雷達(dá)圖是以從同一點(diǎn)開始的軸上表示的三個(gè)或更多個(gè)定量變量的二維圖表的形式顯示多變量數(shù)據(jù)的圖形方法,雷達(dá)圖通常用于綜合分析多個(gè)指標(biāo)，具有完整，清晰和直觀的優(yōu)點(diǎn)。
這類雷達(dá)圖一般用于比較同類事物不同緯度性能的優(yōu)劣，以奧迪A4L時(shí)尚動(dòng)感型和凱迪拉克CT4精英型為例，我們來畫一下這兩種汽車的雷達(dá)圖，代碼如下：
import pyecharts.options as opts
from pyecharts.charts import Radar
v1 = [[110, 9.7, 6.2, 56, 150, 1610]]
v2 = [[174, 6.9, 6.8, 66, 237, 1540]]
c=(
Radar(init_opts=opts.InitOpts(bg_color="#3CB371")) #設(shè)置背景顏色
.add_schema(
schema=[
opts.RadarIndicatorItem(name="最大功率率（KW）", max_=200),
opts.RadarIndicatorItem(name="百米提速（秒）", max_=12),
opts.RadarIndicatorItem(name="綜合油耗（L/100KM）", max_=20),
opts.RadarIndicatorItem(name="油箱容積（L）", max_=100),
opts.RadarIndicatorItem(name="馬力(Ps)", max_=300),
opts.RadarIndicatorItem(name="整車質(zhì)量KG（）", max_=2000),
],
splitarea_opt=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1) #是否顯示分隔區(qū)域，透明度設(shè)置為1
),
textstyle_opts=opts.TextStyleOpts(color="#fff"),
)
.add(
series_name="奧迪A4L時(shí)尚動(dòng)感型",
data=v1,
linestyle_opts=opts.LineStyleOpts(color="#8B008B",width=2), #線的顏色、寬度
)
.add(
series_name="凱迪拉克CT4精英型",
data=v2,
linestyle_opts=opts.LineStyleOpts(color="#FFA500",width=2), #線的顏色、寬度
)
.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) #不顯示數(shù)字
.set_global_opts(
title_opts=opts.TitleOpts(title="汽車性能比較"), legend_opts=opts.LegendOpts()
)
)
c.render_notebook()
from pyecharts import options as opts
from pyecharts.charts import Radar
value_bj = [
[55, 9, 56, 0.46, 18, 6, 1],
[25, 11, 21, 0.65, 34, 9, 2],
[56, 7, 63, 0.3, 14, 5, 3],
[33, 7, 29, 0.33, 16, 6, 4],
[42, 24, 44, 0.76, 40, 16, 5],
[82, 58, 90, 1.77, 68, 33, 6],
[74, 49, 77, 1.46, 48, 27, 7],
[78, 55, 80, 1.29, 59, 29, 8],
[267, 216, 280, 4.8, 108, 64, 9],
[185, 127, 216, 2.52, 61, 27, 10],
[39, 19, 38, 0.57, 31, 15, 11],
[41, 11, 40, 0.43, 21, 7, 12],
]
value_sh = [
[91, 45, 125, 0.82, 34, 23, 1],
[65, 27, 78, 0.86, 45, 29, 2],
[83, 60, 84, 1.09, 73, 27, 3],
[109, 81, 121, 1.28, 68, 51, 4],
[106, 77, 114, 1.07, 55, 51, 5],
[109, 81, 121, 1.28, 68, 51, 6],
[106, 77, 114, 1.07, 55, 51, 7],
[89, 65, 78, 0.86, 51, 26, 8],
[53, 33, 47, 0.64, 50, 17, 9],
[80, 55, 80, 1.01, 75, 24, 10],
[117, 81, 124, 1.03, 45, 24, 11],
[99, 71, 142, 1.1, 62, 42, 12],
]
c_schema = [
{"name": "AQI", "max": 300, "min": 5},
{"name": "PM2.5", "max": 250, "min": 20},
{"name": "PM10", "max": 300, "min": 5},
{"name": "CO", "max": 5},
{"name": "NO2", "max": 200},
{"name": "SO2", "max": 100},
]
c = (
Radar(init_opts=opts.InitOpts(bg_color="#8B658B"))
.add_schema(schema=c_schema, shape="polygon")
.add("北京", value_bj,color="#8B008B",linestyle_opts=opts.LineStyleOpts(width=2))
.add("上海", value_sh,color="#FF4500",linestyle_opts=opts.LineStyleOpts(width=2))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="一線城市空氣質(zhì)量比較"))
)
c.render_notebook()
通過增加數(shù)據(jù)種類，可以比較同一緯度、不同時(shí)間下的差距，如上圖，通過展示北京、上海兩座城市12天的天氣情況，可以清晰的看出上海的天氣要比北京好。
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