Your business likely requires customized views of the data that is core to the creation of your product. Decades ago, accomplishing this involved development of large data analysis software libraries that were customized to a company’s particular needs, along with user interfaces that enabled employees to view the data, so they could respond to anomalies.

Much has changed in recent decades. Data analysis code that had to be custom-developed decades ago is now available in software libraries such as Python’s NumPy and SciPy. Similarly, modern data display libraries available, for example, Matplotlib. This code is well tested and verified by a large user community. By utilizing these libraries, developers can simply call verified functions, rather than developing new custom code.

Meanwhile, Application Programming Interfaces (APIs) have immensely simplified the programming effort required to access large amounts of important data. In particular, JavaScript Object Notation (JSON) facilitates downloading data from APIs in a format that is easily ingested by most modern programming languages that are suited to data analysis, for example, Java and Python…

In my previous two posts, I illustrated how your team can Use cron and curl to Regularly Download API Performance Data and How to Use Python to Extract API Performance Data. In this post, I illustrate how you can use the Python MatPlotLib library to create plots of the downloaded data that will be of […]