[原创]-Day5.数据可视化之Pyecharts

 #安装
 pip install pyecharts

 #卸载
 pip uninstall pycharts
 
 #方案1：安装
 pip install -i https://pypi.tuna.tsinghua.edu.cn/simple pyecharts
 
 #方案2：
 #去官网下载：
 https://pypi.org/project/pyecharts/0.1.9.4/#files
 #把下载下来的whl文件放在相映路径下，接着在改路径下执行cmd。
 #使用
 pip install pyecharts-0.1.9.4-py2.py3-none-any.whl
 
 # 报错
 init() got an unexpected keyword argument ‘title_color’
 
 # 原因版本太新了没有安装拓展库 所以这里用老版本演示
 
 pip uninstall pyecharts
 #安装指定版本
 pip install pyecharts==0.5.0
 
 # 地图库安装
  pip install echarts-countries-pypkg         
  pip install echarts-china-provinces-pypkg 
  pip install echarts-china-cities-pypkg 
  pip install echarts-china-counties-pypkg
  pip install echarts-china-misc-pypkg
  pip install echarts-united-kingdom-pypkg

 #报错找不到 是因为 新版本更改了引入方式
 from pyecharts import Geo
 
 #新版本引入如下
 from pyecharts.charts import Geo
 
 
 # 旧版本测试地理图
 data = [
   ("海门", 95), ("鄂尔多斯", 15), ("招远", 12), ("舟山", 12), ("齐齐哈尔", 14), ("盐城", 15),
   ("赤峰", 16), ("青岛", 18), ("乳山", 18), ("金昌", 19), ("泉州", 21), ("莱西", 21),
   ("日照", 21), ("胶南", 22), ("南通", 23), ("拉萨", 24), ("云浮", 24), ("梅州", 25),
   ("文登", 25), ("上海", 25), ("攀枝花", 25), ("威海", 25), ("承德", 25), ("厦门", 26),
   ("汕尾", 26), ("潮州", 26), ("丹东", 27), ("太仓", 27), ("曲靖", 27), ("烟台", 28),
   ("福州", 29), ("瓦房店", 30), ("即墨", 50), ("抚顺", 31), ("玉溪", 31), ("张家口", 31),
   ("阳泉", 31), ("莱州", 42), ("湖州", 32), ("汕头", 32), ("昆山", 33), ("宁波", 33),
   ("湛江", 33), ("揭阳", 44), ("荣成", 54), ("连云港", 35), ("葫芦岛", 35), ("常熟", 36),
   ("东莞", 36), ("河源", 36), ("淮安", 56), ("泰州", 36), ("南宁", 47), ("营口", 37),
   ("惠州", 37), ("江阴", 67), ("蓬莱", 57), ("韶关", 38), ("嘉峪关", 78), ("广州", 38),
   ("延安", 38), ("太原", 69), ("清远", 59), ("中山", 39), ("昆明", 79), ("寿光", 40),
   ("盘锦", 40), ("长治", 61), ("深圳", 51), ("珠海", 42), ("宿迁", 73), ("咸阳", 43),
   ("铜川", 44), ("平度", 64), ("佛山", 54), ("海口", 44), ("江门", 75), ("章丘", 45),
   ("肇庆", 46), ("大连", 67), ("临汾", 57), ("吴江", 47), ("石嘴山", 9), ("沈阳", 50),
   ("苏州", 50), ("茂名", 60), ("嘉兴", 41), ("长春", 51), ("胶州", 52), ("银川", 52),
   ("张家港", 52), ("三门峡", 53), ("锦州", 54), ("南昌", 54), ("柳州", 54), ("三亚", 54),
   ("自贡", 56), ("吉林", 56), ("阳江", 57), ("泸州", 57), ("西宁", 57), ("宜宾", 58),
   ("呼和浩特", 58), ("成都", 58), ("大同", 58), ("镇江", 59), ("桂林", 59), ("张家界", 59),
   ("宜兴", 59), ("北海", 60), ("西安", 61), ("金坛", 62), ("东营", 62), ("牡丹江", 63),
   ("遵义", 63), ("绍兴", 63), ("扬州", 64), ("常州", 64), ("潍坊", 65), ("重庆", 66),
   ("台州", 67), ("南京", 67), ("滨州", 70), ("贵阳", 71), ("无锡", 71), ("本溪", 71),
   ("克拉玛依", 72), ("渭南", 72), ("马鞍山", 72), ("宝鸡", 72), ("焦作", 75), ("句容", 75),
   ("北京", 79), ("徐州", 79), ("衡水", 80), ("包头", 80), ("绵阳", 80), ("乌鲁木齐", 84),
   ("枣庄", 84), ("杭州", 84), ("淄博", 85), ("鞍山", 86), ("溧阳", 86), ("库尔勒", 86),
   ("安阳", 90), ("开封", 90), ("济南", 92), ("德阳", 93), ("温州", 95), ("九江", 96),
   ("邯郸", 98), ("临安", 99), ("兰州", 99), ("沧州", 100), ("临沂", 103), ("南充", 104),
   ("天津", 105), ("富阳", 106), ("泰安", 112), ("诸暨", 112), ("郑州", 113), ("哈尔滨", 114),
   ("聊城", 116), ("芜湖", 117), ("唐山", 119), ("平顶山", 119), ("邢台", 119), ("德州", 120),
   ("济宁", 120), ("荆州", 127), ("宜昌", 130), ("义乌", 132), ("丽水", 133), ("洛阳", 134),
   ("秦皇岛", 136), ("株洲", 143), ("石家庄", 147), ("莱芜", 148), ("常德", 152), ("保定", 153),
   ("湘潭", 154), ("金华", 157), ("岳阳", 169), ("长沙", 175), ("衢州", 177), ("廊坊", 193),
   ("菏泽", 194), ("合肥", 229), ("武汉", 273), ("大庆", 279)]
 
 geo = Geo("全国主要城市空气质量", "data from pm2.5", title_color="b",
         title_pos = "center", width = 1200,
         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.render()
 
 geo

 attr, value = geo.cast(data)
 geo.add("", attr, value, type="effectScatter", is_random=True, effect_scale=5)
 geo.render("effect.html")

 geo.add("", attr, value, type="heatmap", is_visualmap=True, visual_range=[0, 300],
        visual_text_color='#fff')
 geo.render("headmap.html")

 from pyecharts import GeoLines, Style
 style = Style(title_top="#fff",title_pos = "center",width=1200,height=600,background_color="#404a59")
 style_geo = style.add(is_label_show=True,line_curve=0.2,line_opacity=0.6,legend_text_color="#eee",
    legend_pos="right",geo_effect_symbol="plane",geo_effect_symbolsize=15,label_color=['#a6c84c', '#ffa022', '#46bee9'],
    label_pos="right",label_formatter="{b}",label_text_color="#eee",)
 data_guangzhou = [
   ["广州", "上海"],
   ["广州", "北京"],
   ["广州", "呼和浩特"],
   ["广州", "太原"],
   ["广州", "长沙"],
   ["广州", "吐鲁番"]
 ]
 geolines = GeoLines("GeoLines 示例 -DataScience", **style.init_style)
 geolines.add("从广州出发", data_guangzhou, **style_geo)
 geolines.render()

 from pyecharts import Graph
 
 nodes = [{"name": "a", "symbolSize": 10},
         {"name": "b", "symbolSize": 20},
         {"name": "c", "symbolSize": 30},
         {"name": "d", "symbolSize": 40},
         {"name": "e", "symbolSize": 50},
         {"name": "f", "symbolSize": 40},
         {"name": "g", "symbolSize": 30},
         {"name": "h", "symbolSize": 20}]
 links = []
 for i in nodes:
   for j in nodes:
       links.append({"source": i.get('name'), "target": j.get('name')})
 graph = Graph("关系图-环形布局示例-DataScience 提供")
 graph.add("", nodes, links, is_label_show=True,
         graph_repulsion=8000, graph_layout='circular',
         label_text_color=None)
 graph.render("graph.html")

 # 数据指标随机生成的，名字是安装本期活动顺序写的
 
 from pyecharts import WordCloud
 
 name = [
   '喵呜', '四方', '映琴', '梧桐', 'Ray',
   '子逐', '小明', '小小外星人', '大熊喵喵喵', 'Tony', '木夫',
   '平凡人', '不染', 'W.', '白饭', 'vicky',
   '不是', '挖掘机', '陌影', '赛茜']
 value = [
   10000, 6181, 4386, 4055, 2467, 2244, 1898, 1484, 1112,
   965, 847, 582, 555, 550, 462, 366, 360, 282, 273, 265]
 wordcloud = WordCloud(width=1300, height=620)
 wordcloud.add("", name, value, word_size_range=[20, 100])
 wordcloud.render("wordcloud.html")

 #coding=utf-8
 from __future__ import unicode_literals
 
 #首先来我的绘制第一个图表
 
 from pyecharts import Bar
 
 bar = Bar("我的第一个图表","这里是一个副标题(DataScience)")
 bar.add("分类",["A","B","C","D","E","F"],[15,18,35,66,6,88])
 #bar.print_echarts_options() # 该行只为了打印配置项，方便调试时使用
 bar.render() #生成本地 HTML 文件
 bar

 # note ：使用Pandas & Numpy 时，整数类型请确保为int， 而不是 numpy.int32
 
 #当然你可以采用更加酷炫的方式，使用jupyter notebook 来展示图表，matplotib有的，
 # pyecharts 也会有的
 
 #Note :从v0.1.9.2版本开始，废弃 render_notebook() 方法，现在已采用更加pythonic的做法。
 #直接调用本身实例就可以了。
 
 #EG 案例
 from pyecharts import Bar
 
 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]
 
 bar = Bar("柱状图示例-DataScience")
 bar.add("增长数",attr,v1,mark_line=["average"],mark_point=["max","min"])
 bar.add("取关数",attr,v2,mark_line=["average"],mark_point=["max","min"])
 bar

 # 饼图
 from pyecharts import Pie
 
 attr = ["算法","产品","大数据","数据分析","Python","数据可视化"]
 v1 = [11,12,13,10,10,10]
 v2 = [19,21,32,20,20,33]
 
 pie= Pie("饼图-玫瑰图示例-DataScience",title_pos='center',width=900)
 pie.add("品种A",attr,v1,center=[25,50],is_random=True,redius=[30,75],rosetype='redius')
 pie.add("品种B",attr,v2,center=[75,50],is_random=True,redius=[30,75],rosetype='area',is_legend_show=False,is_label_show=True)
 
 pie

 # 如果使用的是 自定义类，直接调用自定义类示例即可
 
 from pyecharts import Bar,Line,Overlap
 
 attr = ["A","B","C","D","E","F"]
 v1 = [10,20,30,40,50,60]
 v2 = [38,28,58,48,88,68]
 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