1. Aprender
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  2. Projetos
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  3. Analyzing US Census Data in Python
  • 1

    Decennial Census of Population and Housing

    Gratuito

    Start exploring Census data products with the Decennial Census. Use the Census API and the requests package to retrieve data, load into pandas data frames, and conduct exploratory visualization in seaborn. Learn about important Census geographies, including states, counties, and tracts.

  • 2

    American Community Survey

    Explore topics such as health insurance coverage and gentrification using the American Community Survey. Calculate Margins of Error and explore change over time. Create choropleth maps using geopandas.

  • 3

    Measuring Segregation

    Explore racial segregation in America. Calculate the Index of Dissimilarity, and important measure of segregation. Learn about and use Metropolitan Statistical Areas, and important geography for urban research. Study segregation changes over time in Chicago.

  • 4

    Exploring Census Topics

    In this chapter, you will apply what you have learned to four topical studies. Explore unemployment by race and ethnicity; commuting patterns and worker density; immigration and state-to-state population flows; and rent burden in San Francisco.

Initializing

Exercicio

Labor Force Participation

Unemployment could be going down because labor force participation is decreasing, that is, fewer people are looking for work! In this exercise, you will investigate that possiblity. You will start with a DataFrame, lf_by_race, of percent labor force participation by year for 25 to 54 year olds in four racial groups (White, Black, Asian, and Hispanic) and both sexes. You will create a bar plot of labor force participation against year. In order to condition the bar plot by demographic group, you will first melt the DataFrame. The DataFrame already has suitable column names.

pandas and seaborn have been imported with the usual aliases. unemp_by_race is loaded and five columns are displayed in the console.

Instruções

100 XP
  • melt the lf_by_race DataFrame, setting var_name to "demographic" and value_name to "labor_force_participation"; can you determine the appropriate column for the id_vars parameter?
  • Call sns.barplot with the year on the x-axis and labor force participation on the y-axis; then use the hue parameter to show sex grouped by year.