政府网站建设问题分析百度热搜关键词排名

Pyecharts是一个用于生成 Echarts 图表的 Python 库。Echarts 是一个基于 JavaScript 的数据可视化库，提供了丰富的图表类型和交互功能。通过 Pyecharts，你可以使用 Python 代码生成各种类型的 Echarts 图表，例如折线图、柱状图、饼图、散点图等。

Pyecharts 提供了许多方便的 API 和方法，可以让你轻松地创建和定制自己的图表。以下是一些常用的 Pyecharts 方法：

Bar()：用于创建柱状图

Line()：用于创建折线图

Pie()：用于创建饼图

Scatter()：用于创建散点图

Option()：用于设置图表选项

render()：用于生成 HTML 文件并打开图表

01百分比源码如下

from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeType#两组数据
list2 = [{"value": 12, "percent": 12 / (12 + 3)},{"value": 23, "percent": 23 / (23 + 21)},{"value": 33, "percent": 33 / (33 + 5)},{"value": 3, "percent": 3 / (3 + 52)},{"value": 33, "percent": 33 / (33 + 43)},
]list3 = [{"value": 3, "percent": 3 / (12 + 3)},{"value": 21, "percent": 21 / (23 + 21)},{"value": 5, "percent": 5 / (33 + 5)},{"value": 52, "percent": 52 / (3 + 52)},{"value": 43, "percent": 43 / (33 + 43)},
]c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis([1, 2, 3, 4, 5]).add_yaxis("product1", list2, stack="stack1", category_gap="50%").add_yaxis("product2", list3, stack="stack1", category_gap="50%").set_series_opts(label_opts=opts.LabelOpts(position="right",formatter=JsCode("function(x){return Number(x.data.percent * 100).toFixed() + '%';}"),)).render("stack_bar_percent.html")
)

02源码示例

from pyecharts import options as opts
from pyecharts.charts import Barc = (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("bar_rotate_xaxis_label.html")
)

03源码示例

import pyecharts.options as opts
from pyecharts.charts import Bar, Linex_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),).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),).extend_axis(yaxis=opts.AxisOpts(name="温度",type_="value",min_=0,max_=25,interval=5,axislabel_opts=opts.LabelOpts(formatter="{value} °C"),)).set_global_opts(tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),xaxis_opts=opts.AxisOpts(type_="category",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"),axistick_opts=opts.AxisTickOpts(is_show=True),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("mixed_bar_and_line.html")

04示例源码

from pyecharts.charts import Bar
from pyecharts import options as optsx_data = [f"11月{str(i)}日" for i in range(1, 12)]
y_total = [0, 900, 1245, 1530, 1376, 1376, 1511, 1689, 1856, 1495, 1292]
y_in = [900, 345, 393, "-", "-", 135, 178, 286, "-", "-", "-"]
y_out = ["-", "-", "-", 108, 154, "-", "-", "-", 119, 361, 203]bar = (Bar().add_xaxis(xaxis_data=x_data).add_yaxis(series_name="",y_axis=y_total,stack="总量",itemstyle_opts=opts.ItemStyleOpts(color="rgba(0,0,0,0)"),).add_yaxis(series_name="收入", y_axis=y_in, stack="总量").add_yaxis(series_name="支出", y_axis=y_out, stack="总量").set_global_opts(yaxis_opts=opts.AxisOpts(type_="value")).render("bar_waterfall_plot.html")
)