3+4-CleaningData.ipynb

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    "colab": {
      "name": "CleaningData",
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      "cell_type": "markdown",
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      "source": [
        "# What are my features?  \n",
        "It’s pretty easy to infer the following features from the column names:\n",
        "\n",
        "**ST_NUM:** Street number\n",
        "\n",
        "**ST_NAME:** Street name\n",
        "\n",
        "**OWN_OCCUPIED:** Is the residence owner occupied\n",
        "\n",
        "**NUM_BEDROOMS:** Number of bedrooms\n",
        "\n",
        "We can also answer, what are the expected types?\n",
        "\n",
        "**ST_NUM:** float or int… some sort of numeric type\n",
        "\n",
        "**ST_NAME:** string\n",
        "\n",
        "**OWN_OCCUPIED:** string… Y (“Yes”) or N (“No”)\n",
        "\n",
        "**NUM_BEDROOMS:** float or int, a numeric type"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ThatcSM3su43",
        "colab_type": "text"
      },
      "source": [
        " <img src=\"https://raw.githubusercontent.com/fazlyrabbi77/DataProcessing/master/data-cleaning-from-ibm-analytics.jpg\">"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZW32LWsfesy",
        "colab_type": "code",
        "outputId": "dbfdacb4-a5ca-4587-cea4-5246efbc8b54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        }
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import numpy as np"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGKfFEjJlQHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#reading the dataset\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/fazlyrabbi77/DataProcessing/master/real-estate.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZRdS3O81lecS",
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        "outputId": "3ad88edc-01ba-48f0-e85b-fc6a38655961",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>NUM_BEDROOMS</th>\n",
              "      <th>NUM_BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>na</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y            3        1  1000\n",
              "1  100002000.0   197.0   LEXINGTON            N            3      1.5    --\n",
              "2  100003000.0     NaN   LEXINGTON            N          NaN        1   850\n",
              "3  100004000.0   201.0    BERKELEY           12            1      NaN   700\n",
              "4          NaN   203.0    BERKELEY            Y            3        2  1600\n",
              "5  100006000.0   207.0    BERKELEY            Y          NaN        1   800\n",
              "6  100007000.0     NaN  WASHINGTON          NaN            2   HURLEY   950\n",
              "7  100008000.0   213.0     TREMONT            Y            1        1   NaN\n",
              "8  100009000.0   215.0     TREMONT            Y           na        2  1800"
            ]
          },
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            "tags": []
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      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 109
        }
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>NUM_BEDROOMS</th>\n",
              "      <th>NUM_BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM    ST_NAME OWN_OCCUPIED NUM_BEDROOMS NUM_BATH SQ_FT\n",
              "0  100001000.0   104.0     PUTNAM            Y            3        1  1000\n",
              "1  100002000.0   197.0  LEXINGTON            N            3      1.5    --"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9HYBnZKpGpb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Renaming Columns with inplace\n",
        "df.rename(columns = {\"NUM_BEDROOMS\": \"BEDROOMS\",  \"NUM_BATH\":\"BATH\"},inplace=True) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7JFGyDJapnfz",
        "colab_type": "code",
        "outputId": "6bacb613-fd10-4e5e-8083-062047f2dbeb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM    ST_NAME OWN_OCCUPIED BEDROOMS BATH SQ_FT\n",
              "0  100001000.0   104.0     PUTNAM            Y        3    1  1000\n",
              "1  100002000.0   197.0  LEXINGTON            N        3  1.5    --\n",
              "2  100003000.0     NaN  LEXINGTON            N      NaN    1   850\n",
              "3  100004000.0   201.0   BERKELEY           12        1  NaN   700\n",
              "4          NaN   203.0   BERKELEY            Y        3    2  1600"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wexc3cCDqeSj",
        "colab_type": "code",
        "outputId": "671f7b9a-13f2-4ae6-eeca-6e2c9d434502",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\n",
        "#Check Null\n",
        "df.isnull().values.any()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9st-ZOCmuiHn",
        "colab_type": "code",
        "outputId": "17d589cd-07d6-47a6-a64a-4d6ca86f4971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#Count the number of Null values\n",
        "df.isnull().values.sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxQRs4UNu3L-",
        "colab_type": "code",
        "outputId": "78e79d54-cf23-4378-e9aa-95d67c9c18f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "#Showing the null values as per attributes\n",
        "null_columns=df.columns[df.isnull().any()]\n",
        "df[null_columns].isnull().sum()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PID             1\n",
              "ST_NUM          2\n",
              "OWN_OCCUPIED    1\n",
              "BEDROOMS        2\n",
              "BATH            1\n",
              "SQ_FT           1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7IxvPVeu6Ts",
        "colab_type": "code",
        "outputId": "bfa1020e-a0fc-4c93-a2f7-0ef01ab7b0ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "# Showing Null Values\n",
        "print(df[df.isnull().any(axis=1)][null_columns].head())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "           PID  ST_NUM OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
            "2  100003000.0     NaN            N      NaN       1   850\n",
            "3  100004000.0   201.0           12        1     NaN   700\n",
            "4          NaN   203.0            Y        3       2  1600\n",
            "5  100006000.0   207.0            Y      NaN       1   800\n",
            "6  100007000.0     NaN          NaN        2  HURLEY   950\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UzvNRYugvHSF",
        "colab_type": "code",
        "outputId": "c5d8a93b-6e65-4813-ab1f-1fc4a1576381",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "#Dropping columns without inplace\n",
        "df.drop(['PID'], axis=1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>na</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   ST_NUM     ST_NAME OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
              "0   104.0      PUTNAM            Y        3       1  1000\n",
              "1   197.0   LEXINGTON            N        3     1.5    --\n",
              "2     NaN   LEXINGTON            N      NaN       1   850\n",
              "3   201.0    BERKELEY           12        1     NaN   700\n",
              "4   203.0    BERKELEY            Y        3       2  1600\n",
              "5   207.0    BERKELEY            Y      NaN       1   800\n",
              "6     NaN  WASHINGTON          NaN        2  HURLEY   950\n",
              "7   213.0     TREMONT            Y        1       1   NaN\n",
              "8   215.0     TREMONT            Y       na       2  1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SfO0tJQwcFD",
        "colab_type": "code",
        "outputId": "a1820bbe-986e-4033-86e6-0fbfbbf90d85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "#dropping rows without inplace\n",
        "df.drop([7,8])"
      ],
      "execution_count": 0,
      "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",
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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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y        3       1  1000\n",
              "1  100002000.0   197.0   LEXINGTON            N        3     1.5    --\n",
              "2  100003000.0     NaN   LEXINGTON            N      NaN       1   850\n",
              "3  100004000.0   201.0    BERKELEY           12        1     NaN   700\n",
              "4          NaN   203.0    BERKELEY            Y        3       2  1600\n",
              "5  100006000.0   207.0    BERKELEY            Y      NaN       1   800\n",
              "6  100007000.0     NaN  WASHINGTON          NaN        2  HURLEY   950"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuu3y272wuT9",
        "colab_type": "code",
        "outputId": "b5747d08-d320-46eb-b18f-df57fa6e7e28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>3</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>na</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y        3       1  1000\n",
              "1  100002000.0   197.0   LEXINGTON            N        3     1.5    --\n",
              "2  100003000.0     NaN   LEXINGTON            N      NaN       1   850\n",
              "3  100004000.0   201.0    BERKELEY           12        1     NaN   700\n",
              "4          NaN   203.0    BERKELEY            Y        3       2  1600\n",
              "5  100006000.0   207.0    BERKELEY            Y      NaN       1   800\n",
              "6  100007000.0     NaN  WASHINGTON          NaN        2  HURLEY   950\n",
              "7  100008000.0   213.0     TREMONT            Y        1       1   NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       na       2  1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPPd-aGgwyc8",
        "colab_type": "code",
        "outputId": "8e2f580b-c6e1-4d11-cb06-ccbcf011c34b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "# Filling null values with specific value\n",
        "df['PID'].fillna(100005000,inplace=True)\n",
        "df"
      ],
      "execution_count": 0,
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>na</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y        3       1  1000\n",
              "1  100002000.0   197.0   LEXINGTON            N        3     1.5    --\n",
              "2  100003000.0     NaN   LEXINGTON            N      NaN       1   850\n",
              "3  100004000.0   201.0    BERKELEY           12        1     NaN   700\n",
              "4  100005000.0   203.0    BERKELEY            Y        3       2  1600\n",
              "5  100006000.0   207.0    BERKELEY            Y      NaN       1   800\n",
              "6  100007000.0     NaN  WASHINGTON          NaN        2  HURLEY   950\n",
              "7  100008000.0   213.0     TREMONT            Y        1       1   NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       na       2  1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "STH-7R1a33t-",
        "colab_type": "code",
        "outputId": "e1bd63af-cad6-441c-ddef-bb91825d6e3f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "# Row wise data filling\n",
        "df.loc[2,'ST_NUM'] = 197\n",
        "df.loc[6,'ST_NUM'] = 208\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>na</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y        3       1  1000\n",
              "1  100002000.0   197.0   LEXINGTON            N        3     1.5    --\n",
              "2  100003000.0   197.0   LEXINGTON            N      NaN       1   850\n",
              "3  100004000.0   201.0    BERKELEY           12        1     NaN   700\n",
              "4  100005000.0   203.0    BERKELEY            Y        3       2  1600\n",
              "5  100006000.0   207.0    BERKELEY            Y      NaN       1   800\n",
              "6  100007000.0   208.0  WASHINGTON          NaN        2  HURLEY   950\n",
              "7  100008000.0   213.0     TREMONT            Y        1       1   NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       na       2  1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ydYD5iVvBLCL",
        "colab_type": "code",
        "outputId": "60cc3861-83b7-454b-e86b-8eeb639fd082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "df.head(3)"
      ],
      "execution_count": 0,
      "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",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM    ST_NAME OWN_OCCUPIED BEDROOMS BATH SQ_FT\n",
              "0  100001000.0   104.0     PUTNAM            Y        3    1  1000\n",
              "1  100002000.0   197.0  LEXINGTON            N        3  1.5    --\n",
              "2  100003000.0   197.0  LEXINGTON            N      NaN    1   850"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suSrI1E_4y1W",
        "colab_type": "code",
        "outputId": "99be3ca8-6265-4182-8bd8-7fe481b8876a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "#unwanted value treatment\n",
        "cnt=0\n",
        "for row in df['OWN_OCCUPIED']:\n",
        "    try:\n",
        "        int(row)\n",
        "        df.loc[cnt, 'OWN_OCCUPIED']=np.nan\n",
        "    except ValueError:\n",
        "        pass\n",
        "    cnt+=1\n",
        "df"
      ],
      "execution_count": 0,
      "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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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1.5</td>\n",
              "      <td>--</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NaN</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>HURLEY</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>na</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED BEDROOMS    BATH SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y        3       1  1000\n",
              "1  100002000.0   197.0   LEXINGTON            N        3     1.5    --\n",
              "2  100003000.0     NaN   LEXINGTON            N      NaN       1   850\n",
              "3  100004000.0   201.0    BERKELEY          NaN        1     NaN   700\n",
              "4          NaN   203.0    BERKELEY            Y        3       2  1600\n",
              "5  100006000.0   207.0    BERKELEY            Y      NaN       1   800\n",
              "6  100007000.0     NaN  WASHINGTON          NaN        2  HURLEY   950\n",
              "7  100008000.0   213.0     TREMONT            Y        1       1   NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       na       2  1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yGTDHvFuDaZu",
        "colab_type": "code",
        "outputId": "671743cf-7763-492b-e152-22f4a84357a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df['BEDROOMS'] = pd.to_numeric(df['BEDROOMS'], errors='coerce')\n",
        "df['BATH'] = pd.to_numeric(df['BATH'], errors='coerce')\n",
        "df['SQ_FT'] = pd.to_numeric(df['SQ_FT'], errors='coerce')\n",
        "df"
      ],
      "execution_count": 0,
      "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",
              "    .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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5     NaN\n",
              "2  100003000.0   197.0   LEXINGTON            N       NaN   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY          NaN       1.0   NaN   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       NaN   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON          NaN       2.0   NaN   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0     NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       NaN   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-oK8JN-F-3vH",
        "colab_type": "code",
        "outputId": "3faa970e-f0e3-44b9-ffc8-c72512fe2d97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df['OWN_OCCUPIED'].fillna(df['OWN_OCCUPIED'].mode()[0], inplace=True)\n",
        "df"
      ],
      "execution_count": 0,
      "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",
              "    .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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5     NaN\n",
              "2  100003000.0   197.0   LEXINGTON            N       NaN   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY            Y       1.0   NaN   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       NaN   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON            Y       2.0   NaN   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0     NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       NaN   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KVrPPXtw_sG2",
        "colab_type": "code",
        "outputId": "b8450723-769e-4521-d236-6b368e87f95d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
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              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5     NaN\n",
              "2  100003000.0   197.0   LEXINGTON            N       NaN   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY            Y       1.0   NaN   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       NaN   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON            Y       2.0   NaN   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0     NaN\n",
              "8  100009000.0   215.0     TREMONT            Y       NaN   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7beGsKQPSAJ1",
        "colab_type": "code",
        "outputId": "fcee57f4-f243-4c83-f219-c852a88abccb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "#Group By parameter check\n",
        "df.groupby('BEDROOMS')['SQ_FT'].median()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BEDROOMS\n",
              "1.0     700.0\n",
              "2.0     950.0\n",
              "3.0    1300.0\n",
              "Name: SQ_FT, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qdc5yiVnUzKk",
        "colab_type": "code",
        "outputId": "dfca1eb2-3d97-41c2-ec63-5703104eaeb2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "# Filling Null with group by vparameter\n",
        "df['SQ_FT'] = df['SQ_FT'].fillna(df.groupby('BEDROOMS')['SQ_FT'].transform('median'))\n",
        "df['SQ_FT'] = df['SQ_FT'].fillna(df['SQ_FT'].median())\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1300.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5  1300.0\n",
              "2  100003000.0   197.0   LEXINGTON            N       NaN   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY            Y       1.0   NaN   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       NaN   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON            Y       2.0   NaN   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0   700.0\n",
              "8  100009000.0   215.0     TREMONT            Y       NaN   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgFuJkWkcVpJ",
        "colab_type": "code",
        "outputId": "18791889-6090-4845-b529-0965fc597c49",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df.loc[2,'BEDROOMS'] = 1\n",
        "df.loc[5,'BEDROOMS'] = 1\n",
        "df.loc[8,'BEDROOMS'] = 3\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
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              "      <td>Y</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
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              "      <td>N</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5  1300.0\n",
              "2  100003000.0   197.0   LEXINGTON            N       1.0   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY            Y       1.0   NaN   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       1.0   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON            Y       2.0   NaN   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0   700.0\n",
              "8  100009000.0   215.0     TREMONT            Y       3.0   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5bhUzugUYSXd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Filling Null with group by vparameter\n",
        "df['BEDROOMS'] = df['BEDROOMS'].fillna(df.groupby('SQ_FT')['BEDROOMS'].transform('median'))\n",
        "df['BATH'] = df['BATH'].fillna(df['BATH'].median())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2RQqF9UbGPN",
        "colab_type": "code",
        "outputId": "6cb72046-aa93-4c4f-b537-d08de8d74baa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "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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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000.0</td>\n",
              "      <td>104.0</td>\n",
              "      <td>PUTNAM</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1300.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
              "      <td>197.0</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5  1300.0\n",
              "2  100003000.0   197.0   LEXINGTON            N       1.0   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY            Y       1.0   1.0   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       1.0   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON            Y       2.0   1.0   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0   700.0\n",
              "8  100009000.0   215.0     TREMONT            Y       3.0   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qooZiYcUgELy",
        "colab_type": "code",
        "outputId": "120823fa-16f8-43fc-dd58-b953889fc5fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df['BATH']=df['BATH'].fillna(method='bfill')\n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000.0</td>\n",
              "      <td>197.0</td>\n",
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              "      <td>3.0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1300.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000.0</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>850.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000.0</td>\n",
              "      <td>201.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000.0</td>\n",
              "      <td>203.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000.0</td>\n",
              "      <td>207.0</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000.0</td>\n",
              "      <td>208.0</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>950.0</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000.0</td>\n",
              "      <td>213.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>700.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000.0</td>\n",
              "      <td>215.0</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1800.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH   SQ_FT\n",
              "0  100001000.0   104.0      PUTNAM            Y       3.0   1.0  1000.0\n",
              "1  100002000.0   197.0   LEXINGTON            N       3.0   1.5  1300.0\n",
              "2  100003000.0   197.0   LEXINGTON            N       1.0   1.0   850.0\n",
              "3  100004000.0   201.0    BERKELEY            Y       1.0   1.0   700.0\n",
              "4  100005000.0   203.0    BERKELEY            Y       3.0   2.0  1600.0\n",
              "5  100006000.0   207.0    BERKELEY            Y       1.0   1.0   800.0\n",
              "6  100007000.0   208.0  WASHINGTON            Y       2.0   1.0   950.0\n",
              "7  100008000.0   213.0     TREMONT            Y       1.0   1.0   700.0\n",
              "8  100009000.0   215.0     TREMONT            Y       3.0   2.0  1800.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RUEPPPZMA2ZP",
        "colab_type": "code",
        "outputId": "e0b21098-4e1d-4770-927a-dc45d4f1e76d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "#Converting street number to int\n",
        "df.PID = df.PID.astype('int64') \n",
        "df.ST_NUM = df.ST_NUM.astype('int64') \n",
        "df.BEDROOMS = df.BEDROOMS.astype('int64') \n",
        "df.BATH = df.BATH.astype('int64') \n",
        "df.SQ_FT = df.SQ_FT.astype('int64') \n",
        "df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000</td>\n",
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              "      <td>1300</td>\n",
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              "      <th>3</th>\n",
              "      <td>100004000</td>\n",
              "      <td>201</td>\n",
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              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000</td>\n",
              "      <td>203</td>\n",
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              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000</td>\n",
              "      <td>207</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000</td>\n",
              "      <td>208</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>950</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000</td>\n",
              "      <td>213</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000</td>\n",
              "      <td>215</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH  SQ_FT\n",
              "0  100001000     104      PUTNAM            Y         3     1   1000\n",
              "1  100002000     197   LEXINGTON            N         3     1   1300\n",
              "2  100003000     197   LEXINGTON            N         1     1    850\n",
              "3  100004000     201    BERKELEY            Y         1     1    700\n",
              "4  100005000     203    BERKELEY            Y         3     2   1600\n",
              "5  100006000     207    BERKELEY            Y         1     1    800\n",
              "6  100007000     208  WASHINGTON            Y         2     1    950\n",
              "7  100008000     213     TREMONT            Y         1     1    700\n",
              "8  100009000     215     TREMONT            Y         3     2   1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vRctxkeTrdB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df2=df\n",
        "df1=df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWIRENFBUBe1",
        "colab_type": "code",
        "outputId": "c80b4140-ff1d-4ab1-97c6-b52203bbc78b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df2"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
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              "      <th>0</th>\n",
              "      <td>100001000</td>\n",
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              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000</td>\n",
              "      <td>197</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000</td>\n",
              "      <td>197</td>\n",
              "      <td>LEXINGTON</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000</td>\n",
              "      <td>201</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000</td>\n",
              "      <td>203</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000</td>\n",
              "      <td>207</td>\n",
              "      <td>BERKELEY</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000</td>\n",
              "      <td>208</td>\n",
              "      <td>WASHINGTON</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000</td>\n",
              "      <td>213</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000</td>\n",
              "      <td>215</td>\n",
              "      <td>TREMONT</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PID  ST_NUM     ST_NAME OWN_OCCUPIED  BEDROOMS  BATH  SQ_FT\n",
              "0  100001000     104      PUTNAM            Y         3     1   1000\n",
              "1  100002000     197   LEXINGTON            N         3     1   1300\n",
              "2  100003000     197   LEXINGTON            N         1     1    850\n",
              "3  100004000     201    BERKELEY            Y         1     1    700\n",
              "4  100005000     203    BERKELEY            Y         3     2   1600\n",
              "5  100006000     207    BERKELEY            Y         1     1    800\n",
              "6  100007000     208  WASHINGTON            Y         2     1    950\n",
              "7  100008000     213     TREMONT            Y         1     1    700\n",
              "8  100009000     215     TREMONT            Y         3     2   1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OEqzCkXnbHFD",
        "colab_type": "code",
        "outputId": "581bc096-ad7d-4f4b-f3e6-3ea9dc342fa7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "# Label Encoding\n",
        "from sklearn.preprocessing import LabelEncoder \n",
        "le = LabelEncoder() \n",
        "df['ST_NAME']= le.fit_transform(df['ST_NAME']) \n",
        "df"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000</td>\n",
              "      <td>104</td>\n",
              "      <td>2</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000</td>\n",
              "      <td>201</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000</td>\n",
              "      <td>207</td>\n",
              "      <td>0</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000</td>\n",
              "      <td>208</td>\n",
              "      <td>4</td>\n",
              "      <td>Y</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>Y</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000</td>\n",
              "      <td>215</td>\n",
              "      <td>3</td>\n",
              "      <td>Y</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PID  ST_NUM  ST_NAME OWN_OCCUPIED  BEDROOMS  BATH  SQ_FT\n",
              "0  100001000     104        2            Y         3     1   1000\n",
              "1  100002000     197        1            N         3     1   1300\n",
              "2  100003000     197        1            N         1     1    850\n",
              "3  100004000     201        0            Y         1     1    700\n",
              "4  100005000     203        0            Y         3     2   1600\n",
              "5  100006000     207        0            Y         1     1    800\n",
              "6  100007000     208        4            Y         2     1    950\n",
              "7  100008000     213        3            Y         1     1    700\n",
              "8  100009000     215        3            Y         3     2   1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "td3grbtxixz1",
        "colab_type": "code",
        "outputId": "59e02372-3bae-4424-c280-179442a8a815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "#use of mapping function\n",
        "mapping = {'Y' :1 , 'N' : 2 }\n",
        "df['OWN_OCCUPIED'] = df['OWN_OCCUPIED'].map(mapping)\n",
        "df"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000</td>\n",
              "      <td>104</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000</td>\n",
              "      <td>201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000</td>\n",
              "      <td>207</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000</td>\n",
              "      <td>208</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000</td>\n",
              "      <td>215</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PID  ST_NUM  ST_NAME  OWN_OCCUPIED  BEDROOMS  BATH  SQ_FT\n",
              "0  100001000     104        2             1         3     1   1000\n",
              "1  100002000     197        1             2         3     1   1300\n",
              "2  100003000     197        1             2         1     1    850\n",
              "3  100004000     201        0             1         1     1    700\n",
              "4  100005000     203        0             1         3     2   1600\n",
              "5  100006000     207        0             1         1     1    800\n",
              "6  100007000     208        4             1         2     1    950\n",
              "7  100008000     213        3             1         1     1    700\n",
              "8  100009000     215        3             1         3     2   1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnUNTVVpeGwP",
        "colab_type": "code",
        "outputId": "05641497-267d-40ed-baf8-49940a26986f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "#One Hot Encoding for nominal data\n",
        "df1 = pd.get_dummies(df, columns=['OWN_OCCUPIED'])\n",
        "df1"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "      <th>OWN_OCCUPIED_1</th>\n",
              "      <th>OWN_OCCUPIED_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000</td>\n",
              "      <td>104</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1300</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000</td>\n",
              "      <td>201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000</td>\n",
              "      <td>207</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000</td>\n",
              "      <td>208</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>950</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000</td>\n",
              "      <td>215</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PID  ST_NUM  ST_NAME  ...  SQ_FT  OWN_OCCUPIED_1  OWN_OCCUPIED_2\n",
              "0  100001000     104        2  ...   1000               1               0\n",
              "1  100002000     197        1  ...   1300               0               1\n",
              "2  100003000     197        1  ...    850               0               1\n",
              "3  100004000     201        0  ...    700               1               0\n",
              "4  100005000     203        0  ...   1600               1               0\n",
              "5  100006000     207        0  ...    800               1               0\n",
              "6  100007000     208        4  ...    950               1               0\n",
              "7  100008000     213        3  ...    700               1               0\n",
              "8  100009000     215        3  ...   1800               1               0\n",
              "\n",
              "[9 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzbCEHp1iDro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "std = StandardScaler()\n",
        "\n",
        "df2 = pd.DataFrame(std.fit_transform(df.values.reshape(-1, 1)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5CDm2Sb9M9R",
        "colab_type": "code",
        "outputId": "d7d61de1-1b92-4c32-e622-e7ec86a5b6c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "df2"
      ],
      "execution_count": 0,
      "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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.449375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.408251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.408254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.408254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.408254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>-0.408254</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>-0.408254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>-0.408254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>-0.408254</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>-0.408203</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>63 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0\n",
              "0   2.449375\n",
              "1  -0.408251\n",
              "2  -0.408254\n",
              "3  -0.408254\n",
              "4  -0.408254\n",
              "..       ...\n",
              "58 -0.408254\n",
              "59 -0.408254\n",
              "60 -0.408254\n",
              "61 -0.408254\n",
              "62 -0.408203\n",
              "\n",
              "[63 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EL00AoSBp2n",
        "colab_type": "code",
        "outputId": "953a4e58-8d35-4b43-ff95-2e6ea31d28a3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(df)\n",
        "scaled = pd.DataFrame(scaler.transform(df),columns=df.columns)\n",
        "scaled"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.272727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.125</td>\n",
              "      <td>0.837838</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.545455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.250</td>\n",
              "      <td>0.837838</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.136364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.375</td>\n",
              "      <td>0.873874</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.500</td>\n",
              "      <td>0.891892</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.818182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.625</td>\n",
              "      <td>0.927928</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.090909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.750</td>\n",
              "      <td>0.936937</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.227273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.875</td>\n",
              "      <td>0.981982</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     PID    ST_NUM  ST_NAME  OWN_OCCUPIED  BEDROOMS  BATH     SQ_FT\n",
              "0  0.000  0.000000     0.50           0.0       1.0   0.0  0.272727\n",
              "1  0.125  0.837838     0.25           1.0       1.0   0.0  0.545455\n",
              "2  0.250  0.837838     0.25           1.0       0.0   0.0  0.136364\n",
              "3  0.375  0.873874     0.00           0.0       0.0   0.0  0.000000\n",
              "4  0.500  0.891892     0.00           0.0       1.0   1.0  0.818182\n",
              "5  0.625  0.927928     0.00           0.0       0.0   0.0  0.090909\n",
              "6  0.750  0.936937     1.00           0.0       0.5   0.0  0.227273\n",
              "7  0.875  0.981982     0.75           0.0       0.0   0.0  0.000000\n",
              "8  1.000  1.000000     0.75           0.0       1.0   1.0  1.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lq_xxAkIVNZl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df3=df"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emllggV8FJcb",
        "colab_type": "code",
        "outputId": "d751ecd7-dec4-4171-f25f-1d591578adc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "df"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>100001000</td>\n",
              "      <td>104</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100002000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100003000</td>\n",
              "      <td>197</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100004000</td>\n",
              "      <td>201</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100005000</td>\n",
              "      <td>203</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100006000</td>\n",
              "      <td>207</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100007000</td>\n",
              "      <td>208</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100008000</td>\n",
              "      <td>213</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100009000</td>\n",
              "      <td>215</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         PID  ST_NUM  ST_NAME  OWN_OCCUPIED  BEDROOMS  BATH  SQ_FT\n",
              "0  100001000     104        2             1         3     1   1000\n",
              "1  100002000     197        1             2         3     1   1300\n",
              "2  100003000     197        1             2         1     1    850\n",
              "3  100004000     201        0             1         1     1    700\n",
              "4  100005000     203        0             1         3     2   1600\n",
              "5  100006000     207        0             1         1     1    800\n",
              "6  100007000     208        4             1         2     1    950\n",
              "7  100008000     213        3             1         1     1    700\n",
              "8  100009000     215        3             1         3     2   1800"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JjujN_iJRBOf",
        "colab_type": "code",
        "outputId": "216459e7-e317-47b8-f4e1-c2770a025c0e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "from sklearn.preprocessing import normalize\n",
        "data_normalized = normalize(df)\n",
        "data_normalized = pd.DataFrame(data_normalized, columns=df.columns)\n",
        "data_normalized"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000001</td>\n",
              "      <td>1.999980e-08</td>\n",
              "      <td>9.999900e-09</td>\n",
              "      <td>2.999970e-08</td>\n",
              "      <td>9.999900e-09</td>\n",
              "      <td>0.000010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>9.999800e-09</td>\n",
              "      <td>1.999960e-08</td>\n",
              "      <td>2.999940e-08</td>\n",
              "      <td>9.999800e-09</td>\n",
              "      <td>0.000013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>9.999700e-09</td>\n",
              "      <td>1.999940e-08</td>\n",
              "      <td>9.999700e-09</td>\n",
              "      <td>9.999700e-09</td>\n",
              "      <td>0.000008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>9.999600e-09</td>\n",
              "      <td>9.999600e-09</td>\n",
              "      <td>9.999600e-09</td>\n",
              "      <td>0.000007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>9.999500e-09</td>\n",
              "      <td>2.999850e-08</td>\n",
              "      <td>1.999900e-08</td>\n",
              "      <td>0.000016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>9.999400e-09</td>\n",
              "      <td>9.999400e-09</td>\n",
              "      <td>9.999400e-09</td>\n",
              "      <td>0.000008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>3.999720e-08</td>\n",
              "      <td>9.999300e-09</td>\n",
              "      <td>1.999860e-08</td>\n",
              "      <td>9.999300e-09</td>\n",
              "      <td>0.000009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>2.999760e-08</td>\n",
              "      <td>9.999200e-09</td>\n",
              "      <td>9.999200e-09</td>\n",
              "      <td>9.999200e-09</td>\n",
              "      <td>0.000007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000002</td>\n",
              "      <td>2.999730e-08</td>\n",
              "      <td>9.999100e-09</td>\n",
              "      <td>2.999730e-08</td>\n",
              "      <td>1.999820e-08</td>\n",
              "      <td>0.000018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   PID    ST_NUM       ST_NAME  ...      BEDROOMS          BATH     SQ_FT\n",
              "0  1.0  0.000001  1.999980e-08  ...  2.999970e-08  9.999900e-09  0.000010\n",
              "1  1.0  0.000002  9.999800e-09  ...  2.999940e-08  9.999800e-09  0.000013\n",
              "2  1.0  0.000002  9.999700e-09  ...  9.999700e-09  9.999700e-09  0.000008\n",
              "3  1.0  0.000002  0.000000e+00  ...  9.999600e-09  9.999600e-09  0.000007\n",
              "4  1.0  0.000002  0.000000e+00  ...  2.999850e-08  1.999900e-08  0.000016\n",
              "5  1.0  0.000002  0.000000e+00  ...  9.999400e-09  9.999400e-09  0.000008\n",
              "6  1.0  0.000002  3.999720e-08  ...  1.999860e-08  9.999300e-09  0.000009\n",
              "7  1.0  0.000002  2.999760e-08  ...  9.999200e-09  9.999200e-09  0.000007\n",
              "8  1.0  0.000002  2.999730e-08  ...  2.999730e-08  1.999820e-08  0.000018\n",
              "\n",
              "[9 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NzubtuhsVW1W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "std = StandardScaler()\n",
        "\n",
        "standard= pd.DataFrame(std.fit_transform(df3.values.reshape(-1, 1)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rKbeezAVVsrV",
        "colab_type": "code",
        "outputId": "d98fc577-bb45-4c42-f5f6-f01a8557bd11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "source": [
        "standard"
      ],
      "execution_count": 0,
      "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",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.645630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.377967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>-0.377916</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>-0.377970</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>72 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           0\n",
              "0   2.645630\n",
              "1  -0.377967\n",
              "2  -0.377970\n",
              "3  -0.377970\n",
              "4  -0.377970\n",
              "..       ...\n",
              "67 -0.377970\n",
              "68 -0.377970\n",
              "69 -0.377970\n",
              "70 -0.377916\n",
              "71 -0.377970\n",
              "\n",
              "[72 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guAYBa2kVvky",
        "colab_type": "code",
        "outputId": "b624c6fa-9cc2-421b-f130-78bda2c28983",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "import pandas as pd    \n",
        "from sklearn.preprocessing import StandardScaler\n",
        "ss = StandardScaler()\n",
        "df_scaledstd = pd.DataFrame(ss.fit_transform(df),columns = df.columns)\n",
        "df_scaledstd"
      ],
      "execution_count": 0,
      "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",
              "\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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.549193</td>\n",
              "      <td>-2.779199</td>\n",
              "      <td>0.312348</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.206195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.161895</td>\n",
              "      <td>0.096190</td>\n",
              "      <td>-0.390434</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>0.589128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.774597</td>\n",
              "      <td>0.096190</td>\n",
              "      <td>-0.390434</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.603856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.387298</td>\n",
              "      <td>0.219862</td>\n",
              "      <td>-1.093216</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.001517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.281699</td>\n",
              "      <td>-1.093216</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>1.384450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.387298</td>\n",
              "      <td>0.405371</td>\n",
              "      <td>-1.093216</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.736410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.774597</td>\n",
              "      <td>0.436290</td>\n",
              "      <td>1.717911</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.338748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.161895</td>\n",
              "      <td>0.590880</td>\n",
              "      <td>1.015129</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.001517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.549193</td>\n",
              "      <td>0.652717</td>\n",
              "      <td>1.015129</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>1.914665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PID    ST_NUM   ST_NAME  OWN_OCCUPIED  BEDROOMS      BATH     SQ_FT\n",
              "0 -1.549193 -2.779199  0.312348     -0.534522   1.06066 -0.534522 -0.206195\n",
              "1 -1.161895  0.096190 -0.390434      1.870829   1.06066 -0.534522  0.589128\n",
              "2 -0.774597  0.096190 -0.390434      1.870829  -1.06066 -0.534522 -0.603856\n",
              "3 -0.387298  0.219862 -1.093216     -0.534522  -1.06066 -0.534522 -1.001517\n",
              "4  0.000000  0.281699 -1.093216     -0.534522   1.06066  1.870829  1.384450\n",
              "5  0.387298  0.405371 -1.093216     -0.534522  -1.06066 -0.534522 -0.736410\n",
              "6  0.774597  0.436290  1.717911     -0.534522   0.00000 -0.534522 -0.338748\n",
              "7  1.161895  0.590880  1.015129     -0.534522  -1.06066 -0.534522 -1.001517\n",
              "8  1.549193  0.652717  1.015129     -0.534522   1.06066  1.870829  1.914665"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v-zDQ_spFrN8",
        "colab_type": "code",
        "outputId": "77babb1a-28d5-4cb0-c31b-d04548f8f607",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.549193</td>\n",
              "      <td>-2.779199</td>\n",
              "      <td>0.312348</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.206195</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-1.161895</td>\n",
              "      <td>0.096190</td>\n",
              "      <td>-0.390434</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>0.589128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.774597</td>\n",
              "      <td>0.096190</td>\n",
              "      <td>-0.390434</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.603856</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.387298</td>\n",
              "      <td>0.219862</td>\n",
              "      <td>-1.093216</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.001517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.281699</td>\n",
              "      <td>-1.093216</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>1.384450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.387298</td>\n",
              "      <td>0.405371</td>\n",
              "      <td>-1.093216</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.736410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.774597</td>\n",
              "      <td>0.436290</td>\n",
              "      <td>1.717911</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-0.338748</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.161895</td>\n",
              "      <td>0.590880</td>\n",
              "      <td>1.015129</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.06066</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>-1.001517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.549193</td>\n",
              "      <td>0.652717</td>\n",
              "      <td>1.015129</td>\n",
              "      <td>-0.534522</td>\n",
              "      <td>1.06066</td>\n",
              "      <td>1.870829</td>\n",
              "      <td>1.914665</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        PID    ST_NUM   ST_NAME  OWN_OCCUPIED  BEDROOMS      BATH     SQ_FT\n",
              "0 -1.549193 -2.779199  0.312348     -0.534522   1.06066 -0.534522 -0.206195\n",
              "1 -1.161895  0.096190 -0.390434      1.870829   1.06066 -0.534522  0.589128\n",
              "2 -0.774597  0.096190 -0.390434      1.870829  -1.06066 -0.534522 -0.603856\n",
              "3 -0.387298  0.219862 -1.093216     -0.534522  -1.06066 -0.534522 -1.001517\n",
              "4  0.000000  0.281699 -1.093216     -0.534522   1.06066  1.870829  1.384450\n",
              "5  0.387298  0.405371 -1.093216     -0.534522  -1.06066 -0.534522 -0.736410\n",
              "6  0.774597  0.436290  1.717911     -0.534522   0.00000 -0.534522 -0.338748\n",
              "7  1.161895  0.590880  1.015129     -0.534522  -1.06066 -0.534522 -1.001517\n",
              "8  1.549193  0.652717  1.015129     -0.534522   1.06066  1.870829  1.914665"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qdx33MWsFsVL",
        "colab_type": "code",
        "outputId": "0d88ae72-a637-4774-e081-d492a691bca0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        }
      },
      "source": [
        "from sklearn.preprocessing import RobustScaler\n",
        "robust = RobustScaler()\n",
        "robust_scaled_df = robust.fit_transform(df)\n",
        "robust_scaled_df = pd.DataFrame(robust_scaled_df, columns=df.columns)\n",
        "robust_scaled_df"
      ],
      "execution_count": 0,
      "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>PID</th>\n",
              "      <th>ST_NUM</th>\n",
              "      <th>ST_NAME</th>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <th>BEDROOMS</th>\n",
              "      <th>BATH</th>\n",
              "      <th>SQ_FT</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-1.00</td>\n",
              "      <td>-9.000000</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.75</td>\n",
              "      <td>-0.545455</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.50</td>\n",
              "      <td>-0.545455</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.25</td>\n",
              "      <td>-0.181818</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.25</td>\n",
              "      <td>0.363636</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.454545</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>-0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.00</td>\n",
              "      <td>1.090909</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    PID    ST_NUM   ST_NAME  OWN_OCCUPIED  BEDROOMS  BATH  SQ_FT\n",
              "0 -1.00 -9.000000  0.333333           0.0       0.5   0.0    0.1\n",
              "1 -0.75 -0.545455  0.000000           1.0       0.5   0.0    0.7\n",
              "2 -0.50 -0.545455  0.000000           1.0      -0.5   0.0   -0.2\n",
              "3 -0.25 -0.181818 -0.333333           0.0      -0.5   0.0   -0.5\n",
              "4  0.00  0.000000 -0.333333           0.0       0.5   1.0    1.3\n",
              "5  0.25  0.363636 -0.333333           0.0      -0.5   0.0   -0.3\n",
              "6  0.50  0.454545  1.000000           0.0       0.0   0.0    0.0\n",
              "7  0.75  0.909091  0.666667           0.0      -0.5   0.0   -0.5\n",
              "8  1.00  1.090909  0.666667           0.0       0.5   1.0    1.7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZ2nUT3HO2lQ",
        "colab_type": "code",
        "outputId": "8deba320-4c42-4eb5-b416-8ed2eaeb8eff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        }
      },
      "source": [
        "transposed_df=df.transpose()\n",
        "transposed_df"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>PID</th>\n",
              "      <td>100001000</td>\n",
              "      <td>100002000</td>\n",
              "      <td>100003000</td>\n",
              "      <td>100004000</td>\n",
              "      <td>100005000</td>\n",
              "      <td>100006000</td>\n",
              "      <td>100007000</td>\n",
              "      <td>100008000</td>\n",
              "      <td>100009000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ST_NUM</th>\n",
              "      <td>104</td>\n",
              "      <td>197</td>\n",
              "      <td>197</td>\n",
              "      <td>201</td>\n",
              "      <td>203</td>\n",
              "      <td>207</td>\n",
              "      <td>208</td>\n",
              "      <td>213</td>\n",
              "      <td>215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ST_NAME</th>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OWN_OCCUPIED</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BEDROOMS</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BATH</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SQ_FT</th>\n",
              "      <td>1000</td>\n",
              "      <td>1300</td>\n",
              "      <td>850</td>\n",
              "      <td>700</td>\n",
              "      <td>1600</td>\n",
              "      <td>800</td>\n",
              "      <td>950</td>\n",
              "      <td>700</td>\n",
              "      <td>1800</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      0          1          2  ...          6          7          8\n",
              "PID           100001000  100002000  100003000  ...  100007000  100008000  100009000\n",
              "ST_NUM              104        197        197  ...        208        213        215\n",
              "ST_NAME               2          1          1  ...          4          3          3\n",
              "OWN_OCCUPIED          1          2          2  ...          1          1          1\n",
              "BEDROOMS              3          3          1  ...          2          1          3\n",
              "BATH                  1          1          1  ...          1          1          2\n",
              "SQ_FT              1000       1300        850  ...        950        700       1800\n",
              "\n",
              "[7 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7GY5cMNWQQrz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}