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We can extract the year and month from a Datetime column using pandas.Series.dt.year()the and methods respectively. If the data is not of type, you need to convert it to first . We can also extract the year and month using the and methods .pandas.Series.dt.month()DatetimeDatetimepandas.DatetimeIndex.monthpandas.DatetimeIndex.yearstrftime()

pandas.Series.dt.year()and pandas.Series.dt.month()methods to extract month and year

pandas.Series.dt.year()The and methods applied to the Datetime type pandas.Series.dt.month()return numpy arrays of the year and month, respectively, of the Datetime entries in the Series object.

import pandas as pd
import numpy as np
import datetime

list_of_dates = ["2019-11-20", "2020-01-02", "2020-02-05", "2020-03-10", "2020-04-16"]
employees = ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"]
df = pd.DataFrame({"Joined date": pd.to_datetime(list_of_dates)}, index=employees)

df["Year"] = df["Joined date"].dt.year
df["Month"] = df["Joined date"].dt.month
print(df)

Output:

        Joined date  Year  Month
Hisila   2019-11-20  2019     11
Shristi  2020-01-02  2020      1
Zeppy    2020-02-05  2020      2
Alina    2020-03-10  2020      3
Jerry    2020-04-16  2020      4

However, if the column is not Datetimeof type, you should first to_datetime()convert the column to Datetimetype using the method.

import pandas as pd
import numpy as np
import datetime

list_of_dates = ["11/20/2019", "01/02/2020", "02/05/2020", "03/10/2020", "04/16/2020"]
employees = ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"]
df = pd.DataFrame({"Joined date": pd.to_datetime(list_of_dates)}, index=employees)
df["Joined date"] = pd.to_datetime(df["Joined date"])

df["Year"] = df["Joined date"].dt.year
df["Month"] = df["Joined date"].dt.month
print(df)

Output:

        Joined date  Year  Month
Hisila   2019-11-20  2019     11
Shristi  2020-01-02  2020      1
Zeppy    2020-02-05  2020      2
Alina    2020-03-10  2020      3
Jerry    2020-04-16  2020      4

strftime()Method to extract year and month

strftime()The method takes a Datetime, a format code as input, and returns a string representing the specific format specified in the output. We use %Yand %mas the format code to extract the year and month.

import pandas as pd
import numpy as np
import datetime

list_of_dates = ["2019-11-20", "2020-01-02", "2020-02-05", "2020-03-10", "2020-04-16"]
employees = ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"]
df = pd.DataFrame({"Joined date": pd.to_datetime(list_of_dates)}, index=employees)

df["year"] = df["Joined date"].dt.strftime("%Y")
df["month"] = df["Joined date"].dt.strftime("%m")

print(df)

Output:

        Joined date  year month
Hisila   2019-11-20  2019    11
Shristi  2020-01-02  2020    01
Zeppy    2020-02-05  2020    02
Alina    2020-03-10  2020    03
Jerry    2020-04-16  2020    04

pandas.DatetimeIndex.monthand pandas.DatetimeIndex.yearextract the year and month

DatetimeAnother simple way to extract the month and year from a column is to retrieve the values ​​of the year and month attributes of a pandas.DatetimeIndex object of class .

import pandas as pd
import numpy as np
import datetime

list_of_dates = ["2019-11-20", "2020-01-02", "2020-02-05", "2020-03-10", "2020-04-16"]
employees = ["Hisila", "Shristi", "Zeppy", "Alina", "Jerry"]
df = pd.DataFrame({"Joined date": pd.to_datetime(list_of_dates)}, index=employees)

df["year"] = pd.DatetimeIndex(df["Joined date"]).year
df["month"] = pd.DatetimeIndex(df["Joined date"]).month

print(df)

Output:

        Joined date  Year  Month
Hisila   2019-11-20  2019     11
Shristi  2020-01-02  2020      1
Zeppy    2020-02-05  2020      2
Alina    2020-03-10  2020      3
Jerry    2020-04-16  2020      4

pandas.DatetimeIndexA class is datetime64an immutable type of data type ndarray. It has properties like year, month, day, etc.