05-Machine-Learning-Code/数据分析工具/Pandas/3_Set_reset_use_indexes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "people = {\n",
    "    \"first\": [\"Corey\", 'Jane', 'John'], \n",
    "    \"last\": [\"Schafer\", 'Doe', 'Doe'], \n",
    "    \"email\": [\"CoreyMSchafer@gmail.com\", 'JaneDoe@email.com', 'JohnDoe@email.com']\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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       "    }\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>first</th>\n",
       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@email.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John      Doe        JohnDoe@email.com"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    CoreyMSchafer@gmail.com\n",
       "1          JaneDoe@email.com\n",
       "2          JohnDoe@email.com\n",
       "Name: email, dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['email']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>first</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>JohnDoe@email.com</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         first     last\n",
       "email                                  \n",
       "CoreyMSchafer@gmail.com  Corey  Schafer\n",
       "JaneDoe@email.com         Jane      Doe\n",
       "JohnDoe@email.com         John      Doe"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index('email')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>first</th>\n",
       "      <th>last</th>\n",
       "      <th>email</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "      <td>JohnDoe@email.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first     last                    email\n",
       "0  Corey  Schafer  CoreyMSchafer@gmail.com\n",
       "1   Jane      Doe        JaneDoe@email.com\n",
       "2   John      Doe        JohnDoe@email.com"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df # actually do not change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('email',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>first</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>email</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>JohnDoe@email.com</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         first     last\n",
       "email                                  \n",
       "CoreyMSchafer@gmail.com  Corey  Schafer\n",
       "JaneDoe@email.com         Jane      Doe\n",
       "JohnDoe@email.com         John      Doe"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CoreyMSchafer@gmail.com', 'JaneDoe@email.com', 'JohnDoe@email.com'], dtype='object', name='email')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first      Corey\n",
       "last     Schafer\n",
       "Name: CoreyMSchafer@gmail.com, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['CoreyMSchafer@gmail.com']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Schafer'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['CoreyMSchafer@gmail.com','last']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-11-7eaf75073732>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mE:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1422\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1423\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1424\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1425\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1426\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1847\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1848\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1849\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1850\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1851\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_key\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1723\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1724\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_list_like_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1725\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1726\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1727\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mTuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m    272\u001b[0m         \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m         \u001b[1;31m# a scalar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 274\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_scalar_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_convert_slice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_convert_scalar_indexer\u001b[1;34m(self, key, kind)\u001b[0m\n\u001b[0;32m   3136\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mkind\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"loc\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3137\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3138\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"label\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3139\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3140\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_invalid_indexer\u001b[1;34m(self, form, key)\u001b[0m\n\u001b[0;32m   3338\u001b[0m             \u001b[1;34m\"cannot do {form} indexing on {klass} with these \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3339\u001b[0m             \"indexers [{key}] of {kind}\".format(\n\u001b[1;32m-> 3340\u001b[1;33m                 \u001b[0mform\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mform\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mklass\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3341\u001b[0m             )\n\u001b[0;32m   3342\u001b[0m         )\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot do label indexing on <class 'pandas.core.indexes.base.Index'> with these indexers [0] of <class 'int'>"
     ]
    }
   ],
   "source": [
    "df.loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "first      Corey\n",
       "last     Schafer\n",
       "Name: CoreyMSchafer@gmail.com, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>email</th>\n",
       "      <th>first</th>\n",
       "      <th>last</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>CoreyMSchafer@gmail.com</td>\n",
       "      <td>Corey</td>\n",
       "      <td>Schafer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>JaneDoe@email.com</td>\n",
       "      <td>Jane</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>JohnDoe@email.com</td>\n",
       "      <td>John</td>\n",
       "      <td>Doe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     email  first     last\n",
       "0  CoreyMSchafer@gmail.com  Corey  Schafer\n",
       "1        JaneDoe@email.com   Jane      Doe\n",
       "2        JohnDoe@email.com   John      Doe"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('survey_results_public.csv',index_col='Respondent')\n",
    "schema_df = pd.read_csv('survey_results_schema.csv',index_col='Column')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns',10)\n",
    "pd.set_option('display.max_rows',10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Hobby</th>\n",
       "      <th>OpenSource</th>\n",
       "      <th>Country</th>\n",
       "      <th>Student</th>\n",
       "      <th>Employment</th>\n",
       "      <th>...</th>\n",
       "      <th>Age</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>MilitaryUS</th>\n",
       "      <th>SurveyTooLong</th>\n",
       "      <th>SurveyEasy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Respondent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Kenya</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed part-time</td>\n",
       "      <td>...</td>\n",
       "      <td>25 - 34 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Very easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>United States</td>\n",
       "      <td>No</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>35 - 44 years old</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>Yes, part-time</td>\n",
       "      <td>Employed full-time</td>\n",
       "      <td>...</td>\n",
       "      <td>18 - 24 years old</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The survey was an appropriate length</td>\n",
       "      <td>Somewhat easy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 128 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Hobby OpenSource         Country         Student  \\\n",
       "Respondent                                                    \n",
       "1            Yes         No           Kenya              No   \n",
       "3            Yes        Yes  United Kingdom              No   \n",
       "4            Yes        Yes   United States              No   \n",
       "5             No         No   United States              No   \n",
       "7            Yes         No    South Africa  Yes, part-time   \n",
       "\n",
       "                    Employment  ...                Age Dependents MilitaryUS  \\\n",
       "Respondent                      ...                                            \n",
       "1           Employed part-time  ...  25 - 34 years old        Yes        NaN   \n",
       "3           Employed full-time  ...  35 - 44 years old        Yes        NaN   \n",
       "4           Employed full-time  ...                NaN        NaN        NaN   \n",
       "5           Employed full-time  ...  35 - 44 years old         No         No   \n",
       "7           Employed full-time  ...  18 - 24 years old        Yes        NaN   \n",
       "\n",
       "                                   SurveyTooLong     SurveyEasy  \n",
       "Respondent                                                       \n",
       "1           The survey was an appropriate length      Very easy  \n",
       "3           The survey was an appropriate length  Somewhat easy  \n",
       "4                                            NaN            NaN  \n",
       "5           The survey was an appropriate length  Somewhat easy  \n",
       "7           The survey was an appropriate length  Somewhat easy  \n",
       "\n",
       "[5 rows x 128 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hobby                                             Yes\n",
       "OpenSource                                         No\n",
       "Country                                         Kenya\n",
       "Student                                            No\n",
       "Employment                         Employed part-time\n",
       "                                 ...                 \n",
       "Age                                 25 - 34 years old\n",
       "Dependents                                        Yes\n",
       "MilitaryUS                                        NaN\n",
       "SurveyTooLong    The survey was an appropriate length\n",
       "SurveyEasy                                  Very easy\n",
       "Name: 1, Length: 128, dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\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></th>\n",
       "      <th>QuestionText</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Column</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>Respondent</td>\n",
       "      <td>Randomized respondent ID number (not in order ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Hobby</td>\n",
       "      <td>Do you code as a hobby?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>OpenSource</td>\n",
       "      <td>Do you contribute to open source projects?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Country</td>\n",
       "      <td>In which country do you currently reside?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Student</td>\n",
       "      <td>Are you currently enrolled in a formal, degree...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Age</td>\n",
       "      <td>What is your age? If you prefer not to answer,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>Dependents</td>\n",
       "      <td>Do you have any children or other dependents t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>MilitaryUS</td>\n",
       "      <td>Are you currently serving or have you ever ser...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SurveyTooLong</td>\n",
       "      <td>How do you feel about the length of the survey...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>SurveyEasy</td>\n",
       "      <td>How easy or difficult was this survey to compl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>129 rows × 1 columns</p>\n",
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      ],
      "text/plain": [
       "                                                    QuestionText\n",
       "Column                                                          \n",
       "Respondent     Randomized respondent ID number (not in order ...\n",
       "Hobby                                    Do you code as a hobby?\n",
       "OpenSource            Do you contribute to open source projects?\n",
       "Country                In which country do you currently reside?\n",
       "Student        Are you currently enrolled in a formal, degree...\n",
       "...                                                          ...\n",
       "Age            What is your age? If you prefer not to answer,...\n",
       "Dependents     Do you have any children or other dependents t...\n",
       "MilitaryUS     Are you currently serving or have you ever ser...\n",
       "SurveyTooLong  How do you feel about the length of the survey...\n",
       "SurveyEasy     How easy or difficult was this survey to compl...\n",
       "\n",
       "[129 rows x 1 columns]"
      ]
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     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
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    " schema_df"
   ]
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  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QuestionText    Do you code as a hobby?\n",
       "Name: Hobby, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "schema_df.loc['Hobby']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "QuestionText    Do you contribute to open source projects?\n",
       "Name: OpenSource, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "schema_df.loc['OpenSource']"
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  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Do you code as a hobby?'"
      ]
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     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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    "schema_df.loc['Hobby','QuestionText']"
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       "                                                      QuestionText\n",
       "Column                                                            \n",
       "AIDangerous      What do you think is the most dangerous aspect...\n",
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       "AIInteresting    What do you think is the most exciting aspect ...\n",
       "AIResponsible    Whose responsibility is it, <u>primarily</u>, ...\n",
       "AdBlocker        Do you have ad-blocking software installed on ...\n",
       "...                                                            ...\n",
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       "VersionControl   What version control systems do you use regula...\n",
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       "YearsCoding      Including any education, for how many years ha...\n",
       "YearsCodingProf  For how many years have you coded professional...\n",
       "\n",
       "[129 rows x 1 columns]"
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       "                                                      QuestionText\n",
       "Column                                                            \n",
       "YearsCodingProf  For how many years have you coded professional...\n",
       "YearsCoding      Including any education, for how many years ha...\n",
       "WakeTime         On days when you work, what time do you typica...\n",
       "VersionControl   What version control systems do you use regula...\n",
       "UpdateCV         Think back to the last time you updated your r...\n",
       "...                                                            ...\n",
       "AdBlocker        Do you have ad-blocking software installed on ...\n",
       "AIResponsible    Whose responsibility is it, <u>primarily</u>, ...\n",
       "AIInteresting    What do you think is the most exciting aspect ...\n",
       "AIFuture         Overall, what's your take on the future of art...\n",
       "AIDangerous      What do you think is the most dangerous aspect...\n",
       "\n",
       "[129 rows x 1 columns]"
      ]
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     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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    "schema_df.sort_index(ascending=False)"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "schema_df.sort_index(inplace=True)"
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  {
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   "execution_count": 39,
   "metadata": {},
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       "                                                      QuestionText\n",
       "Column                                                            \n",
       "AIDangerous      What do you think is the most dangerous aspect...\n",
       "AIFuture         Overall, what's your take on the future of art...\n",
       "AIInteresting    What do you think is the most exciting aspect ...\n",
       "AIResponsible    Whose responsibility is it, <u>primarily</u>, ...\n",
       "AdBlocker        Do you have ad-blocking software installed on ...\n",
       "...                                                            ...\n",
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       "VersionControl   What version control systems do you use regula...\n",
       "WakeTime         On days when you work, what time do you typica...\n",
       "YearsCoding      Including any education, for how many years ha...\n",
       "YearsCodingProf  For how many years have you coded professional...\n",
       "\n",
       "[129 rows x 1 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
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