Blog_Binning_num_features.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.184955Z",
     "start_time": "2021-04-04T17:03:14.571922Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.189120Z",
     "start_time": "2021-04-04T17:03:21.186707Z"
    }
   },
   "outputs": [],
   "source": [
    "pd.options.display.max_columns = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.488088Z",
     "start_time": "2021-04-04T17:03:21.191480Z"
    }
   },
   "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>age</th>\n",
       "      <th>birth_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65</td>\n",
       "      <td>1955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>1963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>1973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>72</td>\n",
       "      <td>1948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  birth_year\n",
       "0   65        1955\n",
       "1   57        1963\n",
       "2   47        1973\n",
       "3   72        1948\n",
       "4   53        1967"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Data/UC_gen_2020.gz',\n",
    "                     usecols=['birth_age_adj',\n",
    "                              'birth_year'])\n",
    "df.rename(columns={'birth_age_adj':'age'}, inplace=True)\n",
    "df['age'] = df['age'].astype(int)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.504327Z",
     "start_time": "2021-04-04T17:03:21.491051Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 168070 entries, 0 to 168069\n",
      "Data columns (total 2 columns):\n",
      " #   Column      Non-Null Count   Dtype\n",
      "---  ------      --------------   -----\n",
      " 0   age         168070 non-null  int64\n",
      " 1   birth_year  168070 non-null  int64\n",
      "dtypes: int64(2)\n",
      "memory usage: 2.6 MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.510235Z",
     "start_time": "2021-04-04T17:03:21.506558Z"
    }
   },
   "outputs": [],
   "source": [
    "def check_unique(col, df, dropna=False):\n",
    "\n",
    "    unique_vals = pd.DataFrame()\n",
    "    unique_vals['count'] = pd.Series(df[col].value_counts(dropna=dropna))\n",
    "    unique_vals['%'] = pd.Series(round(df[col].value_counts(normalize=True, dropna=dropna)*100, 2))\n",
    "    \n",
    "    return unique_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.517474Z",
     "start_time": "2021-04-04T17:03:21.511916Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_hist(variable, data, rotation=0, ha='center', figsize=(10,7)):\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    ax = sns.histplot(x=data[variable],\n",
    "                       palette='nipy_spectral')\n",
    "    \n",
    "    if variable == 'age':\n",
    "        xtick_lab = [0,20,40,60,80,100]\n",
    "    if variable == 'birth_year':\n",
    "        xtick_lab = [1900,1920,1940,1960,1980,2000]\n",
    "    ax.set_xticklabels(xtick_lab,\n",
    "                           fontsize=12, weight='bold')\n",
    "    ax.set_title(f'{variable.title()} Distribution', \n",
    "                 fontsize=16,\n",
    "                 weight='bold')\n",
    "    ax.set_xlabel(f'{variable.title()}',\n",
    "                  fontsize=14,\n",
    "                  weight='bold')\n",
    "    ax.set_ylabel('Count', fontsize=14, weight='bold')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.527553Z",
     "start_time": "2021-04-04T17:03:21.519314Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_count(variable, data, rotation=0, ha='center', figsize=(8,6)):\n",
    "    \n",
    "    if variable == 'generation':\n",
    "        order = ['GenZ', 'Millennial', 'GenX', 'Boomer', 'Greatest-Silent']\n",
    "    else:\n",
    "        order = ['Teens', \"20's\", \"30's\", \"40's\", \"50's\", \"60's\", \"70's\", \"80's\", \"90's\", \"100's\"]\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    ax = sns.countplot(x=data[variable],\n",
    "                       order=order,\n",
    "                       palette='nipy_spectral')\n",
    "    \n",
    "    ax.set_xticklabels(ax.get_xticklabels(),\n",
    "                       rotation=rotation,\n",
    "                       horizontalalignment=ha,\n",
    "                       fontsize=12, weight='bold')\n",
    "    ax.set_title(f'{variable.title()} Counts', \n",
    "                 fontsize=16,\n",
    "                 weight='bold')\n",
    "    ax.set_xlabel(f'{variable.title()}',\n",
    "                  fontsize=14,\n",
    "                  weight='bold')\n",
    "    ax.set_ylabel('Count', fontsize=14, weight='bold')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.540467Z",
     "start_time": "2021-04-04T17:03:21.531263Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_hist_highlight(variable, data, low, high, rotation=0, ha='center', figsize=(10,7)):\n",
    "    \n",
    "    plt.figure(figsize=figsize)\n",
    "    ax = sns.histplot(x=data[variable], kde=True,\n",
    "                       palette='nipy_spectral')\n",
    "    \n",
    "    if variable == 'age':\n",
    "        xtick_lab = [0,20,40,60,80,100]\n",
    "    if variable == 'birth_year':\n",
    "        xtick_lab = [1900,1920,1940,1960,1980,2000]\n",
    "    ax.set_xticklabels(xtick_lab,\n",
    "                       fontsize=12, weight='bold')\n",
    "\n",
    "    kde_x, kde_y = ax.lines[0].get_data()\n",
    "    print(ax.lines)\n",
    "    \n",
    "    #plotting the two lines\n",
    "    p1 = plt.axvline(x=low, color='black', ls='--', lw=4)\n",
    "    p2 = plt.axvline(x=high, color='black', ls='--', lw=4)\n",
    "\n",
    "\n",
    "    ax.fill_between(kde_x, kde_y, where=(kde_x<low) | (kde_x>high) , \n",
    "                interpolate=True, color=['red', 'yellow'])\n",
    "    \n",
    "    ax.set_title(f'{variable.title()} Distribution', \n",
    "                 fontsize=16,\n",
    "                 weight='bold')\n",
    "    ax.set_xlabel(f'{variable.title()}',\n",
    "                  fontsize=14,\n",
    "                  weight='bold')\n",
    "    ax.set_ylabel('Count', fontsize=14, weight='bold')\n",
    "    ax.spines['right'].set_visible(False)\n",
    "    ax.spines['top'].set_visible(False)\n",
    "    \n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.932509Z",
     "start_time": "2021-04-04T17:03:21.544310Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/maxsteele/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('age', df);\n",
    "# plt.savefig('Figures/blog_bin_num_features/age_distr', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.947671Z",
     "start_time": "2021-04-04T17:03:21.934173Z"
    }
   },
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>154322</td>\n",
       "      <td>91.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13748</td>\n",
       "      <td>8.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count      %\n",
       "0  154322  91.82\n",
       "1   13748   8.18"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['first_pres_elec'] = np.where(df['age']<22, 1, 0)\n",
    "check_unique('first_pres_elec', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:21.966330Z",
     "start_time": "2021-04-04T17:03:21.949413Z"
    }
   },
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>137614</td>\n",
       "      <td>81.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30456</td>\n",
       "      <td>18.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    count      %\n",
       "0  137614  81.88\n",
       "1   30456  18.12"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['senior'] = np.where(df['age']>=65, 1, 0)\n",
    "check_unique('senior', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:22.918222Z",
     "start_time": "2021-04-04T17:03:21.969263Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/maxsteele/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[<matplotlib.lines.Line2D object at 0x7f853b3dd588>]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist_highlight('age', df, 22, 65)\n",
    "plt.savefig('Figures/blog_bin_num_features/age_thresh', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:03:23.276666Z",
     "start_time": "2021-04-04T17:03:22.956226Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/maxsteele/opt/anaconda3/envs/learn-env/lib/python3.6/site-packages/ipykernel_launcher.py:12: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  if sys.path[0] == '':\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_hist('birth_year', df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:33:53.727333Z",
     "start_time": "2021-04-04T17:33:53.719746Z"
    }
   },
   "outputs": [],
   "source": [
    "## Define function for grouping into generation categories by birth year\n",
    "def get_gen_grp(birth_year):\n",
    "    \n",
    "    if birth_year < 1946:\n",
    "        return 'Greatest-Silent'\n",
    "    \n",
    "    elif (birth_year > 1945) & (birth_year < 1965):\n",
    "        return 'Boomer'\n",
    "    \n",
    "    elif (birth_year > 1964) & (birth_year < 1981):\n",
    "        return 'GenX'\n",
    "    \n",
    "    elif (birth_year > 1980) & (birth_year < 1997):\n",
    "        return 'Millennial'\n",
    "    \n",
    "    elif birth_year > 1996:\n",
    "        return 'GenZ'\n",
    "    \n",
    "    else:\n",
    "        return 'Missing'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:33:54.053321Z",
     "start_time": "2021-04-04T17:33:53.975904Z"
    }
   },
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GenX</th>\n",
       "      <td>53717</td>\n",
       "      <td>31.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boomer</th>\n",
       "      <td>44171</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Millennial</th>\n",
       "      <td>38600</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenZ</th>\n",
       "      <td>19834</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greatest-Silent</th>\n",
       "      <td>11748</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      %\n",
       "GenX             53717  31.96\n",
       "Boomer           44171  26.28\n",
       "Millennial       38600  22.97\n",
       "GenZ             19834  11.80\n",
       "Greatest-Silent  11748   6.99"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Create a new column grouping birth_year into generations\n",
    "df['generation'] = df['birth_year'].apply(get_gen_grp)\n",
    "\n",
    "## Check distribution\n",
    "check_unique('generation', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:33:54.635437Z",
     "start_time": "2021-04-04T17:33:54.324687Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('generation', df)\n",
    "plt.savefig('Figures/blog_bin_num_features/gen_grps', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:59:03.007877Z",
     "start_time": "2021-04-04T17:59:02.976995Z"
    }
   },
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GenX</th>\n",
       "      <td>53717</td>\n",
       "      <td>31.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boomer</th>\n",
       "      <td>44171</td>\n",
       "      <td>26.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Millennial</th>\n",
       "      <td>38600</td>\n",
       "      <td>22.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GenZ</th>\n",
       "      <td>19834</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greatest-Silent</th>\n",
       "      <td>11748</td>\n",
       "      <td>6.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 count      %\n",
       "GenX             53717  31.96\n",
       "Boomer           44171  26.28\n",
       "Millennial       38600  22.97\n",
       "GenZ             19834  11.80\n",
       "Greatest-Silent  11748   6.99"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Bin registered voters into generation groups using pd.cut\n",
    "# Define group labels\n",
    "cut_labels = ['Greatest-Silent', 'Boomer', 'GenX', 'Millennial', 'GenZ']\n",
    "\n",
    "# Define bin edges\n",
    "cut_bins = [0, 1945, 1964, 1980, 1996, 2100]\n",
    "\n",
    "# Create a new column grouping birth_year into generations\n",
    "df['cut_generation'] = pd.cut(df['birth_year'], bins=cut_bins, labels=cut_labels)\n",
    "\n",
    "## Check distribution\n",
    "check_unique('cut_generation', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:59:03.429182Z",
     "start_time": "2021-04-04T17:59:03.396955Z"
    }
   },
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>(40, 50]</th>\n",
       "      <td>33349</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(50, 60]</th>\n",
       "      <td>32924</td>\n",
       "      <td>19.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(20, 30]</th>\n",
       "      <td>26411</td>\n",
       "      <td>15.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(30, 40]</th>\n",
       "      <td>24427</td>\n",
       "      <td>14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(60, 70]</th>\n",
       "      <td>21781</td>\n",
       "      <td>12.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(70, 80]</th>\n",
       "      <td>13614</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(10, 20]</th>\n",
       "      <td>10520</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(80, 90]</th>\n",
       "      <td>4349</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(90, 100]</th>\n",
       "      <td>681</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(100, 110]</th>\n",
       "      <td>14</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count      %\n",
       "(40, 50]    33349  19.84\n",
       "(50, 60]    32924  19.59\n",
       "(20, 30]    26411  15.71\n",
       "(30, 40]    24427  14.53\n",
       "(60, 70]    21781  12.96\n",
       "(70, 80]    13614   8.10\n",
       "(10, 20]    10520   6.26\n",
       "(80, 90]     4349   2.59\n",
       "(90, 100]     681   0.41\n",
       "(100, 110]     14   0.01"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Bin registered voters into generation groups using pd.cut\n",
    "# Define bin edges\n",
    "cut_bins = [10, 20, 30, 40, 50 ,60 ,70, 80, 90, 100, 110]\n",
    "\n",
    "# Create a new column grouping birth_year into generations\n",
    "df['cut_age'] = pd.cut(df['age'], bins=cut_bins)\n",
    "\n",
    "## Check distribution\n",
    "check_unique('cut_age', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:59:03.762423Z",
     "start_time": "2021-04-04T17:59:03.756512Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10,  20,  30,  40,  50,  60,  70,  80,  90, 100, 110])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10, 111, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:59:04.255694Z",
     "start_time": "2021-04-04T17:59:04.227673Z"
    }
   },
   "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>count</th>\n",
       "      <th>%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>40's</th>\n",
       "      <td>33349</td>\n",
       "      <td>19.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50's</th>\n",
       "      <td>32924</td>\n",
       "      <td>19.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20's</th>\n",
       "      <td>26411</td>\n",
       "      <td>15.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30's</th>\n",
       "      <td>24427</td>\n",
       "      <td>14.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60's</th>\n",
       "      <td>21781</td>\n",
       "      <td>12.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70's</th>\n",
       "      <td>13614</td>\n",
       "      <td>8.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Teens</th>\n",
       "      <td>10520</td>\n",
       "      <td>6.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80's</th>\n",
       "      <td>4349</td>\n",
       "      <td>2.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90's</th>\n",
       "      <td>681</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100's</th>\n",
       "      <td>14</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       count      %\n",
       "40's   33349  19.84\n",
       "50's   32924  19.59\n",
       "20's   26411  15.71\n",
       "30's   24427  14.53\n",
       "60's   21781  12.96\n",
       "70's   13614   8.10\n",
       "Teens  10520   6.26\n",
       "80's    4349   2.59\n",
       "90's     681   0.41\n",
       "100's     14   0.01"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Bin registered voters into generation groups using pd.cut\n",
    "# Define group labels\n",
    "cut_labels = ['Teens', \"20's\", \"30's\", \"40's\", \"50's\", \"60's\", \"70's\", \"80's\", \"90's\", \"100's\"]\n",
    "\n",
    "# Define bin edges\n",
    "cut_bins = np.arange(10, 111, 10)\n",
    "\n",
    "# Create a new column grouping birth_year into generations\n",
    "df['cut_age'] = pd.cut(df['age'], bins=cut_bins, labels=cut_labels)\n",
    "\n",
    "## Check distribution\n",
    "check_unique('cut_age', df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-04-04T17:59:05.003762Z",
     "start_time": "2021-04-04T17:59:04.679905Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_count('cut_age', df)\n",
    "plt.savefig('Figures/blog_bin_num_features/decade_grps', bbox_inches = \"tight\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "learn-env",
   "language": "python",
   "name": "learn-env"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}