Pandas Extract Month and Year from Datetime

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  • Post last modified:January 26, 2023
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You can extract month and year from the DateTime (date) column in pandas in several ways. In this article, I will explain how to get a year and get a month from the Datetime column using pandas.Series.dt.year and pandas.Series.dt.month methods respectively.

If the data is not in Datetime type, you need to convert it first to Datetime by using pd.to_datetime() method. Also, I will cover extract year and month using pandas.DatetimeIndex properties year and month strftime() methods.

1. Quick Examples of Extract Month and Year from Datetime

If you are in a hurry, below are some quick examples of how to extract month and year separately from the pandas DataFrame DateTime column.


# Use Datetime.strftime() Method to extract month and year
df['Year'] = df['InsertedDate'].dt.strftime('%Y')
df['Month'] = df['InsertedDate'].dt.strftime('%m')

# Using pandas.Series.dt.year() & pandas.Series.dt.month() method
df['Year'] = df['InsertedDate'].dt.year 
df['Month'] = df['InsertedDate'].dt.month 

# Using pandas.DatetimeIndex() to extract month and year
df['year'] = pd.DatetimeIndex(df['InsertedDate']).year
df['month'] = pd.DatetimeIndex(df['InsertedDate']).month

# Use datetime.to_period() method to extract month and year
df['Month_Year'] = df['InsertedDate'].dt.to_period('M')

# Use DataFrame.apply() with lambda function and strftime()
df['Month_Year'] = df['InsertedDate'].apply(lambda x: x.strftime('%B-%Y')) 

# Use Pandas.to_datetime() and datetime.strftime() method
df['yyyy-mm'] = pd.to_datetime(df['InsertedDate']).dt.strftime('%Y-%m')

2. Pandas Extract Month and Year using Datetime.strftime()

strftime() method takes datetime format and returns a string representing the specific format. You can use %Y and %m as format codes to extract year and month respectively from the pandas DataFrame.


import pandas as pd
import numpy as np
import datetime
Dates = ["2018-08-14","2019-10-17","2020-11-14","2020-05-17","2021-09-15","2021-12-14"]
Courses =["Spark","PySpark","Hadoop","Python","Pandas","Hadoop"]
df = pd.DataFrame({'InsertedDate': pd.to_datetime(Dates)},index=Courses)

# Use Datetime.strftime() Method to extract month and year
df['Year'] = df['InsertedDate'].dt.strftime('%Y')
df['Month'] = df['InsertedDate'].dt.strftime('%m')
print(df)

Yields below output.


        InsertedDate  Year Month
Spark     2018-08-14  2018    08
PySpark   2019-10-17  2019    10
Hadoop    2020-11-14  2020    11
Python    2020-05-17  2020    05
Pandas    2021-09-15  2021    09
Hadoop    2021-12-14  2021    12

3. Extract Month and Year Using pandas.Series.dt.year() & month()

You can use pandas.Series.dt.year() and pandas.Series.dt.month() methods to get year and month but, these return a series object. Assign these to a column to get a DataFrame with year and month columns.


# Using pandas.Series.dt.year() & pandas.Series.dt.month() method
df['Year'] = df['InsertedDate'].dt.year 
df['Month'] = df['InsertedDate'].dt.month 
print(df)

Yields below output.


        InsertedDate  Year  Month
Spark     2018-08-14  2018      8
PySpark   2019-10-17  2019     10
Hadoop    2020-11-14  2020     11
Python    2020-05-17  2020      5
Pandas    2021-09-15  2021      9
Hadoop    2021-12-14  2021     12

4. Use pandas DatetimeIndex() to Extract Month and Year

Also, to extract the month and year from the pandas Datetime column, use DatetimeIndex.month attribute to find the month and use DatetimeIndex.year attribute to find the year present in the date. Note that this method takes a date as an argument.


# Using pandas.DatetimeIndex() to extract month and year
df['year'] = pd.DatetimeIndex(df['InsertedDate']).year
df['month'] = pd.DatetimeIndex(df['InsertedDate']).month
print(df)

Yields the same output as above.

5. Use Datetime.to_period() Method to Extract Month and Year

You can also use df['Month_Year']=df['InsertedDate'].dt.to_period('M') method. The df['date_column'] has to be in datetime format.


# Use datetime.to_period() method to extract month and year
df['Month_Year'] = df['InsertedDate'].dt.to_period('M')
print(df)

Yields below output.


        InsertedDate   Month_Year
Spark     2018-08-14    2018-08
PySpark   2019-10-17    2019-10
Hadoop    2020-11-14    2020-11
Python    2020-05-17    2020-05
Pandas    2021-09-15    2021-09
Hadoop    2021-12-14    2021-12

6. Use DataFrame.apply() With Lambda Function and strftime()

Let’s see how to get the month and year by using Pandas DataFrame.apply() and lambda function.


# Use DataFrame.apply() with lambda function and strftime()
df['Month_Year'] = df['InsertedDate'].apply(lambda x: x.strftime('%B-%Y')) 
print(df)

Yields below output.


        InsertedDate      Month_Year
Spark     2018-08-14     August-2018
PySpark   2019-10-17    October-2019
Hadoop    2020-11-14   November-2020
Python    2020-05-17        May-2020
Pandas    2021-09-15  September-2021
Hadoop    2021-12-14   December-2021

7. Use Pandas.to_datetime() and datetime.strftime() Method

To add a column with ‘year-month’ pairs


# Use Pandas.to_datetime() and datetime.strftime() method
df['yyyy-mm'] = pd.to_datetime(df['InsertedDate']).dt.strftime('%Y-%m')
print(df)

Yields below output.


        InsertedDate  yyyy-mm
Spark     2018-08-14  2018-08
PySpark   2019-10-17  2019-10
Hadoop    2020-11-14  2020-11
Python    2020-05-17  2020-05
Pandas    2021-09-15  2021-09
Hadoop    2021-12-14  2021-12

8. Complete Example to Get Month and Year from Panda Datetime


import pandas as pd
import numpy as np
import datetime
Dates = ["2018-08-14","2019-10-17","2020-11-14","2020-05-17","2021-09-15","2021-12-14"]
Courses =["Spark","PySpark","Hadoop","Python","Pandas","Hadoop"]
df = pd.DataFrame({'InsertedDate': pd.to_datetime(Dates)},index=Courses)

# Use Datetime.strftime() Method to extract month and year
df['Year'] = df['InsertedDate'].dt.strftime('%Y')
df['Month'] = df['InsertedDate'].dt.strftime('%m')
print(df)

# Using pandas.Series.dt.year() & pandas.Series.dt.month() method
df['Year'] = df['InsertedDate'].dt.year 
df['Month'] = df['InsertedDate'].dt.month 
print(df)

# Using pandas.DatetimeIndex() to extract month and year
df['year'] = pd.DatetimeIndex(df['InsertedDate']).year
df['month'] = pd.DatetimeIndex(df['InsertedDate']).month
print(df)

# Use datetime.to_period() method to extract month and year
df['Month_Year'] = df['InsertedDate'].dt.to_period('M')
print(df)

# Use DataFrame.apply() with lambda function and strftime()
df['Month_Year'] = df['InsertedDate'].apply(lambda x: x.strftime('%B-%Y')) 
print(df)

# Use Pandas.to_datetime() and datetime.strftime() method
df['yyyy-mm'] = pd.to_datetime(df['InsertedDate']).dt.strftime('%Y-%m')
print(df)

# Use datetime.strftime() method
df['yyyy'] = pd.to_datetime(df['InsertedDate']).dt.strftime('%Y')
df['mm'] = pd.to_datetime(df['InsertedDate']).dt.strftime('%m')
print(df)

Conclusion

In this article, you have learned how to extract month and year separately from the pandas DateTime column by using pandas.Series.dt.strftime(), pandas.DatetimeIndex(), datetime.to_period() and DataFrame.apply() methods with examples.

Happy Learning !!

References

You may also like reading:

  1. Pandas Convert Multiple Columns To DateTime Type
  2. Pandas Convert Date (datetime) to String Format
  3. Pandas Filter DataFrame Rows on Dates
  4. Pandas Convert Integer to Datetime Type
  5. Pandas Count The Frequency of a Value in Column
  6. Pandas Iterate Over Rows with Examples
  7. Pandas.DataFrame.query() by Examples
  8. Pandas apply() Function to Single & Multiple Column(s)
  9. Pandas Create Conditional Column in DataFrame
  10. Pandas Replace Blank Values (empty) with NaN
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