In this post, we will learn How can I convert these Dates to the correct format in a Pandas data frame which we can perform using to datetime function for the same we have given an example of pseudo code.
Dates Formate Conversions
As we know at different places we need different types of date formats where at some times we need to get the data where the year is first then month and then day and at some places, we have the year as the last and at first and second places we have day and months.
To regulate the same we need to convert the dates from one format to another we need to perform certain steps that we will let you know. And by default, we get the date in the form of dd mm yyyy in India and the US we have the default format as mm dd yyyy. As at times we need to get the dates in different formats at different points in the wold.
Here we will see how we can convert one format of date to another format we need to so following the same follow the code given below Suppose we have the following date formate
Date
0 2023-01-01
1 2023-02-01
2 2023-03-01
Now to convert the date column to the correct format we can use the following code.
import pandas as pd
# Creating the date series
date_ sr = pd. Series( pd. date_ range(
'2019-12-31', periods=3, freq= 'M', tz= 'Asia/ Calcutta'))
# Creating the index
ind = ['Day 1', 'Day 2', 'Day 3']
# set the index
date_ sr. index = ind
change_ format = date_sr.dt. strftime(' %d,%m,%Y')
# Print the formatted date
print( change_ format)
Here %y stands for the year, %m is for the month, and %d is for Day
It will convert the Date column to a datetime format, which you can then work with using the various datetime related functions in Pandas. example, you can extract the year, month, or day from the dates, or you can perform arithmetic operations on them (e.g., calculating the difference between two dates).
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