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5分钟学会Pyecharts数据可视化
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这是涛哥给你推荐的第40篇好文
来源 ：Python与算法之美
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 Bar
x = ["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 Scatter
import 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
# 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
往期阅读
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