Business_Case_Yulu (1).ipynb

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            "--2024-06-21 11:05:25--  https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/428/original/bike_sharing.csv\n",
            "Resolving d2beiqkhq929f0.cloudfront.net (d2beiqkhq929f0.cloudfront.net)... 3.162.7.188, 3.162.7.94, 3.162.7.58, ...\n",
            "Connecting to d2beiqkhq929f0.cloudfront.net (d2beiqkhq929f0.cloudfront.net)|3.162.7.188|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 648353 (633K) [text/plain]\n",
            "Saving to: ‘bike_sharing.csv.1’\n",
            "\n",
            "bike_sharing.csv.1  100%[===================>] 633.16K  --.-KB/s    in 0.09s   \n",
            "\n",
            "2024-06-21 11:05:25 (6.91 MB/s) - ‘bike_sharing.csv.1’ saved [648353/648353]\n",
            "\n"
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      "source": [
        "!wget --no-check-certificate https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/428/original/bike_sharing.csv"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "khiE4P7CW7eh"
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      "source": [
        "df=pd.read_csv('bike_sharing.csv')"
      ],
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      "outputs": []
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        "df.head()"
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              "              datetime  season  holiday  workingday  weather  temp   atemp  \\\n",
              "0  2011-01-01 00:00:00       1        0           0        1  9.84  14.395   \n",
              "1  2011-01-01 01:00:00       1        0           0        1  9.02  13.635   \n",
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              "4  2011-01-01 04:00:00       1        0           0        1  9.84  14.395   \n",
              "\n",
              "   humidity  windspeed  casual  registered  count  \n",
              "0        81        0.0       3          13     16  \n",
              "1        80        0.0       8          32     40  \n",
              "2        80        0.0       5          27     32  \n",
              "3        75        0.0       3          10     13  \n",
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        },
        "id": "g5G_XhX3ARoV",
        "outputId": "d5e9aa9d-627b-4aa8-b3d5-4796ee6a6d12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10886 entries, 0 to 10885\n",
            "Data columns (total 12 columns):\n",
            " #   Column      Non-Null Count  Dtype  \n",
            "---  ------      --------------  -----  \n",
            " 0   datetime    10886 non-null  object \n",
            " 1   season      10886 non-null  int64  \n",
            " 2   holiday     10886 non-null  int64  \n",
            " 3   workingday  10886 non-null  int64  \n",
            " 4   weather     10886 non-null  int64  \n",
            " 5   temp        10886 non-null  float64\n",
            " 6   atemp       10886 non-null  float64\n",
            " 7   humidity    10886 non-null  int64  \n",
            " 8   windspeed   10886 non-null  float64\n",
            " 9   casual      10886 non-null  int64  \n",
            " 10  registered  10886 non-null  int64  \n",
            " 11  count       10886 non-null  int64  \n",
            "dtypes: float64(3), int64(8), object(1)\n",
            "memory usage: 1020.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['datetime']=pd.to_datetime(df['datetime'])"
      ],
      "metadata": {
        "id": "-sOY0GHAXWri"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['datetime'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKJ4hjlaBwPx",
        "outputId": "e29b0980-6c85-442d-bba8-2e36b26d1d02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('<M8[ns]')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5qHTFduXbQp",
        "outputId": "ddd30e04-4f3b-4c9e-9dd3-aa99fbcae827"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXKHQefIX7ly",
        "outputId": "01cacbfe-6d88-4b19-b9cf-91e807c35f58"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "datetime      0\n",
              "season        0\n",
              "holiday       0\n",
              "workingday    0\n",
              "weather       0\n",
              "temp          0\n",
              "atemp         0\n",
              "humidity      0\n",
              "windspeed     0\n",
              "casual        0\n",
              "registered    0\n",
              "count         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['weather'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C1sMnQFRYRrB",
        "outputId": "380164a0-f999-42d3-b4e2-05ce1a87fd9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weather\n",
              "1    7192\n",
              "2    2834\n",
              "3     859\n",
              "4       1\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['date']=df['datetime'].dt.date"
      ],
      "metadata": {
        "id": "3jDRSVwfEoEx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'].min(),df['date'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9kgDJblKEvhF",
        "outputId": "8065238f-ddd1-464f-817b-d8c0a1beff42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(datetime.date(2011, 1, 1), datetime.date(2012, 12, 19))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['date'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5IOML8cFRTa",
        "outputId": "8d0244b0-6e2a-444f-90dd-2654bd3e0fda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "456"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df.drop_duplicates(subset='date')"
      ],
      "metadata": {
        "id": "3YpKxsiSFxfj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1['season'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sw5jRFuOZHu9",
        "outputId": "af673765-7734-4d48-d1b6-37a0ba05e4c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "season\n",
              "1    114\n",
              "2    114\n",
              "3    114\n",
              "4    114\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1['workingday'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWIQHnq_ZNmC",
        "outputId": "fd44d349-441b-4819-d76a-74c3487e8215"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "workingday\n",
              "1    311\n",
              "0    145\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,4))\n",
        "plt.subplot(1,3,1)\n",
        "sns.histplot(df['count'])\n",
        "ax=plt.subplot(1,3,2)\n",
        "sns.histplot(df['registered'],ax=ax)\n",
        "ax.set(ylabel='')\n",
        "ax=plt.subplot(1,3,3)\n",
        "sns.histplot(df['casual'],ax=ax)\n",
        "ax.set(ylabel='')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "LfoOWG-nHH9-",
        "outputId": "9f180e9b-581a-4db8-ecbc-0e0bb2df4730"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df.groupby(['date','season','holiday','workingday']).aggregate({'casual':sum,'registered':sum,'count':sum})\n",
        "#grouped by date, retaining the data about season, holiday and workingday columns and sum of hiring counts\n",
        "df1.reset_index(inplace=True) #reseting the index from date and multilevel column indexing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "fHqG2FV9LBQo",
        "outputId": "057753e7-2156-416d-f41e-c4fbae2434d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'date'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-5-dc129ac4cf35>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'date'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'season'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'holiday'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'workingday'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maggregate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'casual'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'registered'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#grouped by date, retaining the data about season, holiday and workingday columns and sum of hiring counts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mdf1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minplace\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#reseting the index from date and multilevel column indexing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[0;34m(self, by, axis, level, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m   8250\u001b[0m         \u001b[0maxis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8251\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 8252\u001b[0;31m         return DataFrameGroupBy(\n\u001b[0m\u001b[1;32m   8253\u001b[0m             \u001b[0mobj\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   8254\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, observed, dropna)\u001b[0m\n\u001b[1;32m    929\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    930\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mgrouper\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 931\u001b[0;31m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[1;32m    932\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/groupby/grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[0;34m(obj, key, axis, level, sort, observed, validate, dropna)\u001b[0m\n\u001b[1;32m    983\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    984\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 985\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    986\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    987\u001b[0m             \u001b[0;31m# Add key to exclusions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'date'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,4))\n",
        "plt.subplot(1,3,1)\n",
        "sns.histplot(df1['count'])\n",
        "plt.subplot(1,3,2)\n",
        "sns.histplot(df1['registered'])\n",
        "plt.subplot(1,3,3)\n",
        "sns.histplot(df1['casual'])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "id": "ObZeGvKZNlKl",
        "outputId": "d7227b9a-b463-4941-cc74-356474033ec0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df, y='casual')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "IgVXxp7RvKAy",
        "outputId": "c00df67d-6377-403a-c350-dcc2c9f27043"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='casual'>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#converting season codes to season names\n",
        "def season (s):\n",
        "  if(s==1):\n",
        "    return 'spring'\n",
        "  elif(s==2):\n",
        "    return 'summer'\n",
        "  elif(s==3):\n",
        "    return 'fall'\n",
        "  else:\n",
        "    return 'winter'\n",
        "df['seasonw']=df['season'].apply(season)"
      ],
      "metadata": {
        "id": "t4ZuISHXmPWs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uDZ38mDlm7In",
        "outputId": "6e30139e-ac01-4eab-bed4-76cacc7f45c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  datetime  season  holiday  workingday  weather   temp  \\\n",
              "0      2011-01-01 00:00:00       1        0           0        1   9.84   \n",
              "1      2011-01-01 01:00:00       1        0           0        1   9.02   \n",
              "2      2011-01-01 02:00:00       1        0           0        1   9.02   \n",
              "3      2011-01-01 03:00:00       1        0           0        1   9.84   \n",
              "4      2011-01-01 04:00:00       1        0           0        1   9.84   \n",
              "...                    ...     ...      ...         ...      ...    ...   \n",
              "10881  2012-12-19 19:00:00       4        0           1        1  15.58   \n",
              "10882  2012-12-19 20:00:00       4        0           1        1  14.76   \n",
              "10883  2012-12-19 21:00:00       4        0           1        1  13.94   \n",
              "10884  2012-12-19 22:00:00       4        0           1        1  13.94   \n",
              "10885  2012-12-19 23:00:00       4        0           1        1  13.12   \n",
              "\n",
              "        atemp  humidity  windspeed  casual  registered  count seasonw  \n",
              "0      14.395        81     0.0000       3          13     16  spring  \n",
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10886,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 10886,\n        \"samples\": [\n          \"2011-07-19 11:00:00\",\n          \"2012-01-16 06:00:00\",\n          \"2011-12-11 18:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.791589843987567,\n        \"min\": 0.82,\n        \"max\": 41.0,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          6.56,\n          1.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"atemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.474600626484948,\n        \"min\": 0.76,\n        \"max\": 45.455,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          14.395,\n          16.665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          29,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.164537326838689,\n        \"min\": 0.0,\n        \"max\": 56.9969,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          22.0028,\n          43.0006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 0,\n        \"max\": 367,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          287,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"registered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151,\n        \"min\": 0,\n        \"max\": 886,\n        \"num_unique_values\": 731,\n        \"samples\": [\n          566,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 1,\n        \"max\": 977,\n        \"num_unique_values\": 822,\n        \"samples\": [\n          626,\n          256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"summer\",\n          \"winter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "season_count=df.groupby('seasonw')['count'].sum()\n",
        "plt.figure(figsize = (6,6))\n",
        "plt.pie(season_count,\n",
        "       labels=season_count.index,\n",
        "       startangle=90,\n",
        "       autopct = '%.2f%%')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "DbU1FONmoo6P",
        "outputId": "46017a14-b89f-4a70-aa86-c7a4fd6aba14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "season_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LZHNIyYKqqld",
        "outputId": "927dcc55-9744-42c5-e40a-801c084fdc1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "seasonw\n",
              "fall      640662\n",
              "spring    312498\n",
              "summer    588282\n",
              "winter    544034\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=df,x='seasonw',y='count',hue='workingday',estimator='mean')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "pA7ADTkAZZMF",
        "outputId": "9a4d510f-4275-4be6-aacd-d5e6ad275341"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='seasonw', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='weather',hue='workingday',y='count',data=df,estimator='sum')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "9pRuCG__Zxh8",
        "outputId": "8691063e-7583-4723-d36a-089e87c47e2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='weather', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(hue='season',x='weather',data=df,color='g')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "iYxIeMLgaXja",
        "outputId": "7c3f0b88-d7d1-4f65-8daa-4d91c97fe1af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-eb3eda238735>:1: FutureWarning: \n",
            "\n",
            "Setting a gradient palette using color= is deprecated and will be removed in v0.14.0. Set `palette='dark:g'` for the same effect.\n",
            "\n",
            "  sns.countplot(hue='season',x='weather',data=df,color='g')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='weather', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(df['temp'],df['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "IeLwQ2YCnL0x",
        "outputId": "ffc45941-a8b1-4127-9d9f-bda81abc588e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x79c6fb291bd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(data=df,x='workingday',y='count')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "VJo3Vq7natjm",
        "outputId": "0793fea7-74bc-4020-efd7-1484cdc6e75a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='workingday', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import ttest_ind"
      ],
      "metadata": {
        "id": "yko_V13ea6KY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=df.groupby(['date','seasonw','holiday','workingday']).aggregate({'casual':sum,'registered':sum,'count':sum})\n",
        "#grouped by date, retaining the data about season, holiday and workingday columns and sum of hiring counts\n",
        "df1.reset_index(inplace=True) #reseting the index from date and multilevel column indexing"
      ],
      "metadata": {
        "id": "n1aoq4lx1Y7d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "jbahWdi311yr",
        "outputId": "8164d9af-3898-41dd-f9c9-981133ff9127"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           date  season  holiday  workingday  casual  registered  count\n",
              "0    2011-01-01       1        0           0     331         654    985\n",
              "1    2011-01-02       1        0           0     131         670    801\n",
              "2    2011-01-03       1        0           1     120        1229   1349\n",
              "3    2011-01-04       1        0           1     108        1454   1562\n",
              "4    2011-01-05       1        0           1      82        1518   1600\n",
              "..          ...     ...      ...         ...     ...         ...    ...\n",
              "451  2012-12-15       4        0           0     767        4280   5047\n",
              "452  2012-12-16       4        0           0     538        3248   3786\n",
              "453  2012-12-17       4        0           1     212        4373   4585\n",
              "454  2012-12-18       4        0           1     433        5124   5557\n",
              "455  2012-12-19       4        0           1     333        4934   5267\n",
              "\n",
              "[456 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-504b7c12-bfdc-429a-9afa-824b5a1dbe9b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>casual</th>\n",
              "      <th>registered</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>331</td>\n",
              "      <td>654</td>\n",
              "      <td>985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-02</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>131</td>\n",
              "      <td>670</td>\n",
              "      <td>801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-03</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>1229</td>\n",
              "      <td>1349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-04</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>1454</td>\n",
              "      <td>1562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-05</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>2012-12-15</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>767</td>\n",
              "      <td>4280</td>\n",
              "      <td>5047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>452</th>\n",
              "      <td>2012-12-16</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>538</td>\n",
              "      <td>3248</td>\n",
              "      <td>3786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>453</th>\n",
              "      <td>2012-12-17</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>212</td>\n",
              "      <td>4373</td>\n",
              "      <td>4585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>454</th>\n",
              "      <td>2012-12-18</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>433</td>\n",
              "      <td>5124</td>\n",
              "      <td>5557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>455</th>\n",
              "      <td>2012-12-19</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>333</td>\n",
              "      <td>4934</td>\n",
              "      <td>5267</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>456 rows × 7 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-504b7c12-bfdc-429a-9afa-824b5a1dbe9b')\"\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-504b7c12-bfdc-429a-9afa-824b5a1dbe9b 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-504b7c12-bfdc-429a-9afa-824b5a1dbe9b');\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-55c85310-b3fc-479d-885b-d881b577ceea\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-55c85310-b3fc-479d-885b-d881b577ceea')\"\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-55c85310-b3fc-479d-885b-d881b577ceea button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_f53b072f-e0d3-4ccc-b936-37a2a2852749\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df1')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_f53b072f-e0d3-4ccc-b936-37a2a2852749 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1",
              "summary": "{\n  \"name\": \"df1\",\n  \"rows\": 456,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-01-01\",\n        \"max\": \"2012-12-19\",\n        \"num_unique_values\": 456,\n        \"samples\": [\n          \"2012-08-13\",\n          \"2011-03-02\",\n          \"2012-06-18\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 698,\n        \"min\": 9,\n        \"max\": 3410,\n        \"num_unique_values\": 410,\n        \"samples\": [\n          667,\n          1504\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"registered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1494,\n        \"min\": 491,\n        \"max\": 6911,\n        \"num_unique_values\": 430,\n        \"samples\": [\n          5219,\n          3819\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1868,\n        \"min\": 605,\n        \"max\": 8714,\n        \"num_unique_values\": 446,\n        \"samples\": [\n          6436,\n          6824\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#the data of working and non working days has been seggregated\n",
        "df_working=df1[df1['workingday']==1]\n",
        "df_nonworking=df1[df1['workingday']==0]"
      ],
      "metadata": {
        "id": "79R92sJV4yKK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_working['count'].mean(),df_nonworking['count'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_3_epk99Llh",
        "outputId": "aee4dc27-fca2-4a2b-c568-35398f7ce787"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4600.012861736334, 4516.358620689655)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ttest_ind(df_working['count'],df_nonworking['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "egm3wG5F45lX",
        "outputId": "421eed8e-e27c-401e-c774-c47167821b51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TtestResult(statistic=0.44477221614881995, pvalue=0.656696335987859, df=454.0)"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df_working['count'],bins=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "crG8yZvD5AwY",
        "outputId": "e6349824-c507-4f62-e718-adba34b02c49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AAEBd1CvMLFy4sKH7AAAAqJcrumdm+/bteuONN/TGG29o586dDdWTT01NjfLz85WamqqYmBhdf/31eu6552SMafB9AQAAO9XrzExZWZkeeOABrVu3Ti1btpQknTx5UoMHD9bSpUvVtm3bBmnuhRde0Pz58/XnP/9ZaWlp2rZtm8aMGaO4uDg9/vjjDbIPAABgt3qdmXnsscdUUVGhvXv36sSJEzpx4oRKSkrk8XgaNGR89NFHuueeezR8+HB16NBB999/vzIzM/Xxxx832D4AAIDd6hVmVqxYoT/84Q/q2rWrb1m3bt00b948ffDBBw3W3K233qqioiLt379fkrRr1y5t3LhRw4YNu+hjqqur5fF4/CYAABC+6nWZyev1KjIy8rzlkZGR8nq9V9zUOVOnTpXH41GXLl3UtGlT1dTUaObMmRo1atRFH1NQUKAZM2Y0WA9Xq7T0XnK73bXWuFwu7d1dHJiGEBA2HndPRaXi2yZcoqYiQN0EVl3GHmrHC1eO436+eoWZIUOGaOLEiVqyZImSkpIkSUePHtUTTzyhoUOHNlhzb731lt58800tXrxYaWlpKi4u1qRJk5SUlHTR74DKy8tTbm6ub97j8Sg5ObnBerpauN1uZc4srLVm1bTsgPSCwLHxuBuv95I9L5swJDDNBFhdxh5qxwtXjuN+vnqFmblz5+ruu+9Whw4dfEHhyJEj6t69u954440Ga27y5MmaOnWqHnjgAUlSjx49dOjQIRUUFFw0zDidTjmdzgbrAQAAhLZ6hZnk5GTt2LFDH374oT777DNJUteuXZWRkdGgzZ06dUpNmvjf1tO0adMGvZQFAADsdllhZs2aNZowYYI2b96s2NhY/fjHP9aPf/xjSVJ5ebnS0tK0YMEC3XbbbQ3S3IgRIzRz5kylpKQoLS1NO3fu1OzZszV27NgG2T4AALDfZYWZOXPm6Oc//7liY2PPWxcXF6df/vKXmj17doOFmd///vfKz8/Xo48+qrKyMiUlJemXv/ylnn766QbZPgAAsN9lvTV7165duuOOOy66PjMzU9u3b7/ips5p0aKF5syZo0OHDun06dP6/PPP9Zvf/EZRUVENtg8AAGC3ywozx48fv+Bbss+JiIjQ119/fcVNAQAA1NVlhZlrr71WJSUlF12/e/duuVyuK24KAACgri4rzNx5553Kz8/XmTNnzlt3+vRpTZ8+XXfddVeDNQcAAHApl3UD8K9//Wu9/fbbuvHGGzVhwgR17txZkvTZZ59p3rx5qqmp0bRp0xqlUQAAgAu5rDCTkJCgjz76SOPGjVNeXp6MMZIkh8OhrKwszZs3TwkJtX/EMgAAQEO67A/Na9++vd5//3198803OnjwoIwx6tSpk1q1atUY/QEAANSqXp8ALEmtWrVS3759G7IXAACAy3ZZNwADAACEGsIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBq9f7QPMBTUan4trV/fcWp02fULCa61hqXy6W9u4trrUlL7yW3233F27ma1eU59FRUXHI7dTnuddlOuKrL8xPIn9VQ6yeQeN24ehBmUG/G61XmzMJaa5ZNGKLM2StqrVk1LfuS+3K73ZfcV122czWry3O4bMKQS26nrsf9alWX5yeQP6uh1k8g8bpx9eAyEwAAsBphBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGC1kA8zR48e1UMPPaTWrVsrJiZGPXr00LZt24LdFgAACBERwW6gNt98840GDBigwYMH64MPPlDbtm114MABtWrVKtitAQCAEBHSYeaFF15QcnKyFi5c6FuWmpoaxI4AAECoCenLTO+884769Omjn/zkJ2rXrp169+6t1157rdbHVFdXy+Px+E0AACB8hfSZmS+++ELz589Xbm6ufvWrX2nr1q16/PHHFRUVpZycnAs+pqCgQDNmzAhwp0BweSoqFd824RI1FQHqBrgydfl5drlc2ru7ODANIeSFdJjxer3q06ePnn/+eUlS7969VVJSogULFlw0zOTl5Sk3N9c37/F4lJycHJB+gWAxXq8yZxbWWrNswpDANANcobr8PK+alh2QXmCHkL7M5HK51K1bN79lXbt21eHDhy/6GKfTqdjYWL8JAACEr5AOMwMGDNC+ffv8lu3fv1/t27cPUkcAACDUhHSYeeKJJ7R582Y9//zzOnjwoBYvXqxXX31V48ePD3ZrAAAgRIR0mOnbt6+WL1+uJUuWqHv37nruuec0Z84cjRo1KtitAQCAEBHSNwBL0l133aW77ror2G0AAIAQFdJnZgAAAC6FMAMAAKxGmAEAAFYjzAAAAKsRZgAAgNUIMwAAwGqEGQAAYDXCDAAAsBphBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgtYhgN4DAS0vvJbfbXWuNp6IiQN1InopKxbdNuERN4PppKHV5nl0ul/buLg5MQ0AYaajXjbps59TpM2oWE11rDb/LwUWYuQq53W5lziystWbZhCGBaUaS8XpDqp+GUpfnedW07ID0AoSbhnrdqOt2MmevqLWG3+Xg4jITAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYjzAAAAKsRZgAAgNUIMwAAwGqEGQAAYDWrwsysWbPkcDg0adKkYLcCAABChDVhZuvWrXrllVeUnp4e7FYAAEAIsSLMVFZWatSoUXrttdfUqlWrYLcDAABCiBVhZvz48Ro+fLgyMjIuWVtdXS2Px+M3AQCA8BUR7AYuZenSpdqxY4e2bt1ap/qCggLNmDGjkbsKXWnpveR2u2ut8VRUBKibwPJUVCq+bUKtNS6XS3t3F1/xvq7m5xlXri4/q4H8+alLP6dOn1GzmOhaaxrq9wu4XCEdZo4cOaKJEydq9erVio6u/ZfonLy8POXm5vrmPR6PkpOTG6vFkON2u5U5s7DWmmUThgSmmQAzXu8lx75qWnaD7Otqfp5x5erysxrIn5+69pM5e0WtNQ31+wVcrpAOM9u3b1dZWZluuukm37Kamhpt2LBBc+fOVXV1tZo2ber3GKfTKafTGehWAQBAkIR0mBk6dKj27Nnjt2zMmDHq0qWLpkyZcl6QAQAAV5+QDjMtWrRQ9+7d/ZY1b95crVu3Pm85AAC4OlnxbiYAAICLCekzMxeybt26YLcAAABCCGdmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFaLCHYDtktL7yW3211rjcvl0t7dxYFpCFesLsfUU1HRIPvyVFQqvm1CQPYFNLar+ec51MZel37C6W8TYeYKud1uZc4srLVm1bTsgPSChlGXY7pswpAG2ZfxegO2L6CxXc0/z6E29rr0E05/m7jMBAAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFgt5MNMQUGB+vbtqxYtWqhdu3bKzs7Wvn37gt0WAAAIESEfZtavX6/x48dr8+bNWr16tc6ePavMzExVVVUFuzUAABACIoLdwKWsWLHCb37RokVq166dtm/frttvvz1IXQEAgFAR8mHmX5WXl0uS4uPjL7i+urpa1dXVvnmPxxOQvgAAQHBYFWa8Xq8mTZqkAQMGqHv37hesKSgo0IwZMwLcGQAA4SctvZfcbnetNS6XS3t3FwemoYuwKsyMHz9eJSUl2rhx40Vr8vLylJub65v3eDxKTk4ORHsAAIQVt9utzJmFtdasmpYdkF5qY02YmTBhgt59911t2LBB11133UXrnE6nnE5nADsDAADBFPJhxhijxx57TMuXL9e6deuUmpoa7JYAAEAICfkwM378eC1evFh//etf1aJFCx07dkySFBcXp5iYmCB3BwAAgi3kP2dm/vz5Ki8v16BBg+RyuXzTX/7yl2C3BgAAQkDIn5kxxgS7BQAAEMJC/swMAABAbQgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFaLCHYDqLu09F5yu9211ngqKgLUjZ08FZWKb5twiRqeQwDhL5xeDwkzFnG73cqcWVhrzbIJQwLTjKWM18tzCAAKr9dDLjMBAACrEWYAAIDVCDMAAMBqhBkAAGA1wgwAALAaYQYAAFiNMAMAAKxGmAEAAFYjzAAAAKsRZgAAgNUIMwAAwGqEGQAAYDXCDAAAsBphBgAAWI0wAwAArEaYAQAAVrMizMybN08dOnRQdHS0+vXrp48//jjYLQEAgBAR8mHmL3/5i3JzczV9+nTt2LFDPXv2VFZWlsrKyoLdGgAACAEhH2Zmz56tn//85xozZoy6deumBQsWqFmzZvrTn/4U7NYAAEAIiAh2A7X59ttvtX37duXl5fmWNWnSRBkZGdq0adMFH1NdXa3q6mrffHl5uSTJ4/E0So/G69XZ01WXrGmI/ddpX8ZQQw011FBDTeBqGuhv3L86t01jzKWLTQg7evSokWQ++ugjv+WTJ082t9xyywUfM336dCOJiYmJiYmJKQymI0eOXDIvhPSZmfrIy8tTbm6ub97r9erEiRNq3bq1HA5HEDurO4/Ho+TkZB05ckSxsbHBbqfRMd7wxnjD19U0VonxBpoxRhUVFUpKSrpkbUiHmTZt2qhp06Y6fvy43/Ljx48rMTHxgo9xOp1yOp1+y1q2bNlYLTaq2NjYq+IX5hzGG94Yb/i6msYqMd5AiouLq1NdSN8AHBUVpZtvvllFRUW+ZV6vV0VFRerfv38QOwMAAKEipM/MSFJubq5ycnLUp08f3XLLLZozZ46qqqo0ZsyYYLcGAABCQMiHmZEjR+rrr7/W008/rWPHjqlXr15asWKFEhISgt1ao3E6nZo+ffp5l8vCFeMNb4w3fF1NY5UYbyhzGFOX9zwBAACEppC+ZwYAAOBSCDMAAMBqhBkAAGA1wgwAALAaYaaRbNiwQSNGjFBSUpIcDocKCwv91htj9PTTT8vlcikmJkYZGRk6cOCAX82JEyc0atQoxcbGqmXLlvrP//xPVVZW+tXs3r1bt912m6Kjo5WcnKwXX3yxsYd2noKCAvXt21ctWrRQu3btlJ2drX379vnVnDlzRuPHj1fr1q11zTXX6N///d/P+zDEw4cPa/jw4WrWrJnatWunyZMn67vvvvOrWbdunW666SY5nU7dcMMNWrRoUWMP7zzz589Xenq674Ok+vfvrw8++MC3PpzGeiGzZs2Sw+HQpEmTfMvCaczPPPOMHA6H39SlSxff+nAa6zlHjx7VQw89pNatWysmJkY9evTQtm3bfOvD6fWqQ4cO5x1fh8Oh8ePHSwq/41tTU6P8/HylpqYqJiZG119/vZ577jm/7zsKi+N7Zd+ehIt5//33zbRp08zbb79tJJnly5f7rZ81a5aJi4szhYWFZteuXebuu+82qamp5vTp076aO+64w/Ts2dNs3rzZ/P3vfzc33HCDefDBB33ry8vLTUJCghk1apQpKSkxS5YsMTExMeaVV14J1DCNMcZkZWWZhQsXmpKSElNcXGzuvPNOk5KSYiorK301jzzyiElOTjZFRUVm27Zt5t/+7d/Mrbfe6lv/3Xffme7du5uMjAyzc+dO8/7775s2bdqYvLw8X80XX3xhmjVrZnJzc80nn3xifv/735umTZuaFStWBHS877zzjnnvvffM/v37zb59+8yvfvUrExkZaUpKSsJurP/q448/Nh06dDDp6elm4sSJvuXhNObp06ebtLQ043a7fdPXX38dlmM1xpgTJ06Y9u3bm4cffths2bLFfPHFF2blypXm4MGDvppwer0qKyvzO7arV682kszatWuNMeF3fGfOnGlat25t3n33XVNaWmqWLVtmrrnmGvPSSy/5asLh+BJmAuBfw4zX6zWJiYnmv/7rv3zLTp48aZxOp1myZIkxxphPPvnESDJbt2711XzwwQfG4XCYo0ePGmOM+cMf/mBatWplqqurfTVTpkwxnTt3buQR1a6srMxIMuvXrzfGfD+2yMhIs2zZMl/Np59+aiSZTZs2GWO+D39NmjQxx44d89XMnz/fxMbG+sb31FNPmbS0NL99jRw50mRlZTX2kC6pVatW5r//+7/DeqwVFRWmU6dOZvXq1eZHP/qRL8yE25inT59uevbsecF14TZWY75/zRg4cOBF14f769XEiRPN9ddfb7xeb1ge3+HDh5uxY8f6LbvvvvvMqFGjjDHhc3y5zBQEpaWlOnbsmDIyMnzL4uLi1K9fP23atEmStGnTJrVs2VJ9+vTx1WRkZKhJkybasmWLr+b2229XVFSUryYrK0v79u3TN998E6DRnK+8vFySFB8fL0navn27zp496zfeLl26KCUlxW+8PXr08PswxKysLHk8Hu3du9dX88NtnKs5t41gqKmp0dKlS1VVVaX+/fuH9VjHjx+v4cOHn9dXOI75wIEDSkpKUseOHTVq1CgdPnxYUniO9Z133lGfPn30k5/8RO3atVPv3r312muv+daH8+vVt99+qzfeeENjx46Vw+EIy+N76623qqioSPv375ck7dq1Sxs3btSwYcMkhc/xJcwEwbFjxyTpvE8xTkhI8K07duyY2rVr57c+IiJC8fHxfjUX2sYP9xFoXq9XkyZN0oABA9S9e3dfL1FRUed94ee/jvdSY7lYjcfj0enTpxtjOBe1Z88eXXPNNXI6nXrkkUe0fPlydevWLSzHKklLly7Vjh07VFBQcN66cBtzv379tGjRIq1YsULz589XaWmpbrvtNlVUVITdWCXpiy++0Pz589WpUyetXLlS48aN0+OPP64///nPfj2H4+tVYWGhTp48qYcfftjXR7gd36lTp+qBBx5Qly5dFBkZqd69e2vSpEkaNWqUX8+2H9+Q/zoD2GX8+PEqKSnRxo0bg91Ko+rcubOKi4tVXl6u//3f/1VOTo7Wr18f7LYaxZEjRzRx4kStXr1a0dHRwW6n0Z37H6skpaenq1+/fmrfvr3eeustxcTEBLGzxuH1etWnTx89//zzkqTevXurpKRECxYsUE5OTpC7a1x//OMfNWzYMCUlJQW7lUbz1ltv6c0339TixYuVlpam4uJiTZo0SUlJSWF1fDkzEwSJiYmSdN4d8sePH/etS0xMVFlZmd/67777TidOnPCrudA2friPQJowYYLeffddrV27Vtddd51veWJior799ludPHnSr/5fx3upsVysJjY2NuB/ZKKionTDDTfo5ptvVkFBgXr27KmXXnopLMe6fft2lZWV6aabblJERIQiIiK0fv16vfzyy4qIiFBCQkLYjfmHWrZsqRtvvFEHDx4My+PrcrnUrVs3v2Vdu3b1XVoL19erQ4cO6cMPP9TPfvYz37JwPL6TJ0/2nZ3p0aOHRo8erSeeeMJ3ljVcji9hJghSU1OVmJiooqIi3zKPx6MtW7aof//+kqT+/fvr5MmT2r59u69mzZo18nq96tevn69mw4YNOnv2rK9m9erV6ty5s1q1ahWg0Xz/tr4JEyZo+fLlWrNmjVJTU/3W33zzzYqMjPQb7759+3T48GG/8e7Zs8fvF2b16tWKjY31vdD279/fbxvnas5tI5i8Xq+qq6vDcqxDhw7Vnj17VFxc7Jv69OmjUaNG+f4dbmP+ocrKSn3++edyuVxheXwHDBhw3kcp7N+/X+3bt5cUfq9X5yxcuFDt2rXT8OHDfcvC8fieOnVKTZr4/6lv2rSpvF6vpDA6vgG5zfgqVFFRYXbu3Gl27txpJJnZs2ebnTt3mkOHDhljvn8rXMuWLc1f//pXs3v3bnPPPfdc8K1wvXv3Nlu2bDEbN240nTp18nsr3MmTJ01CQoIZPXq0KSkpMUuXLjXNmjUL+Fsdx40bZ+Li4sy6dev83vJ46tQpX80jjzxiUlJSzJo1a8y2bdtM//79Tf/+/X3rz73dMTMz0xQXF5sVK1aYtm3bXvDtjpMnTzaffvqpmTdvXlDe7jh16lSzfv16U1paanbv3m2mTp1qHA6HWbVqVdiN9WJ++G4mY8JrzE8++aRZt26dKS0tNf/4xz9MRkaGadOmjSkrKwu7sRrz/dvtIyIizMyZM82BAwfMm2++aZo1a2beeOMNX004vV4ZY0xNTY1JSUkxU6ZMOW9duB3fnJwcc+211/remv3222+bNm3amKeeespXEw7HlzDTSNauXWsknTfl5OQYY75/O1x+fr5JSEgwTqfTDB061Ozbt89vG//85z/Ngw8+aK655hoTGxtrxowZYyoqKvxqdu3aZQYOHGicTqe59tprzaxZswI1RJ8LjVOSWbhwoa/m9OnT5tFHHzWtWrUyzZo1M/fee69xu91+2/nyyy/NsGHDTExMjGnTpo158sknzdmzZ/1q1q5da3r16mWioqJMx44d/fYRKGPHjjXt27c3UVFRpm3btmbo0KG+IGNMeI31Yv41zITTmEeOHGlcLpeJiooy1157rRk5cqTfZ66E01jP+dvf/ma6d+9unE6n6dKli3n11Vf91ofT65UxxqxcudJIOm8MxoTf8fV4PGbixIkmJSXFREdHm44dO5pp06b5vYU6HI6vw5gffAwgAACAZbhnBgAAWI0wAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTAD4Kr05ZdfyuFwqLi4ONitALhChBkAAGA1wgyAoPB6vXrxxRd1ww03yOl0KiUlRTNnzpQk7dmzR0OGDFFMTIxat26tX/ziF6qsrPQ9dtCgQZo0aZLf9rKzs/Xwww/75jt06KDnn39eY8eOVYsWLZSSkqJXX33Vtz41NVWS1Lt3bzkcDg0aNKjRxgqgcRFmAARFXl6eZs2apfz8fH3yySdavHixEhISVFVVpaysLLVq1Upbt27VsmXL9OGHH2rChAmXvY/f/va36tOnj3bu3KlHH31U48aN0759+yRJH3/8sSTpww8/lNvt1ttvv92g4wMQOBHBbgDA1aeiokIvvfSS5s6dq5ycHEnS9ddfr4EDB+q1117TmTNn9Prrr6t58+aSpLlz52rEiBF64YUXlJCQUOf93HnnnXr00UclSVOmTNHvfvc7rV27Vp07d1bbtm0lSa1bt1ZiYmIDjxBAIHFmBkDAffrpp6qurtbQoUMvuK5nz56+ICNJAwYMkNfr9Z1Vqav09HTfvx0OhxITE1VWVlb/xgGEJMIMgICLiYm5osc3adJExhi/ZWfPnj2vLjIy0m/e4XDI6/Ve0b4BhB7CDICA69Spk2JiYlRUVHTeuq5du2rXrl2qqqryLfvHP/6hJk2aqHPnzpKktm3byu12+9bX1NSopKTksnqIioryPRaA3QgzAAIuOjpaU6ZM0VNPPaXXX39dn3/+uTZv3qw//vGPGjVqlKKjo5WTk6OSkhKtXbtWjz32mEaPHu27X2bIkCF677339N577+mzzz7TuHHjdPLkycvqoV27doqJidGKFSt0/PhxlZeXN8JIAQQCYQZAUOTn5+vJJ5/U008/ra5du2rkyJEqKytTs2bNtHLlSp04cUJ9+/bV/fffr6FDh2ru3Lm+x44dO1Y5OTn66U9/qh/96Efq2LGjBg8efFn7j4iI0Msvv6xXXnlFSUlJuueeexp6iAACxGH+9cIzAACARTgzAwAArEaYAQAAViPMAAAAqxFmAACA1QgzAADAaoQZAABgNcIMAACwGmEGAABYjTADAACsRpgBAABWI8wAAACr/T+AmN445TwRagAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(df_nonworking['count'],bins=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "tdAzTOEa1GJw",
        "outputId": "a533aa78-bfbb-4d67-cad8-801e263553d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='count', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "oHrOIK5L5oU-",
        "outputId": "03f0bdac-7067-475c-c01c-91f02781ac72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 datetime  season  holiday  workingday  weather   temp  \\\n",
              "0     2011-01-01 00:00:00       1        0           0        1   9.84   \n",
              "1     2011-01-01 01:00:00       1        0           0        1   9.02   \n",
              "2     2011-01-01 02:00:00       1        0           0        1   9.02   \n",
              "3     2011-01-01 03:00:00       1        0           0        1   9.84   \n",
              "4     2011-01-01 04:00:00       1        0           0        1   9.84   \n",
              "...                   ...     ...      ...         ...      ...    ...   \n",
              "10881 2012-12-19 19:00:00       4        0           1        1  15.58   \n",
              "10882 2012-12-19 20:00:00       4        0           1        1  14.76   \n",
              "10883 2012-12-19 21:00:00       4        0           1        1  13.94   \n",
              "10884 2012-12-19 22:00:00       4        0           1        1  13.94   \n",
              "10885 2012-12-19 23:00:00       4        0           1        1  13.12   \n",
              "\n",
              "        atemp  humidity  windspeed  casual  registered  count seasonw  \\\n",
              "0      14.395        81     0.0000       3          13     16  spring   \n",
              "1      13.635        80     0.0000       8          32     40  spring   \n",
              "2      13.635        80     0.0000       5          27     32  spring   \n",
              "3      14.395        75     0.0000       3          10     13  spring   \n",
              "4      14.395        75     0.0000       0           1      1  spring   \n",
              "...       ...       ...        ...     ...         ...    ...     ...   \n",
              "10881  19.695        50    26.0027       7         329    336  winter   \n",
              "10882  17.425        57    15.0013      10         231    241  winter   \n",
              "10883  15.910        61    15.0013       4         164    168  winter   \n",
              "10884  17.425        61     6.0032      12         117    129  winter   \n",
              "10885  16.665        66     8.9981       4          84     88  winter   \n",
              "\n",
              "             date  \n",
              "0      2011-01-01  \n",
              "1      2011-01-01  \n",
              "2      2011-01-01  \n",
              "3      2011-01-01  \n",
              "4      2011-01-01  \n",
              "...           ...  \n",
              "10881  2012-12-19  \n",
              "10882  2012-12-19  \n",
              "10883  2012-12-19  \n",
              "10884  2012-12-19  \n",
              "10885  2012-12-19  \n",
              "\n",
              "[10886 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e55a0ae0-b9ae-40e2-a8fe-36e13edd1b26\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday</th>\n",
              "      <th>workingday</th>\n",
              "      <th>weather</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",
              "      <th>seasonw</th>\n",
              "      <th>date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2011-01-01 00:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>81</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3</td>\n",
              "      <td>13</td>\n",
              "      <td>16</td>\n",
              "      <td>spring</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2011-01-01 01:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>8</td>\n",
              "      <td>32</td>\n",
              "      <td>40</td>\n",
              "      <td>spring</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01 02:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.02</td>\n",
              "      <td>13.635</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>27</td>\n",
              "      <td>32</td>\n",
              "      <td>spring</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2011-01-01 03:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>13</td>\n",
              "      <td>spring</td>\n",
              "      <td>2011-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2011-01-01 04:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9.84</td>\n",
              "      <td>14.395</td>\n",
              "      <td>75</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>spring</td>\n",
              "      <td>2011-01-01</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10881</th>\n",
              "      <td>2012-12-19 19:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15.58</td>\n",
              "      <td>19.695</td>\n",
              "      <td>50</td>\n",
              "      <td>26.0027</td>\n",
              "      <td>7</td>\n",
              "      <td>329</td>\n",
              "      <td>336</td>\n",
              "      <td>winter</td>\n",
              "      <td>2012-12-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10882</th>\n",
              "      <td>2012-12-19 20:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>14.76</td>\n",
              "      <td>17.425</td>\n",
              "      <td>57</td>\n",
              "      <td>15.0013</td>\n",
              "      <td>10</td>\n",
              "      <td>231</td>\n",
              "      <td>241</td>\n",
              "      <td>winter</td>\n",
              "      <td>2012-12-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10883</th>\n",
              "      <td>2012-12-19 21:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>15.910</td>\n",
              "      <td>61</td>\n",
              "      <td>15.0013</td>\n",
              "      <td>4</td>\n",
              "      <td>164</td>\n",
              "      <td>168</td>\n",
              "      <td>winter</td>\n",
              "      <td>2012-12-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10884</th>\n",
              "      <td>2012-12-19 22:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.94</td>\n",
              "      <td>17.425</td>\n",
              "      <td>61</td>\n",
              "      <td>6.0032</td>\n",
              "      <td>12</td>\n",
              "      <td>117</td>\n",
              "      <td>129</td>\n",
              "      <td>winter</td>\n",
              "      <td>2012-12-19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10885</th>\n",
              "      <td>2012-12-19 23:00:00</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>13.12</td>\n",
              "      <td>16.665</td>\n",
              "      <td>66</td>\n",
              "      <td>8.9981</td>\n",
              "      <td>4</td>\n",
              "      <td>84</td>\n",
              "      <td>88</td>\n",
              "      <td>winter</td>\n",
              "      <td>2012-12-19</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10886 rows × 14 columns</p>\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 10886,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-01-01 00:00:00\",\n        \"max\": \"2012-12-19 23:00:00\",\n        \"num_unique_values\": 10886,\n        \"samples\": [\n          \"2011-07-19 11:00:00\",\n          \"2012-01-16 06:00:00\",\n          \"2011-12-11 18:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"holiday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"workingday\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"weather\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.791589843987567,\n        \"min\": 0.82,\n        \"max\": 41.0,\n        \"num_unique_values\": 49,\n        \"samples\": [\n          6.56,\n          1.64\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"atemp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.474600626484948,\n        \"min\": 0.76,\n        \"max\": 45.455,\n        \"num_unique_values\": 60,\n        \"samples\": [\n          14.395,\n          16.665\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"humidity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 100,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          29,\n          61\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"windspeed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.164537326838689,\n        \"min\": 0.0,\n        \"max\": 56.9969,\n        \"num_unique_values\": 28,\n        \"samples\": [\n          22.0028,\n          43.0006\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"casual\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49,\n        \"min\": 0,\n        \"max\": 367,\n        \"num_unique_values\": 309,\n        \"samples\": [\n          287,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"registered\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 151,\n        \"min\": 0,\n        \"max\": 886,\n        \"num_unique_values\": 731,\n        \"samples\": [\n          566,\n          9\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 181,\n        \"min\": 1,\n        \"max\": 977,\n        \"num_unique_values\": 822,\n        \"samples\": [\n          626,\n          256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seasonw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"summer\",\n          \"winter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2011-01-01\",\n        \"max\": \"2012-12-19\",\n        \"num_unique_values\": 456,\n        \"samples\": [\n          \"2012-08-13\",\n          \"2011-03-02\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#segregating the count based on weather\n",
        "df_weather1=df[df['weather']==1]\n",
        "df_weather2=df[df['weather']==2]\n",
        "df_weather3=df[df['weather']==3]\n",
        "df_weather4=df[df['weather']==4]"
      ],
      "metadata": {
        "id": "fJA__rtW5Vps"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Assumptions in ANOVA\n",
        "from statsmodels.graphics.gofplots import qqplot"
      ],
      "metadata": {
        "id": "cPcDHR1sHzPe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,3))\n",
        "qqplot(df_weather1['count'],line='s')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 898
        },
        "id": "zkhVI78SIJpZ",
        "outputId": "ac3466d0-dbb8-4a43-db9b-2052692918a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x300 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import shapiro"
      ],
      "metadata": {
        "id": "7aVFcwvfINLs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "countsub=df_weather1['count'].sample(200)\n",
        "shapiro(countsub)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zlhuIrgcI1Gs",
        "outputId": "09880a8a-3f07-4029-aee9-ca2f3e26ebed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8852612376213074, pvalue=3.1881906836783713e-11)"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countsub2=df_weather2['count'].sample(200)\n",
        "shapiro(countsub2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfgDKnFtI2sr",
        "outputId": "97544065-e29b-4cfe-8ed4-e9eeb9e8ac9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.8723453283309937, pvalue=6.005850764628784e-12)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "countsub3=df_weather3['count'].sample(200)\n",
        "shapiro(countsub3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zph1fM53LSQp",
        "outputId": "87eaf9ac-dbd7-4232-e092-c67c7b4aa798"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.7585265040397644, pvalue=8.141081148500356e-17)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shapiro(df_weather4['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 263
        },
        "id": "RohxxpkdLYnm",
        "outputId": "d4be72e2-4722-4a6d-e877-5f118057ef48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Data must be at least length 3.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-62-9d41d30d1cce>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mshapiro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_weather4\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/scipy/stats/_morestats.py\u001b[0m in \u001b[0;36mshapiro\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1867\u001b[0m     \u001b[0mN\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1868\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mN\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1869\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data must be at least length 3.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1870\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1871\u001b[0m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmedian\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Data must be at least length 3."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import levene\n"
      ],
      "metadata": {
        "id": "NtpughJzLkNi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "levene(df_weather1['count'],df_weather2['count'],df_weather3['count'],df_weather4['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IkAVG62feWZs",
        "outputId": "c4f9b5dc-8709-4a44-f285-53c77a72e5f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=54.85106195954556, pvalue=3.504937946833238e-35)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import f_oneway"
      ],
      "metadata": {
        "id": "LbsSaibOegEF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f_oneway(df_weather1['count'],df_weather2['count'],df_weather3['count'],df_weather4['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Npo4jvr0e95S",
        "outputId": "a5e43cd7-be8a-446a-e5ad-5198429ee41f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F_onewayResult(statistic=65.53024112793271, pvalue=5.482069475935669e-42)"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import kruskal"
      ],
      "metadata": {
        "id": "DU3PvAcVe_xX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kruskal(df_weather1['count'],df_weather2['count'],df_weather3['count'],df_weather4['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1L3A2FNcgCAg",
        "outputId": "4d2e780f-cebd-4143-ac0a-96b0716b5552"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KruskalResult(statistic=205.00216514479087, pvalue=3.501611300708679e-44)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#segregating the count based on seasons\n",
        "df_spring=df1[df1['seasonw']=='spring']\n",
        "df_summer=df1[df1['seasonw']=='summer']\n",
        "df_fall=df1[df1['seasonw']=='fall']\n",
        "df_winter=df1[df1['seasonw']=='winter']\n"
      ],
      "metadata": {
        "id": "N0LUjW5OgDgQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Assumptions in ANOVA\n",
        "from statsmodels.graphics.gofplots import qqplot\n",
        "qqplot(df_spring['count'],line='s')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "o8czmV8Dgy4U",
        "outputId": "ed6203fc-7eaf-4752-cb18-68c4da21968d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import shapiro\n",
        "sub1=df_spring['count'].sample(200)\n",
        "shapiro(sub1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "AfEUe9W-hxMu",
        "outputId": "65d2a0e5-9058-4875-8826-73a991f5dbf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Cannot take a larger sample than population when 'replace=False'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-90-7b8944f07c2c>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshapiro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0msub1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf_spring\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'count'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mshapiro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msub1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m   5856\u001b[0m             \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocess_weights\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5857\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5858\u001b[0;31m         \u001b[0msampled_indices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreplace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5859\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msampled_indices\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5860\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/sample.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Invalid weights: weights sum to zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     return random_state.choice(obj_len, size=size, replace=replace, p=weights).astype(\n\u001b[0m\u001b[1;32m    152\u001b[0m         \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     )\n",
            "\u001b[0;32mmtrand.pyx\u001b[0m in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot take a larger sample than population when 'replace=False'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shapiro(df_summer['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "77YCZWcziAz4",
        "outputId": "5a4f5720-792b-4831-e0c0-4624120cbff5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9752597212791443, pvalue=0.032791439443826675)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shapiro(df_fall['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j0Uf-ay6iC33",
        "outputId": "6c9ca2af-b8af-4d60-bb47-1b03c0107cfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9641115069389343, pvalue=0.003765953006222844)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shapiro(df_winter['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fmlpcb6xiDzC",
        "outputId": "98d91dae-b7a3-416e-a2ee-b8fca6f6fd77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ShapiroResult(statistic=0.9835520386695862, pvalue=0.17639805376529694)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "qqplot(df_winter['count'],line='s')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "id": "hhs7puTeiE21",
        "outputId": "6b2a5196-ffeb-4df4-e073-d79691964f1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import levene\n",
        "levene(df_spring['count'],df_summer['count'],df_fall['count'],df_winter['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YTKkXzHuiJYf",
        "outputId": "02a210bd-dbd5-4aa4-ce26-65d39335c669"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LeveneResult(statistic=1.5071252673249398, pvalue=0.21194448921499898)"
            ]
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import f_oneway\n",
        "f_oneway(df_spring['count'],df_summer['count'],df_fall['count'],df_winter['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sQ7tO-IpixR2",
        "outputId": "57ea70c6-9e3c-4000-a6d5-79f53296a105"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "F_onewayResult(statistic=80.0504789788067, pvalue=1.506580502991204e-41)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kruskal(df_spring['count'],df_summer['count'],df_fall['count'],df_winter['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KgIouddOjFqK",
        "outputId": "8af1781f-1a98-457e-8218-bc7ba70c62e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KruskalResult(statistic=152.13785107938023, pvalue=9.111105591373848e-33)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import kruskal\n",
        "kruskal(df_spring['count'],df_summer['count'],df_fall['count'],df_winter['count'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8zTWcpfjHUD",
        "outputId": "2ddd0f1c-403b-41d7-db08-43e8ab6c1d55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KruskalResult(statistic=152.13785107938023, pvalue=9.111105591373848e-33)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#segregating the weather details based on season\n",
        "spring=df[df['seasonw']=='spring']['weather']\n",
        "summer=df[df['seasonw']=='summer']['weather']\n",
        "fall=df[df['seasonw']=='fall']['weather']\n",
        "winter=df[df['seasonw']=='winter']['weather']"
      ],
      "metadata": {
        "id": "LpTvsw6MjgpB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import chisquare"
      ],
      "metadata": {
        "id": "DxWDlctLligJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(index=df['seasonw'],columns=df['weather'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "wR36xVZalrLv",
        "outputId": "9a24dfa8-813f-4d51-8e57-106291c9e64e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "weather     1    2    3  4\n",
              "seasonw                   \n",
              "fall     1930  604  199  0\n",
              "spring   1759  715  211  1\n",
              "summer   1801  708  224  0\n",
              "winter   1702  807  225  0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-30f7564a-182a-4158-8437-9cd8a187ac14\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>weather</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seasonw</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fall</th>\n",
              "      <td>1930</td>\n",
              "      <td>604</td>\n",
              "      <td>199</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spring</th>\n",
              "      <td>1759</td>\n",
              "      <td>715</td>\n",
              "      <td>211</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>summer</th>\n",
              "      <td>1801</td>\n",
              "      <td>708</td>\n",
              "      <td>224</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>winter</th>\n",
              "      <td>1702</td>\n",
              "      <td>807</td>\n",
              "      <td>225</td>\n",
              "      <td>0</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-30f7564a-182a-4158-8437-9cd8a187ac14')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-30f7564a-182a-4158-8437-9cd8a187ac14 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-30f7564a-182a-4158-8437-9cd8a187ac14');\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",
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              "\n",
              "\n",
              "<div id=\"df-705aa99f-b178-4144-9d0e-556cbed969cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-705aa99f-b178-4144-9d0e-556cbed969cd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      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-705aa99f-b178-4144-9d0e-556cbed969cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"seasonw\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"spring\",\n          \"winter\",\n          \"fall\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 96,\n        \"min\": 1702,\n        \"max\": 1930,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          1759,\n          1702,\n          1930\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 82,\n        \"min\": 604,\n        \"max\": 807,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          715,\n          807,\n          604\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 199,\n        \"max\": 225,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          211,\n          225,\n          199\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chisquare(pd.crosstab(index=df['seasonw'],columns=df['weather']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zRBdM3s_ltSv",
        "outputId": "ec39bab6-99ee-4476-ba92-942c99a41466"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Power_divergenceResult(statistic=array([15.66740823, 29.16725476,  2.10826542,  3.        ]), pvalue=array([1.32664927e-03, 2.06532807e-06, 5.50242422e-01, 3.91625176e-01]))"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import chi2_contingency"
      ],
      "metadata": {
        "id": "ioSqSjxGmp7g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chi2_contingency(pd.crosstab(index=df['seasonw'],columns=df['weather']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5HrTI4oinB-d",
        "outputId": "2b9b98c3-e680-4440-a9ea-93990007be56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Chi2ContingencyResult(statistic=49.15865559689363, pvalue=1.5499250736864862e-07, dof=9, expected_freq=array([[1.80559765e+03, 7.11493845e+02, 2.15657450e+02, 2.51056403e-01],\n",
              "       [1.77454639e+03, 6.99258130e+02, 2.11948742e+02, 2.46738931e-01],\n",
              "       [1.80559765e+03, 7.11493845e+02, 2.15657450e+02, 2.51056403e-01],\n",
              "       [1.80625831e+03, 7.11754180e+02, 2.15736359e+02, 2.51148264e-01]]))"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YSGZPUoNn5Nl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6BMLPAjBnDqr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}