The reason it is not displayed is because the geojson file you are using does not have the zip code data. Therefore, it is necessary to prepare a geojson file with zip codes. As an example, I created a graph with sample data from here. Depending on your data, if you want to handle zip codes for the entire US, the number of data will be huge and will affect the performance.
from urllib.request import urlopen import json import requests # Nevada Zip code url = 'https://raw.githubusercontent.com/OpenDataDE/State-zip-code-GeoJSON/master/nv_nevada_zip_codes_geo.min.json' with urlopen(url) as response: nv_zip_json = json.load(response) zip_code = [] for i in range(len(nv_zip_json['features'])): code = nv_zip_json['features'][i]['properties']['ZCTA5CE10'] zip_code.append(code) import pandas as pd import numpy as np df = pd.DataFrame() df['zip_code'] = df['zip_code'].astype(str) import plotly.express as px fig = px.choropleth(df, geojson= nv_zip_json, locations='zip_code', featureidkey="properties.ZCTA5CE10", color='value', color_continuous_scale="blues", projection="mercator", ) fig.update_geos(fitbounds="locations", visible=False) fig.update_layout(margin=) fig.show()
To draw a map by county using the current data, the following code can be used.
import plotly.express as px fig = px.choropleth(df, geojson= counties, locations='county', featureidkey="properties.NAME", scope="usa", color='user_id', color_continuous_scale="blues", ) fig.show() 