Master Pandas Date Handling: Two Simple Methods to Convert Month Strings

1. Introduction

Hello, I am a Python advanced learner. Recently a question about Pandas date handling was posted in a Python community.

2. Implementation

The initial idea was to simply append "31" to each date and format, but month lengths vary, so a proper function is needed.

Method One

Using MonthEnd from pandas.tseries.offsets:

import pandas as pd
from pandas.tseries.offsets import MonthEnd

df = pd.DataFrame({'date': ['2022-01', '2022-02', '2022-03', '2022-04', '2022-05', '2022-06', '2022-07']})
df['new_date'] = df['date'].astype('datetime64').map(MonthEnd())
print(df)

Method Two

For strings like "202201", convert with pd.to_datetime and then apply MonthEnd:

import pandas as pd
from pandas.tseries.offsets import MonthEnd

df = pd.DataFrame({'date': ['202201', '202202', '202203', '202204', '202205', '202206', '202207']})
df['new_date'] = pd.to_datetime(df['date'], format='%Y%m').map(MonthEnd())
print(df)

An edge case was discovered: when the original date is the last day of the month, the conversion shifts to the last day of the next month.

A third approach avoids extra imports by using pure Python arithmetic (illustrated below).

Community members also shared performance tips, such as using the plus operator for faster processing (shown in the images).

3. Summary

This article presented a specific Pandas time‑handling problem, explained two reliable solutions with complete code, highlighted an edge case, and shared community‑derived optimizations to help readers solve similar date‑processing tasks efficiently.

Thanks to the contributors for their insights and code examples.