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')