pyecharts可视化

pip install pyecharts==1.7.1
在使用pyecharts过程中，它的数据一定是python原生的list

from pyecharts.globals import ThemeType
import pyecharts.options as opts   # 导入配置项
from pyecharts.charts import Line  # 导入折线图类
# from pyecharts.faker import Faker

c = (
    Line(init_opts=opts.InitOpts(theme=ThemeType.CHALK, width="1800px", height="800px"))                         # 创建折线图对象
    .add_xaxis([1,2,3,4])          # 添加x轴,数据
    .add_yaxis("商家A", [1,3,4], is_smooth=True)  # 添加y轴：图例,y数据
    .add_yaxis("商家B", [5,6,7,8], is_smooth=True)  # 添加y轴：图例,数据
    .set_global_opts(title_opts=opts.TitleOpts(title="Line-smooth"),
            tooltip_opts=opts.TooltipOpts(trigger="axis"),
            toolbox_opts=opts.ToolboxOpts(is_show=True, orient="vertical", pos_left="1%",  pos_top="10%"),
            xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False,),) # 全局配置项,指定标题
#     .render("line_smooth.html")    # 渲染到本地的网页呈现
)
c.render()     # 渲染到编辑器中

>pip install pyecharts==0.1.9.4
# #导入柱状图-Bar
# from pyecharts import Bar
# #设置行名
columns = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
#设置数据
data1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
data2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
# #设置柱状图的主标题与副标题
# bar = Bar("柱状图", "一年的降水量与蒸发量")
# #添加柱状图的数据及配置项
# bar.add("降水量", columns, data1, mark_line=["average"], mark_point=["max", "min"])
# bar.add("蒸发量", columns, data2, mark_line=["average"], mark_point=["max", "min"])
# #生成本地文件（默认为.html文件）
# bar.render()


# #导入饼图Pie
# from pyecharts import Pie
# #设置主标题与副标题，标题设置居中，设置宽度为900
# pie = Pie("饼状图", "一年的降水量与蒸发量",title_pos='center',width=900)
# #加入数据，设置坐标位置为【25，50】，上方的colums选项取消显示
# pie.add("降水量", columns, data1 ,center=[25,50],is_legend_show=False)
# #加入数据，设置坐标位置为【75，50】，上方的colums选项取消显示，显示label标签
# pie.add("蒸发量", columns, data2 ,center=[75,50],is_legend_show=False,is_label_show=True)
# #保存图表
# pie.render()


from pyecharts import Line
line = Line("折线图","一年的降水量与蒸发量")
#is_label_show是设置上方数据是否显示
line.add("降水量", columns, data1, is_label_show=True)
line.add("蒸发量", columns, data2, is_label_show=True)
line.render()

# from pyecharts import Radar
# radar = Radar("雷达图", "一年的降水量与蒸发量")
# #由于雷达图传入的数据得为多维数据，所以这里需要做一下处理
# radar_data1 = [[2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]]
# radar_data2 = [[2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]]
# #设置column的最大值，为了雷达图更为直观，这里的月份最大值设置有所不同
# schema = [ 
#     ("Jan", 5), ("Feb",10), ("Mar", 10),
#     ("Apr", 50), ("May", 50), ("Jun", 200),
#     ("Jul", 200), ("Aug", 200), ("Sep", 50),
#     ("Oct", 50), ("Nov", 10), ("Dec", 5)
# ]
# #传入坐标
# radar.config(schema)
# radar.add("降水量",radar_data1)
# #一般默认为同一种颜色，这里为了便于区分，需要设置item的颜色
# radar.add("蒸发量",radar_data2,item_color="#1C86EE")
# radar.render()

# from pyecharts import Scatter
# scatter = Scatter("散点图", "一年的降水量与蒸发量")
# #xais_name是设置横坐标名称，这里由于显示问题，还需要将y轴名称与y轴的距离进行设置
# scatter.add("降水量与蒸发量的散点分布", data1,data2,xaxis_name="降水量",yaxis_name="蒸发量",
#             yaxis_name_gap=40)
# scatter.render()


# #图表布局 Grid

# from pyecharts import Grid
# #设置折线图标题位置
# line = Line("折线图","一年的降水量与蒸发量",title_top="45%")
# line.add("降水量", columns, data1, is_label_show=True)
# line.add("蒸发量", columns, data2, is_label_show=True)
# grid = Grid()
# #设置两个图表的相对位置
# grid.add(bar, grid_bottom="60%")
# grid.add(line, grid_top="60%")
# grid.render()



# from pyecharts import Overlap
# overlap = Overlap()
# bar = Bar("柱状图-折线图合并", "一年的降水量与蒸发量")
# bar.add("降水量", columns, data1, mark_point=["max", "min"])
# bar.add("蒸发量", columns, data2, mark_point=["max", "min"])
# overlap.add(bar)
# overlap.add(line)
# overlap.render()