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30分钟学会pyecharts数据可视化。网友:非常好用!

30分钟学会pyecharts数据可视化。网友:非常好用!

作者: 0de1595b4560 | 来源:发表于2019-05-06 19:20 被阅读328次

    小红:你先跟我说说什么是pyecharts吧。

    小明:Echarts 是一个由百度开源的数据可视化javascript库,凭借着良好的交互性,精巧的图表设计,得到了众多开发者的认可。而 Python 是一门富有表达力的语言,很适合用于数据处理。当数据分析遇上数据可视化时,pyecharts诞生了。简单地说,pyecharts就是百度开源的一个强大的javascript数据可视化库Echarts的python接口。

    小红:明白,那这个pyecharts要怎么安装呢?

    小明:很简单,用pip就可以安装了。下面是在jupyter notebook中安装这个库的一个示范。

    # 安装pyecharts!pip install pyecharts==0.5.11# pyecharts_snapshot
     提供图片导出功能!pip install pyecharts_snapshot
    

    一,基本图表

    小红:好了,我已经安装成功了。你可不可以给我举一些常用图表的范例。

    小明:OK,在数据分析中最常用的3种图表就是柱形图,折线图和散点图了。下面我们就来看一下pyecharts绘制这3种常用图表的范例吧。

    1,柱形图

    柱形图适合表现几组数据之间的对比关系,柱形图表现的数据的数量一般不宜太多,多了的话会像一堆杂草。

    from  pyecharts import Barx = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋"]
    y1 = [5, 20, 36, 10, 75]
    y2 = [10, 25, 8, 60, 20]
    bar = Bar(title = "产品月销量",width = 600,height = 420)
    bar.add(name = "商家A", x_axis = x, y_axis = y1)
    bar.add(name = "商家B", x_axis = x, y_axis = y2,is_xaxis_boundarygap =True)
    # 导出绘图html文件,
    可直接用浏览器打开bar.render('柱形图示范.html')
    bar
    

    2,折线图

    折线图适合描述两个变量之间的函数关系,例如常用它来描述一个变量随时间的变化趋势。

    from  pyecharts import Line
    x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
    y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
    y2 [8,20,24,36,40,36,40,45,50,53,48,58]
    line = Line(title = "月销售总额",width = 600,height = 420)
    line.add(name = "商家A", x_axis = x, y_axis = y1,       
      line_width = 3,line_color = 'red')
    line.add(name = "商家B", 
    x_axis = x, y_axis = y2,        
     yaxis_min = 0,yaxis_max = 100,is_xaxis_boundarygap = False,    
     is_datazoom_show =True,line_width = 2,line_color = 'cyan')
    line.render('折线图示范.html')
    line
    

    3,散点图

    散点图适合表现大量样本的多个属性的分布规律。散点图的每个点表示一个样本,每个坐标维度表示一个属性。

    from pyecharts
     import Scatterimport pandas as pd
     dfboy = pd.DataFrame()
    dfboy['weight'] = [56,67,65,70,57,60,80,85,76,64]
    dfboy['height'] = [162,170,168,172,168,172,180,176,178,170]
    dfgirl = pd.DataFrame()
    dfgirl['weight'] = [50,62,60,70,57,45,62,65,70,56]
    dfgirl['height'] = [155,162,165,170,166,158,160,170,172,165]
    scatter = Scatter(title = "体格数据",width = 600,height = 420)
    scatter.add(name = "boy", x_axis = dfboy['weight'], y_axis = dfboy['height'])
    scatter.add(name = "girl", x_axis = dfgirl['weight'], y_axis = dfgirl['height'],           
    yaxis_min = 130,yaxis_max = 200,xaxis_min = 30,xaxis_max = 100)
    scatter.render("散点图示范.html")
    scatter
    

    当样本属性维度多于2个时,散点图可以使用点的颜色或大小等方式来表达更多属性维度。下面示范使用点的大小表示第3个维度。

    from pyecharts import Scatter
    import pandas as pd 
    
    def custom_formatter(params):
        return (params.value[3] + ':' +
                 str(params.value[0]) +','
                 +str(params.value[1]) + ','
                 +str(params.value[2]))
    
    df = pd.DataFrame()
    df['country'] = ["中国",'美国','德国','法国','英国','日本','俄罗斯','印度','澳大利亚','加拿大']
    df['life-expectancy'] = [76.9,79.1,81.1,81.9,81.4,83.5,73.13,66.8,81.8,81.7]
    df['capita-gdp'] = [13334,53354,44053,37599,38225,36162,23038,5903,44056,43294]
    df['population'] = [1376048943,321773631,80688545,64395345,64715810,126573481,143456918,
                        1311050527,23968973,35939927]
    
    scatter = Scatter(title = "各国发展水平",width = 600,height = 420)
    scatter.add(name = '',
                x_axis = df['capita-gdp'],  # params.values[0]
                y_axis = df['life-expectancy'], # params.values[1]
                extra_data = df['population'].values.tolist(), # params.values[2]
                extra_name = df['country'].values.tolist(), # params.values[3]
                tooltip_formatter=custom_formatter,  #自定义提示框格式内容
                is_visualmap=True, 
                visual_orient="horizontal",
                visual_type = 'size',  #可以是size或者color
                visual_dimension=2,
                visual_range=[20000000, 1500000000],
               )
    scatter
    

    小红:你上面说的这些范例大部分还是挺简单的,可能这个用散点图来显示各个国家的发展水平的例子相对复杂一些,是不是如果有第3维数据就要用extra_data来传入呢?然后用visual_dimension来指定用点的颜色或者点的大小表示的是第几维的数据。这里的话,用点的大小表示了population也就是各个国家人口的多少。对吗?

    小明:一点也没有错,你可以仿照着这个例子试试用点的颜色来表示第3维度的数据,应该不难懂的。

    小红:棒棒的。除了这三种最常用图表外。还有一些别的好用的表现力强的图表可以推荐一些吗?

    小明:你还真是学而不厌。那我再给你演示一下箱型图,词云图以及地理坐标系图吧。

    4,箱型图

    箱型图适合表现一组数据的统计分布规律,它能显示出一组数据的最大值、最小值、中位数、及上下四分位数。

    箱型图的进阶版本是小提琴图,可以展示数据的密度估计曲线,可以用seaborn画出。

    from pyecharts import Boxplot
    
    x =['1班','2班','3班','4班']
    y1=[78, 98, 56, 78, 90.0, 45, 78, 20, 87, 86, 74, 89, 94]
    y2=[89, 82, 45, 67, 68, 78.0, 79, 98, 71, 56, 78, 81, 80]
    y3=[90, 80, 60, 89, 76, 73.0, 72, 92, 89, 87, 65, 66, 76]
    y4=[82, 72, 55, 100, 90.0, 78, 69, 67, 87, 66, 78, 71, 82]
    
    box = Boxplot(title = '考试成绩箱型图',width = 600,height = 420)
    
    # 预处理数据计算最大值,最小值,中位数以及上下四分位数
    y_prepared = box.prepare_data([y1,y2,y3,y4]) 
    box.add(name = '',x_axis = x,y_axis = y_prepared)
    

    附:用seaborn 进行小提琴图的绘制

    import seaborn as sns
    %matplotlib inline
    %config InlineBackend.figure_format = 'svg'
    
    #设置风格
    sns.set(style="white", context="notebook")
    #处理中文问题
    sns.set_style({'font.sans-serif':['simhei', 'Arial']}) 
    
    dfdata = pd.DataFrame()
    dfdata['score'] = y1 + y2 + y3 + y4
    dfdata['class'] = ['1班']*len(y1)+['2班']*len(y2)+['3班']*len(y3)+['4班']*len(y4)
    
    ax = sns.violinplot(x= 'class', y = 'score',data = dfdata,
                palette = 'hls', # 设置调色板
                inner = 'box'# 设置内部显示类型 → “box”, “quartile”, “point”, “stick”, None
               )
    

    5,词云图

    词云图适合表现不同关键词的出现频率或重要性程度。

    from pyecharts import WordCloud
    
    words = ['python','jupyter','numpy','pandas','matplotlib','sklearn',
            'xgboost','lightGBM','simpy','keras','tensorflow',
             'hive','hadoop','spark']
    counts = [100,90,65,95,50,60,70,70,20,70,80,80,60,60]
    
    cloud = WordCloud(title = '数据算法常用工具',width = 600,height = 420)
    cloud.add(name = 'utils',attr = words,value = counts,
              shape = "circle",word_size_range = (10,70))
    cloud
    
    

    6,地理坐标系图

    地理坐标系图适合表现和国家,省份,以及城市,经纬度位置相关联的数据分布规律。

    pyecharts中Geo表达和城市关联的数据,Map表达和国家和省份关联的数据。

    # 安装地图附属包
    !pip install echarts-countries-pypkg
    !pip install echarts-china-provinces-pypkg
    !pip install echarts-china-cities-pypkg
    
    # 全国城市地图示例
    from pyecharts import Geo
    
    data = [
        ("海门", 9),("鄂尔多斯", 12),("招远", 12),("舟山", 12),("齐齐哈尔", 14),("盐城", 15),
        ("惠州", 37),("江阴", 37),("蓬莱", 37),("韶关", 38),("嘉峪关", 38),("广州", 38),
        ("张家港", 52),("三门峡", 53),("锦州", 54),("南昌", 54),("柳州", 54),("三亚", 54),
        ("呼和浩特", 58),("成都", 58),("大同", 58),("镇江", 59),("桂林", 59),("张家界", 59),
        ("北京", 79),("徐州", 79),("衡水", 80),("包头", 80),("绵阳", 80),("乌鲁木齐", 84),
        ("菏泽", 194),("合肥", 229),("武汉", 273),("大庆", 279)]
    
    geo = Geo(
        "全国部分城市空气质量",
        title_color="#fff",
        title_pos="center",
        width=800,
        height=600,
        background_color="#404a59",
    )
    attr, value = geo.cast(data)
    geo.add(
        "",
        attr,
        value,
        visual_range=[0, 200],
        visual_text_color="#fff",
        symbol_size=15,
        is_visualmap=True,
    )
    geo
    
    #  全国省份地图
    from pyecharts import Map
    value = [155, 10, 66, 78, 44, 38, 88, 50, 20]
    attr = ["福建","山东","北京","上海","江西","新疆","内蒙古","云南","重庆"]
    m = Map("全国省份地图", width=600, height=400)
    m.add("", attr, value, maptype='china',
            is_visualmap=True, 
            is_piecewise=True,
            visual_text_color="#000",
            visual_range_text=["", ""],
            pieces=[
                {"max": 160, "min": 81, "label": "高"},
                {"max": 80, "min": 51, "label": "中"},
                {"max": 50, "min": 0, "label": "低"},
            ])
    m
    
    # 世界地图示例
    from pyecharts import Map
    countries= ["China", "Canada", "India", "Russia", "United States","Japan"]
    capita_gdp = [13334, 43294, 5903, 23038, 53354,36162]
    population = [1376048943, 35939927, 1311050527, 143456918, 321773631,126573481]
    life_expectancy = [76.9,81.7,66.8,73.13,79.1,73.13]
    
    m = Map("世界经济发展水平", width=800, height=500)
    m.add(
        "人均GDP",
        attr = countries,
        value = capita_gdp,
        maptype="world",
        is_visualmap=True,
        visual_range = [5000,60000],
        visual_text_color="#000",
        is_map_symbol_show=False,
        visual_orient="horizontal"
    )
    m
    

    二,图表配置

    小红:你上面介绍的这些基本图表都蛮实用的,做起来好像也不难。但是如果我想对图表尺寸,线型颜色,坐标轴刻度等一些细节进行调整,而不是采用默认配置,应该怎么做呢?

    小明:你说的这个叫做图表配置。在pyecharts里有3种进行图表配置的方法。

    第一个是修改图表主题风格:利用configure或use_theme指定图表主题风格,对图表整体颜色风格产生影响。

    第二个是初始化图表通用属性:在创建图表时指定图表height,title等属性,对图像尺寸和标题等通用属性产生影响。

    第三个是配置特定元素属性:可以使用add给图表配置xyAxis,datazoom,lineStyle等特定元素属性。

    1,修改图表主题风格

    可以使用use_theme修改单个图表主题,也可以用configure修改全局图表风格。

    # 默认主题效果
    import random
    from pyecharts import Bar
    
    X_AXIS = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    bar = Bar("默认主题效果", "这里是副标题")
    bar.add("商家A", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家B", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家C", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家D", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar
    
    image
    # 使用dark主题
    import random
    from pyecharts import Bar
    
    
    X_AXIS = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    bar = Bar("dark主题展示", "这里是副标题")
    bar.use_theme("dark")
    bar.add("商家A", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家B", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家C", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家D", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar
    
    # 安装主题插件获取更多主题
    !pip install echarts-themes-pypkg
    

    主题插件支持以下主题

    • vintage

    • macarons 美

    • shine 美+++

    • roma 美

    • westeros

    • wonderland

    • chalk

    • halloween 美+

    • essos

    • walden

    • purple-passion

    • romantic

    import random
    from pyecharts import Bar
    
    X_AXIS = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    bar = Bar("shine主题展示", "这里是副标题")
    bar.use_theme("shine")
    bar.add("商家A", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家B", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家C", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar.add("商家D", X_AXIS, [random.randint(10, 100) for _ in range(6)])
    bar
    

    如果要设置某个主题风格应用到所有图表,可以在绘图开始前用configure进行设置。

    from pyecharts import configure
    # 将这行代码置于首部
    configure(global_theme='shine')
    
    
    from pyecharts import Pie
    attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    value = [11, 12, 13, 10, 10, 10]
    pie = Pie("销售额占比",width = 800,height = 520)
    pie.add("", attr, value, is_label_show=True)
    

    2,修改图表通用属性

    图表通用属性指的是图表的title,subtitle,height,width,title_pos,title_color,title_text_size,background_color等属性,这些属性对所有类型的图表都适用。

    所有通用属性设置方式详细参见pyecharts的官方文档:https://github.com/lyhue1991/pyecharts/blob/master/docs/zh-cn/charts_configure.md

    from pyecharts import Pie
    attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    value = [11, 12, 13, 10, 10, 10]
    
    # 初始化图表通用属性
    pie = Pie(title = "销售额占比",
              title_pos = 'center', # 标题居中
              title_top = 'bottom', # 标题在底部
              title_color = '#0000ff', # 标题颜色设置为蓝色,256位rgb格式
              background_color = "#aee", # 设置背景颜色,16位rgb格式
              width = 600,height = 420)
    
    pie.add("", attr, value, is_label_show=True)
    

    3,配置特定元素属性

    特定元素属性包括xyAxis,dataZoom,lineStyle,markLine-markPoint,visualMap等元素属性,这些元素只在某些种类的图表中适用。例如xyAxis只在Line、Bar、Scatter、EffectScatter、Kline这几类图表中适用。

    所有特定元素属性设置方式详细参见官方文档:https://github.com/lyhue1991/pyecharts/blob/master/docs/zh-cn/charts_configure.md

    # 设置xyAxis示范
    
    from  pyecharts import Line
    
    x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
    y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
    y2 = [8,20,24,36,40,36,40,45,50,53,48,58]
    
    line = Line(title = "月销售总额",width = 600,height = 420)
    
    line.add(name = "商家A", x_axis = x, y_axis = y1)
    line.add(name = "商家B", x_axis = x, y_axis = y2,
             #
             #=====设置xyAxis=====
             yaxis_min = 0,yaxis_max = 100,  # 设置y坐标轴刻度范围
             xaxis_name = '月份', yaxis_name = '销售额', #x轴名称,y轴名称
             xaxis_name_gap =  40, # x轴名称与轴距离
             xaxis_rotate = 30,  # x轴刻度旋转角度
             is_splitline_show = True, # 显示y轴网格线
             is_xaxislabel_align = True # x轴刻度和标签是否对齐
            )
    line
    
    # 设置dataZoom示范
    from  pyecharts import Line
    
    x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
    y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
    y2 = [8,20,24,36,40,36,40,45,50,53,48,58]
    
    line = Line(title = "月销售总额",width = 600,height = 420)
    
    line.add(name = "商家A", x_axis = x, y_axis = y1)
    line.add(name = "商家B", x_axis = x, y_axis = y2,
             #
             #=====设置xyAxis=====
             is_xaxis_boundarygap = False,  # x坐标刻度对准数据,而不是作为分类边界
             #
             #=====设置dataZoom=====
             is_datazoom_show = True, #显示 dataZoom控制条
             datazoom_type = 'both' # 可以是slider,inside或both
             #
             #
            )
    line
    
    # 设置lineStyle示范
    
    from  pyecharts import Line
    
    x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
    y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
    y2 = [8,20,24,36,40,36,40,45,50,53,48,58]
    
    line = Line(title = "月销售总额",width = 600,height = 420)
    
    line.add(name = "商家A", x_axis = x, y_axis = y1,
             #
             #=====设置lineStyle=====
             line_width = 2,
             line_opacity = 0.5, # 透明度
             line_color = 'red'
            )
    line.add(name = "商家B", x_axis = x, y_axis = y2,
             #
             #=====设置xyAxis=====
             is_xaxis_boundarygap = False,  # x坐标刻度对准数据,而不是作为分类边界
             #
             #=====设置dataZoom=====
             is_datazoom_show = True, #显示 dataZoom控制条
             #
             #=====设置lineStyle=====
             line_width = 3,
             line_color = '#11ffbb',
             line_type = 'dashed', # 线型,可以是solid,dashed,或者dotted
            )
    line
    
    # 设置markPoint和markLine示范
    
    from  pyecharts import Line
    
    x = ['2018-{:0>2d}'.format(s) for s in range(1,13)]
    y1 = [5,10,26,30,35,30,20,26,40,46,40,50]
    y2 = [8,20,24,36,40,36,40,45,50,53,48,58]
    
    line = Line(title = "月销售总额",width = 600,height = 420)
    
    line.add(name = "商家A", x_axis = x, y_axis = y1,
             #
             #=====设置lineStyle=====
             line_width = 2,
             line_opacity = 0.5, # 透明度
             line_color = 'red',
             #
             #=====设置markPoint&markLine=====
             mark_point = ['min','max'], #标记点
             mark_line = ['average'] #标记线
            )
    line.add(name = "商家B", x_axis = x, y_axis = y2,
             #
             #=====设置xyAxis=====
             is_xaxis_boundarygap = False,  # x坐标刻度对准数据,而不是作为分类边界
             yaxis_min = 0,yaxis_max = 100,  # 设置y坐标轴刻度范围
             #
             #=====设置dataZoom=====
             is_datazoom_show = True, #显示 dataZoom控制条
             #
             #=====设置lineStyle=====
             line_width = 3,
             line_type = 'dashed', # 线型,可以是solid,dashed,或者dotted
    
             #=====设置markPoint&markLine=====
             mark_point = [{"coord": ['2018-09', 60], "name": "2018/09销售目标"}, 
              {"coord": ['2018-11', 80], "name": "2018/10销售目标"}]  # 自定义标记点
            )
    line
    

    如果需要反复使用相同的配置,可以使用Style类简化这个过程。

    from pyecharts import Pie,Style
    
    pie = Pie('各类电影中"好片"所占的比例', "数据来自豆瓣", title_pos='center')
    style = Style()
    pie_style = style.add(
        label_pos="center",
        is_label_show=True,
        label_text_color=None
    )
    
    pie.add(
        "", ["剧情", ""], [25, 75], center=[10, 30], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["奇幻", ""], [24, 76], center=[30, 30], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["爱情", ""], [14, 86], center=[50, 30], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["惊悚", ""], [11, 89], center=[70, 30], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["冒险", ""], [27, 73], center=[90, 30], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["动作", ""], [15, 85], center=[10, 70], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["喜剧", ""], [54, 46], center=[30, 70], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["科幻", ""], [26, 74], center=[50, 70], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["悬疑", ""], [25, 75], center=[70, 70], radius=[18, 24], **pie_style
    )
    pie.add(
        "", ["犯罪", ""],[28, 72],center=[90, 70], radius=[18, 24], legend_top="bottom", 
        **pie_style
    )
    pie
    
    

    三,图表组合

    小红:你上面展示的这些图表配置的范例我基本上get到了,简单地说,就是可以修改图表主题,配置图表通用属性,以及配置特定元素属性,找到相应的范例参照来改就可以了。但有时候我想把多个不同类型的图表画在一张图上,比如在一张图上同时画柱状图和折线图,绘制包含多个子图的图表,pyecharts可以做到吗?

    小明:当然可以喽。pyecharts可以利用图表组合将多个基本图表加工成内容更加丰富,表现力更强的组合图表。在pyecharts中对图表进行组合的方式主要有:Grid, Overlap, Page, Timeline这四种方式。

    1,Grid图表组合

    Grid图表组合可以并行显示多张图,类似子图的作用。Grid中的子图可以是Overlap。

    from pyecharts import Bar, Line, Grid
    
    x = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    y1 = [5, 20, 36, 10, 75, 90]
    y2 = [10, 25, 8, 60, 20, 80]
    bar = Bar("柱状图示例", height=720)
    bar.add("商家A", x, y1, is_stack=True)
    bar.add("商家B", x, y2, is_stack=True)
    
    
    line = Line("折线图示例", title_top="50%")
    x = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
    line.add(
        "最高气温",
        x,
        [11, 11, 15, 13, 12, 13, 10],
        mark_point=["max", "min"],
        mark_line=["average"],
    )
    line.add(
        "最低气温",
        x,
        [1, -2, 2, 5, 3, 2, 0],
        mark_point=["max", "min"],
        mark_line=["average"],
        legend_top="50%",
    )
    
    grid = Grid()
    #利用grid_bottom,grid_top,grid_left,grid_right四个参数控制子图的相对位置
    grid.add(bar, grid_bottom="60%")  
    grid.add(line, grid_top="60%")
    grid
    

    利用Grid解决dataZoom与X轴标签重叠的问题

    from pyecharts import Bar, Grid
    
    x = [
        "2019-01-01",
        "2019-01-02",
        "2019-01-03",
        "2019-01-04",
        "2019-01-05",
        "2019-01-06",
        "2019-01-07",
        "2019-01-08",
        "2019-01-09",
    ]
    y = [10, 20, 30, 40, 50, 60, 70, 80, 90]
    
    grid = Grid()
    bar = Bar("利用 Grid 解决 dataZoom 与 X 轴标签重叠问题")
    bar.add("", x, y, is_datazoom_show=True, xaxis_interval=0, xaxis_rotate=30)
    
    # 把 bar 加入到 grid 中,并适当调整 grid_bottom 参数,使 bar 图整体上移
    grid.add(bar, grid_bottom="25%")
    

    2,Overlap图表组合

    Overlap图表组合可以将不同类型的图表画在同一张图上。

    from pyecharts import Bar, Line, Overlap
    
    attr = ['A', 'B', 'C', 'D', 'E', 'F']
    v1 = [10, 20, 30, 40, 50, 60]
    v2 = [38, 28, 58, 48, 78, 68]
    bar = Bar("Line - Bar 示例")
    bar.add("bar", attr, v1)
    line = Line()
    line.add("line", attr, v2)
    
    overlap = Overlap()
    overlap.add(bar)
    overlap.add(line)
    overlap
    

    Overlap显示双坐标轴

    from pyecharts import Line, Bar, Overlap
    
    attr = ["{}月".format(i) for i in range(1, 13)]
    v1 = [2.0, 4.9, 7.0, 23.2, 25.6, 76.7, 135.6, 162.2, 32.6, 20.0, 6.4, 3.3]
    v2 = [2.6, 5.9, 9.0, 26.4, 28.7, 70.7, 175.6, 182.2, 48.7, 18.8, 6.0, 2.3]
    v3 = [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()
    bar.add("蒸发量", attr, v1)
    bar.add("降水量", attr, v2, yaxis_formatter=" ml",
            yaxis_interval=50, yaxis_max=250)
    
    line = Line()
    line.add("平均温度", attr, v3, yaxis_formatter=" °C", yaxis_interval=5)
    
    overlap = Overlap(width=800, height=500)
    # 默认不新增 x y 轴,并且 x y 轴的索引都为 0
    overlap.add(bar)
    # 新增一个 y 轴,此时 y 轴的数量为 2,第二个 y 轴的索引为 1(索引从 0 开始),所以设置 yaxis_index = 1
    # 由于使用的是同一个 x 轴,所以 x 轴部分不用做出改变
    overlap.add(line, yaxis_index=1, is_add_yaxis=True)
    overlap
    

    3,Pages图表组合

    Pages可以将多张图表按顺序展示在一张网页中,适合制作图形化报表。Pages中的图表可以是Grid,Overlap或Timeline.

    from pyecharts import Bar, Scatter3D
    from pyecharts import Page
    
    page = Page()         
    
    # bar
    attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    v1 = [5, 20, 36, 10, 75, 90]
    v2 = [10, 25, 8, 60, 20, 80]
    bar = Bar("柱状图数据堆叠示例",width = 500,height = 300)
    bar.add("商家A", attr, v1, is_stack=True)
    bar.add("商家B", attr, v2, is_stack=True)
    page.add(bar)         
    
    # scatter3D
    import random
    data = [
        [random.randint(0, 100),
        random.randint(0, 100),
        random.randint(0, 100)] for _ in range(80)
    ]
    range_color = [
        '#313695', '#4575b4', '#74add1', '#abd9e9', '#e0f3f8', '#ffffbf',
        '#fee090', '#fdae61', '#f46d43', '#d73027', '#a50026']
    scatter3D = Scatter3D("3D 散点图示例", width= 500, height=300)
    scatter3D.add("", data, is_visualmap=True, visual_range_color=range_color)
    
    page.add(scatter3D)  
    
    page
    

    4,Timeline图表组合

    Timeline可以将多个图表制作成动画。

    from pyecharts import Bar, Timeline
    from random import randint
    
    attr = ["衬衫", "羊毛衫", "雪纺衫", "裤子", "高跟鞋", "袜子"]
    bar_1 = Bar("2012 年销量", "数据纯属虚构")
    bar_1.add("春季", attr, [randint(10, 100) for _ in range(6)])
    bar_1.add("夏季", attr, [randint(10, 100) for _ in range(6)])
    bar_1.add("秋季", attr, [randint(10, 100) for _ in range(6)])
    bar_1.add("冬季", attr, [randint(10, 100) for _ in range(6)])
    
    bar_2 = Bar("2013 年销量", "数据纯属虚构")
    bar_2.add("春季", attr, [randint(10, 100) for _ in range(6)])
    bar_2.add("夏季", attr, [randint(10, 100) for _ in range(6)])
    bar_2.add("秋季", attr, [randint(10, 100) for _ in range(6)])
    bar_2.add("冬季", attr, [randint(10, 100) for _ in range(6)])
    
    bar_3 = Bar("2014 年销量", "数据纯属虚构")
    bar_3.add("春季", attr, [randint(10, 100) for _ in range(6)])
    bar_3.add("夏季", attr, [randint(10, 100) for _ in range(6)])
    bar_3.add("秋季", attr, [randint(10, 100) for _ in range(6)])
    bar_3.add("冬季", attr, [randint(10, 100) for _ in range(6)])
    
    bar_4 = Bar("2015 年销量", "数据纯属虚构")
    bar_4.add("春季", attr, [randint(10, 100) for _ in range(6)])
    bar_4.add("夏季", attr, [randint(10, 100) for _ in range(6)])
    bar_4.add("秋季", attr, [randint(10, 100) for _ in range(6)])
    bar_4.add("冬季", attr, [randint(10, 100) for _ in range(6)])
    
    bar_5 = Bar("2016 年销量", "数据纯属虚构")
    bar_5.add("春季", attr, [randint(10, 100) for _ in range(6)])
    bar_5.add("夏季", attr, [randint(10, 100) for _ in range(6)])
    bar_5.add("秋季", attr, [randint(10, 100) for _ in range(6)])
    bar_5.add("冬季", attr, [randint(10, 100) for _ in range(6)], is_legend_show=True)
    
    timeline = Timeline(is_auto_play=True,
                        timeline_bottom=0,
                        timeline_play_interval=800  # 每800ms播放一张
                       )
    
    timeline.add(bar_1, '2012 年')
    timeline.add(bar_2, '2013 年')
    timeline.add(bar_3, '2014 年')
    timeline.add(bar_4, '2015 年')
    timeline.add(bar_5, '2016 年')
    timeline
    
    

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