Global Footprint of Survival8 (Nov 2022)

Download Code and Data

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: 

Tags: Data Visualization,Technology,Investment,

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
    

In [ ]: