Convert a series of date strings to a time series in Pandas Dataframe

Pandas Series dt.strftime() Method | Change Date Format in Series

Last Updated : 12 Jul, 2024

The dt.strftime() method converts the datetime objects in the Pandas Series to a specified date format.

The function returns an index of formatted strings specified by date_format, which supports the same string format as the Python standard library.

Example

Python

import pandas as pd 
sr = pd.Series(['2012-12-31 08:45', '2019-1-1 12:30', '2008-02-2 10:30', 
            '2010-1-1 09:25', '2019-12-31 00:00']) 
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5'] 
sr.index = idx 
sr = pd.to_datetime(sr) 
result = sr.dt.strftime('% B % d, % Y, % r')
print(result)

Output:

dt.strftime output example

Syntax

Syntax: Series.dt.strftime(Date_Format)
Parameter
date_format : Date format string (e.g. “%Y-%m-%d”)
Returns: NumPy ndarray of formatted string

How to change the Date Format of DateTime objects in a Pandas Series

To change the date format of DateTime objects in a Pandas Series we use the dt.strftime method of the Pandas library in Python.

To understand it better, let us look at some example

Example:

Python

# importing pandas as pd
import pandas as pd

# Creating the Series
sr = pd.Series(pd.date_range('2012-12-31 09:45', periods = 5, freq = 'M',
                            tz = 'Asia / Calcutta'))

# Creating the index
idx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']

# set the index
sr.index = idx

# Print the series
print(sr)

Output:

datetime series created

Now we will use the Series dt.strftime() function to convert the dates in the series object to the specified format.

Python

# convert to the given date format
result = sr.dt.strftime('% d % m % Y, % r')

# print the result
print(result)

Output :

date format changed after dt.strftime

As we can see in the output, the dt.strftime() function has successfully converted the dates in the given series object to the specified format.

Convert a series of date strings to a time series in Pandas Dataframe

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Shubham__Ranjan

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