How can I plot a map of a specfic country using plotly - matplotlib

Question

Using geopandas and matplotlib I have ploted a map of india showing Air Quality Index.
The link to my data is:
https://drive.google.com/file/d/1-xihM-LCB6dNfONbK28CJWOP_PVgXA8C/view?usp=share_link
I want to plot an interactive map with names of the cities and borders of regions of India using plotly?
from matplotlib import cm, colors
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
#restricted to India.
ax = world[world.name == 'India'].plot(color='grey', edgecolor='white')
city_day_gdf.plot(column='AQI_Bucket', ax=ax, cmap='PuBuGn', markersize=city_day_gdf['AQI'])
norm = colors.Normalize(city_day_gdf.AQI.min(), city_day_gdf.AQI.max())
plt.colorbar(cm.ScalarMappable(norm=norm,cmap='PuBuGn'), ax=ax)
plt.title("A Map showing the descriptions of Air Quality Index in terms of AQI magnitude across India between 2015 and 2020")
plt.show()

Answer 1

you can scatter your data using px.scatter_mapbox(). Have reduced data to just newest reading per city. You have not stated how you want to treat time series
with requirement for plotting regions of India, you need some geometry for this. Have used GeoJson from a repo on GitHub for this. Have simplified the geometry as it is very detailed and hence will slow down plotly significantly
finally pull it all together, layer scatter on top of choropleth
import pandas as pd
import geopandas as gpd
import shapely.wkt
import plotly.express as px
import requests
url = "https://drive.google.com/file/d/1-xihM-LCB6dNfONbK28CJWOP_PVgXA8C/view?usp=share_link"
df = pd.read_csv("https://drive.google.com/uc?id=" + url.split("/")[-2], index_col=0)
df["Date"] = pd.to_datetime(df["Date"])
# just reduce data to last date for each city. Nothing in question indicates
# how to dela with time series
df = df.sort_values(["City", "Date"]).groupby("City", as_index=False).last()
# get some geometry for regions of india
gdf_region = gpd.read_file(
"https://raw.githubusercontent.com/Subhash9325/GeoJson-Data-of-Indian-States/master/Indian_States"
)
gdf_region["geometry"] = (
gdf_region.to_crs(gdf_region.estimate_utm_crs())
.simplify(5000)
.to_crs(gdf_region.crs)
)
fig = px.choropleth_mapbox(
gdf_region,
geojson=gdf_region.__geo_interface__,
locations=gdf_region.index,
color="NAME_1",
).update_traces(showlegend=False)
fig.add_traces(
px.scatter_mapbox(
df, lat="Lat", lon="Lon", color="AQI_Bucket", hover_data=["City"]
).data
)
fig.update_layout(
mapbox=dict(
style="carto-positron",
zoom=3,
center=dict(lat=df["Lat"].mean(), lon=df["Lon"].mean()),
)
)