Plotly choropleth map in jupyter notebooks not showing color - jupyter-notebook

Question

Trying to make a choropleth map in plotly using some data I have in a csv file. Have created This is what i get in result(my map)
Below are the coding that I have did to the work:
import json
import pandas as pd
import plotly.express as px
asean_country = json.load(open("aseancovidmap.geojson","r"))
df= pd.read_csv("covidcases.csv")
df["iso-2"]=df['Country'].apply(lambda x: id_map[x])
id_map={}
for feature in asean_country['features']:
feature['id']= feature['properties']['sform']
id_map[feature['properties']['name']]=feature['id']
figure=px.choropleth(df,locations='iso-2',locationmode='country names',geojson=asean_country,color='Ttlcases',scope='asia',title='Total COVID 19 cases in ASEAN Countries as on 10/1/2022')
figure.show()

Answer 1

clearly I don't have access to your files, so have sourced geometry and COVID data. For reference this is at end of answer.
the key change I have made. *Don't loop over geojson Define locations as column in dataframe and featureidkey
clearly this is coloring countries
solution
import json
import pandas as pd
import plotly.express as px
# asean_country = json.load(open("aseancovidmap.geojson","r"))
asean_country = gdf_asean.rename(columns={"adm0_a3": "iso_a2"}).__geo_interface__
# df= pd.read_csv("covidcases.csv")
df = gdf_asean_cases.loc[:, ["iso_code", "adm0_a3", "total_cases", "date"]].rename(
columns={"iso_code": "iso_a2", "total_cases": "Ttlcases"}
)
figure = px.choropleth(
df,
locations="iso_a2",
featureidkey="properties.iso_a2",
geojson=asean_country,
color="Ttlcases",
title="Total COVID 19 cases in ASEAN Countries as on 10/1/2022",
).update_geos(fitbounds="locations", visible=True).update_layout(margin={"t":40,"b":0,"l":0,"r":0})
figure.show()
data sourcing
import requests, io
import geopandas as gpd
import pandas as pd
# get asia geometry
gdf = gpd.read_file(
"https://gist.githubusercontent.com/hrbrmstr/94bdd47705d05a50f9cf/raw/0ccc6b926e1aa64448e239ac024f04e518d63954/asia.geojson"
)
# get countries that make up ASEAN
df = pd.read_html("https://en.wikipedia.org/wiki/List_of_ASEAN_countries_by_GDP")[1].loc[1:]
# no geometry for singapore.... just ASEAN geometry
gdf_asean = (
gdf.loc[:, ["admin", "adm0_a3", "geometry"]]
.merge(
df.loc[:, ["Country", "Rank"]], left_on="admin", right_on="Country", how="right"
)
)
# get COVID data
dfall = pd.read_csv("https://raw.githubusercontent.com/owid/covid-19-data/master/public/data/owid-covid-data.csv")
# filter to last date in data
dfall["date"] = pd.to_datetime(dfall["date"])
dflatest = dfall.groupby(["iso_code"], as_index=False).last()
# merge geometry and COVID data
gdf_asean_cases = gdf_asean.merge(
dflatest.loc[:, ["iso_code", "total_cases", "date"]], left_on="adm0_a3", right_on="iso_code"
)