Python可視化神器pyecharts繪制折線(xiàn)圖詳情
折線(xiàn)圖介紹
折線(xiàn)圖和柱狀圖一樣是我們?nèi)粘?梢暬疃嗟囊粋€(gè)圖例,當(dāng)然它的優(yōu)勢(shì)和適用場(chǎng)景相信大家肯定不陌生,要想快速的得出趨勢(shì),抓住趨勢(shì)二字,就會(huì)很快的想到要用折線(xiàn)圖來(lái)表示了。折線(xiàn)圖是通過(guò)直線(xiàn)將這些點(diǎn)按照某種順序連接起來(lái)形成的圖,適用于數(shù)據(jù)在一個(gè)有序的因變量上的變化,它的特點(diǎn)是反應(yīng)事物隨類(lèi)別而變化的趨勢(shì),可以清晰展現(xiàn)數(shù)據(jù)的增減趨勢(shì)、增減的速率、增減的規(guī)律、峰值等特征。
優(yōu)點(diǎn):
- 能很好的展現(xiàn)沿某個(gè)維度的變化趨勢(shì)
- 能比較多組數(shù)據(jù)在同一個(gè)維度上的趨勢(shì)
- 適合展現(xiàn)較大數(shù)據(jù)集
缺點(diǎn):每張圖上不適合展示太多折線(xiàn)
折線(xiàn)圖模板系列
雙折線(xiàn)圖(氣溫最高最低溫度趨勢(shì)顯示)
雙折線(xiàn)圖在一張圖里面顯示,肯定有一個(gè)相同的維度,然后有兩個(gè)不同的數(shù)據(jù)集。比如一天的溫度有最高的和最低的溫度,我們就可以用這個(gè)來(lái)作為展示了。
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(
series_name="最高氣溫",
y_axis=high_temperature,
# 顯示最大值和最小值
# markpoint_opts=opts.MarkPointOpts(
# data=[
# opts.MarkPointItem(type_="max", name="最大值"),
# opts.MarkPointItem(type_="min", name="最小值"),
# ]
# ),
# 顯示平均值
# markline_opts=opts.MarkLineOpts(
# data=[opts.MarkLineItem(type_="average", name="平均值")]
# ),
)
.add_yaxis(
series_name="最低氣溫",
y_axis=low_temperature,
# 設(shè)置刻度標(biāo)簽
# 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="最高點(diǎn)"),
# ]
# ),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="未來(lái)一周氣溫變化", subtitle="副標(biāo)題"),
# tooltip_opts=opts.TooltipOpts(trigger="axis"),
# toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
.render("最低最高溫度折線(xiàn)圖.html")
)
print("圖表已生成!請(qǐng)查收!")
面積折線(xiàn)圖(緊貼Y軸)
還記得二重積分嗎,面積代表什么?有時(shí)候我們就想要看誰(shuí)圍出來(lái)的面積大,這個(gè)在物理的實(shí)際運(yùn)用中比較常見(jiàn),下面來(lái)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())
.add_yaxis("商家A", Faker.values(), is_smooth=True)
.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="標(biāo)題"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
name='類(lèi)別',
name_location='middle',
name_gap=30, # 標(biāo)簽與軸線(xiàn)之間的距離,默認(rèn)為20,最好不要設(shè)置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標(biāo)簽字體大小
)),
yaxis_opts=opts.AxisOpts(
name='數(shù)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() # 工具選項(xiàng)
)
.render("面積折線(xiàn)圖-緊貼Y軸.html")
)
print("請(qǐng)查收!")
簡(jiǎn)單折線(xiàn)圖(無(wú)動(dòng)態(tài)和數(shù)據(jù)標(biāo)簽)
此模板和Excel里面的可視化差不多,沒(méi)有一點(diǎn)功能元素,雖然它是最簡(jiǎn)潔的,但是我們可以通過(guò)這個(gè)進(jìn)行改動(dòng),在上面創(chuàng)作的畫(huà)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"]
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='類(lèi)別',
name_location='middle',
name_gap=30, # 標(biāo)簽與軸線(xiàn)之間的距離,默認(rèn)為20,最好不要設(shè)置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標(biāo)簽字體大小
)),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name='數(shù)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(
series_name="",
y_axis=y_data,
symbol="emptyCircle",
is_symbol_show=True,
label_opts=opts.LabelOpts(is_show=False),
)
.render("簡(jiǎn)單折線(xiàn)圖.html")
)
連接空白數(shù)據(jù)折線(xiàn)圖
有時(shí)候我們?cè)谔幚頂?shù)據(jù)的時(shí)候,發(fā)現(xiàn)有些類(lèi)別的數(shù)據(jù)缺失了,這個(gè)時(shí)候我們想要它可以自動(dòng)連接起來(lái),那么這個(gè)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="標(biāo)題"),
xaxis_opts=opts.AxisOpts(
name='類(lèi)別',
name_location='middle',
name_gap=30, # 標(biāo)簽與軸線(xiàn)之間的距離,默認(rèn)為20,最好不要設(shè)置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標(biāo)簽字體大小
)),
yaxis_opts=opts.AxisOpts(
name='數(shù)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() # 工具選項(xiàng))
.render("數(shù)據(jù)缺失折線(xiàn)圖.html")
)
對(duì)數(shù)軸折線(xiàn)圖示例
此圖例未必用的上,當(dāng)然也可以作為一個(gè)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(
series_name="1/2的指數(shù)",
y_axis=y_data_05,
linestyle_opts=opts.LineStyleOpts(width=2),
)
.add_yaxis(
series_name="2的指數(shù)", y_axis=y_data_2, linestyle_opts=opts.LineStyleOpts(width=2)
)
.add_yaxis(
series_name="3的指數(shù)", y_axis=y_data_3, linestyle_opts=opts.LineStyleOpts(width=2)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="對(duì)數(shù)軸示例", pos_left="center"),
tooltip_opts=opts.TooltipOpts(trigger="item", formatter="{a} <br/> : {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("對(duì)數(shù)軸折線(xiàn)圖.html")
)
折線(xiàn)圖堆疊(適合多個(gè)折線(xiàn)圖展示)
多個(gè)折線(xiàn)圖展示要注意的是,數(shù)據(jù)量不能過(guò)于的接近,不然密密麻麻的折線(xiàn),反而讓人看起來(lái)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="郵件營(yíng)銷(xiāo)",
stack="總量",
y_axis=[120, 132, 101, 134, 90, 230, 210],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="聯(lián)盟廣告",
stack="總量",
y_axis=[220, 182, 191, 234, 290, 330, 310],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="視頻廣告",
stack="總量",
y_axis=[150, 232, 201, 154, 190, 330, 410],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="直接訪(fǎng)問(wèn)",
stack="總量",
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="總量",
y_axis=[820, 932, 901, 934, 1290, 1330, 1320],
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="折線(xiàn)圖堆疊"),
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='數(shù)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='類(lèi)別',
name_location='middle',
name_gap=30, # 標(biāo)簽與軸線(xiàn)之間的距離,默認(rèn)為20,最好不要設(shè)置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標(biāo)簽字體大小
)),
)
.render("折線(xiàn)圖堆疊.html")
)
二維曲線(xiàn)折線(xiàn)圖(兩個(gè)數(shù)據(jù))
有時(shí)候需要在一個(gè)圖里面進(jìn)行對(duì)比,那么我們應(yīng)該如何呈現(xiàn)一個(gè)絲滑般的曲線(xiàn)折線(xiàn)圖呢?看看這個(gè)
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) # 如果不想變成曲線(xiàn)就刪除即可
.add_yaxis("商家B", Faker.values(), is_smooth=True)
.set_global_opts(title_opts=opts.TitleOpts(title="標(biāo)題"),
xaxis_opts=opts.AxisOpts(
name='類(lèi)別',
name_location='middle',
name_gap=30, # 標(biāo)簽與軸線(xiàn)之間的距離,默認(rèn)為20,最好不要設(shè)置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標(biāo)簽字體大小
)),
yaxis_opts=opts.AxisOpts(
name='數(shù)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() # 工具選項(xiàng)
)
.render("二維折線(xiàn)圖.html")
)
多維度折線(xiàn)圖(顏色對(duì)比)
次模板的最大的好處就是可以移動(dòng)鼠標(biāo)智能顯示數(shù)據(jù)
import pyecharts.options as opts
from pyecharts.charts import Line
# 將在 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("多維顏色多維折線(xiàn)圖.html")
)
階梯折線(xiàn)圖
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="標(biāo)題"),
xaxis_opts=opts.AxisOpts(
name='類(lèi)別',
name_location='middle',
name_gap=30, # 標(biāo)簽與軸線(xiàn)之間的距離,默認(rèn)為20,最好不要設(shè)置20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標(biāo)簽字體大小
)),
yaxis_opts=opts.AxisOpts(
name='數(shù)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() # 工具選項(xiàng)
)
.render("階梯折線(xiàn)圖.html")
)
js高渲染折線(xiàn)圖
里面的渲染效果相當(dāng)好看,可以適用于炫酷的展示,數(shù)據(jù)集可以展示也可以不展示,在相應(yīng)的位置更改參數(shù)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="注冊(cè)總量",
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")
)

所有圖表均可配置,無(wú)論是字體的大小,還是顏色,還是背景都可以自己配置喲!下期文章我們繼續(xù)探索折線(xiàn)圖的魅力喲!
到此這篇關(guān)于Python可視化神器pyecharts繪制折線(xiàn)圖詳情的文章就介紹到這了,更多相關(guān)python繪制折線(xiàn)圖內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家!
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