Python中怎么绘制各种折线图，很多新手对此不是很清楚，为了帮助大家解决这个难题，下面小编将为大家详细讲解，有这方面需求的人可以来学习下，希望你能有所收获。
创新互联建站是一家集网站建设,喀左企业网站建设,喀左品牌网站建设,网站定制,喀左网站建设报价,网络营销,网络优化,喀左网站推广为一体的创新建站企业，帮助传统企业提升企业形象加强企业竞争力。可充分满足这一群体相比中小企业更为丰富、高端、多元的互联网需求。同时我们时刻保持专业、时尚、前沿，时刻以成就客户成长自我，坚持不断学习、思考、沉淀、净化自己，让我们为更多的企业打造出实用型网站。
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 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_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,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()
is_smooth：平滑曲线标志
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()
重庆分公司
重庆分公司
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。
看完上述内容是否对您有帮助呢？如果还想对相关知识有进一步的了解或阅读更多相关文章，请关注创新互联行业资讯频道，感谢您对创新互联的支持。
