Subsetting data based on a categorical variable often involves using the "or" operator (|) to select rows from multiple categories. This can get tedious when you want all states in one of three different regions, for example. Instead, use the .isin() method, which will allow you to tackle this problem by writing one condition instead of three se…

Subsetting data based on a categorical variable often involves using the "or" operator (|) to select rows from multiple categories. This can get tedious when you want all states in one of three different regions, for example. Instead, use the .isin() method, which will allow you to tackle this problem by writing one condition instead of three separate ones. colors = ["brown", "black", "tan"] condition = dogs["color"].isin(colors) dogs[condition] homelessness is available and pandas is loaded as pd.

# Subset for rows in South Atlantic or Mid-Atlantic regions
south_mid_atlantic = homelessness [(homelessness["region"]=="South Atlantic") | (homelessness["region"]=="Mid-Atlantic")]

# See the result
print(south_mid_atlantic)