Fill Countries in Python Basemap

I googled many Python methods to color countries in a world map. But most of them require additional Python packages, like cartopy, and shapelib. Because I am using Anaconda (Python 3.5) on Windows, it's not easy to install these packages. Actually, I tried many times and failed. Finally, I found a great solution from Ramiro Gómez. I listed my workable code based on Ramiro Gómez as well as the created figures.

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

from geonamescache import GeonamesCache
from matplotlib.patches import Polygon
from matplotlib.collections import PatchCollection
from mpl_toolkits.basemap import Basemap

filename = 'bookmakers_nations.csv'
shapefile = 'ne_10m_admin_0_countries_lakes\\ne_10m_admin_0_countries_lakes'
num_colors = 27
year = '2012'
cols = ['Country Name', 'Country Code', year]
title = 'The_Distribution_of_Soccer_Bookmakers'
imgfile = 'img/{}.pdf'.format(title)

# descripton = '''
# Forest area is land under natural or planted stands of trees of at least 5 meters in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations
# and agroforestry systems) and trees in urban parks and gardens. Countries without data are shown in grey. Data: World Bank - worldbank.org • Author: Ramiro Gómez - ramiro.org'''.strip()

gc = GeonamesCache()
iso3_codes = list(gc.get_dataset_by_key(gc.get_countries(), 'iso3').keys())


# df = pd.read_csv(filename, skiprows=4, usecols=cols)
df = pd.read_csv(filename, usecols=cols)
df.set_index('Country Code', inplace=True)
df = df.ix[iso3_codes].dropna() # Filter out non-countries and missing values.


values = df[year]
# cm = plt.get_cmap('Greens')
cm = plt.get_cmap('Oranges')
scheme = [cm(i / num_colors) for i in range(num_colors)]
bins = np.linspace(values.min(), values.max(), num_colors)
df['bin'] = np.digitize(values, bins) - 1
df.sort_values('bin', ascending=False).head(10)

# mpl.style.use('map')
# mpl.style.use('ggplot')
fig = plt.figure(figsize=(22, 12))

ax = fig.add_subplot(111, axisbg='w', frame_on=False)
fig.suptitle('The Distribution of Soccer Bookmakers', fontsize=30, y=.95)

# m = Basemap(lon_0=0, projection='robin')
m = Basemap(lon_0=0, projection='mill')
# m = Basemap(lon_0=0, projection='merc')
m.drawmapboundary(color='w')

m.readshapefile(shapefile, 'units', color='#444444', linewidth=.2)
for info, shape in zip(m.units_info, m.units):
    iso3 = info['ADM0_A3']
    if iso3 not in df.index:
        color = '#dddddd'
    else:
        color = scheme[df.ix[iso3]['bin']]

    patches = [Polygon(np.array(shape), True)]
    pc = PatchCollection(patches)
    pc.set_facecolor(color)
    ax.add_collection(pc)

# Cover up Antarctica so legend can be placed over it.
ax.axhspan(0, 1000 * 1800 * 4, facecolor='w', edgecolor='w', zorder=2)

# Draw color legend.
ax_legend = fig.add_axes([0.35, 0.24, 0.3, 0.03], zorder=3)
cmap = mpl.colors.ListedColormap(scheme)
cb = mpl.colorbar.ColorbarBase(ax_legend, cmap=cmap, ticks=bins, boundaries=bins, orientation='horizontal')
cb.ax.set_xticklabels([str(int(round(i, 1))) for i in bins])

# Set the map footer.
# plt.annotate(descripton, xy=(-.8, -3.2), size=14, xycoords='axes fraction')

plt.savefig(imgfile, bbox_inches='tight', pad_inches=.2)