👀 Views: 80 💬 Answers: 1 📅 Created: 2025-06-25

pandas csv datetime Python

I'm performance testing and I'm testing a new approach and I'm currently working on a data processing script using Pandas to parse a CSV file that contains a column with dates. The scenario arises because the date formats are inconsistent; some dates are in `YYYY-MM-DD` format, while others are in `MM/DD/YYYY`. This discrepancy leads to a `ValueError` when I attempt to convert the column to datetime using `pd.to_datetime()`. Here's a snippet of my code: ```python import pandas as pd data = { 'name': ['Alice', 'Bob', 'Charlie'], 'date_of_birth': ['1990-05-01', '05/21/1992', '1988-12-15'] } # Create DataFrame df = pd.DataFrame(data) # Attempt to convert the date column try: df['date_of_birth'] = pd.to_datetime(df['date_of_birth']) except ValueError as e: print(f'behavior: {e}') ``` When I run this, I get the following behavior: ``` behavior: Unable to parse string "05/21/1992" at position 1 ``` To address this, I’ve tried using the `errors='coerce'` parameter: ```python df['date_of_birth'] = pd.to_datetime(df['date_of_birth'], errors='coerce') ``` However, this results in `NaT` for the improperly formatted dates, which isn't ideal since I want to retain all the information. I also considered preprocessing the dates with a custom function to identify and convert different formats, but I’m not sure what the best approach would be. Is there a more efficient way to handle mixed date formats in this situation without losing data or resulting in `NaT`? Any insights or best practices would be greatly appreciated! Am I approaching this the right way? This is for a microservice running on Debian.