The pandas (Python data analysis) library is just a collection of fancy wrappers around NumPy, Matplotlib, and other Python libraries. You can find more information including installation instructions on the pandas website at http://pandas.pydata.org/pandas-docs/stable/install.html. Most good APIs such as NumPy seem to follow the Unix philosophy—keep it simple and do one thing well. This philosophy results in many small tools and utilities that can be used as building blocks for something bigger. The pandas library mimics the R programming language in its approach.

The pandas library has plotting subroutines, which can plot lag and autocorrelation plots. Autocorrelation is correlation within a dataset and can be indicative of a trend. For instance, if we have a lag of one day, we can see if the average temperature of yesterday influences the temperature today. For that to be the case, the autocorrelation value needs to be relatively...