Map Template Series
China Map
Shows all the provinces in China, a complete simple geographical figure of China, making it easier for you to understand the distribution of cities!
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map() .add("城市", [list(z) for z in zip(Faker.provinces, Faker.values())], "china") .set_global_opts(title_opts=opts.TitleOpts(title="中国地图")) .render("中国地图.html") ) print([list(z) for z in zip(Faker.provinces, Faker.values())])
Provincial data map (Chongqing map)
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker x=["巫山县","万州区","云阳县","奉节县"] y=[123,560,456,362] c = ( Map(init_opts=opts.InitOpts(width="1400px", height="700px")) .add("城市", [list(z) for z in zip(x,y)], "重庆") .set_global_opts( title_opts=opts.TitleOpts(title="重庆地图"), visualmap_opts=opts.VisualMapOpts(max_=560) ) .render("重庆地图.html") )
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map(init_opts=opts.InitOpts(width="1400px", height="700px")) .add("城市", [list(z) for z in zip(Faker.provinces, Faker.values())], "china") .set_global_opts( title_opts=opts.TitleOpts(title="中国人口地图)"), visualmap_opts=opts.VisualMapOpts(max_=200, is_piecewise=True), ) .render("中国分段地图.html") )
{ "Somalia": "索马里", "Liechtenstein": "列支敦士登", "Morocco": "摩洛哥", "W. Sahara": "西撒哈拉", "Serbia": "塞尔维亚", "Afghanistan": "阿富汗", "Angola": "安哥拉", "Albania": "阿尔巴尼亚", "Andorra": "安道尔共和国", "United Arab Emirates": "阿拉伯联合酋长国", "Argentina": "阿根廷", "Armenia": "亚美尼亚", "Australia": "澳大利亚", "Austria": "奥地利", "Azerbaijan": "阿塞拜疆", "Burundi": "布隆迪", "Belgium": "比利时", "Benin": "贝宁", "Burkina Faso": "布基纳法索", "Bangladesh": "孟加拉国", "Bulgaria": "保加利亚", "Bahrain": "巴林", "Bahamas": "巴哈马", "Bosnia and Herz.": "波斯尼亚和黑塞哥维那", "Belarus": "白俄罗斯", "Belize": "伯利兹", "Bermuda": "百慕大", "Bolivia": "玻利维亚", "Brazil": "巴西", "Barbados": "巴巴多斯", "Brunei": "文莱", "Bhutan": "不丹", "Botswana": "博茨瓦纳", "Central African Rep.": "中非", "Canada": "加拿大", "Switzerland": "瑞士", "Chile": "智利", "China": "中国", "Côte d'Ivoire": "科特迪瓦", "Cameroon": "喀麦隆", "Dem. Rep. Congo": "刚果民主共和国", "Congo": "刚果", "Colombia": "哥伦比亚", "Cape Verde": "佛得角", "Costa Rica": "哥斯达黎加", "Cuba": "古巴", "N. Cyprus": "北塞浦路斯", "Cyprus": "塞浦路斯", "Czech Rep.": "捷克", "Germany": "德国", "Djibouti": "吉布提", "Denmark": "丹麦", "Dominican Rep.": "多米尼加", "Algeria": "阿尔及利亚", "Ecuador": "厄瓜多尔", "Egypt": "埃及", "Eritrea": "厄立特里亚", "Spain": "西班牙", "Estonia": "爱沙尼亚", "Ethiopia": "埃塞俄比亚", "Finland": "芬兰", "Fiji": "斐济", "France": "法国", "Gabon": "加蓬", "United Kingdom": "英国", "Georgia": "格鲁吉亚", "Ghana": "加纳", "Guinea": "几内亚", "Gambia": "冈比亚", "Guinea-Bissau": "几内亚比绍", "Eq. Guinea": "赤道几内亚", "Greece": "希腊", "Grenada": "格林纳达", "Greenland": "格陵兰", "Guatemala": "危地马拉", "Guam": "关岛", "Guyana": "圭亚那", "Honduras": "洪都拉斯", "Croatia": "克罗地亚", "Haiti": "海地", "Hungary": "匈牙利", "Indonesia": "印度尼西亚", "India": "印度", "Br. Indian Ocean Ter.": "英属印度洋领土", "Ireland": "爱尔兰", "Iran": "伊朗", "Iraq": "伊拉克", "Iceland": "冰岛", "Israel": "以色列", "Italy": "意大利", "Jamaica": "牙买加", "Jordan": "约旦", "Japan": "日本", "Siachen Glacier": "锡亚琴冰川", "Kazakhstan": "哈萨克斯坦", "Kenya": "肯尼亚", "Kyrgyzstan": "吉尔吉斯坦", "Cambodia": "柬埔寨", "Korea": "韩国", "Kuwait": "科威特", "Lao PDR": "老挝", "Lebanon": "黎巴嫩", "Liberia": "利比里亚", "Libya": "利比亚", "Sri Lanka": "斯里兰卡", "Lesotho": "莱索托", "Lithuania": "立陶宛", "Luxembourg": "卢森堡", "Latvia": "拉脱维亚", "Moldova": "摩尔多瓦", "Madagascar": "马达加斯加", "Mexico": "墨西哥", "Macedonia": "马其顿", "Mali": "马里", "Malta": "马耳他", "Myanmar": "缅甸", "Montenegro": "黑山", "Mongolia": "蒙古", "Mozambique": "莫桑比克", "Mauritania": "毛里塔尼亚", "Mauritius": "毛里求斯", "Malawi": "马拉维", "Malaysia": "马来西亚", "Namibia": "纳米比亚", "New Caledonia": "新喀里多尼亚", "Niger": "尼日尔", "Nigeria": "尼日利亚", "Nicaragua": "尼加拉瓜", "Netherlands": "荷兰", "Norway": "挪威", "Nepal": "尼泊尔", "New Zealand": "新西兰", "Oman": "阿曼", "Pakistan": "巴基斯坦", "Panama": "巴拿马", "Peru": "秘鲁", "Philippines": "菲律宾", "Papua New Guinea": "巴布亚新几内亚", "Poland": "波兰", "Puerto Rico": "波多黎各", "Dem. Rep. Korea": "朝鲜", "Portugal": "葡萄牙", "Paraguay": "巴拉圭", "Palestine": "巴勒斯坦", "Qatar": "卡塔尔", "Romania": "罗马尼亚", "Russia": "俄罗斯", "Rwanda": "卢旺达", "Saudi Arabia": "沙特阿拉伯", "Sudan": "苏丹", "S. Sudan": "南苏丹", "Senegal": "塞内加尔", "Singapore": "新加坡", "Solomon Is.": "所罗门群岛", "Sierra Leone": "塞拉利昂", "El Salvador": "萨尔瓦多", "Suriname": "苏里南", "Slovakia": "斯洛伐克", "Slovenia": "斯洛文尼亚", "Sweden": "瑞典", "Swaziland": "斯威士兰", "Seychelles": "塞舌尔", "Syria": "叙利亚", "Chad": "乍得", "Togo": "多哥", "Thailand": "泰国", "Tajikistan": "塔吉克斯坦", "Turkmenistan": "土库曼斯坦", "Timor-Leste": "东帝汶", "Tonga": "汤加", "Trinidad and Tobago": "特立尼达和多巴哥", "Tunisia": "突尼斯", "Turkey": "土耳其", "Tanzania": "坦桑尼亚", "Uganda": "乌干达", "Ukraine": "乌克兰", "Uruguay": "乌拉圭", "United States": "美国", "Uzbekistan": "乌兹别克斯坦", "Venezuela": "委内瑞拉", "Vietnam": "越南", "Vanuatu": "瓦努阿图", "Yemen": "也门", "South Africa": "南非", "Zambia": "赞比亚", "Zimbabwe": "津巴布韦", "Aland": "奥兰群岛", "American Samoa": "美属萨摩亚", "Fr. S. Antarctic Lands": "南极洲", "Antigua and Barb.": "安提瓜和巴布达", "Comoros": "科摩罗", "Curaçao": "库拉索岛", "Cayman Is.": "开曼群岛", "Dominica": "多米尼加", "Falkland Is.": "马尔维纳斯群岛(福克兰)", "Faeroe Is.": "法罗群岛", "Micronesia": "密克罗尼西亚", "Heard I. and McDonald Is.": "赫德岛和麦克唐纳群岛", "Isle of Man": "曼岛", "Jersey": "泽西岛", "Kiribati": "基里巴斯", "Saint Lucia": "圣卢西亚", "N. Mariana Is.": "北马里亚纳群岛", "Montserrat": "蒙特塞拉特", "Niue": "纽埃", "Palau": "帕劳", "Fr. Polynesia": "法属波利尼西亚", "S. Geo. and S. Sandw. Is.": "南乔治亚岛和南桑威奇群岛", "Saint Helena": "圣赫勒拿", "St. Pierre and Miquelon": "圣皮埃尔和密克隆群岛", "São Tomé and Principe": "圣多美和普林西比", "Turks and Caicos Is.": "特克斯和凯科斯群岛", "St. Vin. and Gren.": "圣文森特和格林纳丁斯", "U.S. Virgin Is.": "美属维尔京群岛", "Samoa": "萨摩亚" }
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map(init_opts=opts.InitOpts(width="1400px", height="700px")) .add("国家", [list(z) for z in zip(Faker.country, Faker.values())], "world") .set_series_opts(label_opts=opts.LabelOpts(is_show=False)) .set_global_opts( title_opts=opts.TitleOpts(title="世界地图"), visualmap_opts=opts.VisualMapOpts(max_=200), ) .render("世界地图.html") )
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map(init_opts=opts.InitOpts(width="1400px", height="700px")) .add( "城市", [list(z) for z in zip(Faker.guangdong_city, Faker.values())], "china-cities", label_opts=opts.LabelOpts(is_show=False), ) .set_global_opts( title_opts=opts.TitleOpts(title="中国地图(带城市)"), visualmap_opts=opts.VisualMapOpts(), ) .render("中国地图带城市.html") )
from pyecharts import options as opts from pyecharts.charts import Map from pyecharts.faker import Faker c = ( Map(init_opts=opts.InitOpts(width="1400px", height="700px")) .add("城市", [list(z) for z in zip(Faker.provinces, Faker.values())], "china") .set_global_opts( title_opts=opts.TitleOpts(title="(标题)"), visualmap_opts=opts.VisualMapOpts(max_=200), ) .render("连续数据地图.html") )
The above is the detailed content of How to use pyecharts to draw geographical charts in Python. For more information, please follow other related articles on the PHP Chinese website!

Python is easier to learn and use, while C is more powerful but complex. 1. Python syntax is concise and suitable for beginners. Dynamic typing and automatic memory management make it easy to use, but may cause runtime errors. 2.C provides low-level control and advanced features, suitable for high-performance applications, but has a high learning threshold and requires manual memory and type safety management.

Python and C have significant differences in memory management and control. 1. Python uses automatic memory management, based on reference counting and garbage collection, simplifying the work of programmers. 2.C requires manual management of memory, providing more control but increasing complexity and error risk. Which language to choose should be based on project requirements and team technology stack.

Python's applications in scientific computing include data analysis, machine learning, numerical simulation and visualization. 1.Numpy provides efficient multi-dimensional arrays and mathematical functions. 2. SciPy extends Numpy functionality and provides optimization and linear algebra tools. 3. Pandas is used for data processing and analysis. 4.Matplotlib is used to generate various graphs and visual results.

Whether to choose Python or C depends on project requirements: 1) Python is suitable for rapid development, data science, and scripting because of its concise syntax and rich libraries; 2) C is suitable for scenarios that require high performance and underlying control, such as system programming and game development, because of its compilation and manual memory management.

Python is widely used in data science and machine learning, mainly relying on its simplicity and a powerful library ecosystem. 1) Pandas is used for data processing and analysis, 2) Numpy provides efficient numerical calculations, and 3) Scikit-learn is used for machine learning model construction and optimization, these libraries make Python an ideal tool for data science and machine learning.

Is it enough to learn Python for two hours a day? It depends on your goals and learning methods. 1) Develop a clear learning plan, 2) Select appropriate learning resources and methods, 3) Practice and review and consolidate hands-on practice and review and consolidate, and you can gradually master the basic knowledge and advanced functions of Python during this period.

Key applications of Python in web development include the use of Django and Flask frameworks, API development, data analysis and visualization, machine learning and AI, and performance optimization. 1. Django and Flask framework: Django is suitable for rapid development of complex applications, and Flask is suitable for small or highly customized projects. 2. API development: Use Flask or DjangoRESTFramework to build RESTfulAPI. 3. Data analysis and visualization: Use Python to process data and display it through the web interface. 4. Machine Learning and AI: Python is used to build intelligent web applications. 5. Performance optimization: optimized through asynchronous programming, caching and code

Python is better than C in development efficiency, but C is higher in execution performance. 1. Python's concise syntax and rich libraries improve development efficiency. 2.C's compilation-type characteristics and hardware control improve execution performance. When making a choice, you need to weigh the development speed and execution efficiency based on project needs.


Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

Video Face Swap
Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Article

Hot Tools

Atom editor mac version download
The most popular open source editor

SublimeText3 Linux new version
SublimeText3 Linux latest version

SublimeText3 Mac version
God-level code editing software (SublimeText3)

SublimeText3 English version
Recommended: Win version, supports code prompts!

SAP NetWeaver Server Adapter for Eclipse
Integrate Eclipse with SAP NetWeaver application server.