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

Introduction

The author encountered a Pandas data‑processing issue where a dataset contained mixed British and American date formats, making further analysis impossible.

Problem Details

The raw data showed inconsistent date strings, as illustrated below.

Additionally, the file contained empty lines that interfered with processing.

Solution Approach

The proposed solution first normalizes the date column using pd.to_datetime with a specific format and error handling, then removes empty rows.

Code Implementation

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

This converts valid dates to timestamps; invalid entries become pd.NaT, which Pandas treats as missing values.

Result

After applying the conversion, the dataset displays clean, uniform date values.

The article concludes that the provided steps and code effectively resolve the mixed‑date issue in Pandas.