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Analyzing US Census Data in Python

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Zaktualizowano 07.2023

Learn to use the Census API to work with demographic and socioeconomic data.

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PythonExploratory Data Analysis5 godz.16 videos57 Exercises4,850 PD7,371Oświadczenie o osiągnięciu

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Opis kursu

Data scientists in diverse fields, from marketing to public health to civic hacking, need to work with demographic and socioeconomic data. Government census agencies offer richly detailed, high-quality datasets, but the number of variables and intricacies of administrative geographies (what is a Census tract anyway?) can make approaching this goldmine a daunting process. This course will introduce you to the Decennial Census and the annual American Community Survey, and show you where to find data on household income, commuting, race, family structure, and other topics that may interest you. You will use Python to request this data using the Census API for large and small geographies. You will manipulate the data using pandas, and create derived data such as a measure of segregation. You will also get a taste of the mapping capabilities of geopandas.

Wymagania wstępne

Data Manipulation with pandas

1

Decennial Census of Population and Housing

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.

Census Subject Tables

Aggregate and Calculate Proportions

Calculate Proportions

Identify Extreme Values

Using the Census API

The Basic API Request

The API Response and Pandas

API to Visualization: Group Quarters

Census Geographies

Specific Places

Congressional Districts by State

Zip Code Tabulation Areas

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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.

Annual Change

Home Values in California

Health Insurance Coverage

Finding ACS Tables by Subject

Margins of Error

Plotting Margins of Error over Time

Significance of Difference of Estimates

Significance of Difference of Proportions

Basic Mapping with Geopandas

Choropleth Map of Internet Access

Proportional Symbol Map of Households w/ Internet

Bivariate Map of Broadband Access

Neighborhood Change

Identifying Gentrifiable Tracts

Identifying Gentrifying Tracts

Mapping Gentrification

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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.

Measuring Segregation: The Index of Dissimilarity

Calculating D for One State

Calculating D in a Loop

Calculating D Using Grouping in Pandas

Metropolitan Segregation

Joining Tracts and Metropolitan Areas

Create Function to Calculate D

Characteristics of Segregated Metros

Segregation Impacts: Unemployment

Calculating Unemployment

Impacts of Black-White Segregation by Sex

White and Black Unemployment

Neighborhood Segregation Over Time

Tract Demographics in a Segregated City

Segregation Begets More Segregation

Population Decline in Segregated Neighborhoods

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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.

Employment and the Labor Force

Unemployment

Labor Force Participation

Heatmap of Travel Times By Commute Mode

Worker Population

Immigration

State-to-State Flows

Is the Rent Too Damn High?

Rent Burden in San Francisco

High Rent and Rent Burden

Congratulations!

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Analyzing US Census Data in Python

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