Automate your visualization
One of the main strengths of Matplotlib is that it can be automated to adapt to the data that it receives as input. For example, if you receive data that has an unknown number of categories, you can still create a bar plot that has bars for each category.
This is what you will do in this exercise. You will be visualizing data about medal winners in the 2016 summer Olympic Games again, but this time you will have a dataset that has some unknown number of branches of sports in it. This will be loaded into memory as a pandas DataFrame object called summer_2016_medals, which has a column called "Sport" that tells you to which branch of sport each row corresponds. There is also a "Weight" column that tells you the weight of each athlete.
This exercise is part of the course
Introduction to Data Visualization with Matplotlib
Exercise instructions
- Iterate over the values of
sportssettingsportas your loop variable. - In each iteration, extract the rows where the
"Sport"column is equal tosport. - Add a bar to the provided
axobject, labeled with the sport name, with the mean of the"Weight"column as its height, and the standard deviation as a y-axis error bar. - Save the figure into the file
"sports_weights.png".
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
fig, ax = plt.subplots()
# Loop over the different sports branches
for ____ in ____:
# Extract the rows only for this sport
sport_df = ____
# Add a bar for the "Weight" mean with std y error bar
____
ax.set_ylabel("Weight")
ax.set_xticklabels(sports, rotation=90)
# Save the figure to file
____