Rent Burden in San Francisco
In this exercise, you will look at rent burden (households paying 30% or more of their income in rent) in San Francisco, one of the highest price housing markets in the country.
The rent DataFrame contains the number of households in each of 7 income categories crossed with 8 rent-share-of-income categories. For each income category, You will use a loop to calculate the percentage of rent burdened households in each income category. The column name prefixes associated with each income category are in a list:
incomes = ["inc_under_10k", "inc_10k_to_20k", "inc_20k_to_35k", "inc_35k_to_50k",
"inc_50k_to_75k", "inc_75k_to_100k", "inc_over_100k"]
pandas and seaborn are imported using the usual aliases.
This exercise is part of the course
Analyzing US Census Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate percentage of rent burdened households
rent_burden = rent[["name"]]
for income in incomes:
rent_burden[income] = 100 * (rent[____] +
rent[____] + rent[____] +
rent[____]) / (rent[income] - rent[income + "_rent_not_computed"])