平面直角坐标系
- 直方图
- 折线图
- 箱形图
- 散点图
- 带涟漪效果散点图
- k线图
- 热力图
- 象型图
- 层叠图
地理图表
- GEO-地理坐标系
- MAP-地图
- BMAP-百度地图
基本图表
- 饼图
- 漏斗图
- 仪表盘
- 水球图
- 日历图
- 关系图
- 平行坐标系
- 极坐标系
- 雷达图
- 旭日图
- 桑基图
- 河流图
- 词云图
- 表格
3D图表
- 3D散点图
- 3D折线图
- 3D直方图
- 3D地图
树型图表
- 树图
- 矩形树图

以下默认都是在Jupyter Notebook展示
也可以将每个图代码的最后一行换为
所创建的对象.render('名字.html')转换为html文件就可以查看啦

平面直角坐标系

直方图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]bar = (Bar().add_xaxis(x_data).add_yaxis('', y_data))bar.render_notebook()

折线图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]line = (Line().add_xaxis(x_data).add_yaxis('', y_data))line.render_notebook()

箱形图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [[random.randint(100, 200) for i in range(10)] for item in x_data]Box = Boxplot()
Box.add_xaxis(x_data)
Box.add_yaxis("", Box.prepare_data(y_data))
Box.render_notebook()

散点图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]scatter = (Scatter().add_xaxis(x_data).add_yaxis('', y_data))scatter.render_notebook()

带涟漪效果散点图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]effectScatter = (EffectScatter().add_xaxis(x_data).add_yaxis('', y_data))effectScatter.render_notebook()

k线图

date_list = ["2020/4/{}".format(i + 1) for i in range(30)]
y_data = [[2320.26, 2320.26, 2287.3, 2362.94],[2300, 2291.3, 2288.26, 2308.38],[2295.35, 2346.5, 2295.35, 2345.92],[2347.22, 2358.98, 2337.35, 2363.8],[2360.75, 2382.48, 2347.89, 2383.76],[2383.43, 2385.42, 2371.23, 2391.82],[2377.41, 2419.02, 2369.57, 2421.15],[2425.92, 2428.15, 2417.58, 2440.38],[2411, 2433.13, 2403.3, 2437.42],[2432.68, 2334.48, 2427.7, 2441.73],[2430.69, 2418.53, 2394.22, 2433.89],[2416.62, 2432.4, 2414.4, 2443.03],[2441.91, 2421.56, 2418.43, 2444.8],[2420.26, 2382.91, 2373.53, 2427.07],[2383.49, 2397.18, 2370.61, 2397.94],[2378.82, 2325.95, 2309.17, 2378.82],[2322.94, 2314.16, 2308.76, 2330.88],[2320.62, 2325.82, 2315.01, 2338.78],[2313.74, 2293.34, 2289.89, 2340.71],[2297.77, 2313.22, 2292.03, 2324.63],[2322.32, 2365.59, 2308.92, 2366.16],[2364.54, 2359.51, 2330.86, 2369.65],[2332.08, 2273.4, 2259.25, 2333.54],[2274.81, 2326.31, 2270.1, 2328.14],[2333.61, 2347.18, 2321.6, 2351.44],[2340.44, 2324.29, 2304.27, 2352.02],[2326.42, 2318.61, 2314.59, 2333.67],[2314.68, 2310.59, 2296.58, 2320.96],[2309.16, 2286.6, 2264.83, 2333.29],[2282.17, 2263.97, 2253.25, 2286.33],
]kline = (Kline().add_xaxis(date_list).add_yaxis('', y_data))kline.render_notebook()

热力图

data = [[i, j, random.randint(0, 100)] for i in range(24) for j in range(7)]
hour_list = [str(i) for i in range(24)]
week_list = ['周日', '周一', '周二', '周三', '周四', '周五', '周六']heat = (HeatMap().add_xaxis(hour_list).add_yaxis("", week_list, data))heat.render_notebook()

象型图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]pictorialBar = (PictorialBar().add_xaxis(x_data).add_yaxis('', y_data))pictorialBar.render_notebook()

层叠图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data_bar = [123, 153, 89, 107, 98, 23]
y_data_line = [153, 107, 23, 89, 123, 107]bar = (Bar().add_xaxis(x_data).add_yaxis('', y_data_bar))line = (Line().add_xaxis(x_data).add_yaxis('', y_data_line))overlap = bar.overlap(line)
overlap.render_notebook()

地理图表

GEO-地理坐标系

province = ['广东','湖北','湖南','四川','重庆','黑龙江','浙江','山西','河北','安徽','河南','山东','西藏']
data = [(i, random.randint(50, 150)) for i in province]geo = (Geo().add_schema(maptype="china").add("", data)
)
geo.render_notebook()

MAP-地图

province = ['广东','湖北','湖南','四川','重庆','黑龙江','浙江','山西','河北','安徽','河南','山东','西藏']
data = [(i, random.randint(50, 150)) for i in province]map_ = (Map().add("", data, 'china')
)
map_.render_notebook()

BMAP-百度地图

province = ['广东','湖北','湖南','四川','重庆','黑龙江','浙江','山西','河北','安徽','河南','山东','西藏']
data = [(i, random.randint(50, 150)) for i in province]bmap = (BMap().add_schema(baidu_ak="FAKE_AK", center=[120.13066322374, 30.240018034923]).add("", data)
)
bmap.render_notebook()

基本图表

饼图

# 虚假数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data = [123, 153, 89, 107, 98, 23]pie = (Pie().add('', [list(z) for z in zip(cate, data)]))pie.render_notebook()

漏斗图

# 虚假数据
cate = ['访问', '注册', '加入购物车', '提交订单', '付款成功']
data = [30398, 15230, 10045, 3109, 1698]funnel = (Funnel().add("", [list(z) for z in zip(cate, data)]))funnel.render_notebook()

仪表盘

gauge = (Gauge().add("", [('转化率',34)]))gauge.render_notebook()

水球图

liquid = (Liquid().add("", [0.52, 0.44]))liquid.render_notebook()

日历图

import math# 虚假数据
begin = datetime.date(2019, 1, 1)
end = datetime.date(2019, 12, 31)
data = [[str(begin + datetime.timedelta(days=i)), abs(math.cos(i/100))* random.randint(1000, 1200)]for i in range((end - begin).days + 1)]calendar = (Calendar().add("", data, calendar_opts=opts.CalendarOpts(range_="2019")))calendar.render_notebook()

关系图

nodes = [{"name": "结点1", "symbolSize": 1},{"name": "结点2", "symbolSize": 2},{"name": "结点3", "symbolSize": 3},{"name": "结点4", "symbolSize": 4},{"name": "结点5", "symbolSize": 5},{"name": "结点6", "symbolSize": 6},{"name": "结点7", "symbolSize": 7},{"name": "结点8", "symbolSize": 8},
]
links = [{'source': '结点1', 'target': '结点2'},{'source': '结点1', 'target': '结点3'},{'source': '结点1', 'target': '结点4'},{'source': '结点2', 'target': '结点1'},{'source': '结点3', 'target': '结点4'},{'source': '结点3', 'target': '结点5'},{'source': '结点3', 'target': '结点6'},{'source': '结点4', 'target': '结点1'},{'source': '结点4', 'target': '结点2'},{'source': '结点4', 'target': '结点7'},{'source': '结点4', 'target': '结点8'},{'source': '结点5', 'target': '结点1'},{'source': '结点5', 'target': '结点4'},{'source': '结点5', 'target': '结点6'},{'source': '结点5', 'target': '结点7'},{'source': '结点5', 'target': '结点8'},{'source': '结点6', 'target': '结点1'},{'source': '结点6', 'target': '结点7'},{'source': '结点6', 'target': '结点8'},{'source': '结点7', 'target': '结点1'},{'source': '结点7', 'target': '结点2'},{'source': '结点7', 'target': '结点8'},{'source': '结点8', 'target': '结点1'},{'source': '结点8', 'target': '结点2'},{'source': '结点8', 'target': '结点3'},]graph = (Graph().add("", nodes, links)
)graph.render_notebook()

平行坐标系

# 虚假数据
data = [['一班', 78, 91, 123, 78, 82, 67, "优秀"],['二班', 89, 101, 127, 88, 86, 75, "良好"],['三班', 86, 93, 101, 84, 90, 73, "合格"],
]parallel = (Parallel().add_schema([opts.ParallelAxisOpts(dim=0,name="班级",type_="category",data=["一班", "二班", "三班"],),opts.ParallelAxisOpts(dim=1, name="英语"),opts.ParallelAxisOpts(dim=2, name="数学"),opts.ParallelAxisOpts(dim=3, name="语文"),opts.ParallelAxisOpts(dim=4, name="物理"),opts.ParallelAxisOpts(dim=5, name="生物"),opts.ParallelAxisOpts(dim=6, name="化学"),opts.ParallelAxisOpts(dim=7,name="评级",type_="category",data=["优秀", "良好", "合格"],),]).add("", data)
)parallel.render_notebook()

极坐标系

# 虚假数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data = [123, 153, 89, 107, 98, 23]polar = (Polar().add_schema(radiusaxis_opts=opts.RadiusAxisOpts(data=cate, type_="category"),).add("", data, type_='bar')
)polar.render_notebook()

雷达图

# 虚假数据
data = [[78, 91, 123, 78, 82, 67],[89, 101, 127, 88, 86, 75],[86, 93, 101, 84, 90, 73],
]radar = (Radar().add_schema(schema=[opts.RadarIndicatorItem(name="语文", max_=150),opts.RadarIndicatorItem(name="数学", max_=150),opts.RadarIndicatorItem(name="英语", max_=150),opts.RadarIndicatorItem(name="物理", max_=100),opts.RadarIndicatorItem(name="生物", max_=100),opts.RadarIndicatorItem(name="化学", max_=100),]
).add('', data)
)
radar.render_notebook()

旭日图

# 虚假数据
data = [{"name": "湖南","children": [{"name": "长沙","children": [{"name": "雨花区", "value": 55},{"name": "岳麓区", "value": 34},{"name": "天心区", "value": 144},]},{"name": "常德","children": [{"name": "武陵区", "value": 156},{"name": "鼎城区", "value": 134},]},{"name": "湘潭", "value": 87},{"name": "株洲", "value": 23},],},{"name": "湖北","children": [{"name": "武汉","children": [{"name": "洪山区", "value": 55},{"name": "东湖高新", "value": 78},{"name": "江夏区", "value": 34},]},{"name": "鄂州", "value": 67},{"name": "襄阳", "value": 34},],},{"name": "北京", "value": 235}
]sunburst = (Sunburst().add("", data_pair=data))sunburst.render_notebook()

桑基图

# 虚假数据
nodes = [{"name": "访问"},{"name": "注册"},{"name": "付费"},
]links = [{"source": "访问", "target": "注册", "value": 50},{"source": "注册", "target": "付费", "value": 30},
]sankey = (Sankey().add("", nodes, links)
)sankey.render_notebook()

河流图

# 虚假数据
cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
date_list = ["2020/4/{}".format(i + 1) for i in range(30)]data = [[day, random.randint(10, 50), c] for day in date_list for c in cate]river = (ThemeRiver().add(series_name=cate,data=data,singleaxis_opts=opts.SingleAxisOpts(type_="time"))
)river.render_notebook()

词云图

words = [("hey", 230),("jude", 124),("dont", 436),("make", 255),("it", 247),("bad", 244),("Take", 138),("a sad song", 184),("and", 12),("make", 165),("it", 247),("better", 182),("remember", 255),("to", 150),("let", 162),("her", 266),("into", 60),("your", 82),("heart", 173),("then", 365),("you", 360),("can", 282),("start", 273),("make", 265),
]wc = (WordCloud().add("", words)
)wc.render_notebook()

表格

from pyecharts.components import Tabletable = Table()headers = ["City name", "Area", "Population", "Annual Rainfall"]
rows = [["Brisbane", 5905, 1857594, 1146.4],["Adelaide", 1295, 1158259, 600.5],["Darwin", 112, 120900, 1714.7],["Hobart", 1357, 205556, 619.5],["Sydney", 2058, 4336374, 1214.8],["Melbourne", 1566, 3806092, 646.9],["Perth", 5386, 1554769, 869.4],
]
table.add(headers, rows)table.render_notebook()

City name Area Population Annual Rainfall
Brisbane 5905 1857594 1146.4
Adelaide 1295 1158259 600.5
Darwin 112 120900 1714.7
Hobart 1357 205556 619.5
Sydney 2058 4336374 1214.8
Melbourne 1566 3806092 646.9
Perth 5386 1554769 869.4

3D图表

3D散点图

data = [(random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)) for _ in range(100)]scatter3D = (Scatter3D().add("", data))scatter3D.render_notebook()

3D折线图

data = []
for t in range(0, 1000):x = math.cos(t/10)y = math.sin(t/10)z = t/10data.append([x, y, z])line3D = (Line3D().add("", data,xaxis3d_opts=opts.Axis3DOpts(type_="value"),yaxis3d_opts=opts.Axis3DOpts(type_="value")))line3D.render_notebook()

3D直方图

data = [[i, j, random.randint(0, 100)] for i in range(24) for j in range(7)]
hour_list = [str(i) for i in range(24)]
week_list = ['周日', '周一', '周二', '周三', '周四', '周五', '周六']bar3D = (Bar3D().add("",data,xaxis3d_opts=opts.Axis3DOpts(hour_list, type_="category"),yaxis3d_opts=opts.Axis3DOpts(week_list, type_="category"),zaxis3d_opts=opts.Axis3DOpts(type_="value"),)
)bar3D.render_notebook()

3D地图

# 虚假数据
province = ['广东','湖北','湖南','四川','重庆','黑龙江','浙江','山西','河北','安徽','河南','山东','西藏']
data = [(i, random.randint(50, 150)) for i in province]map3d = (Map3D().add("", data_pair=data, maptype='china')
)
map3d.render_notebook()
3D地球
from pyecharts.faker import POPULATIONmapglobe = (MapGlobe().add_schema().add(series_name="",maptype="world",data_pair=POPULATION[1:])
)mapglobe.render_notebook()

树型图表

树图

# 虚假数据
data = [{"name": "湖南","children": [{"name": "长沙","children": [{"name": "雨花区", "value": 55},{"name": "岳麓区", "value": 34},{"name": "天心区", "value": 144},]},{"name": "常德","children": [{"name": "武陵区", "value": 156},{"name": "鼎城区", "value": 134},]},{"name": "湘潭", "value": 87},{"name": "株洲", "value": 23},],}
]tree = (Tree().add("", data)
)tree.render_notebook()

矩形树图

# 虚假数据
data = [{"name": "湖南","children": [{"name": "长沙","children": [{"name": "雨花区", "value": 55},{"name": "岳麓区", "value": 34},{"name": "天心区", "value": 144},]},{"name": "常德","children": [{"name": "武陵区", "value": 156},{"name": "鼎城区", "value": 134},]},{"name": "湘潭", "value": 87},{"name": "株洲", "value": 23},],},{"name": "湖北","children": [{"name": "武汉","children": [{"name": "洪山区", "value": 55},{"name": "东湖高新", "value": 78},{"name": "江夏区", "value": 34},]},{"name": "鄂州", "value": 67},{"name": "襄阳", "value": 34},],},{"name": "北京", "value": 235}
]treemap = (TreeMap().add("", data)
)treemap.render_notebook()

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pyecharts各种图表实现（超级全）

目录