本篇内容介绍了“如何用Pyecharts生成云词”的有关知识，在实际案例的操作过程中，不少人都会遇到这样的困境，接下来就让小编带领大家学习一下如何处理这些情况吧！希望大家仔细阅读，能够学有所成！

前言

首先我们得先了解两个概念——上胸围 & 下胸围，具体看示意图：

通过上胸围与下胸围的差值，我们就可以确定罩杯的大小了，具体的对应关系可参考下图：

有了下胸围 & 罩杯就能确定文胸对应的尺码了～
当然这又有分为英式尺码和国际尺码，具体参考下图：

from pyecharts.charts import *from pyecharts import options as optsfrom pyecharts.commons.utils import JsCodefrom collections import Counterimport reimport pandas as pdimport jiebaimport jieba.posseg as psgfrom stylecloud import gen_stylecloudfrom IPython.display import Image

数据处理

原始数据是txt格式，为了方便处理，这边转为Dataframe～

尺码部分通过正则表达式提取出对应的下胸围和罩杯，具体代码如下：

In [2]:

patterns = re.compile(r'(?P<datetime>.*),颜色分类:(?P<color>.*?);尺码:(?P<size>.*?),(?P<comment>.*)')with open('/home/kesci/input/cup6439/cup_all.txt', 'r') as f:
    data = f.readlines()obj_list = []for item in data:
    obj = patterns.search(item)
    obj_list.append(obj.groupdict())
    data = pd.DataFrame(obj_list)data = pd.concat([data, data['size'].str.extract('(?P<circumference>[7-9]{1}[0|5]{1}).*(?P<cup>[a-zA-Z])', 
                                          expand=True)], axis=1)data.head()

Out[2]:

商品类别

我们通过jieba分词来看看商品分类中最常出现的是哪些关键词～

• 颜色：肤色 > 黑色 > 粉色 > 白色；

• 薄款 > 厚款；

• 钢圈似乎是个比较重要的卖点；

In [3]:

w_all = []for item in data.color:
    w_l = psg.cut(item)
    w_l = [w for w, f in w_l if f in ('n', 'nr') and len(w)>1]
    w_all.extend(w_l)c = Counter(w_all)

Building prefix dict from the default dictionary ...
Dumping model to file cache /tmp/jieba.cache
Loading model cost 0.769 seconds.
Prefix dict has been built succesfully.

In [4]:

counter = c.most_common(50)bar = (Bar(init_opts=opts.InitOpts(theme='purple-passion', width='1000px', height='800px'))
       .add_xaxis([x for x, y in counter[::-1]])
       .add_yaxis('出现次数', [y for x, y in counter[::-1]], category_gap='30%')
       .set_global_opts(title_opts=opts.TitleOpts(title="出现最多的关键词",
                                                  pos_left="center",
                                                  title_textstyle_opts=opts.TextStyleOpts(font_size=20)),
                        datazoom_opts=opts.DataZoomOpts(range_start=70, range_end=100, orient='vertical'),
                        visualmap_opts=opts.VisualMapOpts(is_show=False, max_=6e4, min_=3000, dimension=0,
                                range_color=['#f5d69f', '#f5898b', '#ef5055']),
                        legend_opts=opts.LegendOpts(is_show=False),
                        xaxis_opts=opts.AxisOpts(is_show=False,),
                        yaxis_opts=opts.AxisOpts(axistick_opts=opts.AxisTickOpts(is_show=False),
                                                 axisline_opts=opts.AxisLineOpts(is_show=False)))
       .set_series_opts(label_opts=opts.LabelOpts(is_show=True,
                                                  position='right',
                                                  font_style='italic'),
                        itemstyle_opts={"normal": {
                                                    "barBorderRadius": [30, 30, 30, 30],
                                                    'shadowBlur': 10,
                                                    'shadowColor': 'rgba(120, 36, 50, 0.5)',
                                                    'shadowOffsetY': 5,
                                                }
                                       }).reversal_axis())bar.render_notebook()

Out[4]:

In [5]:

t_data = data.groupby(['circumference', 'cup'])['datetime'].count().reset_index()t_data.columns = ['circumference', 'cup', 'num']#t_data.num = round(t_data.num.div(t_data.num.sum(axis=0), axis=0) * 100, 1)data_pair = [
            {"name": 'A',
              "label":{"show": True},
              "children": []},
            {"name": 'B',
              "label":{"show": True},
              "children": []},
            {"name": 'C',
              "label":{"show": True},
              'shadowBlur': 10,
              'shadowColor': 'rgba(120, 36, 50, 0.5)',
              'shadowOffsetY': 5,
              "children": []},
            {"name": 'D',
              "label":{"show": False},
              "children": []},
            {"name": 'E',
              "label":{"show": False},
              "children": []}
    ]for idx, row in t_data.iterrows():
    t_dict = {"name": row.cup,
              "label":{"show": True},
              "children": []}
    if row.num > 3000:
        child_data = {"name": '{}-{}'.format(row.circumference, row.cup), "value":row.num, "label":{"show": True}}
    else:
        child_data = {"name": '{}-{}'.format(row.circumference, row.cup), "value":row.num, "label":{"show": False}}
    if row.cup == "A":
        data_pair[0]['children'].append(child_data)   
    elif row.cup == "B":
        data_pair[1]['children'].append(child_data)   
    elif row.cup == "C":
        data_pair[2]['children'].append(child_data)  
    elif row.cup == "D":
        data_pair[3]['children'].append(child_data)  
    elif row.cup == "E":
        data_pair[4]['children'].append(child_data)

尺码分布

• 单看罩杯的话：B > A > C

• 细分到具体尺码：75B > 80B > 75A > 70A

In [6]:

c = (Sunburst(
        init_opts=opts.InitOpts(
            theme='purple-passion',
            width="1000px",
            height="1000px"))
    .add(
        "",
        data_pair=data_pair,
        highlight_policy="ancestor",
        radius=[0, "100%"],
        sort_='null',
        levels=[
            {},
            {
                "r0": "20%",
                "r": "48%",
                "itemStyle": {"borderColor": 'rgb(220,220,220)', "borderWidth": 2}
            },
            {"r0": "50%", "r": "80%", "label": {"align": "right"},
                "itemStyle": {"borderColor": 'rgb(220,220,220)', "borderWidth": 1}}
        ],
    )
    .set_global_opts(
        visualmap_opts=opts.VisualMapOpts(is_show=False, max_=90000, min_=3000, 
                                range_color=['#f5d69f', '#f5898b', '#ef5055']),
        title_opts=opts.TitleOpts(title="文 胸\n\n尺 码 分 布",
                                               pos_left="center",
                                               pos_top="center",
                                               title_textstyle_opts=opts.TextStyleOpts(font_style='oblique', font_size=30),))
    .set_series_opts(label_opts=opts.LabelOpts(font_size=18, formatter="{b}: {c}")))c.render_notebook()

Out[6]:

罩杯分布

我们通过不同的胸围来看看罩杯的比例：

• 下胸围=70：A > B > C

• 下胸围=75：B > A > C

• 下胸围=80：B > A > C

• 下胸围=85：B > C > A

• 下胸围=90：C > B > A

• 下胸围=95：C > B > D

In [7]:

grid = Grid(init_opts=opts.InitOpts(theme='purple-passion', width='1000px', height='1000px'))for idx, c in enumerate(['70', '75', '80', '85', '90', '95']):
    
    if idx % 2 == 0:
        x = 30
        y = int(idx/2) * 30 + 20
    else:
        x = 70
        y = int(idx/2) * 30 + 20
    pos_x = str(x)+'%'
    pos_y = str(y)+'%'
    
    pie = Pie(init_opts=opts.InitOpts())
    
    pie.add(
            c,
            [[row.cup, row.num]for i, row in t_data[t_data.circumference==c].iterrows()],
            center=[pos_x, pos_y],
            radius=[70, 100],
            label_opts=opts.LabelOpts(formatter='{b}:{d}%'),
    )
    
    pie.set_global_opts(
        title_opts=opts.TitleOpts(title="下胸围={}".format(c),
                                  pos_top=str(y-1)+'%', pos_left=str(x-4)+'%',
                                  title_textstyle_opts=opts.TextStyleOpts(font_size=15)),
        legend_opts=opts.LegendOpts(is_show=True))
    grid.add(pie,grid_opts=opts.GridOpts(pos_left='20%'))grid.render_notebook()

Out[7]:

评论词云

最后我们来看看评论中经常说到的是什么词语吧～

In [8]:

w_all = []for item in data.comment:
    w_l = jieba.lcut(item)
    w_all.extend(w_l)c = Counter(w_all)

In [10]:

gen_stylecloud(' '.join(w_all),
              size=1000,
              #max_words=1000,
              font_path='/home/kesci/work/font/simhei.ttf',
              #palette='palettable.tableau.TableauMedium_10',
              icon_name='fas fa-heartbeat',
              output_name='comment.png',
              custom_stopwords=['没有','用户','填写','评论']
              )Image(filename='comment.png')

Out[10]:

“如何用Pyecharts生成云词”的内容就介绍到这里了，感谢大家的阅读。如果想了解更多行业相关的知识可以关注天达云网站，小编将为大家输出更多高质量的实用文章！