Data cleaning often takes more time than training a model. The article presents five Python libraries that make this work clearer and more reliable. 🐍
pyjanitor adds chainable operations to pandas for normalizing names, dropping nulls, and transforming columns. Great Expectations declares rules for types, ranges, uniqueness, and missing values while generating quality reports. ftfy repairs corrupted text, mojibake, and Unicode problems.
For quick dataset exploration, ydata-profiling creates an EDA report with distributions, duplicates, correlations, and outliers. Finally, Cerberus validates dictionaries and JSON through schemas, type coercion, and nested rules. 🧹
💡 Explanation in a nutshell#
These tools turn repetitive tasks into declarative steps: we describe what data should look like, and the library detects or fixes problems. This finds errors before they reach a model, dashboard, or API.
There is no universal library: pyjanitor fits DataFrames, Great Expectations pipeline checks, ftfy text, and Cerberus JSON structures.
More information at the link 👇
