data visualization2_막대그래프 실습(silicon_valley).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   "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>race_ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>268883</td>\n",
       "      <td>41.257252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>105560</td>\n",
       "      <td>16.197065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>17508</td>\n",
       "      <td>2.686417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>11479</td>\n",
       "      <td>1.761331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>125347</td>\n",
       "      <td>19.233171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Female</td>\n",
       "      <td>58049</td>\n",
       "      <td>8.907005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Male</td>\n",
       "      <td>32201</td>\n",
       "      <td>4.940903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Female</td>\n",
       "      <td>15512</td>\n",
       "      <td>2.380152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>All</td>\n",
       "      <td>Male</td>\n",
       "      <td>454813</td>\n",
       "      <td>69.786244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>All</td>\n",
       "      <td>Female</td>\n",
       "      <td>196910</td>\n",
       "      <td>30.213756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All workers</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Both</td>\n",
       "      <td>651723</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>7282</td>\n",
       "      <td>58.678485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>1818</td>\n",
       "      <td>14.649476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>120</td>\n",
       "      <td>0.966962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>53</td>\n",
       "      <td>0.427075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>2023</td>\n",
       "      <td>16.301370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Female</td>\n",
       "      <td>556</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Male</td>\n",
       "      <td>266</td>\n",
       "      <td>2.143433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Female</td>\n",
       "      <td>103</td>\n",
       "      <td>0.829976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>All</td>\n",
       "      <td>Male</td>\n",
       "      <td>9824</td>\n",
       "      <td>79.161966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>All</td>\n",
       "      <td>Female</td>\n",
       "      <td>2586</td>\n",
       "      <td>20.838034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Executives</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Both</td>\n",
       "      <td>12410</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>48311</td>\n",
       "      <td>46.479253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>18935</td>\n",
       "      <td>18.217065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1575</td>\n",
       "      <td>1.515283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>978</td>\n",
       "      <td>0.940918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>18563</td>\n",
       "      <td>17.859170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Female</td>\n",
       "      <td>8084</td>\n",
       "      <td>7.777489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Male</td>\n",
       "      <td>3741</td>\n",
       "      <td>3.599157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Female</td>\n",
       "      <td>1642</td>\n",
       "      <td>1.579742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>All</td>\n",
       "      <td>Male</td>\n",
       "      <td>73526</td>\n",
       "      <td>70.738207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>All</td>\n",
       "      <td>Female</td>\n",
       "      <td>30415</td>\n",
       "      <td>29.261793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Both</td>\n",
       "      <td>103941</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>133311</td>\n",
       "      <td>38.660592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>White</td>\n",
       "      <td>Female</td>\n",
       "      <td>47505</td>\n",
       "      <td>13.776593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>6301</td>\n",
       "      <td>1.827309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Female</td>\n",
       "      <td>3756</td>\n",
       "      <td>1.089251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>89365</td>\n",
       "      <td>25.916120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Female</td>\n",
       "      <td>39902</td>\n",
       "      <td>11.571700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Male</td>\n",
       "      <td>11820</td>\n",
       "      <td>3.427836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Female</td>\n",
       "      <td>5533</td>\n",
       "      <td>1.604587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>All</td>\n",
       "      <td>Male</td>\n",
       "      <td>245461</td>\n",
       "      <td>71.184430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>All</td>\n",
       "      <td>Female</td>\n",
       "      <td>99363</td>\n",
       "      <td>28.815570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Professionals</th>\n",
       "      <td>Totals</td>\n",
       "      <td>Both</td>\n",
       "      <td>344824</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          race_ethnicity  gender   count  percentage\n",
       "job_category                                                        \n",
       "All workers                        White    Male  268883   41.257252\n",
       "All workers                        White  Female  105560   16.197065\n",
       "All workers    Black_or_African American    Male   17508    2.686417\n",
       "All workers    Black_or_African American  Female   11479    1.761331\n",
       "All workers                        Asian    Male  125347   19.233171\n",
       "All workers                        Asian  Female   58049    8.907005\n",
       "All workers           Hispanic_or_Latino    Male   32201    4.940903\n",
       "All workers           Hispanic_or_Latino  Female   15512    2.380152\n",
       "All workers                          All    Male  454813   69.786244\n",
       "All workers                          All  Female  196910   30.213756\n",
       "All workers                       Totals    Both  651723  100.000000\n",
       "Executives                         White    Male    7282   58.678485\n",
       "Executives                         White  Female    1818   14.649476\n",
       "Executives     Black_or_African American    Male     120    0.966962\n",
       "Executives     Black_or_African American  Female      53    0.427075\n",
       "Executives                         Asian    Male    2023   16.301370\n",
       "Executives                         Asian  Female     556    4.500000\n",
       "Executives            Hispanic_or_Latino    Male     266    2.143433\n",
       "Executives            Hispanic_or_Latino  Female     103    0.829976\n",
       "Executives                           All    Male    9824   79.161966\n",
       "Executives                           All  Female    2586   20.838034\n",
       "Executives                        Totals    Both   12410  100.000000\n",
       "Managers                           White    Male   48311   46.479253\n",
       "Managers                           White  Female   18935   18.217065\n",
       "Managers       Black_or_African American    Male    1575    1.515283\n",
       "Managers       Black_or_African American  Female     978    0.940918\n",
       "Managers                           Asian    Male   18563   17.859170\n",
       "Managers                           Asian  Female    8084    7.777489\n",
       "Managers              Hispanic_or_Latino    Male    3741    3.599157\n",
       "Managers              Hispanic_or_Latino  Female    1642    1.579742\n",
       "Managers                             All    Male   73526   70.738207\n",
       "Managers                             All  Female   30415   29.261793\n",
       "Managers                          Totals    Both  103941  100.000000\n",
       "Professionals                      White    Male  133311   38.660592\n",
       "Professionals                      White  Female   47505   13.776593\n",
       "Professionals  Black_or_African American    Male    6301    1.827309\n",
       "Professionals  Black_or_African American  Female    3756    1.089251\n",
       "Professionals                      Asian    Male   89365   25.916120\n",
       "Professionals                      Asian  Female   39902   11.571700\n",
       "Professionals         Hispanic_or_Latino    Male   11820    3.427836\n",
       "Professionals         Hispanic_or_Latino  Female    5533    1.604587\n",
       "Professionals                        All    Male  245461   71.184430\n",
       "Professionals                        All  Female   99363   28.815570\n",
       "Professionals                     Totals    Both  344824  100.000000"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "\n",
    "# 실리콘 밸리에서 일하는 남자 관리자(Manager)에 대한 인종 분포를 막대 그래프로 그려보세요\n",
    "df = pd.read_csv('C:/Users/Kwon JaeWoo/Desktop/codeit_데이터사이언스 입문/3_3silicon_valley_summary.csv', index_col = 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "male_manager = (df['gender'] == 'Male') & (df.index == 'Managers')\n",
    "df1 = df.loc[male_manager, ['race_ethnicity', 'count']]\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.set_index('race_ethnicity', inplace = True)\n",
    "df1.drop('All', axis= 'index', inplace = True)\n",
    "df1['count'].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='race_ethnicity'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = df[(df.index == 'Managers') & (df['gender'] =='Male') & (df['race_ethnicity'] != 'All')]\n",
    "data.plot(kind='bar', x='race_ethnicity', y='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>race_ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>count</th>\n",
       "      <th>percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>48311</td>\n",
       "      <td>46.479253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Black_or_African American</td>\n",
       "      <td>Male</td>\n",
       "      <td>1575</td>\n",
       "      <td>1.515283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>18563</td>\n",
       "      <td>17.859170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Managers</th>\n",
       "      <td>Hispanic_or_Latino</td>\n",
       "      <td>Male</td>\n",
       "      <td>3741</td>\n",
       "      <td>3.599157</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         race_ethnicity gender  count  percentage\n",
       "job_category                                                     \n",
       "Managers                          White   Male  48311   46.479253\n",
       "Managers      Black_or_African American   Male   1575    1.515283\n",
       "Managers                          Asian   Male  18563   17.859170\n",
       "Managers             Hispanic_or_Latino   Male   3741    3.599157"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}