关于echartsaxispointer的信息
本篇文章给大家谈谈echartsaxispointer，以及对应的知识点，希望对各位有所帮助，不要忘了收藏本站喔。
本文目录一览：
Echarts条形堆叠图如何做到如下效果？
app.title = '堆叠条形图';
option = {
tooltip : {
trigger: 'axis',
axisPointer : { // 坐标轴指示器，坐标轴触发有效
type : 'shadow' // 默认为直线，可做腔选为：'line' | 'shadow'
}
},
legend: {
data: ['直接访问', '邮件营销','直接访问','邮件营销','直接访问']
},
grid: {
left: '3%',
right: '4%',
bottom: '3%',
containLabel: true
},
xAxis: {
type: 'value'
},
yAxis: {
type: 'category',
data: ['周一','周二','周三','周四','周五','周六','周日']
},
series: [
{
name: '直接庆码访问',
type: 'bar',
stack: '总量',
label: {
normal: {
show: false,
position: 'insideRight'
}
},
data: [320, 302, 301, 334, 390, 330, 320]
},
{
name: '邮件誉胡哪营销',
type: 'bar',
stack: '总量',
label: {
normal: {
show: false,
position: 'insideRight'
}
},
data: [120, 132, 101, 134, 90, 230, 210]
},
{
name: '直接访问',
type: 'bar',
stack: '总量',
label: {
normal: {
show: false,
position: 'insideRight'
}
},
data: [220, 182, 191, 234, 290, 330, 310]
},
{
name: '邮件营销',
type: 'bar',
stack: '总量',
label: {
normal: {
show: false,
position: 'insideRight'
}
},
data: [150, 212, 201, 154, 190, 330, 410]
},
{
name: '直接访问',
type: 'bar',
stack: '总量',
label: {
normal: {
show: false,
position: 'insideRight'
}
},
data: [820, 832, 901, 934, 1290, 1330, 1320]
},
{
name: '邮件营销',
type: 'bar',
stack: '总量',
label: {
normal: {
show: false,
position: 'insideRight'
}
},
data: [520, 532, 701, 334, 290, 430, 410]
}
]
};
[img]pyecharts柱状图进阶篇
如果想把温度和降雨量画在同一个柱谨锋状图内誉晌银，一个纵坐标就不够用了
import pyecharts.options as opts
from pyecharts.charts import Bar, 庆宴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(init_opts=opts.InitOpts(width="1000px", height="600px"))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="蒸发量",
yaxis_data=[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="平均温度",
yaxis_data=[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),
)
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),
),
)
)
bar.render_notebook()
简单的出场方式已经不能满足我的需要了，我需要酷炫一点的
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
l1=[100,200,300,400,500,400,300]
l2=[300,400,500,400,300,200,100]
bar = (
Bar(
init_opts=opts.InitOpts(
animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="bounceIn"
)
)
)
.add_xaxis(Faker.choose())
.add_yaxis("商家A", l1)
.add_yaxis("商家B", l2)
.set_global_opts(title_opts=opts.TitleOpts(title="Bar-动画配置基本示例", subtitle="我是副标题"))
)
bar.render_notebook()
pyecharts折线图进阶篇
import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y=[100,200,300,400,500,400,300]
line=(
Line()
.set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=False),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
)
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="基本折线图",
y_axis=y,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
)
)
line.render_notebook()
series_name：图形名称
y_axis：数据
symbol：标记的图形，
pyecharts提供的类型包括'circle','rect','roundRect','triangle','diamond','pin','arrow','none'，也可以通过'image://url'设置为图片，其中 URL 为图片的链接。is_symbol_show：是否显示 symbol
有时候我们要分析的数据存在空缺值，需要进行处理才能画出折线图
import pyecharts.options 如此 as opts
from pyecharts.charts import 举配Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y=[100,200,300,400,None,400,300]
line=(
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="连接空数据（折线图）",
y_axis=y,
)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-连接空数据"))
)
line.render_notebook()
import pyecharts.options as opts
from pyecharts.charts import 渣答迅Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
y2=[200,300,200,100,200,300,400]
line=(
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name="y1线",y_axis=y1,symbol="arrow",is_symbol_show=True)
.add_yaxis(series_name="y2线",y_axis=y2)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-多折线重叠"))
)
line.render_notebook()
import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
y2=[200,300,200,100,200,300,400]
line=(
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name="y1线",y_axis=y1, is_smooth=True)
.add_yaxis(series_name="y2线",y_axis=y2, is_smooth=True)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-多折线重叠"))
)
line.render_notebook()
import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
line=(
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name="y1线",y_axis=y1, is_step=True)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-阶梯图"))
)
line.render_notebook()
is_step：阶梯图参数
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.faker import Faker
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
"y1",
y1,
symbol="triangle",
symbol_size=30,
linestyle_opts=opts.LineStyleOpts(color="red", width=4, type_="dashed"),
itemstyle_opts=opts.ItemStyleOpts(
border_width=3, border_color="yellow", color="blue"
),
)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-ItemStyle"))
)
line.render_notebook()
linestyle_opts:折线样式配置color设置颜色，width设置宽度type设置类型，有'solid','dashed','dotted'三种类型 itemstyle_opts：图元样式配置，border_width设置描边宽度，border_color设置描边颜色，color设置纹理填充颜色
import pyecharts.options as opts
from pyecharts.charts import Line
x=['星期一','星期二','星期三','星期四','星期五','星期七','星期日']
y1=[100,200,300,400,100,400,300]
y2=[200,300,200,100,200,300,400]
line=(
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(series_name="y1线",y_axis=y1,areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
.add_yaxis(series_name="y2线",y_axis=y2,areastyle_opts=opts.AreaStyleOpts(opacity=0.5))
.set_global_opts(title_opts=opts.TitleOpts(title="Line-多折线重叠"))
)
line.render_notebook()
import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.commons.utils import JsCode
js_formatter ="""function (params) {
console.log(params);
return '降水量 ' + params.value + (params.seriesData.length ? '：' + params.seriesData[0].data : '');
}"""
line=(
Line()
.add_xaxis(
xaxis_data=[
"2016-1",
"2016-2",
"2016-3",
"2016-4",
"2016-5",
"2016-6",
"2016-7",
"2016-8",
"2016-9",
"2016-10",
"2016-11",
"2016-12",
]
)
.extend_axis(
xaxis_data=[
"2015-1",
"2015-2",
"2015-3",
"2015-4",
"2015-5",
"2015-6",
"2015-7",
"2015-8",
"2015-9",
"2015-10",
"2015-11",
"2015-12",
],
xaxis=opts.AxisOpts(
type_="category",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False, linestyle_opts=opts.LineStyleOpts(color="#6e9ef1")
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, label=opts.LabelOpts(formatter=JsCode(js_formatter))
),
),
)
.add_yaxis(
series_name="2015 降水量",
is_smooth=True,
symbol="emptyCircle",
is_symbol_show=False,
color="#d14a61",
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),
linestyle_opts=opts.LineStyleOpts(width=2),
)
.add_yaxis(
series_name="2016 降水量",
is_smooth=True,
symbol="emptyCircle",
is_symbol_show=False,
color="#6e9ef1",
y_axis=[3.9,5.9,11.1,18.7,48.3,69.2,231.6,46.6,55.4,18.4,10.3,0.7],
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
.set_global_opts(
legend_opts=opts.LegendOpts(),
tooltip_opts=opts.TooltipOpts(trigger="none", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(
type_="category",
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
axisline_opts=opts.AxisLineOpts(
is_on_zero=False, linestyle_opts=opts.LineStyleOpts(color="#d14a61")
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True, label=opts.LabelOpts(formatter=JsCode(js_formatter))
),
),
yaxis_opts=opts.AxisOpts(
type_="value",
splitline_opts=opts.SplitLineOpts(
is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)
),
),
)
)
line.render_notebook()
import pyecharts.options as opts
from pyecharts.charts import Line
x_data = ["00:00","01:15","02:30","03:45","05:00","06:15","07:30","08:45","10:00","11:15","12:30","13:45","15:00","16:15","17:30","18:45","20:00","21:15","22:30","23:45",]
y_data = [300,280,250,260,270,300,550,500,400,390,380,390,400,500,600,750,800,700,600,400,]
line=(
Line()
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="用电量",
y_axis=y_data,
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=2),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="一天用电量分布", subtitle="纯属虚构"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
xaxis_opts=opts.AxisOpts(boundary_gap=False),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(formatter="{value} W"),
splitline_opts=opts.SplitLineOpts(is_show=True),
),
visualmap_opts=opts.VisualMapOpts(
is_piecewise=True,
dimension=0,
pieces=[
{"lte":6,"color":"green"},
{"gt":6,"lte":8,"color":"red"},
{"gt":8,"lte":14,"color":"yellow"},
{"gt":14,"lte":17,"color":"red"},
{"gt":17,"color":"green"},
],
pos_right=0,
pos_bottom=100
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(
data=[
opts.MarkAreaItem(name="早高峰", x=("07:30","10:00")),
opts.MarkAreaItem(name="晚高峰", x=("17:30","21:15")),
]
)
)
)
line.render_notebook()
这里给大家介绍几个关键参数：
①visualmap_opts：视觉映射配置项，可以将折线分段并设置标签（is_piecewise），将不同段设置颜色（pieces）；
②markarea_opts：标记区域配置项，data参数可以设置标记区域名称和位置。
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