利用Python进行数据可视化的实例代码

数据可视化和数据挖掘都是探索数据和分析数据的一种手段,下面这篇文章主要给大家介绍了关于如何利用Python进行数据可视化的相关资料,文中通过示例代码介绍的非常详细,需要的朋友可以参考下

前言

前面写过一篇用Python制作PPT的博客，感兴趣的可以参考

这篇是关于用Python进行数据可视化的，准备作为一个长贴，随时更新有价值的Python可视化用例,都是网上搜集来的，与君共享,本文所有测试均基于Python3.

首先搭建环境

 $pip install pyecharts -U $pip install echarts-themes-pypkg $pip install snapshot_selenium $pip install echarts-countries-pypkg $pip install echarts-cities-pypkg $pip install echarts-china-provinces-pypkg $pip install echarts-china-cities-pypkg $pip install echarts-china-counties-pypkg $pip install echarts-china-misc-pypkg $pip install echarts-united-kingdom-pypkg $pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyecharts $git clone https://github.com/pyecharts/pyecharts.git $cd pyecharts/ $pip install -r requirements.txt $python setup.py install

一顿操作下来，该装的不该装的都装上了，多装一些包没坏处，说不定哪天就用上了呢

实例代码

例子1：

 from pyecharts.charts import Bar from pyecharts import options as opts bar = ( Bar() .add_xaxis(["chenshan", "maoyi", "longdai", "kuzi", "fengyi", "gaogenxie", "wazi"]) .add_yaxis("storeA", [114, 55, 27, 101, 125, 27, 105]) .add_yaxis("storeB", [57, 134, 137, 129, 145, 60, 49]) .set_global_opts(title_opts=opts.TitleOpts(title="sales")) ) #bar.render_notebook() bar.render()

render():默认将会在根目录下生成一个 render.html 的文件，支持 path 参数，设置文件保存位置，如 render("./xx/xxx.html").

结果是以网页的形式输出的，执行后，在当前目录下生成render.html，用浏览器打开,最好事先安装chrome浏览器.

例子2：

 from pyecharts import options as opts from pyecharts.charts import Pie from pyecharts.faker import Faker pie = ( 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=opts.TitleOpts(title="Pie-设置颜色")) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) ) pie.render()

例子3:

 import pyecharts.options as opts from pyecharts.charts import Line from pyecharts.faker import Faker c = ( Line() .add_xaxis(Faker.choose()) .add_yaxis("storeA", Faker.values(), is_smooth=True) .add_yaxis("storeB", Faker.values(), is_smooth=True) .set_global_opts(title_opts=opts.TitleOpts(title="Line-smooth")) ) c.render()

例子4:

 from pyecharts import options as opts from pyecharts.charts import Kline 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], [2255.77, 2270.28, 2253.31, 2276.22], ] k = ( Kline() .add_xaxis(["2017/7/{}".format(i + 1) for i in range(31)]) .add_yaxis("k-line", data) .set_global_opts( yaxis_opts=opts.AxisOpts(is_scale=True), xaxis_opts=opts.AxisOpts(is_scale=True), title_opts=opts.TitleOpts(title="Kline-examples"), ) ) k.render()

例子5:

 from pyecharts import options as opts from pyecharts.charts import Gauge g = ( Gauge() .add("", [("complete", 66.6)]) .set_global_opts(title_opts=opts.TitleOpts(title="Gauge-basic examples")) ) g.render()

例子6:

 from pyecharts import options as opts from pyecharts.charts import Bar ( Bar() .add_xaxis( [ "名字很长的X轴标签1", "名字很长的X轴标签2", "名字很长的X轴标签3", "名字很长的X轴标签4", "名字很长的X轴标签5", "名字很长的X轴标签6", ] ) .add_yaxis("商家A", [10, 20, 30, 40, 50, 40]) .add_yaxis("商家B", [20, 10, 40, 30, 40, 50]) .set_global_opts( xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)), title_opts=opts.TitleOpts(title="Bar-旋转X轴标签", subtitle="解决标签名字过长的问题"), ) .render() )

 from pyecharts import options as opts from pyecharts.faker import Faker from pyecharts.charts import Page, Pie l1 = ['aa','bb','cc','dd','ee'] num =[10,20,15,25,30] c = ( Pie() .add( "", [list(z) for z in zip(l1, num)], radius=["40%", "75%"],   # 圆环的粗细和大小 ) .set_global_opts( title_opts=opts.TitleOpts(title="Pie-Radius"), legend_opts=opts.LegendOpts( orient="vertical", pos_top="5%", pos_left="2%"  # 左面比例尺 ), ) .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")) ) c.render()

 from pyecharts.faker import Faker from pyecharts import options as opts from pyecharts.charts import Page, Pie l1 = ['aa','bb','cc','dd','ee'] num =[10,20,15,25,30] c = ( Pie() .add( "", [list(z) for z in zip(l1, num)], radius=["40%", "55%"], label_opts=opts.LabelOpts( position="outside", formatter="{a|{a}}{abg|} {hr|} {b|{b}: }{c}  {per|{d}%}  ", background_color="#eee", border_color="#aaa", border_width=1, border_radius=4, rich={ "a": {"color": "#999", "lineHeight": 22, "align": "center"}, "abg": { "backgroundColor": "#e3e3e3", "width": "100%", "align": "right", "height": 22, "borderRadius": [4, 4, 0, 0], }, "hr": { "borderColor": "#aaa", "width": "100%", "borderWidth": 0.5, "height": 0, }, "b": {"fontSize": 16, "lineHeight": 33}, "per": { "color": "#eee", "backgroundColor": "#334455", "padding": [2, 4], "borderRadius": 2, }, }, ), ) .set_global_opts(title_opts=opts.TitleOpts(title="Pie-富文本示例")) ) c.render()

 from pyecharts import options as opts from pyecharts.charts import Line, Bar, Grid bar = ( Bar() .add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"]) .add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105]) .add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49]) .set_global_opts(title_opts=opts.TitleOpts(title="运维之路"),) ) week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"] high_temperature = [11, 11, 15, 13, 12, 13, 10] low_temperature = [1, -2, 2, 5, 3, 2, 0] line2 = ( Line(init_opts=opts.InitOpts(width="1600px", height="800px")) .add_xaxis(xaxis_data=week_name_list) .add_yaxis( series_name="最高气温", y_axis=high_temperature, markpoint_opts=opts.MarkPointOpts( data=[ opts.MarkPointItem(type_="max", name="最大值"), opts.MarkPointItem(type_="min", name="最小值"), ] ), markline_opts=opts.MarkLineOpts( data=[opts.MarkLineItem(type_="average", name="平均值")] ), ) .add_yaxis( series_name="最低气温", y_axis=low_temperature, markpoint_opts=opts.MarkPointOpts( data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)] ), markline_opts=opts.MarkLineOpts( data=[ opts.MarkLineItem(type_="average", name="平均值"), opts.MarkLineItem(symbol="none", x="90%", y="max"), opts.MarkLineItem(symbol="circle", type_="max", name="最高点"), ] ), ) .set_global_opts( #title_opts=opts.TitleOpts(title="气温变化", subtitle="纯属虚构"), tooltip_opts=opts.TooltipOpts(trigger="axis"), toolbox_opts=opts.ToolboxOpts(is_show=True), xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False), #legend_opts=opts.LegendOpts(pos_left="right"), ) #.render("temperature_change_line_chart.html") ) # 最后的 Grid #grid_chart = Grid(init_opts=opts.InitOpts(width="1400px", height="800px")) grid_chart = Grid() grid_chart.add( bar, grid_opts=opts.GridOpts( pos_left="3%", pos_right="1%", height="20%" ), ) # wr grid_chart.add( line2, grid_opts=opts.GridOpts( pos_left="3%", pos_right="1%", pos_top="40%", height="35%" ), ) #grid_chart.render("professional_kline_chart.html") grid_chart.render()

 from pyecharts import options as opts from pyecharts.charts import Radar v1=[[83, 92, 87, 49, 89, 86]]  # 数据必须为二维数组，否则会集中一个指示器显示 v2=[[88, 95, 66, 43, 86, 96]] v3=[[80, 92, 87, 58, 78, 81]] radar1=( Radar() .add_schema(# 添加schema架构 schema=[ opts.RadarIndicatorItem(name='传球',max_=100),# 设置指示器名称和最大值 opts.RadarIndicatorItem(name='射门',max_=100), opts.RadarIndicatorItem(name='身体',max_=100), opts.RadarIndicatorItem(name='防守',max_=100), opts.RadarIndicatorItem(name='速度',max_=100), opts.RadarIndicatorItem(name='盘带',max_=100), ] ) .add('罗纳尔多',v1,color="#f9713c") # 添加一条数据，参数1为数据名，参数2为数据，参数3为颜色 .add('梅西',v2,color="#4169E1") .add('苏亚雷斯',v3,color="#00BFFF") .set_global_opts(title_opts=opts.TitleOpts(title='雷达图'),) ) radar1.render()

 import math import random from pyecharts.faker import Faker from pyecharts import options as opts from pyecharts.charts import Page, Polar c = ( Polar() .add_schema( angleaxis_opts=opts.AngleAxisOpts(data=Faker.week, type_="category") ) .add("A", [1, 2, 3, 4, 3, 5, 1], type_="bar", stack="stack0") .add("B", [2, 4, 6, 1, 2, 3, 1], type_="bar", stack="stack0") .add("C", [1, 2, 3, 4, 1, 2, 5], type_="bar", stack="stack0") .set_global_opts(title_opts=opts.TitleOpts(title="Polar-AngleAxis")) ) c.render()

 import math import random from pyecharts.faker import Faker from pyecharts import options as opts from pyecharts.charts import Page, Polar data = [(i, random.randint(1, 100)) for i in range(10)] c = ( Polar() .add("", data, type_="effectScatter", effect_opts=opts.EffectOpts(scale=10, period=5), label_opts=opts.LabelOpts(is_show=False)) #  type默认为"line", #  "effectScatter"，scatter,bar .set_global_opts(title_opts=opts.TitleOpts(title="Polar-Scatter0")) ) c.render()

 import math import random from pyecharts.faker import Faker from pyecharts import options as opts from pyecharts.charts import Page, Polar c = ( Polar() .add_schema( radiusaxis_opts=opts.RadiusAxisOpts(data=Faker.week, type_="category") ) .add("A", [1, 2, 3, 4, 3, 5, 1], type_="bar", stack="stack0") .add("B", [2, 4, 6, 1, 2, 3, 1], type_="bar", stack="stack0") .add("C", [1, 2, 3, 4, 1, 2, 5], type_="bar", stack="stack0") .set_global_opts(title_opts=opts.TitleOpts(title="Polar-RadiusAxis")) ) c.render()

 from pyecharts import options as opts from pyecharts.charts import Liquid, Page from pyecharts.globals import SymbolType c = ( Liquid() .add("lq", [0.61, 0.7],shape='rect',is_outline_show=False) # 水球外形，有' circle', 'rect', 'roundRect', 'triangle', 'diamond', 'pin', 'arrow' 可选。 # 默认 'circle'。也可以为自定义的 SVG 路径。 #is_outline_show设置边框 .set_global_opts(title_opts=opts.TitleOpts(title="Liquid-基本示例")) ) c.render()

散点图：

 from pyecharts.charts import Scatter import pyecharts.options as opts female_height = [161.2,167.5,159.5,157,155.8,170,159.1,166,176.2,160.2,172.5,170.9,172.9,153.4,160,147.2,168.2,175,157,167.6,159.5,175,166.8,176.5,170.2,] female_weight = [51.6,59,49.2,63,53.6,59,47.6,69.8,66.8,75.2,55.2,54.2,62.5,42,50,49.8,49.2,73.2,47.8,68.8,50.6,82.5,57.2,87.8,72.8,54.5,] male_height = [174 ,175.3 ,193.5 ,186.5 ,187.2 ,181.5 ,184 ,184.5 ,175 ,184 ,180 ,177.8 ,192 ,176 ,174 ,184 ,192.7 ,171.5 ,173 ,176 ,176 ,180.5 ,172.7 ,176 ,173.5 ,178 ,] male_weight = [65.6 ,71.8 ,80.7 ,72.6 ,78.8 ,74.8 ,86.4 ,78.4 ,62 ,81.6 ,76.6 ,83.6 ,90 ,74.6 ,71 ,79.6 ,93.8 ,70 ,72.4 ,85.9 ,78.8 ,77.8 ,66.2 ,86.4 ,81.8 ,89.6 ,] scatter = Scatter() scatter.add_xaxis(female_height) scatter.add_xaxis(male_height) scatter.add_yaxis("female", female_weight, symbol_size=15) #散点大小 scatter.add_yaxis("male", male_weight, symbol_size=15) #散点大小 scatter.set_global_opts(title_opts=opts.TitleOpts(title="身高体重分布"), xaxis_opts=opts.AxisOpts( type_ = "value", # 设置x轴为数值轴 splitline_opts=opts.SplitLineOpts(is_show = True)), # x轴分割线 yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True))# y轴分割线 ) scatter.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) scatter.render("./html/scatter_base.html")