A "Thin" Application of Time Series
Google Trends allows users to see how often a term is searched for. We downloaded a file from Google Trends containing the frequency over time for the search word "diet", which is pre-loaded in a DataFrame called diet. A first step when analyzing a time series is to visualize the data with a plot. You should be able to clearly see a gradual decrease in searches for "diet" throughout the calendar year, hitting a low around the December holidays, followed by a spike in searches around the new year as people make New Year's resolutions to lose weight.
Like many time series datasets you will be working with, the index of dates are strings and should be converted to a datetime index before plotting.
This course touches on a lot of concepts you may have forgotten, so if you ever need a quick refresher, download the pandas basics Cheat Sheet and keep it handy!
This exercise is part of the course
Time Series Analysis in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import pandas and plotting modules
import pandas as pd
import matplotlib.pyplot as plt
# Convert the date index to datetime
diet.index = ____.____(____.____)