How Can I Change the Datetime Format of a Pandas DataFrame Column?

Changing Datetime Format in Pandas

In Pandas, dataframes often contain datetime columns, but the default format may not be suitable for all applications. This query delves into how to change the datetime format to meet specific requirements.

The provided dataframe has a DOB column in a custom format (e.g., "1/1/2016"), which is initially recognized as an 'object' by Pandas. Converting it to a date format using df['DOB'] = pd.to_datetime(df['DOB']) results in a "2016-01-26" format, not the desired one.

To overcome this, Pandas offers the dt.strftime method to manipulate datetime formats. It allows converting the datetime object to a string format. For instance, to transform the DOB column to "01/26/2016":

df['DOB1'] = df['DOB'].dt.strftime('%m/%d/%Y')

The %m (month), %d (day), and %Y (year) fields specify the desired format. Note that after this transformation, the DOB1 column will have an 'object' dtype (string), unlike the datetime dtype of the DOB column. This method allows the flexibility to adjust datetime formats to meet specific needs, ensuring consistent and readable date representation in Pandas dataframes.

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