pyecharts

pyecharts简介

官网链接：http://pyecharts.org/#/

pyecharts 是一个用于生成 Echarts 图表的类库。Echarts 是百度开源的一个数据可视化 JS 库。用 Echarts 生成的图可视化效果非常好，pyecharts 是为了与 Python 进行对接，方便在 Python 中直接使用数据生成图。

pyecharts 用于 web 绘图，有较多的绘图种类，且代码量比较少。它是基于一个 Echarts 的库噶欧辰的，而Echarts 是百度开源的一个可视化 JavaScript 库。

pyecharts 可以将图片保存为多种格式，但需要插件，否则只能保存为 html 格式。安装 pyecharts 可以用：
pip install pyecharts
安装图片保存插件可用：
pip install pyecharts-snapshot
以及：
npm install -g phantomjs-prebuilt

柱状图

from pyecharts.charts import Bar
from pyecharts import options as opts
bar = Bar()
bar.add_xaxis(['毛衣','寸衫',"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',[57,134,101,22,69,90,129])
bar.set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况",subtitle='A和B公司'),
                   toolbox_opts=opts.ToolboxOpts(is_show=True))
bar.set_series_opts(label_opts=opts.LabelOpts(position="top"))
bar.render_notebook()    # 在 notebook 中展示
# bar.render(r"D:桌面snapshot.html") 生成 html 文件

bar = Bar()
bar.add_xaxis(['毛衣','寸衫',"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',[57,134,101,22,69,90,129])
bar.set_global_opts(title_opts=opts.TitleOpts(title="某商场销售情况",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()

饼状图

普通饼图

from pyecharts.charts import Pie
from pyecharts import options as opts
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-职称比例"),
                 toolbox_opts = opts.ToolboxOpts(is_show=True))
c.set_series_opts(label_opts = opts.LabelOpts(formatter="{b}:{c}"))
c.render_notebook()

环形图

from pyecharts.charts import Pie
from pyecharts import options as opts
c = Pie()
L1 = ["教授","副教授","讲师","助教","其他"]
num = [20,30,10,12,8]
c.add("",[list(z) for z in zip(L1,num)],radius=["40%","75%"])
c.set_global_opts(title_opts=opts.TitleOpts(title='Pie圆环'),
                 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()

玫瑰图

from pyecharts.charts import Pie
from pyecharts import options as opts
c = Pie()
L1 = ["教授","副教授","讲师","助教","其他"]
num = [20,30,10,12,8]
c.add("",[list(z) for z in zip(L1,num)],radius=["40%","75%"],rosetype="area")
c.set_global_opts(title_opts = opts.TitleOpts(title="玫瑰图"),toolbox_opts = opts.ToolboxOpts(is_show=True),
                 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()

散点图

from pyecharts.charts import Scatter
from pyecharts import options as opts
s = Scatter()
week = ['Mon','Thur','Wed','Tues','Fri','Sar','Sun']
s.add_xaxis(week)
s.add_yaxis('商家A',[11,22,33,44,55,66,77])
s.add_yaxis('商家B',[0,10,20,30,40,50,60])
s.set_global_opts(title_opts=opts.TitleOpts(title='散点图'),
                 toolbox_opts = opts.ToolboxOpts(is_show=True),
                 legend_opts = opts.LegendOpts(orient='vertical',pos_top='5%',pos_left="2%"))
s.set_series_opts(label_opts=opts.LabelOpts(position='top'))
s.render_notebook()

多图绘制

# 如果要在一张图中绘制两幅图，需要用到 网格：

from pyecharts import options as opts
from pyecharts.charts import Bar,Line,Grid
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="多图绘制"))
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="2图",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%"))
grid.render_notebook()

桑基图

一般用于分析原因，流量等：

import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Sankey

df = pd.DataFrame({'性别':['男','男','男','女','女','女'],
                  "熬夜原因":['打游戏','看剧','加班','打游戏','看剧','加班'],
                  '人数':[40,20,40,8,25,36]})
display(df)
def transForm(df):
    nodes = []
    links = []
    for i in range(2):
        values = df.iloc[:,i].unique()
        for value in values:
            dic = {}
            dic['name']=value
            nodes.append(dic)
    for i in df.values:
        dic = {}
        dic['source'] = i[0]
        dic['target'] = i[1]
        dic['value'] = i[2]
        links.append(dic)
    return nodes,links

nodes,links = transForm(df)
print(nodes)
print(links)
sankey = Sankey()
sankey.add("桑基图",nodes,links,
          linestyle_opt = opts.LineStyleOpts(opacity=0.2,curve=0.5,color="source"),
          label_opts = opts.LabelOpts(position='right'))
sankey.set_global_opts(title_opts=opts.TitleOpts(title='桑基图示例'))
sankey.render_notebook()

词云

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》",4386),("水晶饺子",3055),("雨燕中学",4244),("碣石文化广场",2055),
    ("玄武山",8067),("华工",1868),("十一孔",3483),("宋瘄寮",1122),("石洲",980),("红白",1111),("Beautyleg",3000),
    ("Winnie",6666),("toxic_妲己",2222),("绯月樱",4444)
]
c = WordCloud()
c.add("",words,word_size_range=[10,70])
c.set_global_opts(title_opts=opts.TitleOpts(title="词云"))
c.render_notebook()