intro_to_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24.0'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "import pandas as pd\n",
    "pd.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    San Francisco\n",
       "1         San Jose\n",
       "2       Sacramento\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(['San Francisco', 'San Jose', 'Sacramento'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>City name</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City name  Population\n",
       "0  San Francisco      852469\n",
       "1       San Jose     1015785\n",
       "2     Sacramento      485199"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_names = pd.Series(['San Francisco', 'San Jose', 'Sacramento'])\n",
    "population = pd.Series([852469, 1015785, 485199])\n",
    "\n",
    "pd.DataFrame({ 'City name':city_names, 'Population': population })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.562108</td>\n",
       "      <td>35.625225</td>\n",
       "      <td>28.589353</td>\n",
       "      <td>2643.664412</td>\n",
       "      <td>539.410824</td>\n",
       "      <td>1429.573941</td>\n",
       "      <td>501.221941</td>\n",
       "      <td>3.883578</td>\n",
       "      <td>207300.912353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.005166</td>\n",
       "      <td>2.137340</td>\n",
       "      <td>12.586937</td>\n",
       "      <td>2179.947071</td>\n",
       "      <td>421.499452</td>\n",
       "      <td>1147.852959</td>\n",
       "      <td>384.520841</td>\n",
       "      <td>1.908157</td>\n",
       "      <td>115983.764387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-124.350000</td>\n",
       "      <td>32.540000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.499900</td>\n",
       "      <td>14999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-121.790000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1462.000000</td>\n",
       "      <td>297.000000</td>\n",
       "      <td>790.000000</td>\n",
       "      <td>282.000000</td>\n",
       "      <td>2.566375</td>\n",
       "      <td>119400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.250000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>434.000000</td>\n",
       "      <td>1167.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.544600</td>\n",
       "      <td>180400.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.000000</td>\n",
       "      <td>37.720000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3151.250000</td>\n",
       "      <td>648.250000</td>\n",
       "      <td>1721.000000</td>\n",
       "      <td>605.250000</td>\n",
       "      <td>4.767000</td>\n",
       "      <td>265000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>-114.310000</td>\n",
       "      <td>41.950000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>37937.000000</td>\n",
       "      <td>6445.000000</td>\n",
       "      <td>35682.000000</td>\n",
       "      <td>6082.000000</td>\n",
       "      <td>15.000100</td>\n",
       "      <td>500001.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          longitude      latitude  housing_median_age   total_rooms  \\\n",
       "count  17000.000000  17000.000000        17000.000000  17000.000000   \n",
       "mean    -119.562108     35.625225           28.589353   2643.664412   \n",
       "std        2.005166      2.137340           12.586937   2179.947071   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.790000     33.930000           18.000000   1462.000000   \n",
       "50%     -118.490000     34.250000           29.000000   2127.000000   \n",
       "75%     -118.000000     37.720000           37.000000   3151.250000   \n",
       "max     -114.310000     41.950000           52.000000  37937.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    17000.000000  17000.000000  17000.000000   17000.000000   \n",
       "mean       539.410824   1429.573941    501.221941       3.883578   \n",
       "std        421.499452   1147.852959    384.520841       1.908157   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        297.000000    790.000000    282.000000       2.566375   \n",
       "50%        434.000000   1167.000000    409.000000       3.544600   \n",
       "75%        648.250000   1721.000000    605.250000       4.767000   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        17000.000000  \n",
       "mean        207300.912353  \n",
       "std         115983.764387  \n",
       "min          14999.000000  \n",
       "25%         119400.000000  \n",
       "50%         180400.000000  \n",
       "75%         265000.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing_dataframe = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
    "california_housing_dataframe.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>population</th>\n",
       "      <th>households</th>\n",
       "      <th>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-114.31</td>\n",
       "      <td>34.19</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5612.0</td>\n",
       "      <td>1283.0</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>1.4936</td>\n",
       "      <td>66900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-114.47</td>\n",
       "      <td>34.40</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7650.0</td>\n",
       "      <td>1901.0</td>\n",
       "      <td>1129.0</td>\n",
       "      <td>463.0</td>\n",
       "      <td>1.8200</td>\n",
       "      <td>80100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-114.56</td>\n",
       "      <td>33.69</td>\n",
       "      <td>17.0</td>\n",
       "      <td>720.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>333.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.6509</td>\n",
       "      <td>85700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-114.57</td>\n",
       "      <td>33.64</td>\n",
       "      <td>14.0</td>\n",
       "      <td>1501.0</td>\n",
       "      <td>337.0</td>\n",
       "      <td>515.0</td>\n",
       "      <td>226.0</td>\n",
       "      <td>3.1917</td>\n",
       "      <td>73400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-114.57</td>\n",
       "      <td>33.57</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1454.0</td>\n",
       "      <td>326.0</td>\n",
       "      <td>624.0</td>\n",
       "      <td>262.0</td>\n",
       "      <td>1.9250</td>\n",
       "      <td>65500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
       "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
       "2    -114.56     33.69                17.0        720.0           174.0   \n",
       "3    -114.57     33.64                14.0       1501.0           337.0   \n",
       "4    -114.57     33.57                20.0       1454.0           326.0   \n",
       "\n",
       "   population  households  median_income  median_house_value  \n",
       "0      1015.0       472.0         1.4936             66900.0  \n",
       "1      1129.0       463.0         1.8200             80100.0  \n",
       "2       333.0       117.0         1.6509             85700.0  \n",
       "3       515.0       226.0         3.1917             73400.0  \n",
       "4       624.0       262.0         1.9250             65500.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing_dataframe.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001C2DA512F98>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 13,
     "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": [
    "%matplotlib inline\n",
    "california_housing_dataframe.hist('housing_median_age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    San Francisco\n",
       "1         San Jose\n",
       "2       Sacramento\n",
       "Name: City name, dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities = pd.DataFrame({ 'City name': city_names, 'Population': population })\n",
    "print(type(cities['City name']))\n",
    "cities['City name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'str'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'San Jose'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(cities['City name'][1]))\n",
    "cities['City name'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "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>City name</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>852469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       City name  Population\n",
       "0  San Francisco      852469\n",
       "1       San Jose     1015785\n",
       "2     Sacramento      485199"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(cities[0:3]))\n",
    "cities[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     852.469\n",
       "1    1015.785\n",
       "2     485.199\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population / 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    13.655892\n",
       "1    13.831172\n",
       "2    13.092314\n",
       "dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.log(population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "population.apply(lambda val: val > 1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>San Francisco</td>\n",
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       "      <td>46.87</td>\n",
       "      <td>18187.945381</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785</td>\n",
       "      <td>176.53</td>\n",
       "      <td>5754.177760</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sacramento</td>\n",
       "      <td>485199</td>\n",
       "      <td>97.92</td>\n",
       "      <td>4955.055147</td>\n",
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      "text/plain": [
       "       City name  Population  Area square miles  Population density\n",
       "0  San Francisco      852469              46.87        18187.945381\n",
       "1       San Jose     1015785             176.53         5754.177760\n",
       "2     Sacramento      485199              97.92         4955.055147"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities['Area square miles'] = pd.Series([46.87, 176.53, 97.92])\n",
    "cities['Population density'] = cities['Population'] / cities['Area square miles']\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Sacramento</td>\n",
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      "text/plain": [
       "       City name  Population  Area square miles  Population density  \\\n",
       "0  San Francisco      852469              46.87        18187.945381   \n",
       "1       San Jose     1015785             176.53         5754.177760   \n",
       "2     Sacramento      485199              97.92         4955.055147   \n",
       "\n",
       "   Is wide and has saint name  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                       False  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities['Is wide and has saint name'] = (cities['Area square miles'] > 50) & cities['City name'].apply(lambda name: name.startswith('San'))\n",
    "cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "city_names.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=3, step=1)"
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     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "cities.index"
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   "execution_count": 26,
   "metadata": {},
   "outputs": [
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       "       City name  Population  Area square miles  Population density  \\\n",
       "0  San Francisco      852469              46.87        18187.945381   \n",
       "1       San Jose     1015785             176.53         5754.177760   \n",
       "2     Sacramento      485199              97.92         4955.055147   \n",
       "\n",
       "   Is wide and has saint name  \n",
       "0                       False  \n",
       "1                        True  \n",
       "2                       False  "
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   "source": [
    "cities"
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  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "      <td>San Francisco</td>\n",
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      "text/plain": [
       "       City name  Population  Area square miles  Population density  \\\n",
       "2     Sacramento      485199              97.92         4955.055147   \n",
       "0  San Francisco      852469              46.87        18187.945381   \n",
       "1       San Jose     1015785             176.53         5754.177760   \n",
       "\n",
       "   Is wide and has saint name  \n",
       "2                       False  \n",
       "0                       False  \n",
       "1                        True  "
      ]
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     "execution_count": 28,
     "metadata": {},
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   "source": [
    "cities.reindex([2, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       City name  Population  Area square miles  Population density  \\\n",
       "2     Sacramento      485199              97.92         4955.055147   \n",
       "1       San Jose     1015785             176.53         5754.177760   \n",
       "0  San Francisco      852469              46.87        18187.945381   \n",
       "\n",
       "   Is wide and has saint name  \n",
       "2                       False  \n",
       "1                        True  \n",
       "0                       False  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.reindex(np.random.permutation(cities.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>San Jose</td>\n",
       "      <td>1015785.0</td>\n",
       "      <td>176.53</td>\n",
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       "      <td>True</td>\n",
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      ],
      "text/plain": [
       "       City name  Population  Area square miles  Population density  \\\n",
       "0  San Francisco    852469.0              46.87        18187.945381   \n",
       "4            NaN         NaN                NaN                 NaN   \n",
       "5            NaN         NaN                NaN                 NaN   \n",
       "2     Sacramento    485199.0              97.92         4955.055147   \n",
       "1       San Jose   1015785.0             176.53         5754.177760   \n",
       "\n",
       "  Is wide and has saint name  \n",
       "0                      False  \n",
       "4                        NaN  \n",
       "5                        NaN  \n",
       "2                      False  \n",
       "1                       True  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cities.reindex([0, 4, 5, 2, 1])"
   ]
  }
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