Anaconda is a widely used free Python distribution maintained by Continuum (https://www.continuum.io/). We will use the prevailing software stack provided by Anaconda to generate our apps. In this book, we will use PySpark and the PyData ecosystem. The PyData ecosystem is promoted, supported, and maintained by Continuum and powered by the Anaconda Python distribution. The Anaconda Python distribution essentially saves time and aggravation in the installation of the Python environment; we will use it in conjunction with Spark. Anaconda has its own package management that supplements the traditional pip install and easy-install. Anaconda comes with batteries included, namely some of the most important packages such as Pandas, Scikit-Learn, Blaze, Matplotlib, and Bokeh. An upgrade to any of the installed library is a simple command at the console:
$ conda update
A list of installed libraries in our environment can be obtained with command:
$ conda list
The key components of the stack are as follows:
- Anaconda: This is a free Python distribution with almost 200 Python packages for science, math, engineering, and data analysis.
- Conda: This is a package manager that takes care of all the dependencies of installing a complex software stack. This is not restricted to Python and manages the install process for R and other languages.
- Numba: This provides the power to speed up code in Python with high-performance functions and just-in-time compilation.
- Blaze: This enables large scale data analytics by offering a uniform and adaptable interface to access a variety of data providers, which include streaming Python, Pandas, SQLAlchemy, and Spark.
- Bokeh: This provides interactive data visualizations for large and streaming datasets.
- Wakari: This allows us to share and deploy IPython Notebooks and other apps on a hosted environment.
The following figure shows the components of the Anaconda stack:
