Bike_demand_data_pre_processing.ipynb

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      "name": "Bike_demand_data_pre-processing.ipynb",
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        "<a href=\"https://colab.research.google.com/github/hitesh-pathak/Credit_Card_Defaulter_Data_Science/blob/main/Bike_demand_data_pre_processing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "**Import libs and data from google sheet**"
      ],
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        "id": "K4CAH6AceV8Q"
      }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "pd.options.display.max_columns = 999\n"
      ],
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        "id": "xonNdp0KaGzs"
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      "execution_count": 12,
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      "source": [
        "## data import\n",
        "\n",
        "sheet_url = \"https://docs.google.com/spreadsheets/d/1WieH-IPBXkKPj46XCEsjAgShaRdZ3A9BTN2zz27jSN4/edit#gid=917200446\"\n",
        "csv_url = sheet_url.replace('/edit#gid=', '/export?format=csv&gid=')\n",
        "\n",
        "df = pd.read_csv(csv_url)\n",
        "\n",
        "df.head()"
      ],
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          "height": 206
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        "id": "NXjIA_odi-QL",
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              "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
              "0        1  01-01-2018       1   0     1        0        1           1   \n",
              "1        2  02-01-2018       1   0     1        0        2           1   \n",
              "2        3  03-01-2018       1   0     1        0        3           1   \n",
              "3        4  04-01-2018       1   0     1        0        4           1   \n",
              "4        5  05-01-2018       1   0     1        0        5           1   \n",
              "\n",
              "   weathersit       temp     atemp      hum  windspeed  casual  registered  \\\n",
              "0           2  14.110847  18.18125  80.5833  10.749882     331         654   \n",
              "1           2  14.902598  17.68695  69.6087  16.652113     131         670   \n",
              "2           1   8.050924   9.47025  43.7273  16.636703     120        1229   \n",
              "3           1   8.200000  10.60610  59.0435  10.739832     108        1454   \n",
              "4           1   9.305237  11.46350  43.6957  12.522300      82        1518   \n",
              "\n",
              "    cnt  \n",
              "0   985  \n",
              "1   801  \n",
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    {
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      "source": [
        "**Pre processing data**"
      ],
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        "print(df.shape)\n",
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          "text": [
            "(730, 16)\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 730 entries, 0 to 729\n",
            "Data columns (total 16 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   instant     730 non-null    int64  \n",
            " 1   dteday      730 non-null    object \n",
            " 2   season      730 non-null    int64  \n",
            " 3   yr          730 non-null    int64  \n",
            " 4   mnth        730 non-null    int64  \n",
            " 5   holiday     730 non-null    int64  \n",
            " 6   weekday     730 non-null    int64  \n",
            " 7   workingday  730 non-null    int64  \n",
            " 8   weathersit  730 non-null    int64  \n",
            " 9   temp        730 non-null    float64\n",
            " 10  atemp       730 non-null    float64\n",
            " 11  hum         730 non-null    float64\n",
            " 12  windspeed   730 non-null    float64\n",
            " 13  casual      730 non-null    int64  \n",
            " 14  registered  730 non-null    int64  \n",
            " 15  cnt         730 non-null    int64  \n",
            "dtypes: float64(4), int64(11), object(1)\n",
            "memory usage: 91.4+ KB\n"
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    {
      "cell_type": "markdown",
      "source": [
        "Convert to proper datetime fields"
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      "source": [
        "df = df.rename(columns={'weathersit':'weather',\n",
        "                       'yr':'year',\n",
        "                       'mnth':'month',\n",
        "                       'hr':'hour',\n",
        "                       'hum':'humidity',\n",
        "                       'cnt':'count',\n",
        "                       'dteday':'date'})\n",
        "\n",
        "date = pd.DatetimeIndex(df['date'])\n",
        "\n",
        "df['date'] = date.date\n",
        "df['year'] = date.year\n",
        "df['month'] = date.month_name()\n",
        "df['weekday'] = date.day_name()\n",
        "\n",
        "df"
      ],
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              "     instant        date  season  year     month  holiday   weekday  \\\n",
              "0          1  2018-01-01       1  2018   January        0    Monday   \n",
              "1          2  2018-02-01       1  2018  February        0  Thursday   \n",
              "2          3  2018-03-01       1  2018     March        0  Thursday   \n",
              "3          4  2018-04-01       1  2018     April        0    Sunday   \n",
              "4          5  2018-05-01       1  2018       May        0   Tuesday   \n",
              "..       ...         ...     ...   ...       ...      ...       ...   \n",
              "725      726  2019-12-27       1  2019  December        0    Friday   \n",
              "726      727  2019-12-28       1  2019  December        0  Saturday   \n",
              "727      728  2019-12-29       1  2019  December        0    Sunday   \n",
              "728      729  2019-12-30       1  2019  December        0    Monday   \n",
              "729      730  2019-12-31       1  2019  December        0   Tuesday   \n",
              "\n",
              "     workingday  weather       temp     atemp  humidity  windspeed  casual  \\\n",
              "0             1        2  14.110847  18.18125   80.5833  10.749882     331   \n",
              "1             1        2  14.902598  17.68695   69.6087  16.652113     131   \n",
              "2             1        1   8.050924   9.47025   43.7273  16.636703     120   \n",
              "3             1        1   8.200000  10.60610   59.0435  10.739832     108   \n",
              "4             1        1   9.305237  11.46350   43.6957  12.522300      82   \n",
              "..          ...      ...        ...       ...       ...        ...     ...   \n",
              "725           1        2  10.420847  11.33210   65.2917  23.458911     247   \n",
              "726           0        2  10.386653  12.75230   59.0000  10.416557     644   \n",
              "727           0        2  10.386653  12.12000   75.2917   8.333661     159   \n",
              "728           1        1  10.489153  11.58500   48.3333  23.500518     364   \n",
              "729           1        2   8.849153  11.17435   57.7500  10.374682     439   \n",
              "\n",
              "     registered  count  \n",
              "0           654    985  \n",
              "1           670    801  \n",
              "2          1229   1349  \n",
              "3          1454   1562  \n",
              "4          1518   1600  \n",
              "..          ...    ...  \n",
              "725        1867   2114  \n",
              "726        2451   3095  \n",
              "727        1182   1341  \n",
              "728        1432   1796  \n",
              "729        2290   2729  \n",
              "\n",
              "[730 rows x 16 columns]"
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              "      <th>2</th>\n",
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              "      <td>2018-03-01</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>March</td>\n",
              "      <td>0</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.050924</td>\n",
              "      <td>9.47025</td>\n",
              "      <td>43.7273</td>\n",
              "      <td>16.636703</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>2018-04-01</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>April</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>8.200000</td>\n",
              "      <td>10.60610</td>\n",
              "      <td>59.0435</td>\n",
              "      <td>10.739832</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>2018-05-01</td>\n",
              "      <td>1</td>\n",
              "      <td>2018</td>\n",
              "      <td>May</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>9.305237</td>\n",
              "      <td>11.46350</td>\n",
              "      <td>43.6957</td>\n",
              "      <td>12.522300</td>\n",
              "      <td>82</td>\n",
              "      <td>1518</td>\n",
              "      <td>1600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>725</th>\n",
              "      <td>726</td>\n",
              "      <td>2019-12-27</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "      <td>Friday</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>10.420847</td>\n",
              "      <td>11.33210</td>\n",
              "      <td>65.2917</td>\n",
              "      <td>23.458911</td>\n",
              "      <td>247</td>\n",
              "      <td>1867</td>\n",
              "      <td>2114</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>726</th>\n",
              "      <td>727</td>\n",
              "      <td>2019-12-28</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "      <td>Saturday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10.386653</td>\n",
              "      <td>12.75230</td>\n",
              "      <td>59.0000</td>\n",
              "      <td>10.416557</td>\n",
              "      <td>644</td>\n",
              "      <td>2451</td>\n",
              "      <td>3095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>727</th>\n",
              "      <td>728</td>\n",
              "      <td>2019-12-29</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "      <td>Sunday</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>10.386653</td>\n",
              "      <td>12.12000</td>\n",
              "      <td>75.2917</td>\n",
              "      <td>8.333661</td>\n",
              "      <td>159</td>\n",
              "      <td>1182</td>\n",
              "      <td>1341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>728</th>\n",
              "      <td>729</td>\n",
              "      <td>2019-12-30</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>10.489153</td>\n",
              "      <td>11.58500</td>\n",
              "      <td>48.3333</td>\n",
              "      <td>23.500518</td>\n",
              "      <td>364</td>\n",
              "      <td>1432</td>\n",
              "      <td>1796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>729</th>\n",
              "      <td>730</td>\n",
              "      <td>2019-12-31</td>\n",
              "      <td>1</td>\n",
              "      <td>2019</td>\n",
              "      <td>December</td>\n",
              "      <td>0</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>8.849153</td>\n",
              "      <td>11.17435</td>\n",
              "      <td>57.7500</td>\n",
              "      <td>10.374682</td>\n",
              "      <td>439</td>\n",
              "      <td>2290</td>\n",
              "      <td>2729</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>730 rows × 16 columns</p>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Convert appropriate fields to categorial dtype"
      ],
      "metadata": {
        "id": "dWL-0gWp08cH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['season'].unique())\n",
        "df['weather'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WTjrC6TQtIUn",
        "outputId": "d7622241-c628-4b99-8faa-d25df4fd6537"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 1, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['season'] = df['season'].map({1:'s1',2:'s2',3:'s3',4:'s4'})\n",
        "df['weather'] = df['weather'].map({1:'w1',2:'w2',3:'w3'})\n",
        "df.sample(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "hSK1Rha9fziM",
        "outputId": "4c6981bf-36af-46af-80fe-d56b263d1635"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     instant        date season  year     month  holiday    weekday  \\\n",
              "230      231  2018-08-19     s3  2018    August        0     Sunday   \n",
              "155      156  2018-05-06     s2  2018       May        0     Sunday   \n",
              "525      526  2019-10-06     s2  2019   October        0     Sunday   \n",
              "680      681  2019-12-11     s4  2019  December        1  Wednesday   \n",
              "139      140  2018-05-20     s2  2018       May        0     Sunday   \n",
              "\n",
              "     workingday weather       temp     atemp  humidity  windspeed  casual  \\\n",
              "230           0      w2  28.085000  31.66105   72.2917   9.333636     797   \n",
              "155           1      w2  26.581653  30.84020   65.2500   9.292364    1685   \n",
              "525           1      w1  29.793347  33.17585   53.8333   8.959307    2224   \n",
              "680           0      w1  19.885000  23.76915   74.1667  11.625639    1097   \n",
              "139           0      w1  22.003347  26.45210   71.9583   8.375871     909   \n",
              "\n",
              "     registered  count  \n",
              "230        3356   4153  \n",
              "155        3221   4906  \n",
              "525        4374   6598  \n",
              "680        5172   6269  \n",
              "139        4008   4917  "
            ],
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              "      <th>month</th>\n",
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              "      <td>2018-08-19</td>\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-40a397f3-0c61-4ce4-8c2e-84c7e1b37ee5 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-40a397f3-0c61-4ce4-8c2e-84c7e1b37ee5');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here I also drop unnecessary fields like 'Instant' which is just an index. Also I drop 'date' because we have all its data already in other columns."
      ],
      "metadata": {
        "id": "8AwRm_u61DAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cat_fields = [\"season\", \"year\", \"month\", \"weather\", \"holiday\" ,\"workingday\"]\n",
        "drop_cols = ['date', 'instant']\n",
        "df.drop(drop_cols, axis=1, inplace=True)\n",
        "df.loc[:, cat_fields] = df[cat_fields].astype('category')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3wImCH4tZ4q",
        "outputId": "f50c08f9-0060-4a59-a0b0-6fcd3c6310df"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 730 entries, 0 to 729\n",
            "Data columns (total 14 columns):\n",
            " #   Column      Non-Null Count  Dtype   \n",
            "---  ------      --------------  -----   \n",
            " 0   season      730 non-null    category\n",
            " 1   year        730 non-null    category\n",
            " 2   month       730 non-null    category\n",
            " 3   holiday     730 non-null    category\n",
            " 4   weekday     730 non-null    object  \n",
            " 5   workingday  730 non-null    category\n",
            " 6   weather     730 non-null    category\n",
            " 7   temp        730 non-null    float64 \n",
            " 8   atemp       730 non-null    float64 \n",
            " 9   humidity    730 non-null    float64 \n",
            " 10  windspeed   730 non-null    float64 \n",
            " 11  casual      730 non-null    int64   \n",
            " 12  registered  730 non-null    int64   \n",
            " 13  count       730 non-null    int64   \n",
            "dtypes: category(6), float64(4), int64(3), object(1)\n",
            "memory usage: 51.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The above shows that there are no null values in data."
      ],
      "metadata": {
        "id": "nqLqCKQHwOf_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Descriptive stats**"
      ],
      "metadata": {
        "id": "nOmxXgAR1TiN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6S9SHMofveEJ",
        "outputId": "a83251dc-266c-4d6b-e66f-f958565a3708"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             temp       atemp    humidity   windspeed       casual  \\\n",
              "count  730.000000  730.000000  730.000000  730.000000   730.000000   \n",
              "mean    20.319259   23.726322   62.765175   12.763620   849.249315   \n",
              "std      7.506729    8.150308   14.237589    5.195841   686.479875   \n",
              "min      2.424346    3.953480    0.000000    1.500244     2.000000   \n",
              "25%     13.811885   16.889713   52.000000    9.041650   316.250000   \n",
              "50%     20.465826   24.368225   62.625000   12.125325   717.000000   \n",
              "75%     26.880615   30.445775   72.989575   15.625589  1096.500000   \n",
              "max     35.328347   42.044800   97.250000   34.000021  3410.000000   \n",
              "\n",
              "        registered        count  \n",
              "count   730.000000   730.000000  \n",
              "mean   3658.757534  4508.006849  \n",
              "std    1559.758728  1936.011647  \n",
              "min      20.000000    22.000000  \n",
              "25%    2502.250000  3169.750000  \n",
              "50%    3664.500000  4548.500000  \n",
              "75%    4783.250000  5966.000000  \n",
              "max    6946.000000  8714.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5ff798e8-4254-4973-8502-45824dd5b52d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temp</th>\n",
              "      <th>atemp</th>\n",
              "      <th>humidity</th>\n",
              "      <th>windspeed</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>730.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>730.000000</td>\n",
              "      <td>730.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>20.319259</td>\n",
              "      <td>23.726322</td>\n",
              "      <td>62.765175</td>\n",
              "      <td>12.763620</td>\n",
              "      <td>849.249315</td>\n",
              "      <td>3658.757534</td>\n",
              "      <td>4508.006849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7.506729</td>\n",
              "      <td>8.150308</td>\n",
              "      <td>14.237589</td>\n",
              "      <td>5.195841</td>\n",
              "      <td>686.479875</td>\n",
              "      <td>1559.758728</td>\n",
              "      <td>1936.011647</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.424346</td>\n",
              "      <td>3.953480</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.500244</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>22.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>13.811885</td>\n",
              "      <td>16.889713</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>9.041650</td>\n",
              "      <td>316.250000</td>\n",
              "      <td>2502.250000</td>\n",
              "      <td>3169.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>20.465826</td>\n",
              "      <td>24.368225</td>\n",
              "      <td>62.625000</td>\n",
              "      <td>12.125325</td>\n",
              "      <td>717.000000</td>\n",
              "      <td>3664.500000</td>\n",
              "      <td>4548.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>26.880615</td>\n",
              "      <td>30.445775</td>\n",
              "      <td>72.989575</td>\n",
              "      <td>15.625589</td>\n",
              "      <td>1096.500000</td>\n",
              "      <td>4783.250000</td>\n",
              "      <td>5966.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>35.328347</td>\n",
              "      <td>42.044800</td>\n",
              "      <td>97.250000</td>\n",
              "      <td>34.000021</td>\n",
              "      <td>3410.000000</td>\n",
              "      <td>6946.000000</td>\n",
              "      <td>8714.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5ff798e8-4254-4973-8502-45824dd5b52d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-5ff798e8-4254-4973-8502-45824dd5b52d 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-5ff798e8-4254-4973-8502-45824dd5b52d');\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",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## show categorical stats\n",
        "\n",
        "df.describe(exclude=np.number)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "QCN795vpwIBX",
        "outputId": "11e56b81-ae76-46c4-bed1-dd99e1b48ab1"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       season  year   month  holiday weekday  workingday weather\n",
              "count     730   730     730      730     730         730     730\n",
              "unique      4     2      12        2       7           2       3\n",
              "top        s3  2018  August        0  Monday           1      w1\n",
              "freq      188   365      62      709     105         504     463"
            ],
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              "\n",
              "  <div id=\"df-bfc43b28-6f70-47b1-b33d-ca013b7e6480\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>year</th>\n",
              "      <th>month</th>\n",
              "      <th>holiday</th>\n",
              "      <th>weekday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "      <td>730</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>s3</td>\n",
              "      <td>2018</td>\n",
              "      <td>August</td>\n",
              "      <td>0</td>\n",
              "      <td>Monday</td>\n",
              "      <td>1</td>\n",
              "      <td>w1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>188</td>\n",
              "      <td>365</td>\n",
              "      <td>62</td>\n",
              "      <td>709</td>\n",
              "      <td>105</td>\n",
              "      <td>504</td>\n",
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              "\n",
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              "\n",
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              "\n",
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              "        const buttonEl =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-bfc43b28-6f70-47b1-b33d-ca013b7e6480');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Export data set for later use**"
      ],
      "metadata": {
        "id": "ZHw3AMCQxLW8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('./pre_processed_data.csv')"
      ],
      "metadata": {
        "id": "3bCd7Xofxtl7"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "files.download('./pre_processed_data.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "hIETC4Oa2ohm",
        "outputId": "d103c8cb-8c88-4e81-82a8-6d03f50f23d6"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_e340b632-2382-44fc-80ef-29f0d7b420ad\", \"pre_processed_data.csv\", 60565)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "SZDc3C-O2vPC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}