datacamp-logo
Session Ready
Exercise

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 data frame 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.

Instructions 1/3
XP
  • 1
  • 2
  • 3
  • Add the columns that count rent burdened households; these column names begin with income and end with "_rent_30_to_35_pct", "_rent_35_to_40_pct", "_rent_40_to_50_pct", and "_rent_over_50_pct".