Key Points
- A Python library is a collection of pre-written code you import to
extend Python’s capabilities.
-
numpy handles fast numerical computation;
pandas handles tabular data.
-
matplotlib is the standard plotting library;
geopandas adds geographic support.
- The standard aliases (
np, pd,
plt, gpd) are conventions, use them so your
code matches examples you find online.
- Census data supports planning services for specific population
groups
- It can be used for business and facility site selection
- It supports public policy analysis
- It enables spatial analysis of hazard impacts, epidemiological
models, and more
-
tract:* returns all tracts in the specified state
-
county:* returns all counties in the specified
state
- Replace
state:18 with your state’s FIPS code (State
Codes List)
-
state:* is not allowed for tract-level
queries due to dataset size limits — you must specify a state
- The Census API gives you flexible, precise access to ACS data
- You can combine multiple variables in a single API call
-
&descriptive=true adds plain-language descriptions
for each variable
-
&outputFormat=csv makes the data easy to open in
Excel or import into Python
- Always cast Census columns to numeric before analysis — the API
returns everything as strings
- Always check for missing data (
NaN) to avoid
visualization problems later on
- Rename cryptic variable codes to descriptive column names early in
your workflow
- Use
groupby with .agg() to compute
multiple statistics at once across geographic units
- Exploratory plots help you understand your data; explanatory plots
help others understand your findings
- Choropleth maps, histograms, and bar charts each answer a different
question about Census data
- Color scale choices, axis ranges, and aggregation level all affect
how a visualization is interpreted
- Use colorblind-friendly palettes and always label axes, titles, and
legends
- Transparency about data suppression and margins of error is an
ethical requirement when publishing Census visualizations
