关于pyecharts

pyecharts是一个用于生成echart（百度开源的数据可视化javascript库）图表的类库。pyecharts 分为 v0.5.x 和 v1.x 两个大版本，版本不兼容，本篇所有的案例基于v1.6.2。

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柱状图

# 柱状图import randomimport pyecharts.options as optsfrom pyecharts.charts import Barx_vals = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']bar = (    Bar()    .add_xaxis(x_vals)    .add_yaxis('商家A', [random.randint(10, 100) for _ in range(6)])    .add_yaxis('商家B', [random.randint(10, 100) for _ in range(6)])    .add_yaxis('商家C', [random.randint(10, 100) for _ in range(6)])    .add_yaxis('商家D', [random.randint(10, 100) for _ in range(6)])    .set_series_opts(label_opts=opts.LabelOpts(is_show=True, font_size=14),                          markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(y=40, name="达标线=40")]))    .set_global_opts(title_opts=opts.TitleOpts(title='柱状图示例-销量', subtitle='四个商家'),                     xaxis_opts=opts.AxisOpts(name='商品'),                     yaxis_opts=opts.AxisOpts(name='单位:件')))bar.render('柱状图.html')

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堆叠柱状图

# 柱状堆叠图import pyecharts.options as optsfrom pyecharts.charts import Bargoods = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']bar = (    Bar()    .add_xaxis(goods)    .add_yaxis('商家A', [random.randint(10, 100) for _ in range(6)], stack='stack1')    .add_yaxis('商家B', [random.randint(10, 100) for _ in range(6)], stack='stack1')    .add_yaxis('商家C', [random.randint(10, 100) for _ in range(6)], stack='stack1')    .set_series_opts(label_opts=opts.LabelOpts(is_show=False))    .set_global_opts(title_opts=opts.TitleOpts(title='柱状堆叠图示例-商品销量'),                     xaxis_opts=opts.AxisOpts(name='品类'),                            yaxis_opts=opts.AxisOpts(name='销量（单位:件）')))bar.render('柱状堆叠图.html')

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条形图

# 条形图x_vals1 = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']x_vals2 = ['POLO', '篮球鞋', '羽绒服', '皮鞋', '领带', '睡衣']x_vals3 = ['羽毛球服', '羽毛球鞋', '护腕', '护膝', '护踝', '毛巾']y_vals = [random.randint(10, 100) for _ in range(18)]bar = Bar().add_xaxis(x_vals1 + x_vals2 + x_vals3)      bar.add_yaxis('商家A', y_vals,               markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='average'),                                                opts.MarkPointItem(type_='max'),                                                opts.MarkPointItem(type_='min')],                                                 symbol_size=80)              ) bar.set_series_opts(label_opts=opts.LabelOpts(is_show=True, position='right'))bar.set_global_opts(title_opts=opts.TitleOpts(title='条形图示例-商品销量', subtitle='条目较多条形图比较好看点'))bar.reversal_axis() #翻转XY轴，将柱状图转换为条形图bar.render('条形图.html')

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直方图

# 直方图# 直方图import randomimport pyecharts.options as optsfrom pyecharts.charts import Barx_vals = ['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子']xlen = len(x_vals)# 设置成两种颜色y_vals = []for idx, item in enumerate(x_vals):    if idx % 2 == 0:        y_vals.append(            opts.BarItem(                name = item,                value = random.randint(10, 100),                itemstyle_opts = opts.ItemStyleOpts(color="#749f83"),            )        )    else:        y_vals.append(            opts.BarItem(                name = item,                value = random.randint(10, 100),                itemstyle_opts = opts.ItemStyleOpts(color="#d48265"),            )        )bar_histogram = (    Bar()    .add_xaxis(x_vals)    .add_yaxis('商家A', y_vals, category_gap=0)     # .add_yaxis('商家A', [random.randint(10, 100) for _ in range(6)], category_gap=0)    .set_series_opts(label_opts=opts.LabelOpts(is_show=True, font_size=14))    .set_global_opts(title_opts=opts.TitleOpts(title='直方图示例-选择赠品', subtitle=''),                     xaxis_opts=opts.AxisOpts(name='赠品类型'),                     yaxis_opts=opts.AxisOpts(name='选择相应赠品的人数')))bar_histogram.render('直方图.html')

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帕累托图（复合图）

# 帕累托图--# 左边纵坐标表示频数,右边纵坐标表示频率.分析线表示累积频率import randomfrom pyecharts import options as optsfrom pyecharts.charts import Bar, Lineimport pandas as pd# 随机颜色, from fakerdef rand_color() -> str:    return random.choice(        [            "#c23531",            "#2f4554",            "#61a0a8",            "#d48265",            "#749f83",            "#ca8622",            "#bda29a",            "#6e7074",            "#546570",            "#c4ccd3",            "#f05b72",            "#444693",            "#726930",            "#b2d235",            "#6d8346",            "#ac6767",            "#1d953f",            "#6950a1",        ]    )df_origin = pd.DataFrame({'categories':['衬衫', '羊毛衫', '雪纺衫', '裤子', '高跟鞋', '袜子'],'sales': [random.randint(10, 100) for _ in range(6)]})print(df_origin)# 按销量降序排列df_sorted = df_origin.sort_values(by='sales' , ascending=False)print(df_sorted)# 折线图x轴x_line_categories = [*range(7)] # 折线图y轴--向下累积频率cum_percent = df_sorted['sales'].cumsum() / df_sorted['sales'].sum() * 100cum_percent = cum_percent.append(pd.Series([0])) # 添加起始频率0cum_percent = cum_percent.sort_values(ascending=True)print(df_sorted.categories.values.tolist()) print(cum_percent.values.tolist())def pareto_bar() -> Bar:     line = (        Line()        .add_xaxis(x_line_categories)                .add_yaxis("累计百分比",                   cum_percent.values.tolist(),                       xaxis_index=1,                   yaxis_index=1,             # 使用次y坐标轴，即bar中的extend_axis                   label_opts=opts.LabelOpts(is_show=False),                   is_smooth=True,                  )    )        bar = (        Bar()        .add_xaxis(df_sorted.categories.values.tolist())        .add_yaxis('销售额', df_sorted.sales.values.tolist(), category_gap=0)        # .add_yaxis('总额百分比', cum_percent.values.tolist())           .extend_axis(xaxis=opts.AxisOpts(is_show=False, position='top'))         .extend_axis(yaxis=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_inside=True),  # 刻度尺朝内            axislabel_opts=opts.LabelOpts(formatter='{value}%'), position='right') )        .set_series_opts(label_opts=opts.LabelOpts(is_show=True, font_size=14))        .set_global_opts(title_opts=opts.TitleOpts(title='帕累托图示例-销售额', subtitle=''),                             xaxis_opts=opts.AxisOpts(name='商品类型', type_='category'),                             yaxis_opts=opts.AxisOpts(                                axislabel_opts=opts.LabelOpts(formatter="{value} 件")                             )                         )    )    bar.overlap(line)    return barpareto_bar().render('帕累托图.html')

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饼图

# 饼图from pyecharts import options as optsfrom pyecharts.charts import Page, Piepie = (    Pie()    .add('鼠标选中分区后的tip',         [list(z) for z in zip(['20{}年第{}季'.format(year,season)                                           for year in [19, 20]  # count 2                                                                                        for season in range(1,5)] # count 2                ,[random.randint(2, 10) for _ in range(8)])]) # count 8    .set_series_opts(label_opts=opts.LabelOpts(formatter='{b}: {c}万套'))    .set_global_opts(title_opts=opts.TitleOpts(title='饼图实例-近两年季度销售'),                         legend_opts=opts.LegendOpts(is_show=False)))pie.render('饼图.html')

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圆环图

from pyecharts.charts import Piepie = (    Pie()    .add(        '鼠标选中分区后的tip',        [list(z) for z in zip(['20{}年第{}季'.format(year,season)                                       for year in [19, 20]  # count 2                                                                                    for season in range(1,5)] # count 2            ,[random.randint(2, 10) for _ in range(8)])],        radius=['50%', '75%'],          #设置内径外径                   label_opts=opts.LabelOpts(is_show=True)            )    .set_global_opts(title_opts=opts.TitleOpts(title='圆环图示例-近两年季度销售'),                     legend_opts=opts.LegendOpts(is_show=False)))pie.render('圆环图.html')

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玫瑰图

# 玫瑰图from pyecharts.charts import Piepie = (    Pie()    .add(        '鼠标选中分区后的tip',        [list(z) for z in zip(['20{}年第{}季'.format(year,season)                                       for year in [19, 20]  # count 2                                                                                    for season in range(1,5)] # count 2            ,[random.randint(0, 10) for _ in range(8)])],        radius=['10%', '75%'],          #设置内径外径        # rosetype='radius' 圆心角展现数据百分比，半径展现数据大小        # rosetype='area' 圆心角相同，为通过半径展现数据大小        rosetype='radius',                     label_opts=opts.LabelOpts(is_show=True)            )    .set_global_opts(title_opts=opts.TitleOpts(title='玫瑰图示例-近两年季度销售'),                     legend_opts=opts.LegendOpts(is_show=False)))pie.render('玫瑰图.html')

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折线图

# 折线图import randomimport pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.commons.utils import JsCode line = (    Line()    .add_xaxis(['{}月第{}周周赛'.format(y,z)                      for y in range(1, 3)  # 1、2月                           for z in range(1, 5)])  # 1-4周    .add_yaxis('A题', [random.randint(10, 20) for _ in range(8)],                is_smooth=True, # 平滑               markpoint_opts=opts.MarkPointOpts(                           # 使用coord这个属性设置自定义标记点数值，我这儿随便写                           data=[opts.MarkPointItem(name='自定义标记点',coord=[2,18],value='标注值')]                       )               )    .add_yaxis('B题', [random.randint(5, 20) for _ in range(8)])    .add_yaxis('C题', [random.randint(5, 20) for _ in range(8)])    .set_series_opts(label_opts=opts.LabelOpts(                    formatter=JsCode( # 通过定义JavaScript回调函数自定义标签                         "function(params){"                                "return params.value[1].toString() + '%';}"  # 外层单引号内存双引号亲测不行！                    )                ))    .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)), # 设置x轴标签旋转角度                     yaxis_opts=opts.AxisOpts(name='AC率', min_=3),                      title_opts=opts.TitleOpts(title='折线示例_ACM题目分析'))            )line.render('折线图.html')

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折线面积图

# 折线面积图import randomimport pyecharts.options as optsfrom pyecharts.charts import Linefrom pyecharts.commons.utils import JsCode line = (    Line()    .add_xaxis(['{}月第{}周周赛'.format(y,z)                      for y in range(1, 3)  # 1、2月                           for z in range(1, 5)])  # 1-4周    .add_yaxis('蔡队',                [random.randint(10, 20) for _ in range(8)],                is_symbol_show=False,                        areastyle_opts=opts.AreaStyleOpts(opacity=0.5),               markpoint_opts=opts.MarkPointOpts(data=[opts.MarkPointItem(type_='average', name='均值'),                                                   opts.MarkPointItem(type_='max', name='最大值'),                                                   opts.MarkPointItem(type_='min', name='最小值')],                                                    symbol_size=50)                          )    .add_yaxis('旺神',                [random.randint(6, 20) for _ in range(8)],                is_smooth=True,                is_symbol_show=False,               areastyle_opts=opts.AreaStyleOpts(opacity=0.5)               )    .set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)), # 设置x轴标签旋转角度                     yaxis_opts=opts.AxisOpts(name='完成积分', min_=5),                      title_opts=opts.TitleOpts(title='折线面积图示例_周赛分析'))            )line.render('折线面积图.html')

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散点图

# 散点图from pyecharts.charts import Scatterfrom pyecharts import options as optsfrom pyecharts.commons.utils import JsCodeimport pandas as pddef scatter_simple() -> Scatter:    # 数据源    df = pd.DataFrame({'AC':[21,22,23,24,28,30,34,35,40,44,45],  # 刷题数                       'ACB':[140,120,380,120,200,190,160,300,300,400,500],                       '姓名':['小军','NIL','假冒NOI','小白','弱刚','晓雷','窜天','云云','依图','蔡队','旺神',]})    # inplace=True：不创建新的对象，直接对原始对象进行修改    # 升序    df.sort_values('AC', inplace=True, ascending=True)          c = (        Scatter()        .add_xaxis(df.AC.values.tolist())        .add_yaxis(            '刷题_能力_姓名',            df[['ACB','姓名']].values.tolist(),            label_opts=opts.LabelOpts(                formatter=JsCode(                    'function(params){return params.value[2];}' #通过定义JavaScript回调函数自定义标签                )            )        )        .set_global_opts(            title_opts=opts.TitleOpts(title='散点图示例--ACM集训队队员能力'),            xaxis_opts=opts.AxisOpts(name='AC(刷题数)', type_='value', min_=20),  #x轴从20开始，原点不为0            yaxis_opts=opts.AxisOpts(name='ACB(能力值)', min_=100),  # y轴起始点的值            legend_opts=opts.LegendOpts(is_show=True)        )    )    return cscatter_simple().render('散点图.html')

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雷达图

# 雷达图import randomfrom pyecharts import options as optsfrom pyecharts.charts import Page, Radardef radar_simple() -> Radar:    c = (        Radar()        .add_schema(            # 各项的max_值可以不同            schema=[                opts.RadarIndicatorItem(name='计算几何学', max_=100),                opts.RadarIndicatorItem(name='动态规划', max_=100),                opts.RadarIndicatorItem(name='图论', max_=100),                opts.RadarIndicatorItem(name='搜索', max_=100),                opts.RadarIndicatorItem(name='模拟', max_=100),                opts.RadarIndicatorItem(name='数论', max_=100),            ]        )        .add('旺神', [[random.randint(10, 101) for _ in range(6)]],                        color='red',                        areastyle_opts = opts.AreaStyleOpts(  #设置填充的属性                 opacity = 0.5,                                   color='red'                              ),)        .add('蔡队', [[random.randint(10, 101) for _ in range(6)]],             color='blue',             areastyle_opts = opts.AreaStyleOpts(                 opacity = 0.5,#透明度                 color='blue'         ),)        .set_series_opts(label_opts=opts.LabelOpts(is_show=True))        .set_global_opts(title_opts=opts.TitleOpts(title='雷达图示例-ACM集训队队员能力'))    )    return cradar_simple().render('雷达图.html')

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箱线图

# 箱线图--描述离散程度from pyecharts import options as optsfrom pyecharts.charts import Boxplotdef boxpolt_base() -> Boxplot:    v_sophomore = [        [1.1, 2.2, 2.6, 3.2, 3.7, 4.2, 4.7, 4.7, 5.5, 6.3, 8.0],        [2.5, 2.5, 2.8, 3.2, 3.7, 4.2, 4.7, 4.7, 5.5, 6.3, 7.0]    ]    v_junior = [        [3.6, 3.7, 4.7, 4.9, 5.1, 5.2, 5.3, 5.4, 5.7, 5.8, 5.8],        [3.6, 3.7, 4.7, 4.9, 5.1, 5.2, 5.3, 5.4, 5.7, 5.8, 5.8]    ]    # 最小值，下四分位数，中位数、上四分位数、最大值    # [min, Q1, median (or Q2), Q3, max]    c = (        Boxplot()        .add_xaxis(['寒假作业','暑假作业'])        .add_yaxis('大二队员', Boxplot.prepare_data(v_sophomore))        .add_yaxis('大三队员', Boxplot.prepare_data(v_junior))        .set_series_opts(label_opts=opts.LabelOpts(is_show=True))        .set_global_opts(title_opts=opts.TitleOpts(title='ACM集训队祖传练习完成时长离散度'),                         xaxis_opts=opts.AxisOpts(name='单位：小时'),                          legend_opts=opts.LegendOpts(is_show=True))        .reversal_axis() #翻转XY轴    )    return cboxpolt_base().render('箱线图.html')

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词云图

# 词云图from pyecharts import options as optsfrom pyecharts.charts import WordCloudfrom pyecharts.globals import SymbolTypewords = [    ('背包问题', 10000),    ('大整数', 6181),    ('Karatsuba乘法算法', 4386),    ('穷举搜索', 4055),    ('傅里叶变换', 2467),    ('状态树遍历', 2244),    ('剪枝', 1868),    ('Gale-shapley', 1484),    ('最大匹配与匈牙利算法', 1112),    ('线索模型', 865),    ('关键路径算法', 847),    ('最小二乘法曲线拟合', 582),    ('二分逼近法', 555),    ('牛顿迭代法', 550),    ('Bresenham算法', 462),    ('粒子群优化', 366),    ('Dijkstra', 360),    ('A*算法', 282),    ('负极大极搜索算法', 273),    ('估值函数', 265)]def wordcloud_base() -> WordCloud:    c = (        WordCloud()        .add("", words, word_size_range=[20, 100], shape=SymbolType.ROUND_RECT)        .set_global_opts(title_opts=opts.TitleOpts(title='WordCloud示例-OJ搜索关键字'))    )    return cwordcloud_base().render('词云图.html')