def conlanger_map():
foo = dict([(country.iso.lower(), country.count) for country in country_most_common()])
custom_style = Style(
background='#fff',
plot_background='#fff',
foreground='#ffffff',
foreground_light='#ffffff',
foreground_dark='#ffffff',
opacity='.6',
opacity_hover='.9',
transition='400ms ease-in',
colors=('#527C3A', '#E8537A', '#E95355', '#E87653', '#E89B53')
)
chart = Worldmap(style=custom_style)
chart.no_prefix = True
chart.disable_xml_declaration = True
chart.show_legend = False
chart.add('Conlangers', foo)
return chart.render()
import pygal
import pygal_maps_world
#import base64
from pygal.maps.world import World
#from ipywidgets import HTML
from flask import Flask
from flask import request
print("1")
if request.method == 'POST':
print("1")
countryCode = request.form['code']
else:
print('No country code entered!')
wm = World()
wm.force_uri_protocol = 'http'
wm.title = "Women Political leader distribution across countries"
wm.add('Leaders', {'ca': 4, 'mx': 1, 'us': 6})
#wm.chart.render()
wm.render_to_file('map.svg')
#b64 = base64.b64encode(wm.render())
#src = 'data:image/svg+xml;charset=utf-8;base64,'+b64
#HTML('<embed src={}></embed>'.format(src))
filename = 'population_data.json'
with open(filename) as f:
pop_data = json.load(f)
cc_populations = {}
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
cc1, cc2, cc3 = {}, {}, {}
for cc, popu in cc_populations.items():
if popu < 10000000:
cc1[cc] = popu
elif popu < 1000000000:
cc2[cc] = popu
else:
cc3[cc] = popu
wm_style = RotateStyle('#336699', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'Population in world'
wm.add('< 10m', cc1)
wm.add('10m - 1b', cc2)
wm.add('> 1b', cc3)
wm.render_to_file('world_populations_new.svg')
from pygal.maps.world import World
wm = World()
wm.title = 'Populations of Countries in North America'
wm.add("North America", {"ca":3412600, 'us': 309349000, 'mx': 113423000})
wm.render_to_file("C:\\Users\\Asymmetry\\Desktop\\na_populations.svg")
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# Podzielenie państw na trzy grupy według liczebności populacji.
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# Wyświetlenie liczby państw w każdej z trzech grup.
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_uri_protocol = 'http'
wm.title = 'Populacja na świecie w 2010 roku (dane dla poszczególnych państw)'
wm.add('0 - 10 mln', cc_pops_1)
wm.add('10 mln - 1 mld', cc_pops_2)
wm.add('>1 mld', cc_pops_3)
wm.render_to_file('world_population.svg')
cc_gdp = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == '2010':
country_name = gdp_dict['Country Name']
gdp = int(float(gdp_dict['Value']))
code = get_country_code(country_name)
if code:
cc_gdp[code] = gdp
# Group the countries into 3 gdp levels.
cc_gdp_1, cc_gdp_2, cc_gdp_3 = {}, {}, {}
for cc, gdp in cc_gdp.items():
if gdp < 1000000000:
cc_gdp_1[cc] = gdp
elif gdp < 10000000000000:
cc_gdp_2[cc] = gdp
else:
cc_gdp_3[cc] = gdp
# Plot and output to file
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_uri_protocol = 'http'
wm.title = 'World GDP in 2010, by Country'
wm.add('0-1bn', cc_gdp_1)
wm.add('1bn-1tr', cc_gdp_2)
wm.add('>1tr', cc_gdp_3)
wm.render_to_file('world_gdp.svg')
# Open file and extract data
filename = "life_female.csv"
with open(filename) as f:
lifedata = csv.reader(f)
# Get to the proper column line on the csv
for n in range(1, 6):
next(lifedata)
# Make a new dictionary. Translate the country names
# to country_codes, and put them in as keys and the
# life expectancy data as values (taken from the 2014 data set)
lrates = {}
for rowdata in list(lifedata):
ccode = get_country_code(rowdata[0])
try:
lrates[ccode] = float(rowdata[58])
except ValueError:
continue
# Plot new graph and output to file
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_uri_protocol = 'http'
wm.title = 'Female Life Expectancy in 2014, by Country'
wm.add('Years', lrates)
wm.render_to_file('female_mortality.svg')
for gdp_dict in gdp_data:
if gdp_dict['Year'] == 2016:
country_name = gdp_dict['Country Name']
value = int(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
values[code] = value
# Value levels.
cc_value_1, cc_value_2, cc_value_3 = {}, {}, {}
for c, v in values.items():
if v < 100000000000:
cc_value_1[c] = v
elif v < 1000000000000:
cc_value_2[c] = v
else:
cc_value_3[c] = v
# Prints numerical value of the three categories of GDP.
print(len(cc_value_1), len(cc_value_2), len(cc_value_3))
wm = World()
wm.title = "World Gross Domestic Product in 2016, by country"
# Keeps value from being scientific notation.
wm.value_formatter = lambda x: "{:,}".format(x)
wm.add('< 100B', cc_value_1)
wm.add('100B - 1T', cc_value_2)
wm.add('> 1T', cc_value_3)
wm.render_to_file('world_gdp_2016.svg')
print(country_name + ': missing_data')
else:
lf_data[country_name] = lf_value
cc_lf = {}
for country, value in lf_data.items():
code = get_country_code(country)
if code:
cc_lf[code] = value
else:
print('ERROR - ' + country_name)
cc_lf_1, cc_lf_2, cc_lf_3 = {}, {}, {}
for key, value in cc_lf.items():
if value < 5000000:
cc_lf_1[key] = value
elif value < 50000000:
cc_lf_2[key] = value
else:
cc_lf_3[key] = value
print(len(cc_lf_1), len(cc_lf_2), len(cc_lf_3))
wm_style = RotateStyle('#556677', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'Labor force by Country (year: 2010)'
wm.add('0-5m', cc_lf_1)
wm.add('5m-50m', cc_lf_2)
wm.add('>50m', cc_lf_3)
wm.render_to_file('world_labor_force.svg')
from pygal.maps.world import World
wm = World()
wm.force_uri_protocol = 'http'
wm.title = 'Wielkość populacji w krajach Ameryki Północnej'
wm.add('Ameryka Północna', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_populations.svg')
cc_population={}
for pop_dict in pop_date:#将每个字典存储在pop_dict中
if pop_dict['Year']=='2010': #for循环是一次操作一个值
country_name=pop_dict['Country Name']
population=int(float(pop_dict['Value']))
code=get_country_code(country_name)
if code:
cc_population[code]=population #字典的赋值
else:
print("ERROR"+":"+country_name)
for cc,pop in cc_population.items():
if pop<100000000:
cc_pop1[cc]=pop
elif pop<1000000000:
cc_pop2[cc]=pop
else:
cc_pop3[cc]=pop
wm_style=RotateStyle('#336699',base_style=LightColorizedStyle)
wm=World(style=wm_style)
wm.title="World Population in 2010,by Country"
wm.add('0-10m',cc_pop1)
wm.add('10m-1bn',cc_pop2)
wm.add('>1bn',cc_pop3)
wm.render_to_file('word_population.svg')
import json
from country_codes import get_country_code
from pygal.maps.world import World
from pygal.style import RotateStyle
# Wczytanie danych i umieszczenie ich na liście.
filename = 'gdp_json.json'
with open(filename) as f:
pop_data = json.load(f)
# Utworzenie słownika danych dotyczących populacji.
cc_populations = {}
for pop_dict in pop_data:
if str(pop_dict['Year']) == '2010':
country_name = pop_dict['Country Name']
population = pop_dict['Value']
code = get_country_code(country_name)
if code:
cc_populations[code] = float(population)
print(cc_populations)
wm_style = RotateStyle('#336699')
wm = World(style=wm_style)
wm.force_uri_protocol = 'http'
wm.value_formatter = lambda x: "%.2f" % x
wm.add('GDP', cc_populations)
wm.render_to_file('world_gdp.svg')
from pygal.maps.world import World
wm = World()
wm.force_uri_protocol = 'http'
wm.title = 'North, Central, and South America'
wm.add('North America', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add('South America', [
'ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy', 'pe', 'py', 'sr', 'uy',
've'
])
wm.render_to_file('americas.svg')
import pygal
from pygal.maps.world import World
wm = World()
wm.title = "North, Central and South America"
wm.add("North America", ['ca', 'mx', 'us'])
wm.add("Central America", ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add("South America", ['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf',f 'gy', 'pe', 'py', 'sr', 'uy', 've'])
wm.render_to_file('americas.svg')
gdp = int(float(pop_dict['Value']))
code = get_country_code(country)
if code:
cc_gdps[code] = gdp
else:
print('Error - ', country )
# Group the countries into 3 gdp levels.
# Less than 5 billion, less than 50 billion, >= 50 billion.
# Also, convert to billions for displaying values.
cc_gdps_1, cc_gdps_2, cc_gdps_3 = {}, {}, {}
for cc, gdp in cc_gdps.items():
if gdp < 5000000000:
cc_gdps_1[cc] = round(gdp / 1000000000)
elif gdp < 50000000000:
cc_gdps_2[cc] = round(gdp / 1000000000)
else:
cc_gdps_3[cc] = round(gdp / 1000000000)
# See how many countries are in each level.
print(len(cc_gdps_1), len(cc_gdps_2), len(cc_gdps_3))
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.title = 'Global GDP in 2014, by Country (in billions USD)'
wm.add('0-5bn', cc_gdps_1)
wm.add('5bn-50bn', cc_gdps_2)
wm.add('>50bn', cc_gdps_3)
wm.render_to_file('global_gdp.svg')
def get_country_code(country_name):
for code, name in COUNTRIES.items():
if name == country_name:
return code
return None
gdps = r.json()
gdps_lys = []
for gdp in gdps:
if len(gdps_lys) == 0:
gdps_lys.append({'name': gdp['Country Name'], 'year': gdp['Year'],'value': gdp['Value']})
else:
if gdps_lys[-1]['name'] == gdp['Country Name']:
gdps_lys[-1]['year'] = gdp['Year']
gdps_lys[-1]['value'] = gdp['Value']
else:
gdps_lys.append({'name': gdp['Country Name'], 'year': gdp['Year'],'value': gdp['Value']})
countries = {}
ans = 0
for gdp in gdps_lys:
code = get_country_code(gdp['name'])
if code:
countries[code] = gdp['value']*1000000000
else:
print(gdp['name'])
print (ans)
wm = World()
wm.title = 'Gpd of counries'
wm.add('',countries)
wm.render_to_file('Gdps.svg')
from pygal.maps.world import World
wm = World()
wm.title = 'Poppulations of Countries in North America'
wm.add('North America', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_populations.svg')
from pygal.maps.world import World
wm = World()
wm.title = 'North, Central, and South America'
wm.add('North America', ['ca', 'mx', 'us'])
wm.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add('South America', ['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf',
'gy', 'pe', 'py', 'sr', 'uy', 've'])
wm.render_to_file('americas.svg')
# Group the countries into 3 IMF-credit levels;
# this time, we need a separate "n/a" level for countries
# where values were unavailable.
imf_credit_n, imf_credit_1, imf_credit_2, imf_credit_3 = {}, {}, {}, {}
for cc, imf_credit in cc_imfcredits.items():
# none_to_zero needed because pygal skips None's, when plotting
if imf_credit == None:
imf_credit_n[cc] = none_to_zero(imf_credit)
elif imf_credit < 1000000000: # x < 1B
imf_credit_1[cc] = imf_credit
elif imf_credit < 10000000000: # 1B <= x < 10B
imf_credit_2[cc] = imf_credit
else: # 10B <= x
imf_credit_3[cc] = imf_credit
# See how many countries are in each level.
print(len(imf_credit_n), len(imf_credit_1), len(imf_credit_2),
len(imf_credit_3))
# Plot the data.
wm_style = RS('#336699')
wm = World(style=wm_style)
wm.title = 'Use of IMF Credit in 2010 (in USD), by Country'
wm.add('n/a', imf_credit_n)
wm.add('0-1bn', imf_credit_1)
wm.add('1bn-10bn', imf_credit_2)
wm.add('>10bn', imf_credit_3)
wm.render_to_file('use_of_imf_credit.svg')
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# Podzielenie państw na trzy grupy według liczebności populacji.
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# Wyświetlenie liczby państw w każdej z trzech grup.
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_uri_protocol = 'http'
wm.title = 'Populacja na świecie w 2010 roku (dane dla poszczególnych państw)'
wm.add('0 - 10 mln', cc_pops_1)
wm.add('10 mln - 1 mld', cc_pops_2)
wm.add('>1 mld', cc_pops_3)
wm.render_to_file('world_population.svg')
from pygal.maps.world import World
wm = World()
wm.force_uri_protocol = 'http'
wm.title = ('Wielkość populacji w krajach Ameryki Północnej')
wm.add('Ameryka Północna', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_populations.svg')
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# Group the countries into 3 population levels
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# See how many countries are in each level
# print(len(cc_pops_1),len(cc_pops_2),len(cc_pops_3))
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.title = 'World Population in 2010, by Country'
wm.add('0-10m', cc_pops_1)
wm.add('10-1bn', cc_pops_2)
wm.add('>1n', cc_pops_3)
wm.add('2010', cc_populations)
wm.render_to_file('world_population.svg')
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# Group the countries into 3 poplulation levels.
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop <10000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# See how many countries are in each level.
print(len(cc_pops_1), len(cc_pops_2), len(cc_pops_3))
wm = World()
wm_style =RotateStyle('#336699')
wm = World(style=wm_style)
wm.title = 'World Population in 2010, by Country'
wm.add('0-10m', cc_pops_1)
wm.add('10m-1b', cc_pops_2)
wm.add('>1b', cc_pops_3)
wm.render_to_file('world_population.svg')
#print(country_name+": "+str(population))
if code:
#print(code+": "+str(population))
cc_poplations[code] = population
else:
print('ERROR - ' + country_name)
#Agrupa países em três níveis populacionais
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_poplations.items():
if pop < 10000000:
cc_pops_1[cc] = pop
elif pop < 1000000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
# Vê quantos países estão em cada nível
print(len(cc_pops_1), len(cc_pops_1), len(cc_pops_3))
wm_style = RotateStyle('#336699')
wm = World(style=wm_style)
wm.title = 'World Population in 2010, by Country'
#wm.add('2010', cc_poplations)
wm.add('0-10m', cc_pops_1)
wm.add('10m-1bn', cc_pops_2)
wm.add('>1bn', cc_pops_3)
wm.render_to_file('world_population.svg')
from pygal.maps.world import World
wm = World()
wm.force_uri_protocol = 'http'
wm.title = 'Populations of Countries in North America'
wm.add('North America', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_populations.svg')
# if code is not None, add the data to the dictionary
# print(countryName + ": " + '{:,}'.format(gdpValue))
if code:
ccGdp[code] = gdpValue
# to distinguish the countries from each other, we will have to put them in
# separate dictionaries to plot them separately
ccGdp1, ccGdp2, ccGdp3 = {}, {}, {}
# showing results in milliards is IMO better to see the real difference
# between the countries
for code, val in ccGdp.items():
if val < 500000000000:
ccGdp1[code] = round(val / 1000000000)
elif val < 5000000000000:
ccGdp2[code] = round(val / 1000000000)
else:
ccGdp3[code] = round(val / 1000000000)
'''print("over 5,000 mld countries: " + str(len(ccGdp3)))
print("500 - 5,000 mld countries: " + str(len(ccGdp2)))
print("below 500 mld countries: " + str(len(ccGdp1)))'''
# Create a map and fill it with data
wm = World(style=CleanStyle)
wm.title = "Countries GDP, 2016"
wm.add('over 5,000 mld', ccGdp3)
wm.add("500 - 5,000 mld", ccGdp2)
wm.add("below 500 mld", ccGdp1)
wm.render_to_file('world_gdp.svg')
# json.load() converts the data into a format Python can work with
pop_data = json.load(f)
# build a dictionary of population data
cc_populations = {}
for pop_dict in pop_data:
if pop_dict['Year'] == '2010':
country_name = pop_dict['Country Name']
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# group the countries into 3 population levels
cc_pops_1, cc_pops_2, cc_pops_3 = {}, {}, {}
for cc, pop in cc_populations.items():
if pop < 10000000:
cc_pops_1[cc] = pop
elif pop < 10000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
wm = World()
wm.force_uri_protocol = 'http'
wm.title = 'World Population in 2010, by Country'
wm.add('0-10,', cc_pops_1)
wm.add('10m-1bn', cc_pops_2)
wm.add('>1bn', cc_pops_3)
wm.render_to_file('world_population.svg')
gdp_populations = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == 2016:
country_name = gdp_dict['Country Name']
gdp_value = float(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
gdp_populations[code] = gdp_value
# Group the countries into 3 population levels.
gdp_pops_1, gdp_pops_2, gdp_pops_3 = {}, {}, {}
for cc, gdp in gdp_populations.items():
if gdp < 100000000000:
gdp_pops_1[cc] = gdp
elif gdp < 10000000000000:
gdp_pops_2[cc] = gdp
else:
gdp_pops_3[cc] = gdp
# See how many countries are in each level.
print(len(gdp_pops_1), len(gdp_pops_2), len(gdp_pops_3))
wm_style = RotateStyle('#FF0000', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'World GDP in 2016, by Country'
wm.add('0-100bn', gdp_pops_1)
wm.add('100bn-10tn', gdp_pops_2)
wm.add('>10tn', gdp_pops_3)
wm.render_to_file('world_gdp.svg')
#
from pygal.maps.world import World
wm = World()
wm.title = 'North, Central, and South America'
wm.add('North America', ['ca', 'mx', 'us'])
wm.add('Central America', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add('South America', ['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy',
'pe', 'py', 'sr', 'uy', 've'])
wm.render_to_file('americas.svg')
from pygal.maps.world import World
wm = World()
wm.force_uri_protocol = 'http'
wm.title = 'Ameryka Północna, Środkowa i Południowa'
wm.add('Ameryka Północna', ['ca', 'mx', 'us'])
wm.add('Ameryka Środkowa', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add(
'Ameryka Południowa',
['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf', 'gy', 'pe', 'sr', 'uy', 've'])
wm.render_to_file('americas.svg')
