Availability of 'cartopy' package on Anaconda.org
Installation
(base) $ conda list cartopy # packages in environment at /home/ashish/anaconda3: # # Name Version Build Channel cartopy 0.19.0.post1 py39h3b23250_0 conda-forge (base) $ pip show cartopy Name: Cartopy Version: 0.19.0.post1 Summary: A cartographic python library with Matplotlib support for visualisation Home-page: https://scitools.org.uk/cartopy/docs/latest/ Author: UK Met Office Author-email: License: LGPLv3 Location: /home/ashish/anaconda3/lib/python3.9/site-packages Requires: numpy, shapely, pyshp Required-by:
In [1]:
import pandas as pd
In [2]:
df1 = pd.read_csv('report_20221108.csv')
In [3]:
df1
Out[3]:
| Country | Estimated earnings (USD) | Page views | Page RPM (USD) | Impressions | Impression RPM (USD) | Active View Viewable | Clicks | |
|---|---|---|---|---|---|---|---|---|
| 0 | India | 85.56 | 193607 | 0.44 | 513708 | 0.17 | 0.4542 | 1577 |
| 1 | United States | 30.54 | 14041 | 2.17 | 32393 | 0.94 | 0.4668 | 146 |
| 2 | Kenya | 6.34 | 3258 | 1.95 | 5454 | 1.16 | 0.4176 | 136 |
| 3 | United Arab Emirates | 4.41 | 3086 | 1.43 | 7728 | 0.57 | 0.4372 | 44 |
| 4 | United Kingdom | 2.33 | 1833 | 1.27 | 3917 | 0.59 | 0.4513 | 19 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 174 | Mauritania | 0.00 | 1 | 0.00 | 3 | 0.00 | 0.0000 | 0 |
| 175 | Palau | 0.00 | 1 | 0.00 | 2 | 0.00 | 0.5000 | 0 |
| 176 | St. Kitts & Nevis | 0.00 | 1 | 0.36 | 4 | 0.09 | 0.7500 | 0 |
| 177 | Turks & Caicos Islands | 0.00 | 1 | 0.02 | 3 | 0.01 | 0.0000 | 0 |
| 178 | Vanuatu | 0.00 | 1 | 0.00 | 2 | 0.00 | 0.5000 | 0 |
179 rows × 8 columns
In [5]:
top50 = df1['Country'].values.tolist()[0:50]
In [12]:
cartopy_country_lookup = {
"Russia": "Russian Federation"
}
In [13]:
top50 = [cartopy_country_lookup[x] if x in cartopy_country_lookup else x for x in top50]
In [14]:
countries = {k:v for k, v in zip(top50, list(range(100, 50, -1)))}
In [15]:
import cartopy
import cartopy.crs as ccrs
import cartopy.io.shapereader as shpreader
from cartopy.feature import LAND
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from datetime import datetime
In [16]:
# Setting the color to shade the countries.
cmap = mpl.cm.Greys # Other color: mpl.cm.Blues
max_users = float(max(countries.values()))
shapename = 'admin_0_countries'
countries_shp = shpreader.natural_earth(resolution='110m', category='cultural', name=shapename)
ax = plt.axes(projection=ccrs.Robinson())
for country in shpreader.Reader(countries_shp).records():
name = country.attributes['NAME_LONG']
if name in countries:
num_users = countries[name]
else:
num_users = 1
# ax.add_geometries(country.geometry, ccrs.PlateCarree(), facecolor=cmap(num_users/max_users, 1))
# TypeError: 'Polygon' object is not iterable
# name: Fiji, type(country.geometry): [class 'shapely.geometry.multipolygon.MultiPolygon']
# name: Tanzania, type(country.geometry): [class 'shapely.geometry.polygon.Polygon']
try:
ax.add_geometries(country.geometry, ccrs.PlateCarree(), facecolor=cmap(num_users/max_users, 1))
except Exception as e:
#print(e)
import shapely.wkt as wkt
from shapely.geometry import MultiPolygon
list_str_polygons = [str(country.geometry)]
c = MultiPolygon(map(wkt.loads, list_str_polygons))
ax.add_geometries(c, ccrs.PlateCarree(), facecolor=cmap(num_users/max_users, 1))
ax.add_feature(cartopy.feature.OCEAN, color='lightblue')
#Save the image as a file.
plt.savefig('Audience_' + str(datetime.now()).replace(':', '_') + '.png', transparent=True, dpi=900)
print("Done")
Done
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