本文主要是介绍Pyecharts直角坐标系图：柱状图，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
Pyecharts直角坐标系图：柱状图
文章目录
- Pyecharts直角坐标系图：柱状图
- 前言
- Pyecharts柱状图例子
- 1 柱子堆叠百分比形式
- 2 x轴标签旋转
- 3 金融指数
- 4 字典配置
- 5 水平滑动
- 6 柱状图和折线图混合
- 7 指定类型
- 总结
前言
本文主要展示了Pyecharts柱状图的简单使用和案例。
Pyecharts柱状图例子
1 柱子堆叠百分比形式
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeTypelist2 = [{"value": 12, "percent": 12 / (12 + 3)},{"value": 23, "percent": 23 / (23 + 21)},{"value": 33, "percent": 33 / (33 + 5)},{"value": 3, "percent": 3 / (3 + 52)},{"value": 33, "percent": 33 / (33 + 43)},
]list3 = [{"value": 3, "percent": 3 / (12 + 3)},{"value": 21, "percent": 21 / (23 + 21)},{"value": 5, "percent": 5 / (33 + 5)},{"value": 52, "percent": 52 / (3 + 52)},{"value": 43, "percent": 43 / (33 + 43)},
]c = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis([1, 2, 3, 4, 5]).add_yaxis("product1", list2, stack="stack1", category_gap="50%").add_yaxis("product2", list3, stack="stack1", category_gap="50%").set_series_opts(label_opts=opts.LabelOpts(position="right",formatter=JsCode("function(x){return Number(x.data.percent * 100).toFixed() + '%';}"),)).render("stack_bar_percent.html")
)
2 x轴标签旋转
这个建议生成html来看，比较炫酷
from pyecharts import options as opts
from pyecharts.charts import Barc = (Bar().add_xaxis(["名字很长的X轴标签1","名字很长的X轴标签2","名字很长的X轴标签3","名字很长的X轴标签4","名字很长的X轴标签5","名字很长的X轴标签6",]).add_yaxis("商家A", [10, 20, 30, 40, 50, 40]).add_yaxis("商家B", [20, 10, 40, 30, 40, 50]).set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),title_opts=opts.TitleOpts(title="Bar-旋转X轴标签", subtitle="解决标签名字过长的问题"),).render("bar_rotate_xaxis_label.html")
)
3 金融指数
import pyecharts.options as opts
from pyecharts.charts import Timeline, Bar, Pie"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://www.echartsjs.com/examples/editor.html?c=mix-timeline-finance目前无法实现的功能:1、暂无
"""
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",
]
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}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,],2005: [2026.51,2135.07,5271.57,2357.04,1773.21,3869.4,1580.83,2971.68,4381.2,10524.96,7164.75,2245.9,3175.92,1917.47,10478.62,5514.14,2852.12,2612.57,11356.6,1510.68,240.83,1564,3067.23,821.16,1426.42,63.52,1951.36,838.56,264.61,281.05,1164.79,],2004: [1853.58,1685.93,4301.73,1919.4,1248.27,3061.62,1329.68,2487.04,3892.12,8437.99,6250.38,1844.9,2770.49,1566.4,8478.69,4182.1,2320.6,2190.54,9280.73,1253.7,205.6,1376.91,2489.4,681.5,1281.63,52.74,1553.1,713.3,211.7,244.05,914.47,],2003: [1487.15,1337.31,3417.56,1463.38,967.49,2898.89,1098.37,2084.7,3209.02,6787.11,5096.38,1535.29,2340.82,1204.33,6485.05,3310.14,1956.02,1777.74,7592.78,984.08,175.82,1135.31,2014.8,569.37,1047.66,47.64,1221.17,572.02,171.92,194.27,719.54,],2002: [1249.99,1069.08,2911.69,1134.31,754.78,2609.85,943.49,1843.6,2622.45,5604.49,4090.48,1337.04,2036.97,941.77,5184.98,2768.75,1709.89,1523.5,6143.4,846.89,148.88,958.87,1733.38,481.96,934.88,32.72,1007.56,501.69,144.51,153.06,603.15,],
}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,],2005: [4854.33,1658.19,3340.54,1611.07,1542.26,3295.45,1413.83,1857.42,4776.2,6612.22,5360.1,2137.77,2551.41,1411.92,5924.74,3181.27,2655.94,2882.88,9772.5,1560.92,377.17,1440.32,2836.73,815.32,1374.62,137.24,1546.59,787.36,213.37,259.49,929.41,],2004: [4092.27,1319.76,2805.47,1375.67,1270,2811.95,1223.64,1657.77,4097.26,5198.03,4584.22,1963.9,2206.02,1225.8,4764.7,2722.4,2292.55,2428.95,8335.3,1361.92,335.3,1229.62,2510.3,661.8,1192.53,123.3,1234.6,688.41,193.7,227.73,833.36,],2003: [3435.95,1150.81,2439.68,1176.65,1000.79,2487.85,1075.48,1467.9,3404.19,4493.31,3890.79,1638.42,1949.91,1043.08,4112.43,2358.86,2003.08,1995.78,7178.94,1178.25,293.85,1081.35,2189.68,558.28,1013.76,96.76,1063.89,589.91,169.81,195.46,753.91,],2002: [2982.57,997.47,2149.75,992.69,811.47,2258.17,958.88,1319.4,3038.9,3891.92,3227.99,1399.02,1765.8,972.73,3700.52,1978.37,1795.93,1780.79,6343.94,1074.85,270.96,956.12,1943.68,480.37,914.5,89.56,963.62,514.83,148.83,171.14,704.5,],
}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,],2005: [4854.33,1658.19,3340.54,1611.07,1542.26,3295.45,1413.83,1857.42,4776.2,6612.22,5360.1,2137.77,2551.41,1411.92,5924.74,3181.27,2655.94,2882.88,9772.5,1560.92,377.17,1440.32,2836.73,815.32,1374.62,137.24,1546.59,787.36,213.37,259.49,929.41,],2004: [4092.27,1319.76,2805.47,1375.67,1270,2811.95,1223.64,1657.77,4097.26,5198.03,4584.22,1963.9,2206.02,1225.8,4764.7,2722.4,2292.55,2428.95,8335.3,1361.92,335.3,1229.62,2510.3,661.8,1192.53,123.3,1234.6,688.41,193.7,227.73,833.36,],2003: [3435.95,1150.81,2439.68,1176.65,1000.79,2487.85,1075.48,1467.9,3404.19,4493.31,3890.79,1638.42,1949.91,1043.08,4112.43,2358.86,2003.08,1995.78,7178.94,1178.25,293.85,1081.35,2189.68,558.28,1013.76,96.76,1063.89,589.91,169.81,195.46,753.91,],2002: [2982.57,997.47,2149.75,992.69,811.47,2258.17,958.88,1319.4,3038.9,3891.92,3227.99,1399.02,1765.8,972.73,3700.52,1978.37,1795.93,1780.79,6343.94,1074.85,270.96,956.12,1943.68,480.37,914.5,89.56,963.62,514.83,148.83,171.14,704.5,],
}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,],2005: [4854.33,1658.19,3340.54,1611.07,1542.26,3295.45,1413.83,1857.42,4776.2,6612.22,5360.1,2137.77,2551.41,1411.92,5924.74,3181.27,2655.94,2882.88,9772.5,1560.92,377.17,1440.32,2836.73,815.32,1374.62,137.24,1546.59,787.36,213.37,259.49,929.41,],2004: [4092.27,1319.76,2805.47,1375.67,1270,2811.95,1223.64,1657.77,4097.26,5198.03,4584.22,1963.9,2206.02,1225.8,4764.7,2722.4,2292.55,2428.95,8335.3,1361.92,335.3,1229.62,2510.3,661.8,1192.53,123.3,1234.6,688.41,193.7,227.73,833.36,],2003: [3435.95,1150.81,2439.68,1176.65,1000.79,2487.85,1075.48,1467.9,3404.19,4493.31,3890.79,1638.42,1949.91,1043.08,4112.43,2358.86,2003.08,1995.78,7178.94,1178.25,293.85,1081.35,2189.68,558.28,1013.76,96.76,1063.89,589.91,169.81,195.46,753.91,],2002: [2982.57,997.47,2149.75,992.69,811.47,2258.17,958.88,1319.4,3038.9,3891.92,3227.99,1399.02,1765.8,972.73,3700.52,1978.37,1795.93,1780.79,6343.94,1074.85,270.96,956.12,1943.68,480.37,914.5,89.56,963.62,514.83,148.83,171.14,704.5,],
}def format_data(data: dict) -> dict:for year in range(2002, 2012):max_data, sum_data = 0, 0temp = 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) * 100data[str(year) + "sum"] = sum_datareturn 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)#####################################################################################
# 2002 - 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%", "35%"],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(2002, 2012):timeline.add(get_year_overlap_chart(year=y), time_point=str(y))# 1.0.0 版本的 add_schema 暂时没有补上 return self 所以只能这么写着
timeline.add_schema(is_auto_play=True, play_interval=1000)
timeline.render("finance_indices_2002.html")4 字典配置
from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.globals import ThemeTypec = (Bar({"theme": ThemeType.MACARONS}).add_xaxis(Faker.choose()).add_yaxis("商家A", Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(title_opts={"text": "Bar-通过 dict 进行配置", "subtext": "我也是通过 dict 进行配置的"}).render("bar_base_dict_config.html")
)
5 水平滑动
建议自己去生成html再看，比较帅。
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Fakerc = (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")
)6 柱状图和折线图混合
import pyecharts.options as opts
from pyecharts.charts import Bar, Line"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://www.echartsjs.com/examples/editor.html?c=mix-line-bar目前无法实现的功能:1、暂无
"""x_data = ["1月", "2月", "3月", "4月", "5月", "6月", "7月", "8月", "9月", "10月", "11月", "12月"]bar = (Bar(init_opts=opts.InitOpts(width="1600px", height="800px")).add_xaxis(xaxis_data=x_data).add_yaxis(series_name="蒸发量",yaxis_data=[2.0,4.9,7.0,23.2,25.6,76.7,135.6,162.2,32.6,20.0,6.4,3.3,],label_opts=opts.LabelOpts(is_show=False),).add_yaxis(series_name="降水量",yaxis_data=[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),).extend_axis(yaxis=opts.AxisOpts(name="温度",type_="value",min_=0,max_=25,interval=5,axislabel_opts=opts.LabelOpts(formatter="{value} °C"),)).set_global_opts(tooltip_opts=opts.TooltipOpts(is_show=True, trigger="axis", axis_pointer_type="cross"),xaxis_opts=opts.AxisOpts(type_="category",axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"),),yaxis_opts=opts.AxisOpts(name="水量",type_="value",min_=0,max_=250,interval=50,axislabel_opts=opts.LabelOpts(formatter="{value} ml"),axistick_opts=opts.AxisTickOpts(is_show=True),splitline_opts=opts.SplitLineOpts(is_show=True),),)
)line = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(series_name="平均温度",yaxis_index=1,y_axis=[2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2],label_opts=opts.LabelOpts(is_show=False),)
)bar.overlap(line).render("mixed_bar_and_line.html")
7 指定类型
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Fakerc = (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-MarkPoint（指定类型）")).set_series_opts(label_opts=opts.LabelOpts(is_show=False),markpoint_opts=opts.MarkPointOpts(data=[ # 在这指定类型，最大值和最小值opts.MarkPointItem(type_="max", name="最大值"),opts.MarkPointItem(type_="min", name="最小值"),opts.MarkPointItem(type_="average", name="平均值"),]),).render("bar_markpoint_type.html")
)
总结
本文主要是展示了Pyecharts柱状图的简单应用和案例，对于更加有趣的案例，建议查看官方文档。
这篇关于Pyecharts直角坐标系图：柱状图的文章就介绍到这儿，希望我们推荐的文章对编程师们有所帮助！
