Python - Group by date range in pandas dataframe

Python - Group by date range in pandas dataframe

In this article, here we will define two functions just to check we are getting the date ranges we want within groups [Info since left edges are open, need to subtract 1 day]

import pandas as pd

edges = pd.to_datetime([x for year in df.index.year.unique() 
                        for x in [f'{year}-04-20', f'{year}-05-29']])

def min_idx(x):
    return x.index.min()
def max_idx(x):
    return x.index.max()

df.groupby(pd.cut(df.index, bins=edges)).agg([min_idx, max_idx, min, max]).loc[::2, :]

How to filter Pandas DataFrame rows by date in Python

For ex. filtering a DataFrame for all rows between January 1, 2020 and January 31, 2020, would return all rows with date values on January 2020.

Filtering pandas.DataFrame rows by date select all rows which satisfy specified date constraints, based on a column containing date data.

USE pandas.DataFrame.loc TO FILTER ROWS BY DATE

print(filtered_dates)
OUTPUT
   value       date
2      3 2020-01-01
4      5 2020-01-23
print(df)
OUTPUT
   value       date
0      1 2020-03-18
1      2 2020-10-24
2      3 2020-01-01
3      4 2019-01-04
4      5 2020-01-23

startdate = "2020-1-1"
enddate = "2020-1-31"

after_start_date = df["date"] >= startdate 
before_end_date = df["date"] <= enddate
between_two_dates = after_start_date & before_end_date
filtered_dates = df.loc[between_two_dates]

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2020-09-13