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本文是为了帮助大家快速掌握十大顶级绘图方法，重点解释数据是如何呈现在不同类型图中。

使用
pip install pyecharts
安装，安装后的版本为
v1.6
pyecharts几行代码就能绘制出有特色的的图形，绘图API链式调用，使用方便。

1 仪表盘

from
pyecharts
import
charts


# 仪表盘
gauge = charts.Gauge()

gauge.add(
'Python小例子'
, [(
'Python机器学习'
,
30
), (
'Python基础'
,
70.
),

(
'Python正则'
,
90
)])

gauge.render(path=
"./data/仪表盘.html"
)

print(
'ok'
)


仪表盘中共展示三项，每项的比例为30%,70%,90%，如下图默认名称显示第一项：Python机器学习，完成比例为30%
2 漏斗图
from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Funnel, Page

from
random
import
randint


deffunnel_base() -> Funnel:
c = (

Funnel()

.add(
"豪车"
, [list(z)
for
z
in
zip([
'宝马'
,
'法拉利'
,
'奔驰'
,
'奥迪'
,
'大众'
,
'丰田'
,
'特斯拉'
],

[randint(
1
,
20
)
for
_
in
range(
7
)])])

.set_global_opts(title_opts=opts.TitleOpts(title=
"豪车漏斗图"
))

)

return
c


funnel_base().render(
'./img/car_funnel.html'
)

print(
'ok'
)

以7种车型及某个属性值绘制的漏斗图，属性值大越靠近漏斗的大端。
3 日历图
import
datetime

import
random


from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Calendar



defcalendar_interval_1() -> Calendar:
begin = datetime.date(
2019
,
1
,
1
)

end = datetime.date(
2019
,
12
,
27
)

data = [

[str(begin + datetime.timedelta(days=i)), random.randint(
1000
,
25000
)]

for
i
in
range(
0
, (end - begin).days +
1
,
2
)
# 隔天统计
]


calendar = (

Calendar(init_opts=opts.InitOpts(width=
"1200px"
)).add(

""
, data, calendar_opts=opts.CalendarOpts(range_=
"2019"
))

.set_global_opts(

title_opts=opts.TitleOpts(title=
"Calendar-2019年步数统计"
),

visualmap_opts=opts.VisualMapOpts(

max_=
25000
,

min_=
1000
,

orient=
"horizontal"
,

is_piecewise=
True
,

pos_top=
"230px"
,

pos_left=
"100px"
,

),

)

)

return
calendar



calendar_interval_1().render(
'./img/calendar.html'
)

print(
'ok'
)


绘制2019年1月1日到12月27日的步行数，官方给出的图形宽度900px不够，只能显示到9月份，本例使用opts.InitOpts(width="1200px")做出微调，并且visualmap显示所有步数，每隔一天显示一次：
4 图(graph)
import
json

import
os


from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Graph, Page



defgraph_base() -> Graph:
nodes = [

{
"name"
:
"cus1"
,
"symbolSize"
:
10
},

{
"name"
:
"cus2"
,
"symbolSize"
:
30
},

{
"name"
:
"cus3"
,
"symbolSize"
:
20
}

]

links = []

for
i
in
nodes:

if
i.get(
'name'
) ==
'cus1'
:

continue
for
j
in
nodes:

if
j.get(
'name'
) ==
'cus1'
:

continue
links.append({
"source"
: i.get(
"name"
),
"target"
: j.get(
"name"
)})

c = (

Graph()

.add(
""
, nodes, links, repulsion=
8000
)

.set_global_opts(title_opts=opts.TitleOpts(title=
"customer-influence"
))

)

return
c

构建图，其中客户点1与其他两个客户都没有关系(link)，也就是不存在有效边：
5 水球图
from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Liquid, Page

from
pyecharts.globals
import
SymbolType



defliquid() -> Liquid:
c = (

Liquid()

.add(
"lq"
, [
0.67
,
0.30
,
0.15
])

.set_global_opts(title_opts=opts.TitleOpts(title=
"Liquid"
))

)

return
c



liquid().render(
'./img/liquid.html'
)


水球图的取值[0.67, 0.30, 0.15]表示下图中的三个波浪线，一般代表三个百分比:

6 饼图

from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Pie

from
random
import
randint


defpie_base() -> Pie:
c = (

Pie()

.add(
""
, [list(z)
for
z
in
zip([
'宝马'
,
'法拉利'
,
'奔驰'
,
'奥迪'
,
'大众'
,
'丰田'
,
'特斯拉'
],

[randint(
1
,
20
)
for
_
in
range(
7
)])])

.set_global_opts(title_opts=opts.TitleOpts(title=
"Pie-基本示例"
))

.set_series_opts(label_opts=opts.LabelOpts(formatter=
"{b}: {c}"
))

)

return
c


pie_base().render(
'./img/pie_pyecharts.html'
)

7 极坐标

import
random

from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Page, Polar


defpolar_scatter0() -> Polar:
data = [(alpha, random.randint(
1
,
100
))
for
alpha
in
range(
101
)]
# r = random.randint(1, 100)
print(data)

c = (

Polar()

.add(
""
, data, type_=
"bar"
, label_opts=opts.LabelOpts(is_show=
False
))

.set_global_opts(title_opts=opts.TitleOpts(title=
"Polar"
))

)

return
c



polar_scatter0().render(
'./img/polar.html'
)

极坐标表示为(夹角,半径)，如(6,94)表示"夹角"为6，半径94的点：

8 词云图

from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Page, WordCloud

from
pyecharts.globals
import
SymbolType



words = [

(
"Python"
,
100
),

(
"C++"
,
80
),

(
"Java"
,
95
),

(
"R"
,
50
),

(
"JavaScript"
,
79
),

(
"C"
,
65
)

]



defwordcloud() -> WordCloud:
c = (

WordCloud()

# word_size_range: 单词字体大小范围
.add(
""
, words, word_size_range=[
20
,
100
], shape=
'cardioid'
)

.set_global_opts(title_opts=opts.TitleOpts(title=
"WordCloud"
))

)

return
c



wordcloud().render(
'./img/wordcloud.html'
)


("C",65)表示在本次统计中C语言出现65次

9 系列柱状图

from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
Bar

from
random
import
randint



defbar_series() -> Bar:
c = (

Bar()

.add_xaxis([
'宝马'
,
'法拉利'
,
'奔驰'
,
'奥迪'
,
'大众'
,
'丰田'
,
'特斯拉'
])

.add_yaxis(
"销量"
, [randint(
1
,
20
)
for
_
in
range(
7
)])

.add_yaxis(
"产量"
, [randint(
1
,
20
)
for
_
in
range(
7
)])

.set_global_opts(title_opts=opts.TitleOpts(title=
"Bar的主标题"
, subtitle=
"Bar的副标题"
))

)

return
c



bar_series().render(
'./img/bar_series.html'
)

10 热力图

import
random

from
pyecharts
import
options
as
opts

from
pyecharts.charts
import
HeatMap



defheatmap_car() -> HeatMap:
x = [
'宝马'
,
'法拉利'
,
'奔驰'
,
'奥迪'
,
'大众'
,
'丰田'
,
'特斯拉'
]

y = [
'中国'
,
'日本'
,
'南非'
,
'澳大利亚'
,
'阿根廷'
,
'阿尔及利亚'
,
'法国'
,
'意大利'
,
'加拿大'
]

value = [[i, j, random.randint(
0
,
100
)]

for
i
in
range(len(x))
for
j
in
range(len(y))]

c = (

HeatMap()

.add_xaxis(x)

.add_yaxis(
"销量"
, y, value)

.set_global_opts(

title_opts=opts.TitleOpts(title=
"HeatMap"
),

visualmap_opts=opts.VisualMapOpts(),

)

)

return
c


heatmap_car().render(
'./img/heatmap_pyecharts.html'
)

阅读更多：
这个双折线图，惊艳到我了！
这个折线图，惊艳到我了！
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