Python_Pandas_Assignment.ipynb

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    {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/prasunpankaj/100-plus-Python-programming-exercises-extended/blob/master/Python_Pandas_Assignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      },
      "source": [
        "#Library for Data Science\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "df = pd.read_csv(\"/content/sample_data/california_housing_test.csv\")\n",
        "df"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.05</td>\n",
              "      <td>37.37</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.67</td>\n",
              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</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>2995</th>\n",
              "      <td>-119.86</td>\n",
              "      <td>34.42</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>-118.14</td>\n",
              "      <td>34.06</td>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>-119.70</td>\n",
              "      <td>36.30</td>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>-117.12</td>\n",
              "      <td>34.10</td>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>-119.63</td>\n",
              "      <td>34.42</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.5608</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      longitude  latitude  ...  median_income  median_house_value\n",
              "0       -122.05     37.37  ...         6.6085            344700.0\n",
              "1       -118.30     34.26  ...         3.5990            176500.0\n",
              "2       -117.81     33.78  ...         5.7934            270500.0\n",
              "3       -118.36     33.82  ...         6.1359            330000.0\n",
              "4       -119.67     36.33  ...         2.9375             81700.0\n",
              "...         ...       ...  ...            ...                 ...\n",
              "2995    -119.86     34.42  ...         1.1790            225000.0\n",
              "2996    -118.14     34.06  ...         3.3906            237200.0\n",
              "2997    -119.70     36.30  ...         2.2895             62000.0\n",
              "2998    -117.12     34.10  ...         3.2708            162500.0\n",
              "2999    -119.63     34.42  ...         8.5608            500001.0\n",
              "\n",
              "[3000 rows x 9 columns]"
            ]
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            "tags": []
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    {
      "cell_type": "markdown",
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        "id": "E8SBf54usJ46"
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      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "advBKQG0sKYY"
      },
      "source": [
        "# New Section"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jmFBwg8SWHB2"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5DCvfElyWHdg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1cc8102-df3a-4dc5-c7f4-839ca6625696"
      },
      "source": [
        "df.head(10)\n",
        "df.head(20)\n",
        "df.head(50)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.05</td>\n",
              "      <td>37.37</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.67</td>\n",
              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-119.56</td>\n",
              "      <td>36.51</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>663.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1.6635</td>\n",
              "      <td>67000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-121.43</td>\n",
              "      <td>38.63</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>604.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>1.6641</td>\n",
              "      <td>67000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-120.65</td>\n",
              "      <td>35.48</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2310.0</td>\n",
              "      <td>471.0</td>\n",
              "      <td>1341.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>3.2250</td>\n",
              "      <td>166900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-122.84</td>\n",
              "      <td>38.40</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3080.0</td>\n",
              "      <td>617.0</td>\n",
              "      <td>1446.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>3.6696</td>\n",
              "      <td>194400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-118.02</td>\n",
              "      <td>34.08</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2402.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>2830.0</td>\n",
              "      <td>603.0</td>\n",
              "      <td>2.3333</td>\n",
              "      <td>164200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>-118.24</td>\n",
              "      <td>33.98</td>\n",
              "      <td>45.0</td>\n",
              "      <td>972.0</td>\n",
              "      <td>249.0</td>\n",
              "      <td>1288.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>2.2054</td>\n",
              "      <td>125000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>-119.12</td>\n",
              "      <td>35.85</td>\n",
              "      <td>37.0</td>\n",
              "      <td>736.0</td>\n",
              "      <td>166.0</td>\n",
              "      <td>564.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>2.4167</td>\n",
              "      <td>58300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>-121.93</td>\n",
              "      <td>37.25</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1089.0</td>\n",
              "      <td>182.0</td>\n",
              "      <td>535.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>4.6900</td>\n",
              "      <td>252600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>-117.03</td>\n",
              "      <td>32.97</td>\n",
              "      <td>16.0</td>\n",
              "      <td>3936.0</td>\n",
              "      <td>694.0</td>\n",
              "      <td>1935.0</td>\n",
              "      <td>659.0</td>\n",
              "      <td>4.5625</td>\n",
              "      <td>231200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>-117.97</td>\n",
              "      <td>33.73</td>\n",
              "      <td>27.0</td>\n",
              "      <td>2097.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>1217.0</td>\n",
              "      <td>331.0</td>\n",
              "      <td>5.7121</td>\n",
              "      <td>222500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>-117.99</td>\n",
              "      <td>33.81</td>\n",
              "      <td>42.0</td>\n",
              "      <td>161.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>157.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>2.2000</td>\n",
              "      <td>153100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>-120.81</td>\n",
              "      <td>37.53</td>\n",
              "      <td>15.0</td>\n",
              "      <td>570.0</td>\n",
              "      <td>123.0</td>\n",
              "      <td>189.0</td>\n",
              "      <td>107.0</td>\n",
              "      <td>1.8750</td>\n",
              "      <td>181300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>-121.20</td>\n",
              "      <td>38.69</td>\n",
              "      <td>26.0</td>\n",
              "      <td>3077.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1603.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>2.7174</td>\n",
              "      <td>137500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>-118.88</td>\n",
              "      <td>34.21</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1590.0</td>\n",
              "      <td>196.0</td>\n",
              "      <td>654.0</td>\n",
              "      <td>199.0</td>\n",
              "      <td>6.5851</td>\n",
              "      <td>300000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>-122.59</td>\n",
              "      <td>38.01</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8814.0</td>\n",
              "      <td>1307.0</td>\n",
              "      <td>3450.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>6.1724</td>\n",
              "      <td>414300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>-122.15</td>\n",
              "      <td>37.75</td>\n",
              "      <td>40.0</td>\n",
              "      <td>1445.0</td>\n",
              "      <td>256.0</td>\n",
              "      <td>849.0</td>\n",
              "      <td>255.0</td>\n",
              "      <td>3.8913</td>\n",
              "      <td>126300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>-121.37</td>\n",
              "      <td>38.68</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1775.0</td>\n",
              "      <td>296.0</td>\n",
              "      <td>937.0</td>\n",
              "      <td>305.0</td>\n",
              "      <td>3.1786</td>\n",
              "      <td>83400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>-118.16</td>\n",
              "      <td>34.07</td>\n",
              "      <td>47.0</td>\n",
              "      <td>2994.0</td>\n",
              "      <td>543.0</td>\n",
              "      <td>1651.0</td>\n",
              "      <td>561.0</td>\n",
              "      <td>3.8644</td>\n",
              "      <td>241500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>-122.20</td>\n",
              "      <td>37.79</td>\n",
              "      <td>45.0</td>\n",
              "      <td>2021.0</td>\n",
              "      <td>528.0</td>\n",
              "      <td>1410.0</td>\n",
              "      <td>480.0</td>\n",
              "      <td>2.7788</td>\n",
              "      <td>115400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>-117.28</td>\n",
              "      <td>33.28</td>\n",
              "      <td>13.0</td>\n",
              "      <td>6131.0</td>\n",
              "      <td>1040.0</td>\n",
              "      <td>4049.0</td>\n",
              "      <td>940.0</td>\n",
              "      <td>3.8156</td>\n",
              "      <td>150700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>-118.03</td>\n",
              "      <td>34.16</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1401.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>667.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>7.1615</td>\n",
              "      <td>484700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>-122.42</td>\n",
              "      <td>37.76</td>\n",
              "      <td>52.0</td>\n",
              "      <td>3587.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>2259.0</td>\n",
              "      <td>979.0</td>\n",
              "      <td>2.5403</td>\n",
              "      <td>250000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>-118.39</td>\n",
              "      <td>33.99</td>\n",
              "      <td>32.0</td>\n",
              "      <td>2612.0</td>\n",
              "      <td>418.0</td>\n",
              "      <td>1030.0</td>\n",
              "      <td>402.0</td>\n",
              "      <td>6.6030</td>\n",
              "      <td>369200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>-118.45</td>\n",
              "      <td>34.07</td>\n",
              "      <td>19.0</td>\n",
              "      <td>4845.0</td>\n",
              "      <td>1609.0</td>\n",
              "      <td>3751.0</td>\n",
              "      <td>1539.0</td>\n",
              "      <td>1.5830</td>\n",
              "      <td>350000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>-118.48</td>\n",
              "      <td>34.01</td>\n",
              "      <td>30.0</td>\n",
              "      <td>3078.0</td>\n",
              "      <td>954.0</td>\n",
              "      <td>1561.0</td>\n",
              "      <td>901.0</td>\n",
              "      <td>3.4852</td>\n",
              "      <td>425000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>-119.35</td>\n",
              "      <td>36.33</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1195.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>568.0</td>\n",
              "      <td>229.0</td>\n",
              "      <td>3.1486</td>\n",
              "      <td>105600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>33.91</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1617.0</td>\n",
              "      <td>493.0</td>\n",
              "      <td>1530.0</td>\n",
              "      <td>500.0</td>\n",
              "      <td>2.6182</td>\n",
              "      <td>172600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>-121.13</td>\n",
              "      <td>39.31</td>\n",
              "      <td>17.0</td>\n",
              "      <td>3442.0</td>\n",
              "      <td>705.0</td>\n",
              "      <td>1693.0</td>\n",
              "      <td>619.0</td>\n",
              "      <td>2.8102</td>\n",
              "      <td>128900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>-118.08</td>\n",
              "      <td>34.55</td>\n",
              "      <td>5.0</td>\n",
              "      <td>16181.0</td>\n",
              "      <td>2971.0</td>\n",
              "      <td>8152.0</td>\n",
              "      <td>2651.0</td>\n",
              "      <td>4.5237</td>\n",
              "      <td>141800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>-118.32</td>\n",
              "      <td>33.94</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1067.0</td>\n",
              "      <td>170.0</td>\n",
              "      <td>499.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>4.6389</td>\n",
              "      <td>183800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>-118.11</td>\n",
              "      <td>34.00</td>\n",
              "      <td>33.0</td>\n",
              "      <td>2886.0</td>\n",
              "      <td>726.0</td>\n",
              "      <td>2650.0</td>\n",
              "      <td>728.0</td>\n",
              "      <td>2.6250</td>\n",
              "      <td>178700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>-122.53</td>\n",
              "      <td>37.97</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1560.0</td>\n",
              "      <td>451.0</td>\n",
              "      <td>700.0</td>\n",
              "      <td>419.0</td>\n",
              "      <td>2.5125</td>\n",
              "      <td>270800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>-118.02</td>\n",
              "      <td>33.92</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1478.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>5.5238</td>\n",
              "      <td>185300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>-118.05</td>\n",
              "      <td>33.93</td>\n",
              "      <td>31.0</td>\n",
              "      <td>894.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>883.0</td>\n",
              "      <td>190.0</td>\n",
              "      <td>3.6771</td>\n",
              "      <td>141500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>-119.01</td>\n",
              "      <td>34.23</td>\n",
              "      <td>11.0</td>\n",
              "      <td>5785.0</td>\n",
              "      <td>1035.0</td>\n",
              "      <td>2760.0</td>\n",
              "      <td>985.0</td>\n",
              "      <td>4.6930</td>\n",
              "      <td>232200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>-119.32</td>\n",
              "      <td>36.20</td>\n",
              "      <td>15.0</td>\n",
              "      <td>1562.0</td>\n",
              "      <td>275.0</td>\n",
              "      <td>961.0</td>\n",
              "      <td>287.0</td>\n",
              "      <td>3.4231</td>\n",
              "      <td>83300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>-116.92</td>\n",
              "      <td>32.77</td>\n",
              "      <td>16.0</td>\n",
              "      <td>2770.0</td>\n",
              "      <td>406.0</td>\n",
              "      <td>1269.0</td>\n",
              "      <td>429.0</td>\n",
              "      <td>6.6783</td>\n",
              "      <td>275000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>-118.06</td>\n",
              "      <td>34.15</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1980.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>697.0</td>\n",
              "      <td>226.0</td>\n",
              "      <td>15.0001</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>-117.27</td>\n",
              "      <td>34.09</td>\n",
              "      <td>36.0</td>\n",
              "      <td>848.0</td>\n",
              "      <td>186.0</td>\n",
              "      <td>737.0</td>\n",
              "      <td>169.0</td>\n",
              "      <td>0.9838</td>\n",
              "      <td>79300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>-118.23</td>\n",
              "      <td>34.13</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1308.0</td>\n",
              "      <td>286.0</td>\n",
              "      <td>835.0</td>\n",
              "      <td>294.0</td>\n",
              "      <td>4.2891</td>\n",
              "      <td>214800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>-117.24</td>\n",
              "      <td>33.17</td>\n",
              "      <td>4.0</td>\n",
              "      <td>9998.0</td>\n",
              "      <td>1874.0</td>\n",
              "      <td>3925.0</td>\n",
              "      <td>1672.0</td>\n",
              "      <td>4.2826</td>\n",
              "      <td>237500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>-121.91</td>\n",
              "      <td>37.44</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1212.0</td>\n",
              "      <td>251.0</td>\n",
              "      <td>799.0</td>\n",
              "      <td>242.0</td>\n",
              "      <td>5.0808</td>\n",
              "      <td>212500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>-118.29</td>\n",
              "      <td>33.94</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1782.0</td>\n",
              "      <td>338.0</td>\n",
              "      <td>1003.0</td>\n",
              "      <td>329.0</td>\n",
              "      <td>2.5398</td>\n",
              "      <td>105700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>-121.35</td>\n",
              "      <td>38.00</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1649.0</td>\n",
              "      <td>369.0</td>\n",
              "      <td>732.0</td>\n",
              "      <td>350.0</td>\n",
              "      <td>3.4231</td>\n",
              "      <td>123800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>-117.99</td>\n",
              "      <td>33.78</td>\n",
              "      <td>19.0</td>\n",
              "      <td>7399.0</td>\n",
              "      <td>1698.0</td>\n",
              "      <td>3554.0</td>\n",
              "      <td>1593.0</td>\n",
              "      <td>3.1049</td>\n",
              "      <td>173900.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    longitude  latitude  ...  median_income  median_house_value\n",
              "0     -122.05     37.37  ...         6.6085            344700.0\n",
              "1     -118.30     34.26  ...         3.5990            176500.0\n",
              "2     -117.81     33.78  ...         5.7934            270500.0\n",
              "3     -118.36     33.82  ...         6.1359            330000.0\n",
              "4     -119.67     36.33  ...         2.9375             81700.0\n",
              "5     -119.56     36.51  ...         1.6635             67000.0\n",
              "6     -121.43     38.63  ...         1.6641             67000.0\n",
              "7     -120.65     35.48  ...         3.2250            166900.0\n",
              "8     -122.84     38.40  ...         3.6696            194400.0\n",
              "9     -118.02     34.08  ...         2.3333            164200.0\n",
              "10    -118.24     33.98  ...         2.2054            125000.0\n",
              "11    -119.12     35.85  ...         2.4167             58300.0\n",
              "12    -121.93     37.25  ...         4.6900            252600.0\n",
              "13    -117.03     32.97  ...         4.5625            231200.0\n",
              "14    -117.97     33.73  ...         5.7121            222500.0\n",
              "15    -117.99     33.81  ...         2.2000            153100.0\n",
              "16    -120.81     37.53  ...         1.8750            181300.0\n",
              "17    -121.20     38.69  ...         2.7174            137500.0\n",
              "18    -118.88     34.21  ...         6.5851            300000.0\n",
              "19    -122.59     38.01  ...         6.1724            414300.0\n",
              "20    -122.15     37.75  ...         3.8913            126300.0\n",
              "21    -121.37     38.68  ...         3.1786             83400.0\n",
              "22    -118.16     34.07  ...         3.8644            241500.0\n",
              "23    -122.20     37.79  ...         2.7788            115400.0\n",
              "24    -117.28     33.28  ...         3.8156            150700.0\n",
              "25    -118.03     34.16  ...         7.1615            484700.0\n",
              "26    -122.42     37.76  ...         2.5403            250000.0\n",
              "27    -118.39     33.99  ...         6.6030            369200.0\n",
              "28    -118.45     34.07  ...         1.5830            350000.0\n",
              "29    -118.48     34.01  ...         3.4852            425000.0\n",
              "30    -119.35     36.33  ...         3.1486            105600.0\n",
              "31    -118.30     33.91  ...         2.6182            172600.0\n",
              "32    -121.13     39.31  ...         2.8102            128900.0\n",
              "33    -118.08     34.55  ...         4.5237            141800.0\n",
              "34    -118.32     33.94  ...         4.6389            183800.0\n",
              "35    -118.11     34.00  ...         2.6250            178700.0\n",
              "36    -122.53     37.97  ...         2.5125            270800.0\n",
              "37    -118.02     33.92  ...         5.5238            185300.0\n",
              "38    -118.05     33.93  ...         3.6771            141500.0\n",
              "39    -119.01     34.23  ...         4.6930            232200.0\n",
              "40    -119.32     36.20  ...         3.4231             83300.0\n",
              "41    -116.92     32.77  ...         6.6783            275000.0\n",
              "42    -118.06     34.15  ...        15.0001            500001.0\n",
              "43    -117.27     34.09  ...         0.9838             79300.0\n",
              "44    -118.23     34.13  ...         4.2891            214800.0\n",
              "45    -117.24     33.17  ...         4.2826            237500.0\n",
              "46    -121.91     37.44  ...         5.0808            212500.0\n",
              "47    -118.29     33.94  ...         2.5398            105700.0\n",
              "48    -121.35     38.00  ...         3.4231            123800.0\n",
              "49    -117.99     33.78  ...         3.1049            173900.0\n",
              "\n",
              "[50 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecmDHR91baRH",
        "outputId": "b28a784d-b41f-4498-baef-20489728fc24"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "       'total_bedrooms', 'population', 'households', 'median_income',\n",
              "       'median_house_value'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fX-LXWFebgtd",
        "outputId": "c918b48c-91d0-483f-ec3c-36f7d83b4372"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude             float64\n",
              "latitude              float64\n",
              "housing_median_age    float64\n",
              "total_rooms           float64\n",
              "total_bedrooms        float64\n",
              "population            float64\n",
              "households            float64\n",
              "median_income         float64\n",
              "median_house_value    float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sc6pOrKoUbbP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "db5ec371-2488-4258-cf1f-f451450ec98e"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3000, 9)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xl4Iz2Yfiuga",
        "outputId": "1757bef6-d041-41ea-e91d-850007937d27"
      },
      "source": [
        "df.max"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method DataFrame.max of       longitude  latitude  ...  median_income  median_house_value\n",
              "0       -122.05     37.37  ...         6.6085            344700.0\n",
              "1       -118.30     34.26  ...         3.5990            176500.0\n",
              "2       -117.81     33.78  ...         5.7934            270500.0\n",
              "3       -118.36     33.82  ...         6.1359            330000.0\n",
              "4       -119.67     36.33  ...         2.9375             81700.0\n",
              "...         ...       ...  ...            ...                 ...\n",
              "2995    -119.86     34.42  ...         1.1790            225000.0\n",
              "2996    -118.14     34.06  ...         3.3906            237200.0\n",
              "2997    -119.70     36.30  ...         2.2895             62000.0\n",
              "2998    -117.12     34.10  ...         3.2708            162500.0\n",
              "2999    -119.63     34.42  ...         8.5608            500001.0\n",
              "\n",
              "[3000 rows x 9 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KJ2N9MsxixxJ",
        "outputId": "c29e211d-f113-43da-e791-240a0934a47b"
      },
      "source": [
        "df.axes"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[RangeIndex(start=0, stop=3000, step=1),\n",
              " Index(['longitude', 'latitude', 'housing_median_age', 'total_rooms',\n",
              "        'total_bedrooms', 'population', 'households', 'median_income',\n",
              "        'median_house_value'],\n",
              "       dtype='object')]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zEQILD9Mi1_a",
        "outputId": "b0f6a90c-d133-4239-fe02-bee39b1c8c1f"
      },
      "source": [
        "df.ndim"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1b85d9r2UbbQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "f65f2c62-1b52-41e1-e4cc-a04462755dcc"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "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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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.589200</td>\n",
              "      <td>35.63539</td>\n",
              "      <td>28.845333</td>\n",
              "      <td>2599.578667</td>\n",
              "      <td>529.950667</td>\n",
              "      <td>1402.798667</td>\n",
              "      <td>489.91200</td>\n",
              "      <td>3.807272</td>\n",
              "      <td>205846.27500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.994936</td>\n",
              "      <td>2.12967</td>\n",
              "      <td>12.555396</td>\n",
              "      <td>2155.593332</td>\n",
              "      <td>415.654368</td>\n",
              "      <td>1030.543012</td>\n",
              "      <td>365.42271</td>\n",
              "      <td>1.854512</td>\n",
              "      <td>113119.68747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.180000</td>\n",
              "      <td>32.56000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>22500.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.810000</td>\n",
              "      <td>33.93000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1401.000000</td>\n",
              "      <td>291.000000</td>\n",
              "      <td>780.000000</td>\n",
              "      <td>273.00000</td>\n",
              "      <td>2.544000</td>\n",
              "      <td>121200.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.485000</td>\n",
              "      <td>34.27000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2106.000000</td>\n",
              "      <td>437.000000</td>\n",
              "      <td>1155.000000</td>\n",
              "      <td>409.50000</td>\n",
              "      <td>3.487150</td>\n",
              "      <td>177650.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.020000</td>\n",
              "      <td>37.69000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3129.000000</td>\n",
              "      <td>636.000000</td>\n",
              "      <td>1742.750000</td>\n",
              "      <td>597.25000</td>\n",
              "      <td>4.656475</td>\n",
              "      <td>263975.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.490000</td>\n",
              "      <td>41.92000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>30450.000000</td>\n",
              "      <td>5419.000000</td>\n",
              "      <td>11935.000000</td>\n",
              "      <td>4930.00000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         longitude    latitude  ...  median_income  median_house_value\n",
              "count  3000.000000  3000.00000  ...    3000.000000          3000.00000\n",
              "mean   -119.589200    35.63539  ...       3.807272        205846.27500\n",
              "std       1.994936     2.12967  ...       1.854512        113119.68747\n",
              "min    -124.180000    32.56000  ...       0.499900         22500.00000\n",
              "25%    -121.810000    33.93000  ...       2.544000        121200.00000\n",
              "50%    -118.485000    34.27000  ...       3.487150        177650.00000\n",
              "75%    -118.020000    37.69000  ...       4.656475        263975.00000\n",
              "max    -114.490000    41.92000  ...      15.000100        500001.00000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-N-b_QgZUbbQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7af530f-4075-4ec3-8b1e-aca1b0a127d8"
      },
      "source": [
        "df.values"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-1.22050e+02,  3.73700e+01,  2.70000e+01, ...,  6.06000e+02,\n",
              "         6.60850e+00,  3.44700e+05],\n",
              "       [-1.18300e+02,  3.42600e+01,  4.30000e+01, ...,  2.77000e+02,\n",
              "         3.59900e+00,  1.76500e+05],\n",
              "       [-1.17810e+02,  3.37800e+01,  2.70000e+01, ...,  4.95000e+02,\n",
              "         5.79340e+00,  2.70500e+05],\n",
              "       ...,\n",
              "       [-1.19700e+02,  3.63000e+01,  1.00000e+01, ...,  2.20000e+02,\n",
              "         2.28950e+00,  6.20000e+04],\n",
              "       [-1.17120e+02,  3.41000e+01,  4.00000e+01, ...,  1.40000e+01,\n",
              "         3.27080e+00,  1.62500e+05],\n",
              "       [-1.19630e+02,  3.44200e+01,  4.20000e+01, ...,  2.60000e+02,\n",
              "         8.56080e+00,  5.00001e+05]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "wlml6v0SihX-",
        "outputId": "ab0e5a7d-774b-40bc-bf1f-c55c73b60689"
      },
      "source": [
        "df.tail(10)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2990</th>\n",
              "      <td>-118.23</td>\n",
              "      <td>34.09</td>\n",
              "      <td>49.0</td>\n",
              "      <td>1638.0</td>\n",
              "      <td>456.0</td>\n",
              "      <td>1500.0</td>\n",
              "      <td>430.0</td>\n",
              "      <td>2.6923</td>\n",
              "      <td>150000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2991</th>\n",
              "      <td>-117.17</td>\n",
              "      <td>34.28</td>\n",
              "      <td>13.0</td>\n",
              "      <td>4867.0</td>\n",
              "      <td>718.0</td>\n",
              "      <td>780.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>7.1997</td>\n",
              "      <td>253800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2992</th>\n",
              "      <td>-122.33</td>\n",
              "      <td>37.39</td>\n",
              "      <td>52.0</td>\n",
              "      <td>573.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>232.0</td>\n",
              "      <td>92.0</td>\n",
              "      <td>6.2263</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2993</th>\n",
              "      <td>-117.91</td>\n",
              "      <td>33.60</td>\n",
              "      <td>37.0</td>\n",
              "      <td>2088.0</td>\n",
              "      <td>510.0</td>\n",
              "      <td>673.0</td>\n",
              "      <td>390.0</td>\n",
              "      <td>5.1048</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2994</th>\n",
              "      <td>-117.93</td>\n",
              "      <td>33.86</td>\n",
              "      <td>35.0</td>\n",
              "      <td>931.0</td>\n",
              "      <td>181.0</td>\n",
              "      <td>516.0</td>\n",
              "      <td>174.0</td>\n",
              "      <td>5.5867</td>\n",
              "      <td>182500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2995</th>\n",
              "      <td>-119.86</td>\n",
              "      <td>34.42</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1450.0</td>\n",
              "      <td>642.0</td>\n",
              "      <td>1258.0</td>\n",
              "      <td>607.0</td>\n",
              "      <td>1.1790</td>\n",
              "      <td>225000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2996</th>\n",
              "      <td>-118.14</td>\n",
              "      <td>34.06</td>\n",
              "      <td>27.0</td>\n",
              "      <td>5257.0</td>\n",
              "      <td>1082.0</td>\n",
              "      <td>3496.0</td>\n",
              "      <td>1036.0</td>\n",
              "      <td>3.3906</td>\n",
              "      <td>237200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>-119.70</td>\n",
              "      <td>36.30</td>\n",
              "      <td>10.0</td>\n",
              "      <td>956.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>693.0</td>\n",
              "      <td>220.0</td>\n",
              "      <td>2.2895</td>\n",
              "      <td>62000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>-117.12</td>\n",
              "      <td>34.10</td>\n",
              "      <td>40.0</td>\n",
              "      <td>96.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>3.2708</td>\n",
              "      <td>162500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>-119.63</td>\n",
              "      <td>34.42</td>\n",
              "      <td>42.0</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>263.0</td>\n",
              "      <td>753.0</td>\n",
              "      <td>260.0</td>\n",
              "      <td>8.5608</td>\n",
              "      <td>500001.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      longitude  latitude  ...  median_income  median_house_value\n",
              "2990    -118.23     34.09  ...         2.6923            150000.0\n",
              "2991    -117.17     34.28  ...         7.1997            253800.0\n",
              "2992    -122.33     37.39  ...         6.2263            500001.0\n",
              "2993    -117.91     33.60  ...         5.1048            500001.0\n",
              "2994    -117.93     33.86  ...         5.5867            182500.0\n",
              "2995    -119.86     34.42  ...         1.1790            225000.0\n",
              "2996    -118.14     34.06  ...         3.3906            237200.0\n",
              "2997    -119.70     36.30  ...         2.2895             62000.0\n",
              "2998    -117.12     34.10  ...         3.2708            162500.0\n",
              "2999    -119.63     34.42  ...         8.5608            500001.0\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abVidL8-jKRJ",
        "outputId": "3dec40c0-f129-4054-a5d5-861fbd0564d3"
      },
      "source": [
        "df.size"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FeyFtTxPlRWB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "873c5bda-3b0e-4478-b842-9ce8a788af7b"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 9 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   longitude           3000 non-null   float64\n",
            " 1   latitude            3000 non-null   float64\n",
            " 2   housing_median_age  3000 non-null   float64\n",
            " 3   total_rooms         3000 non-null   float64\n",
            " 4   total_bedrooms      3000 non-null   float64\n",
            " 5   population          3000 non-null   float64\n",
            " 6   households          3000 non-null   float64\n",
            " 7   median_income       3000 non-null   float64\n",
            " 8   median_house_value  3000 non-null   float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 211.1 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVTB00R9luz1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e9ca480-2377-4346-dece-b55cd8b4376d"
      },
      "source": [
        "len(df.columns)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "id": "UFyRGDt8mJzR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e763f863-74c4-47e0-9c44-d5de239d63d4"
      },
      "source": [
        "dir(df)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T',\n",
              " '_AXIS_LEN',\n",
              " '_AXIS_NAMES',\n",
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              " '__doc__',\n",
              " '__eq__',\n",
              " '__finalize__',\n",
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              " '__getattr__',\n",
              " '__getattribute__',\n",
              " '__getitem__',\n",
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              " 'households',\n",
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              " 'iat',\n",
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              " 'le',\n",
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              " 'lookup',\n",
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              " 'mad',\n",
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              " 'median',\n",
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              " 'pct_change',\n",
              " 'pipe',\n",
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              " 'plot',\n",
              " 'pop',\n",
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              " 'rank',\n",
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              " 'tail',\n",
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              " 'to_excel',\n",
              " 'to_feather',\n",
              " 'to_gbq',\n",
              " 'to_hdf',\n",
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              " 'to_json',\n",
              " 'to_latex',\n",
              " 'to_markdown',\n",
              " 'to_numpy',\n",
              " 'to_parquet',\n",
              " 'to_period',\n",
              " 'to_pickle',\n",
              " 'to_records',\n",
              " 'to_sql',\n",
              " 'to_stata',\n",
              " 'to_string',\n",
              " 'to_timestamp',\n",
              " 'to_xarray',\n",
              " 'total_bedrooms',\n",
              " 'total_rooms',\n",
              " 'transform',\n",
              " 'transpose',\n",
              " 'truediv',\n",
              " 'truncate',\n",
              " 'tz_convert',\n",
              " 'tz_localize',\n",
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              " 'value_counts',\n",
              " 'values',\n",
              " 'var',\n",
              " 'where',\n",
              " 'xs']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yurOaRKgmfym",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "9bdeadd2-16ff-4bb0-85e2-16949f5c3868"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>3000.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.589200</td>\n",
              "      <td>35.63539</td>\n",
              "      <td>28.845333</td>\n",
              "      <td>2599.578667</td>\n",
              "      <td>529.950667</td>\n",
              "      <td>1402.798667</td>\n",
              "      <td>489.91200</td>\n",
              "      <td>3.807272</td>\n",
              "      <td>205846.27500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.994936</td>\n",
              "      <td>2.12967</td>\n",
              "      <td>12.555396</td>\n",
              "      <td>2155.593332</td>\n",
              "      <td>415.654368</td>\n",
              "      <td>1030.543012</td>\n",
              "      <td>365.42271</td>\n",
              "      <td>1.854512</td>\n",
              "      <td>113119.68747</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.180000</td>\n",
              "      <td>32.56000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.00000</td>\n",
              "      <td>0.499900</td>\n",
              "      <td>22500.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.810000</td>\n",
              "      <td>33.93000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>1401.000000</td>\n",
              "      <td>291.000000</td>\n",
              "      <td>780.000000</td>\n",
              "      <td>273.00000</td>\n",
              "      <td>2.544000</td>\n",
              "      <td>121200.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.485000</td>\n",
              "      <td>34.27000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>2106.000000</td>\n",
              "      <td>437.000000</td>\n",
              "      <td>1155.000000</td>\n",
              "      <td>409.50000</td>\n",
              "      <td>3.487150</td>\n",
              "      <td>177650.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.020000</td>\n",
              "      <td>37.69000</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3129.000000</td>\n",
              "      <td>636.000000</td>\n",
              "      <td>1742.750000</td>\n",
              "      <td>597.25000</td>\n",
              "      <td>4.656475</td>\n",
              "      <td>263975.00000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.490000</td>\n",
              "      <td>41.92000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>30450.000000</td>\n",
              "      <td>5419.000000</td>\n",
              "      <td>11935.000000</td>\n",
              "      <td>4930.00000</td>\n",
              "      <td>15.000100</td>\n",
              "      <td>500001.00000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         longitude    latitude  ...  median_income  median_house_value\n",
              "count  3000.000000  3000.00000  ...    3000.000000          3000.00000\n",
              "mean   -119.589200    35.63539  ...       3.807272        205846.27500\n",
              "std       1.994936     2.12967  ...       1.854512        113119.68747\n",
              "min    -124.180000    32.56000  ...       0.499900         22500.00000\n",
              "25%    -121.810000    33.93000  ...       2.544000        121200.00000\n",
              "50%    -118.485000    34.27000  ...       3.487150        177650.00000\n",
              "75%    -118.020000    37.69000  ...       4.656475        263975.00000\n",
              "max    -114.490000    41.92000  ...      15.000100        500001.00000\n",
              "\n",
              "[8 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PO-bmm9KmmLV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14cf7e84-63eb-41f8-f00b-0946b61da5ce"
      },
      "source": [
        "df.max()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude               -114.4900\n",
              "latitude                  41.9200\n",
              "housing_median_age        52.0000\n",
              "total_rooms            30450.0000\n",
              "total_bedrooms          5419.0000\n",
              "population             11935.0000\n",
              "households              4930.0000\n",
              "median_income             15.0001\n",
              "median_house_value    500001.0000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_0yI8Y2mrTE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb967c3d-7b0e-4c4f-d05c-1d21941ada53"
      },
      "source": [
        "df.min()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude              -124.1800\n",
              "latitude                 32.5600\n",
              "housing_median_age        1.0000\n",
              "total_rooms               6.0000\n",
              "total_bedrooms            2.0000\n",
              "population                5.0000\n",
              "households                2.0000\n",
              "median_income             0.4999\n",
              "median_house_value    22500.0000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vi000XyIm-30",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6b969a7-4809-4b80-9e35-3b2c124f4655"
      },
      "source": [
        "df.head(50).mean()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude               -119.377200\n",
              "latitude                  35.400200\n",
              "housing_median_age        29.300000\n",
              "total_rooms             2793.100000\n",
              "total_bedrooms           556.200000\n",
              "population              1525.440000\n",
              "households               528.080000\n",
              "median_income              4.024894\n",
              "median_house_value    205638.020000\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l00hPChlnE_a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fab7b8cc-0e61-4923-f923-87c03bb70dbb"
      },
      "source": [
        "df.longitude.value_counts()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-118.26    26\n",
              "-118.21    26\n",
              "-118.28    25\n",
              "-118.27    25\n",
              "-118.29    25\n",
              "           ..\n",
              "-123.39     1\n",
              "-116.66     1\n",
              "-120.59     1\n",
              "-119.61     1\n",
              "-120.10     1\n",
              "Name: longitude, Length: 607, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MOvnLHUGnpEH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ba7e0ff-b74f-4139-b350-111093b2fd7c"
      },
      "source": [
        "df.std()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "longitude                  1.994936\n",
              "latitude                   2.129670\n",
              "housing_median_age        12.555396\n",
              "total_rooms             2155.593332\n",
              "total_bedrooms           415.654368\n",
              "population              1030.543012\n",
              "households               365.422710\n",
              "median_income              1.854512\n",
              "median_house_value    113119.687470\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXORia9RqQo5",
        "outputId": "117aea5c-8c0d-4dd1-e898-d49a1ed83d98"
      },
      "source": [
        "df.longitude"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      -122.05\n",
              "1      -118.30\n",
              "2      -117.81\n",
              "3      -118.36\n",
              "4      -119.67\n",
              "         ...  \n",
              "2995   -119.86\n",
              "2996   -118.14\n",
              "2997   -119.70\n",
              "2998   -117.12\n",
              "2999   -119.63\n",
              "Name: longitude, Length: 3000, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TOoJKPgGqWVg",
        "outputId": "5ff1d8f3-77e6-4e1a-dd10-b8d5fbce8d60"
      },
      "source": [
        "#This will retrun data frame\n",
        "new_data = df[[\"longitude\"]]\n",
        "print(new_data);"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      longitude\n",
            "0       -122.05\n",
            "1       -118.30\n",
            "2       -117.81\n",
            "3       -118.36\n",
            "4       -119.67\n",
            "...         ...\n",
            "2995    -119.86\n",
            "2996    -118.14\n",
            "2997    -119.70\n",
            "2998    -117.12\n",
            "2999    -119.63\n",
            "\n",
            "[3000 rows x 1 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPVqEr6nqeqw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "d35ef6c4-ecc5-4cc6-8546-6ced4f1f9f2c"
      },
      "source": [
        "phd_data_f = df[[\"total_rooms\"]]\n",
        "phd_data_f.describe()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_rooms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2599.578667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2155.593332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1401.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>2106.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3129.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>30450.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        total_rooms\n",
              "count   3000.000000\n",
              "mean    2599.578667\n",
              "std     2155.593332\n",
              "min        6.000000\n",
              "25%     1401.000000\n",
              "50%     2106.000000\n",
              "75%     3129.000000\n",
              "max    30450.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XunO2dXyrtsG",
        "outputId": "df9ebbbf-e565-468a-ed30-b367422eca97"
      },
      "source": [
        "phd_data_f.info()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3000 entries, 0 to 2999\n",
            "Data columns (total 1 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   total_rooms  3000 non-null   float64\n",
            "dtypes: float64(1)\n",
            "memory usage: 23.6 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMc2bBKvr3q5",
        "outputId": "7f638935-c306-48f2-f4b4-472348c507fc"
      },
      "source": [
        "phd_data_f.median()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "total_rooms    2106.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPoGvD2-sV2z",
        "outputId": "ad6a964e-848d-4e27-e7e8-e62998a40ad0"
      },
      "source": [
        "phd_data_f.mean()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "total_rooms    2599.578667\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tzo6S1YGyf1u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "28b78d18-6fab-4089-9efe-397afc0c92ab"
      },
      "source": [
        "df_population = df[(df['latitude'] == 37.37) & (df.total_rooms > 3000)]\n",
        "df_population"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.05</td>\n",
              "      <td>37.37</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307</th>\n",
              "      <td>-121.88</td>\n",
              "      <td>37.37</td>\n",
              "      <td>14.0</td>\n",
              "      <td>6016.0</td>\n",
              "      <td>1404.0</td>\n",
              "      <td>3258.0</td>\n",
              "      <td>1316.0</td>\n",
              "      <td>3.5745</td>\n",
              "      <td>333700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2430</th>\n",
              "      <td>-120.50</td>\n",
              "      <td>37.37</td>\n",
              "      <td>18.0</td>\n",
              "      <td>8606.0</td>\n",
              "      <td>1678.0</td>\n",
              "      <td>5303.0</td>\n",
              "      <td>1644.0</td>\n",
              "      <td>2.4012</td>\n",
              "      <td>79700.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      longitude  latitude  ...  median_income  median_house_value\n",
              "0       -122.05     37.37  ...         6.6085            344700.0\n",
              "307     -121.88     37.37  ...         3.5745            333700.0\n",
              "2430    -120.50     37.37  ...         2.4012             79700.0\n",
              "\n",
              "[3 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JE3itg2V7gMb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "6839b6ea-03ad-4977-bc0f-6f47a1bb5486"
      },
      "source": [
        "df.iloc[1:4,0:3]"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  housing_median_age\n",
              "1    -118.30     34.26                43.0\n",
              "2    -117.81     33.78                27.0\n",
              "3    -118.36     33.82                28.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BE-Erx2-71my",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "outputId": "8ce99e3f-fab1-4efc-e14a-65194db2971c"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-122.05</td>\n",
              "      <td>37.37</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3885.0</td>\n",
              "      <td>661.0</td>\n",
              "      <td>1537.0</td>\n",
              "      <td>606.0</td>\n",
              "      <td>6.6085</td>\n",
              "      <td>344700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-118.30</td>\n",
              "      <td>34.26</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1510.0</td>\n",
              "      <td>310.0</td>\n",
              "      <td>809.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>3.5990</td>\n",
              "      <td>176500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-117.81</td>\n",
              "      <td>33.78</td>\n",
              "      <td>27.0</td>\n",
              "      <td>3589.0</td>\n",
              "      <td>507.0</td>\n",
              "      <td>1484.0</td>\n",
              "      <td>495.0</td>\n",
              "      <td>5.7934</td>\n",
              "      <td>270500.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-118.36</td>\n",
              "      <td>33.82</td>\n",
              "      <td>28.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6.1359</td>\n",
              "      <td>330000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-119.67</td>\n",
              "      <td>36.33</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1241.0</td>\n",
              "      <td>244.0</td>\n",
              "      <td>850.0</td>\n",
              "      <td>237.0</td>\n",
              "      <td>2.9375</td>\n",
              "      <td>81700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-119.56</td>\n",
              "      <td>36.51</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1018.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>663.0</td>\n",
              "      <td>204.0</td>\n",
              "      <td>1.6635</td>\n",
              "      <td>67000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>-121.43</td>\n",
              "      <td>38.63</td>\n",
              "      <td>43.0</td>\n",
              "      <td>1009.0</td>\n",
              "      <td>225.0</td>\n",
              "      <td>604.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>1.6641</td>\n",
              "      <td>67000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>-120.65</td>\n",
              "      <td>35.48</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2310.0</td>\n",
              "      <td>471.0</td>\n",
              "      <td>1341.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>3.2250</td>\n",
              "      <td>166900.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>-122.84</td>\n",
              "      <td>38.40</td>\n",
              "      <td>15.0</td>\n",
              "      <td>3080.0</td>\n",
              "      <td>617.0</td>\n",
              "      <td>1446.0</td>\n",
              "      <td>599.0</td>\n",
              "      <td>3.6696</td>\n",
              "      <td>194400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>-118.02</td>\n",
              "      <td>34.08</td>\n",
              "      <td>31.0</td>\n",
              "      <td>2402.0</td>\n",
              "      <td>632.0</td>\n",
              "      <td>2830.0</td>\n",
              "      <td>603.0</td>\n",
              "      <td>2.3333</td>\n",
              "      <td>164200.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   longitude  latitude  ...  median_income  median_house_value\n",
              "0    -122.05     37.37  ...         6.6085            344700.0\n",
              "1    -118.30     34.26  ...         3.5990            176500.0\n",
              "2    -117.81     33.78  ...         5.7934            270500.0\n",
              "3    -118.36     33.82  ...         6.1359            330000.0\n",
              "4    -119.67     36.33  ...         2.9375             81700.0\n",
              "5    -119.56     36.51  ...         1.6635             67000.0\n",
              "6    -121.43     38.63  ...         1.6641             67000.0\n",
              "7    -120.65     35.48  ...         3.2250            166900.0\n",
              "8    -122.84     38.40  ...         3.6696            194400.0\n",
              "9    -118.02     34.08  ...         2.3333            164200.0\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8gZaWXgt9KNJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "76a1d8f9-0bda-493b-8b60-5ebc0f5efef7"
      },
      "source": [
        "df[['total_rooms','total_bedrooms']].agg(['mean','max'])"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2599.578667</td>\n",
              "      <td>529.950667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>30450.000000</td>\n",
              "      <td>5419.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       total_rooms  total_bedrooms\n",
              "mean   2599.578667      529.950667\n",
              "max   30450.000000     5419.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    }
  ]
}