DataAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>bioguide_id</th>\n",
       "      <th>govtrack_id</th>\n",
       "      <th>icpsr_id</th>\n",
       "      <th>wikipedia</th>\n",
       "      <th>wikidata</th>\n",
       "      <th>first_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>gender</th>\n",
       "      <th>birthday</th>\n",
       "      <th>chamber</th>\n",
       "      <th>start</th>\n",
       "      <th>end</th>\n",
       "      <th>state</th>\n",
       "      <th>district</th>\n",
       "      <th>party</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>1993-01-05</td>\n",
       "      <td>1995-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>1995-01-04</td>\n",
       "      <td>1997-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>1997-01-07</td>\n",
       "      <td>1999-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>1999-01-06</td>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>2001-01-03</td>\n",
       "      <td>2003-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>2003-01-07</td>\n",
       "      <td>2005-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>House</td>\n",
       "      <td>2005-01-04</td>\n",
       "      <td>2007-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>13.0000000000</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>Senate</td>\n",
       "      <td>2007-01-04</td>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1.0000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>Senate</td>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1.0000000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B000944</td>\n",
       "      <td>400050</td>\n",
       "      <td>29,389.0000000000</td>\n",
       "      <td>Sherrod Brown</td>\n",
       "      <td>Q381880</td>\n",
       "      <td>Sherrod</td>\n",
       "      <td>Brown</td>\n",
       "      <td>M</td>\n",
       "      <td>1952-11-09</td>\n",
       "      <td>Senate</td>\n",
       "      <td>2019-01-03</td>\n",
       "      <td>2025-01-03</td>\n",
       "      <td>OH</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Democrat</td>\n",
       "      <td>1.0000000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  bioguide_id  govtrack_id          icpsr_id      wikipedia wikidata  \\\n",
       "0     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "1     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "2     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "3     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "4     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "5     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "6     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "7     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "8     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "9     B000944       400050 29,389.0000000000  Sherrod Brown  Q381880   \n",
       "\n",
       "  first_name last_name gender   birthday chamber      start        end state  \\\n",
       "0    Sherrod     Brown      M 1952-11-09   House 1993-01-05 1995-01-03    OH   \n",
       "1    Sherrod     Brown      M 1952-11-09   House 1995-01-04 1997-01-03    OH   \n",
       "2    Sherrod     Brown      M 1952-11-09   House 1997-01-07 1999-01-03    OH   \n",
       "3    Sherrod     Brown      M 1952-11-09   House 1999-01-06 2001-01-03    OH   \n",
       "4    Sherrod     Brown      M 1952-11-09   House 2001-01-03 2003-01-03    OH   \n",
       "5    Sherrod     Brown      M 1952-11-09   House 2003-01-07 2005-01-03    OH   \n",
       "6    Sherrod     Brown      M 1952-11-09   House 2005-01-04 2007-01-03    OH   \n",
       "7    Sherrod     Brown      M 1952-11-09  Senate 2007-01-04 2013-01-03    OH   \n",
       "8    Sherrod     Brown      M 1952-11-09  Senate 2013-01-03 2019-01-03    OH   \n",
       "9    Sherrod     Brown      M 1952-11-09  Senate 2019-01-03 2025-01-03    OH   \n",
       "\n",
       "       district     party        class  \n",
       "0 13.0000000000  Democrat          NaN  \n",
       "1 13.0000000000  Democrat          NaN  \n",
       "2 13.0000000000  Democrat          NaN  \n",
       "3 13.0000000000  Democrat          NaN  \n",
       "4 13.0000000000  Democrat          NaN  \n",
       "5 13.0000000000  Democrat          NaN  \n",
       "6 13.0000000000  Democrat          NaN  \n",
       "7           NaN  Democrat 1.0000000000  \n",
       "8           NaN  Democrat 1.0000000000  \n",
       "9           NaN  Democrat 1.0000000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "\n",
    "'''\n",
    "    INITIAL SET UP\n",
    "'''\n",
    "# These commands below set some options for pandas and to have matplotlib show the charts in the notebook\n",
    "pd.set_option('display.max_rows', 1000)\n",
    "pd.options.display.float_format = '{:,.10f}'.format\n",
    "%matplotlib inline\n",
    "\n",
    "# Load the data\n",
    "# We have this defaulted to the folder OUTSIDE of your repo - please change it as needed\n",
    "population = pd.read_csv('Population by Age and Sex - US, States, Counties.csv')\n",
    "\n",
    "def load_json(file_name: str):\n",
    "    with open(file_name, 'r') as file:\n",
    "        file_data = json.load(file)\n",
    "    rows = []\n",
    "    for person in file_data:\n",
    "        base_info = {\n",
    "            \"bioguide_id\": person[\"id\"].get(\"bioguide\"),\n",
    "            \"govtrack_id\": person[\"id\"].get(\"govtrack\"),\n",
    "            \"icpsr_id\": person[\"id\"].get(\"icpsr\"),\n",
    "            \"wikipedia\": person[\"id\"].get(\"wikipedia\"),\n",
    "            \"wikidata\": person[\"id\"].get(\"wikidata\"),\n",
    "            \"first_name\": person[\"name\"][\"first\"],\n",
    "            \"last_name\": person[\"name\"][\"last\"],\n",
    "            \"gender\": person[\"bio\"].get(\"gender\"),\n",
    "            \"birthday\": person[\"bio\"].get(\"birthday\"),\n",
    "        }\n",
    "        for term in person[\"terms\"]:\n",
    "            row = base_info.copy()\n",
    "            row.update({\n",
    "                \"type\": term[\"type\"],\n",
    "                \"start\": term[\"start\"],\n",
    "                \"end\": term[\"end\"],\n",
    "                \"state\": term[\"state\"],\n",
    "                \"district\": term.get(\"district\"),\n",
    "                \"party\": term.get(\"party\"),\n",
    "                \"class\": term.get(\"class\"),\n",
    "            })\n",
    "            rows.append(row)\n",
    "\n",
    "    # Convert to DataFrame\n",
    "    legislators = pd.DataFrame(rows)\n",
    "    return legislators\n",
    "\n",
    "# load both historical & current into dataframes\n",
    "incumbent = load_json('legislators-current.json')\n",
    "non_incumbent = load_json('legislators-historical.json')\n",
    "\n",
    "# filter historical from people born after 1900\n",
    "non_incumbent['birthday'] = pd.to_datetime(non_incumbent['birthday'])\n",
    "non_incumbent = non_incumbent[non_incumbent['birthday'].dt.year >= 1900]\n",
    "incumbent['birthday'] = pd.to_datetime(incumbent['birthday'])\n",
    "\n",
    "# Convert terms start and end to datetime\n",
    "incumbent['start'] = pd.to_datetime(incumbent['start'])\n",
    "incumbent['end'] = pd.to_datetime(incumbent['end'])\n",
    "non_incumbent['start'] = pd.to_datetime(non_incumbent['start'])\n",
    "non_incumbent['end'] = pd.to_datetime(non_incumbent['end'])\n",
    "\n",
    "# Change type column/values of type for readability\n",
    "incumbent = incumbent.rename(columns={'type': 'chamber'})\n",
    "incumbent['chamber'] = incumbent['chamber'].replace(\n",
    "    {'rep': 'House'})\n",
    "incumbent['chamber'] = incumbent['chamber'].replace(\n",
    "    {'sen': 'Senate'})\n",
    "grouped_incumbent = incumbent.groupby('bioguide_id').first().reset_index()\n",
    "\n",
    "non_incumbent = non_incumbent.rename(columns={'type': 'chamber'})\n",
    "non_incumbent['chamber'] = non_incumbent['chamber'].replace(\n",
    "    {'rep': 'House'})\n",
    "non_incumbent['chamber'] = non_incumbent['chamber'].replace(\n",
    "    {'sen': 'Senate'})\n",
    "\n",
    "# merged dataframe: all legislators incumbent & non_incumbent\n",
    "all_legislators = pd.concat([incumbent, non_incumbent], ignore_index=True)\n",
    "\n",
    "# Incumbent without grouping by bioguide_id have duplicated candidates due to multiple terms\n",
    "merged = all_legislators.groupby('bioguide_id').first().reset_index()\n",
    "\n",
    "all_legislators.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "    GROUPING FUNCTIONS for exploring and analyzing data\n",
    "'''\n",
    "def group_party(df):\n",
    "    party_count = df.groupby(['party']).agg(\n",
    "        party_count=('party', 'count'),\n",
    "    ).reset_index()\n",
    "    return party_count\n",
    "\n",
    "def group_chamber(df):\n",
    "    chamber_count = df.groupby(['chamber']).agg(\n",
    "        chamber_count=('chamber', 'count'),\n",
    "    ).reset_index()\n",
    "    return chamber_count\n",
    "\n",
    "def group_age(df):\n",
    "    df['age'] = df['birthday'].apply(\n",
    "        lambda x: min((datetime.now() - pd.to_datetime(x)).days // 365, 100))\n",
    "\n",
    "    # Group by party and calculate age statistics\n",
    "    age_summary = df.groupby(['party', 'type', 'gender']).agg(\n",
    "        average_age=('age', 'mean'),\n",
    "        min_age=('age', 'min'),\n",
    "        max_age=('age', 'max'),\n",
    "    ).reset_index()\n",
    "    # Format average_age to 2 decimal points without trailing zeros\n",
    "    age_summary['average_age'] = age_summary['average_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    return age_summary\n",
    "\n",
    "def group_gender(df):\n",
    "    gender_count = df.groupby(['party', 'gender']).agg(\n",
    "        count=('gender', 'count')\n",
    "    ).reset_index()\n",
    "    return gender_count\n",
    "\n",
    "def group_years_of_service(df):\n",
    "    df['years_of_service'] = (pd.to_datetime(\n",
    "        df['end']) - pd.to_datetime(df['start'])).dt.days // 365\n",
    "\n",
    "    # Group by party and calculate years of service statistics\n",
    "    service_summary = df.groupby('party').agg(\n",
    "        total_years=('years_of_service', 'sum'),\n",
    "        average_years=('years_of_service', 'mean'),\n",
    "        min_years=('years_of_service', 'min'),\n",
    "        max_years=('years_of_service', 'max'),\n",
    "    ).reset_index()\n",
    "    return service_summary\n",
    "\n",
    "def group_state_party(df):\n",
    "    state_party_count = df.groupby(['state', 'party', 'type']).agg(\n",
    "        legislator_count=('bioguide_id', 'count')\n",
    "    ).reset_index()\n",
    "    return state_party_count\n",
    "\n",
    "def group_dates(df):\n",
    "    date_summary = df.groupby('party').agg(\n",
    "        average_start=('start', lambda x: pd.to_datetime(x).mean()),\n",
    "        median_start=('start', lambda x: pd.to_datetime(x).median()),\n",
    "        average_end=('end', lambda x: pd.to_datetime(x).mean()),\n",
    "        median_end=('end', lambda x: pd.to_datetime(x).median()),\n",
    "    ).reset_index()\n",
    "    return date_summary\n",
    "\n",
    "def group_decade(df):\n",
    "    # Add a \"start_decade\" column\n",
    "    df['start_decade'] = pd.to_datetime(df['start']).dt.year // 10 * 10\n",
    "\n",
    "    # Group by decade and party\n",
    "    decade_summary = incumbent.groupby(['start_decade', 'party']).agg(\n",
    "        legislator_count=('bioguide_id', 'count')\n",
    "    ).reset_index()\n",
    "    return decade_summary\n",
    "\n",
    "def group_top_states(df):\n",
    "    top_states = df.groupby(['state', 'party', 'type']).agg(\n",
    "        legislator_count=('bioguide_id', 'count')\n",
    "    ).reset_index()\n",
    "    # Sort and find the top states for each party\n",
    "    top_states = top_states.sort_values(\n",
    "        ['party', 'legislator_count'], ascending=[True, False])\n",
    "    return top_states\n",
    "\n",
    "def group_age_start(df):\n",
    "    # Calculate age at term start\n",
    "    df['age_at_start'] = df.apply(\n",
    "        lambda row: (row['start'] - row['birthday']).days // 365, axis=1)\n",
    "\n",
    "    # Group by start date and calculate average age\n",
    "    avg_age_by_term = df.groupby('start').agg(\n",
    "        average_age=('age_at_start', 'mean')\n",
    "    ).reset_index()\n",
    "\n",
    "    avg_age_by_term['average_age'] = avg_age_by_term['average_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    return avg_age_by_term\n",
    "\n",
    "def age_by_party(df):\n",
    "    age_summary = df.groupby('party').agg(\n",
    "        count=('party', 'count'),\n",
    "        average_age=('age', 'mean'),\n",
    "        median_age=('age', 'median'),\n",
    "        mode_age=('age', lambda x: x.mode(\n",
    "        ).iloc[0] if not x.mode().empty else None),\n",
    "        min_age=('age', 'min'),\n",
    "        max_age=('age', 'max'),\n",
    "    ).reset_index()\n",
    "    age_summary['average_age'] = age_summary['average_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    age_summary['median_age'] = age_summary['median_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    return age_summary\n",
    "\n",
    "def age_by_chamber(df):\n",
    "    age_summary = df.groupby('chamber').agg(\n",
    "        count=('party', 'count'),\n",
    "        average_age=('age', 'mean'),\n",
    "        median_age=('age', 'median'),\n",
    "        mode_age=('age', lambda x: x.mode(\n",
    "        ).iloc[0] if not x.mode().empty else None),\n",
    "        min_age=('age', 'min'),\n",
    "        max_age=('age', 'max'),\n",
    "    ).reset_index()\n",
    "    age_summary['average_age'] = age_summary['average_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    age_summary['median_age'] = age_summary['median_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    return age_summary\n",
    "\n",
    "def age_by_gender(df):\n",
    "    age_summary = df.groupby('gender').agg(\n",
    "        count=('party', 'count'),\n",
    "        average_age=('age', 'mean'),\n",
    "        median_age=('age', 'median'),\n",
    "        mode_age=('age', lambda x: x.mode(\n",
    "        ).iloc[0] if not x.mode().empty else None),\n",
    "        min_age=('age', 'min'),\n",
    "        max_age=('age', 'max'),\n",
    "    ).reset_index()\n",
    "    age_summary['average_age'] = age_summary['average_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    age_summary['median_age'] = age_summary['median_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    return age_summary\n",
    "\n",
    "def age_by_all(df):\n",
    "    age_summary = df.groupby(['party', 'chamber', 'gender']).agg(\n",
    "        count=('party', 'count'),\n",
    "        average_age=('age', 'mean'),\n",
    "        median_age=('age', 'median'),\n",
    "        mode_age=('age', lambda x: x.mode(\n",
    "        ).iloc[0] if not x.mode().empty else None),\n",
    "        min_age=('age', 'min'),\n",
    "        max_age=('age', 'max'),\n",
    "    ).reset_index()\n",
    "    age_summary['average_age'] = age_summary['average_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    age_summary['median_age'] = age_summary['median_age'].map(\n",
    "        lambda x: f\"{x:.2f}\".rstrip('0').rstrip('.')\n",
    "    )\n",
    "    return age_summary\n",
    "\n",
    "def gender_by_party(df):\n",
    "    # Group by party and gender, then count\n",
    "    gender_party_counts = df.groupby(\n",
    "        ['party', 'gender']).size().reset_index(name='count')\n",
    "\n",
    "    # Calculate total counts for each party\n",
    "    total_by_party = gender_party_counts.groupby(\n",
    "        'party')['count'].transform('sum')\n",
    "\n",
    "    # Add a proportion column\n",
    "    gender_party_counts['proportion'] = gender_party_counts['count'] / \\\n",
    "        total_by_party\n",
    "    return gender_party_counts\n",
    "\n",
    "def gender_by_chamber(df):\n",
    "    # Group by chamber and gender, then count\n",
    "    gender_chamber_counts = df.groupby(\n",
    "        ['chamber', 'gender']).size().reset_index(name='count')\n",
    "\n",
    "    # Calculate total counts for each chamber\n",
    "    total_by_chamber = gender_chamber_counts.groupby(\n",
    "        'chamber')['count'].transform('sum')\n",
    "\n",
    "    # Add a proportion column\n",
    "    gender_chamber_counts['proportion'] = gender_chamber_counts['count'] / \\\n",
    "        total_by_chamber\n",
    "    return gender_chamber_counts\n",
    "\n",
    "def add_age(df):\n",
    "    df['age'] = df.apply(\n",
    "        lambda row: (row['start'] - row['birthday']).days // 365, axis=1\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "    CONGRESS ANALYSIS\n",
    "'''\n",
    "# Party Affil Stats - Dems/Repubs\n",
    "all_party = group_party(merged)\n",
    "\n",
    "# Chamber Stats - House(rep)/Senate(sen)\n",
    "all_chamber = group_chamber(merged)\n",
    "\n",
    "add_age(merged)\n",
    "\n",
    "# Age Stats by Party\n",
    "all_age_party = age_by_party(merged)\n",
    "\n",
    "# Age Stats by Chamber\n",
    "all_age_chamber = age_by_chamber(merged)\n",
    "\n",
    "# Age Stats By Gender\n",
    "all_age_gender = age_by_gender(merged)\n",
    "\n",
    "# Age Stats By All\n",
    "all_age_all = age_by_all(merged)\n",
    "\n",
    "# Gender by Party\n",
    "all_gender_party = gender_by_party(merged)\n",
    "\n",
    "# Gender by Chamber\n",
    "all_gender_chamber = gender_by_chamber(merged)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add data label to each bar\n",
    "def label_bars(ax):\n",
    "    for container in ax.containers:\n",
    "        for bar in container:\n",
    "            height = bar.get_height()  # Get the height of each bar\n",
    "            if height > 0:  # Only label bars with a positive height\n",
    "                ax.text(\n",
    "                    bar.get_x() + bar.get_width() / 2,  # X-coordinate\n",
    "                    height,  # Y-coordinate (bar height)\n",
    "                    f'{height:.1f}',  # Label text (formatted to 1 decimal place)\n",
    "                    ha='center', va='bottom', fontsize=10  # Center alignment\n",
    "                )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Chart By Party\n",
    "all_age_party = all_age_party[all_age_party['party'].isin(['Democrat', 'Republican', 'Independent'])]\n",
    "all_age_party = all_age_party.astype({'min_age': 'float64', 'average_age': 'float64',\n",
    "                                     'median_age': 'float64', 'mode_age': 'float64', 'max_age': 'float64'})\n",
    "plot_age_party = all_age_party.set_index('party')\n",
    "ax = plot_age_party[['average_age', 'median_age', 'mode_age', 'min_age', 'max_age']].plot(\n",
    "    kind='bar', figsize=(12, 6))  # , 'median_age', 'mode_age'\n",
    "label_bars\n",
    "plt.title('Age Statistics by Political Party')\n",
    "plt.xlabel('Political Party')\n",
    "plt.ylabel('Age')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(['Average Age', 'Median Age', 'Mode Age', 'Min Age', 'Max Age'])\n",
    "plt.title('Congressional Age Statistics by Party')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Chart By Gender\n",
    "all_age_gender = all_age_gender.astype({'count': 'float64', 'min_age': 'float64', 'average_age': 'float64', 'median_age': 'float64', 'mode_age': 'float64', 'max_age': 'float64'})\n",
    "plot_age_gen = all_age_gender.set_index('gender')\n",
    "ax = plot_age_gen[['average_age', 'median_age', 'mode_age',\n",
    "              'min_age', 'max_age']].plot(kind='bar', figsize=(12, 6))\n",
    "label_bars(ax)\n",
    "            \n",
    "plt.xlabel('Gender')\n",
    "plt.ylabel('Age')\n",
    "ax.set_xticklabels(\n",
    "    ['Female' if label == 'F' else 'Male' for label in plot_age_gen.index], rotation=0)\n",
    "plt.legend(['Average Age', 'Median Age', 'Mode Age', 'Min Age', 'Max Age'])\n",
    "plt.title('Congressional Age Statistics by Gender')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Age Chart By Chamber\n",
    "all_age_chamber = all_age_chamber.astype({'min_age': 'float64', 'average_age': 'float64',\n",
    "                                     'median_age': 'float64', 'mode_age': 'float64', 'max_age': 'float64'})\n",
    "plot_age_chamber = all_age_chamber.set_index('chamber')\n",
    "ax = plot_age_chamber[['average_age', 'median_age', 'mode_age', 'min_age', 'max_age']].plot(\n",
    "    kind='bar', figsize=(12, 6))  # , 'median_age', 'mode_age'\n",
    "label_bars(ax)\n",
    "\n",
    "plt.title('Age Statistics by Political Party')\n",
    "plt.xlabel('Chamber')\n",
    "plt.ylabel('Age')\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(['Average Age', 'Median Age', 'Mode Age', 'Min Age', 'Max Age'])\n",
    "plt.title('Congressional Age Statistics by Chamber')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CONGRESS AGE OVER TIME\n",
    "# Use the entire all_legislators dataset to track new cohorts each new term\n",
    "# Compare US Population Age to every new Congress term\n",
    "\n",
    "all_avg_over_time = group_age_start(all_legislators)\n",
    "all_avg_over_time['year'] = all_avg_over_time['start'].dt.year\n",
    "all_avg_over_time['average_age'] = pd.to_numeric(\n",
    "    all_avg_over_time['average_age'], errors='coerce')\n",
    "all_avg_over_time = all_avg_over_time.groupby('year').agg(\n",
    "    average_age=('average_age', 'mean')\n",
    ").reset_index()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(all_avg_over_time['year'],\n",
    "         all_avg_over_time['average_age'], marker='o', label='Average Age')\n",
    "\n",
    "# Annotate each point with its year and average age\n",
    "for i, row in all_avg_over_time.iterrows():\n",
    "    # Annotate average age slightly below the point\n",
    "    plt.text(row['year'], row['average_age'] + .5,  # Adjust y-coordinate for average age\n",
    "             f\"{row['average_age']:.1f}\", fontsize=8, ha='center', va='bottom')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Age')\n",
    "plt.title('Congress Average Age Over Time', fontsize=14)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "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>Description</th>\n",
       "      <th>Year</th>\n",
       "      <th>Total Population</th>\n",
       "      <th>Population 0-4</th>\n",
       "      <th>Population 5-17</th>\n",
       "      <th>Population 18-24</th>\n",
       "      <th>Population 25-44</th>\n",
       "      <th>Population 45-64</th>\n",
       "      <th>Population 65+</th>\n",
       "      <th>Population Under 18</th>\n",
       "      <th>...</th>\n",
       "      <th>% Population 18-24</th>\n",
       "      <th>% Population 25-44</th>\n",
       "      <th>% Population 45-64</th>\n",
       "      <th>% Population 65+</th>\n",
       "      <th>% Population Under 18</th>\n",
       "      <th>% Population 18-54</th>\n",
       "      <th>% Population 55+</th>\n",
       "      <th>% Male Population</th>\n",
       "      <th>% Female Population</th>\n",
       "      <th>Average Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>2000</td>\n",
       "      <td>282,162,411.0000000000</td>\n",
       "      <td>19,178,293.0000000000</td>\n",
       "      <td>53,197,896.0000000000</td>\n",
       "      <td>27,315,274.0000000000</td>\n",
       "      <td>84,973,340.0000000000</td>\n",
       "      <td>62,428,040.0000000000</td>\n",
       "      <td>35,069,568.0000000000</td>\n",
       "      <td>72,376,189.0000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0968069202</td>\n",
       "      <td>0.3011504605</td>\n",
       "      <td>0.2212486056</td>\n",
       "      <td>0.1242885892</td>\n",
       "      <td>0.2565054245</td>\n",
       "      <td>0.5326279552</td>\n",
       "      <td>0.2108666204</td>\n",
       "      <td>0.4906514887</td>\n",
       "      <td>0.5093485113</td>\n",
       "      <td>36.0461526979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>2001</td>\n",
       "      <td>284,968,955.0000000000</td>\n",
       "      <td>19,298,217.0000000000</td>\n",
       "      <td>53,372,958.0000000000</td>\n",
       "      <td>27,992,652.0000000000</td>\n",
       "      <td>84,523,274.0000000000</td>\n",
       "      <td>64,491,563.0000000000</td>\n",
       "      <td>35,290,291.0000000000</td>\n",
       "      <td>72,671,175.0000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0982305318</td>\n",
       "      <td>0.2966052004</td>\n",
       "      <td>0.2263108380</td>\n",
       "      <td>0.1238390722</td>\n",
       "      <td>0.2550143576</td>\n",
       "      <td>0.5330482192</td>\n",
       "      <td>0.2119374231</td>\n",
       "      <td>0.4909008141</td>\n",
       "      <td>0.5090991859</td>\n",
       "      <td>36.1471260404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>2002</td>\n",
       "      <td>287,625,193.0000000000</td>\n",
       "      <td>19,429,192.0000000000</td>\n",
       "      <td>53,507,265.0000000000</td>\n",
       "      <td>28,480,708.0000000000</td>\n",
       "      <td>83,990,295.0000000000</td>\n",
       "      <td>66,695,526.0000000000</td>\n",
       "      <td>35,522,207.0000000000</td>\n",
       "      <td>72,936,457.0000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0990202134</td>\n",
       "      <td>0.2920129983</td>\n",
       "      <td>0.2318834637</td>\n",
       "      <td>0.1235017233</td>\n",
       "      <td>0.2535816012</td>\n",
       "      <td>0.5300759485</td>\n",
       "      <td>0.2163424502</td>\n",
       "      <td>0.4910229091</td>\n",
       "      <td>0.5089770909</td>\n",
       "      <td>36.2693702999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>2003</td>\n",
       "      <td>290,107,933.0000000000</td>\n",
       "      <td>19,592,446.0000000000</td>\n",
       "      <td>53,508,312.0000000000</td>\n",
       "      <td>28,916,746.0000000000</td>\n",
       "      <td>83,398,001.0000000000</td>\n",
       "      <td>68,828,899.0000000000</td>\n",
       "      <td>35,863,529.0000000000</td>\n",
       "      <td>73,100,758.0000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0996758196</td>\n",
       "      <td>0.2874723216</td>\n",
       "      <td>0.2372527297</td>\n",
       "      <td>0.1236213317</td>\n",
       "      <td>0.2519777975</td>\n",
       "      <td>0.5278542349</td>\n",
       "      <td>0.2201679676</td>\n",
       "      <td>0.4909514039</td>\n",
       "      <td>0.5090485961</td>\n",
       "      <td>36.4105690209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>U.S.</td>\n",
       "      <td>2004</td>\n",
       "      <td>292,805,298.0000000000</td>\n",
       "      <td>19,785,885.0000000000</td>\n",
       "      <td>53,511,850.0000000000</td>\n",
       "      <td>29,302,179.0000000000</td>\n",
       "      <td>83,066,831.0000000000</td>\n",
       "      <td>70,935,234.0000000000</td>\n",
       "      <td>36,203,319.0000000000</td>\n",
       "      <td>73,297,735.0000000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1000739372</td>\n",
       "      <td>0.2836930601</td>\n",
       "      <td>0.2422607599</td>\n",
       "      <td>0.1236429779</td>\n",
       "      <td>0.2503292649</td>\n",
       "      <td>0.5259431474</td>\n",
       "      <td>0.2237275877</td>\n",
       "      <td>0.4912069999</td>\n",
       "      <td>0.5087930001</td>\n",
       "      <td>36.5446449982</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Description  Year       Total Population        Population 0-4  \\\n",
       "0        U.S.  2000 282,162,411.0000000000 19,178,293.0000000000   \n",
       "1        U.S.  2001 284,968,955.0000000000 19,298,217.0000000000   \n",
       "2        U.S.  2002 287,625,193.0000000000 19,429,192.0000000000   \n",
       "3        U.S.  2003 290,107,933.0000000000 19,592,446.0000000000   \n",
       "4        U.S.  2004 292,805,298.0000000000 19,785,885.0000000000   \n",
       "\n",
       "        Population 5-17      Population 18-24      Population 25-44  \\\n",
       "0 53,197,896.0000000000 27,315,274.0000000000 84,973,340.0000000000   \n",
       "1 53,372,958.0000000000 27,992,652.0000000000 84,523,274.0000000000   \n",
       "2 53,507,265.0000000000 28,480,708.0000000000 83,990,295.0000000000   \n",
       "3 53,508,312.0000000000 28,916,746.0000000000 83,398,001.0000000000   \n",
       "4 53,511,850.0000000000 29,302,179.0000000000 83,066,831.0000000000   \n",
       "\n",
       "       Population 45-64        Population 65+   Population Under 18  ...  \\\n",
       "0 62,428,040.0000000000 35,069,568.0000000000 72,376,189.0000000000  ...   \n",
       "1 64,491,563.0000000000 35,290,291.0000000000 72,671,175.0000000000  ...   \n",
       "2 66,695,526.0000000000 35,522,207.0000000000 72,936,457.0000000000  ...   \n",
       "3 68,828,899.0000000000 35,863,529.0000000000 73,100,758.0000000000  ...   \n",
       "4 70,935,234.0000000000 36,203,319.0000000000 73,297,735.0000000000  ...   \n",
       "\n",
       "   % Population 18-24  % Population 25-44  % Population 45-64  \\\n",
       "0        0.0968069202        0.3011504605        0.2212486056   \n",
       "1        0.0982305318        0.2966052004        0.2263108380   \n",
       "2        0.0990202134        0.2920129983        0.2318834637   \n",
       "3        0.0996758196        0.2874723216        0.2372527297   \n",
       "4        0.1000739372        0.2836930601        0.2422607599   \n",
       "\n",
       "   % Population 65+  % Population Under 18  % Population 18-54  \\\n",
       "0      0.1242885892           0.2565054245        0.5326279552   \n",
       "1      0.1238390722           0.2550143576        0.5330482192   \n",
       "2      0.1235017233           0.2535816012        0.5300759485   \n",
       "3      0.1236213317           0.2519777975        0.5278542349   \n",
       "4      0.1236429779           0.2503292649        0.5259431474   \n",
       "\n",
       "   % Population 55+  % Male Population  % Female Population   Average Age  \n",
       "0      0.2108666204       0.4906514887         0.5093485113 36.0461526979  \n",
       "1      0.2119374231       0.4909008141         0.5090991859 36.1471260404  \n",
       "2      0.2163424502       0.4910229091         0.5089770909 36.2693702999  \n",
       "3      0.2201679676       0.4909514039         0.5090485961 36.4105690209  \n",
       "4      0.2237275877       0.4912069999         0.5087930001 36.5446449982  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "    POPULATION ANALYSIS\n",
    "'''\n",
    "filtered_pop = population[population['Description'] == 'U.S.']\n",
    "\n",
    "# Remove unnecessary columns\n",
    "filtered_pop = filtered_pop.drop(\n",
    "    ['IBRC_Geo_ID', 'Statefips', 'Countyfips'], axis=1)\n",
    "\n",
    "# Function to get percentages of populations\n",
    "def percent(row, name):\n",
    "    base = row['Total Population']\n",
    "    return row[name]/base\n",
    "\n",
    "\n",
    "for c in filtered_pop.columns:\n",
    "    if 'population' in c.lower():\n",
    "        filtered_pop['% ' +\n",
    "                     c] = filtered_pop.apply(lambda row: percent(row, c), axis=1)\n",
    "\n",
    "def get_avg_age(row):\n",
    "    base = row['Total Population']\n",
    "    populations = [row['Population 0-4'], row['Population 5-17'], row['Population 18-24'],\n",
    "                   row['Population 25-44'], row['Population 45-64'], row['Population 65+']]  # Populations by group\n",
    "    midpoints = [2.5, 11, 21, 34.5, 54.5, 75]  # Midpoints of age groups\n",
    "    weighted_ages = sum(pop * mid for pop, mid in zip(populations, midpoints))\n",
    "    return weighted_ages / base  # Weighted sum of ages\n",
    "\n",
    "filtered_pop['Average Age'] = filtered_pop.apply(\n",
    "    lambda row: get_avg_age(row), axis=1)\n",
    "filtered_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1998.0, 2021.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "    POPULATION CHARTS\n",
    "'''\n",
    "fig_width = len(filtered_pop)*1.25\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3, 1, sharex=True, figsize=(fig_width, 10))\n",
    "\n",
    "ax1.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Population 0-4'], label='Age 0-4')\n",
    "ax1.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Population 5-17'], label='Age 5-17')\n",
    "ax1.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Population 18-24'], label='Age 18-24')\n",
    "ax1.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Population 25-44'], label='Age 25-44')\n",
    "ax1.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Population 45-64'], label='Age 45-64')\n",
    "ax1.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Population 65+'], label='Age 65+')\n",
    "ax1.set_xlabel('Year')\n",
    "ax1.set_ylabel('Population %')\n",
    "ax1.set_title('Year vs. Population % of US Population from 2000-2019')\n",
    "ax1.legend(loc='center left')\n",
    "\n",
    "ax2.plot(filtered_pop['Year'], filtered_pop['Average Age'],\n",
    "         label='Est. Avg Age', marker='o')\n",
    "ax2.set_xlabel('Year')\n",
    "ax2.set_ylabel('Estimated Average Age')\n",
    "ax2.set_title('Year vs. Estimated Average Age of US Population from 2000-2019')\n",
    "ax2.legend(loc='center left')\n",
    "\n",
    "ax3.plot(filtered_pop['Year'],\n",
    "         filtered_pop['% Male Population'], label='% Male', marker='o')\n",
    "ax3.plot(filtered_pop['Year'], filtered_pop['% Female Population'],\n",
    "         label='% Female', marker='o')\n",
    "ax3.set_xlabel('Year')\n",
    "ax3.set_ylabel('% Male/Female Population')\n",
    "ax3.set_title('Year vs. % Male/Female of US Population from 2000-2019')\n",
    "ax3.legend(loc='center left')\n",
    "\n",
    "\n",
    "plt.xticks(filtered_pop['Year'])\n",
    "plt.xlim(1998, 2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' CONGRESS VS. POPULATION AGE BY YEAR '''\n",
    "congress_age_by_year = group_age_start(all_legislators)\n",
    "congress_age_by_year['year'] = congress_age_by_year['start'].dt.year\n",
    "congress_age_by_year['average_age'] = pd.to_numeric(\n",
    "    congress_age_by_year['average_age'], errors='coerce')\n",
    "\n",
    "congress_age_by_year = congress_age_by_year.groupby('year').agg(\n",
    "    avg_age=('average_age', 'mean')\n",
    ").reset_index()\n",
    "\n",
    "congress_age_by_year = congress_age_by_year[congress_age_by_year['year'] >= 1999]\n",
    "congress_age_by_year = congress_age_by_year[congress_age_by_year['year'] <= 2019]\n",
    "combined_age_over_time = pd.merge(congress_age_by_year[['year', 'avg_age']], filtered_pop[[\n",
    "                                  'Year', 'Average Age']], left_on='year', right_on='Year')\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(combined_age_over_time['year'], combined_age_over_time['avg_age'],\n",
    "         marker='o', label='Congress Average Age')\n",
    "plt.plot(combined_age_over_time['year'], combined_age_over_time['Average Age'],\n",
    "         marker='o', label='Population Average Age')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Age')\n",
    "plt.title('Congress vs. Population Average Age Over Time', fontsize=14)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.xlim(1998, 2021)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "''' CONGRESS VS. POPULATION GENDER BY YEAR '''\n",
    "def group_gender_start(df):\n",
    "    # Extract year from the start date\n",
    "    df['year'] = df['start'].dt.year\n",
    "    \n",
    "    # Group by year and gender to calculate counts for each year\n",
    "    gender_count = df.groupby(['year', 'gender']).agg(\n",
    "        count=('gender', 'count')\n",
    "    ).reset_index()\n",
    "\n",
    "    # Pivot to create columns for M/F counts\n",
    "    pivoted = gender_count.pivot(\n",
    "        index='year', columns='gender', values='count').fillna(0)\n",
    "\n",
    "    # Add cumulative counts for each gender\n",
    "    pivoted['M_cumulative'] = pivoted.get('M', 0).cumsum()\n",
    "    pivoted['F_cumulative'] = pivoted.get('F', 0).cumsum()\n",
    "\n",
    "    # Calculate total cumulative members\n",
    "    pivoted['total_cumulative'] = pivoted['M_cumulative'] + pivoted['F_cumulative']\n",
    "\n",
    "    # Calculate percentages\n",
    "    pivoted['percent_M'] = (pivoted['M_cumulative'] / pivoted['total_cumulative'])\n",
    "    pivoted['percent_F'] = (pivoted['F_cumulative'] / pivoted['total_cumulative'])\n",
    "\n",
    "    # Reset index for final output\n",
    "    pivoted = pivoted.reset_index()\n",
    "    return pivoted\n",
    "\n",
    "inc_age_gender = group_gender_start(incumbent)\n",
    "inc_age_gender\n",
    "\n",
    "inc_age_gender = inc_age_gender[inc_age_gender['year'] >= 1999]\n",
    "inc_age_gender = inc_age_gender[inc_age_gender['year'] <= 2019]\n",
    "combined_gender_over_time = pd.merge(inc_age_gender[['year', 'percent_F', 'percent_M']], filtered_pop[[\n",
    "                                     'Year', '% Female Population', '% Male Population']], left_on='year', right_on='Year')\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(combined_gender_over_time['year'],\n",
    "         combined_gender_over_time['percent_F'], marker='o', label='Congress Female %')\n",
    "plt.plot(combined_gender_over_time['year'],\n",
    "         combined_gender_over_time['percent_M'], marker='o', label='Congress Male %')\n",
    "plt.plot(combined_gender_over_time['year'],\n",
    "         combined_gender_over_time['% Female Population'], marker='o', label='Population Female %')\n",
    "plt.plot(combined_gender_over_time['year'],\n",
    "         combined_gender_over_time['% Male Population'], marker='o', label='Population Male %')\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('% Male/Female')\n",
    "plt.title('Congress vs. Population Gender % Over Time', fontsize=14)\n",
    "# Move legend outside the plot\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "plt.grid(True)\n",
    "plt.xlim(1998, 2021)\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}