9. Similarity between groupby, pivot_table, crosstab.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 9. Similarity between groupby, pivot_table, crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>dept</th>\n",
       "      <th>salary</th>\n",
       "      <th>race</th>\n",
       "      <th>gender</th>\n",
       "      <th>hire_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POLICE OFFICER</td>\n",
       "      <td>Houston Police Department-HPD</td>\n",
       "      <td>45279.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2015-02-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ENGINEER/OPERATOR</td>\n",
       "      <td>Houston Fire Department (HFD)</td>\n",
       "      <td>63166.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>1982-02-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SENIOR POLICE OFFICER</td>\n",
       "      <td>Houston Police Department-HPD</td>\n",
       "      <td>66614.0</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>1984-11-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ENGINEER</td>\n",
       "      <td>Public Works &amp; Engineering-PWE</td>\n",
       "      <td>71680.0</td>\n",
       "      <td>Asian</td>\n",
       "      <td>Male</td>\n",
       "      <td>2012-03-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CARPENTER</td>\n",
       "      <td>Houston Airport System (HAS)</td>\n",
       "      <td>42390.0</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2013-11-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   title                            dept   salary   race  \\\n",
       "0         POLICE OFFICER   Houston Police Department-HPD  45279.0  White   \n",
       "1      ENGINEER/OPERATOR   Houston Fire Department (HFD)  63166.0  White   \n",
       "2  SENIOR POLICE OFFICER   Houston Police Department-HPD  66614.0  Black   \n",
       "3               ENGINEER  Public Works & Engineering-PWE  71680.0  Asian   \n",
       "4              CARPENTER    Houston Airport System (HAS)  42390.0  White   \n",
       "\n",
       "  gender   hire_date  \n",
       "0   Male  2015-02-03  \n",
       "1   Male  1982-02-08  \n",
       "2   Male  1984-11-26  \n",
       "3   Male  2012-03-26  \n",
       "4   Male  2013-11-04  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp = pd.read_csv('data/employee.csv')\n",
    "emp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dept</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Health &amp; Human Services</th>\n",
       "      <th>Female</th>\n",
       "      <td>49000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>59000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Houston Airport System (HAS)</th>\n",
       "      <th>Female</th>\n",
       "      <td>53000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Houston Fire Department (HFD)</th>\n",
       "      <th>Female</th>\n",
       "      <td>53000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Houston Police Department-HPD</th>\n",
       "      <th>Female</th>\n",
       "      <td>52000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Parks &amp; Recreation</th>\n",
       "      <th>Female</th>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>38000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Public Works &amp; Engineering-PWE</th>\n",
       "      <th>Female</th>\n",
       "      <td>51000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        salary\n",
       "dept                           gender         \n",
       "Health & Human Services        Female  49000.0\n",
       "                               Male    59000.0\n",
       "Houston Airport System (HAS)   Female  53000.0\n",
       "                               Male    54000.0\n",
       "Houston Fire Department (HFD)  Female  53000.0\n",
       "                               Male    60000.0\n",
       "Houston Police Department-HPD  Female  52000.0\n",
       "                               Male    63000.0\n",
       "Parks & Recreation             Female  40000.0\n",
       "                               Male    38000.0\n",
       "Public Works & Engineering-PWE Female  51000.0\n",
       "                               Male    50000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.groupby(['dept', 'gender']).agg({'salary':'mean'}).round(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>gender</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dept</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Health &amp; Human Services</th>\n",
       "      <td>49000.0</td>\n",
       "      <td>59000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston Airport System (HAS)</th>\n",
       "      <td>53000.0</td>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston Fire Department (HFD)</th>\n",
       "      <td>53000.0</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Houston Police Department-HPD</th>\n",
       "      <td>52000.0</td>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parks &amp; Recreation</th>\n",
       "      <td>40000.0</td>\n",
       "      <td>38000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Public Works &amp; Engineering-PWE</th>\n",
       "      <td>51000.0</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "gender                           Female     Male\n",
       "dept                                            \n",
       "Health & Human Services         49000.0  59000.0\n",
       "Houston Airport System (HAS)    53000.0  54000.0\n",
       "Houston Fire Department (HFD)   53000.0  60000.0\n",
       "Houston Police Department-HPD   52000.0  63000.0\n",
       "Parks & Recreation              40000.0  38000.0\n",
       "Public Works & Engineering-PWE  51000.0  50000.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.pivot_table(index='dept', columns='gender', \n",
    "                values='salary', aggfunc='mean').round(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dept</th>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Health &amp; Human Services</th>\n",
       "      <th>Female</th>\n",
       "      <td>49000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>59000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Houston Airport System (HAS)</th>\n",
       "      <th>Female</th>\n",
       "      <td>53000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>54000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Houston Fire Department (HFD)</th>\n",
       "      <th>Female</th>\n",
       "      <td>53000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Houston Police Department-HPD</th>\n",
       "      <th>Female</th>\n",
       "      <td>52000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Parks &amp; Recreation</th>\n",
       "      <th>Female</th>\n",
       "      <td>40000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>38000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Public Works &amp; Engineering-PWE</th>\n",
       "      <th>Female</th>\n",
       "      <td>51000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        salary\n",
       "dept                           gender         \n",
       "Health & Human Services        Female  49000.0\n",
       "                               Male    59000.0\n",
       "Houston Airport System (HAS)   Female  53000.0\n",
       "                               Male    54000.0\n",
       "Houston Fire Department (HFD)  Female  53000.0\n",
       "                               Male    60000.0\n",
       "Houston Police Department-HPD  Female  52000.0\n",
       "                               Male    63000.0\n",
       "Parks & Recreation             Female  40000.0\n",
       "                               Male    38000.0\n",
       "Public Works & Engineering-PWE Female  51000.0\n",
       "                               Male    50000.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.pivot_table(index=['dept','gender'], \n",
    "                values='salary', aggfunc='mean').round(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = emp.groupby('dept').agg({'salary':'mean'}).round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = emp.pivot_table(index='dept', values='salary', aggfunc='mean').round(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.equals(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Native American</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>59000.0</td>\n",
       "      <td>66000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>61000.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race      Asian    Black  Hispanic  Native American    White\n",
       "gender                                                      \n",
       "Female  58000.0  48000.0   44000.0          59000.0  66000.0\n",
       "Male    61000.0  52000.0   55000.0          69000.0  63000.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.pivot_table(index='gender', columns='race', \n",
    "                values='salary', aggfunc='mean').round(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Native American</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>48000.0</td>\n",
       "      <td>44000.0</td>\n",
       "      <td>59000.0</td>\n",
       "      <td>66000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>61000.0</td>\n",
       "      <td>52000.0</td>\n",
       "      <td>55000.0</td>\n",
       "      <td>69000.0</td>\n",
       "      <td>63000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race      Asian    Black  Hispanic  Native American    White\n",
       "gender                                                      \n",
       "Female  58000.0  48000.0   44000.0          59000.0  66000.0\n",
       "Male    61000.0  52000.0   55000.0          69000.0  63000.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=emp['gender'], columns=emp['race'], \n",
    "            values=emp['salary'], aggfunc='mean').round(-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Native American</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>18</td>\n",
       "      <td>216</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>70</td>\n",
       "      <td>326</td>\n",
       "      <td>294</td>\n",
       "      <td>4</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race    Asian  Black  Hispanic  Native American  White\n",
       "gender                                                \n",
       "Female     18    216       101                4     78\n",
       "Male       70    326       294                4    522"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=emp['gender'], columns=emp['race'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Native American</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>18</td>\n",
       "      <td>216</td>\n",
       "      <td>101</td>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>70</td>\n",
       "      <td>326</td>\n",
       "      <td>294</td>\n",
       "      <td>4</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race    Asian  Black  Hispanic  Native American  White\n",
       "gender                                                \n",
       "Female     18    216       101                4     78\n",
       "Male       70    326       294                4    522"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emp.pivot_table(index='gender', columns='race', aggfunc='size')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Native American</th>\n",
       "      <th>White</th>\n",
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       "    <tr>\n",
       "      <th>gender</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "race    Asian  Black  Hispanic  Native American  White\n",
       "gender                                                \n",
       "Female    0.2    0.4      0.26              0.5   0.13\n",
       "Male      0.8    0.6      0.74              0.5   0.87"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=emp['gender'], columns=emp['race'], \n",
    "            normalize='columns').round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>race</th>\n",
       "      <th>Asian</th>\n",
       "      <th>Black</th>\n",
       "      <th>Hispanic</th>\n",
       "      <th>Native American</th>\n",
       "      <th>White</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>0.011</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0.043</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.320</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "race    Asian  Black  Hispanic  Native American  White\n",
       "gender                                                \n",
       "Female  0.011  0.132     0.062            0.002  0.048\n",
       "Male    0.043  0.200     0.180            0.002  0.320"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=emp['gender'], columns=emp['race'], \n",
    "            normalize='all').round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>fruit</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Apples</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Apples</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Peaches</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Peaches</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state    fruit  weight\n",
       "0    Texas  Oranges      10\n",
       "1  Florida  Oranges       8\n",
       "2    Texas   Apples       5\n",
       "3  Florida   Apples       8\n",
       "4    Texas  Peaches      20\n",
       "5  Florida  Peaches      14"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/state_fruit.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>fruit</th>\n",
       "      <th>Apples</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Peaches</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fruit    Apples  Oranges  Peaches\n",
       "state                            \n",
       "Florida       8        8       14\n",
       "Texas         5       10       20"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='state', columns='fruit', values='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fruit    Apples  Oranges  Peaches\n",
       "state                            \n",
       "Florida       8        8       14\n",
       "Texas         5       10       20"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot_table(index='state', columns='fruit', \n",
    "               values='weight', aggfunc='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Apples</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Apples</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Peaches</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Peaches</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state    fruit  weight\n",
       "0    Texas  Oranges      10\n",
       "1  Florida  Oranges       8\n",
       "2    Texas   Apples       5\n",
       "3  Florida   Apples       8\n",
       "4    Texas  Peaches      20\n",
       "5  Florida  Peaches      14\n",
       "6    Texas  Oranges       4\n",
       "7  Florida  Oranges       2"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('data/state_fruit2.csv')\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>fruit</th>\n",
       "      <th>Apples</th>\n",
       "      <th>Oranges</th>\n",
       "      <th>Peaches</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "fruit    Apples  Oranges  Peaches\n",
       "state                            \n",
       "Florida       8        8       14\n",
       "Texas         5       10       20"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.pivot(index='state', columns='fruit', values='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>fruit</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Oranges</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Apples</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Apples</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Peaches</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Peaches</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     state    fruit  weight\n",
       "0    Texas  Oranges      10\n",
       "1  Florida  Oranges       8\n",
       "2    Texas   Apples       5\n",
       "3  Florida   Apples       8\n",
       "4    Texas  Peaches      20\n",
       "5  Florida  Peaches      14"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = df.state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Ted/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "s[0] = 'werew'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      werew\n",
       "1    Florida\n",
       "2      Texas\n",
       "3    Florida\n",
       "4      Texas\n",
       "5    Florida\n",
       "Name: state, dtype: object"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
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  {
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   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "     state    fruit  weight\n",
       "0    werew  Oranges      10\n",
       "1  Florida  Oranges       8\n",
       "2    Texas   Apples       5\n",
       "3  Florida   Apples       8\n",
       "4    Texas  Peaches      20\n",
       "5  Florida  Peaches      14"
      ]
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     "execution_count": 137,
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.state is df['state']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = df[['fruit']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Ted/anaconda3/lib/python3.6/site-packages/pandas/core/indexing.py:190: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._setitem_with_indexer(indexer, value)\n",
      "/Users/Ted/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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   "source": [
    "d.iloc[0] = 'werwe'"
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  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     state    fruit  weight\n",
       "0    werew  Oranges      10\n",
       "1  Florida  Oranges       8\n",
       "2    Texas   Apples       5\n",
       "3  Florida   Apples       8\n",
       "4    Texas  Peaches      20\n",
       "5  Florida  Peaches      14"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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