Python視覺化神器pyecharts繪製折線圖詳情

2022-07-07 18:02:39

雙摺線圖（氣溫最高最低溫度趨勢顯示）
面積折線圖（緊貼Y軸）
簡單折線圖（無動態和資料標籤）
連線空白資料折線圖
對數軸折線圖範例
折線圖堆疊（適合多個折線圖展示）
二維曲線折線圖（兩個資料）
多維度折線圖（顏色對比）
階梯折線圖
js高渲染折線圖

折線圖介紹

折線圖和柱狀圖一樣是我們日常視覺化最多的一個圖例，當然它的優勢和適用場景相信大家肯定不陌生，要想快速的得出趨勢，抓住趨勢二字，就會很快的想到要用折線圖來表示了。折線圖是通過直線將這些點按照某種順序連線起來形成的圖，適用於資料在一個有序的因變數上的變化，它的特點是反應事物隨類別而變化的趨勢，可以清晰展現資料的增減趨勢、增減的速率、增減的規律、峰值等特徵。

優點：

能很好的展現沿某個維度的變化趨勢
能比較多組資料在同一個維度上的趨勢
適合展現較巨量資料集

缺點：每張圖上不適合展示太多折線

折線圖模板系列

雙摺線圖（氣溫最高最低溫度趨勢顯示）

雙摺線圖在一張圖裡面顯示，肯定有一個相同的維度，然後有兩個不同的資料集。比如一天的溫度有最高的和最低的溫度，我們就可以用這個來作為展示了。

import pyecharts.options as opts
from pyecharts.charts import Line
week_name_list = ["週一", "週二", "週三", "週四", "週五", "週六", "週日"]
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,
# 設定刻度標籤
# 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"),
# ]
# ),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="未來一週氣溫變化", subtitle="副標題"),
# tooltip_opts=opts.TooltipOpts(trigger="axis"),
# toolbox_opts=opts.ToolboxOpts(is_show=True),
xaxis_opts=opts.AxisOpts(type_="category", boundary_gap=False),
)
.render("最低最高溫度折線圖.html")
)
print("圖表已生成！請查收！")

面積折線圖（緊貼Y軸）

還記得二重積分嗎，面積代表什麼？有時候我們就想要看誰圍出來的面積大，這個在物理的實際運用中比較常見，下面來看看效果吧。

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="標題"),
xaxis_opts=opts.AxisOpts(
axistick_opts=opts.AxisTickOpts(is_align_with_label=True),
is_scale=False,
boundary_gap=False,
name='類別',
name_location='middle',
name_gap=30, # 標籤與軸線之間的距離，預設為20，最好不要設定20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標籤字型大小
)),

yaxis_opts=opts.AxisOpts(
name='數量',
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
)
.render("面積折線圖-緊貼Y軸.html")
)
print("請查收！")

簡單折線圖（無動態和資料標籤）

此模板和Excel裡面的視覺化差不多，沒有一點功能元素，雖然它是最簡潔的，但是我們可以通過這個進行改動，在上面創作的畫作。

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='類別',
name_location='middle',
name_gap=30, # 標籤與軸線之間的距離，預設為20，最好不要設定20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標籤字型大小
)),
yaxis_opts=opts.AxisOpts(
type_="value",
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
name='數量',
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("簡單折線圖.html")
)

連線空白資料折線圖

有時候我們在處理資料的時候，發現有些類別的資料缺失了，這個時候我們想要它可以自動連線起來，那麼這個模板就可以用到了。

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="標題"),
xaxis_opts=opts.AxisOpts(
name='類別',
name_location='middle',
name_gap=30, # 標籤與軸線之間的距離，預設為20，最好不要設定20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標籤字型大小
)),
yaxis_opts=opts.AxisOpts(
name='數量',
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)
.render("資料缺失折線圖.html")
)

對數軸折線圖範例

此圖例未必用的上，當然也可以作為一個模板分享於此。

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",
y_axis=y_data_05,
linestyle_opts=opts.LineStyleOpts(width=2),
)
.add_yaxis(
series_name="2的指數", y_axis=y_data_2, linestyle_opts=opts.LineStyleOpts(width=2)
)
.add_yaxis(
series_name="3的指數", y_axis=y_data_3, linestyle_opts=opts.LineStyleOpts(width=2)
)
.set_global_opts(
title_opts=opts.TitleOpts(title="對數軸範例", 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("對數軸折線圖.html")
)

折線圖堆疊（適合多個折線圖展示）

多個折線圖展示要注意的是，資料量不能過於的接近，不然密密麻麻的折線，反而讓人看起來不舒服。

import pyecharts.options as opts
from pyecharts.charts import Line
from pyecharts.globals import ThemeType
x_data = ["週一", "週二", "週三", "週四", "週五", "週六", "週日"]
y_data = [820, 932, 901, 934, 1290, 1330, 1320]

(
Line({"theme": ThemeType.MACARONS})
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="郵件行銷",
stack="總量",
y_axis=[120, 132, 101, 134, 90, 230, 210],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="聯盟廣告",
stack="總量",
y_axis=[220, 182, 191, 234, 290, 330, 310],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="視訊廣告",
stack="總量",
y_axis=[150, 232, 201, 154, 190, 330, 410],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="直接存取",
stack="總量",
y_axis=[320, 332, 301, 334, 390, 330, 320],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="搜尋引擎",
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='類別',
name_location='middle',
name_gap=30, # 標籤與軸線之間的距離，預設為20，最好不要設定20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標籤字型大小
)),
)
.render("折線圖堆疊.html")
)

二維曲線折線圖（兩個資料）

有時候需要在一個圖裡面進行對比，那麼我們應該如何呈現一個絲滑般的曲線折線圖呢？看看這個

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) # 如果不想變成曲線就刪除即可
.add_yaxis("商家B", Faker.values(), is_smooth=True)
.set_global_opts(title_opts=opts.TitleOpts(title="標題"),
xaxis_opts=opts.AxisOpts(
name='類別',
name_location='middle',
name_gap=30, # 標籤與軸線之間的距離，預設為20，最好不要設定20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標籤字型大小
)),
yaxis_opts=opts.AxisOpts(
name='數量',
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
)

.render("二維折線圖.html")
)

多維度折線圖（顏色對比）

次模板的最大的好處就是可以移動滑鼠智慧顯示資料

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("多維顏色多維折線圖.html")
)

階梯折線圖

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="標題"),
xaxis_opts=opts.AxisOpts(
name='類別',
name_location='middle',
name_gap=30, # 標籤與軸線之間的距離，預設為20，最好不要設定20
name_textstyle_opts=opts.TextStyleOpts(
font_family='Times New Roman',
font_size=16 # 標籤字型大小
)),
yaxis_opts=opts.AxisOpts(
name='數量',
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
)
.render("階梯折線圖.html")
)

js高渲染折線圖

裡面的渲染效果相當好看，可以適用於炫酷的展示，資料集可以展示也可以不展示，在相應的位置更改引數即可。

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="註冊總量",
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")
)