Using the Census API

Phân tích dữ liệu Điều tra Dân số Hoa Kỳ bằng Python

Lee Hachadoorian

Asst. Professor of Instruction, Temple University

Structure of a Census API Request

https://api.census.gov/data/2010/dec/sf1?get=NAME,P001001,&for=state:*

Structure of a Census API Request

https://api.census.gov/data/2010/dec/sf1?

Base URL
- Host = https://api.census.gov/data
- Year = 2010
- Dataset = dec/sf1

Structure of a Census API Request

https://api.census.gov/data/2010/dec/sf1?get=NAME,P001001,&for=state:*

Base URL
- Host = https://api.census.gov/data
- Year = 2010
- Dataset = dec/sf1
Parameters
- get - List of variables
- for - Geography of interest

The requests Library

import requests 

HOST = "https://api.census.gov/data"
year = "2010"
dataset = "dec/sf1"

base_url = "/".join([HOST, year, dataset])

predicates = {}

get_vars = ["NAME", "AREALAND", "P001001"]

predicates["get"] = ",".join(get_vars)

predicates["for"] = "state:*"

r = requests.get(base_url, params=predicates)

Examine the Response

print(r.text)

[["NAME","AREALAND","P001001","state"],
["Alabama","131170787086","4779736","01"],
["Alaska","1477953211577","710231","02"],
["Arizona","294207314414","6392017","04"],
...

Response Errors

print(r.text)

error: unknown variable 'nonexistentvariable'

Create User-Friendly Column Names

print(r.json()[0])

['NAME', 'AREALAND', 'P001001', 'state']

Create easy to remember column names using snake_case:

col_names = ["name", "area_m2", "total_pop", "state"]

Load into Pandas DataFrame

import pandas as pd 

df = pd.DataFrame(columns=col_names, data=r.json()[1:]) 

# Fix data types
df["area_m2"] = df["area_m2"].astype(int)
df["total_pop"] = df["total_pop"].astype(int)

print(df.head())

         name        area_m2  total_pop state
0     Alabama   131170787086    4779736    01
1      Alaska  1477953211577     710231    02
2     Arizona   294207314414    6392017    04
3    Arkansas   134771261408    2915918    05
4  California   403466310059   37253956    06

Find 3 Most Densely Settled States

# Create new column
df["pop_per_km2"] = 1000**2 * df["total_pop"] / df["area_m2"]

# Find top 3
df.nlargest(3, "pop_per_km2")

                    name      area_m2  total_pop state  pop_per_km2
8   District of Columbia    158114680     601723    11  3805.611218
30            New Jersey  19047341691    8791894    34   461.581156
51           Puerto Rico   8867536532    3725789    72   420.160547

Let's practice!

Phân tích dữ liệu Điều tra Dân số Hoa Kỳ bằng Python