Python_Basics_13(Pandas).ipynb

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            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "dtype: int64\n",
            "1    1\n",
            "2    2\n",
            "3    3\n",
            "4    4\n",
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            "1    a\n",
            "2    b\n",
            "3    c\n",
            "4    d\n",
            "dtype: object\n"
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      "source": [
        "#Pandas\n",
        "\n",
        "#importing pandas\n",
        "import pandas as pd\n",
        "\n",
        "#pd.Series => 1D array constructor\n",
        "x = [1,2,3,4]\n",
        "s = pd.Series(x)\n",
        "print(s)\n",
        "\n",
        "#printing with customized index\n",
        "x = [1,2,3,4]\n",
        "s = pd.Series(x,index = [1,2,3,4])\n",
        "print(s)\n",
        "\n",
        "x = ['a','b','c','d']\n",
        "s = pd.Series(x,index = [1,2,3,4])\n",
        "print(s)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating pandas series from dictionary\n",
        "\n",
        "dict = {'name' : 'bob','country': 'india','job':'jobless'}\n",
        "print(pd.Series(dict))"
      ],
      "metadata": {
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      },
      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
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          "text": [
            "name           bob\n",
            "country      india\n",
            "job        jobless\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a constant pandas series\n",
        "\n",
        "print(pd.Series(1,index = [1,2,3,4]))\n"
      ],
      "metadata": {
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          "text": [
            "1    1\n",
            "2    1\n",
            "3    1\n",
            "4    1\n",
            "dtype: int64\n"
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    {
      "cell_type": "code",
      "source": [
        "#Creating a pandas series using linspace\n",
        "#linspace(start,end,no of items) => syntax\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "print(pd.Series(np.linspace(5,20,10),index = [1,2,3,4,5,6,7,8,9,10]))\n"
      ],
      "metadata": {
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          "text": [
            "1      5.000000\n",
            "2      6.666667\n",
            "3      8.333333\n",
            "4     10.000000\n",
            "5     11.666667\n",
            "6     13.333333\n",
            "7     15.000000\n",
            "8     16.666667\n",
            "9     18.333333\n",
            "10    20.000000\n",
            "dtype: float64\n"
          ]
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      "cell_type": "code",
      "source": [
        "#DataFrames => 2D\n",
        "\n",
        "#Pandas DataFrame  => table or spreadsheet format of data (Rowa and Cols)\n",
        "#can be of different types (int,float,object....)\n",
        "\n",
        "#Dataframe column => Series\n",
        "\n",
        "#pandas.DataFrame(data,index,columns,dtype,copy) => syntax\n",
        "\n",
        "#Creation\n",
        "\n",
        "#From Lists\n",
        "data = [\n",
        "    ['Asabeneh','Finland','Helsink'],\n",
        "    ['David','Uk','London'],\n",
        "    ['John','Sweden','Stockholm'],\n",
        "]\n",
        "\n",
        "data_frame = pd.DataFrame(data,columns= ['name','country','city'])\n",
        "print(data_frame)"
      ],
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      "execution_count": null,
      "outputs": [
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          "text": [
            "       name  country       city\n",
            "0  Asabeneh  Finland    Helsink\n",
            "1     David       Uk     London\n",
            "2      John   Sweden  Stockholm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#From Dictionary\n",
        "\n",
        "data = {\n",
        "    'name':['Asabeneh','David','John'],\n",
        "    'country':['Finland','Uk','Sweden'],\n",
        "    'city': ['Helsink','London','Stockholm']\n",
        "}\n",
        "\n",
        "data_frame = pd.DataFrame(data)\n",
        "print(data_frame)"
      ],
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      "execution_count": null,
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          "text": [
            "       name  country       city\n",
            "0  Asabeneh  Finland    Helsink\n",
            "1     David       Uk     London\n",
            "2      John   Sweden  Stockholm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#From List of Dictionary\n",
        "\n",
        "data = [\n",
        "    { 'name' : 'Asabeneh', 'country' : 'Finland', 'city' : 'Helsink'},\n",
        "    { 'name' : 'David', 'country' : 'UK', 'city' : 'London'},\n",
        "    { 'name' : 'John', 'country' : 'Sweden', 'city' : 'Stockholm'},\n",
        "\n",
        "]\n",
        "\n",
        "\n",
        "data_frame = pd.DataFrame(data)\n",
        "print(data_frame)"
      ],
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          "output_type": "stream",
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          "text": [
            "       name  country       city\n",
            "0  Asabeneh  Finland    Helsink\n",
            "1     David       UK     London\n",
            "2      John   Sweden  Stockholm\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a series with custom index\n",
        "\n",
        "print(pd.Series([1,2,3],index = ['a','b','c']))\n",
        "\n",
        "\n",
        "#Dictionary\n",
        "\n",
        "dict = pd.DataFrame({'col1':[1,2,3,4],'col2':['a','b','c','d']})\n",
        "print(dict)"
      ],
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        "outputId": "b5d676d7-7f6b-4268-ce0b-912835fea009"
      },
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "dtype: int64\n",
            "   col1 col2\n",
            "0     1    a\n",
            "1     2    b\n",
            "2     3    c\n",
            "3     4    d\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "#Extracting a series from  dataframe\n",
        "\n",
        "col1_series = dict['col1']\n",
        "print(col1_series)\n",
        "\n",
        "#or\n",
        "\n",
        "col1__series = dict.col1\n",
        "print(col1__series)\n",
        "\n"
      ],
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          "text": [
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "Name: col1, dtype: int64\n",
            "0    1\n",
            "1    2\n",
            "2    3\n",
            "3    4\n",
            "Name: col1, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a dataframe from series\n",
        "\n",
        "s1 = pd.Series([1,2,3])\n",
        "s2 = pd.Series([4,5,6])\n",
        "\n",
        "df = pd.DataFrame({'col1':s1,'col2':s2})\n",
        "print(df)"
      ],
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        },
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        "outputId": "f6496fa7-5bb4-4b92-feee-b68d4c760408"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   col1  col2\n",
            "0     1     4\n",
            "1     2     5\n",
            "2     3     6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Reading csv from pandas\n",
        "\n",
        "data_set = pd.read_csv('weight-height.csv')\n",
        "print(data_set)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e_DoKChWUepv",
        "outputId": "3fb2be71-d1d5-4c9a-dcfe-b67efe020b1a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      Gender     Height      Weight\n",
            "0       Male  73.847017  241.893563\n",
            "1       Male  68.781904  162.310473\n",
            "2       Male  74.110105  212.740856\n",
            "3       Male  71.730978  220.042470\n",
            "4       Male  69.881796  206.349801\n",
            "...      ...        ...         ...\n",
            "9995  Female  66.172652  136.777454\n",
            "9996  Female  67.067155  170.867906\n",
            "9997  Female  63.867992  128.475319\n",
            "9998  Female  69.034243  163.852461\n",
            "9999  Female  61.944246  113.649103\n",
            "\n",
            "[10000 rows x 3 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.head()\n"
      ],
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              "  Gender     Height      Weight\n",
              "0   Male  73.847017  241.893563\n",
              "1   Male  68.781904  162.310473\n",
              "2   Male  74.110105  212.740856\n",
              "3   Male  71.730978  220.042470\n",
              "4   Male  69.881796  206.349801"
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              "  .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-51f1af2c-f062-47a5-bf7a-237ed811e89f 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",
              "variable_name": "data_set",
              "summary": "{\n  \"name\": \"data_set\",\n  \"rows\": 10000,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.8475281207732293,\n        \"min\": 54.2631333250971,\n        \"max\": 78.9987423463896,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          68.6872321923365,\n          68.8293337723017\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.108439006519596,\n        \"min\": 64.700126712753,\n        \"max\": 269.989698505106,\n        \"num_unique_values\": 10000,\n        \"samples\": [\n          173.115812588004,\n          195.162042097346\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9DS84IvSWbe8",
        "outputId": "176ff9f4-c1ca-4b2c-eef1-d98ef8f69387"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Gender     Height      Weight\n",
              "9995  Female  66.172652  136.777454\n",
              "9996  Female  67.067155  170.867906\n",
              "9997  Female  63.867992  128.475319\n",
              "9998  Female  69.034243  163.852461\n",
              "9999  Female  61.944246  113.649103"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-35bfac1a-5c36-4556-9b08-3ae211a84859\" class=\"colab-df-container\">\n",
              "    <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",
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              "\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>Gender</th>\n",
              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>9995</th>\n",
              "      <td>Female</td>\n",
              "      <td>66.172652</td>\n",
              "      <td>136.777454</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>Female</td>\n",
              "      <td>67.067155</td>\n",
              "      <td>170.867906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>Female</td>\n",
              "      <td>63.867992</td>\n",
              "      <td>128.475319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>Female</td>\n",
              "      <td>69.034243</td>\n",
              "      <td>163.852461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>Female</td>\n",
              "      <td>61.944246</td>\n",
              "      <td>113.649103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-35bfac1a-5c36-4556-9b08-3ae211a84859')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-35bfac1a-5c36-4556-9b08-3ae211a84859 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-35bfac1a-5c36-4556-9b08-3ae211a84859');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7973be1d-ac1c-4798-afd7-989ab189f8cc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7973be1d-ac1c-4798-afd7-989ab189f8cc')\"\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",
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              "    </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-7973be1d-ac1c-4798-afd7-989ab189f8cc 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\": \"data_set\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Height\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.767553033101086,\n        \"min\": 61.9442458795172,\n        \"max\": 69.0342431307346,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          67.067154649054\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Weight\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.094746806287993,\n        \"min\": 113.649102675312,\n        \"max\": 170.867905890713,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          170.867905890713\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_set.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TDcnys7AWfJk",
        "outputId": "2945ac98-0b42-49f3-c129-46e368b50c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    }
  ]
}