jupyter

In my last post, I wrote about some basic functions of Pandas and DataFrames. Today, I show you how to read DataFrames from Excel. The Scenario is (again) about configuration generation, but this time I like to focus on the data gathering part.

I like to write today about a topic that I used quite frequently within the last weeks/months: pandas DataFrames. At some point in your automation story, you need some data for whatever reason. One example are connection data for some devices. Another example might be the collection of configuration information. I think you know that many people use Excel for this purpose and today, I like to show you, how you can work with DataFrames. In my next post, I show you how to read and work with data from an Excel file.

I like to write about a tool today, that I used since several months to write python code for some smaller tasks: Jupyter. It provides a web-based python environment running on your local machine or on a server. This is very useful from my perspective to get started with python programming, play with new libraries and share scripts.