Tablesaw

Overview

Tablesaw is a dataframe and visualization library that supports loading, cleaning, transforming, filtering, and summarizing data.

Features

Data processing & transformation

• Import and export data: RDBMS, Excel, CSV, TSV, JSON, HTML, Fixed Width text 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.

Statistics

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

Visualization

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.

Getting started

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

<dependency>
    <groupId>com.github.grooviter</groupId>
    <artifactId>tablesaw-core</artifactId>
    <version>VERSION_NUMBER_GOES_HERE</version>
</dependency>

Or as a gradle dependency:

implementation "com.github.grooviter:tablesaw-core:VERSION_NUMBER_GOES_HERE"

You may also add supporting projects:

• tablesaw-beakerx - for using Tablesaw inside BeakerX
• 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 the Apache Parquet file format with Tablesaw (report issue)

Documentation and support

• Start with the tutorial section
• Then check the full documentation page
• Feature requests and bug reports can be made on the issues tab.

Integrations

Jupyter Notebooks

• We recommend trying Tablesaw inside Jupyter notebooks, which lets you experiment with Tablesaw in a more interactive manner. Get started by installing BeakerX and trying the sample Tablesaw notebook
• A second way to use Tablesaw inside Jupyter notebooks is with IJava, which has built-in support for Tablesaw. Gary Sharpe has written an 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 use Google Colab. Again, Gary Sharpe has an excellent tutorial:Getting Started with Dataframes using Java and Google Colab

Other integrations

• Eclipse uses may find etablesaw 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 with Smile check out the sample Tablesaw Jupyter notebook
• You may use quandl4j-tablesaw if you'd like to load financial and economic data from Quandl into Tablesaw. This is demonstrated in the sample Tablesaw notebook as well

Credits

This repository was born as a fork of the original Tablesaw project.

License

This project is licensed under the Apache 2.0