def world_map_test():
map_style = RotateStyle('#44ff77')
map = World(style=map_style,
value_formatter=lambda x: '{}'.format(x) + u'国家')
map.title = 'world map scope'
country_list = []
chinese_name = {
'United States': u'大美利坚',
'United Kingdom': u'大英帝国',
'France': u'法兰西',
'Italy': u'意大利',
'Japan': u'东瀛',
'Germany': u'德意志',
'Canada': u'加拿大'
}
detail_dict = {}
print(type(chinese_name))
for code, name in COUNTRIES.items():
if name in ('United States', 'United Kingdom', 'France', 'Italy',
'Japan', 'Germany', 'Canada'):
country_list.append(code)
detail_dict[code] = chinese_name[name]
print(code)
#if name == 'Russian Federation':
# unique=code
for x in detail_dict.items():
map.add(u'G7国家', )
#map.add(u'G8国家',country_list+[unique])
map.render_to_file('/Users/qmp/Desktop/map1.svg')
def world_population():
filename = "population_data.json"
with open(filename) as f:
pop_data = json.load(f)
cc_pop = {}
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_pop[code] = population
#grouping countries into 3 pop levels
cc_pops1, cc_pops2, cc_pops3 = {}, {}, {}
for cc, pop in cc_pop.items():
if pop < 10000000:
cc_pops1[cc] = pop
elif pop < 1000000000:
cc_pops2[cc] = pop
else:
cc_pops3[cc] = pop
print(len(cc_pops1), len(cc_pops2), len(cc_pops3))
#building a world map
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_pops1)
wm.add('10m-1bn', cc_pops2)
wm.add('>1bn', cc_pops3)
wm.render_to_file('world_population.svg')
def main():
# Get dict of code to population at year 2010 for plotting
world_pop_2010 = build_pop_dict("population_data.json", 2010)
# Divide dict into three smaller dicts corresponding to population levels (low, medium, high)
low_pop_2010, medium_pop_2010, high_pop_2010 = {}, {}, {}
for code, pop in world_pop_2010.items():
if pop < 10000000:
low_pop_2010[code] = pop
elif pop < 1000000000:
medium_pop_2010[code] = pop
else:
high_pop_2010[code] = pop
# Create pygal maps plotting object
map_style = ps.DarkSolarizedStyle
wm = World(style=map_style)
wm.title = 'World populations'
# Add the shading for countries in each population level
wm.add('0-10m', low_pop_2010)
wm.add('10m-1b', medium_pop_2010)
wm.add('>1b', high_pop_2010)
# Render graph to svg file
wm.render_to_file('world.svg')
def one_country_map():
'''一国地图'''
wm_o = World()
wm_o.force_uri_protocol = 'http'
wm_o.title = '世界地图(中国)'
wm_o.add('China', {'中国': 'cn'})
wm_o.render_to_file('country_map.svg')
def world_country_map():
'''世界各国地图'''
wm_c = World()
wm_c.force_uri_protocol = 'http'
wm_c.title = '世界地图'
for code, name in COUNTRIES.items():
# print(name, code)
wm_c.add(name, code)
wm_c.add('Yemen', {'ye': 'Yemen'})
wm_c.render_to_file('world_map.svg')
def generate_worldmap(df_nation_counts):
"""displays a worldmap coded with the pygal-library in HTML format (interactive Chart)
Parameters: Nations Count Dataframe
Returns: HTML worldmap
"""
total_dict = df_nation_counts.set_index('Nation').to_dict('dict').get('Anzahl Gesuche')
dict_1_5000 = dict(filter(lambda elem: elem[1] <= 5000, total_dict.items()))
dict_5000_20000 = dict(filter(lambda elem: (elem[1] > 5000 and elem[1] <= 20000), total_dict.items()))
dict_20000_plus = dict(filter(lambda elem: (elem[1] > 20000), total_dict.items()))
base_html = """
<!DOCTYPE html>
<html>
<head>
<script type="text/javascript" src="https://kozea.github.io/pygal.js/2.0.x/pygal-tooltips.min.js"></script>
<script type="text/javascript" src="http://kozea.github.com/pygal.js/javascripts/pygal-tooltips.js"></script>
</head>
<body>
<figure>
{rendered_chart}
</figure>
</body>
</html>
"""
#styles
custom_style = Style(
colors=('#e4ed80', '#7dccbc', '#63192e'),
tooltip_font_size=8,
legend_font_size=7,
title_font_size=10,
background='#ffffff')
#define chart
worldmap_chart = World(style=custom_style)
worldmap_chart.title = 'Anzahl Asylgesuche nach Nation'
worldmap_chart.add('1-5000 Gesuche', dict_1_5000)
worldmap_chart.add('5000-20000 Gesuche', dict_5000_20000)
worldmap_chart.add('20000+ Gesuche', dict_20000_plus)
#render
rendered_chart = worldmap_chart.render(is_unicode=True)
plot_html = base_html.format(rendered_chart=rendered_chart)
return display(HTML(plot_html))
def csv_data_handle():
with open(
'/Users/qmp/Downloads/API_TX.VAL.TECH.MF.ZS_DS2_en_csv_v2/API_TX.VAL.TECH.MF.ZS_DS2_en_csv_v2.csv'
) as f:
reader = csv.reader(f)
#print(next(reader))
map_style = RotateStyle('#4455ee', base_style=LightStyle)
map = World()
info_dict = {}
for x in reader:
if len(x) == 62:
print(x[0], x[1], x[-2])
code = counrty_list_show(x[0])
info_dict[code] = float((x[-2])) if x[-2] != '' else None
map.title = 'High-technology exports (% of manufactured exports)'
map.add('', info_dict)
map.render_to_file('/Users/qmp/Desktop/High_Tech_map.svg')
def plot_global_sex_ratio():
below_20 = country_ratios.loc[country_ratios["Sex"] <= 0.20]
below_20 = below_20.reset_index()
below_40 = country_ratios.loc[country_ratios["Sex"] <= 0.40].loc[
country_ratios["Sex"] > 0.20]
below_40 = below_40.reset_index()
below_60 = country_ratios.loc[country_ratios["Sex"] > 0.40].loc[
country_ratios["Sex"] <= 0.60]
below_60 = below_60.reset_index()
below_80 = country_ratios.loc[country_ratios["Sex"] > 0.60].loc[
country_ratios["Sex"] <= 0.80]
below_80 = below_80.reset_index()
above_80 = country_ratios.loc[country_ratios["Sex"] > 0.80]
above_80 = above_80.reset_index()
worldmap_chart = World()
worldmap_chart.title = 'Countrywise Particpation Percentages of Females'
worldmap_chart.add("0%-20%", [
'af', 'dz', 'au', 'ar', 'am', 'be', 'bj', 'bz', 'cz', 'bw', 'bn', 'cl',
'dk', 'dj', 'eg', 'er', 'fi', 'gh', 'ht', 'in', 'ir', 'iq', 'sa', 'kw',
'ly', 'lr', 'lb', 'lu', 'my', 'ma', 'mw', 'mc', 'mr', 'ni', 'om', 'pk',
'py', 'ph', 'pt', 'pr', 'zw', 'de', 'rs', 'sh', 'sm', 'so', 'sd', 'ch',
'sz', 'sy', 'tz', 'tg', 'tn', 'tr', 'ae', 'ug', 'uy', 'vn', 'ye', 'mk',
'zm'
])
worldmap_chart.add('20%-40%', [
'al', 'ad', 'au', 'at', 'az', 'bi', 'bo', 'br', 'bh', 'bg', 'bf', 'cf',
'kh', 'ca', 'cg', 'td', 'cm', 'cd', 'co', 'cr', 'hr', 'cu', 'cy', 'cz',
'do', 'ec', 'sv', 'es', 'ee', 'et', 'ru', 'fr', 'de', 'ga', 'gm', 'gb',
'gw', 'ge', 'gq', 'gr', 'gn', 'gu', 'gy', 'cn', 'hn', 'hu', 'id', 'ie',
'is', 'il', 'it', 'jo', 'jp', 'kz', 'ke', 'kg', 'kr', 'la', 'lv', 'ls',
'li', 'lt', 'md', 'mv', 'mx', 'mn', 'mk', 'ml', 'mt', 'me', 'mz', 'mu',
'mm', 'na', 'nl', 'np', 'ng', 'ne', 'no', 'nz', 'pa', 'pe', 'ps', 'pg',
'pl', 'ro', 'za', 'rw', 'sc', 'sg', 'sl', 'si', 'rs', 'lk', 'sd', 'st',
'sr', 'sk', 'se', 'cz', 'th', 'tj', 'tw', 'ru', 'us', 'uz', 've', 'zw'
])
worldmap_chart.add('40%-60%', [
'ao', 'bt', 'by', 'cn', 'cv', 'jm', 'mg', 'kp', 'ru', 'tm', 'tl', 'ua',
'vn'
])
worldmap_chart.add('60%-80%', [])
worldmap_chart.add('80%-100%', [])
worldmap_chart.render_in_browser()
def result():
if request.method == 'POST' or request.method == 'GET':
engine = sqlite3.connect('csv_test10.db')
result = request.form['q']
query = qu_op(result)
b = query.split(' ')
yr = []
for i in b:
if 'year' in i:
yr.append(i[5:len(i)])
df = pd.read_sql_query(query, engine)
print(df)
if df.loc[0, 'graph'] == '2':
bar_chart = pygal.Bar(width=800,
height=600,
legend_at_bottom=True,
human_readable=True,
background='white',
title=df.columns.values[1].capitalize() +
' across months in ' + yr[0],
x_title='Months',
y_title=df.columns.values[1].capitalize())
for index, row in df.iterrows():
bar_chart.add(row[2], row[1])
final = bar_chart.render_data_uri()
#df = df.sort_values('sales',ascending=False)
elif df.loc[0, 'graph'] == '1':
country_dict = {}
wm = World(show_legend=False)
wm.title = df.columns.values[2].capitalize(
) + " across Top Countries in " + yr[0]
for i in range(len(df)):
for code, name in COUNTRIES.items():
if name.lower() == df.iloc[i, 1].lower():
country_dict[code] = df.iloc[i, 2]
wm.add("Country:", country_dict)
final = wm.render_data_uri()
return render_template('result.html', result=final)
def json_data_handle():
gdp_style = RotateStyle('#5555ff')
map = World(style=gdp_style)
package = Package('/Users/qmp/Downloads/gdp.zip')
resources = package.descriptor['resources']
resourceList = [resources[x]['name'] for x in range(0, len(resources))]
#print(resourceList)
data = package.resources[0].read()
show_dict = {}
for x in data:
if x[2] == 2016:
code = counrty_list_show(x[0])
#print(code,x)
gdp = long(x[-1])
print(gdp)
show_dict[code] = gdp
print(show_dict)
map.title = u'2016世界所有经济体GDP'
map.add(u'经济体', show_dict)
map.render_to_file('/Users/qmp/Desktop/map_gdp.svg')
def clickedBtn(self):
try:
global capitala
capitala = self.label_capitala.text()
if capitala:
path = easygui.filesavebox(msg="",
title="Salvati datele",
default=selected_country + ".txt",
filetypes=None)
# salvare date in fisier txt
file = open(path, "w")
file.write("Tara: " + tara + "\n")
file.write("Continent: " + continent + "\n")
file.write("Capitala: " + capitala + "\n")
file.write("Populatie: " + populatie + "\n")
file.write("Suprafata: " + suprafata + "\n")
file.write("Moneda: " + moneda + "\n")
file.write("Ora GMT: " + timezone + "\n")
file.write("Provincii: " + str(provincii) + "\n")
file.write(
"All data: " +
json.dumps(countries.get_all_info_for_country(tara)) +
"\n")
file.close()
# desenam harta lumii si o salvam
worldmap_chart = World()
worldmap_chart.title = tara
worldmap_chart.add(
'Tara aleasa',
[countries.get_country_code_from_country(tara)])
path = path[:len(path) - 4]
print(path)
worldmap_chart.render_to_file(path + ".svg")
else:
self.prompt_message("Trebuie sa selectati o tara!", "Eroare")
except Exception as ex:
print(ex)
def render_world_map(gdpinfo, plot_countries, year, map_file):
"""
Inputs:
gdpinfo - A GDP information dictionary
plot_countries - Dictionary whose keys are plot library country codes
and values are the corresponding country name
year - String year to create GDP mapping for
map_file - Name of output file to create
Output:
Returns None.
Action:
Creates a world map plot of the GDP data for the given year and
writes it to a file named by map_file.
"""
worldmap_chart = World()
worldmap_chart.title = 'GDP by Country (Log Scale) for ' + year
gdp_dict, missing_data, missing_data_year = build_map_dict_by_name(gdpinfo, plot_countries, year)
worldmap_chart.add('GDP for '+year, gdp_dict)
worldmap_chart.add('Missing '+year+' data', missing_data_year)
worldmap_chart.add('No record', missing_data)
worldmap_chart.render_to_file(map_file)
from pygal_maps_world.maps 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'])
wm.add('South America', ['ar', 'bo', 'br', 'cl', 'co', 'ec', 'gf','gy', 'pe', 'py', 'sr', 'uy', 've'])
wm.render_to_file('americas.svg')
code = get_country_code(country_name)
if code:
tmp = {code: export_value_index}
tx_dic.update(tmp)
else:
print("Błąd - " + country_name)
#print(tx_dic)
# Podzielenie państw na trzy grupy według wielkości indexu
tx_dic_100, tx_dic_300, tx_dic_more = {}, {}, {}
for cc, pop in tx_dic.items():
if pop < 100:
tx_dic_100[cc] = pop
elif pop < 300:
tx_dic_300[cc] = pop
else:
tx_dic_more[cc] = pop
# Przygotowanie wykresu
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.force_url_protocol = 'http'
wm.title = 'Export value index w 2017 roku. (dane dla poszczególnych państw)'
wm.add('< 100', tx_dic_100)
wm.add('100 < 300', tx_dic_300)
wm.add('< 300', tx_dic_more)
wm.render_to_file('Export_value_index_2017.svg')
filename = 'gdp_json.json'
with open(filename) as f:
gdp_data = json.load(f)
cc_gdps = {}
for gdp_dict in gdp_data:
if gdp_dict['Year'] == 2010:
country_name = gdp_dict['Country Name']
gdp = int(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
cc_gdps[code] = gdp
#根据gdp数值对国家进行分组
cc_gdps_1, cc_gdps_2, cc_gdps_3 = {}, {}, {}
for cc, gdp in cc_gdps.items():
if gdp < 10000000000:
cc_gdps_1[cc] = gdp
elif gdp < 1000000000000:
cc_gdps_2[cc] = gdp
else:
cc_gdps_3[cc] = gdp
#绘制世界地图
wm = World()
wm.title = 'World GDP in 2010'
wm.add('0-10bn', cc_gdps_1)
wm.add('10bn-1000bn', cc_gdps_2)
wm.add('>1000bn', cc_gdps_3)
wm.render_to_file('World_GDP.svg')
cc_populations = {}
for pop_dict in pop_data:
if pop_dict['Year'] == 2015:
country_name = pop_dict['Country Name']
# 有些值是小数，先转为float再转为int
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
cc_populations[code] = population
# 为了使颜色分层更加明显
cc_populations_1, cc_populations_2, cc_populations_3 = {}, {}, {}
for cc, population in cc_populations.items():
if population < 10000000:
cc_populations_1[cc] = population
elif population < 1000000000:
cc_populations_2[cc] = population
else:
cc_populations_3[cc] = population
wm_style = RotateStyle('#336699', base_style=LightColorizedStyle)
world = World(style=wm_style)
world.title = 'World Populations in 2015, By Country'
world.add('0-10m', cc_populations_1)
world.add('10m-1bn', cc_populations_2)
world.add('>1bn', cc_populations_3)
world.render_to_file('world_population_2015.svg')
# https://www.jianshu.com/p/6fcd47b3528b
for pop_dict in pop_data:
#print(pop_dict)
if int(pop_dict['Year']) == 2010:
country_name = pop_dict['Country Name']
population = int(pop_dict['Value'])
code = get_country_code(country_name)
if code:
cc_populations[code] = population
#print(code + ": " + str(population))
else:
print('ERROR - ' + country_name)
#根据人口数量将所有的国家分成三组
cc_pops_1,cc_pops_2,cc_pops_3 = {},{},{}
for cc, pop in cc_populations.items():
if pop < 100000000:
cc_pops_1[cc] = pop
elif pop < 100000000:
cc_pops_2[cc] = pop
else:
cc_pops_3[cc] = pop
#看看每组包含了多少个国家
wm = World()
print(len(cc_pops_1),len(cc_pops_2),len(cc_pops_3))
wm_style = RS('#336699', base_style=NS)
wm = World(style = wm_style)
wm.title = 'World Population in 2010, by Country'
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.maps import World
wm = World()
wm.title = 'Americas'
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.maps 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')
cc_population = {}
# population of per country
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_name(country_name)
if code:
cc_population[code] = population
else:
print("Error - " + country_name)
# grouping countries by 3 types of pipulations
cc_pop_1, cc_pop_2, cc_pop_3 = {}, {}, {}
for cc, pop in cc_population.items():
if pop < 10000000: # 10 000 000
cc_pop_1[cc] = pop
elif pop < 1000000000 : # 1 000 000 000
cc_pop_2[cc] = pop
else:
cc_pop_3[cc] = pop
wm = World()
wm.title = 'World population in 2010, By countries'
wm.add('0-10m', cc_pop_1)
wm.add('10m-1bn', cc_pop_2)
wm.add('>1bn', cc_pop_3)
wm.render_to_file('word_pop_by_groups.svg')
import pandas as pd
from pygal.style import *
from pygal_maps_world.maps import World
from country_codes import get_country_code
csv = pd.read_csv("co2_emissions.csv", keep_default_na=False, skiprows=4)
csv['code'] = csv['Country Name'].apply(get_country_code)
# 需要把CO2排放量转换为数字格式
csv['digit_2013'] = pd.to_numeric(csv['2013'])
# 把CO2排放量分3个层次
# 并把国家码和对应的2013年CO2排放量取出来放在字典中
co2_1, co2_2, co2_3 = {}, {}, {}
for code, co2 in zip(csv['code'], csv['digit_2013']):
if co2 <= 5:
co2_1[code] = co2
elif co2 <= 10:
co2_2[code] = co2
else:
co2_3[code] = co2
wm = World(fill=True, style=RedBlueStyle)
wm.title = "World's CO2 emissions at 2013"
wm.add("CO2 <= 5", co2_1)
wm.add("CO2 <= 10", co2_2)
wm.add("CO2 > 10", co2_3)
wm.render_to_file("practice_16-7.svg")
from pygal_maps_world.maps import World
wm = World()
wm.force_url_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')
from pygal_maps_world.maps import World
wm = World()
wm.title = '北美洲国家人口分布'
wm.add("北美", {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_populations.svg')
population = int(float(pop_dict['Value']))
code = get_country_code(country_name)
if code:
# print(code + ':' + str(population))
whole_population[code] = population
# else:
# print('Error - ' + country_name)
# 根据人口数量将所有的国家分成三组
world_population_1, world_population_2, world_population_3 = {}, {}, {}
for world_code, population in whole_population.items():
if population < 10000000:
world_population_1[world_code] = population
elif population < 1000000000:
world_population_2[world_code] = population
else:
world_population_3[world_code] = population
# 看看每组分别包含多少个国家
print(len(world_population_1), len(world_population_2),
len(world_population_3))
world_map_style = RotateStyle('#336699', base_style=LightColorizedStyle)
# world_map_style = LightColorizedStyle
world_map = World(style=world_map_style)
world_map.title = 'World Population In 2010, By Country'
world_map.add('0 - 10M', world_population_1)
world_map.add('10M - 1BN', world_population_2)
world_map.add('>1BN', world_population_3)
world_map.render_to_file('threelevel_light_style.svg')
import json
from pygal_maps_world.maps import World
from country_codes import get_country_code
from pygal.style import LightColorizedStyle as LCS, RotateStyle as RS
filename = 'gdp_json.json'
with open(filename) as f:
pop_data = json.load(f)
gdp = {}
for gdp_dict in pop_data:
if gdp_dict['Year'] == 2010:
country_name = gdp_dict['Country Name']
gdp_val = float(gdp_dict['Value'])
code = get_country_code(country_name)
if code:
gdp[code] = gdp_val
wm_style = RS('#336699', base_style=LCS)
wm = World(style=wm_style)
wm.title = "World GDP in 2010, by Country"
wm.add("GDP", gdp)
wm.render_to_file('world_gdp.svg')
else:
print('ERROR - ' + country_name)
# 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 < 100000000:
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', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = 'World Population in 2010, by Country'
# wm.add('2010', cc_populations)
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.maps import World
wm = World()
wm.title = 'Populations of Countries in North America'
wm.add('North America', {'ca': 34126000, 'us': 309349000, 'mx': 113423000})
wm.render_to_file('na_population.svg')
# valorConvertido = int(value)
# print(valorConvertido)
# valorConvertido += 1
# print(valorConvertido)
# mapaMundi = World()
# mapaMundi.title = 'Norte, Central e Sul'
# mapaMundi.add('Norte',['ca','mx','us'])
# mapaMundi.add('Central',['ma', 'mc', 'md', 'me', 'mg','mk', 'ml', 'mm', 'mn', 'mo','mr', 'mt', 'mu', 'mv', 'mw'])
# mapaMundi.add('Sul',['ar','br','cl'])
# mapaMundi.render_in_browser()
# Plotando dados numéricos em um mapa-múndi
mapaMundi = World()
mapaMundi.title = 'Norte, Central e Sul'
mapaMundi.add('Norte', {'ca': 21, 'mx': 22, 'us': 23})
mapaMundi.add(
'Central', {
'ma': 42,
'mc': 33,
'md': 421,
'me': 45,
'mg': 31,
'mk': 11,
'ml': 48,
'mm': 44,
'mn': 76,
'mo': 9
})
mapaMundi.add('Sul', {'ar': 22, 'br': 11, 'cl': 34})
# -*- coding: utf-8 -*-
"""
-------------------------------------------------
File Name： NorthAmericaPopulation
Description :
Author : Liangz
Date： 2018/10/26
-------------------------------------------------
Change Activity:
2018/10/26:
-------------------------------------------------
"""
__author__ = 'Liangz'
from pygal_maps_world.maps import World
world_map = World()
world_map.title = 'Populations Of Countries In North America'
world_map.add('North America', {
'ca': 34126000,
'us': 309349000,
'mx': 113423000
})
world_map.render_to_file('NA_population.svg')
# 为了使颜色分层更加明显
cc_level_1, cc_level_2, cc_level_3,cc_level_4,cc_level_5,cc_level_6,cc_level_7 = {}, {}, {},{}, {}, {},{}
for cc, number1 in cc_movie_number.items():
if number1 > 3000:
cc_level_7[cc] = number1
elif number1 < 3000 and number1 > 2000:
cc_level_6[cc] = number1
elif number1 < 2000 and number1 > 900:
cc_level_5[cc] = number1
elif number1 < 900 and number1 > 675:
cc_level_4[cc] = number1
elif number1 < 675 and number1 > 450:
cc_level_3[cc] = number1
elif number1 < 450 and number1 > 225:
cc_level_2[cc] = number1
else:
cc_level_1[cc] = number1
wm_style = RotateStyle(color='#fde0dc', base_style=LightColorizedStyle)
world = World(style=wm_style)
world.title = '近10年世界范围各个地区的电影数量'
world.add('不足225部', cc_level_1)
world.add('225——450部', cc_level_2)
world.add('450——675部', cc_level_3)
world.add('675——900部', cc_level_4)
world.add('900——2000部', cc_level_5)
world.add('2000——3000部', cc_level_6)
world.add('超过3000部', cc_level_7)
world.render_to_file('近10年世界范围各个地区的电影数量.svg')
from pygal_maps_world.maps import World
wm = World()
wm.title = 'North, Central, and 南非'
wm.add('North America', ['ca', 'mx', 'us'])
wm.add('Central', ['bz', 'cr', 'gt', 'hn', 'ni', 'pa', 'sv'])
wm.add('South America', ['ar', 'bo', 'br'])
wm.render_to_file('america.svg')
f = open("gdp.json")
gdp = json.load(f)
gdps = {}
# 把所有国家的名称通过get_country_code函数以得到其国别码
# 并把其GDP存入字典
for item in gdp:
if item["Year"] == "2016":
code = get_country_code(item["Country Name"])
if code:
gdps[code] = int(float(item["Value"])) / 100000000
# 根据GDP数量级别不同把国家分成三组
gdp_1, gdp_2, gdp_3 = {}, {}, {}
for c, g in gdps.items():
if g < 1000:
gdp_1[c] = g
elif g < 10000:
gdp_2[c] = g
else:
gdp_3[c] = g
wm_style = RotateStyle('#336699', base_style=LightColorizedStyle)
wm = World(style=wm_style)
wm.title = "GDP of the Whold World - 2016"
wm.add("0-100bn", gdp_1)
wm.add("100-1000bn", gdp_2)
wm.add("1000-10000bn", gdp_3)
wm.render_to_file("gdps.svg")
from pygal_maps_world.maps import World
wm = World()
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')