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  3. Analyzing US Census Data in Python

Exercise

Impacts of Black-White Segregation by Sex

seaborn lets us plot two variables conditioned on a third variable. The two variables will be dissimilarity and unemployment, and we will condition the scatterplot on a third variable, sex, by changing the color of the points and regression line based on the sex being reported. But first we have to turn msa_black_emp into a "tidy" DataFrame.

msa_black_emp has been loaded, with columns "pct_male_unemp" and "pct_female_unemp" as calculated in the last exercise.

pandas and seaborn have been loaded using the usual aliases.

Instructions 1/3

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  • Restrict msa_black_emp to only contain the columns "msa", "D", "pct_male_unemp", and "pct_female_unemp".
  • Rename the columns to "msa", "D", "male", and "female". (Note, there is no change to the first two columns.)