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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.

Cet exercice fait partie du cours

Python for MATLAB Users

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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 and turboprop to filter the strikes dataset.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de 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()
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