Development Tools

  1. tox automation project - standarize testing in Python
  2. pytest
  3. bump2version

Front

  1. Django
  2. Flask
  3. Jinja2

Various

Multiprocessing and Async

Visual

  1. Pandas
  2. Seaborn
  3. Bokeh
  4. Pygal
  5. Plotly

The Pandas states for “panel data”, a term for multidmensional structured data-set. Pandas alone has an ability to produce simple tabular plots. However, it is impossible to customize the graph into more detaild visualisation just by using Pandas.

Seaborn is open-source library for data analysis «and visualisation» which integrates with Pandas. Seaborn is popular for making appealing statistical data graphs.

Bookeh was developed by Anaconda’s team with funding from DARPA. Open source project. An interactive library created for modern web browsers to visualise highly interactive plots and data applications. Bokeh’s method can create any kind of graphical plot including dash boards and variety of charts. The graph looks much nicer and cleaner. Bokeh method has a lot of customization option and functionality. Even though it looks nice, it does not make sense to use for a simple bar visualisation.

Pygal is a part of Python’s library that exports vector chars in different shapes and styles. Options for visualizations are wide open and include pie chars, bar graphs, histograms, maps and on.

Plotly aka Plot.ly because of its main platform online and interactive. Everything that is being created with the tool is posted on the web. Integrates well with Pandas.

  1. Python Data Visualization - Comparing 5 Tools (Medium)

Bokeh

  1. Bookeh Project
  2. Data Visualization with Bokeh in Python - Medium post

Geo

  1. Geo-Python 2018
  2. Automated GIS-process
  3. PyKML