Distribution of inflation rates in China, India, and the US
As you saw in the video, the boxplot()
function displays key quantiles of a distribution with respect to categories, where y
represents a quantitative variable, and x
a categorical variable. In statistics, this kind of distribution is known as a box-and-whisker plot.
A complement to a box plot is a swarmplot()
, which draws a categorical scatterplot that displays all categorical observations without overlapping; it takes similar arguments to boxplot()
:
seaborn.boxplot(x=None, y=None, data=None, ...)
seaborn.swarmplot(x=None, y=None, data=None, ...)
In this final exercise, you will compare the historical distributions of inflation rates by country - specifically China, India, and the US - instead of by time series trends. 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
- Create and show a boxplot of the
inflation
data with'Country'
forx
and'Inflation'
fory
. - Create and show
sns.swarmplot()
with the same arguments.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create boxplot
sns.____(x=____, y=____, data=____)
# Show the plot
plt.show()
# Close the plot
plt.close()
# Create swarmplot
sns.swarmplot(x=____, y=____, data=____)
# Show the plot
plt.show()