Pandas functions for EDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"C:/Users/bolla/OneDrive/Desktop/data science/practice files/panadas functions/housing_dataset.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Head and Tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>452600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>358500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>352100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>341300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>342200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880           129.0   \n",
       "1    -122.22     37.86                  21         7099          1106.0   \n",
       "2    -122.24     37.85                  52         1467           190.0   \n",
       "3    -122.25     37.85                  52         1274           235.0   \n",
       "4    -122.25     37.85                  52         1627           280.0   \n",
       "\n",
       "   population  households  median_income ocean_proximity  median_house_value  \n",
       "0         322         126         8.3252        NEAR BAY              452600  \n",
       "1        2401        1138         8.3014        NEAR BAY              358500  \n",
       "2         496         177         7.2574        NEAR BAY              352100  \n",
       "3         558         219         5.6431        NEAR BAY              341300  \n",
       "4         565         259         3.8462        NEAR BAY              342200  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25</td>\n",
       "      <td>1665</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845</td>\n",
       "      <td>330</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>78100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18</td>\n",
       "      <td>697</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356</td>\n",
       "      <td>114</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>77100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17</td>\n",
       "      <td>2254</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007</td>\n",
       "      <td>433</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>92300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18</td>\n",
       "      <td>1860</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741</td>\n",
       "      <td>349</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>84700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16</td>\n",
       "      <td>2785</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387</td>\n",
       "      <td>530</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>89400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "20635    -121.09     39.48                  25         1665           374.0   \n",
       "20636    -121.21     39.49                  18          697           150.0   \n",
       "20637    -121.22     39.43                  17         2254           485.0   \n",
       "20638    -121.32     39.43                  18         1860           409.0   \n",
       "20639    -121.24     39.37                  16         2785           616.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \\\n",
       "20635         845         330         1.5603          INLAND   \n",
       "20636         356         114         2.5568          INLAND   \n",
       "20637        1007         433         1.7000          INLAND   \n",
       "20638         741         349         1.8672          INLAND   \n",
       "20639        1387         530         2.3886          INLAND   \n",
       "\n",
       "       median_house_value  \n",
       "20635               78100  \n",
       "20636               77100  \n",
       "20637               92300  \n",
       "20638               84700  \n",
       "20639               89400  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Information "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20640 entries, 0 to 20639\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   longitude           20640 non-null  float64\n",
      " 1   latitude            20640 non-null  float64\n",
      " 2   housing_median_age  20640 non-null  int64  \n",
      " 3   total_rooms         20640 non-null  int64  \n",
      " 4   total_bedrooms      20433 non-null  float64\n",
      " 5   population          20640 non-null  int64  \n",
      " 6   households          20640 non-null  int64  \n",
      " 7   median_income       20640 non-null  float64\n",
      " 8   ocean_proximity     20640 non-null  object \n",
      " 9   median_house_value  20640 non-null  int64  \n",
      "dtypes: float64(4), int64(5), object(1)\n",
      "memory usage: 1.6+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20640, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             float64\n",
       "latitude              float64\n",
       "housing_median_age      int64\n",
       "total_rooms             int64\n",
       "total_bedrooms        float64\n",
       "population              int64\n",
       "households              int64\n",
       "median_income         float64\n",
       "ocean_proximity        object\n",
       "median_house_value      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "206400"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20433.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "      <td>20640.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-119.569704</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>206855.816909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.003532</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>115395.615874</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>-121.800000</td>\n",
       "      <td>33.930000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>1447.750000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>787.000000</td>\n",
       "      <td>280.000000</td>\n",
       "      <td>2.563400</td>\n",
       "      <td>119600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>-118.490000</td>\n",
       "      <td>34.260000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>2127.000000</td>\n",
       "      <td>435.000000</td>\n",
       "      <td>1166.000000</td>\n",
       "      <td>409.000000</td>\n",
       "      <td>3.534800</td>\n",
       "      <td>179700.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>-118.010000</td>\n",
       "      <td>37.710000</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>3148.000000</td>\n",
       "      <td>647.000000</td>\n",
       "      <td>1725.000000</td>\n",
       "      <td>605.000000</td>\n",
       "      <td>4.743250</td>\n",
       "      <td>264725.000000</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>39320.000000</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  20640.000000  20640.000000        20640.000000  20640.000000   \n",
       "mean    -119.569704     35.631861           28.639486   2635.763081   \n",
       "std        2.003532      2.135952           12.585558   2181.615252   \n",
       "min     -124.350000     32.540000            1.000000      2.000000   \n",
       "25%     -121.800000     33.930000           18.000000   1447.750000   \n",
       "50%     -118.490000     34.260000           29.000000   2127.000000   \n",
       "75%     -118.010000     37.710000           37.000000   3148.000000   \n",
       "max     -114.310000     41.950000           52.000000  39320.000000   \n",
       "\n",
       "       total_bedrooms    population    households  median_income  \\\n",
       "count    20433.000000  20640.000000  20640.000000   20640.000000   \n",
       "mean       537.870553   1425.476744    499.539680       3.870671   \n",
       "std        421.385070   1132.462122    382.329753       1.899822   \n",
       "min          1.000000      3.000000      1.000000       0.499900   \n",
       "25%        296.000000    787.000000    280.000000       2.563400   \n",
       "50%        435.000000   1166.000000    409.000000       3.534800   \n",
       "75%        647.000000   1725.000000    605.000000       4.743250   \n",
       "max       6445.000000  35682.000000   6082.000000      15.000100   \n",
       "\n",
       "       median_house_value  \n",
       "count        20640.000000  \n",
       "mean        206855.816909  \n",
       "std         115395.615874  \n",
       "min          14999.000000  \n",
       "25%         119600.000000  \n",
       "50%         179700.000000  \n",
       "75%         264725.000000  \n",
       "max         500001.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>longitude</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>-119.569704</td>\n",
       "      <td>2.003532</td>\n",
       "      <td>-124.3500</td>\n",
       "      <td>-121.8000</td>\n",
       "      <td>-118.4900</td>\n",
       "      <td>-118.01000</td>\n",
       "      <td>-114.3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>latitude</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>35.631861</td>\n",
       "      <td>2.135952</td>\n",
       "      <td>32.5400</td>\n",
       "      <td>33.9300</td>\n",
       "      <td>34.2600</td>\n",
       "      <td>37.71000</td>\n",
       "      <td>41.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>28.639486</td>\n",
       "      <td>12.585558</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>18.0000</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>37.00000</td>\n",
       "      <td>52.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_rooms</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>2635.763081</td>\n",
       "      <td>2181.615252</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1447.7500</td>\n",
       "      <td>2127.0000</td>\n",
       "      <td>3148.00000</td>\n",
       "      <td>39320.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_bedrooms</th>\n",
       "      <td>20433.0</td>\n",
       "      <td>537.870553</td>\n",
       "      <td>421.385070</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>296.0000</td>\n",
       "      <td>435.0000</td>\n",
       "      <td>647.00000</td>\n",
       "      <td>6445.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>1425.476744</td>\n",
       "      <td>1132.462122</td>\n",
       "      <td>3.0000</td>\n",
       "      <td>787.0000</td>\n",
       "      <td>1166.0000</td>\n",
       "      <td>1725.00000</td>\n",
       "      <td>35682.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>households</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>499.539680</td>\n",
       "      <td>382.329753</td>\n",
       "      <td>1.0000</td>\n",
       "      <td>280.0000</td>\n",
       "      <td>409.0000</td>\n",
       "      <td>605.00000</td>\n",
       "      <td>6082.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_income</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>3.870671</td>\n",
       "      <td>1.899822</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>2.5634</td>\n",
       "      <td>3.5348</td>\n",
       "      <td>4.74325</td>\n",
       "      <td>15.0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <td>20640.0</td>\n",
       "      <td>206855.816909</td>\n",
       "      <td>115395.615874</td>\n",
       "      <td>14999.0000</td>\n",
       "      <td>119600.0000</td>\n",
       "      <td>179700.0000</td>\n",
       "      <td>264725.00000</td>\n",
       "      <td>500001.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      count           mean            std         min  \\\n",
       "longitude           20640.0    -119.569704       2.003532   -124.3500   \n",
       "latitude            20640.0      35.631861       2.135952     32.5400   \n",
       "housing_median_age  20640.0      28.639486      12.585558      1.0000   \n",
       "total_rooms         20640.0    2635.763081    2181.615252      2.0000   \n",
       "total_bedrooms      20433.0     537.870553     421.385070      1.0000   \n",
       "population          20640.0    1425.476744    1132.462122      3.0000   \n",
       "households          20640.0     499.539680     382.329753      1.0000   \n",
       "median_income       20640.0       3.870671       1.899822      0.4999   \n",
       "median_house_value  20640.0  206855.816909  115395.615874  14999.0000   \n",
       "\n",
       "                            25%          50%           75%          max  \n",
       "longitude             -121.8000    -118.4900    -118.01000    -114.3100  \n",
       "latitude                33.9300      34.2600      37.71000      41.9500  \n",
       "housing_median_age      18.0000      29.0000      37.00000      52.0000  \n",
       "total_rooms           1447.7500    2127.0000    3148.00000   39320.0000  \n",
       "total_bedrooms         296.0000     435.0000     647.00000    6445.0000  \n",
       "population             787.0000    1166.0000    1725.00000   35682.0000  \n",
       "households             280.0000     409.0000     605.00000    6082.0000  \n",
       "median_income            2.5634       3.5348       4.74325      15.0001  \n",
       "median_house_value  119600.0000  179700.0000  264725.00000  500001.0000  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing values in the dataset "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0          False     False               False        False           False   \n",
       "1          False     False               False        False           False   \n",
       "2          False     False               False        False           False   \n",
       "3          False     False               False        False           False   \n",
       "4          False     False               False        False           False   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20635      False     False               False        False           False   \n",
       "20636      False     False               False        False           False   \n",
       "20637      False     False               False        False           False   \n",
       "20638      False     False               False        False           False   \n",
       "20639      False     False               False        False           False   \n",
       "\n",
       "       population  households  median_income  ocean_proximity  \\\n",
       "0           False       False          False            False   \n",
       "1           False       False          False            False   \n",
       "2           False       False          False            False   \n",
       "3           False       False          False            False   \n",
       "4           False       False          False            False   \n",
       "...           ...         ...            ...              ...   \n",
       "20635       False       False          False            False   \n",
       "20636       False       False          False            False   \n",
       "20637       False       False          False            False   \n",
       "20638       False       False          False            False   \n",
       "20639       False       False          False            False   \n",
       "\n",
       "       median_house_value  \n",
       "0                   False  \n",
       "1                   False  \n",
       "2                   False  \n",
       "3                   False  \n",
       "4                   False  \n",
       "...                   ...  \n",
       "20635               False  \n",
       "20636               False  \n",
       "20637               False  \n",
       "20638               False  \n",
       "20639               False  \n",
       "\n",
       "[20640 rows x 10 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude             False\n",
       "latitude              False\n",
       "housing_median_age    False\n",
       "total_rooms           False\n",
       "total_bedrooms         True\n",
       "population            False\n",
       "households            False\n",
       "median_income         False\n",
       "ocean_proximity       False\n",
       "median_house_value    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               0\n",
       "latitude                0\n",
       "housing_median_age      0\n",
       "total_rooms             0\n",
       "total_bedrooms        207\n",
       "population              0\n",
       "households              0\n",
       "median_income           0\n",
       "ocean_proximity         0\n",
       "median_house_value      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loc and iloc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loc  is used to select by labels \n",
    "# The Label of the column or column names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>median_income</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.0368</td>\n",
       "      <td>269700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   median_income  median_house_value\n",
       "0         8.3252              452600\n",
       "1         8.3014              358500\n",
       "2         7.2574              352100\n",
       "3         5.6431              341300\n",
       "4         3.8462              342200\n",
       "5         4.0368              269700"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[:5,['median_income','median_house_value']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  iloc is used to select by position \n",
    "# it used for selecting rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
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       "      <td>21</td>\n",
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       "      <td>2401</td>\n",
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       "      <th>2</th>\n",
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       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880           129.0   \n",
       "1    -122.22     37.86                  21         7099          1106.0   \n",
       "2    -122.24     37.85                  52         1467           190.0   \n",
       "3    -122.25     37.85                  52         1274           235.0   \n",
       "\n",
       "   population  \n",
       "0         322  \n",
       "1        2401  \n",
       "2         496  \n",
       "3         558  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[:4,:6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>452600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>358500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>352100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>341300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880           129.0   \n",
       "1    -122.22     37.86                  21         7099          1106.0   \n",
       "2    -122.24     37.85                  52         1467           190.0   \n",
       "3    -122.25     37.85                  52         1274           235.0   \n",
       "\n",
       "   population  households  median_income ocean_proximity  median_house_value  \n",
       "0         322         126         8.3252        NEAR BAY              452600  \n",
       "1        2401        1138         8.3014        NEAR BAY              358500  \n",
       "2         496         177         7.2574        NEAR BAY              352100  \n",
       "3         558         219         5.6431        NEAR BAY              341300  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nunique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nunique  counts the number of unique entries over the column (or) rows \n",
    "# it is very useful in caterogical features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "longitude               844\n",
       "latitude                862\n",
       "housing_median_age       52\n",
       "total_rooms            5926\n",
       "total_bedrooms         1923\n",
       "population             3888\n",
       "households             1815\n",
       "median_income         12928\n",
       "ocean_proximity           5\n",
       "median_house_value     3842\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Memory usage "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index                    128\n",
       "longitude             165120\n",
       "latitude              165120\n",
       "housing_median_age    165120\n",
       "total_rooms           165120\n",
       "total_bedrooms        165120\n",
       "population            165120\n",
       "households            165120\n",
       "median_income         165120\n",
       "ocean_proximity       165120\n",
       "median_house_value    165120\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.memory_usage()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample "
   ]
  },
  {
   "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",
       "    }\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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5210</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.92</td>\n",
       "      <td>39</td>\n",
       "      <td>1274</td>\n",
       "      <td>282.0</td>\n",
       "      <td>975</td>\n",
       "      <td>277</td>\n",
       "      <td>1.5114</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>90400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14803</th>\n",
       "      <td>-117.13</td>\n",
       "      <td>32.63</td>\n",
       "      <td>10</td>\n",
       "      <td>7374</td>\n",
       "      <td>1157.0</td>\n",
       "      <td>1900</td>\n",
       "      <td>794</td>\n",
       "      <td>8.7991</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>478500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19868</th>\n",
       "      <td>-119.34</td>\n",
       "      <td>36.33</td>\n",
       "      <td>17</td>\n",
       "      <td>2250</td>\n",
       "      <td>430.0</td>\n",
       "      <td>1218</td>\n",
       "      <td>468</td>\n",
       "      <td>4.1812</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>93700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19678</th>\n",
       "      <td>-121.62</td>\n",
       "      <td>39.16</td>\n",
       "      <td>16</td>\n",
       "      <td>2037</td>\n",
       "      <td>464.0</td>\n",
       "      <td>1267</td>\n",
       "      <td>451</td>\n",
       "      <td>2.4556</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>97100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>-118.23</td>\n",
       "      <td>33.89</td>\n",
       "      <td>35</td>\n",
       "      <td>1255</td>\n",
       "      <td>344.0</td>\n",
       "      <td>1782</td>\n",
       "      <td>343</td>\n",
       "      <td>2.1949</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>95100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18302</th>\n",
       "      <td>-122.14</td>\n",
       "      <td>37.42</td>\n",
       "      <td>46</td>\n",
       "      <td>206</td>\n",
       "      <td>44.0</td>\n",
       "      <td>134</td>\n",
       "      <td>51</td>\n",
       "      <td>4.1500</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>265000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3534</th>\n",
       "      <td>-118.55</td>\n",
       "      <td>34.26</td>\n",
       "      <td>21</td>\n",
       "      <td>4018</td>\n",
       "      <td>536.0</td>\n",
       "      <td>1508</td>\n",
       "      <td>529</td>\n",
       "      <td>8.2030</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>445400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6828</th>\n",
       "      <td>-118.11</td>\n",
       "      <td>34.08</td>\n",
       "      <td>30</td>\n",
       "      <td>2350</td>\n",
       "      <td>472.0</td>\n",
       "      <td>945</td>\n",
       "      <td>467</td>\n",
       "      <td>3.3421</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>201000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "5210     -118.28     33.92                  39         1274           282.0   \n",
       "14803    -117.13     32.63                  10         7374          1157.0   \n",
       "19868    -119.34     36.33                  17         2250           430.0   \n",
       "19678    -121.62     39.16                  16         2037           464.0   \n",
       "7612     -118.23     33.89                  35         1255           344.0   \n",
       "18302    -122.14     37.42                  46          206            44.0   \n",
       "3534     -118.55     34.26                  21         4018           536.0   \n",
       "6828     -118.11     34.08                  30         2350           472.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \\\n",
       "5210          975         277         1.5114       <1H OCEAN   \n",
       "14803        1900         794         8.7991      NEAR OCEAN   \n",
       "19868        1218         468         4.1812          INLAND   \n",
       "19678        1267         451         2.4556          INLAND   \n",
       "7612         1782         343         2.1949       <1H OCEAN   \n",
       "18302         134          51         4.1500        NEAR BAY   \n",
       "3534         1508         529         8.2030       <1H OCEAN   \n",
       "6828          945         467         3.3421       <1H OCEAN   \n",
       "\n",
       "       median_house_value  \n",
       "5210                90400  \n",
       "14803              478500  \n",
       "19868               93700  \n",
       "19678               97100  \n",
       "7612                95100  \n",
       "18302              265000  \n",
       "3534               445400  \n",
       "6828               201000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sample(n=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby Function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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></th>\n",
       "      <th>total_rooms</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1</th>\n",
       "      <th>55000</th>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141700</th>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189200</th>\n",
       "      <td>2254.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191300</th>\n",
       "      <td>2062.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>47500</th>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">52</th>\n",
       "      <th>494400</th>\n",
       "      <td>1345.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494700</th>\n",
       "      <td>2425.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498400</th>\n",
       "      <td>3034.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>2058.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500001</th>\n",
       "      <td>2281.964706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17532 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       total_rooms\n",
       "housing_median_age median_house_value             \n",
       "1                  55000                  6.000000\n",
       "                   141700                83.000000\n",
       "                   189200              2254.000000\n",
       "                   191300              2062.000000\n",
       "2                  47500                 96.000000\n",
       "...                                            ...\n",
       "52                 494400              1345.000000\n",
       "                   494700              2425.000000\n",
       "                   498400              3034.000000\n",
       "                   500000              2058.300000\n",
       "                   500001              2281.964706\n",
       "\n",
       "[17532 rows x 1 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['housing_median_age','median_house_value','total_rooms']].groupby(['housing_median_age','median_house_value']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe thead tr th {\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">housing_median_age</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_house_value</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14999</th>\n",
       "      <td>30.750000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17500</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22500</th>\n",
       "      <td>36.250000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25000</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26600</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498800</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499000</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499100</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500000</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500001</th>\n",
       "      <td>33.802073</td>\n",
       "      <td>965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3842 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   housing_median_age      \n",
       "                                 mean count\n",
       "median_house_value                         \n",
       "14999                       30.750000     4\n",
       "17500                       39.000000     1\n",
       "22500                       36.250000     4\n",
       "25000                       21.000000     1\n",
       "26600                       34.000000     1\n",
       "...                               ...   ...\n",
       "498800                      29.000000     1\n",
       "499000                      18.000000     1\n",
       "499100                      28.000000     1\n",
       "500000                      38.000000    27\n",
       "500001                      33.802073   965\n",
       "\n",
       "[3842 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[['median_house_value','housing_median_age']].groupby('median_house_value').agg(['mean','count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=20640, step=1)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
       "       'total_bedrooms', 'population', 'households', 'median_income',\n",
       "       'ocean_proximity', 'median_house_value'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting\n",
    "# Dataframe by Sorting the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>households</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>median_house_value</th>\n",
       "      <th>median_income</th>\n",
       "      <th>ocean_proximity</th>\n",
       "      <th>population</th>\n",
       "      <th>total_bedrooms</th>\n",
       "      <th>total_rooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>126</td>\n",
       "      <td>41</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>452600</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>322</td>\n",
       "      <td>129.0</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1138</td>\n",
       "      <td>21</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>358500</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>2401</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>7099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>177</td>\n",
       "      <td>52</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>352100</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>496</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>219</td>\n",
       "      <td>52</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>341300</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>558</td>\n",
       "      <td>235.0</td>\n",
       "      <td>1274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>259</td>\n",
       "      <td>52</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>342200</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>565</td>\n",
       "      <td>280.0</td>\n",
       "      <td>1627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>330</td>\n",
       "      <td>25</td>\n",
       "      <td>39.48</td>\n",
       "      <td>-121.09</td>\n",
       "      <td>78100</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>845</td>\n",
       "      <td>374.0</td>\n",
       "      <td>1665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>114</td>\n",
       "      <td>18</td>\n",
       "      <td>39.49</td>\n",
       "      <td>-121.21</td>\n",
       "      <td>77100</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>356</td>\n",
       "      <td>150.0</td>\n",
       "      <td>697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>433</td>\n",
       "      <td>17</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.22</td>\n",
       "      <td>92300</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1007</td>\n",
       "      <td>485.0</td>\n",
       "      <td>2254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>349</td>\n",
       "      <td>18</td>\n",
       "      <td>39.43</td>\n",
       "      <td>-121.32</td>\n",
       "      <td>84700</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>741</td>\n",
       "      <td>409.0</td>\n",
       "      <td>1860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>530</td>\n",
       "      <td>16</td>\n",
       "      <td>39.37</td>\n",
       "      <td>-121.24</td>\n",
       "      <td>89400</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>1387</td>\n",
       "      <td>616.0</td>\n",
       "      <td>2785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       households  housing_median_age  latitude  longitude  \\\n",
       "0             126                  41     37.88    -122.23   \n",
       "1            1138                  21     37.86    -122.22   \n",
       "2             177                  52     37.85    -122.24   \n",
       "3             219                  52     37.85    -122.25   \n",
       "4             259                  52     37.85    -122.25   \n",
       "...           ...                 ...       ...        ...   \n",
       "20635         330                  25     39.48    -121.09   \n",
       "20636         114                  18     39.49    -121.21   \n",
       "20637         433                  17     39.43    -121.22   \n",
       "20638         349                  18     39.43    -121.32   \n",
       "20639         530                  16     39.37    -121.24   \n",
       "\n",
       "       median_house_value  median_income ocean_proximity  population  \\\n",
       "0                  452600         8.3252        NEAR BAY         322   \n",
       "1                  358500         8.3014        NEAR BAY        2401   \n",
       "2                  352100         7.2574        NEAR BAY         496   \n",
       "3                  341300         5.6431        NEAR BAY         558   \n",
       "4                  342200         3.8462        NEAR BAY         565   \n",
       "...                   ...            ...             ...         ...   \n",
       "20635               78100         1.5603          INLAND         845   \n",
       "20636               77100         2.5568          INLAND         356   \n",
       "20637               92300         1.7000          INLAND        1007   \n",
       "20638               84700         1.8672          INLAND         741   \n",
       "20639               89400         2.3886          INLAND        1387   \n",
       "\n",
       "       total_bedrooms  total_rooms  \n",
       "0               129.0          880  \n",
       "1              1106.0         7099  \n",
       "2               190.0         1467  \n",
       "3               235.0         1274  \n",
       "4               280.0         1627  \n",
       "...               ...          ...  \n",
       "20635           374.0         1665  \n",
       "20636           150.0          697  \n",
       "20637           485.0         2254  \n",
       "20638           409.0         1860  \n",
       "20639           616.0         2785  \n",
       "\n",
       "[20640 rows x 10 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_index(axis=1,ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sort by values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9188</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.24</td>\n",
       "      <td>52</td>\n",
       "      <td>803</td>\n",
       "      <td>267.0</td>\n",
       "      <td>628</td>\n",
       "      <td>225</td>\n",
       "      <td>4.1932</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19802</th>\n",
       "      <td>-123.17</td>\n",
       "      <td>40.31</td>\n",
       "      <td>36</td>\n",
       "      <td>98</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5360</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>-117.02</td>\n",
       "      <td>36.40</td>\n",
       "      <td>19</td>\n",
       "      <td>619</td>\n",
       "      <td>239.0</td>\n",
       "      <td>490</td>\n",
       "      <td>164</td>\n",
       "      <td>2.1000</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>-122.74</td>\n",
       "      <td>39.71</td>\n",
       "      <td>16</td>\n",
       "      <td>255</td>\n",
       "      <td>73.0</td>\n",
       "      <td>85</td>\n",
       "      <td>38</td>\n",
       "      <td>1.6607</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>-118.33</td>\n",
       "      <td>34.15</td>\n",
       "      <td>39</td>\n",
       "      <td>493</td>\n",
       "      <td>168.0</td>\n",
       "      <td>259</td>\n",
       "      <td>138</td>\n",
       "      <td>2.3667</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>17500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16938</th>\n",
       "      <td>-122.33</td>\n",
       "      <td>37.56</td>\n",
       "      <td>50</td>\n",
       "      <td>1975</td>\n",
       "      <td>245.0</td>\n",
       "      <td>644</td>\n",
       "      <td>251</td>\n",
       "      <td>10.0743</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16936</th>\n",
       "      <td>-122.32</td>\n",
       "      <td>37.56</td>\n",
       "      <td>49</td>\n",
       "      <td>2016</td>\n",
       "      <td>299.0</td>\n",
       "      <td>691</td>\n",
       "      <td>288</td>\n",
       "      <td>5.5490</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16935</th>\n",
       "      <td>-122.33</td>\n",
       "      <td>37.56</td>\n",
       "      <td>34</td>\n",
       "      <td>6394</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>2400</td>\n",
       "      <td>1496</td>\n",
       "      <td>3.4902</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8291</th>\n",
       "      <td>-118.15</td>\n",
       "      <td>33.76</td>\n",
       "      <td>36</td>\n",
       "      <td>2916</td>\n",
       "      <td>785.0</td>\n",
       "      <td>1183</td>\n",
       "      <td>749</td>\n",
       "      <td>3.5985</td>\n",
       "      <td>NEAR OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18055</th>\n",
       "      <td>-121.99</td>\n",
       "      <td>37.25</td>\n",
       "      <td>22</td>\n",
       "      <td>4240</td>\n",
       "      <td>532.0</td>\n",
       "      <td>1480</td>\n",
       "      <td>514</td>\n",
       "      <td>11.2463</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "9188     -117.86     34.24                  52          803           267.0   \n",
       "19802    -123.17     40.31                  36           98            28.0   \n",
       "2799     -117.02     36.40                  19          619           239.0   \n",
       "2521     -122.74     39.71                  16          255            73.0   \n",
       "5887     -118.33     34.15                  39          493           168.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "16938    -122.33     37.56                  50         1975           245.0   \n",
       "16936    -122.32     37.56                  49         2016           299.0   \n",
       "16935    -122.33     37.56                  34         6394          1619.0   \n",
       "8291     -118.15     33.76                  36         2916           785.0   \n",
       "18055    -121.99     37.25                  22         4240           532.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \\\n",
       "9188          628         225         4.1932          INLAND   \n",
       "19802          18           8         0.5360          INLAND   \n",
       "2799          490         164         2.1000          INLAND   \n",
       "2521           85          38         1.6607          INLAND   \n",
       "5887          259         138         2.3667       <1H OCEAN   \n",
       "...           ...         ...            ...             ...   \n",
       "16938         644         251        10.0743      NEAR OCEAN   \n",
       "16936         691         288         5.5490      NEAR OCEAN   \n",
       "16935        2400        1496         3.4902      NEAR OCEAN   \n",
       "8291         1183         749         3.5985      NEAR OCEAN   \n",
       "18055        1480         514        11.2463       <1H OCEAN   \n",
       "\n",
       "       median_house_value  \n",
       "9188                14999  \n",
       "19802               14999  \n",
       "2799                14999  \n",
       "2521                14999  \n",
       "5887                17500  \n",
       "...                   ...  \n",
       "16938              500001  \n",
       "16936              500001  \n",
       "16935              500001  \n",
       "8291               500001  \n",
       "18055              500001  \n",
       "\n",
       "[20640 rows x 10 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sort_values(by='median_house_value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# nsmallest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2521</th>\n",
       "      <td>-122.74</td>\n",
       "      <td>39.71</td>\n",
       "      <td>16</td>\n",
       "      <td>255</td>\n",
       "      <td>73.0</td>\n",
       "      <td>85</td>\n",
       "      <td>38</td>\n",
       "      <td>1.6607</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2799</th>\n",
       "      <td>-117.02</td>\n",
       "      <td>36.40</td>\n",
       "      <td>19</td>\n",
       "      <td>619</td>\n",
       "      <td>239.0</td>\n",
       "      <td>490</td>\n",
       "      <td>164</td>\n",
       "      <td>2.1000</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9188</th>\n",
       "      <td>-117.86</td>\n",
       "      <td>34.24</td>\n",
       "      <td>52</td>\n",
       "      <td>803</td>\n",
       "      <td>267.0</td>\n",
       "      <td>628</td>\n",
       "      <td>225</td>\n",
       "      <td>4.1932</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19802</th>\n",
       "      <td>-123.17</td>\n",
       "      <td>40.31</td>\n",
       "      <td>36</td>\n",
       "      <td>98</td>\n",
       "      <td>28.0</td>\n",
       "      <td>18</td>\n",
       "      <td>8</td>\n",
       "      <td>0.5360</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>14999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5887</th>\n",
       "      <td>-118.33</td>\n",
       "      <td>34.15</td>\n",
       "      <td>39</td>\n",
       "      <td>493</td>\n",
       "      <td>168.0</td>\n",
       "      <td>259</td>\n",
       "      <td>138</td>\n",
       "      <td>2.3667</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>17500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "2521     -122.74     39.71                  16          255            73.0   \n",
       "2799     -117.02     36.40                  19          619           239.0   \n",
       "9188     -117.86     34.24                  52          803           267.0   \n",
       "19802    -123.17     40.31                  36           98            28.0   \n",
       "5887     -118.33     34.15                  39          493           168.0   \n",
       "\n",
       "       population  households  median_income ocean_proximity  \\\n",
       "2521           85          38         1.6607          INLAND   \n",
       "2799          490         164         2.1000          INLAND   \n",
       "9188          628         225         4.1932          INLAND   \n",
       "19802          18           8         0.5360          INLAND   \n",
       "5887          259         138         2.3667       <1H OCEAN   \n",
       "\n",
       "       median_house_value  \n",
       "2521                14999  \n",
       "2799                14999  \n",
       "9188                14999  \n",
       "19802               14999  \n",
       "5887                17500  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nsmallest(5,'median_house_value')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>-122.29</td>\n",
       "      <td>37.81</td>\n",
       "      <td>46</td>\n",
       "      <td>12</td>\n",
       "      <td>4.0</td>\n",
       "      <td>18</td>\n",
       "      <td>7</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>67500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3249</th>\n",
       "      <td>-119.99</td>\n",
       "      <td>36.09</td>\n",
       "      <td>23</td>\n",
       "      <td>333</td>\n",
       "      <td>92.0</td>\n",
       "      <td>198</td>\n",
       "      <td>55</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>-122.89</td>\n",
       "      <td>39.42</td>\n",
       "      <td>16</td>\n",
       "      <td>411</td>\n",
       "      <td>114.0</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>INLAND</td>\n",
       "      <td>73500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4861</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>34.02</td>\n",
       "      <td>29</td>\n",
       "      <td>515</td>\n",
       "      <td>229.0</td>\n",
       "      <td>2690</td>\n",
       "      <td>217</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5213</th>\n",
       "      <td>-118.28</td>\n",
       "      <td>33.93</td>\n",
       "      <td>52</td>\n",
       "      <td>117</td>\n",
       "      <td>33.0</td>\n",
       "      <td>74</td>\n",
       "      <td>45</td>\n",
       "      <td>0.4999</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>90600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "73      -122.29     37.81                  46           12             4.0   \n",
       "3249    -119.99     36.09                  23          333            92.0   \n",
       "3258    -122.89     39.42                  16          411           114.0   \n",
       "4861    -118.28     34.02                  29          515           229.0   \n",
       "5213    -118.28     33.93                  52          117            33.0   \n",
       "\n",
       "      population  households  median_income ocean_proximity  \\\n",
       "73            18           7         0.4999        NEAR BAY   \n",
       "3249         198          55         0.4999          INLAND   \n",
       "3258          26          19         0.4999          INLAND   \n",
       "4861        2690         217         0.4999       <1H OCEAN   \n",
       "5213          74          45         0.4999       <1H OCEAN   \n",
       "\n",
       "      median_house_value  \n",
       "73                 67500  \n",
       "3249              100000  \n",
       "3258               73500  \n",
       "4861              500001  \n",
       "5213               90600  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nsmallest(5,'median_income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# nlargest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>-121.96</td>\n",
       "      <td>37.74</td>\n",
       "      <td>2</td>\n",
       "      <td>200</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4352</th>\n",
       "      <td>-118.37</td>\n",
       "      <td>34.10</td>\n",
       "      <td>37</td>\n",
       "      <td>407</td>\n",
       "      <td>67.0</td>\n",
       "      <td>100</td>\n",
       "      <td>47</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4604</th>\n",
       "      <td>-118.33</td>\n",
       "      <td>34.06</td>\n",
       "      <td>52</td>\n",
       "      <td>1841</td>\n",
       "      <td>240.0</td>\n",
       "      <td>693</td>\n",
       "      <td>218</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4605</th>\n",
       "      <td>-118.33</td>\n",
       "      <td>34.07</td>\n",
       "      <td>52</td>\n",
       "      <td>2248</td>\n",
       "      <td>255.0</td>\n",
       "      <td>813</td>\n",
       "      <td>265</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4606</th>\n",
       "      <td>-118.33</td>\n",
       "      <td>34.07</td>\n",
       "      <td>52</td>\n",
       "      <td>1482</td>\n",
       "      <td>171.0</td>\n",
       "      <td>531</td>\n",
       "      <td>161</td>\n",
       "      <td>15.0001</td>\n",
       "      <td>&lt;1H OCEAN</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "1566    -121.96     37.74                   2          200            20.0   \n",
       "4352    -118.37     34.10                  37          407            67.0   \n",
       "4604    -118.33     34.06                  52         1841           240.0   \n",
       "4605    -118.33     34.07                  52         2248           255.0   \n",
       "4606    -118.33     34.07                  52         1482           171.0   \n",
       "\n",
       "      population  households  median_income ocean_proximity  \\\n",
       "1566          25           9        15.0001       <1H OCEAN   \n",
       "4352         100          47        15.0001       <1H OCEAN   \n",
       "4604         693         218        15.0001       <1H OCEAN   \n",
       "4605         813         265        15.0001       <1H OCEAN   \n",
       "4606         531         161        15.0001       <1H OCEAN   \n",
       "\n",
       "      median_house_value  \n",
       "1566              350000  \n",
       "4352              500001  \n",
       "4604              500001  \n",
       "4605              500001  \n",
       "4606              500001  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nlargest(5,'median_income')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>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>ocean_proximity</th>\n",
       "      <th>median_house_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.80</td>\n",
       "      <td>52</td>\n",
       "      <td>249</td>\n",
       "      <td>78.0</td>\n",
       "      <td>396</td>\n",
       "      <td>85</td>\n",
       "      <td>1.2434</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.87</td>\n",
       "      <td>52</td>\n",
       "      <td>609</td>\n",
       "      <td>236.0</td>\n",
       "      <td>1349</td>\n",
       "      <td>250</td>\n",
       "      <td>1.1696</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.86</td>\n",
       "      <td>52</td>\n",
       "      <td>1668</td>\n",
       "      <td>225.0</td>\n",
       "      <td>517</td>\n",
       "      <td>214</td>\n",
       "      <td>7.8521</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>3726</td>\n",
       "      <td>474.0</td>\n",
       "      <td>1366</td>\n",
       "      <td>496</td>\n",
       "      <td>9.3959</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.83</td>\n",
       "      <td>52</td>\n",
       "      <td>2990</td>\n",
       "      <td>379.0</td>\n",
       "      <td>947</td>\n",
       "      <td>361</td>\n",
       "      <td>7.8772</td>\n",
       "      <td>NEAR BAY</td>\n",
       "      <td>500001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "89     -122.27     37.80                  52          249            78.0   \n",
       "459    -122.25     37.87                  52          609           236.0   \n",
       "493    -122.24     37.86                  52         1668           225.0   \n",
       "494    -122.24     37.85                  52         3726           474.0   \n",
       "509    -122.23     37.83                  52         2990           379.0   \n",
       "\n",
       "     population  households  median_income ocean_proximity  median_house_value  \n",
       "89          396          85         1.2434        NEAR BAY              500001  \n",
       "459        1349         250         1.1696        NEAR BAY              500001  \n",
       "493         517         214         7.8521        NEAR BAY              500001  \n",
       "494        1366         496         9.3959        NEAR BAY              500001  \n",
       "509         947         361         7.8772        NEAR BAY              500001  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.nlargest(5,'median_house_value')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# drop_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = ['ocean_proximity']\n",
    "data.drop(drop_cols,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>452600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>358500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>352100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>341300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>342200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20635</th>\n",
       "      <td>-121.09</td>\n",
       "      <td>39.48</td>\n",
       "      <td>25</td>\n",
       "      <td>1665</td>\n",
       "      <td>374.0</td>\n",
       "      <td>845</td>\n",
       "      <td>330</td>\n",
       "      <td>1.5603</td>\n",
       "      <td>78100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20636</th>\n",
       "      <td>-121.21</td>\n",
       "      <td>39.49</td>\n",
       "      <td>18</td>\n",
       "      <td>697</td>\n",
       "      <td>150.0</td>\n",
       "      <td>356</td>\n",
       "      <td>114</td>\n",
       "      <td>2.5568</td>\n",
       "      <td>77100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20637</th>\n",
       "      <td>-121.22</td>\n",
       "      <td>39.43</td>\n",
       "      <td>17</td>\n",
       "      <td>2254</td>\n",
       "      <td>485.0</td>\n",
       "      <td>1007</td>\n",
       "      <td>433</td>\n",
       "      <td>1.7000</td>\n",
       "      <td>92300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20638</th>\n",
       "      <td>-121.32</td>\n",
       "      <td>39.43</td>\n",
       "      <td>18</td>\n",
       "      <td>1860</td>\n",
       "      <td>409.0</td>\n",
       "      <td>741</td>\n",
       "      <td>349</td>\n",
       "      <td>1.8672</td>\n",
       "      <td>84700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20639</th>\n",
       "      <td>-121.24</td>\n",
       "      <td>39.37</td>\n",
       "      <td>16</td>\n",
       "      <td>2785</td>\n",
       "      <td>616.0</td>\n",
       "      <td>1387</td>\n",
       "      <td>530</td>\n",
       "      <td>2.3886</td>\n",
       "      <td>89400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20640 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0        -122.23     37.88                  41          880           129.0   \n",
       "1        -122.22     37.86                  21         7099          1106.0   \n",
       "2        -122.24     37.85                  52         1467           190.0   \n",
       "3        -122.25     37.85                  52         1274           235.0   \n",
       "4        -122.25     37.85                  52         1627           280.0   \n",
       "...          ...       ...                 ...          ...             ...   \n",
       "20635    -121.09     39.48                  25         1665           374.0   \n",
       "20636    -121.21     39.49                  18          697           150.0   \n",
       "20637    -121.22     39.43                  17         2254           485.0   \n",
       "20638    -121.32     39.43                  18         1860           409.0   \n",
       "20639    -121.24     39.37                  16         2785           616.0   \n",
       "\n",
       "       population  households  median_income  median_house_value  \n",
       "0             322         126         8.3252              452600  \n",
       "1            2401        1138         8.3014              358500  \n",
       "2             496         177         7.2574              352100  \n",
       "3             558         219         5.6431              341300  \n",
       "4             565         259         3.8462              342200  \n",
       "...           ...         ...            ...                 ...  \n",
       "20635         845         330         1.5603               78100  \n",
       "20636         356         114         2.5568               77100  \n",
       "20637        1007         433         1.7000               92300  \n",
       "20638         741         349         1.8672               84700  \n",
       "20639        1387         530         2.3886               89400  \n",
       "\n",
       "[20640 rows x 9 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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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       "</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>95</th>\n",
       "      <td>-122.26</td>\n",
       "      <td>37.80</td>\n",
       "      <td>36</td>\n",
       "      <td>5329</td>\n",
       "      <td>2477.0</td>\n",
       "      <td>3469</td>\n",
       "      <td>2323</td>\n",
       "      <td>2.0096</td>\n",
       "      <td>130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.83</td>\n",
       "      <td>28</td>\n",
       "      <td>5022</td>\n",
       "      <td>1750.0</td>\n",
       "      <td>2558</td>\n",
       "      <td>1661</td>\n",
       "      <td>2.4234</td>\n",
       "      <td>218500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.85</td>\n",
       "      <td>28</td>\n",
       "      <td>5287</td>\n",
       "      <td>1048.0</td>\n",
       "      <td>2031</td>\n",
       "      <td>956</td>\n",
       "      <td>5.4570</td>\n",
       "      <td>337300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>-122.21</td>\n",
       "      <td>37.83</td>\n",
       "      <td>34</td>\n",
       "      <td>5065</td>\n",
       "      <td>788.0</td>\n",
       "      <td>1627</td>\n",
       "      <td>766</td>\n",
       "      <td>6.8976</td>\n",
       "      <td>333300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>-122.30</td>\n",
       "      <td>37.90</td>\n",
       "      <td>15</td>\n",
       "      <td>5083</td>\n",
       "      <td>1212.0</td>\n",
       "      <td>2420</td>\n",
       "      <td>1146</td>\n",
       "      <td>4.5824</td>\n",
       "      <td>256100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "95     -122.26     37.80                  36         5329          2477.0   \n",
       "116    -122.25     37.83                  28         5022          1750.0   \n",
       "125    -122.22     37.85                  28         5287          1048.0   \n",
       "133    -122.21     37.83                  34         5065           788.0   \n",
       "391    -122.30     37.90                  15         5083          1212.0   \n",
       "\n",
       "     population  households  median_income  median_house_value  \n",
       "95         3469        2323         2.0096              130000  \n",
       "116        2558        1661         2.4234              218500  \n",
       "125        2031         956         5.4570              337300  \n",
       "133        1627         766         6.8976              333300  \n",
       "391        2420        1146         4.5824              256100  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('5000 < total_rooms < 5500')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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>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>23</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.84</td>\n",
       "      <td>52</td>\n",
       "      <td>1688</td>\n",
       "      <td>337.0</td>\n",
       "      <td>853</td>\n",
       "      <td>325</td>\n",
       "      <td>2.1806</td>\n",
       "      <td>99700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.85</td>\n",
       "      <td>49</td>\n",
       "      <td>1130</td>\n",
       "      <td>244.0</td>\n",
       "      <td>607</td>\n",
       "      <td>239</td>\n",
       "      <td>2.4597</td>\n",
       "      <td>93800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.83</td>\n",
       "      <td>49</td>\n",
       "      <td>1215</td>\n",
       "      <td>282.0</td>\n",
       "      <td>570</td>\n",
       "      <td>264</td>\n",
       "      <td>1.4861</td>\n",
       "      <td>97200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.82</td>\n",
       "      <td>43</td>\n",
       "      <td>1868</td>\n",
       "      <td>456.0</td>\n",
       "      <td>1061</td>\n",
       "      <td>407</td>\n",
       "      <td>1.5045</td>\n",
       "      <td>93800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>-122.27</td>\n",
       "      <td>37.82</td>\n",
       "      <td>41</td>\n",
       "      <td>3221</td>\n",
       "      <td>853.0</td>\n",
       "      <td>1959</td>\n",
       "      <td>720</td>\n",
       "      <td>1.1108</td>\n",
       "      <td>97500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.82</td>\n",
       "      <td>52</td>\n",
       "      <td>1170</td>\n",
       "      <td>235.0</td>\n",
       "      <td>701</td>\n",
       "      <td>233</td>\n",
       "      <td>1.6098</td>\n",
       "      <td>87500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.82</td>\n",
       "      <td>52</td>\n",
       "      <td>945</td>\n",
       "      <td>243.0</td>\n",
       "      <td>576</td>\n",
       "      <td>220</td>\n",
       "      <td>1.4113</td>\n",
       "      <td>83100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>-122.28</td>\n",
       "      <td>37.82</td>\n",
       "      <td>52</td>\n",
       "      <td>1238</td>\n",
       "      <td>288.0</td>\n",
       "      <td>622</td>\n",
       "      <td>259</td>\n",
       "      <td>1.5057</td>\n",
       "      <td>87500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "23    -122.27     37.84                  52         1688           337.0   \n",
       "26    -122.28     37.85                  49         1130           244.0   \n",
       "35    -122.27     37.83                  49         1215           282.0   \n",
       "51    -122.27     37.82                  43         1868           456.0   \n",
       "52    -122.27     37.82                  41         3221           853.0   \n",
       "54    -122.28     37.82                  52         1170           235.0   \n",
       "55    -122.28     37.82                  52          945           243.0   \n",
       "56    -122.28     37.82                  52         1238           288.0   \n",
       "\n",
       "    population  households  median_income  median_house_value  \n",
       "23         853         325         2.1806               99700  \n",
       "26         607         239         2.4597               93800  \n",
       "35         570         264         1.4861               97200  \n",
       "51        1061         407         1.5045               93800  \n",
       "52        1959         720         1.1108               97500  \n",
       "54         701         233         1.6098               87500  \n",
       "55         576         220         1.4113               83100  \n",
       "56         622         259         1.5057               87500  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.query('40000 < median_house_value < 100000')[:8]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Insert column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "#new column\n",
    "new_col=np.random.randn(20640)\n",
    "#insert the new column at position 8\n",
    "data.insert (8,'new_col',new_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>housing_median_age</th>\n",
       "      <th>total_rooms</th>\n",
       "      <th>total_bedrooms</th>\n",
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       "      <th>median_house_value</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-122.23</td>\n",
       "      <td>37.88</td>\n",
       "      <td>41</td>\n",
       "      <td>880</td>\n",
       "      <td>129.0</td>\n",
       "      <td>322</td>\n",
       "      <td>126</td>\n",
       "      <td>8.3252</td>\n",
       "      <td>-0.736363</td>\n",
       "      <td>452600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-122.22</td>\n",
       "      <td>37.86</td>\n",
       "      <td>21</td>\n",
       "      <td>7099</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>2401</td>\n",
       "      <td>1138</td>\n",
       "      <td>8.3014</td>\n",
       "      <td>-0.887190</td>\n",
       "      <td>358500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-122.24</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1467</td>\n",
       "      <td>190.0</td>\n",
       "      <td>496</td>\n",
       "      <td>177</td>\n",
       "      <td>7.2574</td>\n",
       "      <td>0.487336</td>\n",
       "      <td>352100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1274</td>\n",
       "      <td>235.0</td>\n",
       "      <td>558</td>\n",
       "      <td>219</td>\n",
       "      <td>5.6431</td>\n",
       "      <td>-0.502587</td>\n",
       "      <td>341300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-122.25</td>\n",
       "      <td>37.85</td>\n",
       "      <td>52</td>\n",
       "      <td>1627</td>\n",
       "      <td>280.0</td>\n",
       "      <td>565</td>\n",
       "      <td>259</td>\n",
       "      <td>3.8462</td>\n",
       "      <td>0.102627</td>\n",
       "      <td>342200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
       "0    -122.23     37.88                  41          880           129.0   \n",
       "1    -122.22     37.86                  21         7099          1106.0   \n",
       "2    -122.24     37.85                  52         1467           190.0   \n",
       "3    -122.25     37.85                  52         1274           235.0   \n",
       "4    -122.25     37.85                  52         1627           280.0   \n",
       "\n",
       "   population  households  median_income   new_col  median_house_value  \n",
       "0         322         126         8.3252 -0.736363              452600  \n",
       "1        2401        1138         8.3014 -0.887190              358500  \n",
       "2         496         177         7.2574  0.487336              352100  \n",
       "3         558         219         5.6431 -0.502587              341300  \n",
       "4         565         259         3.8462  0.102627              342200  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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