8 Automated EDA Tools That Reduce Plenty of Manual EDA Hard Work

8 automated EDA tools in a single frame.

December 16, 2023 • Reading Time: 5 minutes

EDA is a vital step in all data science projects.

It is important because examining and understanding the data directly aids the modeling stage.

By uncovering hidden insights and patterns, one can make informed decisions about subsequent steps in the project.

Despite its importance, it is often a time-consuming and tedious task.

Below are 8 powerful EDA tools that automate many redundant EDA steps and help you profile your data quickly.

Before I begin:

Please note that these tools are not the ultimate EDA alternatives that will answer all your questions about the dataset.

But given that the preliminary EDA steps in almost all projects are the same — plotting the response variable, checking imbalance, running correlation analysis, missing value analysis, and more, these tools pretty well automate these steps in my opinion.

Also, at times, manual EDA can be prone to human errors and one may miss out on checking a few things.

Automated tools eliminate these risks and provide a standardized report across all projects.

  • SweetViz

    • Creates a variety of data visualizations.

    • Covers information about missing values, data statistics, etc.

    • Integrates with Jupyter Notebook.

    • Get started: GitHub.

  • ydata-profiling

    • Covers info about missing values, data statistics, correlation, etc.

    • Produces data alerts.

    • Plots data feature interactions.

    • Get started: GitHub.

  • DataPrep

    • Produces interactive visualizations.

    • Typically faster than other common tools.

    • Supports Pandas and Dask DataFrames.

    • Covers info about missing values, data statistics, correlation, etc.

    • Plots data feature interactions.

    • Get started: GitHub.

  • AutoViz

    • Supports CSV, TXT, and JSON.

    • Interactive Bokeh charts.

    • Covers info about missing values, data statistics, correlation, etc.

    • Presents data cleaning suggestions.

    • Get started: GitHub.

  • D-Tale

    • Allows you to run many common Pandas operations with no code.

    • Exports code of analysis.

    • Integrates with Jupyter Notebook.

    • Covers info about missing values, data statistics, correlation, etc.

    • Highlights duplicates, outliers, etc.

    • Get started: GitHub.

  • dabl

    • Primarily provides visualizations.

    • Covers a wide range of plots:

      • Target distribution.

      • Scatter pair plots.

      • Histograms.

    • Get started: GitHub.

  • QuickDA

    • Get an overview report of the dataset.

    • Covers info about missing values, data statistics, correlation, etc.

    • Produces data alerts.

    • Plots data feature interactions.

    • Get started: GitHub.

  • Lux

    • Integrates with Jupyter Notebook.

    • Provides visualization recommendations.

    • Supports EDA on a subset of columns.

    • Get started: GitHub.

👉 Over to you: What are some other automated EDA tools that you are aware of?

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