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.
The Slides for this talk are available here
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.
Libraries Used