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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

Load multiple time series

Whether it is during personal projects or your day-to-day work as a Data Scientist, it is likely that you will encounter situations that require the analysis and visualization of multiple time series at the same time.

Provided that the data for each time series is stored in distinct columns of a file, the pandas library makes it easy to work with multiple time series. In the following exercises, you will work with a new time series dataset that contains the amount of different types of meat produced in the USA between 1944 and 2012.

Instructions

100 XP

We've imported pandas using the pd alias.

  • Read in the the csv file located at url_meat into a DataFrame called meat.
  • Convert the date column in meat to the datetime type.
  • Set the date column as the index of meat.
  • Print the summary statistics of all the numeric columns in meat.