如何使用Python对网易云歌单数据分析及可视化

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')  
  
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('h3')[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')

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

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

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

df_songs = np.log(df['songs'])  
df_songs  
df_songs = get_matplot(x=0,y=df_songs,chart='hist',title='华语歌单歌曲收录分布情况',ha='left',size=10,color=color[3])  
df_songs

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]  
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

df_user = pd.read_csv('playlist.csv',encoding="unicode_escape",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  
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

import Wordcloud  
import pandas as pd  
import numpy as np  
from PIL import Image  
data = pd.read_excel('music_message.xlsx')  
#根据播放量排序，只取前五十个  
data = data.sort_values('play',ascending=False).head(50)  
  
#font_path指明用什么样的字体风格，这里用的是电脑上都有的微软雅黑  
w1 = wordcloud.WordCloud(width=1000,height=700,  
                         background_color='black',  
                         font_path='msyh.ttc')  
txt = "\n".join(i for i in data['title'])  
w1.generate(txt)  
w1.to_file('F:\\词云.png')

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)]