How to Filter Pandas DataFrame Rows by Date

Often you may want to filter the rows of a pandas DataFrame by dates. Fortunately this is fairly easy to do and this tutorial explains two ways to do so, depending on the structure of your DataFrame.

Example 1: Filter By Date Using the Index

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'sales': [4, 11, 13, 9],
                   'customers': [2, 6, 9, 7]},
                   index = ['2020-01-15', '2020-01-18', '2020-01-22', '2020-01-24'])

#view DataFrame 
print(df)

                sales	customers
2020-01-15	4	2
2020-01-18	11	6
2020-01-22	13	9
2020-01-24	9	7

Since the dates are in the index of the DataFrame, we can simply use the .loc function to filter the rows based on a date range:

#filter for rows where date is between Jan 15 and Jan 22
df.loc['2020-01-15':'2020-01-22']

                sales	customers
2020-01-15	4	2
2020-01-18	11	6
2020-01-22	13	9

Note that when we filter the rows using df.loc[start:end] that the dates for start and end are included in the output.

Example 2: Filter By Date Using a Column

Suppose we have the following pandas DataFrame:

import pandas as pd

#create DataFrame
df = pd.DataFrame({'sales': [4, 11, 13, 9],
                   'day': ['2020-01-15', '2020-01-18', '2020-01-22', '2020-01-24']})

#view DataFrame 
print(df)

        sales	day
0	4	2020-01-15
1	11	2020-01-18
2	13	2020-01-22
3	9	2020-01-24

Since the dates are in one of the columns of the DataFrame, we can instead use boolean operators to filter the rows based on a date range:

#filter for rows where date is after Jan 15 and before Jan 23 
df[(df['day'] > '2020-01-15') & (df['day'] < '2020-01-23')]

        sales	day
1	11	2020-01-18
2	13	2020-01-22

Note that we can use similar syntax to filter the rows based on dates outside of some range:

#filter for rows where date is before Jan 20 or after Jan 22 
df[(df['day'] < '2020-01-20') | (df['day'] > '2020-01-22')]


        sales	day
0	4	2020-01-15
1	11	2020-01-18
3	9	2020-01-24