Data Analysis: How to Convert Dynamically Different Date Formats to Unique Date Format Using Python

Convert various date formats into a uniform format.

 

 Today, we are going to explore a interesting and important scenario in data analysis using Python. 

At times, it becomes necessary to convert various date formats into a uniform format while working with file data (CSV/Excel) processing.
It can be quite challenging to identify the date formats present in each column, and performing individual conversions for each column can be time-consuming. Moreover, if the original date formats in the source columns are altered, it may result in errors.
To address this issue, we have devised a Python code that can dynamically convert different date formats into a single, standardized format.
 All you have to do is include the required date formats in the date format list provided in the code. 
This approach will help streamline the process and ensure consistency in your data analysis tasks.

Code:

from datetime import datetime

import pandas as pd

from calendar import monthrange

def convert_date(input_date):

    input_date = str(input_date)

    date_formats_1 = ["%d/%m/%Y","%d/%B/%y","%d/%b/%y","%d/%m/%y","%d-%m-%Y","%d-%b-%y","%d-%m-%y"]

    date_formats_2 = ["%m-%y", "%m-%Y", "%m/%Y","%B-%Y","%b-%Y","%b-%y"]

   

    for date_format in date_formats_1:

        try:

            #date_obj = datetime.strptime(input_date, date_format)

            #return date_obj.strftime("%Y-%m-%d")

            return datetime.strptime(input_date, date_format).strftime("%Y-%m-%d")

        except ValueError:

            pass

   

    for date_format in date_formats_2:

        try:

            date_object = datetime.strptime(input_date, date_format)

            last_day_of_month = monthrange(date_object.year,date_object.month)[1]

            new_date = date_object.replace(day=last_day_of_month).strftime("%Y-%m-%d")

            #return date_object.strftime("%Y-%m-%d")

            return new_date

        except ValueError:

            pass

   

    return input_date

# Test the function

def read_file(file):

    df = pd.read_csv(file)

    print(df)

    # Iterate through columns and apply date conversion if it's a date column

    for column in df.columns:

        df[column] = df[column].apply(convert_date)

    print( df)

input_file = 'G:\ETL_Automation\data\diff_date.csv'

read_file(input_file)

Note:

Please make any necessary updates to the code, and if you have any comments or recommendations, please share them.


If you provide the above code with additional dynamism or more advancement , that would be great.