How to Clean Mixed Date Formats in Pandas: A Step-by-Step Guide

Introduction

The author, known as "皮皮", received a Pandas question in the "Python最强王者" group about handling a dataset with inconsistent date formats.

Problem Description

The raw data contains both British (day/month/year) and American (month/day/year) date strings, as shown in the following screenshots:

Additionally, the file includes empty lines that interfere with processing.

Solution Steps

The author outlines the processing workflow, illustrated by several images:

Code Implementation

pd.to_datetime(df['Order Date'], errors='coerce', format='%m/%d/%y %H:%M')

This command converts the "Order Date" column to datetime, coercing invalid entries to pd.NaT, which Pandas treats as missing values.

Result

The cleaned dataset displays uniform datetime values, with problematic entries replaced by pd.NaT:

Conclusion

The tutorial demonstrates how to resolve mixed‑format date issues in Pandas, providing a clear code snippet and visual guidance to help readers clean their data efficiently.