【Pyecharts】20W条淘宝文胸商品评论数据可视化～【Pyecharts】20W条淘宝文胸商品

【【Pyecharts】20W条淘宝文胸商品评论数据可视化～】咳咳～不要怀疑，这是一个正经的可视化项目，而且附带一点科普
数据来源

数据来自爬虫获取，淘宝约50个文胸商品的20W条评论数据～
数据源来自chenjiandongx/cup-size

前言
对于很多只知道A/B/C的绅士们，我们在看数据之前可能先得了解点知识～
首先我们得先了解两个概念——上胸围 & 下胸围，具体看示意图：

文章图片
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：

文章图片
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：

文章图片
好了，接下俩就可以开始我们的可视化了～
依赖模块

from pyecharts.charts import * from pyecharts import options as opts from pyecharts.commons.utils import JsCode from collections import Counter import re import pandas as pd import jieba import jieba.posseg as psg from stylecloud import gen_stylecloud from 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：

patterns = re.compile(r'(?P.*),颜色分类:(?P.*?); 尺码:(?P.*?),(?P.*)')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[7-9]{1}[0|5]{1}).*(?P[a-zA-Z])', expand=True)], axis=1) data.head()

文章图片
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～

代码：

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)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()

文章图片

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；

尺码分布

代码：

t_data = https://www.it610.com/article/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 = https://www.it610.com/article/{"name": '{}-{}'.format(row.circumference, row.cup), "value":row.num, "label":{"show": True}} else: child_data = https://www.it610.com/article/{"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)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()

文章图片

单看罩杯的话：B > A > C

细分到具体尺码：75B > 80B > 75A > 70A

罩杯分布
我们通过不同的胸围来看看罩杯的比例：

代码：

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 + 20pos_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()

文章图片

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

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～

代码：

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