How to use zipcodes to create map plot in python

Question

I have a simple dataframe like

colC   zipcode    count
val1   71023      1
val2   75454      3
val3   77034      2
val2   78223      3
val2   91791      4

these are all US zipcodes. I want to plot the zipcodes and the counts of values in colC on a map. For instance, zipcode 75454 has val2 in colC so it must have a different color than zipcode 71023 which has val1 in colC

Additionally I want to create a heatmap where the count column denotes the intensity of the heatmap across the map.

I went over some documentation for geopandas but looks like i have to convert the zipcodes to either some shape files or geojson in order to define the boundaries. I am not able to figure this step out.

Is geopandas the best tool to achieve this?

Any help is much appreciated

UPDATE

I was able to make some progress as

import pandas as pd
import pandas_bokeh
import matplotlib.pyplot as plt
import pgeocode
import geopandas as gpd
from shapely.geometry import Point
from geopandas import GeoDataFrame
pandas_bokeh.output_notebook()

nomi = pgeocode.Nominatim('us')

edf = pd.read_csv('myFile.tsv', sep='\t',header=None, index_col=False ,names=['colC','zipcode','count'])
edf['Latitude'] = (nomi.query_postal_code(edf['zipcode'].tolist()).latitude)
edf['Longitude'] = (nomi.query_postal_code(edf['zipcode'].tolist()).longitude)

geometry = [Point(xy) for xy in zip(edf['Longitude'], edf['Latitude'])]
gdf = GeoDataFrame(edf, geometry=geometry)

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
gdf.plot(ax=world.plot(figsize=(10, 6)), marker='o', color='red', markersize=15);
plt.savefig('world.jpg')

however, this gives me a map plot of the entire world. how can i reduce it to just show me the US as thats where all of my zipcodes are from?

Answer 1

turns out plotly is best suited for me

import pandas as pd
import pandas_bokeh
import matplotlib.pyplot as plt
import pgeocode
import geopandas as gpd
from shapely.geometry import Point
from geopandas import GeoDataFrame
pandas_bokeh.output_notebook()
import plotly.graph_objects as go

nomi = pgeocode.Nominatim('us')

edf = pd.read_csv('myFile.tsv', sep='\t',header=None, index_col=False ,names=['colC','zipcode','count'])
edf['Latitude'] = (nomi.query_postal_code(edf['zipcode'].tolist()).latitude)
edf['Longitude'] = (nomi.query_postal_code(edf['zipcode'].tolist()).longitude)

fig = go.Figure(data=go.Scattergeo(
        lon = edf['Longitude'],
        lat = edf['Latitude'],
        text = edf['colC'],
        mode = 'markers',
        marker_color = edf['count'],
        ))

fig.update_layout(
        title = 'colC Distribution',
        geo_scope='usa',
    )
fig.show()