Pyecharts-5-绘制柱状图Bar
本文中介绍的是如何利用pyecharts中绘制各种bar柱状图。
主要是参考官网的各种例子进行学习和整理
导入各种库
代码语言：javascript复制from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeType
import pandas as pd
import numpy as np数值 百分比
在柱状图中同时显示数值和百分比，能够对比不同的组之间的数据
代码语言：javascript复制list2 = [
{"value": 18, "percent": 18 / (18 3)},
{"value": 23, "percent": 23 / (23 21)},
{"value": 33, "percent": 33 / (33 5)}
]
list3 = [
{"value": 3, "percent": 3 / (18 3)},
{"value": 21, "percent": 21 / (23 21)},
{"value": 5, "percent": 5 / (33 5)}
]
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)) # 使用的主题
.add_xaxis([1, 2, 3])
.add_yaxis("product1", list2, stack="stack1", category_gap="50%") # 堆叠 间隔
.add_yaxis("product2", list3, stack="stack1", category_gap="50%")
.set_series_opts(
label_opts=opts.LabelOpts(
position="right",
formatter=JsCode( # 显示的形式
"function(x){return Number(x.data.percent * 100).toFixed() '%';}"
),
)
)
)
c.render_notebook()
x轴标题斜体
解决x轴标题很长的问题
代码语言：javascript复制c = (
Bar()
.add_xaxis(
[
"名字很长的X轴标签1", # x轴标签
"名字很长的X轴标签2",
"名字很长的X轴标签3"
]
)
.add_yaxis("商家A", [10, 20, 30]) # 加入两组y轴数据
.add_yaxis("商家B", [20, 10, 40])
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)), # 设置旋转角度
title_opts=opts.TitleOpts(title="Bar-旋转X轴标签", subtitle="标题真的很长"), # 主标题和副标题
)
)
c.render_notebook()
堆叠柱状图-stack
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), stack="stack1") # 两组堆叠数据
.add_yaxis("商家B", Faker.values(), stack="stack1")
.add_yaxis("商家C", Faker.values()) # 不参与堆叠
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠数据（部分）"))
# .render("bar_stack1.html") 用于生成html格式文件
)
c.render_notebook() # notebook在线显示
通过字典设置主副标题
代码语言：javascript复制c = (
Bar({"theme": ThemeType.LIGHT})
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
# 通过字典形式设置两个标题
title_opts={"text": "主标题（字典设置）", "subtext": "副标题（字典配置）"}
)
# .render("bar_base_dict_config.html")
)
c.render_notebook()
加入工具栏目
右上角的工具栏进行多种操作
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-显示 ToolBox"),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
)
# .render("bar_toolbox.html")
)
c.render_notebook()
设置xy轴名称
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-XY设置新名字"),
yaxis_opts=opts.AxisOpts(name="Y轴新名字"),
xaxis_opts=opts.AxisOpts(name="X轴新名字"),
)
# .render("bar_xyaxis_name.html")
)
c.render_notebook()
垂直缩放
按照数值大小进行滑动
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values, color=Faker.rand_color())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（垂直活动条）"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
)
# .render("bar_datazoom_slider_vertical.html")
)
c.render_notebook()
水平缩放
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values)
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-水平）"),
datazoom_opts=opts.DataZoomOpts(),
)
# .render("bar_datazoom_slider.html")
)
c.render_notebook()
双边滑动（水平方向）
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values, color=Faker.rand_color())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（水平方向双边滑动）"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
)
# .render("bar_datazoom_both.html")
)
c.render_notebook()
绘制直方图
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.choose())
# 将间隔设置成0就变成了直方图histogram
.add_yaxis("商家A", Faker.values(), category_gap=0, color=Faker.rand_color())
.set_global_opts(title_opts=opts.TitleOpts(title="Bar-直方图"))
# .render("bar_histogram.html")
)
c.render_notebook()
水平柱状图
代码语言：javascript复制c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values(), category_gap=0)
.add_yaxis("商家B", Faker.values(), category_gap=0)
.reversal_axis() # 翻转坐标轴
.set_series_opts(label_opts=opts.LabelOpts(position="right")) # label的位置
.set_global_opts(title_opts=opts.TitleOpts(title="水平柱状图(间隔为0)"))
# .render("bar_reversal_axis.html")
)
c.render_notebook()
c = (
Bar()
.add_xaxis(Faker.choose())
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.reversal_axis() # 翻转坐标轴
.set_series_opts(label_opts=opts.LabelOpts(position="right")) # label的位置
.set_global_opts(title_opts=opts.TitleOpts(title="水平柱状图"))
# .render("bar_reversal_axis.html")
)
c.render_notebook()
多个指标联动
代码语言：javascript复制import pyecharts.options as opts
from pyecharts.charts import Timeline, Bar, Pie
total_data = {}
name_list = [
"北京",
"天津",
"河北",
"山西",
"内蒙古",
"辽宁",
"吉林",
"黑龙江",
"上海",
"江苏",
"浙江",
"安徽",
"福建",
"江西",
"山东",
"河南",
"湖北",
"湖南",
"广东",
"广西",
"海南",
"重庆",
"四川",
"贵州",
"云南",
"西藏",
"陕西",
"甘肃",
"青海",
"宁夏",
"新疆",
]
data_gdp = {
2011: [
16251.93,
11307.28,
24515.76,
11237.55,
14359.88,
22226.7,
10568.83,
12582,
19195.69,
49110.27,
32318.85,
15300.65,
17560.18,
11702.82,
45361.85,
26931.03,
19632.26,
19669.56,
53210.28,
11720.87,
2522.66,
10011.37,
21026.68,
5701.84,
8893.12,
605.83,
12512.3,
5020.37,
1670.44,
2102.21,
6610.05,
],
2010: [
14113.58,
9224.46,
20394.26,
9200.86,
11672,
18457.27,
8667.58,
10368.6,
17165.98,
41425.48,
27722.31,
12359.33,
14737.12,
9451.26,
39169.92,
23092.36,
15967.61,
16037.96,
46013.06,
9569.85,
2064.5,
7925.58,
17185.48,
4602.16,
7224.18,
507.46,
10123.48,
4120.75,
1350.43,
1689.65,
5437.47,
],
2009: [
12153.03,
7521.85,
17235.48,
7358.31,
9740.25,
15212.49,
7278.75,
8587,
15046.45,
34457.3,
22990.35,
10062.82,
12236.53,
7655.18,
33896.65,
19480.46,
12961.1,
13059.69,
39482.56,
7759.16,
1654.21,
6530.01,
14151.28,
3912.68,
6169.75,
441.36,
8169.8,
3387.56,
1081.27,
1353.31,
4277.05,
],
2008: [
11115,
6719.01,
16011.97,
7315.4,
8496.2,
13668.58,
6426.1,
8314.37,
14069.87,
30981.98,
21462.69,
8851.66,
10823.01,
6971.05,
30933.28,
18018.53,
11328.92,
11555,
36796.71,
7021,
1503.06,
5793.66,
12601.23,
3561.56,
5692.12,
394.85,
7314.58,
3166.82,
1018.62,
1203.92,
4183.21,
],
2007: [
9846.81,
5252.76,
13607.32,
6024.45,
6423.18,
11164.3,
5284.69,
7104,
12494.01,
26018.48,
18753.73,
7360.92,
9248.53,
5800.25,
25776.91,
15012.46,
9333.4,
9439.6,
31777.01,
5823.41,
1254.17,
4676.13,
10562.39,
2884.11,
4772.52,
341.43,
5757.29,
2703.98,
797.35,
919.11,
3523.16,
],
2006: [
8117.78,
4462.74,
11467.6,
4878.61,
4944.25,
9304.52,
4275.12,
6211.8,
10572.24,
21742.05,
15718.47,
6112.5,
7583.85,
4820.53,
21900.19,
12362.79,
7617.47,
7688.67,
26587.76,
4746.16,
1065.67,
3907.23,
8690.24,
2338.98,
3988.14,
290.76,
4743.61,
2277.35,
648.5,
725.9,
3045.26,
]
}
data_pi = {
2011: [
136.27,
159.72,
2905.73,
641.42,
1306.3,
1915.57,
1277.44,
1701.5,
124.94,
3064.78,
1583.04,
2015.31,
1612.24,
1391.07,
3973.85,
3512.24,
2569.3,
2768.03,
2665.2,
2047.23,
659.23,
844.52,
2983.51,
726.22,
1411.01,
74.47,
1220.9,
678.75,
155.08,
184.14,
1139.03,
],
2010: [
124.36,
145.58,
2562.81,
554.48,
1095.28,
1631.08,
1050.15,
1302.9,
114.15,
2540.1,
1360.56,
1729.02,
1363.67,
1206.98,
3588.28,
3258.09,
2147,
2325.5,
2286.98,
1675.06,
539.83,
685.38,
2482.89,
625.03,
1108.38,
68.72,
988.45,
599.28,
134.92,
159.29,
1078.63,
],
2009: [
118.29,
128.85,
2207.34,
477.59,
929.6,
1414.9,
980.57,
1154.33,
113.82,
2261.86,
1163.08,
1495.45,
1182.74,
1098.66,
3226.64,
2769.05,
1795.9,
1969.69,
2010.27,
1458.49,
462.19,
606.8,
2240.61,
550.27,
1067.6,
63.88,
789.64,
497.05,
107.4,
127.25,
759.74,
],
2008: [
112.83,
122.58,
2034.59,
313.58,
907.95,
1302.02,
916.72,
1088.94,
111.8,
2100.11,
1095.96,
1418.09,
1158.17,
1060.38,
3002.65,
2658.78,
1780,
1892.4,
1973.05,
1453.75,
436.04,
575.4,
2216.15,
539.19,
1020.56,
60.62,
753.72,
462.27,
105.57,
118.94,
691.07,
],
2007: [
101.26,
110.19,
1804.72,
311.97,
762.1,
1133.42,
783.8,
915.38,
101.84,
1816.31,
986.02,
1200.18,
1002.11,
905.77,
2509.14,
2217.66,
1378,
1626.48,
1695.57,
1241.35,
361.07,
482.39,
2032,
446.38,
837.35,
54.89,
592.63,
387.55,
83.41,
97.89,
628.72,
],
2006: [
88.8,
103.35,
1461.81,
276.77,
634.94,
939.43,
672.76,
750.14,
93.81,
1545.05,
925.1,
1011.03,
865.98,
786.14,
2138.9,
1916.74,
1140.41,
1272.2,
1532.17,
1032.47,
323.48,
386.38,
1595.48,
382.06,
724.4,
50.9,
484.81,
334,
67.55,
79.54,
527.8,
]
}
data_si = {
2011: [
3752.48,
5928.32,
13126.86,
6635.26,
8037.69,
12152.15,
5611.48,
5962.41,
7927.89,
25203.28,
16555.58,
8309.38,
9069.2,
6390.55,
24017.11,
15427.08,
9815.94,
9361.99,
26447.38,
5675.32,
714.5,
5543.04,
11029.13,
2194.33,
3780.32,
208.79,
6935.59,
2377.83,
975.18,
1056.15,
3225.9,
],
2010: [
3388.38,
4840.23,
10707.68,
5234,
6367.69,
9976.82,
4506.31,
5025.15,
7218.32,
21753.93,
14297.93,
6436.62,
7522.83,
5122.88,
21238.49,
13226.38,
7767.24,
7343.19,
23014.53,
4511.68,
571,
4359.12,
8672.18,
1800.06,
3223.49,
163.92,
5446.1,
1984.97,
744.63,
827.91,
2592.15,
],
2009: [
2855.55,
3987.84,
8959.83,
3993.8,
5114,
7906.34,
3541.92,
4060.72,
6001.78,
18566.37,
11908.49,
4905.22,
6005.3,
3919.45,
18901.83,
11010.5,
6038.08,
5687.19,
19419.7,
3381.54,
443.43,
3448.77,
6711.87,
1476.62,
2582.53,
136.63,
4236.42,
1527.24,
575.33,
662.32,
1929.59,
],
2008: [
2626.41,
3709.78,
8701.34,
4242.36,
4376.19,
7158.84,
3097.12,
4319.75,
6085.84,
16993.34,
11567.42,
4198.93,
5318.44,
3554.81,
17571.98,
10259.99,
5082.07,
5028.93,
18502.2,
3037.74,
423.55,
3057.78,
5823.39,
1370.03,
2452.75,
115.56,
3861.12,
1470.34,
557.12,
609.98,
2070.76,
],
2007: [
2509.4,
2892.53,
7201.88,
3454.49,
3193.67,
5544.14,
2475.45,
3695.58,
5571.06,
14471.26,
10154.25,
3370.96,
4476.42,
2975.53,
14647.53,
8282.83,
4143.06,
3977.72,
16004.61,
2425.29,
364.26,
2368.53,
4648.79,
1124.79,
2038.39,
98.48,
2986.46,
1279.32,
419.03,
455.04,
1647.55,
],
2006: [
2191.43,
2457.08,
6110.43,
2755.66,
2374.96,
4566.83,
1915.29,
3365.31,
4969.95,
12282.89,
8511.51,
2711.18,
3695.04,
2419.74,
12574.03,
6724.61,
3365.08,
3187.05,
13469.77,
1878.56,
308.62,
1871.65,
3775.14,
967.54,
1705.83,
80.1,
2452.44,
1043.19,
331.91,
351.58,
1459.3,
]
}
data_ti = {
2011: [
12363.18,
5219.24,
8483.17,
3960.87,
5015.89,
8158.98,
3679.91,
4918.09,
11142.86,
20842.21,
14180.23,
4975.96,
6878.74,
3921.2,
17370.89,
7991.72,
7247.02,
7539.54,
24097.7,
3998.33,
1148.93,
3623.81,
7014.04,
2781.29,
3701.79,
322.57,
4355.81,
1963.79,
540.18,
861.92,
2245.12,
],
2010: [
10600.84,
4238.65,
7123.77,
3412.38,
4209.03,
6849.37,
3111.12,
4040.55,
9833.51,
17131.45,
12063.82,
4193.69,
5850.62,
3121.4,
14343.14,
6607.89,
6053.37,
6369.27,
20711.55,
3383.11,
953.67,
2881.08,
6030.41,
2177.07,
2892.31,
274.82,
3688.93,
1536.5,
470.88,
702.45,
1766.69,
],
2009: [
9179.19,
3405.16,
6068.31,
2886.92,
3696.65,
5891.25,
2756.26,
3371.95,
8930.85,
13629.07,
9918.78,
3662.15,
5048.49,
2637.07,
11768.18,
5700.91,
5127.12,
5402.81,
18052.59,
2919.13,
748.59,
2474.44,
5198.8,
1885.79,
2519.62,
240.85,
3143.74,
1363.27,
398.54,
563.74,
1587.72,
],
2008: [
8375.76,
2886.65,
5276.04,
2759.46,
3212.06,
5207.72,
2412.26,
2905.68,
7872.23,
11888.53,
8799.31,
3234.64,
4346.4,
2355.86,
10358.64,
5099.76,
4466.85,
4633.67,
16321.46,
2529.51,
643.47,
2160.48,
4561.69,
1652.34,
2218.81,
218.67,
2699.74,
1234.21,
355.93,
475,
1421.38,
],
2007: [
7236.15,
2250.04,
4600.72,
2257.99,
2467.41,
4486.74,
2025.44,
2493.04,
6821.11,
9730.91,
7613.46,
2789.78,
3770,
1918.95,
8620.24,
4511.97,
3812.34,
3835.4,
14076.83,
2156.76,
528.84,
1825.21,
3881.6,
1312.94,
1896.78,
188.06,
2178.2,
1037.11,
294.91,
366.18,
1246.89,
],
2006: [
5837.55,
1902.31,
3895.36,
1846.18,
1934.35,
3798.26,
1687.07,
2096.35,
5508.48,
7914.11,
6281.86,
2390.29,
3022.83,
1614.65,
7187.26,
3721.44,
3111.98,
3229.42,
11585.82,
1835.12,
433.57,
1649.2,
3319.62,
989.38,
1557.91,
159.76,
1806.36,
900.16,
249.04,
294.78,
1058.16,
]
}
data_estate = {
2011: [
12363.18,
5219.24,
8483.17,
3960.87,
5015.89,
8158.98,
3679.91,
4918.09,
11142.86,
20842.21,
14180.23,
4975.96,
6878.74,
3921.2,
17370.89,
7991.72,
7247.02,
7539.54,
24097.7,
3998.33,
1148.93,
3623.81,
7014.04,
2781.29,
3701.79,
322.57,
4355.81,
1963.79,
540.18,
861.92,
2245.12,
],
2010: [
10600.84,
4238.65,
7123.77,
3412.38,
4209.03,
6849.37,
3111.12,
4040.55,
9833.51,
17131.45,
12063.82,
4193.69,
5850.62,
3121.4,
14343.14,
6607.89,
6053.37,
6369.27,
20711.55,
3383.11,
953.67,
2881.08,
6030.41,
2177.07,
2892.31,
274.82,
3688.93,
1536.5,
470.88,
702.45,
1766.69,
],
2009: [
9179.19,
3405.16,
6068.31,
2886.92,
3696.65,
5891.25,
2756.26,
3371.95,
8930.85,
13629.07,
9918.78,
3662.15,
5048.49,
2637.07,
11768.18,
5700.91,
5127.12,
5402.81,
18052.59,
2919.13,
748.59,
2474.44,
5198.8,
1885.79,
2519.62,
240.85,
3143.74,
1363.27,
398.54,
563.74,
1587.72,
],
2008: [
8375.76,
2886.65,
5276.04,
2759.46,
3212.06,
5207.72,
2412.26,
2905.68,
7872.23,
11888.53,
8799.31,
3234.64,
4346.4,
2355.86,
10358.64,
5099.76,
4466.85,
4633.67,
16321.46,
2529.51,
643.47,
2160.48,
4561.69,
1652.34,
2218.81,
218.67,
2699.74,
1234.21,
355.93,
475,
1421.38,
],
2007: [
7236.15,
2250.04,
4600.72,
2257.99,
2467.41,
4486.74,
2025.44,
2493.04,
6821.11,
9730.91,
7613.46,
2789.78,
3770,
1918.95,
8620.24,
4511.97,
3812.34,
3835.4,
14076.83,
2156.76,
528.84,
1825.21,
3881.6,
1312.94,
1896.78,
188.06,
2178.2,
1037.11,
294.91,
366.18,
1246.89,
],
2006: [
5837.55,
1902.31,
3895.36,
1846.18,
1934.35,
3798.26,
1687.07,
2096.35,
5508.48,
7914.11,
6281.86,
2390.29,
3022.83,
1614.65,
7187.26,
3721.44,
3111.98,
3229.42,
11585.82,
1835.12,
433.57,
1649.2,
3319.62,
989.38,
1557.91,
159.76,
1806.36,
900.16,
249.04,
294.78,
1058.16,
]
}
data_financial = {
2011: [
12363.18,
5219.24,
8483.17,
3960.87,
5015.89,
8158.98,
3679.91,
4918.09,
11142.86,
20842.21,
14180.23,
4975.96,
6878.74,
3921.2,
17370.89,
7991.72,
7247.02,
7539.54,
24097.7,
3998.33,
1148.93,
3623.81,
7014.04,
2781.29,
3701.79,
322.57,
4355.81,
1963.79,
540.18,
861.92,
2245.12,
],
2010: [
10600.84,
4238.65,
7123.77,
3412.38,
4209.03,
6849.37,
3111.12,
4040.55,
9833.51,
17131.45,
12063.82,
4193.69,
5850.62,
3121.4,
14343.14,
6607.89,
6053.37,
6369.27,
20711.55,
3383.11,
953.67,
2881.08,
6030.41,
2177.07,
2892.31,
274.82,
3688.93,
1536.5,
470.88,
702.45,
1766.69,
],
2009: [
9179.19,
3405.16,
6068.31,
2886.92,
3696.65,
5891.25,
2756.26,
3371.95,
8930.85,
13629.07,
9918.78,
3662.15,
5048.49,
2637.07,
11768.18,
5700.91,
5127.12,
5402.81,
18052.59,
2919.13,
748.59,
2474.44,
5198.8,
1885.79,
2519.62,
240.85,
3143.74,
1363.27,
398.54,
563.74,
1587.72,
],
2008: [
8375.76,
2886.65,
5276.04,
2759.46,
3212.06,
5207.72,
2412.26,
2905.68,
7872.23,
11888.53,
8799.31,
3234.64,
4346.4,
2355.86,
10358.64,
5099.76,
4466.85,
4633.67,
16321.46,
2529.51,
643.47,
2160.48,
4561.69,
1652.34,
2218.81,
218.67,
2699.74,
1234.21,
355.93,
475,
1421.38,
],
2007: [
7236.15,
2250.04,
4600.72,
2257.99,
2467.41,
4486.74,
2025.44,
2493.04,
6821.11,
9730.91,
7613.46,
2789.78,
3770,
1918.95,
8620.24,
4511.97,
3812.34,
3835.4,
14076.83,
2156.76,
528.84,
1825.21,
3881.6,
1312.94,
1896.78,
188.06,
2178.2,
1037.11,
294.91,
366.18,
1246.89,
],
2006: [
5837.55,
1902.31,
3895.36,
1846.18,
1934.35,
3798.26,
1687.07,
2096.35,
5508.48,
7914.11,
6281.86,
2390.29,
3022.83,
1614.65,
7187.26,
3721.44,
3111.98,
3229.42,
11585.82,
1835.12,
433.57,
1649.2,
3319.62,
989.38,
1557.91,
159.76,
1806.36,
900.16,
249.04,
294.78,
1058.16,
]
}
def format_data(data: dict) -> dict:
for year in range(2006, 2012):
max_data, sum_data = 0, 0
temp = data[year]
max_data = max(temp)
for i in range(len(temp)):
sum_data = temp[i]
data[year][i] = {"name": name_list[i], "value": temp[i]}
data[str(year) "max"] = int(max_data / 100) * 100
data[str(year) "sum"] = sum_data
return data
# GDP
total_data["dataGDP"] = format_data(data=data_gdp)
# 第一产业
total_data["dataPI"] = format_data(data=data_pi)
# 第二产业
total_data["dataSI"] = format_data(data=data_si)
# 第三产业
total_data["dataTI"] = format_data(data=data_ti)
# 房地产
total_data["dataEstate"] = format_data(data=data_estate)
# 金融
total_data["dataFinancial"] = format_data(data=data_financial)
# 2006 - 2011 年的数据
def get_year_overlap_chart(year: int) -> Bar:
bar = (
Bar()
.add_xaxis(xaxis_data=name_list)
.add_yaxis(
series_name="GDP",
yaxis_data=total_data["dataGDP"][year],
is_selected=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="金融",
yaxis_data=total_data["dataFinancial"][year],
is_selected=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="房地产",
yaxis_data=total_data["dataEstate"][year],
is_selected=False,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="第一产业",
yaxis_data=total_data["dataPI"][year],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="第二产业",
yaxis_data=total_data["dataSI"][year],
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="第三产业",
yaxis_data=total_data["dataTI"][year],
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="{}全国宏观经济指标".format(year), subtitle="数据来自国家统计局"
),
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="shadow"
),
)
)
pie = (
Pie()
.add(
series_name="GDP占比",
data_pair=[
["第一产业", total_data["dataPI"]["{}sum".format(year)]],
["第二产业", total_data["dataSI"]["{}sum".format(year)]],
["第三产业", total_data["dataTI"]["{}sum".format(year)]],
],
center=["75%", "25%"],
radius="28%",
)
.set_series_opts(tooltip_opts=opts.TooltipOpts(is_show=True, trigger="item"))
)
return bar.overlap(pie)
# 生成时间轴的图
timeline = Timeline(init_opts=opts.InitOpts(width="1600px", height="800px"))
for y in range(2006, 2012):
timeline.add(get_year_overlap_chart(year=y), time_point=str(y))
timeline.add_schema(is_auto_play=True, play_interval=1000)
# timeline.render("finance_indices_2002.html")
timeline.render_notebook()
感谢官网的资料，个人整理主要是为了更好地使用?
