As a data professional, you know that data cleaning—however unenjoyable it may be—is a necessary task that takes up substantial time. Because it is not data, but clean data, that is helpful.

You’ve probably used pandas for data cleaning. But there are other Python libraries you can use for data cleaning, too. This article gives an overview of such Python libraries.

Of course, we’ll include pandas. But we’ll also include a handful of libraries besides pandas. So you can try out and see if you find a library that makes data cleaning less boring and more enjoyable!

1. Pandas

If you’re reading this article, you’ve likely used Pandas for data cleaning and analysis. As you know, it has functions for all common data quality issues.

Pandas has built-in functions and methods for column transformations, filtering, and joining datasets. You can handle duplicate records, impute missing values. You can rename columns, encode categorical variables, and do much more with pandas.

Learn more: Pandas User Guide

2. Cleanlab

When you’re training machine learning models on real-world datasets, you’ll have noise in both the features and the labels. And Cleanlab can auto-detect and fix issues in both.

Cleanlab uses your existing model to estimate and fix problems in the data quality so you can build better models. So you start with the baseline model and a dataset, use cleanlab to use to fix data quality issues—in both the dataset and the label—and then use the now-improved data set to train a better model.

Learn more: Cleanlab docs

3. Pyjanitor

Janitor is a popular R package for data cleaning, and Pyjanitor is its Python implementation.

There are common data quality issues across datasets: duplicate records, missing values, incorrect formats, categorical variables and more. So data cleaning typically involves a series of steps—to go from raw data to usable form.

With pandas, you can use method chaining to group together these steps. And pyjanitor provides a more expressive data preprocessing DAG for these data cleaning steps.

Learn more: Pyjanitor: Clean APIs for data cleaning

4. Missingno

Most large real-world datasets are messy. And missing values are, perhaps, the most common data quality issue and quite challenging to fix. Missingno is a Python library that helps understand missingness in your dataset better—through helpful visualizations.

You can create a range of visualizations: nullity matrices, heatmaps, dendrograms, and bar plots. With these, you should be able to better understand the patterns in missing value and any possible relationship between missing values amongst columns.

Learn more: missingno: Missing data visualization module for Python

5. Autoviz

Data cleaning and visualization both go into the exploratory data analysis step. And Autoviz makes both data cleaning and visualization a breeze.

It supports creating interactive visualizations of the dataset for better understanding. In addition, it helps you assess data quality issues and fix them—all with a single line of code.

Learn more: Autoviz

Wrapping Up

I hope you found this round-up of Python libraries for data cleaning helpful.

Remember it’s not important which library or framework you use. What is more important is that you understand the dataset—the features, their relevance and relative significance—and then go ahead with the best data cleaning approaches.

This involves factoring in the problem you’re trying to solve, the dataset you’re working with,  and the machine learning model you’re looking to build (if you are).

So happy data cleaning!