Growth rates in Brazil, China, and the US
It's time to extend your analysis beyond the levels of international per capita income to the growth rates. The 'income_growth.csv' file contains the growth rates of per capita income over the last 40 years for Brazil, China, and the US.
You will plot the distribution of the historical growth rates for each country on the same chart using a KDE plot to faciliate visual comparison of the ranges of growth that these markets have experienced over this time period.
From this point in the course onwards, you should always inspect any DataFrame with .info() in your console even if this isn't explicitly in the instructions. pandas as pd, seaborn as sns, and matplotlib.pyplot as plt have been imported.
This exercise is part of the course
Importing and Managing Financial Data in Python
Exercise instructions
- Load the file
'income_growth.csv'into the variablegrowth. Parse the'DATE'column intodtypedatetime64and set it as the index. - Inspect the summary statistics for these three growth rates using the appropriate function.
- Iterate over the
growth.columnsattribute in a for loop to access their labels. Most of the code has been outlined for you.- In each iteration of
distplot(), pass in the iteration variablecolumnto select the respective column, set the keywordhisttoFalse, and setlabeltocolumn. - Show the result.
- In each iteration of
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Load the file into growth
growth = pd.read_csv(____, parse_dates=____).____(____)
# Inspect the summary statistics for the growth rates
growth.____()
# Iterate over the three columns
for column in growth.____:
sns.____(growth[____], hist=____, label=____)
# Show the plot
plt.show()