Cleaning-DataFrames-with-Pandas

What problem does it solve?

This skill provides reusable patterns for cleaning, preprocessing, and standardizing tabular data in Python using pandas. It addresses missing values, deduplication, column normalization, type conversion, and loading CSV or Parquet files.

Core Features & Use Cases

• Normalize column names to snake_case
• Handle missing values (drop, fill, interpolate)
• Deduplicate rows based on key columns
• Convert dtypes (datetime, numeric)
• Load CSV/Parquet
• Provide a reusable cleaning pipeline function
• Use Case: Build a reusable cleaning workflow for multiple vendors.

Quick Start

Create a reusable clean_dataframe function that normalizes columns, drops rows with missing keys, deduplicates on given columns, converts dates and numeric columns, and fills defaults as needed.