目录

十二.PyEcharts常用视图(1)

1-PyEcharts-Faker

2-常用视图

2.1 柱状图

普通柱状图

条形柱状图

堆叠柱状图

象形柱状图

颜色自定义

2.2 折线图

基本折线图

折线图常用属性

面积图

堆叠面积图

2.3 散点图

2.4 双轴图

2.5 饼图

基本饼图

空心饼图

玫瑰图

图例调整

文字调整

2.6 雷达图

单例模式

2.7 时间轴

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感谢度娘及开发者.

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1-PyEcharts-Faker

from pyecharts.faker import Faker

方法属性	说明	对应内容
Faker.choose()		[“衬衫”, “毛衣”, “领带”, “裤子”, “风衣”, “高跟鞋”, “袜子”]
Faker.values()		[106, 111, 145, 33, 20, 138, 141]
Faker.drinks		[“可乐”, “雪碧”, “橙汁”, “绿茶”, “奶茶”, “百威”, “青岛”]
Faker.phones		[“小米”, “三星”, “华为”, “苹果”, “魅族”, “VIVO”, “OPPO”]
Faker.fruits		[“草莓”, “芒果”, “葡萄”, “雪梨”, “西瓜”, “柠檬”, “车厘子”]
Faker.animal		[“河马”, “蟒蛇”, “老虎”, “大象”, “兔子”, “熊猫”, “狮子”]
Faker.cars		[“宝马”, “法拉利”, “奔驰”, “奥迪”, “大众”, “丰田”, “特斯拉”]
Faker.dogs		[“哈士奇”, “萨摩耶”, “泰迪”, “金毛”, “牧羊犬”, “吉娃娃”, “柯基”]
Faker.week		[“周一”, “周二”, “周三”, “周四”, “周五”, “周六”, “周日”]
Faker.week_en		[‘Saturday’, ‘Friday’, ‘Thursday’, ‘Wednesday’, ‘Tuesday’, ‘Monday’, ‘Sunday’]
Faker.clock		[‘12a’,‘1a’,‘2a’,‘3a’,‘4a’,‘5a’,‘6a’,...,4p’,‘5p’,‘6p’,‘7p’,‘8p’,‘9p’,‘10p’,‘11p’]
Faker.visual_color		[ “#313695”, “#4575b4”, “#74add1”, “#abd9e9”, .....]
Faker.months		‘1月’, ‘2月’, ‘3月’, ‘4月’, ‘5月’, ‘6月’, ‘7月’, ‘8月’, ‘9月’, ‘10月’, ‘11月’, ‘12月’]
Faker.provinces		[“广东”, “北京”, “上海”, “江西”, “湖南”, “浙江”, “江苏”]
Faker.guangdong_city		[“汕头市”, “汕尾市”, “揭阳市”, “阳江市”, “肇庆市”, “广州市”, “惠州市”]
Faker.country		[‘China’, ‘Canada’, ‘Brazil’, ‘Russia’, ‘United States’, ‘Africa’, ‘Germany’]
Faker.days_attrs		[‘0天’,‘1天’,‘2天’,‘3天’,‘4天’,‘5天’,‘6天’, ‘7天’,‘8天’,... ...,‘28天’,‘29天’]
Faker.days_values		生成的从1-30之间的随机天数,顺序是打乱的

2-常用视图

import pyecharts.options as opts
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType

2.1 柱状图

普通柱状图

from pyecharts.charts import Bar
bar_img = (Bar().add_xaxis(Faker.choose()).add_yaxis("淘宝",Faker.values()).add_yaxis("京东",Faker.values()).set_global_opts(# 旋转x轴的字体(更美观,防止重合.)xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-45)))
)
bar_img.render_notebook()

条形柱状图

横着显示.

bar_img = (Bar().add_xaxis(Faker.choose()).add_yaxis("淘宝",Faker.values()).add_yaxis("京东",Faker.values())# 翻转轴.reversal_axis().set_series_opts(# 字体显示在右边label_opts=opts.LabelOpts(position="right"))
​
)
bar_img.render_notebook()

堆叠柱状图

# 堆叠柱状图
bar_img = (Bar().add_xaxis(Faker.choose()).add_yaxis("淘宝",Faker.values(),stack="one").add_yaxis("京东",Faker.values(),stack="one").set_series_opts(#柱的值显不显示.label_opts=opts.LabelOpts(is_show=True))
)
bar_img.render_notebook()

象形柱状图

from pyecharts.charts import PictorialBar
from pyecharts.globals import SymbolType
​
pbar_img = (PictorialBar().add_xaxis(["小明","小红","小刚","王强","张伟","大伟","小帅"]).add_yaxis("票数",Faker.values(),# label_opts=opts.LabelOpts(is_show=False), #柱的值显不显示.(默认显示)label_opts=opts.LabelOpts(position="right"),#值在哪边显示.symbol=SymbolType.ROUND_RECT,  # 符号类型symbol_repeat="fixed",         # 重复方式symbol_size=30,                # 符号大小is_symbol_clip=True            # 特征长度.(不一样长)).reversal_axis()    #轴翻转..set_global_opts(xaxis_opts=opts.AxisOpts(is_show=False),  #不显示x轴yaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_show=False),  # y轴刻度隐藏axisline_opts=opts.AxisLineOpts(is_show=False)   # y轴线隐藏))
)
​
pbar_img.render_notebook()

颜色自定义

--嵌套js代码,修改颜色.

from pyecharts.commons.utils import JsCode
##js代码看if条件.
color_function = """function(params){if(params.value>=0 && params.value<=49){return 'red';} else if (params.value>=50 && params.value<=99){return 'green';}else if (params.value>=100 && params.value<=149){return 'blue';}}
"""
bar_img = (Bar().add_xaxis(Faker.choose())# .add_yaxis("淘宝",Faker.values(),itemstyle_opts=opts.ItemStyleOpts(color="blue")).add_yaxis("淘宝",Faker.values(),itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))).add_yaxis("京东",Faker.values(),itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function))).add_yaxis("天猫",Faker.values(),itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
​
)
bar_img.render_notebook()

2.2 折线图

from pyecharts.charts import Line

基本折线图

line_img = (Line().add_xaxis(Faker.choose()).add_yaxis("淘宝",Faker.values()).add_yaxis("京东",Faker.values(),is_smooth=True)   # 平滑曲线(曲线).set_global_opts(#添加提示线.tooltip_opts=opts.TooltipOpts(trigger="axis"))
)
line_img.render_notebook()

折线图常用属性

line_img = (Line(init_opts=opts.InitOpts(width="1000px",height="600px")  #设置像素大小.).add_xaxis(Faker.week).add_yaxis("淘宝",Faker.values()).add_yaxis("京东",Faker.values(),symbol="triangle",    # 形状symbol_size=15,       # 大小# 设置线条样式linestyle_opts=opts.LineStyleOpts(# 颜色，      宽度，   样式.color="green",width=3,type_="dashed"),label_opts=opts.LabelOpts(is_show=False),   # 不显示标签# 设置点的属性itemstyle_opts=opts.ItemStyleOpts(border_color="red",border_width=2),# 标注点markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_="max"),# 最大值标注opts.MarkPointItem(type_="min"),# 最大值标注]),# 标注线markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(type_="average")]))#添加提示线..set_global_opts(tooltip_opts=opts.TooltipOpts(trigger="axis")   )
)
line_img.render_notebook()

面积图

import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
​
line_img = (Line().add_xaxis(Faker.choose()).add_yaxis("商家A", Faker.values(), is_smooth=True).add_yaxis("商家B", Faker.values(), is_smooth=True).set_series_opts(# 添加面积，透明度0.5areastyle_opts=opts.AreaStyleOpts(opacity=0.5),label_opts=opts.LabelOpts(is_show=False),  #显不显示点值(默认显示). ).set_global_opts(title_opts=opts.TitleOpts(title="面积图"),xaxis_opts=opts.AxisOpts(# 紧贴 Y 轴(默认不紧贴,False紧贴)boundary_gap=False, ),#添加提示线.tooltip_opts=opts.TooltipOpts(trigger="axis")
​)
)
line_img.render_notebook()

堆叠面积图

line_img = (Line().add_xaxis(Faker.week).add_yaxis("淘宝",stack="堆叠",y_axis=Faker.values(),areastyle_opts=opts.AreaStyleOpts(opacity=0.7),# 添加面积部分颜色label_opts=opts.LabelOpts(is_show=False)    # 隐藏数据).add_yaxis("天猫",stack="堆叠",y_axis=Faker.values(),areastyle_opts=opts.AreaStyleOpts(opacity=0.7),# 添加面积部分颜色label_opts=opts.LabelOpts(is_show=False)).add_yaxis("京东",stack="堆叠",y_axis=Faker.values(),areastyle_opts=opts.AreaStyleOpts(opacity=0.7),# 添加面积部分颜色label_opts=opts.LabelOpts(is_show=False)).add_yaxis("拼多多",stack="堆叠",y_axis=Faker.values(),areastyle_opts=opts.AreaStyleOpts(opacity=0.7),# 添加面积部分颜色label_opts=opts.LabelOpts(is_show=False))
​.set_global_opts(tooltip_opts=opts.TooltipOpts(trigger="axis"),# 紧贴y轴xaxis_opts=opts.AxisOpts(boundary_gap=False),
​)
)
line_img.render_notebook()

2.3 散点图

from pyecharts.charts import Scatter
from pyecharts.faker import Faker
​
sca_img = (Scatter().add_xaxis(Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis("商家B", Faker.values()).add_yaxis("商家C", Faker.values()).add_yaxis("商家D", Faker.values()).set_global_opts(#设置主标题title_opts=opts.TitleOpts(title="Scatter-VisualMap(Color)"),visualmap_opts=opts.VisualMapOpts(max_=150),
#显不显示网格线:(x轴和y轴的)xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),yaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True)),)
)
sca_img.render_notebook()

2.4 双轴图

import pyecharts.options as opts
from pyecharts.charts import Bar, Line
​
​
x_data = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]
​
bar = (Bar().add_xaxis(xaxis_data=x_data).add_yaxis(series_name="蒸发量",y_axis=[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3],label_opts=opts.LabelOpts(is_show=False), # 不在展示坐标轴的数字).add_yaxis(series_name="降水量",y_axis=[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3],label_opts=opts.LabelOpts(is_show=False), # 不在展示坐标轴的数字).extend_axis(yaxis=opts.AxisOpts(name="温度",type_="value", # 连续数值min_=0,max_=25,interval=5,  # 间隔为5axislabel_opts=opts.LabelOpts(formatter="{value} °C"),)).set_global_opts(tooltip_opts=opts.TooltipOpts(# 提示线展示， 触发类型；轴触发 ，格式化x轴标签is_show=True, trigger="axis",axis_pointer_type="cross"),xaxis_opts=opts.AxisOpts(# 坐标轴指示配置项，展示x轴阴影axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),),yaxis_opts=opts.AxisOpts(name="水量",type_="value",min_=0,max_=250,interval=50,axislabel_opts=opts.LabelOpts(formatter="{value} ml"),# 坐标轴刻度配置项axistick_opts=opts.AxisTickOpts(is_show=True),# x轴的网格线splitline_opts=opts.SplitLineOpts(is_show=True),),)
)
​
line = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(series_name="平均温度",yaxis_index=1,y_axis=[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],label_opts=opts.LabelOpts(is_show=False),)
)
​
bar.overlap(line).render_notebook()

2.5 饼图

基本饼图

from pyecharts.charts import Pie
​
data_pie = list(zip(Faker.choose(),Faker.values()))
pie_img = (Pie().add("",[list(x) for x in data_pie]).set_colors(["red","yellow","orange","pink","green","blue","black"]).set_global_opts(title_opts=opts.TitleOpts(title="饮料价格"),legend_opts=opts.LegendOpts(type_="scroll",pos_right="10px",orient="vertaical")).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c}"))
)
​
pie_img.render_notebook()

空心饼图

from pyecharts.charts import PictorialBar
from pyecharts.globals import SymbolType
​
pie_img = (Pie().add("",[list(z) for z in zip(Faker.choose(), Faker.values())],radius=["40%", "75%"],).set_global_opts(title_opts=opts.TitleOpts(title="空心图s"),legend_opts=opts.LegendOpts(orient="vertical", pos_top="15%", pos_left="2%"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}")))
pie_img.render_notebook()

玫瑰图

key_pie = Faker.choose()
pie_img = (Pie().add("",[list(x) for x in zip(key_pie,Faker.values())],radius=["30%","75%"],# 半径大小center=["25%","50%"], # 位置rosetype="radius").add("",[list(x) for x in zip(key_pie,Faker.values())],radius=["30%","75%"],# 半径大小center=["80%","50%"], # 位置rosetype="radius")
)
pie_img.render_notebook()

图例调整

pie_image = (Pie().add("",[list(z)for z in zip(Faker.choose() + Faker.choose() + Faker.choose(),Faker.values() + Faker.values() + Faker.values(),)],center=["40%", "50%"],).set_global_opts(title_opts=opts.TitleOpts(title="Pie-Legend 滚动"),# 滚动类型，垂直展示legend_opts=opts.LegendOpts(type_="scroll", pos_left="80%", orient="vertical"),).set_series_opts(label_opts=opts.LabelOpts(formatter="{b}: {c}"))
)
pie_image.render_notebook()

文字调整

pie_image = (Pie().add(series_name="商品信息",data_pair=[list(z) for z in zip(Faker.choose(), Faker.values())],radius=["50%", "70%"],label_opts=opts.LabelOpts(is_show=False, position="center"),).set_global_opts(legend_opts=opts.LegendOpts(pos_left="legft", orient="vertical")).set_series_opts(tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b}: {c} ({d}%)"),# label_opts=opts.LabelOpts(formatter="{b}: {c}"))
​
)
pie_image.render_notebook()

2.6 雷达图

from pyecharts.charts import Radar
​
num1 = [[8000,10000,7500,6800,13000,15000]]
num2 = [[7000,8000,9500,8800,11000,12500]]
​
radar_img = (Radar().add_schema(schema=[opts.RadarIndicatorItem(name="demo1",max_=9000),opts.RadarIndicatorItem(name="demo2",max_=11000),opts.RadarIndicatorItem(name="demo3",max_=10000),opts.RadarIndicatorItem(name="demo4",max_=9000),opts.RadarIndicatorItem(name="demo5",max_=14000),opts.RadarIndicatorItem(name="demo6",max_=16000),]).add("数据1",num1,color="green").add("数据2",num2,color="orange")
)
radar_img.render_notebook()

单例模式

----一次只能选一个按钮显示数据.

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

2.7 时间轴

from pyecharts import options as opts
from pyecharts.charts import Pie, Timeline
from pyecharts.faker import Faker
​
attr = Faker.choose()
tl = Timeline()
for i in range(2015, 2020):pie = (Pie().add("商家A",[list(z) for z in zip(attr, Faker.values())],
#             rosetype="radius",radius=["30%", "55%"],).set_global_opts(title_opts=opts.TitleOpts("某商店{}年营业额".format(i))))tl.add(pie, "{}年".format(i))
tl.render_notebook()
#上面为空心饼图--
from pyecharts import options as opts
from pyecharts.charts import Bar, Timeline
from pyecharts.faker import Faker
​
x = Faker.choose()
tl = Timeline()
for i in range(2015, 2020):bar = (Bar().add_xaxis(x).add_yaxis("商家A", Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(title_opts=opts.TitleOpts("某商店{}年营业额".format(i))))tl.add(bar, "{}年".format(i))
tl.render_notebook()

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