GeoPandas

import os

os.environ['PROJ_LIB'] = '/opt/tljh/user/share/proj'
import requests

url="https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json"
output_file = 'counties-fips.json'
response = requests.get(url)

if response.status_code == 200:
    with open(output_file, 'wb') as file:
        file.write(response.content)
    print(f"File downloaded as '{output_file}'")
else:
    print(f"Failed to download file. Status code: {response.status_code}")
File downloaded as 'counties-fips.json'
import geopandas as gpd
import pandas as pd
gdf = gpd.read_file("counties-fips.json")
gdf.shape
(3221, 8)
type(gdf)
geopandas.geodataframe.GeoDataFrame
gdf.head()
id GEO_ID STATE COUNTY NAME LSAD CENSUSAREA geometry
0 01001 0500000US01001 01 001 Autauga County 594.436 POLYGON ((-86.49677 32.34444, -86.71790 32.402...
1 01009 0500000US01009 01 009 Blount County 644.776 POLYGON ((-86.57780 33.76532, -86.75914 33.840...
2 01017 0500000US01017 01 017 Chambers County 596.531 POLYGON ((-85.18413 32.87053, -85.12342 32.772...
3 01021 0500000US01021 01 021 Chilton County 692.854 POLYGON ((-86.51734 33.02057, -86.51596 32.929...
4 01033 0500000US01033 01 033 Colbert County 592.619 POLYGON ((-88.13999 34.58170, -88.13925 34.587... 
gdf.dtypes
id              object
GEO_ID          object
STATE           object
COUNTY          object
NAME            object
LSAD            object
CENSUSAREA     float64
geometry      geometry
dtype: object
gdf.plot()
<Axes: >

gdf.shape
(3221, 8)
gdf.columns
Index(['id', 'GEO_ID', 'STATE', 'COUNTY', 'NAME', 'LSAD', 'CENSUSAREA',
       'geometry'],
      dtype='object')
df = pd.read_excel('PovertyEstimates.xlsx', 'PovertyEstimates', skiprows=4)
df.head()
FIPS_Code Stabr Area_name Rural-urban_Continuum_Code_2003 Urban_Influence_Code_2003 Rural-urban_Continuum_Code_2013 Urban_Influence_Code_ 2013 POVALL_2021 CI90LBALL_2021 CI90UBALL_2021 ... CI90UB517P_2021 MEDHHINC_2021 CI90LBINC_2021 CI90UBINC_2021 POV04_2021 CI90LB04_2021 CI90UB04_2021 PCTPOV04_2021 CI90LB04P_2021 CI90UB04P_2021
0 0 US United States NaN NaN NaN NaN 41393176.0 41149497.0 41636855.0 ... 16.3 69717.0 69583.0 69851.0 3349149.0 3299669.0 3398629.0 18.3 18.0 18.6
1 1000 AL Alabama NaN NaN NaN NaN 800848.0 782169.0 819527.0 ... 22.5 53990.0 53218.0 54762.0 71220.0 66888.0 75552.0 25.1 23.6 26.6
2 1001 AL Autauga County 2.0 2.0 2.0 2.0 6296.0 4772.0 7820.0 ... 20.4 66444.0 60061.0 72827.0 NaN NaN NaN NaN NaN NaN
3 1003 AL Baldwin County 4.0 5.0 3.0 2.0 25526.0 21599.0 29453.0 ... 18.5 65658.0 60723.0 70593.0 NaN NaN NaN NaN NaN NaN
4 1005 AL Barbour County 6.0 6.0 6.0 6.0 5089.0 3773.0 6405.0 ... 44.6 38649.0 34308.0 42990.0 NaN NaN NaN NaN NaN NaN

5 rows × 34 columns

gdf.head()
id GEO_ID STATE COUNTY NAME LSAD CENSUSAREA geometry
0 01001 0500000US01001 01 001 Autauga County 594.436 POLYGON ((-86.49677 32.34444, -86.71790 32.402...
1 01009 0500000US01009 01 009 Blount County 644.776 POLYGON ((-86.57780 33.76532, -86.75914 33.840...
2 01017 0500000US01017 01 017 Chambers County 596.531 POLYGON ((-85.18413 32.87053, -85.12342 32.772...
3 01021 0500000US01021 01 021 Chilton County 692.854 POLYGON ((-86.51734 33.02057, -86.51596 32.929...
4 01033 0500000US01033 01 033 Colbert County 592.619 POLYGON ((-88.13999 34.58170, -88.13925 34.587... 
df['fips'] = df.FIPS_Code.astype(str).str.zfill(5)
df.fips
0       00000
1       01000
2       01001
3       01003
4       01005
        ...  
3190    56037
3191    56039
3192    56041
3193    56043
3194    56045
Name: fips, Length: 3195, dtype: object
gdf1 = gdf.merge(df, left_on='id', right_on='fips', how='left')
gdf1.shape
(3221, 43)
gdf.shape
(3221, 8)
gdf1.head()
id GEO_ID STATE COUNTY NAME LSAD CENSUSAREA geometry FIPS_Code Stabr ... MEDHHINC_2021 CI90LBINC_2021 CI90UBINC_2021 POV04_2021 CI90LB04_2021 CI90UB04_2021 PCTPOV04_2021 CI90LB04P_2021 CI90UB04P_2021 fips
0 01001 0500000US01001 01 001 Autauga County 594.436 POLYGON ((-86.49677 32.34444, -86.71790 32.402... 1001.0 AL ... 66444.0 60061.0 72827.0 NaN NaN NaN NaN NaN NaN 01001
1 01009 0500000US01009 01 009 Blount County 644.776 POLYGON ((-86.57780 33.76532, -86.75914 33.840... 1009.0 AL ... 56894.0 52632.0 61156.0 NaN NaN NaN NaN NaN NaN 01009
2 01017 0500000US01017 01 017 Chambers County 596.531 POLYGON ((-85.18413 32.87053, -85.12342 32.772... 1017.0 AL ... 45447.0 40729.0 50165.0 NaN NaN NaN NaN NaN NaN 01017
3 01021 0500000US01021 01 021 Chilton County 692.854 POLYGON ((-86.51734 33.02057, -86.51596 32.929... 1021.0 AL ... 55142.0 49687.0 60597.0 NaN NaN NaN NaN NaN NaN 01021
4 01033 0500000US01033 01 033 Colbert County 592.619 POLYGON ((-88.13999 34.58170, -88.13925 34.587... 1033.0 AL ... 53341.0 48729.0 57953.0 NaN NaN NaN NaN NaN NaN 01033

5 rows × 43 columns

gdf1.columns
Index(['id', 'GEO_ID', 'STATE', 'COUNTY', 'NAME', 'LSAD', 'CENSUSAREA',
       'geometry', 'FIPS_Code', 'Stabr', 'Area_name',
       'Rural-urban_Continuum_Code_2003', 'Urban_Influence_Code_2003',
       'Rural-urban_Continuum_Code_2013', 'Urban_Influence_Code_ 2013',
       'POVALL_2021', 'CI90LBALL_2021', 'CI90UBALL_2021', 'PCTPOVALL_2021',
       'CI90LBALLP_2021', 'CI90UBALLP_2021', 'POV017_2021', 'CI90LB017_2021',
       'CI90UB017_2021', 'PCTPOV017_2021', 'CI90LB017P_2021',
       'CI90UB017P_2021', 'POV517_2021', 'CI90LB517_2021', 'CI90UB517_2021',
       'PCTPOV517_2021', 'CI90LB517P_2021', 'CI90UB517P_2021', 'MEDHHINC_2021',
       'CI90LBINC_2021', 'CI90UBINC_2021', 'POV04_2021', 'CI90LB04_2021',
       'CI90UB04_2021', 'PCTPOV04_2021', 'CI90LB04P_2021', 'CI90UB04P_2021',
       'fips'],
      dtype='object')
gdf.columns
Index(['id', 'GEO_ID', 'STATE', 'COUNTY', 'NAME', 'LSAD', 'CENSUSAREA',
       'geometry'],
      dtype='object')
ca = gdf1[gdf1.STATE=='06']
ca.shape
(58, 43)
ca.plot(column='PCTPOVALL_2021')
<Axes: >