网易云数据分析实战

怼烎@ 2022-12-29 09:39 219阅读 0赞

# 网易云数据分析 #

字段：title,tag,text,collection,play,songs,comments

### 导入模块，读取数据 ###

import pandas as pd
    import numpy as np
    import matplotlib.pyplot as plt
    import squarify

df = pd.read_excel('D:/Pandas/music_message.xlsx',header=0,names=['title','tag','text','collection','play','songs','comments'])
    df.head()

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>title</th> 
   <th>tag</th> 
   <th>text</th> 
   <th>collection</th> 
   <th>play</th> 
   <th>songs</th> 
   <th>comments</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>梦想无处安放的日子 也要时常拿出来晾晒</td> 
   <td>华语-治愈-感动</td> 
   <td>介绍：和过去的时光，聊一聊 用文字记下的回忆和许下的心愿，如今正裹挟着时光的风霜，与你撞个满...</td> 
   <td>824</td> 
   <td>339509</td> 
   <td>30</td> 
   <td>8</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>治愈说唱 I 越过黑暗的那道光</td> 
   <td>华语-说唱-治愈</td> 
   <td>介绍：今天，你又是为何戴上耳机？才发现，音乐真的是有力量的，治愈的力量。愿你有好运气，如果没...</td> 
   <td>2657</td> 
   <td>387541</td> 
   <td>42</td> 
   <td>12</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>《姐姐的爱乐之程》云南站路演</td> 
   <td>华语-流行-综艺</td> 
   <td>介绍：一个热爱音乐的纯朴村寨，一颗生长在茶山上的苍天大树，一轮雨后艳丽的彩虹，一段闲适的时光...</td> 
   <td>1175</td> 
   <td>204799</td> 
   <td>8</td> 
   <td>31</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>你搜不到的土嗨神曲</td> 
   <td>欧美-摇滚-流行</td> 
   <td>介绍：【不喜勿喷，自用歌单+村民推荐】这歌单容易嗨上头，如果你开车/走路请注意安全！现在很多...</td> 
   <td>167万</td> 
   <td>97074200</td> 
   <td>212</td> 
   <td>3972</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>是你的垚/刘大壮/王小帅/王泽科</td> 
   <td>华语-伤感-翻唱</td> 
   <td>介绍：喜欢歌单的可以点个关注哟歌单制作：小攀哟歌单创建：2020.4.1歌单修改：2020....</td> 
   <td>12万</td> 
   <td>7869796</td> 
   <td>115</td> 
   <td>280</td> 
  </tr> 
 </tbody> 
</table>

df.info()

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 1289 entries, 0 to 1288
    Data columns (total 7 columns):
     #   Column      Non-Null Count  Dtype 
    ---  ------      --------------  ----- 
     0   title       1289 non-null   object
     1   tag         1289 non-null   object
     2   text        1289 non-null   object
     3   collection  1289 non-null   object
     4   play        1289 non-null   int64 
     5   songs       1289 non-null   int64 
     6   comments    1289 non-null   object
    dtypes: int64(2), object(5)
    memory usage: 70.6+ KB

### 数据处理、清洗 ###

df['collection'] = df['collection'].astype('string').str.strip()
    df['collection'] = [int(str(i).replace('万','0000')) for i in df['collection']]
    df['text'] = [str(i)[3:] for i in df['text']]
    df['comments'] = [0 if '评论' in str(i).strip() else int(i) for i in df['comments']]

df.info()

<class 'pandas.core.frame.DataFrame'>
    RangeIndex: 1289 entries, 0 to 1288
    Data columns (total 7 columns):
     #   Column      Non-Null Count  Dtype 
    ---  ------      --------------  ----- 
     0   title       1289 non-null   object
     1   tag         1289 non-null   object
     2   text        1289 non-null   object
     3   collection  1289 non-null   int64 
     4   play        1289 non-null   int64 
     5   songs       1289 non-null   int64 
     6   comments    1289 non-null   int64 
    dtypes: int64(4), object(3)
    memory usage: 70.6+ KB

df.shape

(1289, 7)

### 数据预览 ###

df.head()

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>title</th> 
   <th>tag</th> 
   <th>text</th> 
   <th>collection</th> 
   <th>play</th> 
   <th>songs</th> 
   <th>comments</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>0</th> 
   <td>梦想无处安放的日子 也要时常拿出来晾晒</td> 
   <td>华语-治愈-感动</td> 
   <td>和过去的时光，聊一聊 用文字记下的回忆和许下的心愿，如今正裹挟着时光的风霜，与你撞个满怀。那...</td> 
   <td>824</td> 
   <td>339509</td> 
   <td>30</td> 
   <td>8</td> 
  </tr> 
  <tr> 
   <th>1</th> 
   <td>治愈说唱 I 越过黑暗的那道光</td> 
   <td>华语-说唱-治愈</td> 
   <td>今天，你又是为何戴上耳机？才发现，音乐真的是有力量的，治愈的力量。愿你有好运气，如果没有，愿...</td> 
   <td>2657</td> 
   <td>387541</td> 
   <td>42</td> 
   <td>12</td> 
  </tr> 
  <tr> 
   <th>2</th> 
   <td>《姐姐的爱乐之程》云南站路演</td> 
   <td>华语-流行-综艺</td> 
   <td>一个热爱音乐的纯朴村寨，一颗生长在茶山上的苍天大树，一轮雨后艳丽的彩虹，一段闲适的时光。一场...</td> 
   <td>1175</td> 
   <td>204799</td> 
   <td>8</td> 
   <td>31</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>你搜不到的土嗨神曲</td> 
   <td>欧美-摇滚-流行</td> 
   <td>【不喜勿喷，自用歌单+村民推荐】这歌单容易嗨上头，如果你开车/走路请注意安全！现在很多年轻人...</td> 
   <td>1670000</td> 
   <td>97074200</td> 
   <td>212</td> 
   <td>3972</td> 
  </tr> 
  <tr> 
   <th>4</th> 
   <td>是你的垚/刘大壮/王小帅/王泽科</td> 
   <td>华语-伤感-翻唱</td> 
   <td>喜欢歌单的可以点个关注哟歌单制作：小攀哟歌单创建：2020.4.1歌单修改：2020.9.1...</td> 
   <td>120000</td> 
   <td>7869796</td> 
   <td>115</td> 
   <td>280</td> 
  </tr> 
 </tbody> 
</table>

## 构建通用函数，简化代码 ##

get\_matplot(x,y,chart,title,ha,size,color)  
x表示充当x轴数据；  
y表示充当y轴数据；  
chart表示图标类型，这里分为三种barh、hist、squarify.plot；  
ha表示文本相对朝向；  
size表示字体大小；  
color表示图表颜色；

#构建函数
    def get_matplot(x,y,chart,title,ha,size,color):
        # 设置图片显示属性,字体及大小
        plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']
        plt.rcParams['font.size'] = size
        plt.rcParams['axes.unicode_minus'] = False
        # 设置图片显示属性
        fig = plt.figure(figsize=(16, 8), dpi=80)
        ax = plt.subplot(1, 1, 1)
        ax.patch.set_color('white')
        # 设置坐标轴属性
        lines = plt.gca()
        # 设置显示数据
        if x ==0:
            pass
        else:
            x.reverse()
            y.reverse()
            data = pd.Series(y, index=x)
        # 设置坐标轴颜色
        lines.spines['right'].set_color('none')
        lines.spines['top'].set_color('none')
        lines.spines['left'].set_color((64/255, 64/255, 64/255))
        lines.spines['bottom'].set_color((64/255, 64/255, 64/255))
        # 设置坐标轴刻度
        lines.xaxis.set_ticks_position('none')
        lines.yaxis.set_ticks_position('none')
        if chart == 'barh':
            # 绘制柱状图,设置柱状图颜色
            data.plot.barh(ax=ax, width=0.7, alpha=0.7, color=color)
            # 添加标题,设置字体大小
            ax.set_title(f'{title}', fontsize=18, fontweight='light')
            # 添加歌曲出现次数文本
            for x, y in enumerate(data.values):
                plt.text(y+0.3, x-0.12, '%s' % y, ha=f'{ha}')
        elif chart == 'hist':
            # 绘制直方图,设置柱状图颜色
            ax.hist(y, bins=30, alpha=0.7, color=(21/255, 47/255, 71/255))
            # 添加标题,设置字体大小
            ax.set_title(f'{title}', fontsize=18, fontweight='light')
        elif chart == 'plot':
            colors = ['#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50', 
             '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',
             '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',
             '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',
             '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',
              '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff', '#f7bb5f', '#eafb50',
              '#adb0ff', '#ffb3ff', '#90d595', '#e48381', '#aafbff']
            plot = squarify.plot(sizes=y, label=x, color=colors, alpha=1, value=y, edgecolor='white', linewidth=1.5)
            # 设置标签大小为1
            plt.rc('font', size=6)
            # 设置标题大小
            plot.set_title(f'{title}', fontsize=13, fontweight='light')
            # 除坐标轴
            plt.axis('off')
            # 除上边框和右边框刻度
            plt.tick_params(top=False, right=False)
        # 显示图片
        plt.show()

#构建color序列
    color = [(153/255, 0/255, 102/255),(8/255, 88/255, 121/255),(160/255, 102/255, 50/255),(136/255, 43/255, 48/255),(16/255, 152/255, 168/255),(153/255, 0/255, 102/255)]

### 1.歌单播放量Top10 ###

df_play = df[['title','play']].sort_values('play',ascending=False)
    df_play[:10]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>title</th> 
   <th>play</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>40</th> 
   <td>精选 | 网络热歌分享</td> 
   <td>502522656</td> 
  </tr> 
  <tr> 
   <th>178</th> 
   <td>那些好听到爆了的歌</td> 
   <td>135485104</td> 
  </tr> 
  <tr> 
   <th>182</th> 
   <td>精选｜耳熟的翻唱合集</td> 
   <td>121409168</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>你搜不到的土嗨神曲</td> 
   <td>97074200</td> 
  </tr> 
  <tr> 
   <th>97</th> 
   <td>你会爱上这个流行华语歌单</td> 
   <td>69498312</td> 
  </tr> 
  <tr> 
   <th>384</th> 
   <td>超好听的高潮翻唱呀</td> 
   <td>66282664</td> 
  </tr> 
  <tr> 
   <th>158</th> 
   <td>精选｜Cover的那些循环歌曲</td> 
   <td>66277840</td> 
  </tr> 
  <tr> 
   <th>263</th> 
   <td>「翻唱」好听的歌永远没有完整版。</td> 
   <td>55291720</td> 
  </tr> 
  <tr> 
   <th>188</th> 
   <td>网络热播的那些神仙声音（持续更新）</td> 
   <td>48534648</td> 
  </tr> 
  <tr> 
   <th>19</th> 
   <td>精选｜Cover翻唱百听不腻</td> 
   <td>36318572</td> 
  </tr> 
 </tbody> 
</table>

df_play = df_play[:10]
    _x = df_play['title'].tolist()
    _y = df_play['play'].tolist()

df_play = get_matplot(x=_x,y=_y,chart='barh',title='网易云音乐华语歌单播放 TOP10',ha='left',size=8,color=color[0])
    df_play

![在这里插入图片描述][215f2901dc9d841fb5fab04ccf5bd009.png_pic_center]

### 2.歌单收藏量Top10 ###

df_col = df[['title','collection']].sort_values('collection',ascending=False)
    df_col[:10]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>title</th> 
   <th>collection</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>40</th> 
   <td>精选 | 网络热歌分享</td> 
   <td>5460000</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>你搜不到的土嗨神曲</td> 
   <td>1670000</td> 
  </tr> 
  <tr> 
   <th>178</th> 
   <td>那些好听到爆了的歌</td> 
   <td>1430000</td> 
  </tr> 
  <tr> 
   <th>182</th> 
   <td>精选｜耳熟的翻唱合集</td> 
   <td>1380000</td> 
  </tr> 
  <tr> 
   <th>384</th> 
   <td>超好听的高潮翻唱呀</td> 
   <td>810000</td> 
  </tr> 
  <tr> 
   <th>158</th> 
   <td>精选｜Cover的那些循环歌曲</td> 
   <td>740000</td> 
  </tr> 
  <tr> 
   <th>263</th> 
   <td>「翻唱」好听的歌永远没有完整版。</td> 
   <td>690000</td> 
  </tr> 
  <tr> 
   <th>97</th> 
   <td>你会爱上这个流行华语歌单</td> 
   <td>680000</td> 
  </tr> 
  <tr> 
   <th>188</th> 
   <td>网络热播的那些神仙声音（持续更新）</td> 
   <td>490000</td> 
  </tr> 
  <tr> 
   <th>19</th> 
   <td>精选｜Cover翻唱百听不腻</td> 
   <td>430000</td> 
  </tr> 
 </tbody> 
</table>

df_col = df_col[:10]
    _x = df_col['title'].tolist()
    _y = df_col['collection'].tolist()

df_col = get_matplot(x=_x,y=_y,chart='barh',title='网易云音乐华语歌单收藏 TOP10',ha='left',size=8,color=color[1])
    df_col

![在这里插入图片描述][585d9abd60842248db1608dd1533d829.png_pic_center]

### 3.歌单评论数Top10 ###

df_com = df[['title','comments']].sort_values('comments',ascending=False)
    df_com[:10]

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>title</th> 
   <th>comments</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>40</th> 
   <td>精选 | 网络热歌分享</td> 
   <td>19159</td> 
  </tr> 
  <tr> 
   <th>408</th> 
   <td>献给用生命托起世界的英雄们</td> 
   <td>11628</td> 
  </tr> 
  <tr> 
   <th>202</th> 
   <td>致敬黑暗里的光 感谢你们点亮夜空</td> 
   <td>4700</td> 
  </tr> 
  <tr> 
   <th>14</th> 
   <td>篮球巨星科比，再见</td> 
   <td>4097</td> 
  </tr> 
  <tr> 
   <th>3</th> 
   <td>你搜不到的土嗨神曲</td> 
   <td>3972</td> 
  </tr> 
  <tr> 
   <th>436</th> 
   <td>【刷歌升级】60～70秒超短歌曲一万首</td> 
   <td>3849</td> 
  </tr> 
  <tr> 
   <th>349</th> 
   <td>穿越千万时间线，只想见你</td> 
   <td>3793</td> 
  </tr> 
  <tr> 
   <th>1157</th> 
   <td>【227大团结】美好的东西是不会死的</td> 
   <td>3466</td> 
  </tr> 
  <tr> 
   <th>144</th> 
   <td>『中文说唱』 只想私藏的顶级国语Flow</td> 
   <td>2843</td> 
  </tr> 
  <tr> 
   <th>184</th> 
   <td>摆地摊专用BGM，要的就是回头率！</td> 
   <td>2822</td> 
  </tr> 
 </tbody> 
</table>

df_com = df_com[:10]
    _x = df_com['title'].tolist()
    _y = df_com['comments'].tolist()

df_com = get_matplot(x=_x,y=_y,chart='barh',title='网易云音乐华语歌单评论数 TOP10',ha='left',size=8,color=color[2])
    df_com

![在这里插入图片描述][f6e9593da53024acb2c56dfcf63cfc94.png_pic_center]

### 4.歌单收藏数量分布情况 ###

df_collection = np.log(df['collection'])
    df_collection

0        6.714171
    1        7.884953
    2        7.069023
    3       14.328334
    4       11.695247
              ...    
    1284     4.304065
    1285     2.708050
    1286     3.970292
    1287     6.238325
    1288     4.787492
    Name: collection, Length: 1289, dtype: float64

df_collection = get_matplot(x=0,y=df_collection,chart='hist',title='华语歌单收藏数量分布情况',ha='left',size=10,color=color[3])
    df_collection

![d48e791d12344115eae27254dd181c8a.png_pic_center][]

### 5.歌单标签图 ###

def get_tag(df):
        df = df['tag'].str.split('-')
        datalist = list(set(x for data in df for x in data))
        return  datalist
    df_tag = get_tag(df)
    # df_tag
    def get_lx(x,i):
        if i in str(x):
            return 1
        else:
            return 0
    for i in list(df_tag):#这里的df['all_category'].unique()也可以自己用列表构建，我这里是利用了前面获得的
        df[i] = df['tag'].apply(get_lx,i=f'{i}')
    # df.head()
    Series = df.iloc[:,7:].sum().sort_values(0,ascending=False)
    df_tag = [tag for tag in zip(Series.index.tolist(),Series.values.tolist())]
    df_tag[:10]

[('华语', 1287),
     ('流行', 724),
     ('伤感', 177),
     ('说唱', 172),
     ('治愈', 162),
     ('翻唱', 96),
     ('浪漫', 96),
     ('民谣', 90),
     ('夜晚', 77),
     ('古风', 55)]

df_iex = [index for index in Series.index.tolist()][:20]
    df_tag = [tag for tag in Series.values.tolist()][:20]

df_tagiex = get_matplot(x=df_iex,y=df_tag,chart='plot',title='网易云音乐华语歌单标签图',size=10,ha='center',color=color[3])
    df_tagiex

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ0NzYwOTEy_size_16_color_FFFFFF_t_70]

### 6.歌单介绍词云图 ###

### 词云图1 ###

import stylecloud
    from IPython.display import Image
    stylecloud.gen_stylecloud(text=' '.join(list(df.text)), collocations=False,
                              #palette='cartocolors.qualitative.Pastel_5',
                              font_path=r'‪C:/Windows/Fonts/msyh.ttc',
                              #icon_name='fas fa-dragon',
                              icon_name='fas fa-music',
                              #icon_name='fas fa-cat',
                              #icon_name='fas fa-dove',
                              size=400,
                              output_name='music.png')
    #已生成词云图片，这里展示一下
    Image(filename='music.png')

![\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-PVAJixSs-1608555402642)(wyy\_files/wyy\_37\_0.png)\]][img-PVAJixSs-1608555402642_wyy_files_wyy_37_0.png]

### 词云图2 ###

import os  
    import jieba
    from pyecharts.charts import Bar, Pie, Line, WordCloud, Page
    from pyecharts import options as opts 
    from pyecharts.globals import SymbolType, WarningType
    WarningType.ShowWarning = False
    
    import stylecloud
    from IPython.display import Image # 用于在jupyter lab中显示本地图
    
    def get_cut_words(x_series):
        # 读入停用词表
        stop_words = [] 
        
        with open(r"D:/Pandas/已学习/如何制作stylecloud词云？/stop_words.txt", 'r', encoding='utf-8') as f:
            lines = f.readlines()
            for line in lines:
                stop_words.append(line.strip())
    
        # 添加关键词
        my_words = ['好听', '华语', '喜欢', '音乐', '耳机', '歌手', '网易云']   
        for i in my_words:
            jieba.add_word(i) 
    
        # 自定义停用词
        my_stop_words = ['哈哈哈', '哈哈哈哈', '评论']
        stop_words.extend(my_stop_words)               
    
        # 分词
        word_num = jieba.lcut(x_series.astype('str').str.cat(sep='。'), cut_all=False)
    
        # 条件筛选
        word_num_selected = [i for i in word_num if i not in stop_words and len(i)>=2]
        
        return word_num_selected
    
    text1 = get_cut_words(x_series=df.text)#df.columns某一列
    
    def gen_my_stylecloud(text, file_name, icon_name='fas fa-music'):#这里的icon_name可以自定义更换
        stylecloud.gen_stylecloud(text=' '.join(text), max_words=1000,
                                  collocations=False,
                                  font_path=r'‪C:/Windows/Fonts/msyh.ttc',
                                  icon_name=icon_name,
                                  size=653,
                                  output_name=f'{file_name}.png'
                                 )  
    gen_my_stylecloud(text=text1, file_name='music_text')
    Image(filename='music_text.png')

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ0NzYwOTEy_size_16_color_FFFFFF_t_70 1]

### 7.歌曲出现次数TOP10 ###

dfm = pd.read_csv('D:\Pandas\music_name_pandas.csv',encoding='utf-8')
    dfm.shape
    dfm['name'] = dfm['name'].str.strip()
    dfm['count'] = 0
    df_name = dfm.groupby('name',as_index=False)['count'].count().sort_values('count',ascending=False)
    df_name = df_name[:30]
    df_name

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>name</th> 
   <th>count</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>2047</th> 
   <td>偏爱</td> 
   <td>50</td> 
  </tr> 
  <tr> 
   <th>1821</th> 
   <td>会不会（吉他版）</td> 
   <td>50</td> 
  </tr> 
  <tr> 
   <th>4951</th> 
   <td>永不失联的爱</td> 
   <td>45</td> 
  </tr> 
  <tr> 
   <th>2715</th> 
   <td>在哪里都很好</td> 
   <td>42</td> 
  </tr> 
  <tr> 
   <th>5953</th> 
   <td>耗尽</td> 
   <td>40</td> 
  </tr> 
  <tr> 
   <th>1801</th> 
   <td>他只是经过</td> 
   <td>37</td> 
  </tr> 
  <tr> 
   <th>4563</th> 
   <td>是但求其爱</td> 
   <td>36</td> 
  </tr> 
  <tr> 
   <th>5086</th> 
   <td>海底</td> 
   <td>34</td> 
  </tr> 
  <tr> 
   <th>5871</th> 
   <td>经济舱 (Live)</td> 
   <td>33</td> 
  </tr> 
  <tr> 
   <th>3108</th> 
   <td>如果当时2020</td> 
   <td>31</td> 
  </tr> 
  <tr> 
   <th>4566</th> 
   <td>是你想成为的大人吗</td> 
   <td>30</td> 
  </tr> 
  <tr> 
   <th>4571</th> 
   <td>是想你的声音啊</td> 
   <td>30</td> 
  </tr> 
  <tr> 
   <th>4020</th> 
   <td>我很好（吉他版）</td> 
   <td>30</td> 
  </tr> 
  <tr> 
   <th>4375</th> 
   <td>斯芬克斯星座</td> 
   <td>28</td> 
  </tr> 
  <tr> 
   <th>3601</th> 
   <td>彩券</td> 
   <td>28</td> 
  </tr> 
  <tr> 
   <th>1386</th> 
   <td>三号线（吉他版）</td> 
   <td>27</td> 
  </tr> 
  <tr> 
   <th>4217</th> 
   <td>执迷不悟</td> 
   <td>27</td> 
  </tr> 
  <tr> 
   <th>2943</th> 
   <td>天外来物</td> 
   <td>23</td> 
  </tr> 
  <tr> 
   <th>1376</th> 
   <td>万有引力</td> 
   <td>20</td> 
  </tr> 
  <tr> 
   <th>5361</th> 
   <td>爱，存在</td> 
   <td>20</td> 
  </tr> 
  <tr> 
   <th>5591</th> 
   <td>相安</td> 
   <td>19</td> 
  </tr> 
  <tr> 
   <th>4195</th> 
   <td>所念皆星河</td> 
   <td>19</td> 
  </tr> 
  <tr> 
   <th>6301</th> 
   <td>谁说梦想不能当饭吃</td> 
   <td>19</td> 
  </tr> 
  <tr> 
   <th>3829</th> 
   <td>想见你想见你想见你</td> 
   <td>18</td> 
  </tr> 
  <tr> 
   <th>6661</th> 
   <td>野心</td> 
   <td>18</td> 
  </tr> 
  <tr> 
   <th>3095</th> 
   <td>如果你也这样过</td> 
   <td>17</td> 
  </tr> 
  <tr> 
   <th>4595</th> 
   <td>晚风</td> 
   <td>16</td> 
  </tr> 
  <tr> 
   <th>5732</th> 
   <td>空的城</td> 
   <td>16</td> 
  </tr> 
  <tr> 
   <th>2787</th> 
   <td>夏天的风</td> 
   <td>15</td> 
  </tr> 
  <tr> 
   <th>2723</th> 
   <td>在这个年纪也许不配拥有爱情</td> 
   <td>15</td> 
  </tr> 
 </tbody> 
</table>

df_name = df_name[:10]
    names = df_name['name'].tolist()
    plays = df_name['count'].tolist()

df_name = get_matplot(x=names,y=plays,chart='barh',title='网易云音乐华语歌单歌曲 TOP10',ha='left',size=10,color=color[3])
    df_name

![\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-TJF1leBQ-1608555402651)(wyy\_files/wyy\_43\_0.svg)\]][img-TJF1leBQ-1608555402651_wyy_files_wyy_43_0.svg]

### 8.歌单贡献up主Top10 ###

df_user = pd.read_csv('D:\Pandas\playlist.csv',encoding='utf-8',header=0,names=['url','title','play','user'],sep=',')
    df_user.shape
    df_user = df_user.iloc[:,1:]
    df_user['count'] = 0
    df_user = df_user.groupby('user',as_index=False)['count'].count()
    df_user = df_user.sort_values('count',ascending=False)[:10]
    df_user

<table> 
 <thead> 
  <tr> 
   <th></th> 
   <th>user</th> 
   <th>count</th> 
  </tr> 
 </thead> 
 <tbody> 
  <tr> 
   <th>226</th> 
   <td>原创君</td> 
   <td>32</td> 
  </tr> 
  <tr> 
   <th>289</th> 
   <td>安阳汪涛</td> 
   <td>24</td> 
  </tr> 
  <tr> 
   <th>356</th> 
   <td>我自清欢丨被嫌弃的唐唐的一生</td> 
   <td>19</td> 
  </tr> 
  <tr> 
   <th>550</th> 
   <td>蒙丽娜沙的微笑</td> 
   <td>14</td> 
  </tr> 
  <tr> 
   <th>559</th> 
   <td>螚安Vivienne</td> 
   <td>12</td> 
  </tr> 
  <tr> 
   <th>114</th> 
   <td>mayuko然</td> 
   <td>12</td> 
  </tr> 
  <tr> 
   <th>341</th> 
   <td>情思天鹅</td> 
   <td>12</td> 
  </tr> 
  <tr> 
   <th>260</th> 
   <td>圈圈的呆子</td> 
   <td>11</td> 
  </tr> 
  <tr> 
   <th>273</th> 
   <td>大肥猫好可爱哟</td> 
   <td>11</td> 
  </tr> 
  <tr> 
   <th>541</th> 
   <td>苏奕杰</td> 
   <td>10</td> 
  </tr> 
 </tbody> 
</table>

df_user = df_user[:10]
    names = df_user['user'].tolist()
    nums = df_user['count'].tolist()

df_u = get_matplot(x=names,y=nums,chart='barh',title='歌单贡献UP主 TOP10',ha='left',size=10,color=color[4])
    df_u

![\[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-z3AJnpBX-1608555402660)(wyy\_files/wyy\_47\_0.svg)\]][img-z3AJnpBX-1608555402660_wyy_files_wyy_47_0.svg]

## 总结： ##

csv数据存在部分读取不全的情况，我的解决办法打开一个空的excel，然后打开导入并选中csv，这时会弹出窗口提示你设置分隔符，而不是直接双击csv文件用excel打开，两者有区别。  
然后对每项数据在excel进行简单筛选，因为有些数据会存在格式问题，当然你也可以直接pd读取，然后根据报错去excel调整相应的列，我这里的问题是collection列的数据存在不能转换int，原因就是有一行的数据没有被分割，手动修改或者直接删除即可。这样得到的效果也有缺失，但是比之前只有800多条多了200多条，还是值得的。  
究其原因，可能是爬取保存的数据格式或者是默认分隔符为,所致，因为文字信息里面也存在’,’。

## 补充： ##

加上：error\_bad\_lines=False，上面的问题即可解决，意思是忽略此行

简单测试，成功，数据基本上没有丢失

import pandas as pd
    import numpy as np
    
    df = pd.read_csv('D:\Pandas\网易云数据分析\music_message.csv',header=0,error_bad_lines=False,names=['title','tag','text','collection','play','songs','comments'],encoding='utf-8-sig')
    df.to_excel('demo.xlsx',index=False)

### 爬虫代码： ###

from bs4 import BeautifulSoup
    import requests
    import time
    
    headers = { 
        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
    }
    
    for i in range(0, 1330, 35):
        print(i)
        time.sleep(2)
        url = 'https://music.163.com/discover/playlist/?cat=华语&order=hot&limit=35&offset=' + str(i)#修改这里即可
        response = requests.get(url=url, headers=headers)
        html = response.text
        soup = BeautifulSoup(html, 'html.parser')
        # 获取包含歌单详情页网址的标签
        ids = soup.select('.dec a')
        # 获取包含歌单索引页信息的标签
        lis = soup.select('#m-pl-container li')
        print(len(lis))
        for j in range(len(lis)):
            # 获取歌单详情页地址
            url = ids[j]['href']
            # 获取歌单标题
            title = ids[j]['title']
            # 获取歌单播放量
            play = lis[j].select('.nb')[0].get_text()
            # 获取歌单贡献者名字
            user = lis[j].select('p')[1].select('a')[0].get_text()
            # 输出歌单索引页信息
            print(url, title, play, user)
            # 将信息写入CSV文件中
            with open('playlist.csv', 'a+', encoding='utf-8-sig') as f:
                f.write(url + ',' + title + ',' + play + ',' + user + '\n')

''' Author: 你爸爸lzm Date: 2020-12-20 23:19:00 Notes: 使用built命令快速得到一些常用的snippets,右击py文件可以preview代码 LastEditTime: 2020-12-21 21:56:48 '''
    from bs4 import BeautifulSoup
    import pandas as pd
    import requests
    import time
    
    df = pd.read_csv('playlist.csv', header=None, error_bad_lines=False, names=['url', 'title', 'play', 'user'])
    
    headers = { 
        'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36'
    }
    
    for i in df['url']:
        time.sleep(2)
        url = 'https://music.163.com' + i
        response = requests.get(url=url, headers=headers)
        html = response.text
        soup = BeautifulSoup(html, 'html.parser')
        # 获取歌单标题
        title = soup.select('h2')[0].get_text().replace(',', '，')
        # 获取标签
        tags = []
        tags_message = soup.select('.u-tag i')
        for p in tags_message:
            tags.append(p.get_text())
        # 对标签进行格式化
        if len(tags) > 1:
            tag = '-'.join(tags)
        else:
            tag = tags[0]
        # 获取歌单介绍
        if soup.select('#album-desc-more'):
            text = soup.select('#album-desc-more')[0].get_text().replace('\n', '').replace(',', '，')
        else:
            text = '无'
        # 获取歌单收藏量
        collection = soup.select('#content-operation i')[1].get_text().replace('(', '').replace(')', '')
        # 歌单播放量
        play = soup.select('.s-fc6')[0].get_text()
        # 歌单内歌曲数
        songs = soup.select('#playlist-track-count')[0].get_text()
        # 歌单评论数
        comments = soup.select('#cnt_comment_count')[0].get_text()
        # 输出歌单详情页信息
        print(title, tag, text, collection, play, songs, comments)
        # 将详情页信息写入CSV文件中
        with open('music_message.csv', 'a+', encoding='utf-8') as f:
            # f.write(title + '/' + tag + '/' + text + '/' + collection + '/' + play + '/' + songs + '/' + comments + '\n')
            f.write(title + ',' + tag + ',' + text + ',' + collection + ',' + play + ',' + songs + ',' + comments + '\n')
        # 获取歌单内歌曲名称
        li = soup.select('.f-hide li a')
        for j in li:
            with open('music_name.csv', 'a+', encoding='utf-8') as f:
                f.write(j.get_text() + '\n')

数据集大家自己通过爬虫代码抓取吧。

数据集：  
[链接：https://pan.baidu.com/s/1LtOReq75Put9NMtegKCCCQ  
][https_pan.baidu.com_s_1LtOReq75Put9NMtegKCCCQ]提取码：love

[215f2901dc9d841fb5fab04ccf5bd009.png_pic_center]: /images/20221120/ba3a0f9fc0de49948f8ba219dd16bf18.png
[585d9abd60842248db1608dd1533d829.png_pic_center]: /images/20221120/774fa9f025cf4f4da6830bede5c3b4ec.png
[f6e9593da53024acb2c56dfcf63cfc94.png_pic_center]: /images/20221120/d7de457ee65449dcafa788908f73e47f.png
[d48e791d12344115eae27254dd181c8a.png_pic_center]: /images/20221120/69fdd06d50914d048a7424a02803d890.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ0NzYwOTEy_size_16_color_FFFFFF_t_70]: /images/20221120/ab12e14e3d594352ab6134878a8cdce1.png
[img-PVAJixSs-1608555402642_wyy_files_wyy_37_0.png]: /images/20221120/7ef6d39416e24fe0b6bb2de520bd2cb9.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQ0NzYwOTEy_size_16_color_FFFFFF_t_70 1]: /images/20221120/6dba5bb5471e4990b24dd5acfe2cfc17.png
[img-TJF1leBQ-1608555402651_wyy_files_wyy_43_0.svg]: /images/20221120/7e0df1c1880f4bc6a1750779fb90bcc9.png
[img-z3AJnpBX-1608555402660_wyy_files_wyy_47_0.svg]: /images/20221120/742b11244af34840a6190a1c681293ef.png
[https_pan.baidu.com_s_1LtOReq75Put9NMtegKCCCQ]: https://pan.baidu.com/s/1LtOReq75Put9NMtegKCCCQ