Put the days of trying to decipher meaning from boring spreadsheets behind you. Visualize data to give greater and immediate meaning to all those numbers with Python. We will explore the variety of options available for data visualization in Python using different libraries and understand which ones excel for what type of task. Create maps, statistical graphs and more detailed or interactive visualizations that can also be used on the web, ideal to take that blog post to a whole new level. This presentation tackles boring data by looking at python libraries available for mapping such as basemap and folium, statistical graphs from libraries such as matplotlib and seaborn, as well as libraries such as Bokeh and Plotly that can be used for making interactive graphs.

In order to walk through the Jupyter notebook, you must first download the NYC Restaurant Inspection Results csv from NYC Open Data. The CSV can be found here. I also have the curl link to the csv in the python and jupter notebook files.

Libraries Used

• Pandas
  • Python library to provide data analysis features
  • Built on NumPy, SciPy, and matplotlib
  • Key components
    • Series
    • DataFrames
• Matplotlib
  • MATLAB like plotting framework
• Seaborn
  • Built on top of matplotlib
  • Creates more sophisticated graphs that look more professional
• Basemap
  • library for plotting 2D data on maps in Python
  • Had lots of problems with installation
• Folium
  • Visualize data on a Leaflet map
  • Built-in tilesets from:
    • OpenStreetMap, MapQuest Open, MapQuest Open Aerial, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys.
• Bokeh
  • A Python interactive visualization library that targets modern web browsers for presentations.
• Plot.ly
  • Make interactive charts online from Excel or CSV data.