notebooks/analyze-contributions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loads itemized individual contributions, calculates burn rates, and top fundraising days"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import fecfile\n",
    "import pandas as pd\n",
    "import os\n",
    "import datetime\n",
    "from IPython.display import display"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Time the notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set some viewing options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', 200)\n",
    "pd.set_option('display.max_columns', 40)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read dataframe of filings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>committee_id</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>filing_id</th>\n",
       "      <th>cycle</th>\n",
       "      <th>form_type</th>\n",
       "      <th>date_filed</th>\n",
       "      <th>date_coverage_to</th>\n",
       "      <th>date_coverage_from</th>\n",
       "      <th>report_title</th>\n",
       "      <th>report_period</th>\n",
       "      <th>fec_uri</th>\n",
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       "      <th>original_uri</th>\n",
       "      <th>committee_type</th>\n",
       "      <th>contributions_total</th>\n",
       "      <th>cash_on_hand</th>\n",
       "      <th>disbursements_total</th>\n",
       "      <th>receipts_total</th>\n",
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       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>C00694455</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>1358880</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
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       "      <td>11694156.88</td>\n",
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       "      <td>14578958.89</td>\n",
       "      <td>11849290.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>C00703975</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1358905</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00703975/1358905/</td>\n",
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       "      <td>17650871.16</td>\n",
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       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>C00696419</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>1358890</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
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       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>C00696948</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>1357929</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
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       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00696948/1357929/</td>\n",
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       "      <td>33734559.74</td>\n",
       "      <td>21559952.99</td>\n",
       "      <td>28025463.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>C00693234</td>\n",
       "      <td>Elizabeth Warren</td>\n",
       "      <td>1358497</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
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       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00693234/1358497/</td>\n",
       "      <td>False</td>\n",
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       "      <td>18748452.02</td>\n",
       "      <td>24684963.30</td>\n",
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       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>C00697441</td>\n",
       "      <td>Pete Buttigieg</td>\n",
       "      <td>1358691</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00697441/1358691/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>19144698.19</td>\n",
       "      <td>23378518.02</td>\n",
       "      <td>18501846.47</td>\n",
       "      <td>19211492.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>C00699090</td>\n",
       "      <td>Beto O'Rourke</td>\n",
       "      <td>1358876</td>\n",
       "      <td>2020</td>\n",
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       "      <td>2019-10-15</td>\n",
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       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00699090/1358876/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>4468814.60</td>\n",
       "      <td>3255729.87</td>\n",
       "      <td>6423123.17</td>\n",
       "      <td>4482284.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>C00693044</td>\n",
       "      <td>Julián Castro</td>\n",
       "      <td>1358889</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
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       "      <td>Q3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>3495405.71</td>\n",
       "      <td>672333.49</td>\n",
       "      <td>3960970.81</td>\n",
       "      <td>3497251.00</td>\n",
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       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>C00508416</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>1358358</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00508416/1358358/</td>\n",
       "      <td>False</td>\n",
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       "      <td>464789.26</td>\n",
       "      <td>548060.86</td>\n",
       "      <td>7763004.07</td>\n",
       "      <td>868452.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>C00659938</td>\n",
       "      <td>Andrew Yang</td>\n",
       "      <td>1358975</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-16</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00659938/1358975/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>9921124.76</td>\n",
       "      <td>6357361.37</td>\n",
       "      <td>4412923.43</td>\n",
       "      <td>9922625.52</td>\n",
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       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>C00659938</td>\n",
       "      <td>Andrew Yang</td>\n",
       "      <td>1358962</td>\n",
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       "      <td>2019-10-16</td>\n",
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       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00659938/1358962/</td>\n",
       "      <td>False</td>\n",
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       "      <td>9921124.76</td>\n",
       "      <td>6357361.37</td>\n",
       "      <td>4412923.43</td>\n",
       "      <td>9922625.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>C00693713</td>\n",
       "      <td>Tulsi Gabbard</td>\n",
       "      <td>1358606</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
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       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00693713/1358606/</td>\n",
       "      <td>False</td>\n",
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       "      <td>3029789.92</td>\n",
       "      <td>2138491.08</td>\n",
       "      <td>3332222.40</td>\n",
       "      <td>3032158.63</td>\n",
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       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>C00701979</td>\n",
       "      <td>Tim Ryan</td>\n",
       "      <td>1358899</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00701979/1358899/</td>\n",
       "      <td>False</td>\n",
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       "      <td>425731.01</td>\n",
       "      <td>158348.92</td>\n",
       "      <td>602440.41</td>\n",
       "      <td>425731.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>C00699280</td>\n",
       "      <td>Wayne Messam</td>\n",
       "      <td>1357839</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00699280/1357839/</td>\n",
       "      <td>False</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>5.00</td>\n",
       "      <td>31151.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>C00696054</td>\n",
       "      <td>Marianne Williamson</td>\n",
       "      <td>1358852</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
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       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00696054/1358852/</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>3052666.90</td>\n",
       "      <td>723731.57</td>\n",
       "      <td>2877876.51</td>\n",
       "      <td>3054167.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>C00706416</td>\n",
       "      <td>Steve Bullock</td>\n",
       "      <td>1358836</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00706416/1358836/</td>\n",
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       "      <td>1366143.62</td>\n",
       "      <td>2423527.43</td>\n",
       "      <td>2299449.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>C00705186</td>\n",
       "      <td>Michael Bennet</td>\n",
       "      <td>1358635</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00705186/1358635/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>2111199.33</td>\n",
       "      <td>1863600.33</td>\n",
       "      <td>2444742.40</td>\n",
       "      <td>2115097.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>C00580100</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>1358508</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00580100/1358508/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>14272962.05</td>\n",
       "      <td>83216270.26</td>\n",
       "      <td>14479108.22</td>\n",
       "      <td>40958012.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>C00711614</td>\n",
       "      <td>Tom Steyer</td>\n",
       "      <td>1358950</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-09</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00711614/1358950/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>49645130.22</td>\n",
       "      <td>2623142.12</td>\n",
       "      <td>47021989.49</td>\n",
       "      <td>49645131.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>130</td>\n",
       "      <td>C00711614</td>\n",
       "      <td>Tom Steyer</td>\n",
       "      <td>1358948</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-07-09</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00711614/1358948/</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00711614/1358950/</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>49645130.22</td>\n",
       "      <td>2623142.12</td>\n",
       "      <td>47021989.49</td>\n",
       "      <td>49645131.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>C00710574</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>1357300</td>\n",
       "      <td>2020</td>\n",
       "      <td>F3</td>\n",
       "      <td>2019-10-15</td>\n",
       "      <td>2019-09-30</td>\n",
       "      <td>2019-05-22</td>\n",
       "      <td>OCT QUARTERLY</td>\n",
       "      <td>Q3</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/dcdev/forms/C00710574/1357300/</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>374195.61</td>\n",
       "      <td>204561.18</td>\n",
       "      <td>169634.43</td>\n",
       "      <td>374195.61</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    committee_id       candidate_name  filing_id  cycle form_type  date_filed  \\\n",
       "0      C00695510          Cory Booker    1358922   2020        F3  2019-10-15   \n",
       "7      C00694455        Kamala Harris    1358880   2020        F3  2019-10-15   \n",
       "13     C00703975            Joe Biden    1358905   2020        F3  2019-10-15   \n",
       "17     C00696419        Amy Klobuchar    1358890   2020        F3  2019-10-15   \n",
       "24     C00696948       Bernie Sanders    1357929   2020        F3  2019-10-15   \n",
       "33     C00693234     Elizabeth Warren    1358497   2020        F3  2019-10-15   \n",
       "41     C00697441       Pete Buttigieg    1358691   2020        F3  2019-10-15   \n",
       "51     C00699090        Beto O'Rourke    1358876   2020        F3  2019-10-15   \n",
       "59     C00693044        Julián Castro    1358889   2020        F3  2019-10-15   \n",
       "68     C00508416         John Delaney    1358358   2020        F3  2019-10-15   \n",
       "75     C00659938          Andrew Yang    1358975   2020        F3  2019-10-16   \n",
       "76     C00659938          Andrew Yang    1358962   2020        F3  2019-10-16   \n",
       "81     C00693713        Tulsi Gabbard    1358606   2020        F3  2019-10-15   \n",
       "87     C00701979             Tim Ryan    1358899   2020        F3  2019-10-15   \n",
       "91     C00699280         Wayne Messam    1357839   2020        F3  2019-10-15   \n",
       "99     C00696054  Marianne Williamson    1358852   2020        F3  2019-10-15   \n",
       "108    C00706416        Steve Bullock    1358836   2020        F3  2019-10-15   \n",
       "114    C00705186       Michael Bennet    1358635   2020        F3  2019-10-15   \n",
       "118    C00580100         Donald Trump    1358508   2020        F3  2019-10-15   \n",
       "129    C00711614           Tom Steyer    1358950   2020        F3  2019-10-15   \n",
       "130    C00711614           Tom Steyer    1358948   2020        F3  2019-10-15   \n",
       "132    C00710574           Joe Sestak    1357300   2020        F3  2019-10-15   \n",
       "\n",
       "    date_coverage_to date_coverage_from   report_title report_period  \\\n",
       "0         2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "7         2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "13        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "17        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "24        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "33        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "41        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "51        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "59        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "68        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "75        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "76        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "81        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "87        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "91        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "99        2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "108       2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "114       2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "118       2019-09-30         2019-07-01  OCT QUARTERLY            Q3   \n",
       "129       2019-09-30         2019-07-09  OCT QUARTERLY            Q3   \n",
       "130       2019-09-30         2019-07-09  OCT QUARTERLY            Q3   \n",
       "132       2019-09-30         2019-05-22  OCT QUARTERLY            Q3   \n",
       "\n",
       "                                                            fec_uri  paper  \\\n",
       "0    http://docquery.fec.gov/cgi-bin/dcdev/forms/C00695510/1358922/  False   \n",
       "7    http://docquery.fec.gov/cgi-bin/dcdev/forms/C00694455/1358880/  False   \n",
       "13   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00703975/1358905/  False   \n",
       "17   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00696419/1358890/  False   \n",
       "24   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00696948/1357929/  False   \n",
       "33   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00693234/1358497/  False   \n",
       "41   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00697441/1358691/  False   \n",
       "51   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00699090/1358876/  False   \n",
       "59   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00693044/1358889/  False   \n",
       "68   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00508416/1358358/  False   \n",
       "75   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00659938/1358975/  False   \n",
       "76   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00659938/1358962/  False   \n",
       "81   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00693713/1358606/  False   \n",
       "87   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00701979/1358899/  False   \n",
       "91   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00699280/1357839/  False   \n",
       "99   http://docquery.fec.gov/cgi-bin/dcdev/forms/C00696054/1358852/  False   \n",
       "108  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00706416/1358836/  False   \n",
       "114  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00705186/1358635/  False   \n",
       "118  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00580100/1358508/  False   \n",
       "129  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00711614/1358950/  False   \n",
       "130  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00711614/1358948/  False   \n",
       "132  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00710574/1357300/  False   \n",
       "\n",
       "     amended                                                     amended_uri  \\\n",
       "0      False                                                             NaN   \n",
       "7      False                                                             NaN   \n",
       "13     False                                                             NaN   \n",
       "17     False                                                             NaN   \n",
       "24     False                                                             NaN   \n",
       "33     False                                                             NaN   \n",
       "41     False                                                             NaN   \n",
       "51     False                                                             NaN   \n",
       "59     False                                                             NaN   \n",
       "68     False                                                             NaN   \n",
       "75     False                                                             NaN   \n",
       "76      True  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00659938/1358975/   \n",
       "81     False                                                             NaN   \n",
       "87     False                                                             NaN   \n",
       "91     False                                                             NaN   \n",
       "99     False                                                             NaN   \n",
       "108    False                                                             NaN   \n",
       "114    False                                                             NaN   \n",
       "118    False                                                             NaN   \n",
       "129    False                                                             NaN   \n",
       "130     True  http://docquery.fec.gov/cgi-bin/dcdev/forms/C00711614/1358950/   \n",
       "132    False                                                             NaN   \n",
       "\n",
       "    is_amendment  original_filing original_uri committee_type  \\\n",
       "0            NaN              NaN          NaN              P   \n",
       "7            NaN              NaN          NaN              P   \n",
       "13           NaN              NaN          NaN              P   \n",
       "17           NaN              NaN          NaN              P   \n",
       "24           NaN              NaN          NaN              P   \n",
       "33           NaN              NaN          NaN              P   \n",
       "41           NaN              NaN          NaN              P   \n",
       "51           NaN              NaN          NaN              P   \n",
       "59           NaN              NaN          NaN              P   \n",
       "68           NaN              NaN          NaN              P   \n",
       "75           NaN              NaN          NaN              P   \n",
       "76           NaN              NaN          NaN              P   \n",
       "81           NaN              NaN          NaN              P   \n",
       "87           NaN              NaN          NaN              P   \n",
       "91           NaN              NaN          NaN              P   \n",
       "99           NaN              NaN          NaN              P   \n",
       "108          NaN              NaN          NaN              P   \n",
       "114          NaN              NaN          NaN              P   \n",
       "118          NaN              NaN          NaN              P   \n",
       "129          NaN              NaN          NaN              P   \n",
       "130          NaN              NaN          NaN              P   \n",
       "132          NaN              NaN          NaN              P   \n",
       "\n",
       "     contributions_total  cash_on_hand  disbursements_total  receipts_total  \n",
       "0             6017641.53    4223788.92           7159814.01      6023097.42  \n",
       "7            11694156.88   10542691.73          14578958.89     11849290.45  \n",
       "13           15692077.08    8987627.81          17650871.16     15741432.01  \n",
       "17            4805797.15    3679592.17           7836661.31      4806133.25  \n",
       "24           25252294.27   33734559.74          21559952.99     28025463.17  \n",
       "33           24610428.73   25717673.76          18748452.02     24684963.30  \n",
       "41           19144698.19   23378518.02          18501846.47     19211492.54  \n",
       "51            4468814.60    3255729.87           6423123.17      4482284.35  \n",
       "59            3495405.71     672333.49           3960970.81      3497251.00  \n",
       "68             464789.26     548060.86           7763004.07       868452.26  \n",
       "75            9921124.76    6357361.37           4412923.43      9922625.52  \n",
       "76            9921124.76    6357361.37           4412923.43      9922625.52  \n",
       "81            3029789.92    2138491.08           3332222.40      3032158.63  \n",
       "87             425731.01     158348.92            602440.41       425731.01  \n",
       "91                  5.00      31151.44                 0.00            5.00  \n",
       "99            3052666.90     723731.57           2877876.51      3054167.08  \n",
       "108           2292166.03    1366143.62           2423527.43      2299449.06  \n",
       "114           2111199.33    1863600.33           2444742.40      2115097.72  \n",
       "118          14272962.05   83216270.26          14479108.22     40958012.84  \n",
       "129          49645130.22    2623142.12          47021989.49     49645131.61  \n",
       "130          49645130.22    2623142.12          47021989.49     49645131.61  \n",
       "132            374195.61     204561.18            169634.43       374195.61  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filings = (\n",
    "    pd.read_csv(\"../fetched/filings.csv\")\n",
    ")\n",
    "\n",
    "filings.loc[ \n",
    "    lambda x: x['report_period'] == \"Q3\" ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get only the quarterlies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "quarterlies = (\n",
    "    filings\n",
    "    # get quarters\n",
    "    .loc[\n",
    "        lambda x: x['report_period'].str.contains(\"Q\", na=False)\n",
    "    ]\n",
    "    # remove filings that have been superceded by subsequent filings\n",
    "    .loc[\n",
    "        lambda x: x['amended'] == False\n",
    "    ]\n",
    "    .sort_values(['candidate_name', 'report_period'])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse all the fec files with the filing list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But first, more loading functions.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function, when given a filing ID, returns only the contributions from individual donors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_contributions(filing_id):\n",
    "    filing = fecfile.from_file(f\"../fetched/filings/{filing_id}.fec\")\n",
    "    meta = filing['filing']\n",
    "    \n",
    "    # get only schedule A\n",
    "    schedule_a = pd.DataFrame(filing[\"itemizations\"][\"Schedule A\"])\n",
    "    \n",
    "    # remove time zone\n",
    "    schedule_a[\"contribution_date\"] = schedule_a[\"contribution_date\"].dt.tz_localize(None)\n",
    "    \n",
    "    return (\n",
    "        schedule_a\n",
    "        \n",
    "        # Extract only individual contributions\n",
    "        .loc[lambda df: df[\"entity_type\"] == \"IND\"]\n",
    "        \n",
    "        # Remove memo lines\n",
    "        .loc[lambda df: df[\"memo_code\"] == \"\"]\n",
    "        \n",
    "        .assign(\n",
    "            filing_id = int(filing_id),\n",
    "            \n",
    "        )\n",
    "        # filter schedule A\n",
    "        [[\n",
    "            \"entity_type\",\n",
    "            \"filer_committee_id_number\",\n",
    "            \"filing_id\",\n",
    "            \"transaction_id\",\n",
    "            \"contribution_date\",\n",
    "            \"contribution_amount\",\n",
    "            \"contribution_aggregate\",\n",
    "            \"contributor_organization_name\",\n",
    "            \"contributor_first_name\",\n",
    "            \"contributor_last_name\",\n",
    "            \"contributor_zip_code\",\n",
    "            \"contributor_state\"\n",
    "        ]]\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a unique ID out of first name, last name and 5-digit ZIP code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_donor_ids(df):\n",
    "    return (\n",
    "        df\n",
    "        .assign(\n",
    "            donor_id = lambda df: (\n",
    "                df\n",
    "                .assign(\n",
    "                    zip5 = lambda df: (\n",
    "                        df[\"contributor_zip_code\"]\n",
    "                        .fillna(\"-----\")\n",
    "                        .str.slice(0, 5)\n",
    "                    )\n",
    "                )\n",
    "                [[\n",
    "                    \"contributor_first_name\",\n",
    "                    \"contributor_last_name\",\n",
    "                    \"zip5\",\n",
    "                ]]\n",
    "                .apply(lambda x: (\n",
    "                    x\n",
    "                    .fillna(\"\")\n",
    "                    .astype(str)\n",
    "                    # Remove periods, commas, extra whitespace\n",
    "                    .str.replace(r\"[\\.,\\s]+\", \" \")\n",
    "                    .str.strip()\n",
    "                    # Convert everything to upper-case\n",
    "                    .str.upper()\n",
    "                ))\n",
    "                .apply(\"|\".join, axis = 1)\n",
    "            )\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# takes concatenated contribution dataframe and merges with metadata\n",
    "def get_metadata(contributions):\n",
    "    candidate_filings = (\n",
    "        filings\n",
    "        [[\"filing_id\",\n",
    "        \"candidate_name\",\n",
    "        \"date_filed\",\n",
    "        \"date_coverage_to\",\n",
    "        \"date_coverage_from\",\n",
    "        \"report_title\",\n",
    "        \"report_period\"]]\n",
    "    )\n",
    "    \n",
    "    return (\n",
    "        contributions\n",
    "        .merge(\n",
    "            candidate_filings,\n",
    "            on = \"filing_id\",\n",
    "            how = \"left\"\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Concatenate all the filings data into one big DataFrame, and get individual contributions only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "subset = quarterlies.loc[\n",
    "    lambda x: x[\"candidate_name\"] != \"Wayne Messam\" # He did not file a Schedule A\n",
    "]\n",
    "\n",
    "#subset = quarterlies\n",
    "\n",
    "all_indiv = (\n",
    "    pd\n",
    "    .concat(\n",
    "        [ extract_contributions(e) for e in subset[\"filing_id\"] ]\n",
    "    )\n",
    "    .pipe( get_metadata )\n",
    "    .pipe( make_donor_ids )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove the donors who have given $200 or less, or have been refunded to that level"
   ]
  },
  {
   "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>candidate_name</th>\n",
       "      <th>donor_id</th>\n",
       "      <th>latest_contribution_aggregate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>140991</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>JOHN|WILLIAMS|95614</td>\n",
       "      <td>-4700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171314</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>TOM|WALLACE|32034</td>\n",
       "      <td>-3974.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>174022</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>WILLIAM|WEAVER|46228</td>\n",
       "      <td>-3100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       candidate_name              donor_id  latest_contribution_aggregate\n",
       "140991   Donald Trump   JOHN|WILLIAMS|95614                       -4700.00\n",
       "171314   Donald Trump     TOM|WALLACE|32034                       -3974.79\n",
       "174022   Donald Trump  WILLIAM|WEAVER|46228                       -3100.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First build a frame of latest contributions\n",
    "latest_contribs = (\n",
    "    all_indiv\n",
    "    .sort_values('contribution_date')\n",
    "    .groupby([ 'candidate_name', 'donor_id'])\n",
    "    .pipe(lambda grp: pd.DataFrame({\n",
    "        \"latest_contribution_aggregate\": grp[\"contribution_aggregate\"].last(),\n",
    "        })\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "\n",
    "latest_contribs.sort_values(\n",
    "    'latest_contribution_aggregate',\n",
    "    ascending = True\n",
    ").head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get list of individual contributions from individuals who have given more than $200, the official itemization threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold_indiv = (\n",
    "    all_indiv\n",
    "    .merge(\n",
    "        latest_contribs,\n",
    "        on = ['donor_id', 'candidate_name'],\n",
    "        how= \"left\"\n",
    "    )\n",
    "    .loc[\n",
    "        lambda x: x[\"latest_contribution_aggregate\"] > 200\n",
    "    ]\n",
    "    .loc[\n",
    "        lambda x: x[\"contribution_amount\"] > 0\n",
    "    ]\n",
    "    .sort_values('latest_contribution_aggregate', ascending = False)\n",
    "\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finally, some stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's the burn rate (`amount spent / amount raised`) for this period? (Not calculating donations from affiliated committees)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Candidate burn rates (Q3 only)\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>candidate name</th>\n",
       "      <th>cash on hand</th>\n",
       "      <th>spent</th>\n",
       "      <th>raised</th>\n",
       "      <th>difference</th>\n",
       "      <th>percent spent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>129</td>\n",
       "      <td>Tom Steyer</td>\n",
       "      <td>2623142.12</td>\n",
       "      <td>47021989.49</td>\n",
       "      <td>49645130.22</td>\n",
       "      <td>2623140.73</td>\n",
       "      <td>94.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>33734559.74</td>\n",
       "      <td>21559952.99</td>\n",
       "      <td>25252294.27</td>\n",
       "      <td>3692341.28</td>\n",
       "      <td>85.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>Elizabeth Warren</td>\n",
       "      <td>25717673.76</td>\n",
       "      <td>18748452.02</td>\n",
       "      <td>24610428.73</td>\n",
       "      <td>5861976.71</td>\n",
       "      <td>76.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>41</td>\n",
       "      <td>Pete Buttigieg</td>\n",
       "      <td>23378518.02</td>\n",
       "      <td>18501846.47</td>\n",
       "      <td>19144698.19</td>\n",
       "      <td>642851.72</td>\n",
       "      <td>96.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>8987627.81</td>\n",
       "      <td>17650871.16</td>\n",
       "      <td>15692077.08</td>\n",
       "      <td>-1958794.08</td>\n",
       "      <td>112.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>118</td>\n",
       "      <td>Donald Trump</td>\n",
       "      <td>83216270.26</td>\n",
       "      <td>14479108.22</td>\n",
       "      <td>14272962.05</td>\n",
       "      <td>-206146.17</td>\n",
       "      <td>101.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>10542691.73</td>\n",
       "      <td>14578958.89</td>\n",
       "      <td>11694156.88</td>\n",
       "      <td>-2884802.01</td>\n",
       "      <td>124.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75</td>\n",
       "      <td>Andrew Yang</td>\n",
       "      <td>6357361.37</td>\n",
       "      <td>4412923.43</td>\n",
       "      <td>9921124.76</td>\n",
       "      <td>5508201.33</td>\n",
       "      <td>44.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Cory Booker</td>\n",
       "      <td>4223788.92</td>\n",
       "      <td>7159814.01</td>\n",
       "      <td>6017641.53</td>\n",
       "      <td>-1142172.48</td>\n",
       "      <td>119.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>3679592.17</td>\n",
       "      <td>7836661.31</td>\n",
       "      <td>4805797.15</td>\n",
       "      <td>-3030864.16</td>\n",
       "      <td>163.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>51</td>\n",
       "      <td>Beto O'Rourke</td>\n",
       "      <td>3255729.87</td>\n",
       "      <td>6423123.17</td>\n",
       "      <td>4468814.60</td>\n",
       "      <td>-1954308.57</td>\n",
       "      <td>143.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>Julián Castro</td>\n",
       "      <td>672333.49</td>\n",
       "      <td>3960970.81</td>\n",
       "      <td>3495405.71</td>\n",
       "      <td>-465565.10</td>\n",
       "      <td>113.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>99</td>\n",
       "      <td>Marianne Williamson</td>\n",
       "      <td>723731.57</td>\n",
       "      <td>2877876.51</td>\n",
       "      <td>3052666.90</td>\n",
       "      <td>174790.39</td>\n",
       "      <td>94.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>Tulsi Gabbard</td>\n",
       "      <td>2138491.08</td>\n",
       "      <td>3332222.40</td>\n",
       "      <td>3029789.92</td>\n",
       "      <td>-302432.48</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>108</td>\n",
       "      <td>Steve Bullock</td>\n",
       "      <td>1366143.62</td>\n",
       "      <td>2423527.43</td>\n",
       "      <td>2292166.03</td>\n",
       "      <td>-131361.40</td>\n",
       "      <td>105.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>Michael Bennet</td>\n",
       "      <td>1863600.33</td>\n",
       "      <td>2444742.40</td>\n",
       "      <td>2111199.33</td>\n",
       "      <td>-333543.07</td>\n",
       "      <td>115.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>68</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>548060.86</td>\n",
       "      <td>7763004.07</td>\n",
       "      <td>464789.26</td>\n",
       "      <td>-7298214.81</td>\n",
       "      <td>1670.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>87</td>\n",
       "      <td>Tim Ryan</td>\n",
       "      <td>158348.92</td>\n",
       "      <td>602440.41</td>\n",
       "      <td>425731.01</td>\n",
       "      <td>-176709.40</td>\n",
       "      <td>141.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>132</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>204561.18</td>\n",
       "      <td>169634.43</td>\n",
       "      <td>374195.61</td>\n",
       "      <td>204561.18</td>\n",
       "      <td>45.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>Wayne Messam</td>\n",
       "      <td>31151.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          candidate name  cash on hand        spent       raised  difference  \\\n",
       "129           Tom Steyer    2623142.12  47021989.49  49645130.22  2623140.73   \n",
       "24        Bernie Sanders   33734559.74  21559952.99  25252294.27  3692341.28   \n",
       "33      Elizabeth Warren   25717673.76  18748452.02  24610428.73  5861976.71   \n",
       "41        Pete Buttigieg   23378518.02  18501846.47  19144698.19   642851.72   \n",
       "13             Joe Biden    8987627.81  17650871.16  15692077.08 -1958794.08   \n",
       "118         Donald Trump   83216270.26  14479108.22  14272962.05  -206146.17   \n",
       "7          Kamala Harris   10542691.73  14578958.89  11694156.88 -2884802.01   \n",
       "75           Andrew Yang    6357361.37   4412923.43   9921124.76  5508201.33   \n",
       "0            Cory Booker    4223788.92   7159814.01   6017641.53 -1142172.48   \n",
       "17         Amy Klobuchar    3679592.17   7836661.31   4805797.15 -3030864.16   \n",
       "51         Beto O'Rourke    3255729.87   6423123.17   4468814.60 -1954308.57   \n",
       "59         Julián Castro     672333.49   3960970.81   3495405.71  -465565.10   \n",
       "99   Marianne Williamson     723731.57   2877876.51   3052666.90   174790.39   \n",
       "81         Tulsi Gabbard    2138491.08   3332222.40   3029789.92  -302432.48   \n",
       "108        Steve Bullock    1366143.62   2423527.43   2292166.03  -131361.40   \n",
       "114       Michael Bennet    1863600.33   2444742.40   2111199.33  -333543.07   \n",
       "68          John Delaney     548060.86   7763004.07    464789.26 -7298214.81   \n",
       "87              Tim Ryan     158348.92    602440.41    425731.01  -176709.40   \n",
       "132           Joe Sestak     204561.18    169634.43    374195.61   204561.18   \n",
       "91          Wayne Messam      31151.44         0.00         5.00        5.00   \n",
       "\n",
       "     percent spent  \n",
       "129           94.7  \n",
       "24            85.4  \n",
       "33            76.2  \n",
       "41            96.6  \n",
       "13           112.5  \n",
       "118          101.4  \n",
       "7            124.7  \n",
       "75            44.5  \n",
       "0            119.0  \n",
       "17           163.1  \n",
       "51           143.7  \n",
       "59           113.3  \n",
       "99            94.3  \n",
       "81           110.0  \n",
       "108          105.7  \n",
       "114          115.8  \n",
       "68          1670.2  \n",
       "87           141.5  \n",
       "132           45.3  \n",
       "91             0.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quarter = \"Q3\"\n",
    "\n",
    "# Get latest quarterly\n",
    "latest_period = (\n",
    "    quarterlies\n",
    "    .loc[ lambda df: df['report_period'] == quarter]\n",
    ")\n",
    "\n",
    "burn = pd.DataFrame({\n",
    "    \"candidate name\": latest_period[\"candidate_name\"],\n",
    "    \"cash on hand\": latest_period[\"cash_on_hand\"],\n",
    "    \"spent\": latest_period[\"disbursements_total\"],\n",
    "    \"raised\": latest_period[\"contributions_total\"],\n",
    "    \"difference\": latest_period['contributions_total'] - latest_period['disbursements_total'],\n",
    "    \"percent spent\": (( \n",
    "        latest_period['disbursements_total'] / latest_period['contributions_total'] \n",
    "    ) * 100).round(1)\n",
    "}).sort_values('raised', ascending = False)\n",
    "\n",
    "\n",
    "print(f\"Candidate burn rates ({quarter} only)\")\n",
    "burn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biggest days for each candidate who has filed in Q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 days for Joe Biden\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>contribution_date</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>donor_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>04/25/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>3390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>163</td>\n",
       "      <td>09/30/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>2070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>158</td>\n",
       "      <td>09/25/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>09/12/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>09/29/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>131</td>\n",
       "      <td>08/29/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>102</td>\n",
       "      <td>07/31/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>09/21/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>103</td>\n",
       "      <td>08/01/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>1014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>153</td>\n",
       "      <td>09/20/2019</td>\n",
       "      <td>Joe Biden</td>\n",
       "      <td>963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    contribution_date candidate_name  donor_count\n",
       "5          04/25/2019      Joe Biden         3390\n",
       "163        09/30/2019      Joe Biden         2070\n",
       "158        09/25/2019      Joe Biden         1349\n",
       "145        09/12/2019      Joe Biden         1241\n",
       "162        09/29/2019      Joe Biden         1206\n",
       "131        08/29/2019      Joe Biden         1186\n",
       "102        07/31/2019      Joe Biden         1090\n",
       "154        09/21/2019      Joe Biden         1059\n",
       "103        08/01/2019      Joe Biden         1014\n",
       "153        09/20/2019      Joe Biden          963"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 days for Andrew Yang\n"
     ]
    },
    {
     "data": {
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       "      <td>261</td>\n",
       "      <td>09/30/2019</td>\n",
       "      <td>Andrew Yang</td>\n",
       "      <td>3331</td>\n",
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       "    <tr>\n",
       "      <td>260</td>\n",
       "      <td>09/29/2019</td>\n",
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       "      <td>243</td>\n",
       "      <td>09/12/2019</td>\n",
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       "      <td>1431</td>\n",
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       "    <tr>\n",
       "      <td>244</td>\n",
       "      <td>09/13/2019</td>\n",
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       "      <td>256</td>\n",
       "      <td>09/25/2019</td>\n",
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       "    <tr>\n",
       "      <td>258</td>\n",
       "      <td>09/27/2019</td>\n",
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       "      <td>1045</td>\n",
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       "    <tr>\n",
       "      <td>259</td>\n",
       "      <td>09/28/2019</td>\n",
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       "      <td>970</td>\n",
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       "    <tr>\n",
       "      <td>231</td>\n",
       "      <td>08/31/2019</td>\n",
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       "      <td>950</td>\n",
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       "    <tr>\n",
       "      <td>255</td>\n",
       "      <td>09/24/2019</td>\n",
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       "    <tr>\n",
       "      <td>241</td>\n",
       "      <td>09/10/2019</td>\n",
       "      <td>Andrew Yang</td>\n",
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       "244        09/13/2019    Andrew Yang         1363\n",
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       "258        09/27/2019    Andrew Yang         1045\n",
       "259        09/28/2019    Andrew Yang          970\n",
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     "text": [
      "Top 10 days for Pete Buttigieg\n"
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       "      <td>153</td>\n",
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       "247        09/29/2019  Pete Buttigieg         3591\n",
       "246        09/28/2019  Pete Buttigieg         3323\n",
       "79         04/14/2019  Pete Buttigieg         2641\n",
       "243        09/25/2019  Pete Buttigieg         2614\n",
       "154        06/28/2019  Pete Buttigieg         2574\n",
       "156        06/30/2019  Pete Buttigieg         2393\n",
       "233        09/15/2019  Pete Buttigieg         2235\n",
       "153        06/27/2019  Pete Buttigieg         2201"
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     "text": [
      "Top 10 days for Donald Trump\n"
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       "      <td>269</td>\n",
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       "      <td>271</td>\n",
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       "      <td>267</td>\n",
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       "      <td>119</td>\n",
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       "      <td>961</td>\n",
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       "      <td>270</td>\n",
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       "      <td>Donald Trump</td>\n",
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       "266        09/24/2019   Donald Trump         1268\n",
       "169        06/19/2019   Donald Trump         1234\n",
       "268        09/26/2019   Donald Trump         1209\n",
       "269        09/27/2019   Donald Trump         1140\n",
       "271        09/29/2019   Donald Trump         1052\n",
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       "270        09/28/2019   Donald Trump          889"
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     "text": [
      "Top 10 days for Amy Klobuchar\n"
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       "      <td>235</td>\n",
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       "      <td>218</td>\n",
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       "      <td>143</td>\n",
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       "      <td>174</td>\n",
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       "      <td>Amy Klobuchar</td>\n",
       "      <td>521</td>\n",
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       "      <td>3</td>\n",
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       "      <td>460</td>\n",
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       "    <tr>\n",
       "      <td>231</td>\n",
       "      <td>09/26/2019</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>391</td>\n",
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       "    <tr>\n",
       "      <td>234</td>\n",
       "      <td>09/29/2019</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>390</td>\n",
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       "    <tr>\n",
       "      <td>203</td>\n",
       "      <td>08/29/2019</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>389</td>\n",
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       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>07/30/2019</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>375</td>\n",
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       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>08/02/2019</td>\n",
       "      <td>Amy Klobuchar</td>\n",
       "      <td>367</td>\n",
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       "    contribution_date candidate_name  donor_count\n",
       "235        09/30/2019  Amy Klobuchar          916\n",
       "218        09/13/2019  Amy Klobuchar          644\n",
       "143        06/30/2019  Amy Klobuchar          604\n",
       "174        07/31/2019  Amy Klobuchar          521\n",
       "3          02/10/2019  Amy Klobuchar          460\n",
       "231        09/26/2019  Amy Klobuchar          391\n",
       "234        09/29/2019  Amy Klobuchar          390\n",
       "203        08/29/2019  Amy Klobuchar          389\n",
       "173        07/30/2019  Amy Klobuchar          375\n",
       "176        08/02/2019  Amy Klobuchar          367"
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     "text": [
      "Top 10 days for Kamala Harris\n"
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       "      <td>160</td>\n",
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       "      <td>254</td>\n",
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       "      <td>193</td>\n",
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       "      <td>2</td>\n",
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       "      <td>162</td>\n",
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       "      <td>1471</td>\n",
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       "    <tr>\n",
       "      <td>159</td>\n",
       "      <td>06/27/2019</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>1440</td>\n",
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       "    <tr>\n",
       "      <td>221</td>\n",
       "      <td>08/28/2019</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>1411</td>\n",
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       "    <tr>\n",
       "      <td>190</td>\n",
       "      <td>07/28/2019</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>1298</td>\n",
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       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>06/29/2019</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>1214</td>\n",
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       "    <tr>\n",
       "      <td>209</td>\n",
       "      <td>08/16/2019</td>\n",
       "      <td>Kamala Harris</td>\n",
       "      <td>1188</td>\n",
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       "</table>\n",
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       "    contribution_date candidate_name  donor_count\n",
       "160        06/28/2019  Kamala Harris         3071\n",
       "254        09/30/2019  Kamala Harris         1873\n",
       "193        07/31/2019  Kamala Harris         1550\n",
       "2          01/21/2019  Kamala Harris         1508\n",
       "162        06/30/2019  Kamala Harris         1471\n",
       "159        06/27/2019  Kamala Harris         1440\n",
       "221        08/28/2019  Kamala Harris         1411\n",
       "190        07/28/2019  Kamala Harris         1298\n",
       "161        06/29/2019  Kamala Harris         1214\n",
       "209        08/16/2019  Kamala Harris         1188"
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     "text": [
      "Top 10 days for Bernie Sanders\n"
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       "      <td>223</td>\n",
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       "      <td>Bernie Sanders</td>\n",
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       "      <td>161</td>\n",
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       "      <td>Bernie Sanders</td>\n",
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       "      <td>193</td>\n",
       "      <td>08/31/2019</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>7799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>07/31/2019</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>7671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>205</td>\n",
       "      <td>09/12/2019</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>7112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>181</td>\n",
       "      <td>08/19/2019</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>6954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>222</td>\n",
       "      <td>09/29/2019</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>6609</td>\n",
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       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>07/19/2019</td>\n",
       "      <td>Bernie Sanders</td>\n",
       "      <td>6011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "    contribution_date  candidate_name  donor_count\n",
       "223        09/30/2019  Bernie Sanders        15430\n",
       "212        09/19/2019  Bernie Sanders         8545\n",
       "131        06/30/2019  Bernie Sanders         8035\n",
       "161        07/30/2019  Bernie Sanders         8027\n",
       "193        08/31/2019  Bernie Sanders         7799\n",
       "162        07/31/2019  Bernie Sanders         7671\n",
       "205        09/12/2019  Bernie Sanders         7112\n",
       "181        08/19/2019  Bernie Sanders         6954\n",
       "222        09/29/2019  Bernie Sanders         6609\n",
       "150        07/19/2019  Bernie Sanders         6011"
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     "text": [
      "Top 10 days for Marianne Williamson\n"
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       "268        08/23/2019  Marianne Williamson          500\n",
       "273        08/28/2019  Marianne Williamson          472\n",
       "269        08/24/2019  Marianne Williamson          455\n",
       "244        07/30/2019  Marianne Williamson          428\n",
       "272        08/27/2019  Marianne Williamson          401\n",
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     "text": [
      "Top 10 days for Steve Bullock\n"
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       "146        09/30/2019  Steve Bullock          361\n",
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       "85         07/31/2019  Steve Bullock          223\n",
       "145        09/29/2019  Steve Bullock          193\n",
       "84         07/30/2019  Steve Bullock          149\n",
       "54         06/30/2019  Steve Bullock          131\n",
       "116        08/31/2019  Steve Bullock          126\n",
       "98         08/13/2019  Steve Bullock          119\n",
       "8          05/15/2019  Steve Bullock          119\n",
       "71         07/17/2019  Steve Bullock          116"
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     "text": [
      "Top 10 days for Elizabeth Warren\n"
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       "      <td>242</td>\n",
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       "      <td>269</td>\n",
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       "      <td>Elizabeth Warren</td>\n",
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       "      <td>266</td>\n",
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       "      <td>2410</td>\n",
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       "      <td>239</td>\n",
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       "      <td>Elizabeth Warren</td>\n",
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       "    contribution_date    candidate_name  donor_count\n",
       "273        09/30/2019  Elizabeth Warren         6669\n",
       "212        07/31/2019  Elizabeth Warren         4290\n",
       "181        06/30/2019  Elizabeth Warren         3527\n",
       "211        07/30/2019  Elizabeth Warren         2937\n",
       "243        08/31/2019  Elizabeth Warren         2933\n",
       "242        08/30/2019  Elizabeth Warren         2677\n",
       "269        09/26/2019  Elizabeth Warren         2540\n",
       "266        09/23/2019  Elizabeth Warren         2488\n",
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       "239        08/27/2019  Elizabeth Warren         2367"
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     "text": [
      "Top 10 days for Michael Bennet\n"
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       "      <td>81</td>\n",
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       "      <td>106</td>\n",
       "      <td>08/13/2019</td>\n",
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       "      <td>142</td>\n",
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       "      <td>121</td>\n",
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       "      <td>153</td>\n",
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       "      <td>4</td>\n",
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       "    contribution_date  candidate_name  donor_count\n",
       "154        09/30/2019  Michael Bennet          223\n",
       "3          05/02/2019  Michael Bennet          214\n",
       "94         08/01/2019  Michael Bennet          206\n",
       "95         08/02/2019  Michael Bennet          159\n",
       "107        08/14/2019  Michael Bennet          156\n",
       "81         07/19/2019  Michael Bennet          123\n",
       "106        08/13/2019  Michael Bennet          121\n",
       "142        09/18/2019  Michael Bennet          121\n",
       "153        09/29/2019  Michael Bennet          119\n",
       "4          05/03/2019  Michael Bennet          118"
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     "text": [
      "Top 10 days for Beto O'Rourke\n"
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       "      <td>1223</td>\n",
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       "      <td>182</td>\n",
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       "      <td>139</td>\n",
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       "      <td>Beto O'Rourke</td>\n",
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       "      <td>184</td>\n",
       "      <td>09/14/2019</td>\n",
       "      <td>Beto O'Rourke</td>\n",
       "      <td>1107</td>\n",
       "    </tr>\n",
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       "      <td>199</td>\n",
       "      <td>09/29/2019</td>\n",
       "      <td>Beto O'Rourke</td>\n",
       "      <td>1105</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
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       "    contribution_date candidate_name  donor_count\n",
       "0          03/14/2019  Beto O'Rourke         3789\n",
       "200        09/30/2019  Beto O'Rourke         2259\n",
       "170        08/31/2019  Beto O'Rourke         1362\n",
       "108        06/30/2019  Beto O'Rourke         1361\n",
       "154        08/15/2019  Beto O'Rourke         1238\n",
       "183        09/13/2019  Beto O'Rourke         1223\n",
       "182        09/12/2019  Beto O'Rourke         1180\n",
       "139        07/31/2019  Beto O'Rourke         1156\n",
       "184        09/14/2019  Beto O'Rourke         1107\n",
       "199        09/29/2019  Beto O'Rourke         1105"
      ]
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     "text": [
      "Top 10 days for Julián Castro\n"
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       "      <td>239</td>\n",
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       "      <td>372</td>\n",
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       "      <td>221</td>\n",
       "      <td>08/12/2019</td>\n",
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       "      <td>229</td>\n",
       "      <td>08/20/2019</td>\n",
       "      <td>Julián Castro</td>\n",
       "      <td>316</td>\n",
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       "208        07/30/2019  Julián Castro          445\n",
       "210        08/01/2019  Julián Castro          436\n",
       "211        08/02/2019  Julián Castro          397\n",
       "239        08/30/2019  Julián Castro          372\n",
       "221        08/12/2019  Julián Castro          339\n",
       "240        08/31/2019  Julián Castro          328\n",
       "181        07/03/2019  Julián Castro          317\n",
       "229        08/20/2019  Julián Castro          316"
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     "text": [
      "Top 10 days for Tulsi Gabbard\n"
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       "      <td>239</td>\n",
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       "      <td>Tulsi Gabbard</td>\n",
       "      <td>605</td>\n",
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       "      <td>147</td>\n",
       "      <td>06/30/2019</td>\n",
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       "      <td>525</td>\n",
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       "      <td>235</td>\n",
       "      <td>09/26/2019</td>\n",
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       "      <td>486</td>\n",
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       "      <td>233</td>\n",
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       "      <td>467</td>\n",
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       "      <td>224</td>\n",
       "      <td>09/15/2019</td>\n",
       "      <td>Tulsi Gabbard</td>\n",
       "      <td>400</td>\n",
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       "207        08/29/2019  Tulsi Gabbard          668\n",
       "179        08/01/2019  Tulsi Gabbard          647\n",
       "182        08/04/2019  Tulsi Gabbard          631\n",
       "239        09/30/2019  Tulsi Gabbard          605\n",
       "147        06/30/2019  Tulsi Gabbard          525\n",
       "235        09/26/2019  Tulsi Gabbard          486\n",
       "233        09/24/2019  Tulsi Gabbard          467\n",
       "224        09/15/2019  Tulsi Gabbard          400"
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     "text": [
      "Top 10 days for Tom Steyer\n"
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       "      <td>43</td>\n",
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       "      <td>Tom Steyer</td>\n",
       "      <td>25</td>\n",
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       "      <td>62</td>\n",
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       "      <td>13</td>\n",
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       "27        08/05/2019     Tom Steyer           31\n",
       "31        08/09/2019     Tom Steyer           29\n",
       "43        08/21/2019     Tom Steyer           25\n",
       "62        09/09/2019     Tom Steyer           25\n",
       "13        07/22/2019     Tom Steyer           24\n",
       "20        07/29/2019     Tom Steyer           24\n",
       "22        07/31/2019     Tom Steyer           23"
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     "text": [
      "Top 10 days for Tim Ryan\n"
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       "      <td>4</td>\n",
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       "    <tr>\n",
       "      <td>97</td>\n",
       "      <td>07/09/2019</td>\n",
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       "      <td>102</td>\n",
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       "      <td>84</td>\n",
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       "      <td>07/08/2019</td>\n",
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       "88         06/30/2019       Tim Ryan           29\n",
       "119        07/31/2019       Tim Ryan           26\n",
       "146        08/27/2019       Tim Ryan           22\n",
       "4          04/05/2019       Tim Ryan           21\n",
       "97         07/09/2019       Tim Ryan           21\n",
       "102        07/14/2019       Tim Ryan           20\n",
       "84         06/26/2019       Tim Ryan           19\n",
       "96         07/08/2019       Tim Ryan           18"
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      "Top 10 days for Cory Booker\n"
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       "      <td>233</td>\n",
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       "      <td>400</td>\n",
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       "      <td>0</td>\n",
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       "      <td>395</td>\n",
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       "      <td>224</td>\n",
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       "      <td>375</td>\n",
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       "232        09/21/2019    Cory Booker          682\n",
       "181        08/01/2019    Cory Booker          624\n",
       "241        09/30/2019    Cory Booker          604\n",
       "240        09/29/2019    Cory Booker          537\n",
       "234        09/23/2019    Cory Booker          509\n",
       "180        07/31/2019    Cory Booker          429\n",
       "233        09/22/2019    Cory Booker          423\n",
       "195        08/15/2019    Cory Booker          400\n",
       "0          02/01/2019    Cory Booker          395\n",
       "224        09/13/2019    Cory Booker          375"
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     "text": [
      "Top 10 days for John Delaney\n"
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       "    </tr>\n",
       "    <tr>\n",
       "      <td>250</td>\n",
       "      <td>09/30/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>201</td>\n",
       "      <td>08/12/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>74</td>\n",
       "      <td>03/29/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>220</td>\n",
       "      <td>08/31/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>225</td>\n",
       "      <td>09/05/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>08/21/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>03/11/2019</td>\n",
       "      <td>John Delaney</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    contribution_date candidate_name  donor_count\n",
       "189        07/31/2019   John Delaney           48\n",
       "188        07/30/2019   John Delaney           31\n",
       "59         03/14/2019   John Delaney           31\n",
       "250        09/30/2019   John Delaney           28\n",
       "201        08/12/2019   John Delaney           22\n",
       "74         03/29/2019   John Delaney           20\n",
       "220        08/31/2019   John Delaney           20\n",
       "225        09/05/2019   John Delaney           20\n",
       "210        08/21/2019   John Delaney           20\n",
       "56         03/11/2019   John Delaney           19"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 days for Joe Sestak\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>contribution_date</th>\n",
       "      <th>candidate_name</th>\n",
       "      <th>donor_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>96</td>\n",
       "      <td>09/30/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>07/14/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>72</td>\n",
       "      <td>09/06/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>06/30/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>07/15/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>06/23/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>09/26/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>07/07/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>07/17/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>07/21/2019</td>\n",
       "      <td>Joe Sestak</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   contribution_date candidate_name  donor_count\n",
       "96        09/30/2019     Joe Sestak           40\n",
       "20        07/14/2019     Joe Sestak           37\n",
       "72        09/06/2019     Joe Sestak           24\n",
       "7         06/30/2019     Joe Sestak           21\n",
       "21        07/15/2019     Joe Sestak           19\n",
       "0         06/23/2019     Joe Sestak           17\n",
       "92        09/26/2019     Joe Sestak           15\n",
       "13        07/07/2019     Joe Sestak           12\n",
       "23        07/17/2019     Joe Sestak           11\n",
       "26        07/21/2019     Joe Sestak           10"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get list of candidates\n",
    "candidates = threshold_indiv.loc[ \n",
    "    lambda x: x[\"report_period\"] == \"Q3\"\n",
    "]['candidate_name'].unique()\n",
    "\n",
    "for each in candidates:\n",
    "\n",
    "    top_days = (\n",
    "        threshold_indiv\n",
    "        .loc[\n",
    "            lambda x: x['candidate_name'] == each\n",
    "        ]\n",
    "        .groupby([\n",
    "            pd.Grouper(\n",
    "                key = \"contribution_date\",\n",
    "                freq = 'D'\n",
    "            ),\n",
    "            \"candidate_name\",\n",
    "        ])\n",
    "        [\"donor_id\"]\n",
    "        .nunique()\n",
    "        .to_frame(\"donor_count\")\n",
    "        .reset_index()\n",
    "        .sort_values('donor_count', ascending = False)\n",
    "        [0:10]\n",
    "        .assign(\n",
    "            contribution_date = lambda df: df['contribution_date'].dt.strftime(\"%m/%d/%Y\")\n",
    "        )\n",
    "    )\n",
    "\n",
    "    print(f\"Top 10 days for {each}\")\n",
    "    \n",
    "    display(top_days)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check on timing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The notebook ran for 10.37 minutes'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end = datetime.datetime.now()\n",
    "\n",
    "d = (end - start)\n",
    "\n",
    "f\"The notebook ran for {round(d.total_seconds() / 60, 2) } minutes\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "---\n",
    "\n",
    "---"
   ]
  }
 ],
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