How to Filter Pandas DataFrame Rows by Date Range?

Retrieving DataFrame Rows Within Specified Date Ranges

Problem:

A Pandas DataFrame is created from a CSV file containing a date column. The task is to extract only the rows whose date values fall within a specified date range or between two specified dates.

Solution:

There are two approaches to achieve this:

Option 1: Boolean Masking

1. Ensure the date column is in datetime64[ns] format.
2. Create a boolean mask using date range conditions.
3. Select the desired rows using df.loc[mask].

Example:

import pandas as pd

df = pd.read_csv('data.csv', parse_dates=['date'])

start_date = '2022-01-01'
end_date = '2022-02-28'

mask = (df['date'] >= start_date) & (df['date'] <= end_date)
df_filtered = df.loc[mask]

Option 2: DatetimeIndex

1. Set the date column as the index of the DataFrame.
2. Use df.loc[start_date : end_date] to retrieve the desired rows.

Example:

df = pd.read_csv('data.csv', parse_dates=['date'])
df = df.set_index(['date'])

df_filtered = df.loc[start_date : end_date]

Note: If parse_dates was used while reading the CSV, converting the date column to datetime64 is not necessary.

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