Why Using Pandas in Production Matters

Alright, let me get real for a second. If you’re wrangling data in production and you’re not using pandas, you’re probably making your life harder than it needs to be. I’ve lost count of how many times pandas has saved my bacon when a last-minute data request comes in, or when some CSV from marketing is, let’s say, less than perfect.

Here’s a quick peek at what a typical day looks like for me:

import pandas as pd

def clean_sales_data(input_path, output_path):
    df = pd.read_csv(input_path)
    # Drop rows with missing order IDs
    df = df.dropna(subset=['order_id'])
    # Standardize column names
    df.columns = [col.strip().lower() for col in df.columns]
    # Save the cleaned data
    df.to_parquet(output_path, index=False)

clean_sales_data('raw_sales.csv', 'cleaned_sales.parquet')

This isn’t rocket science, but it’s the kind of thing that keeps the data flowing and the business happy. Pandas in production? Absolutely. Just keep an eye on memory usage and you’ll be golden.