Inflation trends in China, India, and the US
Finally, the seaborn
package includes functions that allow you to visualize the distribution of levels of categorical variables.
In the next two exercises, you will examine the historical inflation data in China, India, and the US over the past 50+ years in data from FRED. Before jumping into using the functions you have just learned, you should first familiarize yourself with the raw data. pandas
as pd
, matplotlib.pyplot
as plt
, and seaborn
as sns
have been imported for you. The FRED inflation data is in your workspace as inflation
.
This exercise is part of the course
Importing and Managing Financial Data in Python
Exercise instructions
- Inspect
inflation
using.info()
. - Group
inflation
by'Country'
and assign toinflation_by_country
. - In a for loop, iterate over
country
,data
pairs returned byinflation_by_country
. In each iteration, use.plot()
ondata
withtitle
set tocountry
to show the historical time series.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Inspect the inflation data
inflation.____()
# Create inflation_by_country
inflation_by_country = inflation.____(____)
# Iterate over inflation_by_country and plot the inflation time series per country
for country, data in inflation_by_country:
# Plot the data
data.____(____=____)
# Show the plot
plt.show()