Extracting just Month and Year separately from Pandas Datetime column

Extracting just Month and Year separately from Pandas Datetime column

To extract just the month and year separately from a Pandas DataFrame's datetime column, you can use the dt accessor along with the month and year attributes. Here's how to do it:

Assuming you have a DataFrame named df with a datetime column named 'date_column':

import pandas as pd # Create a sample DataFrame data = {'date_column': ['2023-01-15', '2023-02-20', '2023-03-25']} df = pd.DataFrame(data) # Convert the 'date_column' to a datetime dtype df['date_column'] = pd.to_datetime(df['date_column']) # Extract month and year separately df['month'] = df['date_column'].dt.month df['year'] = df['date_column'].dt.year # Print the modified DataFrame print(df)

The output will be:

 date_column month year 0 2023-01-15 1 2023 1 2023-02-20 2 2023 2 2023-03-25 3 2023

In this example, the pd.to_datetime() function is used to convert the 'date_column' to a datetime dtype. Then, the .dt.month and .dt.year attributes of the datetime column are used to extract the month and year components, respectively. Finally, the extracted month and year values are added as new columns to the DataFrame.

You can adjust the code to work with your specific DataFrame and datetime column name.

Examples

  1. "Pandas extract month and year from datetime column"

    Description: This query seeks to extract the month and year separately from a datetime column in a Pandas DataFrame.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'date_column': ['2024-03-27', '2023-07-15', '2022-11-10']}) # Convert to datetime df['date_column'] = pd.to_datetime(df['date_column']) # Extract month and year df['month'] = df['date_column'].dt.month df['year'] = df['date_column'].dt.year print(df[['date_column', 'month', 'year']])

  2. "Python Pandas separate month and year from datetime"

    Description: This query is similar to the previous one, looking for a way to separate the month and year components from a datetime column in a Pandas DataFrame using Python.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'date_column': ['2024-03-27', '2023-07-15', '2022-11-10']}) # Convert to datetime df['date_column'] = pd.to_datetime(df['date_column']) # Extract month and year df['month'] = df['date_column'].dt.month df['year'] = df['date_column'].dt.year print(df[['date_column', 'month', 'year']])

  3. "Pandas extract month and year separately"

    Description: This query aims to learn how to extract the month and year separately from a datetime column using Pandas in Python.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'date_column': ['2024-03-27', '2023-07-15', '2022-11-10']}) # Convert to datetime df['date_column'] = pd.to_datetime(df['date_column']) # Extract month and year df['month'] = df['date_column'].dt.month df['year'] = df['date_column'].dt.year print(df[['date_column', 'month', 'year']])

  4. "Python Pandas get month and year from datetime"

    Description: This query seeks a Pythonic way to get the month and year components separately from a datetime column using Pandas library.

    Code:

    import pandas as pd # Sample DataFrame df = pd.DataFrame({'date_column': ['2024-03-27', '2023-07-15', '2022-11-10']}) # Convert to datetime df['date_column'] = pd.to_datetime(df['date_column']) # Extract month and year df['month'] = df['date_column'].dt.month df['year'] = df['date_column'].dt.year print(df[['date_column', 'month', 'year']])

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