Global median per capita income over time
The seaborn
barplot()
function shows point estimates and confidence intervals as rectangular bars; the default function displays the mean, but it can also represent another summary statistic if you pass a particular numpy
function to its estimator
parameter:
seaborn.barplot(x=None, y=None, data=None, estimator=<function mean>, ...)
In this exercise, you will use an imported World Bank dataset containing global income per capita data for 189 countries since the year 2000. To practice displaying summary statistics per category, you will plot and compare the median global income per capita since 2000 to the mean.
pandas
as pd
, numpy
as np
, matplotlib.pyplot
as plt
, and seaborn
as sns
have been imported. The global income data is available in your workspace in income_trend
.
This exercise is part of the course
Importing and Managing Financial Data in Python
Exercise instructions
- Inspect
income_trend
using.info()
. - Create a
sns.barplot()
using the column'Year'
forx
and'Income per Capita'
fory
, and show the result after rotating thexticks
by 45 degrees. - Use
plt.close()
after the initialplt.show()
to be able to show a second plot. - Create a second
sns.barplot()
with the samex
andy
settings, usingestimator=np.median
to calculate the median, and show the result.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Inspect the data
income_trend.____()
# Create barplot
sns.barplot(x=____, y='Income per Capita', data=____)
# Rotate xticks
plt.____(____=____)
# Show the plot
plt.show()
# Close the plot
plt.close()
# Create second barplot
sns.barplot(x=____, y='Income per Capita', data=____, estimator=____)
# Rotate xticks
plt.____(____)
# Show the plot
plt.show()