Proyek_Analisis_Data_Bike_Sharing_Dataset (1).ipynb

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      "metadata": {
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      "source": [
        "# Proyek Analisis Data: Bike Sharing Dataset\n",
        "- **Nama:** Septia Clara Sinaga\n",
        "- **Email:** M232D4KX2951@bangkit.academy\n",
        "- **ID Dicoding:** M232D4KX2951"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE0raob58DC0"
      },
      "source": [
        "## Menentukan Pertanyaan Bisnis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmQeQ5YF8DC0"
      },
      "source": [
        "- Pertanyaan 1: Berapa total jam penyewaan sepeda tertinggi dalam satu hari yang tercatat dalam data rental sepeda dan apa faktor yang mempengaruhi peningkatan penyewaan sepeda pada hari tersebut?\n",
        "- Pertanyaan 2: Apakah penyewaan sepeda dipengaruhi oleh kondisi cuaca?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-z4QGlO8DC1"
      },
      "source": [
        "## Import Semua Packages/Library yang Digunakan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FVYwaObI8DC1"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_Sh51Xy8DC1"
      },
      "source": [
        "## Data Wrangling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXU2GBYu8DC1"
      },
      "source": [
        "### Gathering Data"
      ]
    },
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            "text/plain": [
              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1        0        6           0   \n",
              "1        2  2011-01-02       1   0     1        0        0           0   \n",
              "2        3  2011-01-03       1   0     1        0        1           1   \n",
              "3        4  2011-01-04       1   0     1        0        2           1   \n",
              "4        5  2011-01-05       1   0     1        0        3           1   \n",
              "\n",
              "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
              "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
              "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
              "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
              "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
              "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
              "2  1349  \n",
              "3  1562  \n",
              "4  1600  "
            ]
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              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.2727</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.2879</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</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-9d98bf7d-1029-4070-b2dd-6334d218223d')\"\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",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </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-9d98bf7d-1029-4070-b2dd-6334d218223d 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-9d98bf7d-1029-4070-b2dd-6334d218223d');\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-6af175a1-956f-4ef5-af1c-7864df5599b5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6af175a1-956f-4ef5-af1c-7864df5599b5')\"\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-6af175a1-956f-4ef5-af1c-7864df5599b5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   instant      dteday  season  yr  mnth  hr  holiday  weekday  workingday  \\\n",
              "0        1  2011-01-01       1   0     1   0        0        6           0   \n",
              "1        2  2011-01-01       1   0     1   1        0        6           0   \n",
              "2        3  2011-01-01       1   0     1   2        0        6           0   \n",
              "3        4  2011-01-01       1   0     1   3        0        6           0   \n",
              "4        5  2011-01-01       1   0     1   4        0        6           0   \n",
              "\n",
              "   weathersit  temp   atemp   hum  windspeed  casual  registered  cnt  \n",
              "0           1  0.24  0.2879  0.81        0.0       3          13   16  \n",
              "1           1  0.22  0.2727  0.80        0.0       8          32   40  \n",
              "2           1  0.22  0.2727  0.80        0.0       5          27   32  \n",
              "3           1  0.24  0.2879  0.75        0.0       3          10   13  \n",
              "4           1  0.24  0.2879  0.75        0.0       0           1    1  "
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_df = pd.read_csv(\"hour.csv\")\n",
        "hour_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHSiqaZp8DC1"
      },
      "source": [
        "### Assessing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LtUw83YJVd7K"
      },
      "source": [
        "DAY"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ax-3tEjc9Cj1",
        "outputId": "71db033d-a60a-422f-a934-186d00bb0390"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kqXTHWRuUiIr",
        "outputId": "487057a8-eb23-49db-e4fc-6b62b44c4cab"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjR-7ZPsUiV0",
        "outputId": "aff70616-02a7-41c0-e4a2-433a2175caf0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 731 entries, 0 to 730\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     731 non-null    int64  \n",
            " 1   dteday      731 non-null    object \n",
            " 2   season      731 non-null    int64  \n",
            " 3   yr          731 non-null    int64  \n",
            " 4   mnth        731 non-null    int64  \n",
            " 5   holiday     731 non-null    int64  \n",
            " 6   weekday     731 non-null    int64  \n",
            " 7   workingday  731 non-null    int64  \n",
            " 8   weathersit  731 non-null    int64  \n",
            " 9   temp        731 non-null    float64\n",
            " 10  atemp       731 non-null    float64\n",
            " 11  hum         731 non-null    float64\n",
            " 12  windspeed   731 non-null    float64\n",
            " 13  casual      731 non-null    int64  \n",
            " 14  registered  731 non-null    int64  \n",
            " 15  cnt         731 non-null    int64  \n",
            "dtypes: float64(4), int64(11), object(1)\n",
            "memory usage: 91.5+ KB\n"
          ]
        }
      ],
      "source": [
        "day_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m2woEXNbVmII"
      },
      "source": [
        "HOUR"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ro1e_xPDUevN",
        "outputId": "0cb012df-58ab-4e5d-de75-005f372b48f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "instant       0\n",
              "dteday        0\n",
              "season        0\n",
              "yr            0\n",
              "mnth          0\n",
              "hr            0\n",
              "holiday       0\n",
              "weekday       0\n",
              "workingday    0\n",
              "weathersit    0\n",
              "temp          0\n",
              "atemp         0\n",
              "hum           0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "cnt           0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G-nTH4FHUiPW",
        "outputId": "ba4c331b-1dd8-49f8-a902-1255d92a9739"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-e0tOeU1U5RL",
        "outputId": "60bcec29-2b3f-4bca-c034-2353973746ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17379 entries, 0 to 17378\n",
            "Data columns (total 17 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     17379 non-null  int64  \n",
            " 1   dteday      17379 non-null  object \n",
            " 2   season      17379 non-null  int64  \n",
            " 3   yr          17379 non-null  int64  \n",
            " 4   mnth        17379 non-null  int64  \n",
            " 5   hr          17379 non-null  int64  \n",
            " 6   holiday     17379 non-null  int64  \n",
            " 7   weekday     17379 non-null  int64  \n",
            " 8   workingday  17379 non-null  int64  \n",
            " 9   weathersit  17379 non-null  int64  \n",
            " 10  temp        17379 non-null  float64\n",
            " 11  atemp       17379 non-null  float64\n",
            " 12  hum         17379 non-null  float64\n",
            " 13  windspeed   17379 non-null  float64\n",
            " 14  casual      17379 non-null  int64  \n",
            " 15  registered  17379 non-null  int64  \n",
            " 16  cnt         17379 non-null  int64  \n",
            "dtypes: float64(4), int64(12), object(1)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ],
      "source": [
        "hour_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhN5R4hr8DC1"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jVnYpprE9Evz"
      },
      "outputs": [],
      "source": [
        "day_df.dropna(axis=0, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8UvxXO5JV2wh"
      },
      "outputs": [],
      "source": [
        "hour_df.dropna(axis=0, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gp-Y6wU38DC1"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW7WF2kr8DC1"
      },
      "source": [
        "### Explore ..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "e9CQCZjk8DC2",
        "outputId": "ae169d7e-d99d-4aba-b6fc-db1a8b565dfe"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <th>std</th>\n",
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              "      <td>20.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>183.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>8714.000000</td>\n",
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              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5c2c278c-071e-4c9b-91ea-b9e2c76086c9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          instant      season          yr        mnth     holiday     weekday  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
              "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
              "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
              "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
              "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
              "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
              "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
              "\n",
              "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
              "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
              "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
              "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
              "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
              "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
              "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
              "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
              "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
              "\n",
              "            casual   registered          cnt  \n",
              "count   731.000000   731.000000   731.000000  \n",
              "mean    848.176471  3656.172367  4504.348837  \n",
              "std     686.622488  1560.256377  1937.211452  \n",
              "min       2.000000    20.000000    22.000000  \n",
              "25%     315.500000  2497.000000  3152.000000  \n",
              "50%     713.000000  3662.000000  4548.000000  \n",
              "75%    1096.000000  4776.500000  5956.000000  \n",
              "max    3410.000000  6946.000000  8714.000000  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "day_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "wRsS6cs5mCyF",
        "outputId": "99880618-83ee-45b0-e281-df31faa0d224"
      },
      "outputs": [
        {
          "data": {
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[\n          0.4969871684216583,\n          0.5,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"atemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6144.248469131704,\n        \"min\": 0.0,\n        \"max\": 17379.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.4757751021347604,\n          0.4848,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"hum\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6144.216991945488,\n        \"min\": 0.0,\n        \"max\": 17379.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6272288394038783,\n          0.63,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6144.317742909861,\n        \"min\": 0.0,\n        \"max\": 17379.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.1900976063064618,\n          0.194,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6119.284233238239,\n        \"min\": 0.0,\n        \"max\": 17379.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.67621842453536,\n          17.0,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"registered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6072.038722856437,\n        \"min\": 0.0,\n        \"max\": 17379.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          153.78686920996606,\n          115.0,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cnt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6060.617601280442,\n        \"min\": 1.0,\n        \"max\": 17379.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          189.46308763450142,\n          142.0,\n          17379.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
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              "      <th></th>\n",
              "      <th>instant</th>\n",
              "      <th>season</th>\n",
              "      <th>yr</th>\n",
              "      <th>mnth</th>\n",
              "      <th>hr</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
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              "      <th>weathersit</th>\n",
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              "      <td>17379.0000</td>\n",
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              "      <td>17379.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>8690.0000</td>\n",
              "      <td>2.501640</td>\n",
              "      <td>0.502561</td>\n",
              "      <td>6.537775</td>\n",
              "      <td>11.546752</td>\n",
              "      <td>0.028770</td>\n",
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              "      <td>0.682721</td>\n",
              "      <td>1.425283</td>\n",
              "      <td>0.496987</td>\n",
              "      <td>0.475775</td>\n",
              "      <td>0.627229</td>\n",
              "      <td>0.190098</td>\n",
              "      <td>35.676218</td>\n",
              "      <td>153.786869</td>\n",
              "      <td>189.463088</td>\n",
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              "      <th>std</th>\n",
              "      <td>5017.0295</td>\n",
              "      <td>1.106918</td>\n",
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              "      <td>3.438776</td>\n",
              "      <td>6.914405</td>\n",
              "      <td>0.167165</td>\n",
              "      <td>2.005771</td>\n",
              "      <td>0.465431</td>\n",
              "      <td>0.639357</td>\n",
              "      <td>0.192556</td>\n",
              "      <td>0.171850</td>\n",
              "      <td>0.192930</td>\n",
              "      <td>0.122340</td>\n",
              "      <td>49.305030</td>\n",
              "      <td>151.357286</td>\n",
              "      <td>181.387599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>4345.5000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.333300</td>\n",
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              "      <td>34.000000</td>\n",
              "      <td>40.000000</td>\n",
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              "      <td>7.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.484800</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>0.194000</td>\n",
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              "      <td>115.000000</td>\n",
              "      <td>142.000000</td>\n",
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              "      <td>10.000000</td>\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "          instant        season            yr          mnth            hr  \\\n",
              "count  17379.0000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean    8690.0000      2.501640      0.502561      6.537775     11.546752   \n",
              "std     5017.0295      1.106918      0.500008      3.438776      6.914405   \n",
              "min        1.0000      1.000000      0.000000      1.000000      0.000000   \n",
              "25%     4345.5000      2.000000      0.000000      4.000000      6.000000   \n",
              "50%     8690.0000      3.000000      1.000000      7.000000     12.000000   \n",
              "75%    13034.5000      3.000000      1.000000     10.000000     18.000000   \n",
              "max    17379.0000      4.000000      1.000000     12.000000     23.000000   \n",
              "\n",
              "            holiday       weekday    workingday    weathersit          temp  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.028770      3.003683      0.682721      1.425283      0.496987   \n",
              "std        0.167165      2.005771      0.465431      0.639357      0.192556   \n",
              "min        0.000000      0.000000      0.000000      1.000000      0.020000   \n",
              "25%        0.000000      1.000000      0.000000      1.000000      0.340000   \n",
              "50%        0.000000      3.000000      1.000000      1.000000      0.500000   \n",
              "75%        0.000000      5.000000      1.000000      2.000000      0.660000   \n",
              "max        1.000000      6.000000      1.000000      4.000000      1.000000   \n",
              "\n",
              "              atemp           hum     windspeed        casual    registered  \\\n",
              "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
              "mean       0.475775      0.627229      0.190098     35.676218    153.786869   \n",
              "std        0.171850      0.192930      0.122340     49.305030    151.357286   \n",
              "min        0.000000      0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.333300      0.480000      0.104500      4.000000     34.000000   \n",
              "50%        0.484800      0.630000      0.194000     17.000000    115.000000   \n",
              "75%        0.621200      0.780000      0.253700     48.000000    220.000000   \n",
              "max        1.000000      1.000000      0.850700    367.000000    886.000000   \n",
              "\n",
              "                cnt  \n",
              "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  "
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "YqbKA6e9mH98",
        "outputId": "4e7b83fd-e89b-47c2-b7e7-6e28f6bb8a25"
      },
      "outputs": [
        {
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              "      <th>mnth</th>\n",
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              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "   mnth  yr      dteday  weekday  weathersit\n",
              "0     1   0  2011-01-01        6           2\n",
              "1     1   0  2011-01-02        0           2\n",
              "2     1   0  2011-01-03        1           1\n",
              "3     1   0  2011-01-04        2           1\n",
              "4     1   0  2011-01-05        3           1"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "var_yg_dipilih = [\"mnth\", \"yr\", \"dteday\", \"weekday\", \"weathersit\"]\n",
        "day_df = day_df[var_yg_dipilih]\n",
        "day_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "aOf4fPlBmIGM",
        "outputId": "268f0211-9d9d-41c6-84ff-3a5b113147e9"
      },
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dteday</th>\n",
              "      <th>hr</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>276</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-02</td>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>271</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>273</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\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",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "  }\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",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "       dteday   hr\n",
              "0  2011-01-01  276\n",
              "1  2011-01-02  271\n",
              "2  2011-01-03  271\n",
              "3  2011-01-04  273\n",
              "4  2011-01-05  273"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hour_sum = hour_df.groupby('dteday')['hr'].sum().reset_index()\n",
        "hour_sum.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZxQh3JQSnWVq"
      },
      "outputs": [],
      "source": [
        "merge_df = pd.merge(day_df, hour_sum, on='dteday', how='left')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BzuIiUBVnrzv",
        "outputId": "0ac20ddf-dbd5-469c-a606-98b7418932fa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   month  year        date  day_of_week  weather_condition  hour\n",
            "0      1     0  2011-01-01            6                  2   276\n",
            "1      1     0  2011-01-02            0                  2   271\n",
            "2      1     0  2011-01-03            1                  1   271\n",
            "3      1     0  2011-01-04            2                  1   273\n",
            "4      1     0  2011-01-05            3                  1   273\n"
          ]
        }
      ],
      "source": [
        "bike_df = merge_df.rename(columns={\n",
        "    \"mnth\": \"month\",\n",
        "    \"yr\": \"year\",\n",
        "    \"dteday\": \"date\",\n",
        "    \"weekday\": \"day_of_week\",\n",
        "    \"weathersit\": \"weather_condition\",\n",
        "    \"hr\": \"hour\"\n",
        "})\n",
        "\n",
        "print(bike_df.head())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsyZjqak8DC2"
      },
      "source": [
        "## Visualization & Explanatory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZxOiQ6n8DC2"
      },
      "source": [
        "### Pertanyaan 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlI81votBr-D",
        "outputId": "0c7bc026-1672-4270-8ef0-dc9f35dcf6e5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "     month  year        date  day_of_week  weather_condition  hour\n",
            "730     12     1  2012-12-31            1                  2   276\n",
            "729     12     1  2012-12-30            0                  1   276\n",
            "728     12     1  2012-12-29            6                  2   276\n",
            "727     12     1  2012-12-28            5                  2   276\n",
            "726     12     1  2012-12-27            4                  2   276\n"
          ]
        }
      ],
      "source": [
        "jam_terbanyak = bike_df.sort_values(by='date', ascending=False)\n",
        "top5_jam_terbanyak = jam_terbanyak.head(5)\n",
        "print(top5_jam_terbanyak)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Ow3Qz6pWKK6s",
        "outputId": "cec360b2-0cee-4c33-8476-ea88fae470ee"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=top5_jam_terbanyak.date, y=top5_jam_terbanyak.hour)\n",
        "plt.xlabel('Tanggal')\n",
        "plt.ylabel('Total Jam')\n",
        "plt.title('5 Hari Tertinggi dengan Jumlah Jam Rental Terbanyak')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xei9dQtPWMw1"
      },
      "source": [
        "Terlihat bahwa dari tanggal 27-12-2012 sampai dengan 31-12-2012 menjadi 5 hari tertinggi dengan jumlah jam rental terbanyak yaitu 276 jam pada tiap harinya."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgHI7CiU8DC2"
      },
      "source": [
        "### Pertanyaan 2:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Go0lCsvO8DC2",
        "outputId": "76e56749-6461-47a6-80af-9006459c66eb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Clear              463\n",
            "Mist + Cloudy      247\n",
            "Light Snow/Rain     21\n",
            "Name: weathersit, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "weathersit_nilai = day_df['weathersit'].value_counts()\n",
        "weathersit_nilai = weathersit_nilai.rename({1: 'Clear', 2: 'Mist + Cloudy', 3: 'Light Snow/Rain', 4: 'Heavy Rain'})\n",
        "print(weathersit_nilai)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "LJTxhceXQFkZ",
        "outputId": "45150325-4228-4325-8368-703336cab451"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=weathersit_nilai.index, y=weathersit_nilai.values, hue=weathersit_nilai.index, palette='deep', legend=False)\n",
        "plt.xlabel('Kondisi Cuaca')\n",
        "plt.ylabel('Frekuensi')\n",
        "plt.title('Frekuensi Penyewaan Sepeda Terhadap Kondisi Cuaca')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcRPEjB78jz8"
      },
      "source": [
        "Terlihat bahwa jumlah penyewaan sepeda paling tinggi terjadi saat kondisi cuaca adalah Clear, Few clouds, Partly cloudy, Partly cloudy  dengan total frekuensi penyewaan mencapai 463. Kemudian saat kondisi cuaca adalah Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist frekuensi penyewaan sepeda adalah 247. Lalu saat kondisi cuaca adalah Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds frekuensi penyewaan sepeda adalah 21. Terakhir, saat kondisi Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog frekuensi penyewaan sepeda adalah 0."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WeHlCeX8DC2"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTcyR48Y8DC2"
      },
      "source": [
        "- Conclution pertanyaan 1:\n",
        "\n",
        "Pada hasil barplot yang ditampilkan untuk pertanyaan pertama terlihat bahwa 5 tanggal dengan jumlah jam penyewaan sepeda tertinggi adalah dari tanggal 27-12-2012 hingga 31-12-2012. Dari data ini, dapat disimpulkan bahwa periode tersebut mungkin merupakan hari libur sekolah atau libur pekerjaan dengan konteks liburan pergantian tahun.\n",
        "\n",
        "Dalam konteks ini, masyarakat cenderung memiliki lebih banyak waktu luang untuk beraktivitas termasuk bersepeda. Libur sekolah atau pekerjaan pada akhir tahun sering kali menjadi waktu yang dinantikan untuk bersantai dan menikmati momen bersama keluarga atau teman-teman. Penggunaan sepeda sebagai salah satu sarana rekreasi saat libur dapat menjelaskan mengapa jumlah penyewaan sepeda meningkat pada periode tersebut.\n",
        "\n",
        "Dengan demikian, meningkatnya jumlah penyewaan sepeda pada tanggal-tanggal tersebut dapat diatribusikan kepada kegiatan liburan akhir tahun yang memberikan kesempatan bagi masyarakat untuk menikmati aktivitas rekreasi seperti bersepeda. Liburan ini juga memberikan kesempatan bagi masyarakat untuk melepaskan diri dari rutinitas sehari-hari dan menikmati waktu bersantai dengan menggunakan sepeda.\n",
        "\n",
        "- Conclution pertanyaan 2:\n",
        "\n",
        "Pada hasil barplot yang ditampilkan untuk pertanyaan kedua terlihat bahwa frekuensi penyewaan sepeda berdasarkan kondisi cuaca bervariasi. Dari data yang dianalisis, terlihat bahwa jumlah penyewaan sepeda paling tinggi terjadi saat kondisi cuaca adalah Clear, Few clouds, Partly cloudy, Partly cloudy  dengan total frekuensi penyewaan mencapai 463. Hal ini menunjukkan bahwa masyarakat cenderung lebih aktif meminjam sepeda saat cuaca cerah atau sedikit berawan.\n",
        "\n",
        "Sementara itu, saat kondisi cuaca adalah Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist serta saat kondisi cuaca adalah Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds, atau Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog, terlihat bahwa jumlah frekuensi penyewaan sepeda cenderung lebih rendah. Hal ini memungkinkan disebabkan oleh kondisi cuaca yang kurang mendukung untuk bersepeda, seperti hujan, kabut, atau salju yang membuat masyarakat enggan untuk beraktivitas dalam hal meminjam sepeda.\n",
        "\n",
        "Dari analisis ini dapat disimpulkan bahwa kondisi cuaca memainkan peran penting dalam menentukan tingkat aktivitas penyewaan sepeda. Cuaca yang cerah dan cuaca yang tidak ekstrem cenderung meningkatkan jumlah penyewaan sepeda, sementara cuaca yang buruk atau cuaca ekstrem cenderung mengurangi minat masyarakat untuk bersepeda."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vBEi2lLrGXP7"
      },
      "source": [
        "## Dashboard"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "  Local URL: http://localhost:8501\n",
        "  \n",
        "  Network URL: http://192.168.1.4:8501"
      ],
      "metadata": {
        "id": "jK4dzGPBYATr"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPH2ZTK3M9Ny"
      },
      "source": [
        "## Requirements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dWjbRkRiJM-k",
        "outputId": "420936b9-dc42-4778-84b5-3ea427a7644b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: pipreqs in /usr/local/lib/python3.10/dist-packages (0.5.0)\n",
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            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from nbconvert<8.0.0,>=7.11.0->pipreqs) (1.5.1)\n",
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            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from bleach!=5.0.0->nbconvert<8.0.0,>=7.11.0->pipreqs) (1.16.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach!=5.0.0->nbconvert<8.0.0,>=7.11.0->pipreqs) (0.5.1)\n",
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            "Requirement already satisfied: platformdirs>=2.5 in /usr/local/lib/python3.10/dist-packages (from jupyter-core>=4.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (4.2.0)\n",
            "Requirement already satisfied: jupyter-client>=6.1.12 in /usr/local/lib/python3.10/dist-packages (from nbclient>=0.5.0->nbconvert<8.0.0,>=7.11.0->pipreqs) (6.1.12)\n",
            "Requirement already satisfied: fastjsonschema in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (2.19.1)\n",
            "Requirement already satisfied: jsonschema>=2.6 in /usr/local/lib/python3.10/dist-packages (from nbformat>=5.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (4.19.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython==8.12.3->pipreqs) (0.7.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.37,<3.1.0,>=3.0.30->ipython==8.12.3->pipreqs) (0.2.13)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->nbconvert<8.0.0,>=7.11.0->pipreqs) (2.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->yarg==0.1.9->pipreqs) (3.3.2)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->yarg==0.1.9->pipreqs) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->yarg==0.1.9->pipreqs) (2024.2.2)\n",
            "Requirement already satisfied: executing>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython==8.12.3->pipreqs) (2.0.1)\n",
            "Requirement already satisfied: asttokens>=2.1.0 in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython==8.12.3->pipreqs) (2.4.1)\n",
            "Requirement already satisfied: pure-eval in /usr/local/lib/python3.10/dist-packages (from stack-data->ipython==8.12.3->pipreqs) (0.2.2)\n",
            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (23.2.0)\n",
            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (2023.12.1)\n",
            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (0.33.0)\n",
            "Requirement already satisfied: rpds-py>=0.7.1 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=2.6->nbformat>=5.7->nbconvert<8.0.0,>=7.11.0->pipreqs) (0.18.0)\n",
            "Requirement already satisfied: pyzmq>=13 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert<8.0.0,>=7.11.0->pipreqs) (23.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert<8.0.0,>=7.11.0->pipreqs) (2.8.2)\n",
            "Requirement already satisfied: tornado>=4.1 in /usr/local/lib/python3.10/dist-packages (from jupyter-client>=6.1.12->nbclient>=0.5.0->nbconvert<8.0.0,>=7.11.0->pipreqs) (6.3.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install pipreqs\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EInmn5fvJ3cy"
      },
      "outputs": [],
      "source": [
        "!pip freeze > requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frY36ZacJ3gC",
        "outputId": "80fba6a0-12cb-4afd-c763-6690bac5c7c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: session-info in /usr/local/lib/python3.10/dist-packages (1.0.0)\n",
            "Requirement already satisfied: stdlib-list in /usr/local/lib/python3.10/dist-packages (from session-info) (0.10.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install session-info"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "mxp7CqDkLg2K",
        "outputId": "54b35ab0-9910-4cde-cd49-24e884c05305"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<details>\n",
              "<summary>Click to view session information</summary>\n",
              "<pre>\n",
              "-----\n",
              "google              NA\n",
              "matplotlib          3.7.1\n",
              "numpy               1.25.2\n",
              "pandas              1.5.3\n",
              "seaborn             0.13.1\n",
              "session_info        1.0.0\n",
              "-----\n",
              "</pre>\n",
              "<details>\n",
              "<summary>Click to view modules imported as dependencies</summary>\n",
              "<pre>\n",
              "PIL                 9.4.0\n",
              "asttokens           NA\n",
              "backcall            0.2.0\n",
              "certifi             2024.02.02\n",
              "cffi                1.16.0\n",
              "cloudpickle         2.2.1\n",
              "cycler              0.12.1\n",
              "cython_runtime      NA\n",
              "dateutil            2.8.2\n",
              "debugpy             1.6.6\n",
              "decorator           4.4.2\n",
              "defusedxml          0.7.1\n",
              "executing           2.0.1\n",
              "httplib2            0.22.0\n",
              "ipykernel           5.5.6\n",
              "ipython_genutils    0.2.0\n",
              "ipywidgets          7.7.1\n",
              "jedi                0.19.1\n",
              "kiwisolver          1.4.5\n",
              "matplotlib_inline   0.1.6\n",
              "mpl_toolkits        NA\n",
              "numexpr             2.9.0\n",
              "packaging           23.2\n",
              "parso               0.8.3\n",
              "patsy               0.5.6\n",
              "pexpect             4.9.0\n",
              "pickleshare         0.7.5\n",
              "pkg_resources       NA\n",
              "platformdirs        4.2.0\n",
              "portpicker          NA\n",
              "prompt_toolkit      3.0.43\n",
              "psutil              5.9.5\n",
              "ptyprocess          0.7.0\n",
              "pure_eval           0.2.2\n",
              "pyarrow             14.0.2\n",
              "pydev_ipython       NA\n",
              "pydevconsole        NA\n",
              "pydevd              2.9.5\n",
              "pydevd_file_utils   NA\n",
              "pydevd_plugins      NA\n",
              "pydevd_tracing      NA\n",
              "pygments            2.16.1\n",
              "pyparsing           3.1.1\n",
              "pytz                2023.4\n",
              "scipy               1.11.4\n",
              "setuptools          67.7.2\n",
              "sitecustomize       NA\n",
              "six                 1.16.0\n",
              "socks               1.7.1\n",
              "sphinxcontrib       NA\n",
              "stack_data          0.6.3\n",
              "statsmodels         0.14.1\n",
              "tornado             6.3.2\n",
              "traitlets           5.7.1\n",
              "typing_extensions   NA\n",
              "wcwidth             0.2.13\n",
              "zmq                 23.2.1\n",
              "zoneinfo            NA\n",
              "</pre>\n",
              "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
              "<pre>\n",
              "-----\n",
              "IPython             8.12.3\n",
              "jupyter_client      6.1.12\n",
              "jupyter_core        5.7.1\n",
              "notebook            6.5.5\n",
              "-----\n",
              "Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]\n",
              "Linux-6.1.58+-x86_64-with-glibc2.35\n",
              "-----\n",
              "Session information updated at 2024-03-04 11:21\n",
              "</pre>\n",
              "</details>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 53,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import session_info\n",
        "session_info.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ja18_NpOLg5R",
        "outputId": "dd930079-b924-4a27-d0eb-27b1f5614896"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['sys', 'builtins', '_frozen_importlib', '_imp', '_thread', '_warnings', '_weakref', '_io', 'marshal', 'posix', '_frozen_importlib_external', 'time', 'zipimport', '_codecs', 'codecs', 'encodings.aliases', 'encodings', 'encodings.utf_8', '_signal', '_abc', 'abc', 'io', '__main__', 'types', 'enum', '_sre', 'sre_constants', 'sre_parse', 'sre_compile', '_collections_abc', 'itertools', 'keyword', '_operator', 'operator', 'reprlib', '_collections', 'collections', '_functools', 'functools', '_locale', 'copyreg', 're', 'warnings', '_stat', 'stat', 'genericpath', 'posixpath', 'os.path', 'os', '_sitebuiltins', '_distutils_hack', 'importlib._bootstrap', 'importlib._bootstrap_external', 'importlib', 'importlib._abc', 'contextlib', 'importlib.util', 'importlib.machinery', 'google', 'google.cloud', 'mpl_toolkits', 'sphinxcontrib', 'sitecustomize', 'site', 'runpy', 'ipykernel._version', '_json', 'json.scanner', 'json.decoder', 'json.encoder', 'json', 'errno', 'signal', '_weakrefset', 'threading', 'fcntl', '_posixsubprocess', 'select', 'collections.abc', 'math', 'selectors', 'subprocess', 'IPython.core', 'IPython.core.getipython', 'IPython.core.release', 'atexit', 'weakref', 'copy', 'token', 'tokenize', 'linecache', 'traceback', '_string', 'string', 'logging', 'fnmatch', 'zlib', '_compression', '_bz2', 'bz2', '_lzma', 'lzma', 'shutil', 'ntpath', 'urllib', 'urllib.parse', 'pathlib', '_ast', 'ast', '_opcode', 'opcode', 'dis', 'inspect', 'typing.io', 'typing.re', 'typing', 'traitlets.utils', 'traitlets.utils.bunch', 'traitlets.utils.descriptions', 'traitlets.utils.getargspec', 'traitlets.utils.importstring', 'traitlets.utils.sentinel', 'traitlets.traitlets', 'traitlets._version', 'traitlets.utils.decorators', 'traitlets', 'dataclasses', 'pprint', '_socket', 'array', 'socket', '_struct', 'struct', '_compat_pickle', '_pickle', 'pickle', '_heapq', 'heapq', '_queue', 'queue', 'logging.handlers', 'socketserver', 'logging.config', 'textwrap', 'traitlets.utils.text', 'gettext', 'argparse', 'traitlets.config.loader', 'traitlets.config.configurable', 'traitlets.utils.nested_update', 'traitlets.config.application', 'traitlets.config', 'pkgutil', 'platform', 'sysconfig', '_sysconfigdata__x86_64-linux-gnu', 'pydoc', 'html.entities', 'html', '__future__', 'executing.executing', 'executing.version', 'executing', '_bisect', 'bisect', 'asttokens.line_numbers', 'six', 'six.moves', 'asttokens.util', 'asttokens.asttokens', 'asttokens', '_datetime', 'datetime', 'numbers', '_decimal', 'decimal', 'fractions', 'pure_eval.utils', 'pure_eval.my_getattr_static', 'pure_eval.core', 'pure_eval.version', 'pure_eval', 'stack_data.utils', 'stack_data.core', 'stack_data.formatting', 'stack_data.serializing', 'stack_data.version', 'stack_data', 'pygments', 'pygments.formatters._mapping', 'pygments.plugin', 'pygments.util', 'pygments.formatters', 'pygments.styles', 'pygments.formatter', 'pygments.console', 'pygments.token', 'pygments.style', 'pygments.formatters.terminal256', 'IPython.utils', 'IPython.utils.colorable', 'IPython.utils.ipstruct', 'IPython.utils.coloransi', 'IPython.utils.PyColorize', 'locale', 'IPython.utils.encoding', 'IPython.utils.py3compat', 'IPython.core.excolors', 'cmd', 'bdb', 'codeop', 'code', 'glob', 'pdb', 'IPython.core.debugger', 'IPython.core.display_trap', '_random', '_sha512', 'random', 'pexpect.exceptions', 'pexpect.utils', 'pexpect.expect', 'termios', 'tty', 'pty', 'resource', 'ptyprocess.util', 'ptyprocess.ptyprocess', 'ptyprocess', 'pexpect.spawnbase', 'pexpect.pty_spawn', 'pexpect.run', 'pexpect', 'shlex', 'IPython.utils._process_common', 'IPython.utils._process_posix', 'IPython.utils.process', 'IPython.utils.path', 'IPython.utils.terminal', 'IPython.core.ultratb', 'IPython.utils._sysinfo', 'IPython.utils.sysinfo', 'IPython.core.crashhandler', 'tempfile', 'IPython.utils.importstring', 'IPython.paths', 'IPython.core.profiledir', 'IPython.core.application', 'IPython.terminal', '_hashlib', '_blake2', 'hashlib', 'IPython.core.compilerop', 'IPython.core.error', 'IPython.utils.docs', 'IPython.utils.decorators', 'IPython.utils.text', 'IPython.core.magic_arguments', 'getopt', 'binascii', 'IPython.core.display_functions', 'mimetypes', 'IPython.testing', 'IPython.testing.skipdoctest', 'IPython.core.display', 'IPython.lib', 'IPython.lib.display', 'IPython.display', 'IPython.utils.data', 'IPython.core.page', 'IPython.lib.pretty', 'IPython.utils.openpy', 'IPython.utils.dir2', 'IPython.utils.wildcard', 'pygments.lexers._mapping', 'pygments.modeline', 'pygments.lexers', 'pygments.filter', 'pygments.filters', 'pygments.regexopt', 'pygments.lexer', 'pygments.unistring', 'pygments.lexers.python', 'pygments.formatters.html', 'IPython.core.oinspect', 'IPython.core.inputtransformer2', 'IPython.core.magic', 'pickleshare', 'IPython.core.hooks', 'IPython.core.autocall', 'IPython.core.macro', 'IPython.core.splitinput', 'IPython.core.prefilter', 'IPython.core.alias', 'IPython.core.builtin_trap', 'IPython.core.displayhook', 'IPython.core.displaypub', 'backcall.backcall', 'backcall', 'IPython.core.events', 'IPython.core.extensions', 'decorator', 'IPython.utils.sentinel', 'IPython.core.formatters', '_sqlite3', 'sqlite3.dbapi2', 'sqlite3', 'IPython.core.history', 'IPython.core.logger', 'IPython.core.payload', 'IPython.core.usage', 'IPython.utils.capture', 'IPython.utils.io', 'IPython.utils.strdispatch', 'IPython.utils.syspathcontext', 'concurrent', 'concurrent.futures._base', 'concurrent.futures', '_ssl', 'base64', 'ssl', 'asyncio.constants', 'asyncio.format_helpers', 'asyncio.base_futures', 'asyncio.log', 'asyncio.coroutines', '_contextvars', 'contextvars', 'asyncio.exceptions', 'asyncio.base_tasks', '_asyncio', 'asyncio.events', 'asyncio.futures', 'asyncio.protocols', 'asyncio.transports', 'asyncio.sslproto', 'asyncio.mixins', 'asyncio.tasks', 'asyncio.locks', 'asyncio.staggered', 'asyncio.trsock', 'asyncio.base_events', 'asyncio.runners', 'asyncio.queues', 'asyncio.streams', 'asyncio.subprocess', 'asyncio.threads', 'asyncio.base_subprocess', 'asyncio.selector_events', 'asyncio.unix_events', 'asyncio', 'IPython.core.async_helpers', 'IPython.core.interactiveshell', 'prompt_toolkit.application.current', 'prompt_toolkit.eventloop.utils', 'prompt_toolkit.eventloop.async_generator', 'prompt_toolkit.eventloop.inputhook', 'prompt_toolkit.eventloop', 'prompt_toolkit.application.run_in_terminal', 'prompt_toolkit.selection', 'prompt_toolkit.clipboard.base', 'prompt_toolkit.clipboard.in_memory', 'prompt_toolkit.clipboard', 'prompt_toolkit.cache', 'prompt_toolkit.enums', 'prompt_toolkit.filters.base', 'prompt_toolkit.filters.app', 'prompt_toolkit.filters.cli', 'prompt_toolkit.filters.utils', 'prompt_toolkit.filters', 'prompt_toolkit.document', 'prompt_toolkit.auto_suggest', 'prompt_toolkit.keys', 'prompt_toolkit.key_binding.key_bindings', 'wcwidth.table_vs16', 'wcwidth.table_wide', 'wcwidth.table_zero', 'wcwidth.unicode_versions', 'wcwidth.wcwidth', 'wcwidth', 'prompt_toolkit.utils', 'prompt_toolkit.key_binding.key_processor', 'prompt_toolkit.key_binding', 'prompt_toolkit.key_binding.vi_state', 'prompt_toolkit.cursor_shapes', 'prompt_toolkit.data_structures', 'prompt_toolkit.styles.base', 'prompt_toolkit.styles.named_colors', 'prompt_toolkit.styles.style', 'prompt_toolkit.styles.defaults', 'prompt_toolkit.styles.pygments', 'colorsys', 'prompt_toolkit.styles.style_transformation', 'prompt_toolkit.styles', 'prompt_toolkit.output.color_depth', 'prompt_toolkit.output.base', 'prompt_toolkit.output.flush_stdout', 'prompt_toolkit.output.plain_text', 'prompt_toolkit.output.defaults', 'prompt_toolkit.output', 'prompt_toolkit.output.vt100', 'prompt_toolkit.mouse_events', 'prompt_toolkit.formatted_text.base', 'prompt_toolkit.formatted_text.ansi', 'xml', 'xml.dom.domreg', 'xml.dom', 'xml.dom.minicompat', 'xml.dom.NodeFilter', 'xml.dom.xmlbuilder', 'xml.dom.minidom', 'prompt_toolkit.formatted_text.html', 'prompt_toolkit.formatted_text.pygments', 'prompt_toolkit.formatted_text.utils', 'prompt_toolkit.formatted_text', 'prompt_toolkit.completion.base', 'prompt_toolkit.completion.deduplicate', 'prompt_toolkit.completion.filesystem', 'prompt_toolkit.completion.word_completer', 'prompt_toolkit.completion.fuzzy_completer', 'prompt_toolkit.completion.nested', 'prompt_toolkit.completion', 'prompt_toolkit.history', 'prompt_toolkit.search', 'prompt_toolkit.validation', 'prompt_toolkit.buffer', 'prompt_toolkit.input.base', 'prompt_toolkit.input.defaults', 'prompt_toolkit.input', 'prompt_toolkit.input.typeahead', 'prompt_toolkit.key_binding.bindings', 'prompt_toolkit.key_binding.bindings.scroll', 'prompt_toolkit.key_binding.bindings.page_navigation', 'prompt_toolkit.lexers.base', 'prompt_toolkit.lexers.pygments', 'prompt_toolkit.lexers', 'prompt_toolkit.layout.utils', 'prompt_toolkit.layout.processors', 'prompt_toolkit.layout.controls', 'prompt_toolkit.layout.dimension', 'prompt_toolkit.layout.margins', 'prompt_toolkit.layout.mouse_handlers', 'prompt_toolkit.layout.screen', 'prompt_toolkit.layout.containers', 'prompt_toolkit.layout.layout', 'prompt_toolkit.layout.menus', 'prompt_toolkit.layout.scrollable_pane', 'prompt_toolkit.layout', 'prompt_toolkit.key_binding.bindings.completion', 'prompt_toolkit.key_binding.bindings.named_commands', 'prompt_toolkit.key_binding.bindings.basic', 'prompt_toolkit.key_binding.bindings.cpr', 'prompt_toolkit.key_binding.bindings.emacs', 'prompt_toolkit.key_binding.bindings.mouse', 'prompt_toolkit.input.ansi_escape_sequences', 'prompt_toolkit.input.vt100_parser', 'prompt_toolkit.key_binding.digraphs', 'prompt_toolkit.key_binding.bindings.vi', 'prompt_toolkit.key_binding.defaults', 'prompt_toolkit.key_binding.emacs_state', 'prompt_toolkit.layout.dummy', 'prompt_toolkit.renderer', 'prompt_toolkit.application.application', 'prompt_toolkit.application.dummy', 'prompt_toolkit.application', 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'parso.python.tree', 'parso.python.parser', 'parso.python.diff', 'gc', 'parso.cache', 'parso.normalizer', 'parso.python.errors', 'parso.python.pep8', 'parso.file_io', 'parso.grammar', 'parso', 'jedi.parser_utils', 'jedi.debug', 'jedi.settings', 'jedi.cache', 'jedi.file_io', 'jedi.inference.cache', 'jedi.inference.helpers', 'jedi.inference.utils', 'jedi.inference.base_value', 'jedi.inference.sys_path', 'jedi.inference.recursion', 'jedi.inference.flow_analysis', 'jedi.common', 'jedi.inference.lazy_value', 'jedi.inference.docstrings', 'jedi.plugins', 'jedi.inference.names', 'jedi.inference.filters', 'jedi.inference.compiled.getattr_static', 'jedi.inference.compiled.access', 'jedi.inference.signature', 'jedi.inference.context', 'jedi.inference.compiled.value', 'jedi.inference.compiled', 'jedi.inference.analysis', 'jedi.inference.gradual', 'jedi.inference.value.module', 'jedi.inference.value.dynamic_arrays', 'jedi.inference.value.iterable', 'jedi.inference.arguments', 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'jedi.api.environment', 'jedi.inference.references', 'jedi.api.project', 'jedi.api.errors', 'jedi.inference.value.namespace', 'jedi.api.refactoring', 'jedi.api.refactoring.extract', 'jedi.inference.gradual.utils', 'jedi.api', 'jedi.plugins.stdlib', 'jedi.plugins.flask', 'jedi.plugins.pytest', 'jedi.plugins.django', 'jedi.plugins.registry', 'jedi', 'IPython.core.completer', 'IPython.terminal.ptutils', 'IPython.terminal.shortcuts.auto_match', 'IPython.utils.tokenutil', 'IPython.terminal.shortcuts.filters', 'IPython.terminal.shortcuts.auto_suggest', 'IPython.terminal.shortcuts', 'concurrent.futures.thread', 'IPython.terminal.debugger', 'IPython.lib.clipboard', 'IPython.terminal.magics', 'IPython.terminal.pt_inputhooks', 'IPython.terminal.prompts', 'IPython.terminal.interactiveshell', 'IPython.core.magics.auto', 'IPython.core.magics.basic', 'email', 'http', 'email.errors', 'email.quoprimime', 'email.base64mime', 'quopri', 'email.encoders', 'email.charset', 'email.header', 'calendar', 'email._parseaddr', 'email.utils', 'email._policybase', 'email.feedparser', 'email.parser', 'uu', 'email._encoded_words', 'email.iterators', 'email.message', 'http.client', 'urllib.response', 'urllib.error', 'urllib.request', 'IPython.utils.contexts', 'IPython.core.magics.code', 'IPython.core.magics.config', 'IPython.core.magics.display', '_lsprof', 'profile', 'cProfile', 'pstats', 'timeit', 'IPython.utils.module_paths', 'IPython.utils.timing', 'IPython.core.magics.execution', 'IPython.core.magics.extension', 'IPython.core.magics.history', 'IPython.core.magics.logging', 'IPython.core.magics.namespace', 'IPython.core.magics.osm', 'IPython.core.magics.packaging', 'IPython.core.pylabtools', 'IPython.core.magics.pylab', 'IPython.core.magics.script', 'IPython.core.magics', 'IPython.core.shellapp', 'IPython.extensions', 'IPython.extensions.storemagic', 'IPython.terminal.ipapp', 'IPython.terminal.embed', 'IPython.utils.frame', 'IPython', 'ipython_genutils._version', 'ipython_genutils', 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'jupyter_core.paths', 'jupyter_client.utils', 'jupyter_client.connect', 'traitlets.log', 'jupyter_client.launcher', 'jupyter_client.channelsabc', 'jupyter_client.channels', 'jupyter_client.clientabc', 'jupyter_client.client', 'jupyter_client.kernelspec', 'jupyter_client.managerabc', 'jupyter_client.manager', 'jupyter_client.blocking.channels', 'jupyter_client.blocking.client', 'jupyter_client.blocking', 'zmq._future', 'zmq.asyncio', 'jupyter_client.asynchronous.channels', 'jupyter_client.asynchronous.client', 'jupyter_client.asynchronous', 'jupyter_client.multikernelmanager', 'jupyter_client', 'ipykernel.connect', 'ipykernel', 'tornado', 'tornado.speedups', 'tornado.util', 'tornado.escape', '_curses', 'curses', 'tornado.log', 'tornado.concurrent', 'tornado.ioloop', 'tornado.platform', 'tornado.gen', 'tornado.platform.asyncio', 'zmq.eventloop.ioloop', 'zmq.eventloop', 'zmq.eventloop.zmqstream', 'ipython_genutils.importstring', 'imp', 'hmac', 'dateutil._version', 'dateutil', 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'pandas.compat.pickle_compat', 'pandas.io.pickle', 'pandas.core.computation.pytables', 'pandas.io.pytables', 'pandas.io.sas.sasreader', 'pandas.io.sas', 'pandas.io.spss', 'pandas.io.sql', 'pandas.io.stata', 'pandas.io.xml', 'pandas.io.api', 'pandas.util._tester', 'pandas._version', 'pandas', 'seaborn.external', 'seaborn.external.husl', 'seaborn._core', 'seaborn._core.typing', 'seaborn.external.version', 'seaborn.external.appdirs', 'seaborn.utils', 'seaborn.colors.xkcd_rgb', 'seaborn.colors.crayons', 'seaborn.colors', 'seaborn._compat', 'seaborn.palettes', 'seaborn.rcmod', 'seaborn._core.data', 'seaborn._base', '_statistics', 'statistics', 'scipy._lib._testutils', 'scipy._lib', 'scipy._lib.deprecation', 'scipy.__config__', 'scipy.version', 'scipy._distributor_init', 'scipy._lib._pep440', 'scipy._lib._ccallback_c', 'scipy._lib._ccallback', 'scipy', 'scipy.stats._warnings_errors', 'unittest.util', 'unittest.result', 'unittest.case', 'unittest.suite', 'unittest.loader', 'unittest.signals', 'unittest.runner', 'unittest.main', 'unittest', 'numpy.testing._private', 'numpy.linalg.lapack_lite', 'numpy.testing._private.utils', 'numpy.testing._private.extbuild', 'numpy.testing.overrides', 'numpy.testing', 'scipy.sparse._sputils', 'scipy.sparse._matrix', 'scipy.sparse._base', 'scipy.sparse._sparsetools', 'scipy._lib._util', 'scipy.sparse._data', 'scipy.sparse._index', 'scipy.sparse._compressed', 'scipy.sparse._csr', 'scipy.sparse._csc', '_csparsetools', 'scipy.sparse._csparsetools', 'scipy.sparse._lil', 'scipy.sparse._dok', 'scipy.sparse._coo', 'scipy.sparse._dia', 'scipy.sparse._bsr', 'scipy.sparse._construct', 'scipy.sparse._extract', 'scipy.sparse._matrix_io', 'scipy.sparse.linalg._isolve._iterative', 'scipy.sparse.linalg._interface', 'scipy.sparse.linalg._isolve.utils', 'scipy._lib.decorator', 'scipy._lib._threadsafety', 'scipy.sparse.linalg._isolve.iterative', 'scipy.sparse.linalg._isolve.minres', 'scipy.linalg._fblas', 'scipy.linalg.blas', 'scipy.linalg._flapack', 'scipy.linalg.lapack', 'scipy.linalg._misc', 'scipy.linalg.cython_lapack', 'scipy.linalg._cythonized_array_utils', 'scipy.linalg._decomp', 'scipy.linalg._decomp_svd', 'scipy.linalg._solve_toeplitz', 'scipy.linalg._basic', 'scipy.linalg._decomp_lu_cython', 'scipy.linalg._decomp_lu', 'scipy.linalg._decomp_ldl', 'scipy.linalg._decomp_cholesky', 'scipy.linalg._decomp_qr', 'scipy.linalg._decomp_qz', 'scipy.linalg._decomp_schur', 'scipy.linalg._decomp_polar', 'scipy.linalg._expm_frechet', 'scipy.linalg._matfuncs_sqrtm_triu', 'scipy.linalg._matfuncs_sqrtm', 'scipy.linalg.cython_blas', 'scipy.linalg._matfuncs_expm', 'scipy.linalg._matfuncs', 'scipy.linalg._special_matrices', 'scipy.linalg._solvers', 'scipy.linalg._procrustes', 'scipy.linalg._decomp_update', 'scipy.linalg._sketches', 'scipy.linalg._decomp_cossin', 'scipy.linalg.decomp', 'scipy.linalg.decomp_cholesky', 'scipy.linalg.decomp_lu', 'scipy.linalg.decomp_qr', 'scipy.linalg.decomp_svd', 'scipy.linalg.decomp_schur', 'scipy.linalg.basic', 'scipy.linalg.misc', 'scipy.linalg.special_matrices', 'scipy.linalg._flinalg', 'scipy.linalg._flinalg_py', 'scipy.linalg.flinalg', 'scipy.linalg.matfuncs', 'scipy.linalg', 'scipy.sparse.linalg._isolve._gcrotmk', 'scipy.sparse.linalg._isolve.lgmres', 'scipy.sparse.linalg._isolve.lsqr', 'scipy.sparse.linalg._isolve.lsmr', 'scipy.sparse.linalg._isolve.tfqmr', 'scipy.sparse.linalg._isolve', 'scipy.sparse.linalg._dsolve._superlu', 'scipy.sparse.linalg._dsolve.linsolve', 'scipy.sparse.linalg._dsolve._add_newdocs', 'scipy.sparse.linalg._dsolve', 'scipy.sparse.linalg._eigen.arpack._arpack', 'scipy.sparse.linalg._eigen.arpack.arpack', 'scipy.sparse.linalg._eigen.arpack', 'scipy.sparse.linalg._eigen.lobpcg.lobpcg', 'scipy.sparse.linalg._eigen.lobpcg', 'scipy.sparse.linalg._eigen._svds', 'scipy.sparse.linalg._eigen', 'scipy.sparse.linalg._onenormest', 'scipy.sparse.linalg._expm_multiply', 'scipy.sparse.linalg._matfuncs', 'scipy.sparse.linalg._norm', 'scipy.sparse.linalg.isolve', 'scipy.sparse.linalg.dsolve', 'scipy.sparse.linalg.interface', 'scipy.sparse.linalg.eigen', 'scipy.sparse.linalg.matfuncs', 'scipy.sparse.linalg', 'scipy.sparse.csgraph._laplacian', 'scipy.sparse.csgraph._tools', 'scipy.sparse.csgraph._validation', 'scipy.sparse.csgraph._shortest_path', 'scipy.sparse.csgraph._traversal', 'scipy.sparse.csgraph._min_spanning_tree', 'scipy.sparse.csgraph._flow', 'scipy.sparse.csgraph._matching', 'scipy.sparse.csgraph._reordering', 'scipy.sparse.csgraph', 'scipy.sparse.base', 'scipy.sparse.bsr', 'scipy.sparse.compressed', 'scipy.sparse.construct', 'scipy.sparse.coo', 'scipy.sparse.csc', 'scipy.sparse.csr', 'scipy.sparse.data', 'scipy.sparse.dia', 'scipy.sparse.dok', 'scipy.sparse.extract', 'scipy.sparse.lil', 'scipy.sparse.sparsetools', 'scipy.sparse.sputils', 'scipy.sparse', 'scipy.spatial._ckdtree', 'scipy.spatial._kdtree', 'scipy._lib.messagestream', 'scipy.spatial._qhull', 'scipy.spatial._voronoi', 'scipy.spatial._spherical_voronoi', 'scipy.spatial._plotutils', 'scipy.spatial._procrustes', 'scipy.spatial._distance_wrap', 'scipy.spatial._hausdorff', 'scipy.special._sf_error', 'scipy.special._ufuncs_cxx', 'scipy.special._ufuncs', 'scipy.special._specfun', 'scipy.special._comb', 'scipy.special._basic', 'scipy.special._logsumexp', 'scipy.special._orthogonal', 'scipy.special._spfun_stats', 'scipy.special._ellip_harm_2', 'scipy.special._ellip_harm', 'scipy.special._lambertw', 'scipy.special._spherical_bessel', 'scipy.special._add_newdocs', 'scipy.special.add_newdocs', 'scipy.special.basic', 'scipy.special.orthogonal', 'scipy.special.specfun', 'scipy.special.sf_error', 'scipy.special.spfun_stats', 'scipy.special', 'scipy.spatial._distance_pybind', 'scipy.spatial.distance', 'scipy.spatial._geometric_slerp', 'scipy.spatial.ckdtree', 'scipy.spatial.kdtree', 'scipy.spatial.qhull', 'scipy.constants._codata', 'scipy.constants._constants', 'scipy.constants.codata', 'scipy.constants.constants', 'scipy.constants', 'scipy.spatial.transform._rotation_groups', 'scipy.spatial.transform._rotation', 'scipy.spatial.transform._rotation_spline', 'scipy.spatial.transform.rotation', 'scipy.spatial.transform', 'scipy.spatial', 'scipy.ndimage._ni_support', 'scipy.ndimage._nd_image', 'scipy._lib.doccer', 'scipy.ndimage._ni_docstrings', 'scipy.ndimage._filters', 'scipy.ndimage._fourier', 'scipy.ndimage._interpolation', '_ni_label', 'scipy.ndimage._ni_label', 'scipy.ndimage._morphology', 'scipy.ndimage._measurements', 'scipy.ndimage.filters', 'scipy.ndimage.fourier', 'scipy.ndimage.interpolation', 'scipy.ndimage.measurements', 'scipy.ndimage.morphology', 'scipy.ndimage', 'scipy.stats._distr_params', 'scipy.optimize._minpack2', 'scipy.optimize._linesearch', 'scipy.optimize._group_columns', 'scipy.optimize._numdiff', 'scipy.optimize._hessian_update_strategy', 'scipy.optimize._differentiable_functions', 'scipy.optimize._optimize', 'scipy.optimize._trustregion', 'scipy.optimize._trustregion_dogleg', 'scipy.optimize._trustregion_ncg', 'scipy.optimize._trlib._trlib', 'scipy.optimize._trlib', 'scipy.optimize._trustregion_krylov', 'scipy.optimize._trustregion_exact', 'scipy.optimize._constraints', 'scipy.optimize._trustregion_constr.projections', 'scipy.optimize._trustregion_constr.qp_subproblem', 'scipy.optimize._trustregion_constr.equality_constrained_sqp', 'scipy.optimize._trustregion_constr.canonical_constraint', 'scipy.optimize._trustregion_constr.tr_interior_point', 'scipy.optimize._trustregion_constr.report', 'scipy.optimize._trustregion_constr.minimize_trustregion_constr', 'scipy.optimize._trustregion_constr', 'scipy.optimize._lbfgsb', 'scipy.optimize._lbfgsb_py', '_moduleTNC', 'scipy.optimize._moduleTNC', 'scipy.optimize._tnc', 'scipy.optimize._cobyla', 'scipy.optimize._cobyla_py', 'scipy.optimize._slsqp', 'scipy.optimize._slsqp_py', 'scipy.optimize._minimize', 'scipy.optimize._minpack', 'scipy.optimize._lsq.common', 'scipy.optimize._lsq.trf', 'scipy.optimize._lsq.dogbox', 'scipy.optimize._lsq.least_squares', 'scipy.optimize._lsq.givens_elimination', 'scipy.optimize._lsq.trf_linear', 'scipy.optimize._lsq.bvls', 'scipy.optimize._lsq.lsq_linear', 'scipy.optimize._lsq', 'scipy.optimize._minpack_py', 'scipy.optimize._spectral', 'scipy.optimize._nonlin', 'scipy.optimize._root', 'scipy.optimize._zeros', 'scipy.optimize._zeros_py', 'scipy.optimize._root_scalar', 'scipy.optimize.__nnls', 'scipy.optimize._nnls', 'scipy.optimize._basinhopping', 'scipy.optimize._highs', 'scipy.optimize._highs.cython.src._highs_wrapper', 'scipy.optimize._highs._highs_wrapper', 'scipy.optimize._highs.cython.src._highs_constants', 'scipy.optimize._highs._highs_constants', 'scipy.optimize._linprog_highs', 'scipy.linalg._interpolative', 'scipy.linalg._interpolative_backend', 'scipy.linalg.interpolative', 'scipy.optimize._remove_redundancy', 'scipy.optimize._linprog_util', 'scipy.optimize._linprog_ip', 'scipy.optimize._linprog_simplex', 'scipy.optimize._bglu_dense', 'scipy.optimize._linprog_rs', 'scipy.optimize._linprog_doc', 'scipy.optimize._linprog', 'scipy.optimize._lsap', 'scipy.optimize._differentialevolution', 'scipy.optimize._shgo_lib', 'scipy.optimize._shgo_lib._vertex', 'scipy.optimize._shgo_lib._complex', 'scipy.optimize._shgo', 'scipy.optimize._dual_annealing', 'scipy.optimize._qap', 'scipy.optimize._direct', 'scipy.optimize._direct_py', 'scipy.optimize._milp', 'scipy.optimize.cobyla', 'scipy.optimize.lbfgsb', 'scipy.optimize.linesearch', 'scipy.optimize.minpack', 'scipy.optimize.minpack2', 'scipy.optimize.moduleTNC', 'scipy.optimize.nonlin', 'scipy.optimize.optimize', 'scipy.optimize.slsqp', 'scipy.optimize.tnc', 'scipy.optimize.zeros', 'scipy.optimize', 'scipy.integrate._quadrature', 'scipy.integrate._odepack', 'scipy.integrate._odepack_py', 'scipy.integrate._quadpack', 'scipy.integrate._quadpack_py', 'scipy.integrate._vode', 'scipy.integrate._dop', 'scipy.integrate._lsoda', 'scipy.integrate._ode', 'scipy.integrate._bvp', 'scipy.integrate._ivp.common', 'scipy.integrate._ivp.base', 'scipy.integrate._ivp.bdf', 'scipy.integrate._ivp.radau', 'scipy.integrate._ivp.dop853_coefficients', 'scipy.integrate._ivp.rk', 'scipy.integrate._ivp.lsoda', 'scipy.integrate._ivp.ivp', 'scipy.integrate._ivp', 'scipy.integrate._quad_vec', 'scipy.integrate.dop', 'scipy.integrate.lsoda', 'scipy.integrate.vode', 'scipy.integrate.odepack', 'scipy.integrate.quadpack', 'scipy.integrate', 'scipy._lib._finite_differences', 'scipy.stats._constants', 'scipy.stats._censored_data', 'scipy.stats._distn_infrastructure', 'scipy.special.cython_special', 'scipy.stats._stats', 'scipy.stats._tukeylambda_stats', 'scipy.stats._ksstats', 'scipy.stats.beta_ufunc', 'scipy.stats._boost.beta_ufunc', 'scipy.stats.binom_ufunc', 'scipy.stats._boost.binom_ufunc', 'scipy.stats.nbinom_ufunc', 'scipy.stats._boost.nbinom_ufunc', 'scipy.stats.hypergeom_ufunc', 'scipy.stats._boost.hypergeom_ufunc', 'scipy.stats.ncf_ufunc', 'scipy.stats._boost.ncf_ufunc', 'scipy.stats.ncx2_ufunc', 'scipy.stats._boost.ncx2_ufunc', 'scipy.stats.nct_ufunc', 'scipy.stats._boost.nct_ufunc', 'scipy.stats.skewnorm_ufunc', 'scipy.stats._boost.skewnorm_ufunc', 'scipy.stats.invgauss_ufunc', 'scipy.stats._boost.invgauss_ufunc', 'scipy.stats._boost', 'scipy.stats._continuous_distns', 'scipy.interpolate._fitpack', 'scipy.interpolate.dfitpack', 'scipy.interpolate._fitpack_impl', 'scipy.interpolate._bspl', 'scipy.interpolate._bsplines', 'scipy.interpolate._fitpack_py', 'scipy.interpolate._polyint', 'scipy.interpolate._ppoly', 'scipy.interpolate.interpnd', 'scipy.interpolate._interpolate', 'scipy.interpolate._fitpack2', 'scipy.interpolate._rbf', 'scipy.interpolate._rbfinterp_pythran', 'scipy.interpolate._rbfinterp', 'scipy.interpolate._cubic', 'scipy.interpolate._ndgriddata', 'scipy.interpolate._pade', 'scipy.interpolate._rgi_cython', 'scipy.interpolate._rgi', 'scipy.interpolate.fitpack', 'scipy.interpolate.fitpack2', 'scipy.interpolate.interpolate', 'scipy.interpolate.ndgriddata', 'scipy.interpolate.polyint', 'scipy.interpolate.rbf', 'scipy.interpolate', 'scipy.stats._biasedurn', 'scipy.stats._discrete_distns', 'scipy.stats._levy_stable.levyst', 'scipy.stats._levy_stable', 'scipy.stats._entropy', 'scipy.stats.distributions', 'scipy._lib._bunch', 'scipy.stats._stats_pythran', 'scipy.stats._stats_mstats_common', 'scipy.stats._mstats_basic', 'scipy.stats._common', 'scipy._lib._uarray._uarray', 'scipy._lib._uarray._backend', 'scipy._lib._uarray', 'scipy._lib.uarray', 'scipy.fft._basic', 'scipy.fft._realtransforms', 'scipy.fft._fftlog', 'scipy.fft._fftlog_multimethods', 'scipy.fft._pocketfft.pypocketfft', 'scipy.fft._pocketfft.helper', 'scipy.fft._pocketfft.basic', 'scipy.fft._pocketfft.realtransforms', 'scipy.fft._pocketfft', 'scipy.fft._helper', 'scipy.fft._backend', 'scipy.fft', 'scipy.stats._hypotests', 'scipy._lib._docscrape', 'scipy.stats._axis_nan_policy', 'scipy.stats._resampling', 'scipy.stats._binomtest', 'scipy.stats._stats_py', 'scipy.stats._variation', 'scipy.stats._statlib', 'scipy.stats._fit', 'scipy.stats._relative_risk', 'scipy.stats._crosstab', 'scipy.stats._odds_ratio', 'scipy.stats.contingency', 'scipy.stats._morestats', 'scipy.stats._sobol', 'scipy.stats._qmc_cy', 'scipy.stats._qmc', 'scipy.stats._multicomp', 'scipy.stats._binned_statistic', 'scipy.stats._mvn', 'scipy.stats._kde', 'scipy.stats._mstats_extras', 'scipy.stats.mstats', 'scipy.stats.qmc', 'scipy.stats._covariance', 'scipy.stats._rcont.rcont', 'scipy.stats._rcont', 'scipy.stats._qmvnt', 'scipy.stats._multivariate', 'scipy.stats._rvs_sampling', 'scipy.stats._page_trend_test', 'scipy.stats._mannwhitneyu', 'scipy.stats._sensitivity_analysis', 'scipy.stats._survival', 'scipy.stats.biasedurn', 'scipy.stats.kde', 'scipy.stats.morestats', 'scipy.stats.mstats_basic', 'scipy.stats.mstats_extras', 'scipy.stats.mvn', 'scipy.stats.statlib', 'scipy.stats.stats', 'scipy.stats', 'seaborn.algorithms', 'seaborn._statistics', 'seaborn.external.docscrape', 'seaborn._docstrings', 'seaborn.axisgrid', 'seaborn.relational', 'statsmodels.tools.data', 'statsmodels.tools.validation.validation', 'statsmodels.tools.validation', 'statsmodels.tools.tools', 'statsmodels.tools._testing', 'statsmodels.tools', 'statsmodels.compat.python', 'statsmodels.compat', 'statsmodels.compat.pandas', 'patsy.version', 'patsy.origin', 'patsy.compat', 'patsy.util', 'patsy.infix_parser', 'patsy.tokens', 'patsy.parse_formula', 'patsy.constraint', 'patsy.contrasts', 'patsy.categorical', 'patsy.state', 'patsy.splines', 'patsy.mgcv_cubic_splines', 'patsy.builtins', 'patsy.eval', 'patsy.desc', 'patsy.design_info', 'patsy.redundancy', 'patsy.missing', 'patsy.build', 'patsy.highlevel', 'patsy.user_util', 'patsy', 'statsmodels.compat.patsy', 'statsmodels._version', 'statsmodels', 'seaborn.regression', 'seaborn._stats', 'seaborn._core.rules', 'seaborn._core.groupby', 'seaborn._core.scales', 'seaborn._stats.base', 'seaborn._stats.density', 'seaborn.categorical', 'seaborn._stats.counting', 'seaborn.external.kde', 'seaborn.distributions', 'scipy.cluster._vq', 'scipy.cluster.vq', 'scipy.cluster._hierarchy', 'scipy.cluster._optimal_leaf_ordering', 'scipy._lib._disjoint_set', 'scipy.cluster.hierarchy', 'scipy.cluster', 'seaborn.cm', 'seaborn.matrix', 'seaborn.miscplot', 'ipywidgets._version', 'ipywidgets.widgets.widget', 'ipywidgets.widgets.util', 'ipywidgets.widgets.trait_types', 'ipywidgets.widgets.widget_layout', 'ipywidgets.widgets.widget_style', 'ipywidgets.widgets.domwidget', 'ipywidgets.widgets.valuewidget', 'ipywidgets.widgets.widget_core', 'ipywidgets.widgets.widget_description', 'ipywidgets.widgets.widget_bool', 'ipywidgets.widgets.widget_button', 'ipywidgets.widgets.docutils', 'ipywidgets.widgets.widget_box', 'ipywidgets.widgets.widget_int', 'ipywidgets.widgets.widget_float', 'ipywidgets.widgets.widget_color', 'ipywidgets.widgets.widget_date', 'ipywidgets.widgets.widget_output', 'ipywidgets.widgets.widget_selection', 'ipywidgets.widgets.widget_selectioncontainer', 'ipywidgets.widgets.widget_string', 'ipywidgets.widgets.widget_controller', 'ipywidgets.widgets.interaction', 'ipywidgets.widgets.widget_link', 'ipywidgets.widgets.widget_media', 'ipywidgets.widgets.widget_templates', 'ipywidgets.widgets.widget_upload', 'ipywidgets.widgets', 'ipywidgets', 'seaborn.widgets', 'seaborn', 'pandas.io.formats.string', 'pandas.io.formats.html', 'google.colab._dataframe_summarizer', 'PIL.BmpImagePlugin', 'PIL.GifImagePlugin', 'PIL.ImageOps', 'PIL.TiffImagePlugin', 'PIL.JpegPresets', 'PIL.JpegImagePlugin', 'PIL.PpmImagePlugin'])\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "print(sys.modules.keys())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "SQpVlhWbLg8F",
        "outputId": "501fe54f-797c-4f40-9385-ffd7a8f1d483"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<details>\n",
              "<summary>Click to view session information</summary>\n",
              "<pre>\n",
              "-----\n",
              "google              NA\n",
              "matplotlib          3.7.1\n",
              "numpy               1.25.2\n",
              "pandas              1.5.3\n",
              "seaborn             0.13.1\n",
              "session_info        1.0.0\n",
              "-----\n",
              "</pre>\n",
              "<details>\n",
              "<summary>Click to view modules imported as dependencies</summary>\n",
              "<pre>\n",
              "PIL                 9.4.0\n",
              "asttokens           NA\n",
              "backcall            0.2.0\n",
              "certifi             2024.02.02\n",
              "cffi                1.16.0\n",
              "cloudpickle         2.2.1\n",
              "cycler              0.12.1\n",
              "cython_runtime      NA\n",
              "dateutil            2.8.2\n",
              "debugpy             1.6.6\n",
              "decorator           4.4.2\n",
              "defusedxml          0.7.1\n",
              "executing           2.0.1\n",
              "httplib2            0.22.0\n",
              "ipykernel           5.5.6\n",
              "ipython_genutils    0.2.0\n",
              "ipywidgets          7.7.1\n",
              "jedi                0.19.1\n",
              "kiwisolver          1.4.5\n",
              "matplotlib_inline   0.1.6\n",
              "mpl_toolkits        NA\n",
              "numexpr             2.9.0\n",
              "packaging           23.2\n",
              "parso               0.8.3\n",
              "patsy               0.5.6\n",
              "pexpect             4.9.0\n",
              "pickleshare         0.7.5\n",
              "pkg_resources       NA\n",
              "platformdirs        4.2.0\n",
              "portpicker          NA\n",
              "prompt_toolkit      3.0.43\n",
              "psutil              5.9.5\n",
              "ptyprocess          0.7.0\n",
              "pure_eval           0.2.2\n",
              "pyarrow             14.0.2\n",
              "pydev_ipython       NA\n",
              "pydevconsole        NA\n",
              "pydevd              2.9.5\n",
              "pydevd_file_utils   NA\n",
              "pydevd_plugins      NA\n",
              "pydevd_tracing      NA\n",
              "pygments            2.16.1\n",
              "pyparsing           3.1.1\n",
              "pytz                2023.4\n",
              "scipy               1.11.4\n",
              "setuptools          67.7.2\n",
              "sitecustomize       NA\n",
              "six                 1.16.0\n",
              "socks               1.7.1\n",
              "sphinxcontrib       NA\n",
              "stack_data          0.6.3\n",
              "statsmodels         0.14.1\n",
              "tornado             6.3.2\n",
              "traitlets           5.7.1\n",
              "typing_extensions   NA\n",
              "wcwidth             0.2.13\n",
              "zmq                 23.2.1\n",
              "zoneinfo            NA\n",
              "</pre>\n",
              "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
              "<pre>\n",
              "-----\n",
              "IPython             8.12.3\n",
              "jupyter_client      6.1.12\n",
              "jupyter_core        5.7.1\n",
              "notebook            6.5.5\n",
              "-----\n",
              "Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0]\n",
              "Linux-6.1.58+-x86_64-with-glibc2.35\n",
              "-----\n",
              "Session information updated at 2024-03-04 11:22\n",
              "</pre>\n",
              "</details>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "session_info.show(write_req_file=True, req_file_name='requirements.txt')"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
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      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.0 (tags/v3.8.0:fa919fd, Oct 14 2019, 19:21:23) [MSC v.1916 32 bit (Intel)]"
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}