Pyecharts绘制图表大全——柱形图

说明：本文代码资料等来源于Pyecharts官网，进行了一些重要节点的备注说明梳理，便于学习。

今日学习柱形图！

百分比柱形图

x轴标签旋转

堆叠数据

动态宏观经济指标图

通过 dict 进行配置柱形图

区域选择组件配置项

区域缩放配置项

好全的工具箱！

类似于瀑布图

柱形图与折线组合图

图形组件的使用-可加水印

堆叠部分数据

x轴y轴命名

添加自定义背景图

柱状图动画延迟&分割线

可以垂直滑动的数据区域

直方图（颜色区分）

y轴格式化单位

标记点最大-最小-平均值

3个y轴

自定义柱状图颜色

不同系列柱间距离

标记线最大-最小-平均值

渐变圆柱

单系列柱间距离

鼠标滚轮选择缩放区域

默认取消显示某 Series

翻转 XY 轴

自定义标记多个点

动画配置基本示例

直方图

自定义多条标记线

基本图表示例

水平滑动&鼠标滚轮缩放

百分比柱形图

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.globals import ThemeType
 
list2 = [
    {"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))   #主题
    .add_xaxis([1, 2, 3, 4, 5])
    .add_yaxis("product1", list2, stack="stack1", category_gap="50%")
    # stack数据堆叠，同个类目轴上系列配置相同的stack值可以堆叠放置
    # category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
    .add_yaxis("product2", list3, stack="stack1", category_gap="50%")
    .set_series_opts(   
        label_opts=opts.LabelOpts(   #标签配置项
            position="right",  #标签位置靠右
            formatter=JsCode(   #回调函数
                "function(x){return Number(x.data.percent * 100).toFixed() + '%';}"
            ),
        )
    )
    
#     .render("stack_bar_percent.html")
)
c.render_notebook()    #Jupyter Notebook直接显示复制

x轴标签旋转

 from pyecharts import options as opts
from pyecharts.charts import Bar
 
c = (
    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(  #全局配置项
        xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),  #坐标轴配置项  ，rotate标签旋转。从 -90 度到 90 度。正值是逆时针
        title_opts=opts.TitleOpts(title="Bar-旋转X轴标签", subtitle="解决标签名字过长的问题"),  #标题配置项
    )
#     .render("bar_rotate_xaxis_label.html")
)
c.render_notebook()    #Jupyter Notebook直接显示复制

堆叠数据

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker  #导入伪数据库
 
c = (
    Bar()
    .add_xaxis(Faker.choose())   #随机横坐标
    .add_yaxis("商家A", Faker.values(), stack="stack1", category_gap="50%") # stack数据堆叠，同个类目轴上系列配置相同的stack值可以堆叠放置
    .add_yaxis("商家B", Faker.values(), stack="stack1", category_gap="50%") # category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
    .set_series_opts(label_opts=opts.LabelOpts(is_show=True,position='right'))  #is_show是否显示标签
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠数据（全部）"))
#     .render("bar_stack0.html")
)
c.render_notebook()    #Jupyter Notebook直接显示复制

动态宏观经济指标图

 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",
]
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,
    ],
    2005: [
        6969.52,
        3905.64,
        10012.11,
        4230.53,
        3905.03,
        8047.26,
        3620.27,
        5513.7,
        9247.66,
        18598.69,
        13417.68,
        5350.17,
        6554.69,
        4056.76,
        18366.87,
        10587.42,
        6590.19,
        6596.1,
        22557.37,
        3984.1,
        918.75,
        3467.72,
        7385.1,
        2005.42,
        3462.73,
        248.8,
        3933.72,
        1933.98,
        543.32,
        612.61,
        2604.19,
    ],
    2004: [
        6033.21,
        3110.97,
        8477.63,
        3571.37,
        3041.07,
        6672,
        3122.01,
        4750.6,
        8072.83,
        15003.6,
        11648.7,
        4759.3,
        5763.35,
        3456.7,
        15021.84,
        8553.79,
        5633.24,
        5641.94,
        18864.62,
        3433.5,
        819.66,
        3034.58,
        6379.63,
        1677.8,
        3081.91,
        220.34,
        3175.58,
        1688.49,
        466.1,
        537.11,
        2209.09,
    ],
    2003: [
        5007.21,
        2578.03,
        6921.29,
        2855.23,
        2388.38,
        6002.54,
        2662.08,
        4057.4,
        6694.23,
        12442.87,
        9705.02,
        3923.11,
        4983.67,
        2807.41,
        12078.15,
        6867.7,
        4757.45,
        4659.99,
        15844.64,
        2821.11,
        713.96,
        2555.72,
        5333.09,
        1426.34,
        2556.02,
        185.09,
        2587.72,
        1399.83,
        390.2,
        445.36,
        1886.35,
    ],
    2002: [
        4315,
        2150.76,
        6018.28,
        2324.8,
        1940.94,
        5458.22,
        2348.54,
        3637.2,
        5741.03,
        10606.85,
        8003.67,
        3519.72,
        4467.55,
        2450.48,
        10275.5,
        6035.48,
        4212.82,
        4151.54,
        13502.42,
        2523.73,
        642.73,
        2232.86,
        4725.01,
        1243.43,
        2312.82,
        162.04,
        2253.39,
        1232.03,
        340.65,
        377.16,
        1612.6,
    ],
}
 
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,
    ],
    2005: [
        88.68,
        112.38,
        1400,
        262.42,
        589.56,
        882.41,
        625.61,
        684.6,
        90.26,
        1461.51,
        892.83,
        966.5,
        827.36,
        727.37,
        1963.51,
        1892.01,
        1082.13,
        1100.65,
        1428.27,
        912.5,
        300.75,
        463.4,
        1481.14,
        368.94,
        661.69,
        48.04,
        435.77,
        308.06,
        65.34,
        72.07,
        509.99,
    ],
    2004: [
        87.36,
        105.28,
        1370.43,
        276.3,
        522.8,
        798.43,
        568.69,
        605.79,
        83.45,
        1367.58,
        814.1,
        950.5,
        786.84,
        664.5,
        1778.45,
        1649.29,
        1020.09,
        1022.45,
        1248.59,
        817.88,
        278.76,
        428.05,
        1379.93,
        334.5,
        607.75,
        44.3,
        387.88,
        286.78,
        60.7,
        65.33,
        461.26,
    ],
    2003: [
        84.11,
        89.91,
        1064.05,
        215.19,
        420.1,
        615.8,
        488.23,
        504.8,
        81.02,
        1162.45,
        717.85,
        749.4,
        692.94,
        560,
        1480.67,
        1198.7,
        798.35,
        886.47,
        1072.91,
        658.78,
        244.29,
        339.06,
        1128.61,
        298.69,
        494.6,
        40.7,
        302.66,
        237.91,
        48.47,
        55.63,
        412.9,
    ],
    2002: [
        82.44,
        84.21,
        956.84,
        197.8,
        374.69,
        590.2,
        446.17,
        474.2,
        79.68,
        1110.44,
        685.2,
        783.66,
        664.78,
        535.98,
        1390,
        1288.36,
        707,
        847.25,
        1015.08,
        601.99,
        222.89,
        317.87,
        1047.95,
        281.1,
        463.44,
        39.75,
        282.21,
        215.51,
        47.31,
        52.95,
        305,
    ],
}
 
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,
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data_ti = {
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data_estate = {
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data_financial = {
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        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, 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)
 
 
#####################################################################################
# 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",
            y_axis=total_data["dataGDP"][year],
            is_selected=False,  # is_selected是否选中图例
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add_yaxis(
            series_name="金融",
            y_axis=total_data["dataFinancial"][year],
            is_selected=False, # is_selected是否选中图例
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add_yaxis(
            series_name="房地产",
            y_axis=total_data["dataEstate"][year],
            is_selected=False, # is_selected是否选中图例
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add_yaxis(
            series_name="第一产业",
            y_axis=total_data["dataPI"][year],
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add_yaxis(
            series_name="第二产业",
            y_axis=total_data["dataSI"][year],
            label_opts=opts.LabelOpts(is_show=False),
        )
        .add_yaxis(
            series_name="第三产业",
            y_axis=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")
timeline.render_notebook()    #Jupyter Notebook直接显示复制

通过 dict 进行配置柱形图

 from pyecharts.charts import Bar
from pyecharts.faker import Faker
from pyecharts.globals import ThemeType
 
c = (
    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")
)
c.render_notebook()    #Jupyter Notebook直接显示复制

区域选择组件配置项

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
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-Brush示例", subtitle="我是副标题"),
        brush_opts=opts.BrushOpts(),  #区域选择组件配置项
    )
#     .render("bar_with_brush.html")
)
c.render_notebook()复制

区域缩放配置项

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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(),  #区域缩放配置项
    )
#     .render("bar_datazoom_slider.html")
)
c.render_notebook()复制

好全的工具箱！

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
c = (
    Bar(init_opts=opts.InitOpts(bg_color='white'))  #初始化的背景色，长宽等在这里设置,也可以为16进制的代码，如果不设置为白色，下载下来的图片png就是透明的
    .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(),  #工具箱配置项
        legend_opts=opts.LegendOpts(is_show=False),  #图例配置项
    )
#     .render("bar_toolbox.html")
)
c.render_notebook()复制

类似于瀑布图

 from pyecharts.charts import Bar
from pyecharts import options as opts
 
x_data = [f"11月{str(i)}日" for i in range(1, 12)]
y_total = [0, 900, 1245, 1530, 1376, 1376, 1511, 1689, 1856, 1495, 1292]
y_in = [900, 345, 393, "-", "-", 135, 178, 286, "-", "-", "-"]
y_out = ["-", "-", "-", 108, 154, "-", "-", "-", 119, 361, 203]
 
 
bar = (
    Bar()
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="",
        y_axis=y_total,
        stack="总量",   # stack数据堆叠，同个类目轴上系列配置相同的stack值可以堆叠放置
        itemstyle_opts=opts.ItemStyleOpts(color="rgba(0,0,0,0)"), 
        #图元样式配置项,图形颜色，可以使用 RGBA，比如 'rgba(128, 128, 128, 0.5)'，也可以使用十六进制格式，比如 '#ccc'
        # rgba(0,0,0,0)完全不透明的白色,也即是无色
    )
    .add_yaxis(series_name="收入", y_axis=y_in, stack="总量")
    .add_yaxis(series_name="支出", y_axis=y_out, stack="总量")
    .set_global_opts(yaxis_opts=opts.AxisOpts(type_="value"))   #坐标轴配置项
    # type_坐标轴类型。可选：
    # 'value': 数值轴，适用于连续数据。
    # 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。
    # 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，
    # 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。
    # 'log' 对数轴。适用于对数数据。
#     .render("bar_waterfall_plot.html")
)
bar.render_notebook()复制

柱形图与折线组合图

 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",
        y_axis=[
            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",
        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),
    )
    .extend_axis(  #扩展 X/Y 轴
        yaxis=opts.AxisOpts(   #yaxis新增 Y 坐标轴配置项，AxisOpts坐标轴配置项
            name="温度",
            type_="value",  #'value': 数值轴，适用于连续数据
            min_=0,
            max_=25,
            interval=5, # 强制设置坐标轴分割间隔
            axislabel_opts=opts.LabelOpts(formatter="{value} °C"),# 坐标轴标签配置项      formatter回调函数，value传入的数据值
        )
    )
    .set_global_opts(
        tooltip_opts=opts.TooltipOpts(#TooltipOpts：提示框配置项
            is_show=True, trigger="axis", axis_pointer_type="cross"
            #is_show是否显示提示框组件，trigger触发类型，'axis': 坐标轴触发，主要在柱状图，折线图等会使用类目轴的图表中使用
            #axis_pointer_type指示器类型'cross'：十字准星指示器。其实是种简写，表示启用两个正交的轴的 axisPointer。
        ),
        xaxis_opts=opts.AxisOpts(   #坐标轴配置项
            type_="category", #'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。
            axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), # 坐标轴指示器配置项
        ),  #is_show是否显示坐标轴指示器，type_指示器类型# 'line' 直线指示器'shadow' 阴影指示器'none' 无指示器
        yaxis_opts=opts.AxisOpts(
            name="水量",
            type_="value",
            min_=0,
            max_=250,
            interval=50,  # 强制设置坐标轴分割间隔
            axislabel_opts=opts.LabelOpts(formatter="{value} ml"), # 坐标轴标签配置项      formatter回调函数，value传入的数据值
            axistick_opts=opts.AxisTickOpts(is_show=True), # 坐标轴刻度配置项
            splitline_opts=opts.SplitLineOpts(is_show=True), # 分割线配置项
        ),
    )
)
 
line = (
    Line()
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="平均温度",
        yaxis_index=1,  # 使用的 y 轴的 index，在单个图表实例中存在多个 y 轴的时候有用
        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")
bar.overlap(line).render_notebook()复制

图形组件的使用-可加水印

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
 
 
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-Graphic 组件示例"),
        graphic_opts=[  
            opts.GraphicGroup(#GraphicGroup：原生图形元素组件
                graphic_item=opts.GraphicItem(  # graphic_item图形的配置项
                    rotation=JsCode("Math.PI / 4"),# 旋转（rotation）：默认值是 0。表示旋转的弧度值。正值表示逆时针旋转。
                    bounding="raw",# bounding决定此图形元素在定位时，对自身的包围盒计算方式。可选：
                    # 'all'：（默认） 表示用自身以及子节点整体的经过 transform 后的包围盒进行定位。这种方式易于使整体都限制在父元素范围中。
                    # 'raw'：表示仅仅用自身（不包括子节点）的没经过 tranform 的包围盒进行定位。这种方式易于内容超出父元素范围的定位方式。
                    right=110, # 描述怎么根据父元素进行定位。
                    # 父元素是指：如果是顶层元素，父元素是 echarts 图表容器。如果是 group 的子元素，父元素就是 group 元素。
                    bottom=110,
                    z=100,# z 方向的高度，决定层叠关系。
                ),
                children=[  # 子节点列表，其中项都是一个图形元素定义。
                    # 目前可以选择 GraphicText，GraphicImage，GraphicRect
                    opts.GraphicRect(  #GraphicRect：原生图形矩形配置项
                        graphic_item=opts.GraphicItem(
                            left="center", top="center", z=100
                        ),
                        graphic_shape_opts=opts.GraphicShapeOpts(width=400, height=50),  #GraphicShapeOpts图形的形状配置项
                        graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(# GraphicBasicStyleOpts图形基本配置项
                            fill="rgba(0,0,0,0.3)"  #填充色
                        ),
                    ),
                    opts.GraphicText(#GraphicText：原生图形文本配置项
                        graphic_item=opts.GraphicItem(
                            left="center", top="center", z=100
                        ),
                        graphic_textstyle_opts=opts.GraphicTextStyleOpts(  # 图形文本样式的配置项
                            text="pyecharts bar chart",
                            font="bold 26px Microsoft YaHei",
                            graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
                                fill="#fff"
                            ),
                        ),
                    ),
                ],
            )
        ],
    )
#     .render("bar_graphic_component.html")
)
c.render_notebook()复制

堆叠部分数据

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values(), stack="stack1")
    .add_yaxis("商家B", Faker.values(), stack="stack1")
    .add_yaxis("商家C", Faker.values())
    .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")
)
c.render_notebook()复制

x轴y轴命名

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
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 轴名称"),
        yaxis_opts=opts.AxisOpts(name="我是 Y 轴"),
        xaxis_opts=opts.AxisOpts(name="我是 X 轴"),
    )
#     .render("bar_xyaxis_name.html")
)
c.render_notebook()复制

添加自定义背景图

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
 
c = (
    Bar(
        init_opts=opts.InitOpts(
            bg_color={"type": "pattern", "image": JsCode("img"), "repeat": "no-repeat"}
#         "type": "pattern" 表示我们用图片作背景，
#             "image": JsCode("img") 表示我们用 JavaScript 代码来设置这个背景图，
#             "repeat": "no-repeat" 表示图片不重复。
 
        )
    )
    .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-背景图基本示例",
            subtitle="我是副标题",
            title_textstyle_opts=opts.TextStyleOpts(color="white"),  #TextStyleOpts：文字样式配置项，文字颜色
        )
    )
)
#             而 add_js_funcs 方法就是执行相关的 JavaScript 代码，
#             这里的 JavaScript 代码也很简单，就设置一个名为 img 的变量，
#             指定一下路径。也可以用文件路径
c.add_js_funcs(
    """
    var img = new Image(); img.src = 'https://kr.shanghai-jiuxin.com/file/bizhi/20220927/4gzitkl1lyv.jpg';
    """
)
c.render_notebook()复制

柱状图动画延迟&分割线

 import pyecharts.options as opts
from pyecharts.charts import Bar
 
"""
Gallery 使用 pyecharts 1.1.0
参考地址: https://www.echartsjs.com/examples/editor.html?c=bar-animation-delay
 
目前无法实现的功能:
 
1、动画延迟效果暂时没有加入到代码中
"""
 
category = ["类目{}".format(i) for i in range(0, 100)]
red_bar = [
    0,
    -8.901463875624668,
    -17.025413764148556,
    -24.038196249566663,
    -29.66504684804471,
    -33.699527649688676,
    -36.00971978255796,
    -36.541005056170455,
    -35.31542466107655,
    -32.427752866005996,
    -28.038563739693934,
    -22.364693082297347,
    -15.667600860943732,
    -8.240217424060843,
    -0.3929067389459173,
    7.560799717904647,
    15.318054209871054,
    22.599523033552096,
    29.16065418543528,
    34.800927952557615,
    39.37074152590451,
    42.77569739999406,
    44.97819140223978,
    45.99632376477021,
    45.900279829731865,
    44.806440199911805,
    42.86957840395034,
    40.2735832137877,
    37.22119936652441,
    33.92331243435557,
    30.588309963978517,
    27.412031986865767,
    24.56878097935778,
    22.203796820272576,
    20.427519715115604,
    19.311867685884827,
    18.888649906111855,
    19.150128087782186,
    20.051630602288828,
    21.516023200879346,
    23.439750867099516,
    25.700091656548704,
    28.163208735293757,
    30.692553648214542,
    33.1571635093161,
    35.439407573791215,
    37.44177367693234,
    39.09234039030659,
    40.34865356244595,
    41.19981246258526,
    41.66666666666667,
    41.80012531240646,
    41.67768039516203,
    41.39834040182826,
    41.07625507973403,
    40.833382300579814,
    40.79160029175877,
    41.06470032034727,
    41.75070457358366,
    42.924940903672564,
    44.63427081999565,
    46.89281122872821,
    49.679416561286956,
    52.93709961387478,
    56.574470884754874,
    60.46917221906629,
    64.47317623531558,
    68.41972346252496,
    72.1315793340836,
    75.43021771943799,
    78.14548044723074,
    80.12522637371026,
    81.24447108408411,
    81.41353029256493,
    80.58471628367427,
    78.75719600392792,
    75.97969924353211,
    72.35086229880064,
    68.01710226438443,
    63.16803467673056,
    58.029567166714706,
    52.854918421647554,
    47.91391949819902,
    43.48104807503482,
    39.82272085822884,
    37.18442111754884,
    35.778264289169215,
    35.77160292258658,
    37.27724241244461,
    40.345781666728996,
    44.96051012913295,
    51.035187614675685,
    58.41491053964701,
    66.8801325453253,
    76.15376513468516,
    85.91114110149952,
    95.79248672571518,
    105.41742429574506,
    114.40092042993717,
    122.37001313784816,
]
blue_bar = [
    -50,
    -47.18992898088751,
    -42.54426104547181,
    -36.290773900754886,
    -28.71517529663627,
    -20.146937097399626,
    -10.94374119697364,
    -1.4752538113770308,
    7.893046603320797,
    16.81528588241657,
    24.979206795219028,
    32.11821023962515,
    38.02096119056733,
    42.53821720798438,
    45.58667093073836,
    47.14973738101559,
    47.275355710354944,
    46.07100702178889,
    43.6962693226927,
    40.35333240268025,
    36.275975292575026,
    31.71756381888028,
    26.938653692729076,
    22.194784893913152,
    17.725026430574392,
    13.741778696752679,
    10.422266555457615,
    7.902063853319403,
    6.270884006107842,
    5.570756810898967,
    5.796594266992678,
    6.899033489892203,
    8.7893381290192,
    11.346045936704996,
    14.42297348773613,
    17.858132851517098,
    21.483081596548438,
    25.132218074866262,
    28.651548555679597,
    31.906490373810854,
    34.788333671419466,
    37.21906041552118,
    39.154309232933485,
    40.58437366457342,
    41.5332247510366,
    42.05565130942339,
    42.23270781895,
    42.165745792772285,
    41.969375711588256,
    41.76375960543808,
    41.66666666666667,
    41.7857343479728,
    42.21136481847887,
    43.01065209435119,
    44.22268037417866,
    45.855461823273586,
    47.88469584957917,
    50.25443606443524,
    52.879650371477126,
    55.650558977584225,
    58.43853958732492,
    61.10330341815434,
    63.500974294013034,
    65.49264961151306,
    66.95298925309743,
    67.77836838841961,
    67.89414332224722,
    67.26061575374229,
    65.87733853082335,
    63.785482681031894,
    61.068077697490004,
    57.84804048526095,
    54.284018163297375,
    50.564180830851214,
    46.89820707575337,
    43.50780217852947,
    40.616171775045245,
    38.4369379107128,
    37.16302649485318,
    36.95607267600796,
    37.93688225696513,
    40.17745279877072,
    43.694998595987045,
    48.44834150353593,
    54.33692802801367,
    61.20261650152743,
    68.83425165632042,
    76.97491319735354,
    85.33159602026458,
    93.58695857541488,
    101.4126683297632,
    108.48378461530217,
    114.49355390682695,
    119.16795429637915,
    122.27931702317058,
    123.65837448506679,
    123.20413594805603,
    120.89107255501017,
    116.7731992576505,
    110.98476877890735,
]
 
c = (
    Bar(init_opts=opts.InitOpts(width="1600px", height="800px"))
    .add_xaxis(xaxis_data=category)
    .add_yaxis(
        series_name="bar", y_axis=red_bar, label_opts=opts.LabelOpts(is_show=False)
    )
    .add_yaxis(
        series_name="bar2",
        y_axis=blue_bar,
        label_opts=opts.LabelOpts(is_show=False),
    )
    .set_global_opts(
        title_opts=opts.TitleOpts(title="柱状图动画延迟"),
        xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=False)),  # 分割线配置项,x轴不显示
        yaxis_opts=opts.AxisOpts(
            axistick_opts=opts.AxisTickOpts(is_show=True),# 坐标轴刻度配置项，y轴显示，就是凸起来那个小点
            splitline_opts=opts.SplitLineOpts(is_show=True),  #y轴有分割线
        ),
    )
#     .render("bar_chart_display_delay.html")
)
c.render_notebook()复制

可以垂直滑动的数据区域

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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（slider-垂直）"),
        datazoom_opts=opts.DataZoomOpts(orient="vertical"), #DataZoomOpts：区域缩放配置项， 布局方式是横还是竖
    )
#     .render("bar_datazoom_slider_vertical.html")
)
c.render_notebook()复制

直方图（颜色区分）

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
x = Faker.dogs + Faker.animal
xlen = len(x)
y = []
for idx, item in enumerate(x):
    if idx <= xlen / 2:  #一半一个颜色
        y.append(
            opts.BarItem(  #BarItem：柱状图数据项
                name=item,  # 数据项名称
                value=(idx + 1) * 10, # 单个数据项的数值
                itemstyle_opts=opts.ItemStyleOpts(color="#749f83"),  # 图元样式配置项
            )
        )
    else:
        y.append(
            opts.BarItem(
                name=item,
                value=(xlen + 1 - idx) * 10,
                itemstyle_opts=opts.ItemStyleOpts(color="#d48265"),
            )
        )
 
c = (
    Bar()
    .add_xaxis(x)
    .add_yaxis("series0", y, category_gap=0, color=Faker.rand_color())  # category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-直方图（颜色区分）"))
#     .render("bar_histogram_color.html")
)
c.render_notebook()复制

y轴格式化单位

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
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-Y 轴 formatter"),
        yaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(formatter="{value} /月")),
    )
#     .render("bar_yaxis_formatter.html")
)
c.render_notebook()复制

标记点最大-最小-平均值

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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-MarkPoint（指定类型）"))
    .set_series_opts(
        label_opts=opts.LabelOpts(is_show=False),
        markpoint_opts=opts.MarkPointOpts(# 标记点配置项
            data=[ # 标记点数据
                opts.MarkPointItem(type_="max", name="最大值"),#MarkPointItem：标记点数据项
                opts.MarkPointItem(type_="min", name="最小值"),
                opts.MarkPointItem(type_="average", name="平均值"),
            ]
        ),
    )
#     .render("bar_markpoint_type.html")
)
c.render_notebook()复制

3个y轴

 import pyecharts.options as opts
from pyecharts.charts import Bar, Line
 
"""
Gallery 使用 pyecharts 1.0.0
参考地址: https://www.echartsjs.com/examples/editor.html?c=multiple-y-axis
 
目前无法实现的功能:
 
1、暂无
"""
 
colors = ["#5793f3", "#d14a61", "#675bba"]
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"]
legend_list = ["蒸发量", "降水量", "平均温度"]
evaporation_capacity = [
    2.0,
    4.9,
    7.0,
    23.2,
    25.6,
    76.7,
    135.6,
    162.2,
    32.6,
    20.0,
    6.4,
    3.3,
]
rainfall_capacity = [
    2.6,
    5.9,
    9.0,
    26.4,
    28.7,
    70.7,
    175.6,
    182.2,
    48.7,
    18.8,
    6.0,
    2.3,
]
average_temperature = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]
 
bar = (
    Bar(init_opts=opts.InitOpts(width="1680px", height="800px"))
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="蒸发量",
        y_axis=evaporation_capacity,
        yaxis_index=0,   #多轴时才会有索引，默认轴索引都为0
        color=colors[1],
    )
    .add_yaxis(
        series_name="降水量", y_axis=rainfall_capacity, yaxis_index=1, color=colors[0]
    )
    .extend_axis(
        yaxis=opts.AxisOpts(
            name="蒸发量",
            type_="value",
            min_=0,
            max_=250,
            position="right",  #在右
            axisline_opts=opts.AxisLineOpts(
                linestyle_opts=opts.LineStyleOpts(color=colors[1])
            ),
            axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
        )
    )
    .extend_axis(
        yaxis=opts.AxisOpts(
            type_="value",
            name="温度",
            min_=0,
            max_=25,
            position="left", #在左
            axisline_opts=opts.AxisLineOpts(
                linestyle_opts=opts.LineStyleOpts(color=colors[2])
            ),
            axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
            splitline_opts=opts.SplitLineOpts(
                is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1)  # opacity图形透明度，支持从 0 到 1 的数字，为 0 时不绘制该图形。
            ),
        )
    )
    .set_global_opts(
        yaxis_opts=opts.AxisOpts(
            type_="value",
            name="降水量",
            min_=0,
            max_=250,
            position="right",
            offset=80,# Y 轴相对于默认位置的偏移，在相同的 position 上有多个 Y 轴的时候有用。
            axisline_opts=opts.AxisLineOpts(
                linestyle_opts=opts.LineStyleOpts(color=colors[0])
            ),
            axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
        ),
        tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
        #TooltipOpts：提示框配置项
        # 'axis': 坐标轴触发，主要在柱状图，折线图等会使用类目轴的图表中使用。
        # 'cross'：十字准星指示器。其实是种简写，表示启用两个正交的轴的 axisPointer。
    )
)
 
line = (
    Line()
    .add_xaxis(xaxis_data=x_data)
    .add_yaxis(
        series_name="平均温度", y_axis=average_temperature, yaxis_index=2, color=colors[2]
    )
)
 
# bar.overlap(line).render("multiple_y_axes.html")
bar.overlap(line).render_notebook()复制

自定义柱状图颜色

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
 
 
color_function = """
        function (params) {
            if (params.value > 0 && params.value < 50) {
                return 'red';
            } else if (params.value > 50 && params.value < 100) {
                return 'blue';
            }
            return 'green';
        }
        """
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis(
        "商家A",
        Faker.values(),
        itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)), #ItemStyleOpts：图元样式配置项
    )
    .add_yaxis(
        "商家B",
        Faker.values(),
        itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
    )
    .add_yaxis(
        "商家C",
        Faker.values(),
        itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)),
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-自定义柱状颜色"))
#     .render("bar_custom_bar_color.html")
)
c.render_notebook()复制

不同系列柱间距离

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values(), gap="0%") 
    # 不同系列的柱间距离，为百分比（如 '30%'，表示柱子宽度的 30%）。
    # 如果想要两个系列的柱子重叠，可以设置 gap 为 '-100%'。这在用柱子做背景的时候有用。
    .add_yaxis("商家B", Faker.values(), gap="0%")
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-不同系列柱间距离"))
#     .render("bar_different_series_gap.html")
)
c.render_notebook()复制

标记线最大-最小-平均值

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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-MarkLine（指定类型）"))
    .set_series_opts(
        label_opts=opts.LabelOpts(is_show=False),
        markline_opts=opts.MarkLineOpts( #MarkLineItem：标记线数据项
            data=[
                opts.MarkLineItem(type_="min", name="最小值"),
                opts.MarkLineItem(type_="max", name="最大值"),
                opts.MarkLineItem(type_="average", name="平均值"),
            ]
        ),
    )
)
c.render_notebook()复制

渐变圆柱

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.commons.utils import JsCode
from pyecharts.faker import Faker
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values(), category_gap="60%")# category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
    .set_series_opts(
        itemstyle_opts={  # 图元样式配置项
            "normal": {
                "color": JsCode(
                    """new echarts.graphic.LinearGradient(0, 0, 0, 1, [{
                offset: 0,
                color: 'rgba(0, 244, 255, 1)'
            }, {
                offset: 1,
                color: 'rgba(0, 77, 167, 1)'
            }], false)"""
                ),
                "barBorderRadius": [30, 30, 30, 30],
                "shadowColor": "rgb(0, 160, 221)",
            }
        }
    )
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-渐变圆柱"))
#     .render("bar_border_radius.html")
)
c.render_notebook()复制

单系列柱间距离

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values(), category_gap="80%")
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-单系列柱间距离"))
#     .render("bar_same_series_gap.html")
)
c.render_notebook()复制

鼠标滚轮选择缩放区域

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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（inside）"),
        datazoom_opts=opts.DataZoomOpts(type_="inside"),#DataZoomOpts：区域缩放配置项
        # type_组件类型，可选 "slider", "inside"  ，slider是鼠标左右或者上下选择区域，inside是鼠标滚轮放大缩小
    )
#     .render("bar_datazoom_inside.html")
)
c.render_notebook()复制

默认取消显示某 Series

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values(), is_selected=False)
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-默认取消显示某 Series"))
#     .render("bar_is_selected.html")
)
c.render_notebook()复制

翻转 XY 轴

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values())
    .add_yaxis("商家B", Faker.values())
    .reversal_axis() #翻转 XY 轴数据
    .set_series_opts(label_opts=opts.LabelOpts(position="right"))
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-翻转 XY 轴"))
#     .render("bar_reversal_axis.html")
)
c.render_notebook()复制

自定义标记多个点

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
x, y = Faker.choose(), Faker.values()
c = (
    Bar()
    .add_xaxis(x)
    .add_yaxis(
        "商家A",
        y,
        markpoint_opts=opts.MarkPointOpts(
            data=[opts.MarkPointItem(name="自定义标记点", coord=[x[2], y[2]], value=y[2]),
            opts.MarkPointItem(name="自定义标记点2", coord=[x[5], y[5]], value=y[5])]
        ),  #MarkPointItem：标记点数据项
            # coord标注的坐标。坐标格式视系列的坐标系而定，可以是直角坐标系上的 x, y，
            # 也可以是极坐标系上的 radius, angle。例如 [121, 2323]、['aa', 998]。
            # value标注值，可以不设。
    )
    .add_yaxis("商家B", Faker.values())
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-MarkPoint（自定义）"))
    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
#     .render("bar_markpoint_custom.html")
)
c.render_notebook()复制

动画配置基本示例

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
c = (
    Bar(
        init_opts=opts.InitOpts(
            animation_opts=opts.AnimationOpts(   # animation_opts画图动画初始化配置
                animation_delay=1000, animation_easing="elasticOut"
                 # animation_delay初始动画的延迟，默认值为 0
                # animation_easing# 初始动画的缓动效果
            )
        )
    )
    .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-动画配置基本示例", subtitle="我是副标题"))
#     .render("bar_base_with_animation.html")
)
c.render_notebook()复制

直方图

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
c = (
    Bar()
    .add_xaxis(Faker.choose())
    .add_yaxis("商家A", Faker.values(), category_gap=0, color=Faker.rand_color())# category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
    .set_global_opts(title_opts=opts.TitleOpts(title="Bar-直方图"))
#     .render("bar_histogram.html")
)
c.render_notebook()复制

自定义多条标记线

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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-MarkLine（自定义）"))
    .set_series_opts(
        label_opts=opts.LabelOpts(is_show=False),
        markline_opts=opts.MarkLineOpts(
            data=[opts.MarkLineItem(y=50, name="yAxis=50"),opts.MarkLineItem(y=30, name="yAxis=30"),]
        ),
    )
#     .render("bar_markline_custom.html")
)
c.render_notebook()复制

基本图表示例

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
 
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-基本示例", subtitle="我是副标题"))
#     .render("bar_base.html")
)
c.render_notebook()复制

水平滑动&鼠标滚轮缩放

 from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
 
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（slider+inside）"),
        datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
    )   #DataZoomOpts默认slider
#     .render("bar_datazoom_both.html")
)
c.render_notebook()复制

	from pyecharts import options as opts
	from pyecharts.charts import Bar
	from pyecharts.commons.utils import JsCode
	from pyecharts.globals import ThemeType

	list2 = [
	{"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)) #主题
	.add_xaxis([1, 2, 3, 4, 5])
	.add_yaxis("product1", list2, stack="stack1", category_gap="50%")
	# stack数据堆叠，同个类目轴上系列配置相同的stack值可以堆叠放置
	# category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
	.add_yaxis("product2", list3, stack="stack1", category_gap="50%")
	.set_series_opts(
	label_opts=opts.LabelOpts( #标签配置项
	position="right", #标签位置靠右
	formatter=JsCode( #回调函数
	"function(x){return Number(x.data.percent * 100).toFixed() + '%';}"
	),
	)
	)

	# .render("stack_bar_percent.html")
	)
	c.render_notebook() #Jupyter Notebook直接显示

	from pyecharts import options as opts
	from pyecharts.charts import Bar
	from pyecharts.faker import Faker #导入伪数据库

	c = (
	Bar()
	.add_xaxis(Faker.choose()) #随机横坐标
	.add_yaxis("商家A", Faker.values(), stack="stack1", category_gap="50%") # stack数据堆叠，同个类目轴上系列配置相同的stack值可以堆叠放置
	.add_yaxis("商家B", Faker.values(), stack="stack1", category_gap="50%") # category_gap同一系列的柱间距离，默认为类目间距的 20%，可设固定值
	.set_series_opts(label_opts=opts.LabelOpts(is_show=True,position='right')) #is_show是否显示标签
	.set_global_opts(title_opts=opts.TitleOpts(title="Bar-堆叠数据（全部）"))
	# .render("bar_stack0.html")
	)
	c.render_notebook() #Jupyter Notebook直接显示

	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",
	]
	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,
	],
	2005: [
	6969.52,
	3905.64,
	10012.11,
	4230.53,
	3905.03,
	8047.26,
	3620.27,
	5513.7,
	9247.66,
	18598.69,
	13417.68,
	5350.17,
	6554.69,
	4056.76,
	18366.87,
	10587.42,
	6590.19,
	6596.1,
	22557.37,
	3984.1,
	918.75,
	3467.72,
	7385.1,
	2005.42,
	3462.73,
	248.8,
	3933.72,
	1933.98,
	543.32,
	612.61,
	2604.19,
	],
	2004: [
	6033.21,
	3110.97,
	8477.63,
	3571.37,
	3041.07,
	6672,
	3122.01,
	4750.6,
	8072.83,
	15003.6,
	11648.7,
	4759.3,
	5763.35,
	3456.7,
	15021.84,
	8553.79,
	5633.24,
	5641.94,
	18864.62,
	3433.5,
	819.66,
	3034.58,
	6379.63,
	1677.8,
	3081.91,
	220.34,
	3175.58,
	1688.49,
	466.1,
	537.11,
	2209.09,
	],
	2003: [
	5007.21,
	2578.03,
	6921.29,
	2855.23,
	2388.38,
	6002.54,
	2662.08,
	4057.4,
	6694.23,
	12442.87,
	9705.02,
	3923.11,
	4983.67,
	2807.41,
	12078.15,
	6867.7,
	4757.45,
	4659.99,
	15844.64,
	2821.11,
	713.96,
	2555.72,
	5333.09,
	1426.34,
	2556.02,
	185.09,
	2587.72,
	1399.83,
	390.2,
	445.36,
	1886.35,
	],
	2002: [
	4315,
	2150.76,
	6018.28,
	2324.8,
	1940.94,
	5458.22,
	2348.54,
	3637.2,
	5741.03,
	10606.85,
	8003.67,
	3519.72,
	4467.55,
	2450.48,
	10275.5,
	6035.48,
	4212.82,
	4151.54,
	13502.42,
	2523.73,
	642.73,
	2232.86,
	4725.01,
	1243.43,
	2312.82,
	162.04,
	2253.39,
	1232.03,
	340.65,
	377.16,
	1612.6,
	],
	}

	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,
	],
	2005: [
	88.68,
	112.38,
	1400,
	262.42,
	589.56,
	882.41,
	625.61,
	684.6,
	90.26,
	1461.51,
	892.83,
	966.5,
	827.36,
	727.37,
	1963.51,
	1892.01,
	1082.13,
	1100.65,
	1428.27,
	912.5,
	300.75,
	463.4,
	1481.14,
	368.94,
	661.69,
	48.04,
	435.77,
	308.06,
	65.34,
	72.07,
	509.99,
	],
	2004: [
	87.36,
	105.28,
	1370.43,
	276.3,
	522.8,
	798.43,
	568.69,
	605.79,
	83.45,
	1367.58,
	814.1,
	950.5,
	786.84,
	664.5,
	1778.45,
	1649.29,
	1020.09,
	1022.45,
	1248.59,
	817.88,
	278.76,
	428.05,
	1379.93,
	334.5,
	607.75,
	44.3,
	387.88,
	286.78,
	60.7,
	65.33,
	461.26,
	],
	2003: [
	84.11,
	89.91,
	1064.05,
	215.19,
	420.1,
	615.8,
	488.23,
	504.8,
	81.02,
	1162.45,
	717.85,
	749.4,
	692.94,
	560,
	1480.67,
	1198.7,
	798.35,
	886.47,
	1072.91,
	658.78,
	244.29,
	339.06,
	1128.61,
	298.69,
	494.6,
	40.7,
	302.66,
	237.91,
	48.47,
	55.63,
	412.9,
	],
	2002: [
	82.44,
	84.21,
	956.84,
	197.8,
	374.69,
	590.2,
	446.17,
	474.2,
	79.68,
	1110.44,
	685.2,
	783.66,
	664.78,
	535.98,
	1390,
	1288.36,
	707,
	847.25,
	1015.08,
	601.99,
	222.89,
	317.87,
	1047.95,
	281.1,
	463.44,
	39.75,
	282.21,
	215.51,
	47.31,
	52.95,
	305,
	],
	}

	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,
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	data_ti = {
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	861.92,
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	4040.55,
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	274.82,
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	702.45,
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	6068.31,
	2886.92,
	3696.65,
	5891.25,
	2756.26,
	3371.95,
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	958.88,
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	4040.55,
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	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, 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)


	#####################################################################################
	# 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",
	y_axis=total_data["dataGDP"][year],
	is_selected=False, # is_selected是否选中图例
	label_opts=opts.LabelOpts(is_show=False),
	)
	.add_yaxis(
	series_name="金融",
	y_axis=total_data["dataFinancial"][year],
	is_selected=False, # is_selected是否选中图例
	label_opts=opts.LabelOpts(is_show=False),
	)
	.add_yaxis(
	series_name="房地产",
	y_axis=total_data["dataEstate"][year],
	is_selected=False, # is_selected是否选中图例
	label_opts=opts.LabelOpts(is_show=False),
	)
	.add_yaxis(
	series_name="第一产业",
	y_axis=total_data["dataPI"][year],
	label_opts=opts.LabelOpts(is_show=False),
	)
	.add_yaxis(
	series_name="第二产业",
	y_axis=total_data["dataSI"][year],
	label_opts=opts.LabelOpts(is_show=False),
	)
	.add_yaxis(
	series_name="第三产业",
	y_axis=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")
	timeline.render_notebook() #Jupyter Notebook直接显示

	from pyecharts.charts import Bar
	from pyecharts.faker import Faker
	from pyecharts.globals import ThemeType

	c = (
	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")
	)
	c.render_notebook() #Jupyter Notebook直接显示

	from pyecharts.charts import Bar
	from pyecharts import options as opts

	x_data = [f"11月{str(i)}日" for i in range(1, 12)]
	y_total = [0, 900, 1245, 1530, 1376, 1376, 1511, 1689, 1856, 1495, 1292]
	y_in = [900, 345, 393, "-", "-", 135, 178, 286, "-", "-", "-"]
	y_out = ["-", "-", "-", 108, 154, "-", "-", "-", 119, 361, 203]


	bar = (
	Bar()
	.add_xaxis(xaxis_data=x_data)
	.add_yaxis(
	series_name="",
	y_axis=y_total,
	stack="总量", # stack数据堆叠，同个类目轴上系列配置相同的stack值可以堆叠放置
	itemstyle_opts=opts.ItemStyleOpts(color="rgba(0,0,0,0)"),
	#图元样式配置项,图形颜色，可以使用 RGBA，比如 'rgba(128, 128, 128, 0.5)'，也可以使用十六进制格式，比如 '#ccc'
	# rgba(0,0,0,0)完全不透明的白色,也即是无色
	)
	.add_yaxis(series_name="收入", y_axis=y_in, stack="总量")
	.add_yaxis(series_name="支出", y_axis=y_out, stack="总量")
	.set_global_opts(yaxis_opts=opts.AxisOpts(type_="value")) #坐标轴配置项
	# type_坐标轴类型。可选：
	# 'value': 数值轴，适用于连续数据。
	# 'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。
	# 'time': 时间轴，适用于连续的时序数据，与数值轴相比时间轴带有时间的格式化，在刻度计算上也有所不同，
	# 例如会根据跨度的范围来决定使用月，星期，日还是小时范围的刻度。
	# 'log' 对数轴。适用于对数数据。
	# .render("bar_waterfall_plot.html")
	)
	bar.render_notebook()

	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",
	y_axis=[
	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",
	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),
	)
	.extend_axis( #扩展 X/Y 轴
	yaxis=opts.AxisOpts( #yaxis新增 Y 坐标轴配置项，AxisOpts坐标轴配置项
	name="温度",
	type_="value", #'value': 数值轴，适用于连续数据
	min_=0,
	max_=25,
	interval=5, # 强制设置坐标轴分割间隔
	axislabel_opts=opts.LabelOpts(formatter="{value} °C"),# 坐标轴标签配置项 formatter回调函数，value传入的数据值
	)
	)
	.set_global_opts(
	tooltip_opts=opts.TooltipOpts(#TooltipOpts：提示框配置项
	is_show=True, trigger="axis", axis_pointer_type="cross"
	#is_show是否显示提示框组件，trigger触发类型，'axis': 坐标轴触发，主要在柱状图，折线图等会使用类目轴的图表中使用
	#axis_pointer_type指示器类型'cross'：十字准星指示器。其实是种简写，表示启用两个正交的轴的 axisPointer。
	),
	xaxis_opts=opts.AxisOpts( #坐标轴配置项
	type_="category", #'category': 类目轴，适用于离散的类目数据，为该类型时必须通过 data 设置类目数据。
	axispointer_opts=opts.AxisPointerOpts(is_show=True, type_="shadow"), # 坐标轴指示器配置项
	), #is_show是否显示坐标轴指示器，type_指示器类型# 'line' 直线指示器'shadow' 阴影指示器'none' 无指示器
	yaxis_opts=opts.AxisOpts(
	name="水量",
	type_="value",
	min_=0,
	max_=250,
	interval=50, # 强制设置坐标轴分割间隔
	axislabel_opts=opts.LabelOpts(formatter="{value} ml"), # 坐标轴标签配置项 formatter回调函数，value传入的数据值
	axistick_opts=opts.AxisTickOpts(is_show=True), # 坐标轴刻度配置项
	splitline_opts=opts.SplitLineOpts(is_show=True), # 分割线配置项
	),
	)
	)

	line = (
	Line()
	.add_xaxis(xaxis_data=x_data)
	.add_yaxis(
	series_name="平均温度",
	yaxis_index=1, # 使用的 y 轴的 index，在单个图表实例中存在多个 y 轴的时候有用
	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")
	bar.overlap(line).render_notebook()

	import pyecharts.options as opts
	from pyecharts.charts import Bar, Line

	"""
	Gallery 使用 pyecharts 1.0.0
	参考地址: https://www.echartsjs.com/examples/editor.html?c=multiple-y-axis

	目前无法实现的功能:

	1、暂无
	"""

	colors = ["#5793f3", "#d14a61", "#675bba"]
	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"]
	legend_list = ["蒸发量", "降水量", "平均温度"]
	evaporation_capacity = [
	2.0,
	4.9,
	7.0,
	23.2,
	25.6,
	76.7,
	135.6,
	162.2,
	32.6,
	20.0,
	6.4,
	3.3,
	]
	rainfall_capacity = [
	2.6,
	5.9,
	9.0,
	26.4,
	28.7,
	70.7,
	175.6,
	182.2,
	48.7,
	18.8,
	6.0,
	2.3,
	]
	average_temperature = [2.0, 2.2, 3.3, 4.5, 6.3, 10.2, 20.3, 23.4, 23.0, 16.5, 12.0, 6.2]

	bar = (
	Bar(init_opts=opts.InitOpts(width="1680px", height="800px"))
	.add_xaxis(xaxis_data=x_data)
	.add_yaxis(
	series_name="蒸发量",
	y_axis=evaporation_capacity,
	yaxis_index=0, #多轴时才会有索引，默认轴索引都为0
	color=colors[1],
	)
	.add_yaxis(
	series_name="降水量", y_axis=rainfall_capacity, yaxis_index=1, color=colors[0]
	)
	.extend_axis(
	yaxis=opts.AxisOpts(
	name="蒸发量",
	type_="value",
	min_=0,
	max_=250,
	position="right", #在右
	axisline_opts=opts.AxisLineOpts(
	linestyle_opts=opts.LineStyleOpts(color=colors[1])
	),
	axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
	)
	)
	.extend_axis(
	yaxis=opts.AxisOpts(
	type_="value",
	name="温度",
	min_=0,
	max_=25,
	position="left", #在左
	axisline_opts=opts.AxisLineOpts(
	linestyle_opts=opts.LineStyleOpts(color=colors[2])
	),
	axislabel_opts=opts.LabelOpts(formatter="{value} °C"),
	splitline_opts=opts.SplitLineOpts(
	is_show=True, linestyle_opts=opts.LineStyleOpts(opacity=1) # opacity图形透明度，支持从 0 到 1 的数字，为 0 时不绘制该图形。
	),
	)
	)
	.set_global_opts(
	yaxis_opts=opts.AxisOpts(
	type_="value",
	name="降水量",
	min_=0,
	max_=250,
	position="right",
	offset=80,# Y 轴相对于默认位置的偏移，在相同的 position 上有多个 Y 轴的时候有用。
	axisline_opts=opts.AxisLineOpts(
	linestyle_opts=opts.LineStyleOpts(color=colors[0])
	),
	axislabel_opts=opts.LabelOpts(formatter="{value} ml"),
	),
	tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
	#TooltipOpts：提示框配置项
	# 'axis': 坐标轴触发，主要在柱状图，折线图等会使用类目轴的图表中使用。
	# 'cross'：十字准星指示器。其实是种简写，表示启用两个正交的轴的 axisPointer。
	)
	)

	line = (
	Line()
	.add_xaxis(xaxis_data=x_data)
	.add_yaxis(
	series_name="平均温度", y_axis=average_temperature, yaxis_index=2, color=colors[2]
	)
	)

	# bar.overlap(line).render("multiple_y_axes.html")
	bar.overlap(line).render_notebook()

Pyecharts绘制图表大全——柱形图

百分比柱形图

x轴标签旋转

堆叠数据

动态宏观经济指标图

通过 dict 进行配置柱形图

区域选择组件配置项

区域缩放配置项

好全的工具箱！

类似于瀑布图

柱形图与折线组合图

图形组件的使用-可加水印

堆叠部分数据

x轴y轴命名

添加自定义背景图

柱状图动画延迟&分割线

可以垂直滑动的数据区域

直方图（颜色区分）

y轴格式化单位

标记点最大-最小-平均值

3个y轴

自定义柱状图颜色

不同系列柱间距离

标记线最大-最小-平均值

渐变圆柱

单系列柱间距离

鼠标滚轮选择缩放区域

默认取消显示某 Series

翻转 XY 轴

自定义标记多个点

动画配置基本示例

直方图

自定义多条标记线

基本图表示例

水平滑动&鼠标滚轮缩放

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