SelectByDate: Code Extension (refer issue #53)

[Tanvi-Jain01]

This PR extends the code of Issue #53
@nipunbatra , @patel-zeel

BEFORE:

CODE:

vayu/vayu/selectByDate.py

Lines 16 to 23 in ef99aef

	df.index = pd.to_datetime(df.date)
	df = df.drop("date", axis=1)
	df_n = df[year].resample("1D").mean()
	df_n = df_n.fillna(method="ffill")
	df_n["month"] = df_n.index.month
	df_n.index.dayofweek
	print(df_n)

AFTER:

CODE:

import pandas as pd
    
    df['date'] = pd.to_datetime(df['date'])
    df_year = df[df['date'].dt.year == int(year)]
    
    if group:
        df_grouped = df_year.groupby(group).resample(time_period[0], on='date').mean(numeric_only=True)
        return df_grouped
    
    if time_period == 'month':
        df_month = df_year.resample('M', on='date').mean(numeric_only=True)
        return df_month
    elif time_period == 'year':
        df_yearly = df_year.resample('Y', on='date').mean(numeric_only=True)
        return df_yearly
    
    df_day = df_year.resample('D', on='date').mean(numeric_only=True)
    return df_day

USAGE:

selectByDate(df1,'2022',group=['latitude','longitude','station'], time_period='month')

Additional Time Periods: The modified function introduces the capability to compute the average value of each month or year in addition to daily averages. This provides more granular insights into the data.

Grouping Support: The modified function allows for optional grouping of the data by specified columns. This enables the calculation of average values based on different groups, providing more customized analysis and comparisons.

Resampling Flexibility: The modified function uses the resample method with dynamic frequency parameters based on the selected time period. This allows for greater flexibility in computing average values at different frequencies without hardcoding the resampling periods.

OUTPUT: