16352437_ICP5 (1).ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## **Data wrangling with Python. Performing Single table operations**"
      ],
      "metadata": {
        "id": "ilAnAtaQ_I6L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Sacramento Housing data**"
      ],
      "metadata": {
        "id": "_XhT9v7TBr5h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#checking python version\n",
        "!python --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSBbCx2G_Uqw",
        "outputId": "c255cab3-5089-4600-8fde-92179f531ee2"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Python 3.10.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd #importing pandas"
      ],
      "metadata": {
        "id": "F2rdvl0F_bJ_"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(pd.__version__) #checking library version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EenatMBZ_eku",
        "outputId": "0ff1dd0f-c158-420e-9359-5b1d388d46cb"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ds_url = 'https://raw.githubusercontent.com/JuliaData/CSV.jl/refs/heads/main/test/testfiles/Sacramentorealestatetransactions.csv'\n",
        "df = pd.read_csv(ds_url)"
      ],
      "metadata": {
        "id": "HiRYhBIl_m3m"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
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          "height": 423
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        "id": "x_YqxEaN_0s_",
        "outputId": "c5d96a53-aa52-4acc-e903-2fd499eef47c"
      },
      "execution_count": 21,
      "outputs": [
        {
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              "                  street             city    zip state  beds  baths  sq__ft  \\\n",
              "0           3526 HIGH ST       SACRAMENTO  95838    CA     2      1     836   \n",
              "1            51 OMAHA CT       SACRAMENTO  95823    CA     3      1    1167   \n",
              "2         2796 BRANCH ST       SACRAMENTO  95815    CA     2      1     796   \n",
              "3       2805 JANETTE WAY       SACRAMENTO  95815    CA     2      1     852   \n",
              "4        6001 MCMAHON DR       SACRAMENTO  95824    CA     2      1     797   \n",
              "..                   ...              ...    ...   ...   ...    ...     ...   \n",
              "980   9169 GARLINGTON CT       SACRAMENTO  95829    CA     4      3    2280   \n",
              "981      6932 RUSKUT WAY       SACRAMENTO  95823    CA     3      2    1477   \n",
              "982    7933 DAFFODIL WAY   CITRUS HEIGHTS  95610    CA     3      2    1216   \n",
              "983     8304 RED FOX WAY        ELK GROVE  95758    CA     4      2    1685   \n",
              "984  3882 YELLOWSTONE LN  EL DORADO HILLS  95762    CA     3      2    1362   \n",
              "\n",
              "            type                     sale_date   price   latitude   longitude  \n",
              "0    Residential  Wed May 21 00:00:00 EDT 2008   59222  38.631913 -121.434879  \n",
              "1    Residential  Wed May 21 00:00:00 EDT 2008   68212  38.478902 -121.431028  \n",
              "2    Residential  Wed May 21 00:00:00 EDT 2008   68880  38.618305 -121.443839  \n",
              "3    Residential  Wed May 21 00:00:00 EDT 2008   69307  38.616835 -121.439146  \n",
              "4    Residential  Wed May 21 00:00:00 EDT 2008   81900  38.519470 -121.435768  \n",
              "..           ...                           ...     ...        ...         ...  \n",
              "980  Residential  Thu May 15 00:00:00 EDT 2008  232425  38.457679 -121.359620  \n",
              "981  Residential  Thu May 15 00:00:00 EDT 2008  234000  38.499893 -121.458890  \n",
              "982  Residential  Thu May 15 00:00:00 EDT 2008  235000  38.708824 -121.256803  \n",
              "983  Residential  Thu May 15 00:00:00 EDT 2008  235301  38.417000 -121.397424  \n",
              "984  Residential  Thu May 15 00:00:00 EDT 2008  235738  38.655245 -121.075915  \n",
              "\n",
              "[985 rows x 12 columns]"
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              "        width: 32px;\n",
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              "      .colab-df-generate:hover {\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 985,\n  \"fields\": [\n    {\n      \"column\": \"street\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 981,\n        \"samples\": [\n          \"4204 LUSK DR\",\n          \"4741 PACIFIC PARK DR\",\n          \"7837 ABBINGTON WAY\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 39,\n        \"samples\": [\n          \"SHINGLE SPRINGS\",\n          \"GARDEN VALLEY\",\n          \"NORTH HIGHLANDS\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 85,\n        \"min\": 95603,\n        \"max\": 95864,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          95610,\n          95827,\n          95841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"CA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"beds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"baths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sq__ft\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 853,\n        \"min\": 0,\n        \"max\": 5822,\n        \"num_unique_values\": 603,\n        \"samples\": [\n          2367\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"type\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Condo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sale_date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Tue May 20 00:00:00 EDT 2008\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 138365,\n        \"min\": 1551,\n        \"max\": 884790,\n        \"num_unique_values\": 605,\n        \"samples\": [\n          668365\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14543287915297298,\n        \"min\": 38.241514,\n        \"max\": 39.020808,\n        \"num_unique_values\": 969,\n        \"samples\": [\n          38.886525\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.13827789978738897,\n        \"min\": -121.551704,\n        \"max\": -120.597599,\n        \"num_unique_values\": 967,\n        \"samples\": [\n          -121.372876\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape #checking number of rows and columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XJ1wlRKa_4QX",
        "outputId": "d1280492-f289-4065-b77e-34fd1ee73c64"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(985, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df.size #checking total number of elements"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0lnEDMeDAHpU",
        "outputId": "2b464334-c0be-42b4-8ee0-471cb304c3ff"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11820"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
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      "source": [
        "df.dtypes #checking data types"
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          "height": 460
        },
        "id": "6wozCyFUANWY",
        "outputId": "016ffc61-f879-4a39-993b-4e4c01917bff"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "street        object\n",
              "city          object\n",
              "zip            int64\n",
              "state         object\n",
              "beds           int64\n",
              "baths          int64\n",
              "sq__ft         int64\n",
              "type          object\n",
              "sale_date     object\n",
              "price          int64\n",
              "latitude     float64\n",
              "longitude    float64\n",
              "dtype: object"
            ],
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      "source": [
        "df.describe() #Looks at numerical columns and tells about their distribution"
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        },
        "id": "gKYr8yyHAZH1",
        "outputId": "5dbd84f1-69c1-4f65-c1cf-b02aceaa9bc7"
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      "execution_count": 25,
      "outputs": [
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            "text/plain": [
              "                zip        beds       baths       sq__ft          price  \\\n",
              "count    985.000000  985.000000  985.000000   985.000000     985.000000   \n",
              "mean   95750.697462    2.911675    1.776650  1314.916751  234144.263959   \n",
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              "min    95603.000000    0.000000    0.000000     0.000000    1551.000000   \n",
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              "50%    95762.000000    3.000000    2.000000  1304.000000  213750.000000   \n",
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              "max    95864.000000    8.000000    5.000000  5822.000000  884790.000000   \n",
              "\n",
              "         latitude   longitude  \n",
              "count  985.000000  985.000000  \n",
              "mean    38.607732 -121.355982  \n",
              "std      0.145433    0.138278  \n",
              "min     38.241514 -121.551704  \n",
              "25%     38.482717 -121.446127  \n",
              "50%     38.626582 -121.376220  \n",
              "75%     38.695589 -121.295778  \n",
              "max     39.020808 -120.597599  "
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              "      <th>25%</th>\n",
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              "      <td>952.000000</td>\n",
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              "\n",
              "\n",
              "<div id=\"df-d505d383-9bb4-4c4a-9b8e-ecbbae949e32\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d505d383-9bb4-4c4a-9b8e-ecbbae949e32')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d505d383-9bb4-4c4a-9b8e-ecbbae949e32 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44074.18258443427,\n        \"min\": 85.17607208451658,\n        \"max\": 95864.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          95750.69746192894,\n          95762.0,\n          985.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"beds\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 347.1863335155035,\n        \"min\": 0.0,\n        \"max\": 985.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2.9116751269035532,\n          3.0,\n          985.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"baths\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 347.6131420017901,\n        \"min\": 0.0,\n        \"max\": 985.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          985.0,\n          1.7766497461928934,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sq__ft\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1769.6403414077029,\n        \"min\": 0.0,\n        \"max\": 5822.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1314.9167512690356,\n          1304.0,\n          985.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 281175.5539179375,\n        \"min\": 985.0,\n        \"max\": 884790.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          234144.26395939087,\n          213750.0,\n          985.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 336.81054823444987,\n        \"min\": 0.14543287915297298,\n        \"max\": 985.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.60773208629442,\n          38.626582,\n          985.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 387.3307311582134,\n        \"min\": -121.551704,\n        \"max\": 985.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -121.35598223553299,\n          -121.37622,\n          985.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vCSwxn-kAbPE",
        "outputId": "54351c96-ea12-4264-d848-e926026f99a3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 985 entries, 0 to 984\n",
            "Data columns (total 12 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   street     985 non-null    object \n",
            " 1   city       985 non-null    object \n",
            " 2   zip        985 non-null    int64  \n",
            " 3   state      985 non-null    object \n",
            " 4   beds       985 non-null    int64  \n",
            " 5   baths      985 non-null    int64  \n",
            " 6   sq__ft     985 non-null    int64  \n",
            " 7   type       985 non-null    object \n",
            " 8   sale_date  985 non-null    object \n",
            " 9   price      985 non-null    int64  \n",
            " 10  latitude   985 non-null    float64\n",
            " 11  longitude  985 non-null    float64\n",
            "dtypes: float64(2), int64(5), object(5)\n",
            "memory usage: 92.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Performing the given operations using the functions used in ICP:**"
      ],
      "metadata": {
        "id": "kF5ZuTf0BoBS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ctUQUCWKHfNW",
        "outputId": "80ad128a-8710-497d-a043-2dd8e3be726c"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['street', 'city', 'zip', 'state', 'beds', 'baths', 'sq__ft', 'type',\n",
            "       'sale_date', 'price', 'latitude', 'longitude'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Finding number of columns**"
      ],
      "metadata": {
        "id": "DNHXHLOuHuYV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape[1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jXZPdDuCrWn",
        "outputId": "fba9726e-bdf3-470f-8dd6-032e4aa2c497"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Finding mean of the house prices**"
      ],
      "metadata": {
        "id": "b8UkIGvxH9Yc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ql9n5T7AC5LO",
        "outputId": "0e5b2933-021c-409a-8064-be3a595ecf6c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "234144.26395939087"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Finding median of the house prices**"
      ],
      "metadata": {
        "id": "SZm_v29tIJ4m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['price'].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9gjmRuC2C_xN",
        "outputId": "4793823a-15e9-44c4-ed8c-88e997d0d363"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "213750.0"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Find the different types of property in the dataset**"
      ],
      "metadata": {
        "id": "5K_qhqH8MC0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "N8FTq7neMKDZ",
        "outputId": "28250c44-c8be-435b-fa46-c0a354ccd433"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "Residential     917\n",
              "Condo            54\n",
              "Multi-Family     13\n",
              "Unkown            1\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Residential</th>\n",
              "      <td>917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Condo</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Multi-Family</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unkown</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Alternative way**"
      ],
      "metadata": {
        "id": "fLU-ZVw8NpuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find different types of properties using groupby\n",
        "df.groupby('type').size().index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zx0AUROyNiV3",
        "outputId": "0af61c57-ba07-472a-93c0-9308dc25c037"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Condo', 'Multi-Family', 'Residential', 'Unkown'], dtype='object', name='type')"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Finding different types of properties sorted in alphabatical order**"
      ],
      "metadata": {
        "id": "JDbNF-AaIvvO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('type').size().sort_index(ascending=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "NKSryX24DKxM",
        "outputId": "ff33ee82-6e59-40e9-90ee-e9c7fdda2c49"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "Condo            54\n",
              "Multi-Family     13\n",
              "Residential     917\n",
              "Unkown            1\n",
              "dtype: int64"
            ],
            "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>0</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Condo</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Multi-Family</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residential</th>\n",
              "      <td>917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unkown</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Finding max price of a \"condo\"**"
      ],
      "metadata": {
        "id": "QBmHTnZhI47d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[df['type'] == 'condo']['price'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lozGe4tQDMB8",
        "outputId": "aea8aa69-397a-4fc6-9ede-f21737269782"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "nan"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Finding mean price of each type of property**"
      ],
      "metadata": {
        "id": "668Ql0AWI_x0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby('type')['price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "8d8awCXgDWs3",
        "outputId": "181c4c46-263b-470c-ea47-7af233e42b3f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "Condo           150082.185185\n",
              "Multi-Family    224534.692308\n",
              "Residential     239186.162486\n",
              "Unkown          275000.000000\n",
              "Name: price, dtype: float64"
            ],
            "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>price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Condo</th>\n",
              "      <td>150082.185185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Multi-Family</th>\n",
              "      <td>224534.692308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residential</th>\n",
              "      <td>239186.162486</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unkown</th>\n",
              "      <td>275000.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Counting different property types**"
      ],
      "metadata": {
        "id": "Pk4Q52-iJIH9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['type'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "5OCyd_psDcaM",
        "outputId": "78446556-719c-44b4-d0a9-4e7d9a550776"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "type\n",
              "Residential     917\n",
              "Condo            54\n",
              "Multi-Family     13\n",
              "Unkown            1\n",
              "Name: count, dtype: int64"
            ],
            "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>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>type</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Residential</th>\n",
              "      <td>917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Condo</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Multi-Family</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Unkown</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    }
  ]
}