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
常用图
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()
折线图
\'\'\'折线图\'\'\'
line = (
Line()
.add_xaxis(x_data)
.add_yaxis(\'\',y_data)
)
line.render_notebook()
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()
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()
涟漪图
\'\'\'涟漪图\'\'\'
effectscatter = (EffectScatter()
.add_xaxis(x_data)
.add_yaxis(\'\', y_data)
)
effectscatter.render_notebook()
k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()
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()
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()
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()
地图
GEO-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()
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地图\'\'\'
map_ = (
Map()
.add("", data, \'china\')
)
map_.render_notebook()
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()
其他图
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()
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()
仪表图
\'\'\'仪表图\'\'\'
gauge = (Gauge()
.add(\'\', [(\'转化率\', 74)])
)
gauge.render_notebook()
水球图
\'\'\'水球图\'\'\'
liqiud = (Liquid()
.add(\'\', [0.52, 0.44, 0.04, 0.02])
)
liqiud.render_notebook()
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()
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()
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()
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()
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()
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()
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()
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()
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()
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()
3D图
3D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()
3D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()
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\']
\'\'\'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()
3D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()
3D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()
