Python Pandas Series dt.strftime

Pandas is a popular data analysis and manipulation library in Python. dt is a datetime-enabled class available in the Pandas Series object. dt.strftime is a method that formats datetime objects as a string.

Syntax

The syntax of the dt.strftime method is:

Series.dt.strftime(format, *args, **kwargs)

• format (mandatory): The format string, written using strftime syntax.
• *args: Variable length argument list. These are positional arguments that may be supplied to the format string.
• **kwargs: Keyword arguments that are passed to the format function.

Example

Let's create a Pandas Series of Datetime objects and see how to format them using the dt.strftime method.

import pandas as pd

data = pd.Series([
    '2022-06-18 03:30 PM',
    '2022-06-19 02:45 AM',
    '2022-06-20 06:00 PM',
])

data = pd.to_datetime(data)

# format dates as dd-mm-yyyy
data.dt.strftime("%d-%m-%Y")

# format dates as Month day, year hour:minute AM/PM
data.dt.strftime("%B %d, %Y %I:%M %p")

The above code will output following:

0    18-06-2022
1    19-06-2022
2    20-06-2022
dtype: object

0       June 18, 2022 03:30 PM
1    June 19, 2022 02:45 AM
2       June 20, 2022 06:00 PM
dtype: object

Format Codes

Here are some of the common format codes used in dt.strftime method.

| Code | Description | | ---- | ----------- | | %Y | Year (4 digits) | | %y | Year (2 digits) | | %m | Month (e.g. 01) | | %d | Day of the Month (01-31) | | %B | Full month name (e.g. June) | | %b | Abbreviated month name (e.g. Jun) | | %A | Full weekday name (e.g. Saturday) | | %a | Abbreviated weekday name (e.g. Sat) | | %I | Hour in 12-hour format (01-12)| | %H | Hour in 24-hour format (00-23)| | %M | Minute (00-59) | | %S | Second (00-59) | | %p | AM/PM |

You can find a complete list of strftime format codes on strftime.org.

Conclusion

In summary, the dt.strftime method allows for easy formatting of datetime objects in a Pandas Series object using strptime syntax. It is a powerful tool for data analysts to understand and format datetime data.