ml_ex_ts_feature_engineering.ipynb

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      "source": [
        "# Time-related Feature Engineering"
      ],
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        "id": "Lr6LQoE6reHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
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      },
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        "id": "Gmn3EDEc3NnO"
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      "source": [
        "from sklearn.datasets import fetch_openml"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bike_sharing = fetch_openml('Bike_Sharing_Demand', version= 2, as_frame= True)\n",
        "df = bike_sharing.frame\n",
        "df.shape"
      ],
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/datasets/_openml.py:968: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
            "  warn(\n"
          ]
        },
        {
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            "text/plain": [
              "(17379, 13)"
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          "execution_count": 3
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      "source": [
        "df.head(3)"
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      "execution_count": 4,
      "outputs": [
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            "text/plain": [
              "   season  year  month  hour holiday  weekday workingday weather  temp  \\\n",
              "0  spring     0      1     0   False        6      False   clear  9.84   \n",
              "1  spring     0      1     1   False        6      False   clear  9.02   \n",
              "2  spring     0      1     2   False        6      False   clear  9.02   \n",
              "\n",
              "   feel_temp  humidity  windspeed  count  \n",
              "0     14.395      0.81        0.0     16  \n",
              "1     13.635      0.80        0.0     40  \n",
              "2     13.635      0.80        0.0     32  "
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 17379,\n  \"fields\": [\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"summer\",\n          \"winter\",\n          \"spring\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"True\",\n          \"False\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"True\",\n          \"False\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"misty\",\n          \"heavy_rain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.894800971238598,\n        \"min\": 0.8200000000000001,\n        \"max\": 41.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          6.5600000000000005,\n          33.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feel_temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.592510781767972,\n        \"min\": 0.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          39.395,\n          46.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19292983406291508,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          0.29,\n          0.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.196795314376963,\n        \"min\": 0.0,\n        \"max\": 56.996900000000004,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          56.996900000000004,\n          32.9975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 1,\n        \"max\": 977,\n        \"num_unique_values\": 869,\n        \"samples\": [\n          594,\n          46\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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        "df.dtypes"
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          "data": {
            "text/plain": [
              "season        category\n",
              "year             int64\n",
              "month            int64\n",
              "hour             int64\n",
              "holiday       category\n",
              "weekday          int64\n",
              "workingday    category\n",
              "weather       category\n",
              "temp           float64\n",
              "feel_temp      float64\n",
              "humidity       float64\n",
              "windspeed      float64\n",
              "count            int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
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    {
      "cell_type": "code",
      "source": [
        "df.isna().sum(axis= 0)"
      ],
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        "id": "X1U-SHxT46mp",
        "outputId": "55a69047-56f6-40f5-ab2e-c39038b43727"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season        0\n",
              "year          0\n",
              "month         0\n",
              "hour          0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weather       0\n",
              "temp          0\n",
              "feel_temp     0\n",
              "humidity      0\n",
              "windspeed     0\n",
              "count         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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      "source": [
        "fig, ax = plt.subplots(figsize= (12, 4))\n",
        "average_week_demand = df.groupby(['weekday', 'hour'])['count'].mean()\n",
        "average_week_demand.plot(ax= ax)\n",
        "\n",
        "_ = ax.set(\n",
        "    title= 'Average hourly bike demand during the week',\n",
        "    xticks= [i * 24 for i in range(7)],\n",
        "    xticklabels= ['Sun', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat'],\n",
        "    xlabel= 'Time of the week',\n",
        "    ylabel= 'Number of bike rentals'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "qRiHj6em3dJQ",
        "outputId": "dd87bb25-82c8-4c0e-f117-da7ebe70eeed"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Target: the count of bike rentals on an hourly basis\n",
        "df['count'].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ukSbJRH34eCj",
        "outputId": "a227ebd9-056b-4c7a-80ad-5583013c64ba"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    17379.000000\n",
              "mean       189.463088\n",
              "std        181.387599\n",
              "min          1.000000\n",
              "25%         40.000000\n",
              "50%        142.000000\n",
              "75%        281.000000\n",
              "max        977.000000\n",
              "Name: count, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# normalize the target\n",
        "y = df['count'] / df['count'].max()"
      ],
      "metadata": {
        "id": "StIYByKs4ykC"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize= (12, 4))\n",
        "y.hist(bins= 30, ax= ax)\n",
        "_ = ax.set(\n",
        "    xlabel= 'Fraction of rented fleet demand',\n",
        "    ylabel= 'Number of hours'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "4M63Q9nym1k_",
        "outputId": "9da93bdd-7947-4168-d56e-3990486baa62"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(['count'], axis= 'columns')\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6wfA12CSnHnh",
        "outputId": "37925f66-7d41-4869-9437-f1a3c0ed37a9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17379, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163
        },
        "id": "QASBcjr1nYzQ",
        "outputId": "726b7619-d221-48e2-8d9f-cb91fb7f02df"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   season  year  month  hour holiday  weekday workingday weather  temp  \\\n",
              "0  spring     0      1     0   False        6      False   clear  9.84   \n",
              "1  spring     0      1     1   False        6      False   clear  9.02   \n",
              "2  spring     0      1     2   False        6      False   clear  9.02   \n",
              "\n",
              "   feel_temp  humidity  windspeed  \n",
              "0     14.395      0.81        0.0  \n",
              "1     13.635      0.80        0.0  \n",
              "2     13.635      0.80        0.0  "
            ],
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              "      <td>6</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>6</td>\n",
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              "      <td>clear</td>\n",
              "      <td>9.02</td>\n",
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              "        document.querySelector('#df-37fa879b-df26-45e1-938a-36043a11f482 button.colab-df-convert');\n",
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              "        const element = document.querySelector('#df-37fa879b-df26-45e1-938a-36043a11f482');\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",
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              "        <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-af7e67b0-a37f-4a71-9ce9-ff750cd5377a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 17379,\n  \"fields\": [\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"summer\",\n          \"winter\",\n          \"spring\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"month\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 12,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          11,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hour\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 0,\n        \"max\": 23,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          8,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"True\",\n          \"False\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weekday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"True\",\n          \"False\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"misty\",\n          \"heavy_rain\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.894800971238598,\n        \"min\": 0.8200000000000001,\n        \"max\": 41.0,\n        \"num_unique_values\": 50,\n        \"samples\": [\n          6.5600000000000005,\n          33.62\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"feel_temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.592510781767972,\n        \"min\": 0.0,\n        \"max\": 50.0,\n        \"num_unique_values\": 65,\n        \"samples\": [\n          39.395,\n          46.21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19292983406291508,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          0.29,\n          0.61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.196795314376963,\n        \"min\": 0.0,\n        \"max\": 56.996900000000004,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          56.996900000000004,\n          32.9975\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Introspect the distribution of the categorical variables\n",
        "\n",
        "X['weather'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EeRmCcJnaRR",
        "outputId": "eee36462-edc6-4b9b-a43f-a29a70b8eb00"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weather\n",
              "clear         11413\n",
              "misty          4544\n",
              "rain           1419\n",
              "heavy_rain        3\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# collaps the `heavy_rain` category into `rain` as we have only 3 records for it\n",
        "X['weather'] = (\n",
        "    X['weather']\n",
        "     .astype(object)\n",
        "     .replace(to_replace= 'heavy_rain', value= 'rain')\n",
        "     .astype('category')\n",
        ")\n",
        "\n",
        "X['weather'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWFoSLRNnquE",
        "outputId": "472fb470-7439-4af8-adbd-01f297e81416"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weather\n",
              "clear    11413\n",
              "misty     4544\n",
              "rain      1422\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['weather'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9cJvrr7zoGDa",
        "outputId": "a70f92b6-c0d9-4bae-fcf0-9d9b1f7477a2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoricalDtype(categories=['clear', 'misty', 'rain'], ordered=False)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# `season` is well balanced, as expected.\n",
        "X['season'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgFN9X9MnpKo",
        "outputId": "65aba436-0020-4891-b09a-2290849f0cc4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season\n",
              "fall      4496\n",
              "summer    4409\n",
              "spring    4242\n",
              "winter    4232\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['holiday'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KETQvotZodCz",
        "outputId": "d41c6252-549e-423d-c00f-76578e3243cd"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "holiday\n",
              "False    16879\n",
              "True       500\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['workingday'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45lrbcEporMb",
        "outputId": "05db2791-6843-482b-91a7-b45a9a0e7192"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "workingday\n",
              "True     11865\n",
              "False     5514\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['holiday'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJ-Dmx0Go2Ls",
        "outputId": "6f2b4a6b-43b5-4876-f509-153ccb2d29f0"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoricalDtype(categories=['False', 'True'], ordered=False)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['workingday'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cnGRRTb8o6r3",
        "outputId": "e6012bd7-1005-47f5-dd03-11d0dcdf5875"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoricalDtype(categories=['False', 'True'], ordered=False)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# both `map` & `replace` work\n",
        "X['holiday'] = X['holiday'].map({'False': 0, 'True': 1}).astype(np.float16)\n",
        "X['workingday'] = X['workingday'].replace({'False': 0, 'True': 1}).astype(np.float16)\n",
        "\n",
        "X['holiday'].dtype, X['workingday'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90q7ng7lo9-6",
        "outputId": "d19536d9-7b11-41b4-b1cc-fc7333c52b19"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(dtype('float16'), dtype('float16'))"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X['holiday'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Dv0Qv0Qopfi",
        "outputId": "c68d4663-007c-4238-89a5-14b36228503a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "holiday\n",
              "0.0    16879\n",
              "1.0      500\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KA2xvzVwq51Q"
      },
      "execution_count": 22,
      "outputs": []
    }
  ]
}