1. Working with more than one time series
You have become very strong at working with isolated time series but, in the field of Data Science, you will often come across datasets containing multiple time series. For example, we could be measuring the performance of CPU servers over time and in another case, we could be exploring the stock performance of different companies over time. These situations introduce a number of different questions, and therefore require additional analytical tools and visualization techniques. This chapter builds on the analysis of isolated time-series and shows you how to analyze datasets containing multiple series.
2. Working with multiple time series
As you can see in the example shown here, datasets containing multiple time series are very similar to what we have been working with so far. As long as one the fields has a column with date information, reading files that contain data in this format is extremely straightforward with the pandas library.
3. The Meat production dataset
In this chapter, you will be working with a new dataset that contains volumes of different types of meats produced in the United States between 1944 and 2012.
4. Summarizing and plotting multiple time series
A convenient aspect of pandas is that dealing with multiple time series is very similar to dealing with a single time series. Just like in the previous chapters, you can quickly leverage the dot plot() and dot describe() methods to visualize and produce statistical summaries of the data.
5. Area charts
Another interesting way to plot multiple time series is to use area charts. Area charts are commonly used when dealing with multiple time series, and can be leveraged to represent cumulated totals. With the pandas library, you can simply leverage the dot plot dot area() method as shown on this slide to produce an area chart.
6. Let's practice!
Let's start exploring our new dataset!