Automated EDA Tools That Let You Avoid Manual EDA Tasks

8 automated EDA tools in a single frame.

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.

The above visual summarizes 8 powerful EDA tools, that automate many redundant steps of EDA and help you profile your data in quick time.

  • SweetViz

    • Creates a variety of data visualizations.

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

    • Integrates with Jupyter Notebook.

    • Get started: GitHub.

  • Pandas-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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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.

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