import gmaps
import gmaps.datasets
gmaps.configure(api_key="AI...") # Your Google API key

# load a Numpy array of (latitude, longitude) pairs
locations = gmaps.datasets.load_dataset("taxi_rides")

fig = gmaps.figure()
fig.add_layer(gmaps.heatmap_layer(locations))
fig

from matplotlib.cm import viridis
from matplotlib.colors import to_hex

import gmaps
import gmaps.datasets
import gmaps.geojson_geometries

gmaps.configure(api_key="AI...") # Your Google API key

countries_geojson = gmaps.geojson_geometries.load_geometry('countries') # Load GeoJSON of countries

rows = gmaps.datasets.load_dataset('gini') # 'rows' is a list of tuples
country2gini = dict(rows) # dictionary mapping 'country' -> gini coefficient
min_gini = min(country2gini.values())
max_gini = max(country2gini.values())
gini_range = max_gini - min_gini

def calculate_color(gini):
    """
    Convert the GINI coefficient to a color
    """
    # make gini a number between 0 and 1
    normalized_gini = (gini - min_gini) / gini_range

    # invert gini so that high inequality gives dark color
    inverse_gini = 1.0 - normalized_gini

    # transform the gini coefficient to a matplotlib color
    mpl_color = viridis(inverse_gini)

    # transform from a matplotlib color to a valid CSS color
    gmaps_color = to_hex(mpl_color, keep_alpha=False)

    return gmaps_color

# Calculate a color for each GeoJSON feature
colors = []
for feature in countries_geojson['features']:
    country_name = feature['properties']['name']
    try:
        gini = country2gini[country_name]
        color = calculate_color(gini)
    except KeyError:
        # no GINI for that country: return default color
        color = (0, 0, 0, 0.3)
    colors.append(color)

fig = gmaps.figure()
gini_layer = gmaps.geojson_layer(
    countries_geojson,
    fill_color=colors,
    stroke_color=colors,
    fill_opacity=0.8)
fig.add_layer(gini_layer)
fig

import gmaps
import gmaps.datasets
gmaps.configure(api_key="AI...") # Your Google API key

df = gmaps.datasets.load_dataset_as_df('starbucks_kfc_uk')

starbucks_df = df[df['chain_name'] == 'starbucks']
starbucks_df = starbucks_df[['latitude', 'longitude']]

starbucks_layer = gmaps.symbol_layer(
starbucks_df, fill_color="green", stroke_color="green", scale=2
)
fig = gmaps.figure()
fig.add_layer(starbucks_layer)
fig

gmaps.configure(api_key="AI...")