Boolean indexing and Matplotlib fun
Now let's look at how Boolean indexing can help us explore data visually in just a few lines of code. In this exercise, you'll practice many of the things you've learned - converting data from a dictionary into a useable pandas' DataFrame, indexing using Booleans, and then using matplotlib
to visualize your data to learn about some relationships in the wildlife strikes data.
This exercise is part of the course
Python for MATLAB Users
Exercise instructions
- Convert the
strikes
dictionary to a DataFrame. - Create a Boolean filter for
'Turbofan'
in the'Engine'
column. - Create a Boolean filter for
'Turboprop'
in the'Engine'
column. - Plot two scatter plots using
turbofan
andturboprop
to filter thestrikes
dataset.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Create a dictionary and then a DataFrame from the dictionary
strikes = {'Date': date,'Speed': speed,'Height':height,'Engine':engine}
strikes = pd.____(strikes)
# Filter strikes by engine type
turbofan = strikes['Engine']=='____'
turboprop = strikes['____']=='____'
# Create scatter plot of speed and height for each engine type
plt.scatter(strikes[____]['Speed'],strikes[____]['Height'],label='Turbofan')
plt.scatter(strikes[____]['Speed'],strikes[____]['Height'],label='Turboprop')
plt.legend()
plt.xlabel('Strike speed (knots)')
plt.ylabel('Strike height (feet)')
plt.show()