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  3. Visualizing Time Series Data in Python
  • 1

    Line Plots

    Free

    You will learn how to leverage basic plottings tools in Python, and how to annotate and personalize your time series plots. By the end of this chapter, you will be able to take any static dataset and produce compelling plots of your data.

  • 2

    Summary Statistics and Diagnostics

    In this chapter, you will gain a deeper understanding of your time series data by computing summary statistics and plotting aggregated views of your data.

  • 3

    Seasonality, Trend and Noise

    You will go beyond summary statistics by learning about autocorrelation and partial autocorrelation plots. You will also learn how to automatically detect seasonality, trend and noise in your time series data.

  • 4

    Work with Multiple Time Series

    In the field of Data Science, it is common to be involved in projects where multiple time series need to be studied simultaneously. In this chapter, we will show you how to plot multiple time series at once, and how to discover and describe relationships between multiple time series.

  • 5

    Case Study: Unemployment Rate

    This chapter will give you a chance to practice all the concepts covered in the course. You will visualize the unemployment rate in the US from 2000 to 2010.

Initializing

Exercise

Time series decomposition of the airline dataset

In this exercise, you will apply time series decomposition to the airline dataset, and visualize the trend and seasonal components.

Instructions 1/2

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  • Import statsmodels.api using the alias sm.
  • Perform time series decomposition on the airline DataFrame into a variable called decomposition.
  • Extract the trend and seasonal components.