Show Seasonal Plots

[Tanvi-Jain01]

@nipunbatra , @patel-zeel

Adding an extra seasonal parameter in the plots allows us to analyze and visualize the seasonal trends that occur within a year. This parameter provides flexibility in displaying the data based on the seasons (e.g., Summer, Winter, Spring, Autumn) and enables us to observe any patterns or variations that might be specific to certain seasons.

For example: in environmental monitoring, it can help to identify if certain pollutants have higher concentrations during specific seasons due to weather patterns, human activities, or other factors.

Something like the image below :

I tried to implement it in time_plot

CODE:

import pandas as pd
import matplotlib.pyplot as plt

def time_plot(df: pd.DataFrame, year: str, pollutants: list = ["pm25"], seasonal: bool = True):
    df.index = pd.to_datetime(df.date)
    df_n_1 = df[df.index.year == int(year)]

    if seasonal:
        # Divide the months into seasons
        summer = [6, 7, 8]
        winter = [12, 1, 2]
        spring = [3, 4, 5]
        autumn = [9, 10, 11]

        # Create subplots for each season
        fig, axs = plt.subplots(2, 2, figsize=(12, 10))
        axs = axs.flatten()

        # Plot data for each season
        seasons = [summer, winter, spring, autumn]
        season_labels = ["Summer", "Winter", "Spring", "Autumn"]

        for ax, season, label in zip(axs, seasons, season_labels):
            df_season = df_n_1[df_n_1.index.month.isin(season)]

            for pollutant in pollutants:
                values = df_season[pollutant]

                # Plotting
                ax.plot(values.index, values.values, label=pollutant)

            ax.set_title(label)
            ax.legend()
            ax.tick_params(axis="x", rotation=45)

        fig.tight_layout()
        plt.show()
    else:
        # Plot for the whole year
        fig, ax = plt.subplots(figsize=(10, 8))

        for pollutant in pollutants:
            values = df_n_1[pollutant]

            # Plotting
            ax.plot(values.index, values.values, label=pollutant)

        ax.set_title(year)
        ax.legend()
        ax.tick_params(axis="x", rotation=45)

        plt.tight_layout()
        plt.show()


time_plot(df, "2022", pollutants=["pm25", "pm10"], seasonal=True)

OUTPUT:

When seasonal=True

When seasonal=False