT      CA            6  522958.0   \n",
       "51282  107387227RR0001        30       YT      CA            6  167463.0   \n",
       "53061  107444093RR0001        30       YT      CA            6   71797.0   \n",
       "64366  118841055RR0001        30       YT      CA            6  608491.0   \n",
       "65549  837846583RR0001        40       YT      CA            D   15349.0   \n",
       "65554  815784079RR0001        12       YT      CA            D   74497.0   \n",
       "71663  118824846RR0001        30       YT      CA            6  196389.0   \n",
       "72077  118841055RR0010        30       YT      CA            6  146542.0   \n",
       "75729  107396905RR0062        30       YT      CA            6  172794.0   \n",
       "77207  890904121RR0001        30       YT      CA            D   52028.0   \n",
       "\n",
       "          5050      5100   Overhead  \n",
       "2059       0.0  730292.0  18.653498  \n",
       "3074   19255.0   31686.0   0.000000  \n",
       "13993      0.0    2171.0  40.257946  \n",
       "27522  14840.0  224487.0  46.131847  \n",
       "27528   9122.0   29968.0   0.420448  \n",
       "28301  27299.0  319906.0  25.110189  \n",
       "29882   2870.0   68200.0  91.583578  \n",
       "39376   5100.0   22422.0  54.508964  \n",
       "40701  36530.0  454558.0  21.075418  \n",
       "40920  70102.0  852408.0  16.422300  \n",
       "43426      0.0  514929.0  25.936391  \n",
       "47388  61765.0   71649.0  13.795029  \n",
       "50348  32862.0  667181.0  16.691273  \n",
       "51282  56048.0  226969.0   1.523556  \n",
       "53061   5375.0  105673.0  26.970939  \n",
       "64366   9712.0  645052.0   4.162300  \n",
       "65549      0.0   16934.0   9.359868  \n",
       "65554      0.0   77426.0   3.782967  \n",
       "71663   7200.0  228959.0  11.080586  \n",
       "72077    250.0  203833.0  27.984183  \n",
       "75729  27286.0  200080.0   0.000000  \n",
       "77207  11043.0   63303.0   0.366491  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Province'] == 'YT']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "580b2f97",
   "metadata": {},
   "source": [
    "<h4>Categories of interest.</h4>\n",
    "<a id='Coi'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "2daab68c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAwoAAAHACAYAAAAC1J/aAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAiSUlEQVR4nO3df9Rtd10f+PeHXE2DmJDIJTD3Bm+soQq0CtxJ47RVVqMSB2toK+O1rYlKJzOYoYzTqU1q19DV1bTRYbTiNKxmCRKoElNUklajxCDaThG4QEoIIZISTK4J5KqIaJUa+Mwfe189PN/n3iTPc57z/OD1Wuuss893n70/e+9nP+ec99l7f091dwAAABY9YbsXAAAA2HkEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAb7tnsBNuopT3lKHzp0aLsXAwAAdq33vOc9v9Xd+9cbt2uDwqFDh3L06NHtXgwAANi1quo3TjbOqUcAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwGDfdi/AVjh01c897mk+eu2LtmBJAABgd3JEAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADB41KBQVa+rqoer6gMLbf93VX2oqt5fVT9bVU9eGHd1Vd1bVfdU1QsX2p9fVXfO415dVTW3n15VPzW3v7OqDi13FQEAgMfrsRxReH2SS9a03ZbkOd39F5L8epKrk6SqnpXkSJJnz9NcV1WnzdO8JskVSS6Ybyfm+dIkn+juL0/yw0l+YKMrAwAALMejBoXu/tUkv7Om7a3d/cj88NeSHJyHL01yY3d/urvvS3Jvkgur6ulJzuzud3R3J3lDkhcvTHPDPPzmJBefONoAAABsj2Vco/DdSW6dhw8keWBh3LG57cA8vLb9c6aZw8cnk3zJEpYLAADYoE0Fhar6/iSPJPmJE03rPK1P0X6qadard0VVHa2qo8ePH3+8iwsAADxGGw4KVXV5km9O8rfn04mS6UjBeQtPO5jkwbn94DrtnzNNVe1LclbWnOp0Qndf392Hu/vw/v37N7roAADAo9hQUKiqS5L8wyTf0t3/dWHULUmOzD0ZnZ/pouV3dfdDST5VVRfN1x9cluTmhWkun4e/NcnbFoIHAACwDfY92hOq6k1JXpDkKVV1LMkrM/VydHqS2+brjn+tu//X7r6rqm5K8sFMpyRd2d2fmWf1skw9KJ2R6ZqGE9c1vDbJG6vq3kxHEo4sZ9UAAICNetSg0N3fvk7za0/x/GuSXLNO+9Ekz1mn/Y+SvOTRlgMAAFgdv8wMAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAINHDQpV9bqqeriqPrDQdk5V3VZVH57vz14Yd3VV3VtV91TVCxfan19Vd87jXl1VNbefXlU/Nbe/s6oOLXkdAQCAx+mxHFF4fZJL1rRdleT27r4gye3z41TVs5IcSfLseZrrquq0eZrXJLkiyQXz7cQ8X5rkE9395Ul+OMkPbHRlAACA5XjUoNDdv5rkd9Y0X5rkhnn4hiQvXmi/sbs/3d33Jbk3yYVV9fQkZ3b3O7q7k7xhzTQn5vXmJBefONoAAABsj41eo3Budz+UJPP9U+f2A0keWHjesbntwDy8tv1zpunuR5J8MsmXrFe0qq6oqqNVdfT48eMbXHQAAODRLPti5vWOBPQp2k81zdjYfX13H+7uw/v379/gIgIAAI9mo0Hh4/PpRJnvH57bjyU5b+F5B5M8OLcfXKf9c6apqn1Jzsp4qhMAALBCGw0KtyS5fB6+PMnNC+1H5p6Mzs900fK75tOTPlVVF83XH1y2ZpoT8/rWJG+br2MAAAC2yb5He0JVvSnJC5I8paqOJXllkmuT3FRVL01yf5KXJEl331VVNyX5YJJHklzZ3Z+ZZ/WyTD0onZHk1vmWJK9N8saqujfTkYQjS1kzAABgwx41KHT3t59k1MUnef41Sa5Zp/1okues0/5HmYMGAACwM/hlZgAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAg00Fhar63qq6q6o+UFVvqqo/U1XnVNVtVfXh+f7shedfXVX3VtU9VfXChfbnV9Wd87hXV1VtZrkAAIDN2XBQqKoDSf5eksPd/ZwkpyU5kuSqJLd39wVJbp8fp6qeNY9/dpJLklxXVafNs3tNkiuSXDDfLtnocgEAAJu32VOP9iU5o6r2JXlikgeTXJrkhnn8DUlePA9fmuTG7v50d9+X5N4kF1bV05Oc2d3v6O5O8oaFaQAAgG2w4aDQ3b+Z5FVJ7k/yUJJPdvdbk5zb3Q/Nz3koyVPnSQ4keWBhFsfmtgPz8Np2AABgm2zm1KOzMx0lOD/Jf5fki6rq75xqknXa+hTt69W8oqqOVtXR48ePP95FBgAAHqPNnHr09Unu6+7j3f3HSX4myf+Q5OPz6USZ7x+en38syXkL0x/MdKrSsXl4bfugu6/v7sPdfXj//v2bWHQAAOBUNhMU7k9yUVU9ce6l6OIkdye5Jcnl83MuT3LzPHxLkiNVdXpVnZ/pouV3zacnfaqqLprnc9nCNAAAwDbYt9EJu/udVfXmJO9N8kiS9yW5PsmTktxUVS/NFCZeMj//rqq6KckH5+df2d2fmWf3siSvT3JGklvnGwAAsE02HBSSpLtfmeSVa5o/nenownrPvybJNeu0H03ynM0sCwAAsDx+mRkAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwGBTQaGqnlxVb66qD1XV3VX1NVV1TlXdVlUfnu/PXnj+1VV1b1XdU1UvXGh/flXdOY97dVXVZpYLAADYnM0eUfiRJL/Q3V+R5KuS3J3kqiS3d/cFSW6fH6eqnpXkSJJnJ7kkyXVVddo8n9ckuSLJBfPtkk0uFwAAsAkbDgpVdWaSr03y2iTp7v/W3b+b5NIkN8xPuyHJi+fhS5Pc2N2f7u77ktyb5MKqenqSM7v7Hd3dSd6wMA0AALANNnNE4cuSHE/y41X1vqr6sar6oiTndvdDSTLfP3V+/oEkDyxMf2xuOzAPr20HAAC2yWaCwr4kz0vymu5+bpI/yHya0Umsd91Bn6J9nEHVFVV1tKqOHj9+/PEuLwAA8BhtJigcS3Ksu985P35zpuDw8fl0osz3Dy88/7yF6Q8meXBuP7hO+6C7r+/uw919eP/+/ZtYdAAA4FQ2HBS6+2NJHqiqPzc3XZzkg0luSXL53HZ5kpvn4VuSHKmq06vq/EwXLb9rPj3pU1V10dzb0WUL0wAAANtg3yanf3mSn6iqL0zykSTflSl83FRVL01yf5KXJEl331VVN2UKE48kubK7PzPP52VJXp/kjCS3zjcAAGCbbCoodPcdSQ6vM+rikzz/miTXrNN+NMlzNrMsAADA8vhlZgAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAg00Hhao6rareV1X/fn58TlXdVlUfnu/PXnju1VV1b1XdU1UvXGh/flXdOY97dVXVZpcLAADYuGUcUXhFkrsXHl+V5PbuviDJ7fPjVNWzkhxJ8uwklyS5rqpOm6d5TZIrklww3y5ZwnIBAAAbtKmgUFUHk7woyY8tNF+a5IZ5+IYkL15ov7G7P93d9yW5N8mFVfX0JGd29zu6u5O8YWEaAABgG2z2iMK/TPJ9ST670HZudz+UJPP9U+f2A0keWHjesbntwDy8th0AANgmGw4KVfXNSR7u7vc81knWaetTtK9X84qqOlpVR48fP/4YywIAAI/XZo4o/KUk31JVH01yY5K/WlX/JsnH59OJMt8/PD//WJLzFqY/mOTBuf3gOu2D7r6+uw939+H9+/dvYtEBAIBT2XBQ6O6ru/tgdx/KdJHy27r77yS5Jcnl89MuT3LzPHxLkiNVdXpVnZ/pouV3zacnfaqqLpp7O7psYRoAAGAb7NuCeV6b5KaqemmS+5O8JEm6+66quinJB5M8kuTK7v7MPM3Lkrw+yRlJbp1vAADANllKUOjutyd5+zz820kuPsnzrklyzTrtR5M8ZxnLAgAAbJ5fZgYAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAICBoAAAAAwEBQAAYCAoAAAAA0EBAAAYCAoAAMBAUAAAAAaCAgAAMBAUAACAwYaDQlWdV1W/XFV3V9VdVfWKuf2cqrqtqj4835+9MM3VVXVvVd1TVS9caH9+Vd05j3t1VdXmVgsAANiMzRxReCTJ3+/ur0xyUZIrq+pZSa5Kcnt3X5Dk9vlx5nFHkjw7ySVJrquq0+Z5vSbJFUkumG+XbGK5AACATdpwUOjuh7r7vfPwp5LcneRAkkuT3DA/7YYkL56HL01yY3d/urvvS3Jvkgur6ulJzuzud3R3J3nDwjQAAMA2WMo1ClV1KMlzk7wzybnd/VAyhYkkT52fdiDJAwuTHZvbDszDa9vXq3NFVR2tqqPHjx9fxqIDAADr2HRQqKonJfnpJP97d//eqZ66Tlufon1s7L6+uw939+H9+/c//oUFAAAek00Fhar6gkwh4Se6+2fm5o/PpxNlvn94bj+W5LyFyQ8meXBuP7hOOwAAsE020+tRJXltkru7+4cWRt2S5PJ5+PIkNy+0H6mq06vq/EwXLb9rPj3pU1V10TzPyxamAQAAtsG+TUz7l5J8R5I7q+qOue0fJbk2yU1V9dIk9yd5SZJ0911VdVOSD2bqMenK7v7MPN3Lkrw+yRlJbp1vAADANtlwUOju/5j1ry9IkotPMs01Sa5Zp/1okudsdFm206Grfu5xT/PRa1+0BUsCAADL45eZAQCAgaAAAAAMBAUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwEBQAAAABoICAAAwEBQAAIDBvu1eAB67Q1f93OOe5qPXvmgLlgQAgL3OEQUAAGAgKAAAAANBAQAAGAgKAADAQFAAAAAGej1isJHelRI9LAEA7CWOKAAAAANBAQAAGAgKAADAQFAAAAAGggIAADAQFAAAgIGgAAAADAQFAABgICgAAAADQQEAABgICgAAwGDfdi8An98OXfVzG5ruo9e+aMlLAgDAIkcUAACAgaAAAAAMnHrE5w2nOQEAPHaOKAAAAANHFGALbOTohSMXAMBO4ogCAAAwcEQBdjlHLwCArSAoAI+ZUAIAnz8EBWBHEkoAYHsJCsDnNd3mAsD6BAWAFRFKANhN9HoEAAAMHFEA2INWefTC9SQAe5MjCgAAwGDHHFGoqkuS/EiS05L8WHdfu82LBMAOs8qjF46UAJ/vdkRQqKrTkvyrJN+Q5FiSd1fVLd39we1dMgDYensxALl4H3a/HREUklyY5N7u/kiSVNWNSS5NIigAAKe006/J2Wgt2G47JSgcSPLAwuNjSf7iNi0LAMC224tHmvZyrb2ounu7lyFV9ZIkL+zuvzs//o4kF3b3y9c874okV8wP/1ySezZQ7ilJfmsTi7vT6qil1k6otRfXSS21dkKtvbhOaqm13XXU+lxf2t371xuxU44oHEty3sLjg0keXPuk7r4+yfWbKVRVR7v78GbmsZPqqKXWTqi1F9dJLbV2Qq29uE5qqbXdddR67HZK96jvTnJBVZ1fVV+Y5EiSW7Z5mQAA4PPWjjii0N2PVNX/luQXM3WP+rruvmubFwsAAD5v7YigkCTd/fNJfn4FpTZ16tIOrKOWWjuh1l5cJ7XU2gm19uI6qaXWdtdR6zHaERczAwAAO8tOuUYBAADYQQQFAABgICgAAACDHXMx825XVZXkwky/Mt2ZfgfiXb3Ci0Cq6ru6+8dXVW/ZdsI2BABYlqp6UpJnJvlId//uNi/O47ZnL2auqrOSXJ3kxUlO/Nrcw0luTnLtMv9YVfWNSa5L8uEkvzk3H0zy5Um+p7vfuqxaj7Ic93f3M5Y4v8+7bbjbrTJsrarWHl2nlf1v7UXbsf322hcZK359X+nfay++Zqy61jq1n9rdD6+gzpZ9qF7h6/t13f098/BfTvKTSf5Lps8z/8vcy+eW2Irtt5eDwi8meVuSG7r7Y3Pb05JcnuTru/sblljr7iTf1N0fXdN+fpKf7+6vXGKt959sVJJndvfpS6y1V7fhjviQVlW3dvc3LXF+Kwtbq6q1F9dprrXK/61LuvsX5uGzkvxQkv8+yQeSfG93f3yJtc7M9L91MMmt3f2TC+P+5M1zCXVWtv3mea9qf39aklcm+WyS/yvJy5P8zSR3J3lFdz+0jDpzrVXug6ustVdfM1ZZ65y1TUnek+S5mT43/s4Sa63kQ/WKt997u/t58/AvJ/n73f3eqvqyJDct81eTV7L9untP3pLcs5FxG6z14ST71mn/wiT3LrnWx5N8dZIvXXM7lORB2/Ax1frFJP8wydMW2p42t9225FrPO8nt+UkeWnKtu5McWqf9/CR378Zae3Gd5nmu8n/rvQvDP5bkn82vGd+b5C1LrvXTSa7NFMJvmR+fvnY5dtP2W+W+keQXMoWDq5K8f35NesbcdvOS12mV++Aqa+3V14xV1vpskvvW3P54vv/Ikmstvj79cpLnzcNfluToLt1+i+v0njXj3rfbtt9evkbhN6rq+zJ9g/HxJKmqc5N8Z5IHllzrdUneXVU3Lsz7vCRHkrx2ybX+fZIndfcda0dU1duXXGuvbsND3f0Diw09fcv1A1X13Uuu9e4kv5LpG5m1nrzkWvuSHFun/TeTfMEurbUX1ylZ7f/WosPd/dXz8A9X1eVLnv+f7e6/OQ+/paq+P8nbqupbllxn1dtvVfvGud39o0lSVd+z8Dr1o1X10iXWSVa7DVdZa6++Zqyy1vcl+fok/6C770ySqrqvu89fcp21zuzu9yZJd3+kqk5b4rxXuf2+Yj77o5Icqqqzu/sTVfWELai1aEu2314OCt+W6VuZX5lfkDrTt/G3JPmfllmou/9FVb0lyaVJvibTznEsyd/u7g8uudZJ3yy6+28ts1ZWvw1vTvIt2eJtmNW+ad2d6fDfh9eOqKrdHLbWq/WMTPvMMmtt9zptVa2V/W8leWpV/R+Z/qfOrKrq+SunLL/nu9Or6gnd/dkk6e5rqupYkl9N8qQl1lnl9ktWt28s/j3esGbcMj80JX+6Dd8+b8Nk67bh2r9Xknwsyb/bglp79TVjZbW6+1VznR+e36demel/bCus6kP1qt6zkmTtqdK/P9+fk+mUwmXa8u23Z69RSJKqujBJd/e7q+rZSS7JdIhpyy4k2Uuq6u8l+dnu3spvOFeuqs7O9KZ1aZKnzs0n3iCv7e5PLLHWtya5s7vvWWfci7v7LcuqNc/zWZnC1oH8adi6ZQvCVqrqKzNtwy2ttao6c62VbL+q+otJPtTdn6yqJ2baH5+X5K4k/7y7P7nEWq9c03Rddx+fzxH/we6+bIm1fjDJW7v7l9a0X5LkR7v7gmXVWjP/v5LpIsU7e4s6PljFvlFV/zTT3+T317R/eabXpm9dVq2F+f71TB84H0ny60netMz97xS139jd37FF895zrxlzrZWt10LNv5bk+zMdiX/aFsz/S9c0Pdjdf1xVT0nytd39M0ustfLtt9XW2X4Pdfd/W+b227NBYX5z/KZMR01uy/Qm8iuZDqf9Yndfs6LlWOoFq6tUVZ9M8geZLoz5yST/trt/a4tq7ZQLjFfWxewqa61CVX1Jd//2di/HblBVdyX5qu5+pKquz/R/9tNJLp7b/8aKlmNX7u9V9a7uvnAe/rtJrkzyliTfmOTfdfe1y6izkyz7bzV/EfTNmY72/I9J7kjyiUzB4Xu6++1LrHXLOs1/NdMFzunuZZ+axhJV1RmZTiv8wF5636oV9eS0pubu+0y4jAsdduItyZ2ZDtU+McnvZTp3K0nOSPL+Jdda2QWrK96G78t0KPwbMx2aO57pYrvLk3zxkmud7ALjq7LkC4wfZTnu3621kpyV6ULSDyX57fl299z25CXXujbJU+bh5yf5SKYL0n8jydetaPvduuT5PSnJP830rf4n5/3915J85xYs+90Lw+9dM+6OVWy/udau3N+zcEFgpuuA9s/DX5TpqMKyl/1wpgsF/02mb99vS/K7c+3n7rbtN8/vziSnzcNPTPL2efgZ2YILLudt94IkXzffPzQPf90W1PrHSb5sBX+TM5P8iyRvTPLta8Zdt+RalywMn5WpY4L3Z/oS79ytXteF2sveD0/8vf7sFi/3OevcPprk7CTnLLnWKjsxWdwvnpzps9pS94u9fI3CI939mST/tar+S3f/XpJ09x9W1WeXXGuVF6yuUvd0rvFbk7y1qr4g01Gab0/yqvzpN//LcKjXv8D42qr6riXWebQuZs89ybgdXyvJTZm+oXtBf24XhN+Z5N8mWWaXkS/q7qvm4Vcl+baeTvF7ZqYXqKV0/1ZVzzvZqEy9fy3TTyT52SQvzHTe9BcluTHJP66qZ3b3P1pircVv5v5zVR3u7qPz9vvjJdbZq/v7E+ZTCJ+Q6cj48STp7j+oqkeWWOeE6zKdp/3kJP8pU7ey31BVF8/jvmYZRVb8epFMR9w/k+T0JF+cJN19//xav0yHk7wi0yks/6C776iqP+zuX1lynWT64PfkTNdefCzJm5L8VHc/uAW1fjzTFyQ/neS751NN/1Z3fzrJRUuu9c8zfVGXJP9Ppms8/lqSv5HkX2c6Gr8UK94PT/y9fnmL/16/lemLrEUHMgWVztRL0LKs8jPh4n7xqkwBfLn7xVYmuO28JXlnkifOw09YaD8rS+ymb57nB5JccJJxD2z3ttjEer3vFOPOWHKtt2bqaeHchbZzMx1l+KUl11plF7N7tTvbD2XuzjbJr60Zt7RvdDN9iHlbpm9z197+cMnr9J/XPH73fP+ETNcTLLPWWUlen+m0vndmCgcfyfTm8lW7eB9cSa1M3wR+JHN3jZmPRGY6KnTHMtdpnu/7FobvP9m43bL95lqvyPTN4/Xz//N3ze37k/zqsrfhPO+Dmb60+H/Xbscl1ljsLvKvZApyH5tfM65Ycq071jz+/iT/X5IvyfI/Z7z3FHXvWHKtVe6HK/l7Jfk/M32g/vMLbfctc10W5ruyz4Sr2C/28hGFr+0p1afnHjhmX5Dp1Jll+ic5ee8hL19yrVX6tpON6O4/3IJaq+rFZJVdzO7V7mz/VZKfr6prk/xCVf3LJD+T6Rz7O5ZYZ5W9Rv1BVf3l7v6P8wV8v5NMrx9Vtd43QxvW08Wi31lVX5zpm6x9SY71En/8bMGe29+7+9BJRn020zn2y/ZHNf1g01lJ+kRHBFX1dZnC7LKs7G/V3T9SVb+UqYeWH+ruD83tx5N87TJrLdQ8luQlVfWiTKcEb6nu/g9J/kNVvTzTEdVvyxSMlmVVvXwlq+29bJWvGX9iK/9evdqenP5JVveZcMv3iz17MfOqVdVXZDqM9c5e6LGiFn4VlVObt+HBTN9Q24aP0yp7c5rrvSDJyzL9XPy+TGHkLUle191LOf1jlb1GVdVfyHTe7zMzfSP03d3961W1P9P5x69eVi12l6r6qiQ/mCmIfG+m/f7yTH2w/8/d/Z+2cfFYUFU3dveRFdVaWS9fq+y9bJVW+fdaqLmlPTnNNVbymXAV+4WgsARz7xFXZvr286uTvKK7b57H/clPeXNytuHW2q292+yEOquuxe5i39g99uprxl7dB7dyvbayJ6ed8nlmWeslKCxBVd2Z5Gu6+/er6lCSNyd543xo933d/dztXcKdzzbcWlV1f3c/Yy/V2ovrxO5j39g99uprxl7dB3fre8lO+TyzrPXay9corNJpJw4tdfdH51My3lzTD2Es9dzmPcw23KS92LvNXlwndh/7xu6xV18z9uo+uBffS7LCzzOrWC9BYTk+VlVffeLCnzlFfnOmnwz/89u6ZLuHbbh552bq2nPttQiVqUvH3VhrL64Tu499Y/fYq68Ze3Uf3IvvJav8PLPl6yUoLMdlST7n4s35Ys7Lqupfb88i7Tq24ebtud5tVlhn1bXYXewbu8defc3Yq/vgXnwvWeXnmS1fL9coAAAAg2X3vQsAAOwBggIAADAQFAAAgIGgAAAADAQFAABg8P8DeknNUdTglL0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 936x540 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['Category'].value_counts().plot.bar(figsize=(13,7.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7a6e3e7",
   "metadata": {},
   "source": [
    "Single charity identified for Category 155: **\"Upholding Human Rights\".**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1365ed71",
   "metadata": {
    "scrolled": true
   },
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56847</th>\n",
       "      <td>118777267RR0001</td>\n",
       "      <td>155</td>\n",
       "      <td>QC</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>40270.0</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>64849.0</td>\n",
       "      <td>34.817808</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BN  Category Province Country Section Used     5000  \\\n",
       "56847  118777267RR0001       155       QC      CA            D  40270.0   \n",
       "\n",
       "         5050     5100   Overhead  \n",
       "56847  2000.0  64849.0  34.817808  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Category'] == 155]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea979bf7",
   "metadata": {},
   "source": [
    "Charities identified for Category 175: **\"Agriculture\"**. Note how majority of entities have overheads with double-digit percentages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "275331a8",
   "metadata": {
    "scrolled": true
   },
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16589</th>\n",
       "      <td>870150968RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>18835.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>20407.0</td>\n",
       "      <td>7.213211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21822</th>\n",
       "      <td>119229557RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>AB</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>134037.0</td>\n",
       "      <td>2700.0</td>\n",
       "      <td>146124.0</td>\n",
       "      <td>6.423996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22983</th>\n",
       "      <td>129820023RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>AB</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>831657.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>903917.0</td>\n",
       "      <td>7.994097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26843</th>\n",
       "      <td>118867167RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>120277.0</td>\n",
       "      <td>32210.0</td>\n",
       "      <td>249116.0</td>\n",
       "      <td>38.788757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29249</th>\n",
       "      <td>897454625RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>AB</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3234.0</td>\n",
       "      <td>163483.0</td>\n",
       "      <td>98.021813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29368</th>\n",
       "      <td>876200700RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>5192.0</td>\n",
       "      <td>725.0</td>\n",
       "      <td>20691.0</td>\n",
       "      <td>71.403025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30342</th>\n",
       "      <td>127186138RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>156174.0</td>\n",
       "      <td>1225.0</td>\n",
       "      <td>284874.0</td>\n",
       "      <td>44.747853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31195</th>\n",
       "      <td>895885911RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>10159.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>15982.0</td>\n",
       "      <td>35.183331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31293</th>\n",
       "      <td>868559279RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>4366.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>8689.0</td>\n",
       "      <td>40.568535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32265</th>\n",
       "      <td>843277146RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>35751.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37284.0</td>\n",
       "      <td>4.111683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36864</th>\n",
       "      <td>859345472RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>24668.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>30679.0</td>\n",
       "      <td>17.148538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39374</th>\n",
       "      <td>118822865RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>D</td>\n",
       "      <td>82958.0</td>\n",
       "      <td>887.0</td>\n",
       "      <td>93979.0</td>\n",
       "      <td>10.783260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42491</th>\n",
       "      <td>886318880RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>4743.0</td>\n",
       "      <td>74.383302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67590</th>\n",
       "      <td>889860748RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>18592.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>65279.0</td>\n",
       "      <td>71.488534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72384</th>\n",
       "      <td>127692317RR0001</td>\n",
       "      <td>175</td>\n",
       "      <td>NB</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>284780.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>400465.0</td>\n",
       "      <td>28.837726</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BN  Category Province Country Section Used      5000  \\\n",
       "16589  870150968RR0001       175       ON      CA            D   18835.0   \n",
       "21822  119229557RR0001       175       AB      CA            6  134037.0   \n",
       "22983  129820023RR0001       175       AB      CA            6  831657.0   \n",
       "26843  118867167RR0001       175       ON      CA            6  120277.0   \n",
       "29249  897454625RR0001       175       AB      CA            D       0.0   \n",
       "29368  876200700RR0001       175       ON      CA            6    5192.0   \n",
       "30342  127186138RR0001       175       ON      CA            6  156174.0   \n",
       "31195  895885911RR0001       175       ON      CA            D   10159.0   \n",
       "31293  868559279RR0001       175       ON      CA            6    4366.0   \n",
       "32265  843277146RR0001       175       ON      CA            D   35751.0   \n",
       "36864  859345472RR0001       175       ON      CA            D   24668.0   \n",
       "39374  118822865RR0001       175       ON      CA            D   82958.0   \n",
       "42491  886318880RR0001       175       ON      CA            6    1135.0   \n",
       "67590  889860748RR0001       175       ON      CA            6   18592.0   \n",
       "72384  127692317RR0001       175       NB      CA            6  284780.0   \n",
       "\n",
       "          5050      5100   Overhead  \n",
       "16589    100.0   20407.0   7.213211  \n",
       "21822   2700.0  146124.0   6.423996  \n",
       "22983      0.0  903917.0   7.994097  \n",
       "26843  32210.0  249116.0  38.788757  \n",
       "29249   3234.0  163483.0  98.021813  \n",
       "29368    725.0   20691.0  71.403025  \n",
       "30342   1225.0  284874.0  44.747853  \n",
       "31195    200.0   15982.0  35.183331  \n",
       "31293    798.0    8689.0  40.568535  \n",
       "32265      0.0   37284.0   4.111683  \n",
       "36864    750.0   30679.0  17.148538  \n",
       "39374    887.0   93979.0  10.783260  \n",
       "42491     80.0    4743.0  74.383302  \n",
       "67590     20.0   65279.0  71.488534  \n",
       "72384    200.0  400465.0  28.837726  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Category'] == 175]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "faacc524",
   "metadata": {},
   "source": [
    "Two charities identified for Category 215: **NASOs (National Art Service Organizations).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ab498f92",
   "metadata": {
    "scrolled": true
   },
   "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>BN</th>\n",
       "      <th>Category</th>\n",
       "      <th>Province</th>\n",
       "      <th>Country</th>\n",
       "      <th>Section Used</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57552</th>\n",
       "      <td>758166714RR0001</td>\n",
       "      <td>215</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>169191.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>248316.0</td>\n",
       "      <td>15.756133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68245</th>\n",
       "      <td>100762590RR0001</td>\n",
       "      <td>215</td>\n",
       "      <td>ON</td>\n",
       "      <td>CA</td>\n",
       "      <td>6</td>\n",
       "      <td>3434736.0</td>\n",
       "      <td>10802.0</td>\n",
       "      <td>4015370.0</td>\n",
       "      <td>14.191270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    BN  Category Province Country Section Used       5000  \\\n",
       "57552  758166714RR0001       215       ON      CA            6   169191.0   \n",
       "68245  100762590RR0001       215       ON      CA            6  3434736.0   \n",
       "\n",
       "          5050       5100   Overhead  \n",
       "57552  40000.0   248316.0  15.756133  \n",
       "68245  10802.0  4015370.0  14.191270  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Category'] == 215]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b98b572d",
   "metadata": {},
   "source": [
    "<h1>Testing cells.</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ee4fcbd2",
   "metadata": {
    "scrolled": true
   },
   "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>Province</th>\n",
       "      <th>Category</th>\n",
       "      <th>5000</th>\n",
       "      <th>5050</th>\n",
       "      <th>5100</th>\n",
       "      <th>Overhead</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AB</td>\n",
       "      <td>30</td>\n",
       "      <td>83655.0</td>\n",
       "      <td>11314.0</td>\n",
       "      <td>158259.0</td>\n",
       "      <td>7.786515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BC</td>\n",
       "      <td>30</td>\n",
       "      <td>87319.5</td>\n",
       "      <td>12378.5</td>\n",
       "      <td>154638.0</td>\n",
       "      <td>8.627092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MB</td>\n",
       "      <td>30</td>\n",
       "      <td>76974.0</td>\n",
       "      <td>13394.0</td>\n",
       "      <td>131377.0</td>\n",
       "      <td>7.305550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB</td>\n",
       "      <td>30</td>\n",
       "      <td>57794.0</td>\n",
       "      <td>9314.0</td>\n",
       "      <td>108625.0</td>\n",
       "      <td>8.176723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NL</td>\n",
       "      <td>30</td>\n",
       "      <td>102798.0</td>\n",
       "      <td>7362.5</td>\n",
       "      <td>142446.0</td>\n",
       "      <td>10.587189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NS</td>\n",
       "      <td>30</td>\n",
       "      <td>46268.0</td>\n",
       "      <td>5355.0</td>\n",
       "      <td>83468.0</td>\n",
       "      <td>8.116776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NT</td>\n",
       "      <td>30</td>\n",
       "      <td>55280.0</td>\n",
       "      <td>18564.0</td>\n",
       "      <td>160058.0</td>\n",
       "      <td>5.949832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NU</td>\n",
       "      <td>30</td>\n",
       "      <td>140206.0</td>\n",
       "      <td>4950.0</td>\n",
       "      <td>182176.0</td>\n",
       "      <td>2.142283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ON</td>\n",
       "      <td>30</td>\n",
       "      <td>96154.5</td>\n",
       "      <td>14148.5</td>\n",
       "      <td>164574.0</td>\n",
       "      <td>8.567141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PE</td>\n",
       "      <td>30</td>\n",
       "      <td>67711.0</td>\n",
       "      <td>6952.0</td>\n",
       "      <td>121465.0</td>\n",
       "      <td>8.196015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>QC</td>\n",
       "      <td>30</td>\n",
       "      <td>54562.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>122794.0</td>\n",
       "      <td>9.349130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SK</td>\n",
       "      <td>30</td>\n",
       "      <td>42838.0</td>\n",
       "      <td>8000.0</td>\n",
       "      <td>86774.0</td>\n",
       "      <td>7.993284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>YT</td>\n",
       "      <td>30</td>\n",
       "      <td>126314.5</td>\n",
       "      <td>9417.0</td>\n",
       "      <td>201956.5</td>\n",
       "      <td>16.556786</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Province  Category      5000     5050      5100   Overhead\n",
       "0        AB        30   83655.0  11314.0  158259.0   7.786515\n",
       "1        BC        30   87319.5  12378.5  154638.0   8.627092\n",
       "2        MB        30   76974.0  13394.0  131377.0   7.305550\n",
       "3        NB        30   57794.0   9314.0  108625.0   8.176723\n",
       "4        NL        30  102798.0   7362.5  142446.0  10.587189\n",
       "5        NS        30   46268.0   5355.0   83468.0   8.116776\n",
       "6        NT        30   55280.0  18564.0  160058.0   5.949832\n",
       "7        NU        30  140206.0   4950.0  182176.0   2.142283\n",
       "8        ON        30   96154.5  14148.5  164574.0   8.567141\n",
       "9        PE        30   67711.0   6952.0  121465.0   8.196015\n",
       "10       QC        30   54562.0  10400.0  122794.0   9.349130\n",
       "11       SK        30   42838.0   8000.0   86774.0   7.993284\n",
       "12       YT        30  126314.5   9417.0  201956.5  16.556786"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "provinces"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}