How to Add Days to a Pandas Date Column

Problem Formulation and Solution Overview

This article will show you how to add days to a Pandas DataFrame Date column.

To make it more interesting, we have the following running scenario:

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CheckersTV is a new channel offering streaming news and games. They have a large subscriber base, each paying a monthly fee of $12.99. For Accounting purposes, they want to add three (3) days on to the billing date.

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💬 Question: How would we write code to add days to a Pandas DataFrame Date column?

We can accomplish this task by one of the following options:

• Method 1: Use timedelta()
• Method 2: Use to_datetime() and timedelta()
• Method 3: Use to_datetime() and apply()
• Method 4: Use to_datetime() and DateOffset()
• Bonus: Update Date Range

Preparation

Before moving forward, please ensure the Pandas library is installed. Click here if you require instructions.

Then, add the following code to the top of each script. This snippet will allow the code in this article to run error-free.

import pandas as pd

After importing the Pandas library, this library is referenced by calling the shortcode (pd).

Method 1: Use Timedelta()

This example uses the timedelta() class which allows you to define a specific time interval, such as a day, and add it to a datetime expression.

df = pd.read_csv('checkers_users.csv')

df['charge_date'] = df['charge_date'].astype('datetime64[ns]')
df['charge_date'] = df.charge_date + pd.Timedelta(days=3)
print(df)

The above code reads in the checkers_users.csv file into a DataFrame df.

The following line converts the DataFrame column charge_date into a datetime format. Then, three (3) days are added to the charge_date for each column entry (pd.Timedelta(days=3)). The results save back to df['charge_date'] and are output to the terminal.

Method 2: Use to_datetime() and timedelta()

This example uses to_datetime() and timedelta() to add three (3) days to each Date entry in a DataFrame Column.

import time
from datetime import datetime, timedelta, date, time

df = pd.read_csv('checkers_users.csv')
df['charge_date'] = (pd.to_datetime(df['charge_date']) + timedelta(3))
print(df)

The above code imports the time module and others from the datetime library.

Then, the checkers_users.csv file is read into a DataFrame df.

The following line adds three (3) days to the charge_date for each column entry (timedelta(3)). The results save back to df['charge_date'] and are output to the terminal.

Method 3: Use to_datetime() and apply()

This example uses to_datetime() and apply() to add three (3) days to each Date entry in a DataFrame Column.

df = pd.read_csv('checkers_users.csv')
df['new_charge_date'] = pd.to_datetime(df['charge_date']).apply(pd.DateOffset(3))
print(df)

The above code reads in the checkers_users.csv file into a DataFrame df.

The following line adds three (3) days to the charge_date for each column entry (apply(pd.DateOffset(3)). The results save to a new DataFrame column df['new_charge_date'] and are output to the terminal.

⚡Warning: As of this writing using Python 3.10.4, the following message appears when running this code:

FutureWarning: DateOffset.call is deprecated and will be removed in a future version

Method 4: Use to_datetime() and DateOffset()

This example uses to_datetime() and DateOffset() to add three (3) days to each Date entry in a DataFrame Column.

df = pd.read_csv('checkers_users.csv')
df['charge_date'] = df['charge_date'].astype('datetime64[ns]')
df['charge_date'] = pd.to_datetime(df['charge_date']) + pd.DateOffset(days=3)
print(df)

The above code reads in the checkers_users.csv file into a DataFrame df.

The following line converts the DataFrame column charge_date into a datetime format. Then, three (3) days are added to the charge_date for each column entry (pd.DateOffset(days=3)). The results save back to df['charge_date'] and are output to the terminal.

Bonus: Date Change Based on Condition

CheckersTV has decided to change the customers’ bill day out three (3) days only if they fall within a specific range.

import pandas as pd 
import numpy as np 

df = pd.read_csv('checkers_users.csv')
df['charge_date'] = df['charge_date'].astype('datetime64[ns]')

count = 0
for c in df['charge_date']:
    chrg_day = int(c.strftime("%d"))

    if (chrg_day >= 1 and chrg_day <= 25):
        df.iat[count, 4] = pd.to_datetime(c) + pd.DateOffset(days=3)
        count += 1

✨ Finxter Challenge!
Add code to fix the year so this column isn’t
updated on a yearly basis.

Summary

This article has provided four (4) ways to find the most common element in a Pandas DataFrame column to select the best fitting for your coding requirements.

Good Luck & Happy Coding!

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