Tutorials

Basics

Python

• Basic use in Python: Filling histograms from Numpy arrays and Pandas dataframes, composite histograms, plotting them with Matplotlib, many histograms at once, storing and retrieving them.
• Advanced use in Python: Filling histograms from Spark dataframes, making and configuring many histograms at once from a dataframe.

Scala

• Basic use in Scala: Histogrammar without any custom front-ends (for plotting) or back-ends (for aggregation). Focuses on the basics of making aggregations, what makes Histogrammar different, and ASCII-art plots.

Plotting front-ends

Scala

• Making Bokeh plots in Spark: How to aggregate Apache Spark data in Histogrammar and send it to the Bokeh plotting package in Scala.

Python

• Making Matplotlib plots: How to send Histogrammar data to Matplotlib, the most popular Python plotting library.
• Making PyROOT plots: How to send Histogrammar data to the ROOT analysis package in Python. This tutorial is complete enough that you could start here, if you are a ROOT user.
• Making Bokeh plots: How to send Histogrammar data to the Bokeh plotting package in Python.

Aggregation back-ends

Scala

• Collecting data in Spark: How to use your Apache Spark cluster to make histograms, rather than downloading the data and plotting locally.
• Enhancements for SparkSQL: Special bindings to make histograms directly from Apache SparkSQL tables.

Python

• Collecting data from Numpy: Aggregating over data in Numpy arrays without a Python for loop (i.e. faster).

Utility applications

Python

• HistogrammarWatch (hgwatch): stream aggregated data as JSON by appending to a file, a UNIX pipe, or through a socket or remote ssh connection to send interactive plots through any interface you can read as text.