2016_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for all 2016 campaign data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/lukedobrovic/Data/Spectator/personal-campaign-contributions\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3058: DtypeWarning: Columns (12,15,57) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\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>committee_id</th>\n",
       "      <th>committee_name</th>\n",
       "      <th>report_year</th>\n",
       "      <th>report_type</th>\n",
       "      <th>image_number</th>\n",
       "      <th>line_number</th>\n",
       "      <th>transaction_id</th>\n",
       "      <th>file_number</th>\n",
       "      <th>committee_name.1</th>\n",
       "      <th>entity_type</th>\n",
       "      <th>...</th>\n",
       "      <th>filing_form</th>\n",
       "      <th>link_id</th>\n",
       "      <th>is_individual</th>\n",
       "      <th>memo_text</th>\n",
       "      <th>two_year_transaction_period</th>\n",
       "      <th>schedule_type</th>\n",
       "      <th>increased_limit</th>\n",
       "      <th>sub_id</th>\n",
       "      <th>pdf_url</th>\n",
       "      <th>line_number_label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>C00465492</td>\n",
       "      <td>FRIENDS OF JOE SESTAK</td>\n",
       "      <td>2013</td>\n",
       "      <td>Q2</td>\n",
       "      <td>201507210200225038</td>\n",
       "      <td>11AI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IND</td>\n",
       "      <td>...</td>\n",
       "      <td>F3</td>\n",
       "      <td>1040720160018595573</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>SA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1040720160018595836</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/fecimg/?201507...</td>\n",
       "      <td>Contributions From Individuals/Persons Other T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00575795</td>\n",
       "      <td>HILLARY FOR AMERICA</td>\n",
       "      <td>2016</td>\n",
       "      <td>M10</td>\n",
       "      <td>201701099041150117</td>\n",
       "      <td>17A</td>\n",
       "      <td>C11241249</td>\n",
       "      <td>1137625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IND</td>\n",
       "      <td>...</td>\n",
       "      <td>F3P</td>\n",
       "      <td>4010920171359608009</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>SA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4011120171366214585</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/fecimg/?201701...</td>\n",
       "      <td>Contributions From Individuals/Persons Other T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C00465492</td>\n",
       "      <td>FRIENDS OF JOE SESTAK</td>\n",
       "      <td>2013</td>\n",
       "      <td>Q2</td>\n",
       "      <td>201507210200225036</td>\n",
       "      <td>11AI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IND</td>\n",
       "      <td>...</td>\n",
       "      <td>F3</td>\n",
       "      <td>1040720160018595573</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>SA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1040720160018595829</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/fecimg/?201507...</td>\n",
       "      <td>Contributions From Individuals/Persons Other T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>C00575795</td>\n",
       "      <td>HILLARY FOR AMERICA</td>\n",
       "      <td>2016</td>\n",
       "      <td>12G</td>\n",
       "      <td>201701099041274498</td>\n",
       "      <td>17A</td>\n",
       "      <td>C12244496</td>\n",
       "      <td>1137788</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IND</td>\n",
       "      <td>...</td>\n",
       "      <td>F3P</td>\n",
       "      <td>4010920171359608011</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>SA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4011120171366556953</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/fecimg/?201701...</td>\n",
       "      <td>Contributions From Individuals/Persons Other T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>C00179408</td>\n",
       "      <td>MAINE DEMOCRATIC PARTY</td>\n",
       "      <td>2016</td>\n",
       "      <td>M9</td>\n",
       "      <td>201909059163143517</td>\n",
       "      <td>11AI</td>\n",
       "      <td>11AI-000039245</td>\n",
       "      <td>1350680</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IND</td>\n",
       "      <td>...</td>\n",
       "      <td>F3X</td>\n",
       "      <td>4090520191672279180</td>\n",
       "      <td>t</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>SA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4090620191672313017</td>\n",
       "      <td>http://docquery.fec.gov/cgi-bin/fecimg/?201909...</td>\n",
       "      <td>Contributions From Individuals/Persons Other T...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  committee_id          committee_name  report_year report_type  \\\n",
       "0    C00465492   FRIENDS OF JOE SESTAK         2013          Q2   \n",
       "1    C00575795     HILLARY FOR AMERICA         2016         M10   \n",
       "2    C00465492   FRIENDS OF JOE SESTAK         2013          Q2   \n",
       "3    C00575795     HILLARY FOR AMERICA         2016         12G   \n",
       "4    C00179408  MAINE DEMOCRATIC PARTY         2016          M9   \n",
       "\n",
       "         image_number line_number  transaction_id  file_number  \\\n",
       "0  201507210200225038        11AI             NaN      1020266   \n",
       "1  201701099041150117         17A       C11241249      1137625   \n",
       "2  201507210200225036        11AI             NaN      1020266   \n",
       "3  201701099041274498         17A       C12244496      1137788   \n",
       "4  201909059163143517        11AI  11AI-000039245      1350680   \n",
       "\n",
       "   committee_name.1 entity_type  ... filing_form              link_id  \\\n",
       "0               NaN         IND  ...          F3  1040720160018595573   \n",
       "1               NaN         IND  ...         F3P  4010920171359608009   \n",
       "2               NaN         IND  ...          F3  1040720160018595573   \n",
       "3               NaN         IND  ...         F3P  4010920171359608011   \n",
       "4               NaN         IND  ...         F3X  4090520191672279180   \n",
       "\n",
       "  is_individual memo_text two_year_transaction_period schedule_type  \\\n",
       "0             t       NaN                        2014            SA   \n",
       "1             t       NaN                        2016            SA   \n",
       "2             t       NaN                        2014            SA   \n",
       "3             t       NaN                        2016            SA   \n",
       "4             t       NaN                        2016            SA   \n",
       "\n",
       "  increased_limit               sub_id  \\\n",
       "0             NaN  1040720160018595836   \n",
       "1             NaN  4011120171366214585   \n",
       "2             NaN  1040720160018595829   \n",
       "3             NaN  4011120171366556953   \n",
       "4             NaN  4090620191672313017   \n",
       "\n",
       "                                             pdf_url  \\\n",
       "0  http://docquery.fec.gov/cgi-bin/fecimg/?201507...   \n",
       "1  http://docquery.fec.gov/cgi-bin/fecimg/?201701...   \n",
       "2  http://docquery.fec.gov/cgi-bin/fecimg/?201507...   \n",
       "3  http://docquery.fec.gov/cgi-bin/fecimg/?201701...   \n",
       "4  http://docquery.fec.gov/cgi-bin/fecimg/?201909...   \n",
       "\n",
       "                                   line_number_label  \n",
       "0  Contributions From Individuals/Persons Other T...  \n",
       "1  Contributions From Individuals/Persons Other T...  \n",
       "2  Contributions From Individuals/Persons Other T...  \n",
       "3  Contributions From Individuals/Persons Other T...  \n",
       "4  Contributions From Individuals/Persons Other T...  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "DIR = os.getcwd()\n",
    "print(DIR)\n",
    "\n",
    "df = pd.read_csv(DIR + \"/2015to2016contributions.csv\",\n",
    "                    parse_dates = [\"contribution_receipt_date\"]) \n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning: Select desired columns, remove other \"Columbia\" institutions and remove negative values under contributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.columns\n",
    "df_selections = df[[ \"committee_name\", \"contribution_receipt_date\",\n",
    "                    \"contribution_receipt_amount\", \"contributor_employer\", \n",
    "                   \"contributor_occupation\", \"contributor_first_name\", \n",
    "                   \"contributor_middle_name\", \"contributor_last_name\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "faux = [\"MISSOURI\", \"BRITISH\", \"DISTRICT\", \"CHICAGO\", \"INTERNATIONAL\", \"MO\", \"UNIVERSITY HOSPITAL\", \"FLORIDA\",\n",
    "       \"CAROLINA\", \"SOUTHERN\", \"BRIRISH\"]\n",
    "\n",
    "df_selections = df_selections[~df_selections.contributor_employer.str.contains('|'.join(faux))]\n",
    "\n",
    "df_selections = df_selections[df_selections[\"contribution_receipt_amount\"] >= 0]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COLUMBIA UNIVERSITY', 'TEACHERS COLLEGE, COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY COLLEGE OF PHYSICI',\n",
       "       'TEACHERS COLLEGE COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY MEDICAL CENTER', 'COLUMBIA UNIVERSITY SIPA',\n",
       "       \"COLUMBIA UNIVERSITY WOMENSTRONG INT'L\",\n",
       "       \"COLUMBIA UNIVERSITY WOMENSTRONG INT'L.\",\n",
       "       'COLUMBIA UNIVERSITY BUSINESS SCHOOL',\n",
       "       'UNIVERSITY OF THE DISTRCT OF COLUMBIA',\n",
       "       'COLUMBIA UNIVERSITY PRESS',\n",
       "       'COLUMBIA UNIVERSITY TEACHERS COLLEGE', 'COLUMBIA UNIVERSITY-SSA',\n",
       "       'COLUMBIA UNIVERSITY AND NEW YORK STATE',\n",
       "       'HHMI & COLUMBIA UNIVERSITY', 'CIESIN COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY, NY NY',\n",
       "       'AMERICAN ASSEMBLY, COLUMBIA UNIVERSITY', 'COLUMBIA  UNIVERSITY',\n",
       "       'TEACHERS COLLEGE - COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY IN NYC',\n",
       "       'COLUMBIA UNIVERSITY MAILMAN SCHOOL OF',\n",
       "       'COLUMBIA UNIVERSITY MSPH',\n",
       "       'COLUMBIA UNIVERSITY WEATHERHEAD INSTIT',\n",
       "       'UNIVERSITY OF TORONTO AND COLUMBIA UNI',\n",
       "       'NASA-COLUMBIA UNIVERSITY', 'COLUMBIA UNIVERSITY, NY',\n",
       "       'COLUMBIA UNIVERSITY IN THE CITY OF NEW',\n",
       "       'COLUMBIA UNIVERSITY, SIPA', 'THE SCHOOL AT COLUMBIA UNIVERSITY',\n",
       "       'CUMC,COLUMBIA UNIVERSITY', 'TC COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY (NY)',\n",
       "       'COLUMBIA UNIVERSITY SCHOOL OF SOCIAL W',\n",
       "       'TEACHERS COLLEGE,COLUMBIA UNIVERSITY',\n",
       "       'FORMERLY COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY - NEW YORK, NY',\n",
       "       'COLUMBIA UNIVERSITY, NEW YORK CITY',\n",
       "       'MILLER THEATRE AT COLUMBIA UNIVERSITY', 'COLUMBIA UNIVERSITY NY',\n",
       "       'COLUMBIA UNIVERSITY AND SELF-EMPLIOYED',\n",
       "       'COLUMBIA UNIVERSITY/ALLEN HOSPITAL',\n",
       "       'COLUMBIA UNIVERSITY PRESBYTERIAN HOSPI',\n",
       "       'LDEO COLUMBIA UNIVERSITY', 'IRWGS @ COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY,', 'COLUMBIA UNIVERSITY, RETIRED',\n",
       "       'UNITED STATES NAVY/COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY SCHOOL OF NURSING',\n",
       "       'TEACHERS COLLEGE,.COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY PUBLIC SAFETY',\n",
       "       'COLUMBIA UNIVERSITY LIBRARIES', 'COLUMBIA UNIVERSITY LAW SCHOOL',\n",
       "       'COLUMBIA UNIVERSITY/SELF',\n",
       "       'COLUMBIA UNIVERSITY/GREEN IVY SCHOOLS',\n",
       "       'COLUMBIA UNIVERSITY, NEW YORK, NY',\n",
       "       'TRUSTEES OF COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY, TEACHERS COLLEGE',\n",
       "       'COLUMBIA UNIVERSITY MEDICAL', 'RETIRED, COLUMBIA UNIVERSITY',\n",
       "       'FHI 360 & COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY MEDICAL CEMTER', 'NASA COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY/NYP HOSPITAL',\n",
       "       'COLUMBIA UNIVERSITY MEDICAL CE',\n",
       "       'COLUMBIA UNIVERSITY/SYMBOLIC EXCHANGE',\n",
       "       'SELF EMPLOYED AND COLUMBIA UNIVERSITY',\n",
       "       'PENDING - COLUMBIA UNIVERSITY', 'COLUMBIA (UNIVERSITY OF)',\n",
       "       'UNIVERSITY OF COLUMBIA', 'COLUMBIA UNIVERSITY SCHOOL OF MEDICINE',\n",
       "       'COLUMBIA UNIVERSITY (PART-TIME ADJUNCT',\n",
       "       'COLUMBIA UNIVERSITY NEW YORK',\n",
       "       'BARNARD COLLEGE, COLUMBIA UNIVERSITY',\n",
       "       'BARNARD COLLEGE - COLUMBIA UNIVERSITY',\n",
       "       'FREELANCE/COLUMBIA UNIVERSITY',\n",
       "       'NYP COLUMBIA UNIVERSITY MEDICAL CENTER',\n",
       "       'RETIRED COLUMBIA UNIVERSITY',\n",
       "       'RADON LAW OFFICES; COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY MEDICAL CTR.', 'COLUMBIA.UNIVERSITY',\n",
       "       'UNIVERSITY EVENTS MANAGEMENT, COLUMBIA',\n",
       "       'BARNARD COLLEGE AT COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY COLLEGE OF DENTAL', 'COLUMBIA UNIVERSITY.',\n",
       "       'COLUMBIA UNIVERSITY SIRA', 'UNIVERSITY OF MISSOUR-COLUMBIA',\n",
       "       'COLUMBIA UNIVERSITY STUDENT',\n",
       "       'COLUMBIA UNIVERSITY MEDICAL  CENTER',\n",
       "       'COLUMBIA UNIVERSITY/QUEENS COLLEGE',\n",
       "       'COLUMBIA UNIVERSITY-NEVIS LABS',\n",
       "       'BARNARD COLLEGE OF COLUMBIA UNIVERSITY',\n",
       "       'COLUMBIA UNIVERSITY TEACHERS COLLEG',\n",
       "       'COLUMBIA UNIVERSITY SCHOOL OF ARTS',\n",
       "       'GRADUATE STUDENT (COLUMBIA UNIVERSITY)',\n",
       "       \"COLUMBIA UNIVERSITY TEACHER'S COLLEGE\",\n",
       "       'COLUMBIA UNIVERSITY SCHOOL OF DENTAL M',\n",
       "       'UNIVERSITY OF MISSOUI-COLUMBIA',\n",
       "       'COLUMBIA UNIVERSITY AND THE NEW YORK S',\n",
       "       'COLUMBIA UNIVERSITY, DEPARTMENT OF HIS',\n",
       "       'HHMI/COLUMBIA UNIVERSITY'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selections[\"contributor_employer\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['FRIENDS OF JOE SESTAK', 'HILLARY FOR AMERICA',\n",
       "       'MAINE DEMOCRATIC PARTY', 'FRIENDS OF JOHN PLUMB', 'NORPAC',\n",
       "       \"DNC SERVICES CORP./DEM. NAT'L COMMITTEE\",\n",
       "       'COLLEEN DEACON FOR CONGRESS', 'DR. MATT HEINZ FOR ARIZONA',\n",
       "       'FRIENDS OF CHERI BUSTOS',\n",
       "       'LATINO LEADERS FOR EQUALITY GROWTH OPPORTUNITY PROGRESSIVE ACTION & CHANGE (LLEGO-PAC)',\n",
       "       'KATIE MCGINTY FOR SENATE', 'ZEPHYR TEACHOUT FOR CONGRESS',\n",
       "       'REPUBLICAN NATIONAL COMMITTEE', 'JILL STEIN FOR PRESIDENT',\n",
       "       \"EMILY'S LIST\", 'ACTBLUE',\n",
       "       'POLITICAL ACTION COMMITTEE OF THE AMERICAN ASSOCIATION OF ORTHOPAEDIC SURGEONS--PAC OF AAOS',\n",
       "       'JULIA BROWNLEY FOR CONGRESS', 'KAINE FOR VIRGINIA',\n",
       "       'DEMOCRATIC ACTION', \"O'MALLEY FOR PRESIDENT\", 'MAGGIE FOR NH',\n",
       "       'CAROLINE FAYARD FOR US SENATE', 'DEBORAH ROSS FOR SENATE',\n",
       "       'FRIENDS OF SCHUMER', 'MARCO RUBIO FOR SENATE 2016',\n",
       "       'BENNET FOR COLORADO', 'TAMMY FOR ILLINOIS', 'DSCC',\n",
       "       'KAMALA HARRIS FOR SENATE',\n",
       "       'PROGRESSIVE CHANGE CAMPAIGN COMMITTEE', 'HILLARY ACTION FUND',\n",
       "       'TED DEUTCH FOR CONGRESS COMMITTEE',\n",
       "       'GREEN PARTY OF THE UNITED STATES', 'PROGRESSIVE TURNOUT PROJECT',\n",
       "       'JOSH GOTTHEIMER FOR CONGRESS', 'CAIN FOR CONGRESS', '314 PAC',\n",
       "       'OFF THE SIDELINES PAC', 'BRIAN SIMS FOR CONGRESS',\n",
       "       'DONALD J. TRUMP FOR PRESIDENT, INC.',\n",
       "       'FRIENDS OF ANNA THRONE-HOLST', 'FRIENDS OF SHAUGHNESSY NAUGHTON',\n",
       "       'FRIENDS OF ELIZABETH ESTY', 'HOUSE MAJORITY PAC',\n",
       "       'OBAMA FOR AMERICA', 'MOVEON.ORG POLITICAL ACTION', 'CHC BOLD PAC',\n",
       "       'BILL FOSTER FOR CONGRESS', 'RANGEL FOR CONGRESS',\n",
       "       'FRIENDS OF DENA', 'ERIC KINGSON FOR CONGRESS',\n",
       "       'FRIENDS OF RAJA FOR CONGRESS', 'VAN HOLLEN FOR SENATE',\n",
       "       'ELIZABETH FOR MA INC', 'CATHERINE CORTEZ MASTO FOR SENATE',\n",
       "       'FRIENDS OF SHERROD BROWN',\n",
       "       \"1199 SERVICE EMPLOYEES INT'L UNION FEDERAL POLITICAL ACTION FUND\",\n",
       "       'MARCO RUBIO FOR PRESIDENT', 'JEB 2016, INC.',\n",
       "       'AMERICAN ASSOCIATION OF CHILD & ADOLESCENT PSYCHIATRY PAC (AACAP-PAC)',\n",
       "       'PURPOSE PAC', 'PATAKI FOR PRESIDENT INC',\n",
       "       'PETE AGUILAR FOR CONGRESS', 'WOLF PAC',\n",
       "       'BEND THE ARC JEWISH ACTION INC. PAC',\n",
       "       'MONICA VERNON FOR CONGRESS', 'ESPAILLAT FOR CONGRESS 2016',\n",
       "       'AL FRANKEN FOR SENATE ', 'FRIENDS OF CHRIS MURPHY',\n",
       "       'BOB CASEY FOR SENATE INC',\n",
       "       'SOCIETY OF THORACIC SURGEONS POLITICAL ACTION COMMITTEE', 'DCCC',\n",
       "       'MISSOURIANS FOR KANDER', 'EVAN BAYH COMMITTEE',\n",
       "       'FASO FOR CONGRESS', 'LESSIG2016.US', 'NADLER FOR CONGRESS',\n",
       "       'KIRK FOR SENATE', 'CROWLEY FOR CONGRESS',\n",
       "       'ROYCE CAMPAIGN COMMITTEE',\n",
       "       'AMERICAN COLLEGE OF RADIOLOGY ASSOCIATION PAC',\n",
       "       'STABENOW FOR US SENATE', 'FRIENDS OF BERNIE SANDERS',\n",
       "       'GILLIBRAND FOR SENATE', 'NANCY PELOSI FOR CONGRESS',\n",
       "       '21ST CENTURY DEMOCRATS',\n",
       "       'NEW YORK STATE COMMITTEE OF THE WORKING FAMILIES PARTY',\n",
       "       'MIKE HONDA FOR CONGRESS', 'MALONEY FOR CONGRESS', 'JSTREETPAC',\n",
       "       'GRAHAM FOR CONGRESS', 'LOIS FRANKEL FOR CONGRESS',\n",
       "       'SCHNEIDER FOR CONGRESS', 'TIM CANOVA FOR CONGRESS',\n",
       "       'RON JOHNSON FOR SENATE INC', 'HIMES FOR CONGRESS',\n",
       "       'THE AMERICAN CONGRESS OF OB-GYNS PAC (OB-GYN PAC)',\n",
       "       'SENATE MAJORITY PAC',\n",
       "       'WORKING FAMILIES PARTY NATIONAL POLITICAL ACTION COMMITTEE',\n",
       "       'PATTY JUDGE FOR IOWA', 'LEAHY FOR U.S. SENATOR COMMITTEE',\n",
       "       'FRIENDS OF PAT TOOMEY', 'DONNA EDWARDS FOR SENATE',\n",
       "       'BLUMENTHAL FOR CONNECTICUT', 'BERNIE 2016', 'ENGEL FOR CONGRESS',\n",
       "       'ELISE FOR CONGRESS', 'KATHLEEN MATTHEWS FOR CONGRESS',\n",
       "       'COMMITTEE TO ELECT ALAN GRAYSON', 'DEMOCRATS WIN SEATS (DWS PAC)',\n",
       "       'CLEMENTS FOR CONGRESS', 'MOWRER FOR IOWA',\n",
       "       'TRI-STATE MAXED-OUT WOMEN', 'VA SENATE VICTORY FUND',\n",
       "       'LEAGUE OF CONSERVATION VOTERS ACTION FUND',\n",
       "       'NITA LOWEY FOR CONGRESS',\n",
       "       'AMERICAN SOCIETY OF ANESTHESIOLOGISTS POLITICAL ACTION COMMITTEE',\n",
       "       'DEMOCRACY FOR AMERICA', 'FRIENDS OF BRADY WALKINSHAW',\n",
       "       'KIRK VICTORY 2016', 'AMERICAN ACADEMY OF NEUROLOGY BRAINPAC',\n",
       "       'ANDY HARRIS FOR CONGRESS', 'MISSOURI DEMOCRATIC STATE COMMITTEE',\n",
       "       'COMMITTEE TO RE-ELECT NYDIA M. VELAZQUEZ TO CONGRESS',\n",
       "       'FRIENDS OF MARK WARNER', 'NEW YORK STATE DEMOCRATIC COMMITTEE',\n",
       "       'DEMOCRATIC ADVANCEMENT PAC',\n",
       "       'DEBBIE WASSERMAN SCHULTZ FOR CONGRESS', 'YARMUTH FOR CONGRESS',\n",
       "       'DEMOCRATIC PARTY OF VIRGINIA', 'COLORADO DEMOCRATIC PARTY',\n",
       "       'AMERICAN ASSOCIATION OF CLINICAL UROLOGISTS PAC (UROPAC)',\n",
       "       'COUNCIL FOR A LIVABLE WORLD CANDIDATE FUND',\n",
       "       'WILL YANDIK FOR CONGRESS', 'END CITIZENS UNITED',\n",
       "       'SUZAN JOHNSON COOK FOR CONGRESS', 'NEVADA STATE DEMOCRATIC PARTY',\n",
       "       'SEAN PATRICK MALONEY FOR CONGRESS',\n",
       "       'NORTH CAROLINA DEMOCRATIC PARTY - FEDERAL',\n",
       "       'KUSTER FOR CONGRESS, INC',\n",
       "       'PHYSICIANS MUTUAL INSURANCE COMPANY PAC',\n",
       "       'FRIENDS OF CLYDE WILLIAMS', 'TENNESSEE DEMOCRATIC PARTY',\n",
       "       'AMERICAN ASSOCIATION OF NURSE ANESTHETISTS SEPARATE SEGREGATED FUND (CRNA-PAC)',\n",
       "       'TRUMP MAKE AMERICA GREAT AGAIN COMMITTEE',\n",
       "       'AMERICAN ASSOCIATION OF NEUROLOGICAL SURGEONS POLITICAL ACTION COMMITTEE (NEUROSURGERYPAC)',\n",
       "       'TEXAS DEMOCRATIC PARTY', 'KAPLAN FOR CONGRESS',\n",
       "       'NEW HAMPSHIRE DEMOCRATIC PARTY', 'DEMOCRATIC PARTY OF OREGON',\n",
       "       'OKLAHOMA DEMOCRATIC PARTY', 'FLORES FOR CONGRESS',\n",
       "       'FRIENDS OF PATRICK MURPHY', 'SHELBY FOR U S SENATE',\n",
       "       'TAMMY BALDWIN FOR SENATE', 'GEORGIA FEDERAL ELECTIONS COMMITTEE',\n",
       "       'NRCC', 'MASSACHUSETTS DEMOCRATIC STATE COMMITTEE',\n",
       "       'UTAH STATE DEMOCRATIC COMMITTEE',\n",
       "       'INDIANA DEMOCRATIC CONGRESSIONAL VICTORY COMMITTEE',\n",
       "       'WY DEMOCRATIC STATE CENTRAL COMMITTEE',\n",
       "       'MAGGIE HASSAN VICTORY FUND', 'WOMENCOUNT PAC',\n",
       "       'TIM MURPHY FOR CONGRESS',\n",
       "       'UAW - V - CAP (UAW VOLUNTARY COMMUNITY ACTION PROGRAM)',\n",
       "       'SEAN BARNEY FOR CONGRESS',\n",
       "       'NATIONAL COMMITTEE FOR AN EFFECTIVE CONGRESS',\n",
       "       'SHAHEEN FOR SENATE', 'CORY BOOKER FOR SENATE',\n",
       "       'DEMOCRATIC EXECUTIVE COMMITTEE OF FLORIDA',\n",
       "       'PINGREE FOR CONGRESS', 'DEMOCRATIC PARTY OF SOUTH CAROLINA',\n",
       "       'NY REPUBLICAN FEDERAL CAMPAIGN COMMITTEE',\n",
       "       'AMERICANS FOR RESPONSIBLE SOLUTIONS-PAC',\n",
       "       'MONTANA DEMOCRATIC PARTY', 'IDAHO STATE DEMOCRATIC PARTY',\n",
       "       'KENTUCKY STATE DEMOCRATIC CENTRAL EXECUTIVE COMMITTEE',\n",
       "       'AMERICAN PHYSICAL THERAPY ASSOCIATION PHYSICAL THERAPY POLITICAL ACTION COMMITTEE (PT-PAC)',\n",
       "       'DEMOCRATIC PARTY OF WISCONSIN', 'OHIO DEMOCRATIC PARTY',\n",
       "       'DEMOCRATIC PARTY OF ARKANSAS', 'SENATE CONSERVATIVES FUND',\n",
       "       'MINNESOTA DEMOCRATIC-FARMER-LABOR PARTY', 'HILLARY VICTORY FUND',\n",
       "       'AMERICAN COLLEGE OF SURGEONS PROFESSIONAL ASSOCIATION PAC',\n",
       "       'LINDY LI FOR CONGRESS', 'DGA ACTION', 'RIGHT TO RISE PAC, INC.',\n",
       "       'WELLS FOR SECURITY', 'NRSC', 'WHITEHOUSE FOR SENATE',\n",
       "       'MICHIGAN DEMOCRATIC STATE CENTRAL COMMITEE',\n",
       "       'BARRAGAN FOR CONGRESS', 'MASSACHUSETTS REPUBLICAN PARTY',\n",
       "       'MIDWEST VALUES PAC', 'FRIENDS OF JAY MCGOVERN',\n",
       "       'PLANNED PARENTHOOD VOTES', 'MISSISSIPPI DEMOCRATIC PARTY PAC',\n",
       "       'CHRIS CHRISTIE FOR PRESIDENT INC', 'KIRK BANGSTAD FOR CONGRESS',\n",
       "       'FRIENDS OF CHRISTINA M. HARTMAN', 'DERRICK FOR CONGRESS',\n",
       "       'JON KAIMAN FOR CONGRESS', 'APPLEGATE FOR CONGRESS',\n",
       "       'LEADERSHIP, OPPORTUNITY, INNOVATION SERVICE PAC - LOIS PAC',\n",
       "       'KASICH FOR AMERICA INC',\n",
       "       'RHODE ISLAND DEMOCRATIC STATE COMMITTEE',\n",
       "       'NEW JERSEY DEMOCRATIC STATE COMMITTEE', 'GARY JOHNSON 2016',\n",
       "       'CITIZENS FOR MICHAEL WAGER', 'RUSS VICTORY FUND',\n",
       "       'DEMOCRATIC HOPE FUND', 'FOSTER CAMPBELL FOR THE US SENATE',\n",
       "       'DEMOCRATIC STATE CENTRAL COMMITTEE OF LA',\n",
       "       'SITTENFELD FOR SENATE', 'SANTARSIERO FOR CONGRESS',\n",
       "       'WILL JAWANDO FOR CONGRESS', 'YUNGMAN LEE FOR CONGRESS',\n",
       "       'SAKIMA FOR CONGRESS', 'WRIGHT 2016', 'JAMIE RASKIN FOR CONGRESS',\n",
       "       'RUSS FOR WISCONSIN', 'DENISE JUNEAU FOR CONGRESS',\n",
       "       'FRIENDS OF DON BEYER', 'BARBARA LEE FOR CONGRESS',\n",
       "       'FRIENDS OF JOHN SARBANES', 'GRETCHEN DRISKELL FOR CONGRESS',\n",
       "       'PENNSYLVANIA DEMOCRATIC PARTY', 'WILLIAMS FOR CONGRESS',\n",
       "       'STRICKLAND FOR SENATE', 'JEFFRIES FOR CONGRESS',\n",
       "       \"WE'VE GOT YOUR BACKPAC\", 'HUMAN RIGHTS CAMPAIGN EQUALITY VOTES',\n",
       "       'CHRISTENSEN FOR CONGRESS', 'ANNA THRONE-HOLST VICTORY FUND',\n",
       "       'MURPHY VICTORY FUND', 'KIRKPATRICK FOR SENATE',\n",
       "       'ROSS VICTORY FUND', 'FOSTER/PLUMB VICTORY FUND',\n",
       "       'KIM MYERS FOR CONGRESS', 'SUOZZI FOR CONGRESS',\n",
       "       'ESPAILLAT FOR CONGRESS', 'L PAC',\n",
       "       \"AMERICAN SOCIETY FOR RADIATION ONCOLOGY PAC (''ASTRO-PAC'')\",\n",
       "       'PEOPLE FOR DEREK KILMER', 'AMI BERA FOR CONGRESS',\n",
       "       'AMERICAN PSYCHIATRIC ASSOCIATION POLITICAL ACTION COMMITTEE',\n",
       "       'FRIENDS OF JOHN MCCAIN INC', 'VAL DEMINGS FOR CONGRESS',\n",
       "       'CICILLINE COMMITTEE', 'BRYAN CAFORIO FOR CONGRESS',\n",
       "       'KANDER VICTORY FUND',\n",
       "       'AMERICAN COLLEGE OF NURSE-MIDWIVES MIDWIVES-PAC',\n",
       "       'GREEN MOUNTAIN PAC', 'CRUZ FOR PRESIDENT'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_selections[\"committee_name\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculate total donations before and after three-weeks-out day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "three_weeks = datetime.datetime(2016, 10, 18)\n",
    "\n",
    "df_before = df_selections[df_selections[\"contribution_receipt_date\"] < three_weeks]\n",
    "\n",
    "df_after = df_selections[df_selections[\"contribution_receipt_date\"] >= three_weeks]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total:  2141021.61\n",
      "Before:  1874716.8900000001\n",
      "After:  266304.72\n"
     ]
    }
   ],
   "source": [
    "print(\"Total: \", df_selections[\"contribution_receipt_amount\"].sum())\n",
    "print(\"Before: \", df_before[\"contribution_receipt_amount\"].sum())\n",
    "print(\"After: \", df_after[\"contribution_receipt_amount\"].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create dataframes with contributions to each campaign"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>index</th>\n",
       "      <th>committee_name</th>\n",
       "      <th>contribution_receipt_date</th>\n",
       "      <th>contribution_receipt_amount</th>\n",
       "      <th>contributor_employer</th>\n",
       "      <th>contributor_occupation</th>\n",
       "      <th>contributor_first_name</th>\n",
       "      <th>contributor_middle_name</th>\n",
       "      <th>contributor_last_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>840</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>40.0</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>FINANCIAL ANALYST</td>\n",
       "      <td>BRUCE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MACDONALD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>923</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>80.0</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>CHEMIST</td>\n",
       "      <td>MILAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STOJANOVIC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>924</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>250.0</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>PHYSICIAN</td>\n",
       "      <td>RAGY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GIRGIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>942</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>JAMES</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MCCONNELL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>977</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-08-11</td>\n",
       "      <td>28.0</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>JOSEPH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TERWILLIGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7696</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-10-23</td>\n",
       "      <td>80.0</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>ASSOCIATE PROFESSOR</td>\n",
       "      <td>ANDREI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TKATCHENKO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9620</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>150.8</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>JOSEPH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TERWILLIGER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>26478</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>2.4</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>HOUSTON</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BROWN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>27026</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-12-14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>ELISEO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SANTOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27027</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-12-08</td>\n",
       "      <td>0.8</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>CONSULTANT ARTS &amp; CULTURE</td>\n",
       "      <td>ISKRITSA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OGNIANOVA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27028</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-12-14</td>\n",
       "      <td>40.0</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>ADMIN</td>\n",
       "      <td>CARL</td>\n",
       "      <td>P.</td>\n",
       "      <td>SPARANO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27029</td>\n",
       "      <td>DONALD J. TRUMP FOR PRESIDENT, INC.</td>\n",
       "      <td>2016-12-14</td>\n",
       "      <td>0.8</td>\n",
       "      <td>COLUMBIA UNIVERSITY</td>\n",
       "      <td>STUDENT</td>\n",
       "      <td>ELISEO</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SANTOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index                       committee_name contribution_receipt_date  \\\n",
       "0     840  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-06-28   \n",
       "1     923  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-06-03   \n",
       "2     924  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-08-17   \n",
       "3     942  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-08-06   \n",
       "4     977  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-08-11   \n",
       "5    7696  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-10-23   \n",
       "6    9620  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-03-09   \n",
       "7   26478  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-08-06   \n",
       "8   27026  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-12-14   \n",
       "9   27027  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-12-08   \n",
       "10  27028  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-12-14   \n",
       "11  27029  DONALD J. TRUMP FOR PRESIDENT, INC.                2016-12-14   \n",
       "\n",
       "    contribution_receipt_amount contributor_employer  \\\n",
       "0                          40.0  COLUMBIA UNIVERSITY   \n",
       "1                          80.0  COLUMBIA UNIVERSITY   \n",
       "2                         250.0  COLUMBIA UNIVERSITY   \n",
       "3                           2.4  COLUMBIA UNIVERSITY   \n",
       "4                          28.0  COLUMBIA UNIVERSITY   \n",
       "5                          80.0  COLUMBIA UNIVERSITY   \n",
       "6                         150.8  COLUMBIA UNIVERSITY   \n",
       "7                           2.4  COLUMBIA UNIVERSITY   \n",
       "8                           0.8  COLUMBIA UNIVERSITY   \n",
       "9                           0.8  COLUMBIA UNIVERSITY   \n",
       "10                         40.0  COLUMBIA UNIVERSITY   \n",
       "11                          0.8  COLUMBIA UNIVERSITY   \n",
       "\n",
       "       contributor_occupation contributor_first_name contributor_middle_name  \\\n",
       "0           FINANCIAL ANALYST                  BRUCE                     NaN   \n",
       "1                     CHEMIST                  MILAN                     NaN   \n",
       "2                   PHYSICIAN                   RAGY                     NaN   \n",
       "3                     STUDENT                  JAMES                     NaN   \n",
       "4                   PROFESSOR                 JOSEPH                     NaN   \n",
       "5         ASSOCIATE PROFESSOR                 ANDREI                     NaN   \n",
       "6                   PROFESSOR                 JOSEPH                     NaN   \n",
       "7                     STUDENT                HOUSTON                     NaN   \n",
       "8                     STUDENT                 ELISEO                     NaN   \n",
       "9   CONSULTANT ARTS & CULTURE               ISKRITSA                     NaN   \n",
       "10                      ADMIN                   CARL                      P.   \n",
       "11                    STUDENT                 ELISEO                     NaN   \n",
       "\n",
       "   contributor_last_name  \n",
       "0              MACDONALD  \n",
       "1             STOJANOVIC  \n",
       "2                 GIRGIS  \n",
       "3              MCCONNELL  \n",
       "4            TERWILLIGER  \n",
       "5             TKATCHENKO  \n",
       "6            TERWILLIGER  \n",
       "7                  BROWN  \n",
       "8                 SANTOS  \n",
       "9              OGNIANOVA  \n",
       "10               SPARANO  \n",
       "11                SANTOS  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_HILLARY = df_selections[df_selections[\"committee_name\"].str.contains(\"HILLARY FOR AMERICA\")]\n",
    "\n",
    "df_HILLARY.reset_index()\n",
    "\n",
    "df_trump = df_selections[df_selections[\"committee_name\"].str.contains(\"DONALD J. TRUMP FOR PRESIDENT, INC.\")]\n",
    "\n",
    "df_trump.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CU Republicans preferred other candidates over Trump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_kasich = df_selections[df_selections[\"committee_name\"].str.contains(\"KASICH FOR AMERICA INC\")]\n",
    "\n",
    "df_cruz = df_selections[df_selections[\"committee_name\"].str.contains(\"CRUZ FOR PRESIDENT\")]\n",
    "\n",
    "df_rubio = df_selections[df_selections[\"committee_name\"].str.contains(\"MARCO RUBIO FOR PRESIDENT\")]\n",
    "\n",
    "df_jeb = df_selections[df_selections[\"committee_name\"].str.contains(\"JEB 2016, INC.\")]\n",
    "\n",
    "df_christie = df_selections[df_selections[\"committee_name\"].str.contains(\"CHRIS CHRISTIE FOR PRESIDENT INC\")]\n",
    "\n",
    "df_pataki = df_selections[df_selections[\"committee_name\"].str.contains(\"PATAKI FOR PRESIDENT INC\")]\n",
    "\n",
    "df_RNC = df_selections[df_selections[\"committee_name\"].str.contains(\"REPUBLICAN NATIONAL COMMITTEE\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>contribution_receipt_amount</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>committee_name</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JEB 2016, INC.</th>\n",
       "      <td>5900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARCO RUBIO FOR PRESIDENT</th>\n",
       "      <td>4200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CHRIS CHRISTIE FOR PRESIDENT INC</th>\n",
       "      <td>2700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CRUZ FOR PRESIDENT</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KASICH FOR AMERICA INC</th>\n",
       "      <td>1000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DONALD J. TRUMP FOR PRESIDENT, INC.</th>\n",
       "      <td>676.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PATAKI FOR PRESIDENT INC</th>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     contribution_receipt_amount\n",
       "committee_name                                                  \n",
       "JEB 2016, INC.                                            5900.0\n",
       "MARCO RUBIO FOR PRESIDENT                                 4200.0\n",
       "CHRIS CHRISTIE FOR PRESIDENT INC                          2700.0\n",
       "CRUZ FOR PRESIDENT                                        1000.0\n",
       "KASICH FOR AMERICA INC                                    1000.0\n",
       "DONALD J. TRUMP FOR PRESIDENT, INC.                        676.0\n",
       "PATAKI FOR PRESIDENT INC                                   500.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df_kasich, df_cruz, df_rubio, df_jeb, df_christie, df_pataki, df_trump]\n",
    "df_repubs = pd.concat(frames)\n",
    "df_repubs = df_repubs.groupby([\"committee_name\"]).sum()\n",
    "df_repubs = df_repubs.sort_values(by=\"contribution_receipt_amount\", ascending=False)\n",
    "df_repubs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax0 = df_repubs.plot(kind=\"barh\",\n",
    "                    figsize=(10,6))\n",
    "ax0.set_xlabel(\"Amount raised in 2016 campaign cycle\")\n",
    "ax0.invert_yaxis()\n",
    "plt = ax0\n",
    "# plt.savefig(\"repubs.svg\")\n",
    "\n",
    "# plot = dtf.plot()\n",
    "fig = ax0.get_figure()\n",
    "# fig.savefig(\"repub.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plots for donations per campaign and total donation $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12, 8)\n",
      "(3288, 8)\n"
     ]
    }
   ],
   "source": [
    "print(df_trump.shape)\n",
    "print(df_HILLARY.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt \n",
    "from pywaffle import Waffle "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Waffle size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAARCAYAAACIADjJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAAlUlEQVR4nO3cvQ3CMBRG0ReUHcIyLJIFMhMLZBGWgSVwWrAQNEF8Eud0/incXdmFh9ZaAUCaw68PAACvCBQAkQQKgEgCBUAkgQIg0vhucbms16qaHqbu9Ry12/k0H79xMAD+26cb1NSN+/39OgDswhMfAJEECoBIg58kAEjkBgVAJIECIJJAARBJoACIJFAARBIoACJttn0MTTsN/LMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Waffle size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(FigureClass=Waffle,\n",
    "                 columns=10,\n",
    "                values=[328.8],\n",
    "                figsize=(20,10),\n",
    "                vertical=True,\n",
    "                rounding_rule='floor')\n",
    "#fig.savefig(\"hillary_donations.svg\")\n",
    "fig2 = plt.figure(FigureClass=Waffle, \n",
    "                 columns=100,\n",
    "                 values=[1.2],\n",
    "#                  figsize=(10,6),\n",
    "                 vertical=True,\n",
    "                 rounding_rule='floor')\n",
    "# # fig2.savefig(\"trump_donations.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group contributions by receipt date for each campaign and visualize!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>contribution_receipt_date</th>\n",
       "      <th>contribution_receipt_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>150.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-11</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  contribution_receipt_date  contribution_receipt_amount\n",
       "0                2016-03-09                        150.8\n",
       "1                2016-06-03                         80.0\n",
       "2                2016-06-28                         40.0\n",
       "3                2016-08-06                          4.8\n",
       "4                2016-08-11                         28.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_HILLARY = df_HILLARY.groupby([\"contribution_receipt_date\"], as_index=False).sum()\n",
    "\n",
    "df_trump = df_trump.groupby([\"contribution_receipt_date\"], as_index=False).sum()\n",
    "\n",
    "df_trump.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>contribution_receipt_date</th>\n",
       "      <th>contribution_receipt_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-08-17</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-03-09</td>\n",
       "      <td>150.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-06-03</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-10-23</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-12-14</td>\n",
       "      <td>41.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-06-28</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-11</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-06</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-12-08</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  contribution_receipt_date  contribution_receipt_amount\n",
       "5                2016-08-17                        250.0\n",
       "0                2016-03-09                        150.8\n",
       "1                2016-06-03                         80.0\n",
       "6                2016-10-23                         80.0\n",
       "8                2016-12-14                         41.6\n",
       "2                2016-06-28                         40.0\n",
       "4                2016-08-11                         28.0\n",
       "3                2016-08-06                          4.8\n",
       "7                2016-12-08                          0.8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_max = df_trump.sort_values(by=\"contribution_receipt_amount\", ascending=False)\n",
    "\n",
    "df_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Contributions to Trump Campaign since Announcement of Clinton Candidacy (4/12/2015) even though nobody donated to Trump for forever')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "t = np.arange(datetime(2015,4,12), datetime(2016,11,10), timedelta(days=30)).astype(datetime)\n",
    "\n",
    "ax1 = df_HILLARY.plot(x=\"contribution_receipt_date\", y=\"contribution_receipt_amount\",\n",
    "             figsize=(14, 6))\n",
    "ax1.set_xticks(t)\n",
    "ax1.set_ylim([0, 14000])\n",
    "ax1.set_title(\"Contributions to Clinton Campaign since Announcement of Candidacy (4/12/2015)\")\n",
    "\n",
    "\n",
    "ax2 = df_trump.plot(x=\"contribution_receipt_date\", y=\"contribution_receipt_amount\",\n",
    "                   figsize=(14,6),\n",
    "                   color=\"red\")\n",
    "ax2.set_xticks(t)\n",
    "ax2.set_ylim([0, 14000])\n",
    "ax2.set_title(\"Contributions to Trump Campaign since Announcement of Clinton Candidacy (4/12/2015) even though nobody donated to Trump for forever\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect and visualize top committes by funds raised over 2015-2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>index</th>\n",
       "      <th>committee_name</th>\n",
       "      <th>contribution_receipt_amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>115</td>\n",
       "      <td>HILLARY FOR AMERICA</td>\n",
       "      <td>501596.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>ACTBLUE</td>\n",
       "      <td>333992.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>116</td>\n",
       "      <td>HILLARY VICTORY FUND</td>\n",
       "      <td>254830.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67</td>\n",
       "      <td>DNC SERVICES CORP./DEM. NAT'L COMMITTEE</td>\n",
       "      <td>238756.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>BERNIE 2016</td>\n",
       "      <td>69740.95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                           committee_name  contribution_receipt_amount\n",
       "0    115                      HILLARY FOR AMERICA                    501596.80\n",
       "1      3                                  ACTBLUE                    333992.25\n",
       "2    116                     HILLARY VICTORY FUND                    254830.90\n",
       "3     67  DNC SERVICES CORP./DEM. NAT'L COMMITTEE                    238756.63\n",
       "4     26                              BERNIE 2016                     69740.95"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_committees = df_selections.groupby([\"committee_name\"], as_index=False).sum()\n",
    "df_committees.sort_values(by=\"contribution_receipt_amount\", ascending=False, inplace=True)\n",
    "df_committees.reset_index(inplace=True)\n",
    "\n",
    "df_committees.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax3 = df_committees.head(15).plot(kind=\"barh\",\n",
    "                        x = \"committee_name\",\n",
    "                        y = \"contribution_receipt_amount\",\n",
    "                                 figsize=(10,6))\n",
    "ax3.grid(axis='x')\n",
    "ax3.invert_yaxis()\n",
    "plt.savefig(\"hijessica.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collect and visualize occupation of individual Columbia donors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PROFESSOR                   331\n",
       "STUDENT                     291\n",
       "PHYSICIAN                   142\n",
       "GRADUATE STUDENT             74\n",
       "RESEARCHER                   45\n",
       "SCIENTIST                    42\n",
       "ADMINISTRATOR                32\n",
       "TEACHER                      25\n",
       "LIBRARIAN                    21\n",
       "RESEARCH ASSISTANT           21\n",
       "PHD STUDENT                  19\n",
       "ADJUNCT PROFESSOR            18\n",
       "RESEARCH SCIENTIST           16\n",
       "EDUCATOR                     16\n",
       "ADMINISTRATIVE ASSISTANT     14\n",
       "Name: contributor_occupation, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_occ = df_selections.drop_duplicates(subset=[\"contributor_last_name\", \"contributor_middle_name\",\n",
    "                                              \"contributor_middle_name\"])\n",
    "occupations = df_occ[\"contributor_occupation\"].value_counts()\n",
    "\n",
    "occupations.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax5 = occupations.head(15).plot(kind=\"barh\",\n",
    "                               figsize=(10,6))\n",
    "ax5.invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bubble plot for individual Columbia donors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pprint import pprint as pp\n",
    "import circlify as circ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Circle(x=-0.36796041841749116, y=0.8249867010395682, r=0.0966739644989054, level=1, ex={'datum': 14}),\n",
      " Circle(x=-0.6084101928594475, y=0.5801215325962874, r=0.10334881525030033, level=1, ex={'datum': 16}),\n",
      " Circle(x=-0.762668782140797, y=0.4067569590222166, r=0.10334881525030033, level=1, ex={'datum': 16}),\n",
      " Circle(x=-0.7440304212065247, y=0.19460733250453552, r=0.10961797213662455, level=1, ex={'datum': 18}),\n",
      " Circle(x=0.062296477506674094, y=0.7472803786903747, r=0.1126217604026786, level=1, ex={'datum': 19}),\n",
      " Circle(x=-0.17138818509961587, y=0.7377137236219682, r=0.11840094221712694, level=1, ex={'datum': 21}),\n",
      " Circle(x=-0.3766671396397842, y=0.5992945984305373, r=0.1291860190628754, level=1, ex={'datum': 25}),\n",
      " Circle(x=0.3017518222160936, y=0.6491593411039553, r=0.1461572961821661, level=1, ex={'datum': 32}),\n",
      " Circle(x=-0.5027487962805588, y=0.330793362743383, r=0.16744421828121409, level=1, ex={'datum': 42}),\n",
      " Circle(x=0.4527450572221688, y=0.3676143658467007, r=0.17332123222030388, level=1, ex={'datum': 45}),\n",
      " Circle(x=-0.024696470312014822, y=-0.7773477622299602, r=0.22226003118664472, level=1, ex={'datum': 74}),\n",
      " Circle(x=-0.027452563495487466, y=0.3364620096905197, r=0.30788581701835716, level=1, ex={'datum': 142}),\n",
      " Circle(x=-0.4582921212078517, y=-0.2757735518246719, r=0.4407496799183074, level=1, ex={'datum': 291}),\n",
      " Circle(x=0.4525242820404445, y=-0.2757735518246719, r=0.4700667233299888, level=1, ex={'datum': 331})]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circles = circ.circlify([occupations[0], \n",
    "                          occupations[1], \n",
    "                          occupations[2],\n",
    "                          occupations[3],\n",
    "                          occupations[4],\n",
    "                          occupations[5],\n",
    "                          occupations[6],\n",
    "                          occupations[7],\n",
    "                          occupations[8],\n",
    "                          occupations[10],\n",
    "                          occupations[11],\n",
    "                         occupations[12],\n",
    "                         occupations[13],\n",
    "                         occupations[14]],\n",
    "                         show_enclosure=False)\n",
    "pp(circles)\n",
    "circ.bubbles(circles)"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}