How to Extract Month and Year from Date String in a Pandas DataFrame

Published Nov 27, 2021  ∙  Updated May 2, 2022

Suppose we have a Date column in my Pandas DataFrame.

Date        Num
1950-01-01	1.50
1950-02-01	1.50
1950-03-01	1.50
1950-04-01	1.50

Let’s say we want to create a Year and Month column from Date, but it’s a string.

Convert Date string using DateTimeIndex

We can store the values in our Date column with DateTimeIndex, which is simply a collection of timestamp objects with varying UTC offsets.

date = pd.DatetimeIndex(df['Date'])

Extract month and year

We can then extract the month and year (or day or whatever attributes we want) from this DateTimeIndex.

df['Year'] = date.year
df['Month'] = date.month

Convert Date string using Series.dt

Instead of using pd.DateTimeIndex, we can simply use .dt on any datetimelike column.

date = df['Date'].dt

Extract month and year

Extracting the month and year would be the same as with the DateTimeIndex.

df['Year'] = date.year
df['Month'] = date.month