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“ pyecharts 数据分析原来可以这么炫酷”

Echarts 是一个由百度开源的数据可视化&＃xff0c;凭借着良好的交互性&＃xff0c;精巧的图表设计&＃xff0c;得到了众多开发者的认可。而 Python 是一门富有表达力的语言&＃xff0c;很适合用于数据处理。当数据分析遇上数据可视化时&＃xff0c;pyecharts 诞生了。

目录

1、特性

2、安装

3、最简单的图表

4、3d柱状图

5、k线图

6、仪表盘

7、地理坐标图

8、关系图

9、水球图

10、3d地图

11、饼图

12、雷达图

13、树图

1、特性

• 简洁的 API 设计&＃xff0c;使用如丝滑般流畅&＃xff0c;支持链式调用

• 囊括了 30&＃43; 种常见图表&＃xff0c;应有尽有

• 支持主流 Notebook 环境&＃xff0c;Jupyter Notebook 和 JupyterLab

• 可轻松集成至 Flask&＃xff0c;Django 等主流 Web 框架

• 高度灵活的配置项&＃xff0c;可轻松搭配出精美的图表

• 详细的文档和示例&＃xff0c;帮助开发者更快的上手项目

• 多达 400&＃43; 地图文件以及原生的百度地图&＃xff0c;为地理数据可视化提供强有力的支持

2、安装

先来安装我们需要使用到的模块。

pip install pyecharts

3、最简单的图表

成功安装&＃xff0c;我们一起来开发一个最简单的图表。

from pyecharts.charts import Bar
​
bar &＃61; Bar()#实例化柱状图
bar.add_xaxis(["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"])#添加横坐标
bar.add_yaxis("商家A", [5, 20, 36, 10, 75, 90])#添加统计数据
#bar.add_yaxis("商家", [5, 20, 36, 10, 75, 90])#添加多个统计数据
# render 会生成本地 html 文件&＃xff0c;默认会在当前目录生成 render.html 文件
# 也可以传入路径参数&＃xff0c;如 bar.render("mycharts.html")
bar.render()

4、3d柱状图

上面是最基本的统计图&＃xff0c;柱状图&＃xff0c;那我们来看一下3d柱状图。

import random
​
from pyecharts import options as opts
from pyecharts.charts import Bar3D
from pyecharts.faker import Faker
​
​
data &＃61; [(i, j, random.randint(0, 12)) for i in range(6) for j in range(24)]
c &＃61; (
Bar3D()
.add(
"",
[[d[1], d[0], d[2]] for d in data],
xaxis3d_opts&＃61;opts.Axis3DOpts(Faker.clock, type_&＃61;"category"),
yaxis3d_opts&＃61;opts.Axis3DOpts(Faker.week_en, type_&＃61;"category"),
zaxis3d_opts&＃61;opts.Axis3DOpts(type_&＃61;"value"),
)
.set_global_opts(
visualmap_opts&＃61;opts.VisualMapOpts(max_&＃61;20),
title_opts&＃61;opts.TitleOpts(title&＃61;"Bar3D-基本示例"),
)
.render("bar3d_base.html")
)

5、k线图

以及一些炒股人士使用到的k线图。

from pyecharts import options as opts
from pyecharts.charts import Kline
​
data &＃61; [
[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],
[2255.77, 2270.28, 2253.31, 2276.22],
]
​
c &＃61; (
Kline()
.add_xaxis(["2017/7/".format(i &＃43; 1) for i in range(31)])
.add_yaxis(
"kline",
data,
markline_opts&＃61;opts.MarkLineOpts(
data&＃61;[opts.MarkLineItem(type_&＃61;"max", value_dim&＃61;"close")]
),
)
.set_global_opts(
xaxis_opts&＃61;opts.AxisOpts(is_scale&＃61;True),
yaxis_opts&＃61;opts.AxisOpts(
is_scale&＃61;True,
splitarea_opts&＃61;opts.SplitAreaOpts(
is_show&＃61;True, areastyle_opts&＃61;opts.AreaStyleOpts(opacity&＃61;1)
),
),
title_opts&＃61;opts.TitleOpts(title&＃61;"Kline-MarkLine"),
)
.render("kline_markline.html")
)

6、仪表盘

仪表盘&＃xff0c;完成情况等使用

import pyecharts.options as opts
from pyecharts.charts import Gauge
​
"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://echarts.apache.org/examples/editor.html?c&＃61;gauge
​
目前无法实现的功能:
​
1、暂无
"""
​
(
Gauge(init_opts&＃61;opts.InitOpts(width&＃61;"1600px", height&＃61;"800px"))
.add(series_name&＃61;"业务指标", data_pair&＃61;[["完成率", 55.5]])
.set_global_opts(
legend_opts&＃61;opts.LegendOpts(is_show&＃61;False),
tooltip_opts&＃61;opts.TooltipOpts(is_show&＃61;True, formatter&＃61;"a
b : c%"),
)
.render("gauge.html")
)
​

7、地理坐标图

统计地理位置时使用。

from pyecharts import options as opts
from pyecharts.charts import Geo
from pyecharts.faker import Faker
c &＃61; (
Geo()
.add_schema(maptype&＃61;"china")
.add("geo", [list(z) for z in zip(Faker.provinces, Faker.values())])
.set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False))
.set_global_opts(
visualmap_opts&＃61;opts.VisualMapOpts(), title_opts&＃61;opts.TitleOpts(title&＃61;"Geo-基本示例")
)
.render("geo_base.html")
)

8、关系图

关系图&＃xff0c;每一个人的关系&＃xff0c;看起来就像是一个星座一样​。

import json
from pyecharts import options as opts
from pyecharts.charts import Graph
with open("weibo.json", "r", encoding&＃61;"utf-8") as f:
j &＃61; json.load(f)
nodes, links, categories, cont, mid, userl &＃61; j
c &＃61; (
Graph()
.add(
"",
nodes,
links,
categories,
repulsion&＃61;50,
linestyle_opts&＃61;opts.LineStyleOpts(curve&＃61;0.2),
label_opts&＃61;opts.LabelOpts(is_show&＃61;False),
)
.set_global_opts(
legend_opts&＃61;opts.LegendOpts(is_show&＃61;False),
title_opts&＃61;opts.TitleOpts(title&＃61;"Graph-微博转发关系图"),
)
.render("graph_weibo.html")
)

9、水球图

水球图&＃xff0c;统计完成度&＃xff0c;或者​进度。

from pyecharts import options as opts
from pyecharts.charts import Liquid
from pyecharts.globals import SymbolType
c &＃61; (
Liquid()
.add("lq", [0.3, 0.7], is_outline_show&＃61;False, shape&＃61;SymbolType.DIAMOND)
.set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Liquid-Shape-Diamond"))
.render("liquid_shape_diamond")
)

10、3d地图

让数据在3d地图上显示出来​。

from pyecharts import options as opts
from pyecharts.charts import Map3D
from pyecharts.globals import ChartType
example_data &＃61; [
[[119.107078, 36.70925, 1000], [116.587245, 35.415393, 1000]],
[[117.000923, 36.675807], [120.355173, 36.082982]],
[[118.047648, 36.814939], [118.66471, 37.434564]],
[[121.391382, 37.539297], [119.107078, 36.70925]],
[[116.587245, 35.415393], [122.116394, 37.509691]],
[[119.461208, 35.428588], [118.326443, 35.065282]],
[[116.307428, 37.453968], [115.469381, 35.246531]],
]
c &＃61; (
Map3D()
.add_schema(
maptype&＃61;"山东",
itemstyle_opts&＃61;opts.ItemStyleOpts(
color&＃61;"rgb(5,101,123)",
opacity&＃61;1,
border_width&＃61;0.8,
border_color&＃61;"rgb(62,215,213)",
),
light_opts&＃61;opts.Map3DLightOpts(
main_color&＃61;"#fff",
main_intensity&＃61;1.2,
is_main_shadow&＃61;False,
main_alpha&＃61;55,
main_beta&＃61;10,
ambient_intensity&＃61;0.3,
),
view_control_opts&＃61;opts.Map3DViewControlOpts(center&＃61;[-10, 0, 10]),
post_effect_opts&＃61;opts.Map3DPostEffectOpts(is_enable&＃61;False),
)
.add(
series_name&＃61;"",
data_pair&＃61;example_data,
type_&＃61;ChartType.LINES3D,
effect&＃61;opts.Lines3DEffectOpts(
is_show&＃61;True,
period&＃61;4,
trail_width&＃61;3,
trail_length&＃61;0.5,
trail_color&＃61;"#f00",
trail_opacity&＃61;1,
),
linestyle_opts&＃61;opts.LineStyleOpts(is_show&＃61;False, color&＃61;"#fff", opacity&＃61;0),
)
.set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Map3D-Lines3D"))
.render("map3d_with_lines3d.html")
)

11、饼图

饼图显示

from pyecharts import options as opts
from pyecharts.charts import Pie
from pyecharts.faker import Faker
c &＃61; (
Pie()
.add("", [list(z) for z in zip(Faker.choose(), Faker.values())])
.set_colors(["blue", "green", "yellow", "red", "pink", "orange", "purple"])
.set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Pie-设置颜色"))
.set_series_opts(label_opts&＃61;opts.LabelOpts(formatter&＃61;"b: c"))
.render("pie_set_color.html")
)

12、雷达图

统计各项指标情况​。

from pyecharts import options as opts
from pyecharts.charts import Radar
v1 &＃61; [[4300, 10000, 28000, 35000, 50000, 19000]]
v2 &＃61; [[5000, 14000, 28000, 31000, 42000, 21000]]
c &＃61; (
Radar()
.add_schema(
schema&＃61;[
opts.RadarIndicatorItem(name&＃61;"销售", max_&＃61;6500),
opts.RadarIndicatorItem(name&＃61;"管理", max_&＃61;16000),
opts.RadarIndicatorItem(name&＃61;"信息技术", max_&＃61;30000),
opts.RadarIndicatorItem(name&＃61;"客服", max_&＃61;38000),
opts.RadarIndicatorItem(name&＃61;"研发", max_&＃61;52000),
opts.RadarIndicatorItem(name&＃61;"市场", max_&＃61;25000),
]
)
.add("预算分配", v1)
.add("实际开销", v2)
​ .set_series_opts(label_opts&＃61;opts.LabelOpts(is_show&＃61;False))
.set_global_opts(
legend_opts&＃61;opts.LegendOpts(selected_mode&＃61;"single"),
title_opts&＃61;opts.TitleOpts(title&＃61;"Radar-单例模式"),
)
.render("radar_selected_mode.html")
)

13、树图

树图&＃xff0c;统计导向&＃xff0c;思维​。

from pyecharts import options as opts
from pyecharts.charts import Tree
data &＃61; [

"children": [
"name": "B",

"children": ["children": ["name": "I"], "name": "E", "name": "F"],
"name": "C",
,

"children": [
"children": ["name": "J", "name": "K"], "name": "G",
"name": "H",
],
"name": "D",
,
],
"name": "A",

]
c &＃61; (
Tree()
.add("", data)
.set_global_opts(title_opts&＃61;opts.TitleOpts(title&＃61;"Tree-基本示例"))
.render("tree_base.html")
)

这是常用的几种数据分析统计图&＃xff0c;不同的场景&＃xff0c;应用不同的统计图&＃xff0c;让数据看起来更简单&＃xff0c;更方便分析市场导向&＃xff0c;​掌握先机。