Python for Excel, 2nd Edition

Chapter 7. Time Series Analysis with pandas

Python in Excel or Jupyter Notebook: Your Choice!

To follow along with this chapter, you can either use Python in Excel or a traditional Jupyter notebook. The code samples are presented as written in the Excel workbook ch07.xlsx, which you will find in the xl folder of the companion repository.

If you don’t have access to Python in Excel, simply run the Jupyter notebook ch07.ipynb instead, which you’ll find in the root directory of the companion repository. There are small differences to the Python in Excel version, which are pointed out directly in the Jupyter notebook.

The output of the code samples is formatted like in the rest of the book, i.e., as shown by a traditional Jupyter notebook. It’s very similar, though, to what Python in Excel shows as output in the Python editor—​remember to expand the output section below the cells, as shown in the previous chapter, in Figure 6-13.

A time series is a series of data points along a time-based axis that plays a central role in many different scenarios: while traders use historical stock prices to calculate risk measures, the weather forecast is based on time series generated by sensors measuring temperature, humidity, and air pressure. And the digital marketing department relies on time series generated by web pages, e.g., the source and number of page views per hour, and will use them to draw conclusions with regard to their marketing campaigns.

Time series analysis is one of the main ...