Comparing speeding outcomes by gender
When a driver is pulled over for speeding, many people believe that gender has an impact on whether the driver will receive a ticket or a warning. Can you find evidence of this in the dataset?
First, you'll create two DataFrames of drivers who were stopped for speeding: one containing females and the other containing males.
Then, for each gender, you'll use the stop_outcome column to calculate what percentage of stops resulted in a "Citation" (meaning a ticket) versus a "Warning".
Este ejercicio forma parte del curso
Analyzing Police Activity with pandas
Instrucciones del ejercicio
- Create a DataFrame,
female_and_speeding, that only includes female drivers who were stopped for speeding. - Create a DataFrame,
male_and_speeding, that only includes male drivers who were stopped for speeding. - Count the stop outcomes for the female drivers and express them as proportions.
- Count the stop outcomes for the male drivers and express them as proportions.
Ejercicio interactivo práctico
Prueba este ejercicio y completa el código de muestra.
# Create a DataFrame of female drivers stopped for speeding
female_and_speeding = ri[____]
# Create a DataFrame of male drivers stopped for speeding
male_and_speeding = ri[____]
# Compute the stop outcomes for female drivers (as proportions)
print(female_and_speeding.____)
# Compute the stop outcomes for male drivers (as proportions)
print(male_and_speeding.____)