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pyecharts折线图进阶篇
1. 基本折线图
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('折线图.html')所涉及参数
| 参数名 | 作用 |
|---|---|
| series_name | 图形名称 |
| y_axis | 数据 |
| symbol | 标记的图形（） |
| is_symbol_show | 是否显示 symbol |
- 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
2. 连接空数据（折线图）
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 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,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,
is_connect_nones=True
)
.set_global_opts(title_opts=opts.TitleOpts(title="Line-连接空数据"))
)
line.render()3.多条折线重叠
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,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()4.平滑曲线折线图
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()is_smooth：平滑曲线标志
5.阶梯图
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()is_step：阶梯图参数
6.变换折线的样式
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()所涉及参数
| 参数 | 作用 |
|---|---|
| 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 |
7.折线面积图
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()8.双横坐标折线图
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()9.用电量随时间变化
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()
所涉及参数
| 参数 | 效果 |
|---|---|
| visualmap_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） |
| markarea_opts | 标记区域配置项，data参数可以设置标记区域名称和位置。 |
