Plotting in Pandas

Question:

Plotting in Pandas

If the variable data is a NumPy array or a Pandas Series, just like if it is a list, the code

import matplotlib.pyplot as plt
plt.hist(data)

will create a histogram of the data.

Pandas also has built-in plotting that uses matplotlib behind the scenes, so if data is a Series, you can create a histogram using data.hist().

There's no difference between these two in this case, but sometimes the Pandas wrapper can be more convenient. For example, you can make a line plot of a series using data.plot(). The index of the Series will be used for the x-axis and the values for the y-axis.

In the following quiz, we've created Series containing the various variables we've been looking at this lesson. Pick a country you're interested in, and make a plot of each variable over time.

The Udacity editor will only show one plot each time you click "Test Run", so you can look at multiple plots by clicking "Test Run" multiple times. If you're running plotting code locally, you may need to add the line plt.show() depending on your setup.

Start Quiz:

pandas_plotting.py

import pandas as pd
import seaborn as sns

# The following code reads all the Gapminder data into Pandas DataFrames. You'll
# learn about DataFrames next lesson.

path = '/datasets/ud170/gapminder/'
employment = pd.read_csv(path + 'employment_above_15.csv', index_col='Country')
female_completion = pd.read_csv(path + 'female_completion_rate.csv', index_col='Country')
male_completion = pd.read_csv(path + 'male_completion_rate.csv', index_col='Country')
life_expectancy = pd.read_csv(path + 'life_expectancy.csv', index_col='Country')
gdp = pd.read_csv(path + 'gdp_per_capita.csv', index_col='Country')

# The following code creates a Pandas Series for each variable for the United States.
# You can change the string 'United States' to a country of your choice.

employment_us = employment.loc['United States']
female_completion_us = female_completion.loc['United States']
male_completion_us = male_completion.loc['United States']
life_expectancy_us = life_expectancy.loc['United States']
gdp_us = gdp.loc['United States']

# Uncomment the following line of code to see the available country names
# print employment.index.values

# Use the Series defined above to create a plot of each variable over time for
# the country of your choice. You will only be able to display one plot at a time
# with each "Test Run".

Solution: