pyecharts(1)基本图

from pyecharts.charts import *
from pyecharts.components import Table
from pyecharts import options as opts
from pyecharts.commons.utils import JsCode
import random
import datetime
import math
import numpy as np 

from pyecharts.globals import CurrentConfig
CurrentConfig.ONLINE_HOST = "https://cdn.kesci.com/lib/pyecharts_assets/" # 设置host地址

1|0常用图

1|1直方图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]



'''直方图'''
bar = (
    Bar()
    .add_xaxis(x_data)
    .add_yaxis('', y_data)
)
bar.render_notebook()

1|2折线图

'''折线图'''
line = (
    Line()
    .add_xaxis(x_data)
    .add_yaxis('',y_data)
)
line.render_notebook()

1|3箱线图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [[random.randint(100, 200) for i in range(10)] for item in x_data]

'''箱线图'''
box = (
    Boxplot()
    .add_xaxis(x_data)
)
box.add_yaxis('', box.prepare_data(y_data))
box.render_notebook()

1|4散点图

'''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()

1|5涟漪图

'''涟漪图'''
effectscatter = (EffectScatter()
                 .add_xaxis(x_data)
                 .add_yaxis('', y_data)
)
effectscatter.render_notebook()

1|6k线图

'''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()

1|7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']

'''热力图'''
heat = (HeatMap()
       .add_xaxis(hour_list)
        .add_yaxis('', week_list, data)
        
       )
heat.render_notebook()

1|8象形图

x_data = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
y_data = [123, 153, 89, 107, 98, 23]

'''象形图'''
pictorialbar = (PictorialBar()
               .add_xaxis(x_data)
                .add_yaxis('', y_data)
               )
pictorialbar.render_notebook()

1|9叠加图

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)
        )

'''叠加图'''
overlap = bar.overlap(line)
# overlap = line.overlap(bar)
overlap.render_notebook()

2|0地图

2|1GEO-地理坐标

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-地理坐标'''
geo = (Geo()
      .add_schema(maptype='china')
       .add('', data)
      )
geo.render_notebook()

2|2map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地图'''
map_ = (
    Map()
    .add("", data, 'china')
)
map_.render_notebook()

2|3百度地图

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]
'''百度地图'''
bmap = (
    BMap()
    .add_schema(baidu_ak="FAKE_AK", center=[120.13066322374, 30.240018034923])
    .add("", data)
)
bmap.render_notebook()

3|0其他图

3|1饼图

cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data = [123, 153, 89, 107, 98, 23]

'''饼图'''
pie = (Pie()
      .add('', [list(z) for z in zip(cate, data)])
      )
pie.render_notebook()

3|2漏斗图

cate = ['访问', '注册', '加入购物车', '提交订单', '付款成功']
data = [30398, 15230, 10045, 3109, 1698]

'''漏斗图'''
funnel = (Funnel()
         .add('', [list(z) for z in zip(cate, data)])
         )
funnel.render_notebook()

3|3仪表图

'''仪表图'''
gauge = (Gauge()
        .add('', [('转化率', 74)])
        )
gauge.render_notebook()

3|4水球图

'''水球图'''
liqiud = (Liquid()
         .add('', [0.52, 0.44, 0.04, 0.02])
         )
liqiud.render_notebook()

3|5日历图

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(100, 120)]
        for i in range((end - begin).days + 1)]
'''日历图'''
calendar = (Calendar()
           .add('', data, calendar_opts=opts.CalendarOpts(range_='2019'))
           )
calendar.render_notebook()

3|6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'},
         ]
'''关系图'''
graph = (Graph()
        .add('', nodes, links)
        )


graph.render_notebook()

3|7平行坐标系

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"],
]
'''平行坐标系'''
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()

3|8极坐标

cate = ['Apple', 'Huawei', 'Xiaomi', 'Oppo', 'Vivo', 'Meizu']
data = [123, 153, 89, 107, 98, 23]

'''极坐标'''

polar=(
    Polar()
    .add_schema(
        radiusaxis_opts=opts.RadiusAxisOpts(data=cate)
    )
    .add('', data, type_='bar')
)
polar.render_notebook()

3|9雷达图

data = [
    [78, 91, 123, 78, 82, 67],
    [89, 101, 127, 88, 86, 75],
    [86, 93, 101, 84, 90, 73],
]

'''雷达图'''
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()

3|10旭日图

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}
]

'''旭日图'''
sunburst = (
    Sunburst()
    .add('', data_pair=data)
)
sunburst.render_notebook()

3|11桑基图

nodes = [
    {"name": "访问"},
    {"name": "注册"},
    {"name": "付费"},
    {"name": "离开"},
]

links = [
    {"source": "访问", "target": "注册", "value": 50},
    {"source": "注册", "target": "付费", "value": 10},
    {"source": "注册", "target": "离开", "value": 20},
]

'''桑基图'''
sankey=(
    Sankey()
    .add('', nodes, links)
)
sankey.render_notebook()

3|12河流图

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]



'''河流图'''
river = (
    ThemeRiver()
    .add(
        series_name=cate,
        data=data,
        singleaxis_opts=opts.SingleAxisOpts(type_='time')
    )
)

river.render_notebook()

3|13词云图

words = [
    ('Hichens', 600),
    ("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),
    ('LJ', 600),
]

'''词云图'''

wc = (
    WordCloud()
    .add('', words)
)

wc.render_notebook()

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},
     ],
     }
]

'''树状图'''
tree = (
    Tree()
    .add('', data)
)
tree.render_notebook()

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}
]


'''矩阵树图'''
treemap = (
    TreeMap()
    .add('', data)
)
treemap.render_notebook()

3|14表格

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],
]

'''表格'''
from pyecharts.components import Table

table = (
    Table()
    .add(headers, rows)
)
table.render_notebook()

4|03D图

4|13D散点图

data = [(random.randint(0, 100), random.randint(0, 100), random.randint(0, 100)) for _ in range(100)]

'''3D散点图'''
scatter3D = (
    Scatter3D()
    .add('', data)
)
scatter3D.render_notebook()

4|23D折线图

data = []
for t in range(0, 1000):
    x = math.cos(t/10)
    y = math.sin(t/10)
    z = t/10
    data.append([x, y, z])
    
    
'''3D折线图'''

line3D = (
    Line3D()
    .add('', data,
            xaxis3d_opts=opts.Axis3DOpts(type_='value'),
            yaxis3d_opts=opts.Axis3DOpts(type_='value')
        )
)
line3D.render_notebook()

4|33D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']

'''3D直方图'''
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()

4|43D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]

'''3D地图'''
map3D = (
    Map3D()
    .add('', data_pair=data, maptype='china')
)
map3D.render_notebook()

4|53D地球

'''3D地球'''
from pyecharts.faker import POPULATION

mapglobe = (
    MapGlobe()
    .add_schema()
    .add(
        series_name='',
        maptype='world',
        data_pair=POPULATION[1:]
    )
)
mapglobe.render_notebook()

__EOF__

作　　者：Hichens
出　　处：https://www.cnblogs.com/hichens/p/13531749.html
关于博主：莫得感情的浅度学习机器人
版权声明：@Hichens
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