目录
折线图介绍
折线图和柱状图一样是我们日常可视化最多的一个图例，当然它的优势和适用场景相信大家肯定不陌生，要想快速的得出趋势，抓住趋势二字，就会很快的想到要用折线图来表示了。折线图是通过直线将这些点按照某种顺序连接起来形成的图，适用于数据在一个有序的因变量上的变化，它的特点是反应事物随类别而变化的趋势，可以清晰展现数据的增减趋势、增减的速率、增减的规律、峰值等特征。
优点：
- 能很好的展现沿某个维度的变化趋势
- 能比较多组数据在同一个维度上的趋势
- 适合展现较大数据集
缺点：每张图上不适合展示太多折线
折线图模板系列
双折线图（气温最高最低温度趋势显示）
双折线图在一张图里面显示，肯定有一个相同的维度，然后有两个不同的数据集。比如一天的温度有最高的和最低的温度，我们就可以用这个来作为展示了。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
week_name_list = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
-
high_temperature = [11, 11, 15, 13, 12, 13, 10]
-
low_temperature = [1, -2, 2, 5, 3, 2, 0]
-
(
-
Line(init_opts=opts.InitOpts(width="1000px", height="600px"))
-
.add_xaxis(xaxis_data=week_name_list)
-
.add_yaxis(
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series_name="最高气温",
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y_axis=high_temperature,
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# 显示最大值和最小值
-
# markpoint_opts=opts.MarkPointOpts(
-
# data=[
-
# opts.MarkPointItem(type_="max", name="最大值"),
-
# opts.MarkPointItem(type_="min", name="最小值"),
-
# ]
-
# ),
-
# 显示平均值
-
# markline_opts=opts.MarkLineOpts(
-
# data=[opts.MarkLineItem(type_="average", name="平均值")]
-
# ),
-
)
-
.add_yaxis(
-
series_name="最低气温",
-
y_axis=low_temperature,
-
# 设置刻度标签
-
# markpoint_opts=opts.MarkPointOpts(
-
# data=[opts.MarkPointItem(value=-2, name="周最低", x=1, y=-1.5)]
-
# ),
-
# markline_opts=opts.MarkLineOpts(
-
# data=[
-
# opts.MarkLineItem(type_="average", name="平均值"),
-
# opts.MarkLineItem(symbol="none", x="90%", y="max"),
-
# opts.MarkLineItem(symbol="circle", type_="max", name="最高点"),
-
# ]
-
# ),
-
)
-
.set_global_opts(
-
title_opts=opts.TitleOpts(title="未来一周气温变化", subtitle="副标题"),
-
# tooltip_opts=opts.TooltipOpts(trigger="axis"),
-
# toolbox_opts=opts.ToolboxOpts(is_show=True),
-
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
-
)
-
.render("最低最高温度折线图.html")
-
)
-
print("图表已生成！请查收！")
面积折线图（紧贴Y轴）
还记得二重积分吗，面积代表什么？有时候我们就想要看谁围出来的面积大，这个在物理的实际运用中比较常见，下面来看看效果吧。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.faker import Faker
-
from pyecharts.globals import ThemeType
-
-
c = (
-
Line({"theme": ThemeType.MACARONS})
-
.add_xaxis(Faker.choose())
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.add_yaxis("商家A", Faker.values(), is_smooth=True)
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.add_yaxis("商家B", Faker.values(), is_smooth=True)
-
.set_series_opts(
-
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.set_global_opts(
-
title_opts=opts.TitleOpts(title="标题"),
-
xaxis_opts=opts.AxisOpts(
-
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
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is_scale=False,
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boundary_gap=False,
-
name='类别',
-
name_location='middle',
-
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16 # 标签字体大小
-
)),
-
-
yaxis_opts=opts.AxisOpts(
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name='数量',
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name_location='middle',
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name_gap=30,
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16
-
# font_weight='bolder',
-
)),
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# toolbox_opts=opts.ToolboxOpts() # 工具选项
-
)
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.render("面积折线图-紧贴Y轴.html")
-
)
-
print("请查收！")
简单折线图（无动态和数据标签）
此模板和Excel里面的可视化差不多，没有一点功能元素，虽然它是最简洁的，但是我们可以通过这个进行改动，在上面创作的画作。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.globals import ThemeType
-
-
x_data = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
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y_data = [820, 932, 901, 934, 1290, 1330, 1320]
-
-
(
-
Line({"theme": ThemeType.MACARONS})
-
.set_global_opts(
-
tooltip_opts=opts.TooltipOpts(is_show=False),
-
xaxis_opts=opts.AxisOpts(
-
name='类别',
-
name_location='middle',
-
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16 # 标签字体大小
-
)),
-
yaxis_opts=opts.AxisOpts(
-
type_="value",
-
axistick_opts=opts.AxisTickOpts(is_show=True),
-
splitline_opts=opts.SplitLineOpts(is_show=True),
-
name='数量',
-
name_location='middle',
-
name_gap=30,
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16
-
# font_weight='bolder',
-
)),
-
-
-
)
-
.add_xaxis(xaxis_data=x_data)
-
.add_yaxis(
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series_name="",
-
y_axis=y_data,
-
symbol="emptyCircle",
-
is_symbol_show=True,
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.render("简单折线图.html")
-
)
连接空白数据折线图
有时候我们在处理数据的时候，发现有些类别的数据缺失了，这个时候我们想要它可以自动连接起来，那么这个模板就可以用到了。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.faker import Faker
-
from pyecharts.globals import ThemeType
-
-
y = Faker.values()
-
y[3], y[5] = None, None
-
c = (
-
Line({"theme": ThemeType.WONDERLAND})
-
.add_xaxis(Faker.choose())
-
.add_yaxis("商家A", y, is_connect_nones=True)
-
.set_global_opts(title_opts=opts.TitleOpts(title="标题"),
-
xaxis_opts=opts.AxisOpts(
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name='类别',
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name_location='middle',
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name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16 # 标签字体大小
-
)),
-
yaxis_opts=opts.AxisOpts(
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name='数量',
-
name_location='middle',
-
name_gap=30,
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16
-
# font_weight='bolder',
-
)), )
-
# toolbox_opts=opts.ToolboxOpts() # 工具选项)
-
.render("数据缺失折线图.html")
-
)
对数轴折线图示例
此图例未必用的上，当然也可以作为一个模板分享于此。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
-
x_data = ["一", "二", "三", "四", "五", "六", "七", "八", "九"]
-
y_data_3 = [1, 3, 9, 27, 81, 247, 741, 2223, 6669]
-
y_data_2 = [1, 2, 4, 8, 16, 32, 64, 128, 256]
-
y_data_05 = [1 / 2, 1 / 4, 1 / 8, 1 / 16, 1 / 32, 1 / 64, 1 / 128, 1 / 256, 1 / 512]
-
-
-
(
-
Line(init_opts=opts.InitOpts(width="1200px", height="600px"))
-
.add_xaxis(xaxis_data=x_data)
-
.add_yaxis(
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series_name="1/2的指数",
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y_axis=y_data_05,
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linestyle_opts=opts.LineStyleOpts(width=2),
-
)
-
.add_yaxis(
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series_name="2的指数", y_axis=y_data_2, linestyle_opts=opts.LineStyleOpts(width=2)
-
)
-
.add_yaxis(
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series_name="3的指数", y_axis=y_data_3, linestyle_opts=opts.LineStyleOpts(width=2)
-
)
-
.set_global_opts(
-
title_opts=opts.TitleOpts(title="对数轴示例", pos_left="center"),
-
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/>{b} : {c}"),
-
legend_opts=opts.LegendOpts(pos_left="left"),
-
xaxis_opts=opts.AxisOpts(type_="category", name="x"),
-
yaxis_opts=opts.AxisOpts(
-
type_="log",
-
name="y",
-
splitline_opts=opts.SplitLineOpts(is_show=True),
-
is_scale=True,
-
),
-
)
-
.render("对数轴折线图.html")
-
)
折线图堆叠（适合多个折线图展示）
多个折线图展示要注意的是，数据量不能过于的接近，不然密密麻麻的折线，反而让人看起来不舒服。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.globals import ThemeType
-
-
x_data = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
-
y_data = [820, 932, 901, 934, 1290, 1330, 1320]
-
-
(
-
Line({"theme": ThemeType.MACARONS})
-
.add_xaxis(xaxis_data=x_data)
-
.add_yaxis(
-
series_name="邮件营销",
-
stack="总量",
-
y_axis=[120, 132, 101, 134, 90, 230, 210],
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.add_yaxis(
-
series_name="联盟广告",
-
stack="总量",
-
y_axis=[220, 182, 191, 234, 290, 330, 310],
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.add_yaxis(
-
series_name="视频广告",
-
stack="总量",
-
y_axis=[150, 232, 201, 154, 190, 330, 410],
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.add_yaxis(
-
series_name="直接访问",
-
stack="总量",
-
y_axis=[320, 332, 301, 334, 390, 330, 320],
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.add_yaxis(
-
series_name="搜索引擎",
-
stack="总量",
-
y_axis=[820, 932, 901, 934, 1290, 1330, 1320],
-
label_opts=opts.LabelOpts(is_show=False),
-
)
-
.set_global_opts(
-
title_opts=opts.TitleOpts(title="折线图堆叠"),
-
tooltip_opts=opts.TooltipOpts(trigger="axis"),
-
yaxis_opts=opts.AxisOpts(
-
type_="value",
-
axistick_opts=opts.AxisTickOpts(is_show=True),
-
splitline_opts=opts.SplitLineOpts(is_show=True),
-
name='数量',
-
name_location='middle',
-
name_gap=40,
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16
-
# font_weight='bolder',
-
)),
-
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False,
-
name='类别',
-
name_location='middle',
-
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16 # 标签字体大小
-
)),
-
)
-
.render("折线图堆叠.html")
-
)
二维曲线折线图（两个数据）
有时候需要在一个图里面进行对比，那么我们应该如何呈现一个丝滑般的曲线折线图呢？看看这个
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.faker import Faker
-
-
c = (
-
Line()
-
-
.add_xaxis(Faker.choose())
-
.add_yaxis("商家A", Faker.values(), is_smooth=True) # 如果不想变成曲线就删除即可
-
.add_yaxis("商家B", Faker.values(), is_smooth=True)
-
.set_global_opts(title_opts=opts.TitleOpts(title="标题"),
-
xaxis_opts=opts.AxisOpts(
-
name='类别',
-
name_location='middle',
-
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16 # 标签字体大小
-
)),
-
yaxis_opts=opts.AxisOpts(
-
name='数量',
-
name_location='middle',
-
name_gap=30,
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16
-
# font_weight='bolder',
-
)),
-
# toolbox_opts=opts.ToolboxOpts() # 工具选项
-
)
-
-
.render("二维折线图.html")
-
)
多维度折线图（颜色对比）
次模板的最大的好处就是可以移动鼠标智能显示数据
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
-
# 将在 v1.1.0 中更改
-
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(init_opts=opts.InitOpts(width="1200px", height="600px"))
-
.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,
-
# xaxis_index=1,
-
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)
-
),
-
),
-
)
-
.render("多维颜色多维折线图.html")
-
)
阶梯折线图
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.faker import Faker
-
from pyecharts.globals import ThemeType
-
c = (
-
Line({"theme": ThemeType.MACARONS})
-
.add_xaxis(Faker.choose())
-
.add_yaxis("商家A", Faker.values(), is_step=True)
-
.set_global_opts(title_opts=opts.TitleOpts(title="标题"),
-
xaxis_opts=opts.AxisOpts(
-
name='类别',
-
name_location='middle',
-
name_gap=30, # 标签与轴线之间的距离，默认为20，最好不要设置20
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16 # 标签字体大小
-
)),
-
yaxis_opts=opts.AxisOpts(
-
name='数量',
-
name_location='middle',
-
name_gap=30,
-
name_textstyle_opts=opts.TextStyleOpts(
-
font_family='Times New Roman',
-
font_size=16
-
# font_weight='bolder',
-
)),
-
# toolbox_opts=opts.ToolboxOpts() # 工具选项
-
)
-
.render("阶梯折线图.html")
-
)
js高渲染折线图
里面的渲染效果相当好看，可以适用于炫酷的展示，数据集可以展示也可以不展示，在相应的位置更改参数即可。
-
import pyecharts.options as opts
-
from pyecharts.charts import Line
-
from pyecharts.commons.utils import JsCode
-
-
-
x_data = ["14", "15", "16", "17", "18", "19", "20", "21", "22", "23","24","25","26","27","28","29","30","31","32","33","34","35","36","37","38","39","40"]
-
y_data = [393, 438, 485, 631, 689, 824, 987, 1000, 1100, 1200,1500,1000,1700,1900,2000,500,1200,1300,1500,1800,1500,1900,1700,1000,1900,1800,2100,1600,2200,2300]
-
-
background_color_js = (
-
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
-
"[{offset: 0, color: '#c86589'}, {offset: 1, color: '#06a7ff'}], false)"
-
)
-
area_color_js = (
-
"new echarts.graphic.LinearGradient(0, 0, 0, 1, "
-
"[{offset: 0, color: '#eb64fb'}, {offset: 1, color: '#3fbbff0d'}], false)"
-
)
-
-
c = (
-
Line(init_opts=opts.InitOpts(bg_color=JsCode(background_color_js)))
-
.add_xaxis(xaxis_data=x_data)
-
.add_yaxis(
-
series_name="注册总量",
-
y_axis=y_data,
-
is_smooth=True,
-
is_symbol_show=True,
-
symbol="circle",
-
symbol_size=6,
-
linestyle_opts=opts.LineStyleOpts(color="#fff"),
-
label_opts=opts.LabelOpts(is_show=True, position="top", color="white"),
-
itemstyle_opts=opts.ItemStyleOpts(
-
color="red", border_color="#fff", border_width=3
-
),
-
tooltip_opts=opts.TooltipOpts(is_show=False),
-
areastyle_opts=opts.AreaStyleOpts(color=JsCode(area_color_js), opacity=1),
-
)
-
.set_global_opts(
-
title_opts=opts.TitleOpts(
-
title="OCTOBER 2015",
-
pos_bottom="5%",
-
pos_left="center",
-
title_textstyle_opts=opts.TextStyleOpts(color="#fff", font_size=16),
-
),
-
xaxis_opts=opts.AxisOpts(
-
type_="category",
-
boundary_gap=False,
-
axislabel_opts=opts.LabelOpts(margin=30, color="#ffffff63"),
-
axisline_opts=opts.AxisLineOpts(is_show=False),
-
axistick_opts=opts.AxisTickOpts(
-
is_show=True,
-
length=25,
-
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
-
),
-
splitline_opts=opts.SplitLineOpts(
-
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
-
),
-
),
-
yaxis_opts=opts.AxisOpts(
-
type_="value",
-
position="right",
-
axislabel_opts=opts.LabelOpts(margin=20, color="#ffffff63"),
-
axisline_opts=opts.AxisLineOpts(
-
linestyle_opts=opts.LineStyleOpts(width=2, color="#fff")
-
),
-
axistick_opts=opts.AxisTickOpts(
-
is_show=True,
-
length=15,
-
linestyle_opts=opts.LineStyleOpts(color="#ffffff1f"),
-
),
-
splitline_opts=opts.SplitLineOpts(
-
is_show=True, linestyle_opts=opts.LineStyleOpts(color="#ffffff1f")
-
),
-
),
-
legend_opts=opts.LegendOpts(is_show=False),
-
)
-
.render("高渲染.html")
-
)
所有图表均可配置，无论是字体的大小，还是颜色，还是背景都可以自己配置哟！下期文章我们继续探索折线图的魅力哟！
每文一语
万物皆对象，一切可配置！
文章来源: blog.csdn.net，作者：王小王-123，版权归原作者所有，如需转载，请联系作者。
原文链接：blog.csdn.net/weixin_47723732/article/details/113868461