Pandas Series dt.strftime() Method | Change Date Format in Series
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
Python3
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:
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:
Python3
# importing pandas as pdimport pandas as pd# Creating the Seriessr = pd.Series(pd.date_range('2012-12-31 09:45', periods = 5, freq = 'M', tz = 'Asia / Calcutta'))# Creating the indexidx = ['Day 1', 'Day 2', 'Day 3', 'Day 4', 'Day 5']# set the indexsr.index = idx# Print the seriesprint(sr) |
Output:
Now we will use the Series dt.strftime() function to convert the dates in the series object to the specified format.
Python3
# convert to the given date formatresult = sr.dt.strftime('% d % m % Y, % r')# print the resultprint(result) |
Output :
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.
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