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zephyrzilla/olympics-gdp-rio.py

Created August 23, 2016 22:28
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Python code to visualize ranking of countries based on their performance in Rio Olympics 2016 and their corresponding GDP
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olympics-gdp-rio.py
from matplotlib import pyplot as plt
import operator
# Country wise ranking at Rio Olympics 2016, Brazil
# Data scraped off Google: search term "rio olympics 2016"
# Preprocessed and converted to a Python dictionary
rank_olympics_dict = {'Canada': 20, 'Lithuania': 64, 'Ethiopia': 44, 'Argentina': 27, \
'Bahrain': 48, 'Slovenia': 45, 'Germany': 5, 'Spain': 14, 'Netherlands': 11, 'Jamaica': 16, 'United_States': 1, \
'Cote_dIvoire': 51, 'Australia': 10, 'India': 67, 'Azerbaijan': 39, 'Kenya': 15, 'Tajikistan': 54, \
'Turkey': 41, 'Mongolia': 67, 'France': 7, 'Norway': 74, 'Czech_Republic': 43, 'Singapore': 54, \
'South_Africa': 30, 'China': 3, 'Armenia': 42, 'Ukraine': 31, 'Finland': 78, 'Indonesia': 46, 'Sweden': 29, \
'Belarus': 40, 'Russia': 4, 'Bulgaria': 65, 'Romania': 47, 'Portugal': 78, 'Puerto_Rico': 54, 'Malaysia': 60, \
'Austria': 78, 'Vietnam': 48, 'Hungary': 12, 'Niger': 69, 'Brazil': 13, 'Dominican_Republic': 78, \
'Ireland': 62, 'Nigeria': 78, 'United_Arab_Emirates': 78, 'Iran': 25, 'Algeria': 62, 'Belgium': 35, 'Thailand': 35, \
'Georgia': 38, 'Denmark': 28, 'Poland': 33, 'Moldova': 78, 'Morocco': 78, 'Croatia': 17, 'Switzerland': 24, \
'Grenada': 69, 'Estonia': 78, 'Kosovo': 54, 'Uzbekistan': 21, 'Tunisia': 75, 'Colombia': 23, 'Burundi': 69, \
'Fiji': 54, 'Qatar': 69, 'Italy': 9, 'New_Zealand': 19, 'Venezuela': 65, 'South_Korea': 8, 'Israel': 77, \
'Jordan': 54, 'Kazakhstan': 22, 'Philippines': 69, 'Japan': 6, \
'Trinidad_and_Tobago': 78, 'Mexico': 61, 'Egypt': 75, 'Great_Britain': 2, 'Serbia': 32, 'Greece': 26}
# Country wise GDP (Gross Domestic Product) ranking
# Data obtained from World Bank (http://data.worldbank.org/data-catalog/GDP-ranking-table)
# Preprocessed and converted to a Python dictionary
rank_gdp_dict = {'Brazil': 9, 'Canada': 10, 'Qatar': 46, 'Fiji': 79, 'Italy': 8, \
'Dominican_Republic': 54, 'Kenya': 56, 'Serbia': 65, 'New_Zealand': 47, 'Lithuania': 63, \
'Mongolia': 73, 'France': 6, 'Cote_dIvoire': 67, 'Ethiopia': 55, 'Georgia': 72, 'Trinidad_and_Tobago': 68, \
'Ireland': 37, 'Grenada': 81, 'Nigeria': 20, 'Norway': 28, 'Argentina': 24, 'South_Korea': 11, 'Bahrain': 69, \
'Uzbekistan': 57, 'Israel': 33, 'Australia': 13, 'Iran': 26, 'Algeria': 48, 'Singapore': 35, 'South_Africa': 36, \
'Venezuela': 43, 'Jordan': 64, 'Austria': 27, 'Slovenia': 62, 'Czech_Republic': 44, 'China': 2, 'Armenia': 74, \
'Belgium': 23, 'Germany': 4, 'Philippines': 31, 'Poland': 22, 'Spain': 12, 'Ukraine': 53, 'Hungary': 49, \
'Netherlands': 17, 'Denmark': 34, 'Turkey': 18, 'Indonesia': 16, 'Moldova': 78, 'Morocco': 51, 'Sweden': 21, \
'United_Arab_Emirates': 30, 'Croatia': 58, 'Finland': 39, 'Thailand': 25, 'Switzerland': 19, 'Belarus': 60, \
'Russia': 14, 'Bulgaria': 59, 'Jamaica': 71, 'Romania': 45, 'Estonia': 70, 'Portugal': 40, 'Mexico': 15, \
'Egypt': 29, 'Kazakhstan': 50, 'Great_Britain': 5, 'Kosovo': 76, 'India': 7, 'Azerbaijan': 66, 'Puerto_Rico': 52, \
'Malaysia': 32, 'United_States': 1, 'Vietnam': 41, 'Tunisia': 61, 'Colombia': 38, 'Greece': 42, 'Burundi': 80, \
'Japan': 3, 'Niger': 75, 'Tajikistan': 77}
sorted_rank_olympics_dict = sorted(rank_olympics_dict.items(), key = operator.itemgetter(1))
olympics_country = [_[0] for _ in sorted_rank_olympics_dict]
olympics_rank = [rank_olympics_dict[_] for _ in olympics_country]
gdp_rank = [rank_gdp_dict[_] for _ in olympics_country]
plt.title("Combined Statistics", fontweight = 'bold', fontsize = 18)
plt.ylabel("Ranking", fontweight='bold')
plt.xticks(range(len(olympics_country)), olympics_country, size = 'small', rotation = 'vertical')
plt.plot(range(len(olympics_country)), olympics_rank, label = "Olympics rank")
plt.plot(range(len(olympics_country)), gdp_rank, label = "GDP rank")
plt.legend(loc = 'upper left')
plt.show()
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