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Tablesaw is a dataframe and visualization library that supports loading, cleaning, transforming, filtering, and summarizing data. If you work with data in Java, it may save you time and effort. Tablesaw also supports descriptive statistics and can be used to prepare data for working with machine learning libraries like Smile, Tribuo, H20.ai, DL4J.

• Import data from RDBMS, Excel, CSV, TSV, JSON, HTML, or Fixed Width text files, whether they are local or remote (http, S3, etc.)
• Export data to CSV, JSON, HTML or Fixed Width files.
• Combine tables by appending or joining
• Add and remove columns or rows
• Sort, Group, Filter, Edit, Transpose, etc.
• Map/Reduce operations
• Handle missing values

Tablesaw supports data visualization by providing a wrapper for the Plot.ly JavaScript plotting library. Here are a few examples of the new library in action.

• Descriptive stats: mean, min, max, median, sum, product, standard deviation, variance, percentiles, geometric mean, skewness, kurtosis, etc.

Add tablesaw-core to your project. You can find the version number for the latest release in therelease notes:

<dependency>    <groupId>tech.tablesaw</groupId>    <artifactId>tablesaw-core</artifactId>    <version>VERSION_NUMBER_GOES_HERE</version></dependency>

You may also add supporting projects:

• tablesaw-beakerx - for using Tablesaw insideBeakerX
• tablesaw-excel - for using Excel workbooks
• tablesaw-html - for using HTML
• tablesaw-json - for using JSON
• tablesaw-jsplot - for creating charts

External supporting projects -outside of this organization:

• tablesaw-parquet - for using theApache Parquet file format with Tablesaw (report issue)

• Start here:https://jtablesaw.github.io/tablesaw/gettingstarted
• Then see our documentation page:https://jtablesaw.github.io/tablesaw/ and theTablesaw User Guide.
• Ask questions, make suggestions, or tell us how you're using Tablesaw in the new GitHubdiscussions forum.
• Feature requests and bug reports can be made on theissues tab.

• We recommend trying Tablesaw insideJupyter notebooks, which lets you experiment with Tablesaw in a more interactive manner. Get started byinstalling BeakerX and tryingthe sample Tablesaw notebook
• A second way to use Tablesaw insideJupyter notebooks is withIJava, which has built-in support for Tablesaw. Gary Sharpe has writtenan excellent tutorial that shows you how to use Tablesaw plots. Gary has written a number of other tutorials that feature Tablesaw:
  • Tidy Data with Java & Jupyter
  • Dataframes with Tablesaw — JSON
  • Dataframes with Tablesaw — CSV Files
• A third approach is to useGoogle Colab. Again, Gary Sharpe has an excellent tutorial:Getting Started with Dataframes using Java and Google Colab

• Eclipse uses may findetablesaw useful. It provides Eclipse integration aimed at turning Eclipse into a data workbench.
• You may utilize Tablesaw with many machine learning libraries. To see an example of using Tablesaw withSmile check outthe sample Tablesaw Jupyter notebook
• You may usequandl4j-tablesaw if you'd like to load financial and economic data fromQuandl into Tablesaw. This is demonstrated inthe sample Tablesaw notebook as well