pyecharts全家桶，一招学会python可视化

龙哥

发布于 2021-01-25 10:44:13

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发布于 2021-01-25 10:44:13

文章被收录于专栏：Python绿色通道

pyecharts一直被誉为python可视化的神器，因为它只需较少的代码既可以绘制非常漂亮的图形

今天主要分享一下pyecharts的常见绘图方法，并把官方中文文档.pdf送给大家

1.利用Bar绘制柱状图

from pyecharts.charts import Bar
from pyecharts import options as opts
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])
       .set_global_opts(title_opts = opts.TitleOpts(title = "某商场销售情况")) )
bar.render_notebook()

2.并列柱状图绘制

from pyecharts.charts import Bar
from pyecharts import options as opts
bar = Bar()
bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
bar.set_global_opts(title_opts = opts.TitleOpts(title = "货品销售情况",subtitle  =  "A和B公司"))
bar.render_notebook()

3.绘制水平直方图

from pyecharts.charts import Bar
from pyecharts import options as opts
bar = Bar()
bar.add_xaxis(["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"])
bar.add_yaxis("商家A", [114, 55, 27, 101, 125, 27, 105])
bar.add_yaxis("商家B", [57, 134, 137, 129, 145, 60, 49])
bar.set_global_opts(title_opts=opts.TitleOpts(title="货品销售情况",subtitle = "A和B公司"),toolbox_opts = opts.ToolboxOpts(is_show = True))
bar.set_series_opts(label_opts=opts.LabelOpts(position = "right"))
bar.reversal_axis()
bar.render_notebook()

4.饼图绘制

# from example.commons import Faker
from pyecharts import options as opts
from pyecharts.charts import Page, Pie
L1=['教授','副教授','讲师','助教','其他']
num  = [20,30,10,12,8]
c = Pie()
c.add("", [list(z) for z in zip(L1,num)])
c.set_global_opts(title_opts=opts.TitleOpts(title="Pie-职称类别比例"))
c.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
c.render_notebook()

5.圆形饼图绘制


from pyecharts import options as opts
from pyecharts.charts import Page, Pie
wd = ['教授','副教授','讲师','助教','其他']
num = [20,30,10,12,8]
c = Pie()
c.add("",[list(z) for z in zip(wd, num)],radius = ["40%", "75%"])   
c.set_global_opts( title_opts=opts.TitleOpts(title="Pie-Radius"),legend_opts=opts.LegendOpts( orient="vertical", pos_top="5%", pos_left="2%" ))
c .set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
c.render_notebook()

6.圆形饼图中的rich参数应用

from pyecharts import options as opts
from pyecharts.charts import Page, Pie
wd = ['教授','副教授','讲师','助教','其他']
num = [20,30,10,12,8]
c = Pie()
c.add("",[list(z) for z in zip(wd, num)],radius=["40%", "55%"],
       label_opts=opts.LabelOpts(position="outside",
       formatter="{a|{a}}{abg|}\n{hr|}\n{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,} } ))
c.set_global_opts(title_opts=opts.TitleOpts(title="Pie-富文本示例"))
c.render_notebook()

7.玫瑰图绘制


from pyecharts import options as opts
from pyecharts.charts import Page, Pie
data1 = [45,86,39,52,68]
data2 = [67,36,64,89,123]
labels = ['电脑','手机','彩电','冰箱','洗衣机']
c = Pie()
c.add("",[list(z) for z in zip(labels, data1)],radius=["35%", "70%"],center=[180,220],rosetype='radius')
c.add("",[list(z) for z in zip(labels, data2)],radius=["35%", "70%"],center=[550,220],rosetype='area')
c.set_global_opts(title_opts=opts.TitleOpts(title="玫瑰图"))
c.render_notebook()

8.漏斗图绘制

from pyecharts.charts import Funnel
from pyecharts import options as opts
%matplotlib inline
data = [45,86,39,52,68]
labels = ['电脑','手机','彩电','冰箱','洗衣机']
wf = Funnel()
wf.add('电器销量图',[list(z) for z in zip(labels, data)], is_selected= True)
wf.render_notebook()

9.散点图绘制

from pyecharts import options as opts
from pyecharts.charts import Scatter
week = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
c = Scatter()
c.add_xaxis(week)
c.add_yaxis("商家A", [81,65,48,32,68,92,87])
c.set_global_opts(title_opts=opts.TitleOpts(title="Scatter-一周的销售额（万元）"))
c.render_notebook()

10.K线图绘制

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] ]
c = Kline()
c.add_xaxis(["2019/7/{}".format(i + 1) for i in range(31)])
c.add_yaxis("2019年7月份K线图", data)
c.set_global_opts(yaxis_opts=opts.AxisOpts(is_scale=True),
            xaxis_opts=opts.AxisOpts(is_scale=True),
            title_opts=opts.TitleOpts(title="Kline-基本示例"),)
c.render_notebook()

11.仪表盘图绘制

from pyecharts import options as opts
from pyecharts.charts import Gauge, Page
c = Gauge()
c.add("业务指标",[("完成率", 55.5)],axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color=[(0.3, "#67e0e3"),
 (0.7, "#37a2da"), (1, "#fd666d")], width=30)))
c.set_global_opts(title_opts=opts.TitleOpts(title="Gauge-不同颜色"),
 legend_opts=opts.LegendOpts(is_show=False)) 
c.render_notebook()

12.词云绘制

from pyecharts import options as opts
from pyecharts.charts import Page, WordCloud
from pyecharts.globals import SymbolType
words = [
    ("牛肉面", 7800),("黄河", 6181),
    ("《读者》杂志", 4386), ("甜胚子", 3055),
    ("甘肃省博物馆", 2055),("莫高窟", 8067),("兰州大学", 4244),
    ("西北师范大学", 1868),("中山桥", 3484),
    ("月牙泉", 1112),("五泉山", 980),
    ("五彩丹霞", 865),("黄河母亲", 847),("崆峒山",678),
    ("羊皮筏子", 1582),("兴隆山",868),
    ("兰州交通大学", 1555),("白塔山", 2550),("五泉山", 2550)]
c = WordCloud()
c.add("", words, word_size_range=[20, 80])
c.set_global_opts(title_opts=opts.TitleOpts(title="WordCloud-基本示例"))
c.render_notebook()

13.在指定地图上的城市标示某天最高温度

from pyecharts import options as opts
from pyecharts.charts import Map
temperature=[30,31,27,29,18]
loc = ['兰州市','天水市','白银市','武威市','甘南藏族自治州']
c =  Map()
c.add("甘肃省", [list(z) for z in zip(loc, temperature)], "甘肃",is_roam=True) 
c .set_global_opts(title_opts=opts.TitleOpts(title="甘肃省部分城市最高气温"))
c.render("APP类型.html")
c.render_notebook()

14.组合图表上下布局

from pyecharts import options as opts
from pyecharts.charts import Bar, Grid, Line,Scatter
A = ["小米", "三星", "华为", "苹果", "魅族", "VIVO", "OPPO"]
CA = [100,125,87,90,78,98,118]
B = ["草莓", "芒果", "葡萄", "雪梨", "西瓜", "柠檬", "车厘子"]
CB = [78,95,120,102,88,108,98]
bar = Bar()
bar.add_xaxis(A)
bar.add_yaxis("商家A",CA)
bar.add_yaxis("商家B", CB)
bar.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Bar"))
bar.render_notebook()
line=Line()
line.add_xaxis(B)
line.add_yaxis("商家A", CA)
line.add_yaxis("商家B", CB)
line.set_global_opts(title_opts=opts.TitleOpts(title="Grid-Line", pos_top="48%"),
legend_opts=opts.LegendOpts(pos_top="48%"))
line.render_notebook()
grid = Grid()
grid.add(bar, grid_opts=opts.GridOpts(pos_bottom="60%"))
grid.add(line, grid_opts=opts.GridOpts(pos_top="60%"))