• 03 pyechars 直角坐标系图表（示例代码+效果图）

    目录

    1.Bar柱状图/条形图

    2 Line折线图

    3 Scatter散点图

    4 EffectScatter涟漪特效散点图

    5 Boxplot箱型图

    6 Kline k线图

    7 HeatMap热力图

    8 PictorialBar象型柱状图

    9 overlap层叠图

    9.1 Overlap_bar_line

    9.2 Overlap_line_scatter

    Faker中函数的介绍：

    1.Bar柱状图/条形图

    1. """
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    4. """
    5.  
    6. # 柱状图/条形图
    7. from pyecharts.charts import Bar
    8.  
    9. x_data = ["语文", "数学", "英语", "生物", "物理", "化学"]  # x轴数据 数据必须是字符串
    10. y_data = [114, 95, 107, 81, 85, 87]  # y轴数据 数据必须是整数或者小数
    11. bar = Bar()  # 初始化图表
    12. bar.add_xaxis(x_data)  # x轴
    13. bar.add_yaxis('成绩', y_data)  # y轴
    14. bar.render("bar.html")  # 渲染html文件

    2 Line折线图

    1. # 折线图
    2. from pyecharts.charts import Line
    3.  
    4. x_data = ['1月', '2月', '3月', '4月', '5月', '6月']  # x轴数据 数据必须是字符串
    5. y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y轴数据 数据必须是整数或者小数
    6. line = Line()  # 初始化图表
    7. line.add_xaxis(x_data)  # x轴
    8. line.add_yaxis('月消费', y_data)  # y轴
    9. line.render("line.html")  # 渲染html文件

    3 Scatter散点图

    1. # 散点图
    2. from pyecharts.charts import Scatter
    3.  
    4. x_data = ['1月', '2月', '3月', '4月', '5月', '6月']  # x轴数据
    5. y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y轴数据
    6. scatter = Scatter()  # 初始化图表
    7. scatter.add_xaxis(x_data)  # x轴
    8. scatter.add_yaxis('月消费', y_data)  # y轴
    9. scatter.render("scatter.html")  # 渲染html文件

    4 EffectScatter涟漪特效散点图

    1. from pyecharts.charts import EffectScatter
    2.  
    3. x_data = ['1月', '2月', '3月', '4月', '5月', '6月']  # x轴数据
    4. y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y轴数据
    5. effectScatter = EffectScatter()
    6. effectScatter.add_xaxis(x_data)  # x轴
    7. effectScatter.add_yaxis('月消费', y_data)  # y轴
    8. effectScatter.render("effectScatter.html")  # 渲染html文件

    5 Boxplot箱型图

    1. # 箱型图
    2. from pyecharts.charts import Boxplot
    3. import random
    4.  
    5. x_data = ['A', 'B', 'C', 'D', 'E', 'F']
    6. y_data = [[random.randint(100, 200) for i in range(10)] for item in x_data] #[[164, 102, 161, 106, 144, 186, 197, 186, 196, 125], [158, 131, 143, 111, 110, 175, 109, 107, 104, 153], [163, 174, 182, 142, 134, 158, 113, 149, 176, 183], [198, 142, 182, 125, 105, 149, 171, 122, 105, 156], [146, 126, 100, 164, 149, 117, 118, 170, 138, 155], [177, 110, 119, 178, 126, 164, 131, 176, 195, 134]]
    7.  
    8. print(y_data)
    9. Boxplot = Boxplot()
    10. Boxplot.add_xaxis(x_data)  # x轴
    11. Boxplot.add_yaxis('', Boxplot.prepare_data(y_data))  # y轴
    12. Boxplot.render("Boxplot.html")  # 渲染html文件

    6 Kline k线图

    1. # k线图
    2. from pyecharts.charts import Kline
    3.  
    4. date_list = ["2022/6/{}".format(i + 1) for i in range(30)]
    5. y_data = [
    6.     [2320.26, 2320.26, 2287.3, 2362.94],
    7.     [2300, 2291.3, 2288.26, 2308.38],
    8.     [2295.35, 2346.5, 2295.35, 2345.92],
    9.     [2347.22, 2358.98, 2337.35, 2363.8],
    10.     [2360.75, 2382.48, 2347.89, 2383.76],
    11.     [2383.43, 2385.42, 2371.23, 2391.82],
    12.     [2377.41, 2419.02, 2369.57, 2421.15],
    13.     [2425.92, 2428.15, 2417.58, 2440.38],
    14.     [2411, 2433.13, 2403.3, 2437.42],
    15.     [2432.68, 2334.48, 2427.7, 2441.73],
    16.     [2430.69, 2418.53, 2394.22, 2433.89],
    17.     [2416.62, 2432.4, 2414.4, 2443.03],
    18.     [2441.91, 2421.56, 2418.43, 2444.8],
    19.     [2420.26, 2382.91, 2373.53, 2427.07],
    20.     [2383.49, 2397.18, 2370.61, 2397.94],
    21.     [2378.82, 2325.95, 2309.17, 2378.82],
    22.     [2322.94, 2314.16, 2308.76, 2330.88],
    23.     [2320.62, 2325.82, 2315.01, 2338.78],
    24.     [2313.74, 2293.34, 2289.89, 2340.71],
    25.     [2297.77, 2313.22, 2292.03, 2324.63],
    26.     [2322.32, 2365.59, 2308.92, 2366.16],
    27.     [2364.54, 2359.51, 2330.86, 2369.65],
    28.     [2332.08, 2273.4, 2259.25, 2333.54],
    29.     [2274.81, 2326.31, 2270.1, 2328.14],
    30.     [2333.61, 2347.18, 2321.6, 2351.44],
    31.     [2340.44, 2324.29, 2304.27, 2352.02],
    32.     [2326.42, 2318.61, 2314.59, 2333.67],
    33.     [2314.68, 2310.59, 2296.58, 2320.96],
    34.     [2309.16, 2286.6, 2264.83, 2333.29],
    35.     [2282.17, 2263.97, 2253.25, 2286.33],
    36. ]
    37.  
    38. kline = (Kline()
    39.          .add_xaxis(date_list)
    40.          .add_yaxis('', y_data)
    41.          )
    42.  
    43. kline.render("kline.html")  # 渲染html文件

    7 HeatMap热力图

    1. # 热力图
    2. from pyecharts.charts import HeatMap
    3. import random
    4.  
    5. data = [[i, j, random.randint(0, 100)] for i in range(24) for j in range(7)]
    6. hour_list = [str(i) for i in range(24)]
    7. week_list = ['周日', '周一', '周二', '周三', '周四', '周五', '周六']
    8.  
    9. heat = (HeatMap()
    10.         .add_xaxis(hour_list)
    11.         .add_yaxis("", week_list, data)
    12.         )
    13. heat.render("heat.html")

    8 PictorialBar象型柱状图

    1. # 象型图
    2. from pyecharts.charts import PictorialBar
    3.  
    4. x_data = ['1月', '2月', '3月', '4月', '5月', '6月']  # x轴数据
    5. y_data = [1123, 1153, 1089, 1207, 1298, 1123]  # y轴数据
    6. pictorialBar = PictorialBar()  # 初始化图表
    7. pictorialBar.add_xaxis(x_data)  # x轴
    8. pictorialBar.add_yaxis('月消费', y_data)  # y轴
    9. pictorialBar.render("pictorialBar .html")  # 渲染html文件

    9 overlap层叠图

    9.1 Overlap_bar_line

    1. # 层叠图Overlap_bar_line
    2. from pyecharts.charts import Bar
    3. from pyecharts.charts import Line
    4.  
    5. x_data = ['1月', '2月', '3月', '4月', '5月', '6月']
    6. y_data_bar = [1123, 1153, 1089, 1207, 1298, 1123]
    7. y_data_line = [1300, 1200, 1100, 1400, 1500, 1200]
    8.  
    9. bar = (Bar()
    10.        .add_xaxis(x_data)
    11.        .add_yaxis('', y_data_bar)
    12.        )
    13.  
    14. line = (Line()
    15.         .add_xaxis(x_data)
    16.         .add_yaxis('', y_data_line)
    17.         )
    18.  
    19. overlap = bar.overlap(line)
    20. overlap.render("overlap_bar_line .html")

    9.2 Overlap_line_scatter

    1. # 层叠图 Overlap_line_scatter
    2. from pyecharts import options as opts
    3. from pyecharts.charts import Line, Scatter
    4. from pyecharts.faker import Faker
    5.  
    6. x = Faker.choose()
    7. line = (
    8.     Line()
    9.     .add_xaxis(x)
    10.     .add_yaxis("商家A", Faker.values())
    11.     .add_yaxis("商家B", Faker.values())
    12.     .set_global_opts(title_opts=opts.TitleOpts(title="Overlap-line+scatter"))
    13. )
    14. scatter = (
    15.     Scatter()
    16.     .add_xaxis(x)
    17.     .add_yaxis("商家A", Faker.values())
    18.     .add_yaxis("商家B", Faker.values())
    19. )
    20. line.overlap(scatter)
    21. line.render("overlap_line_scatter.html")

     Faker.values() 详解：生成7个随机整数,这7个随机整数一般是两位数和三位数的组合

    Faker中函数的介绍：

    class _Faker:
    clothes = ["衬衫", "毛衣", "领带", "裤子", "风衣", "高跟鞋", "袜子"]
    drinks = ["可乐", "雪碧", "橙汁", "绿茶", "奶茶", "百威", "青岛"]
    phones = ["小米", "三星", "华为", "苹果", "魅族", "VIVO", "OPPO"]
    fruits = ["草莓", "芒果", "葡萄", "雪梨", "西瓜", "柠檬", "车厘子"]
    animal = ["河马", "蟒蛇", "老虎", "大象", "兔子", "熊猫", "狮子"]
    cars = ["宝马", "法拉利", "奔驰", "奥迪", "大众", "丰田", "特斯拉"]
    dogs = ["哈士奇", "萨摩耶", "泰迪", "金毛", "牧羊犬", "吉娃娃", "柯基"]
    week = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
    week_en = "Saturday Friday Thursday Wednesday Tuesday Monday Sunday".split()
    clock = (
        "12a 1a 2a 3a 4a 5a 6a 7a 8a 9a 10a 11a 12p "
        "1p 2p 3p 4p 5p 6p 7p 8p 9p 10p 11p".split()
    )
    visual_color = [
        "#313695",
        "#4575b4",
        "#74add1",
        "#abd9e9",
        "#e0f3f8",
        "#ffffbf",
        "#fee090",
        "#fdae61",
        "#f46d43",
        "#d73027",
        "#a50026",
    ]
    months = ["{}月".format(i) for i in range(1, 13)]
    provinces = ["广东", "北京", "上海", "江西", "湖南", "浙江", "江苏"]
    guangdong_city = ["汕头市", "汕尾市", "揭阳市", "阳江市", "肇庆市", "广州市", "惠州市"]
    country = [
        "China",
        "Canada",
        "Brazil",
        "Russia",
        "United States",
        "Africa",
        "Germany",
    ]
    days_attrs = ["{}天".format(i) for i in range(30)]
    days_values = [random.randint(1, 30) for _ in range(30)]

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  • 原文地址：https://blog.csdn.net/c_lanxiaofang/article/details/126021659