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 US we have the default formate as mm dd yyyy
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
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).
To learn more about How can I convert these dates to the correct format in a Pandas data frame visit: Conversion of date formats in python using the date time function.
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