我用Python爬取美食網(wǎng)站3032個(gè)菜譜并分析，真香！

 1#?主函數(shù)
 2def?main(x):
 3????url?=?'https://www.douguo.com/caipu/{}/0/{}'.format(caipu,x*20)
 4????print(url)
 5????html?=?get_page(url)
 6????parse_page(html,caipu)
 7
 8if?__name__?==?'__main__':
 9????caipu_list?=?['川菜',?'湘菜','粵菜','東北菜','魯菜','浙菜','湖北菜','清真菜']?#中國(guó)菜系
10????start?=?time.time()??#?計(jì)時(shí)
11????for?caipu?in?caipu_list:
12????????for?i?in?range(22):
13????????????#?爬取多頁(yè)
14????????????main(x=i)
15????????????time.sleep(random.uniform(1,?2))
16????????????print(caipu,"第"?+?str(i+1)?+?"頁(yè)提取完成")
17????end?=?time.time()
18????print('共用時(shí)',round((end?-?start)?/?60,?2),?'分鐘')

 1from?pyecharts?import?options?as?opts
 2from?pyecharts.charts?import?Page,?Pie
 3cut?=?lambda?x?:?'4分以下'?if?x?4?else?('4.1-4.5分'?if?x?<=?4.5?else('4.6-4.9分'?if?x?<=?4.9?else?'5分'))
 4df['評(píng)分分布']?=?df['評(píng)分'].map(cut)
 5df2?=?df.groupby('評(píng)分分布')['評(píng)分'].count()
 6df2?=?df2.sort_values(ascending=False)
 7df2?=?df2.round(2)
 8print(df2)
 9c?=?(
10????????Pie()
11????????.add(
12????????????"",
13????????????[list(z)?for?z?in?zip(df2.index.to_list(),df2.to_list())],
14????????????radius=["20%",?"80%"],#?圓環(huán)的粗細(xì)和大小
15????????????rosetype='area'?#玫瑰圖
16????????)
17????????.set_global_opts(
18????????????title_opts=opts.TitleOpts(title="菜譜評(píng)分分布"
19?????????????????????????????????????),
20????????????legend_opts=opts.LegendOpts(
21????????????????orient="vertical",?pos_top="5%",?pos_left="2%"?,textstyle_opts=opts.TextStyleOpts(font_size=14)#?左面比例尺
22????????????),
23
24
25????????)
26????????.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:go7utgvlrp%",font_size=18),
27??????????????????????)
28????)
29c.render_notebook()

 1from?pyecharts?import?options?as?opts
 2from?pyecharts.charts?import?Page,?Pie?
 3df2?=?df.groupby('菜系')['評(píng)分'].count()?#按菜系分組，對(duì)評(píng)分計(jì)數(shù)
 4df2?=?df2.sort_values(ascending=False)?#降序
 5print(df2)
 6c?=?(
 7????????Pie()
 8????????.add("",?[list(z)?for?z?in?zip(df2.index.to_list(),df2.to_list())])
 9????????.set_global_opts(title_opts=opts.TitleOpts(title="各菜系菜譜數(shù)量占比",subtitle="數(shù)據(jù)來(lái)源：豆果美食"))
10????????.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:?{c}"))
11????)
12c.render_notebook()

 1from?pyecharts?import?options?as?opts
 2from?pyecharts.charts?import?Page,?Pie
 3df2?=?df.groupby('菜系')['評(píng)分'].mean()
 4df2?=?df2.sort_values(ascending=False)
 5df2?=?df2.round(2)
 6print(df2)
 7c?=?(
 8????????Pie()
 9????????.add(
10????????????"",
11????????????[list(z)?for?z?in?zip(df2.index.to_list(),df2.to_list())],
12????????????radius=["40%",?"75%"],???#?圓環(huán)的粗細(xì)和大小
13????????)
14????????.set_global_opts(
15????????????title_opts=opts.TitleOpts(title="各菜系平均評(píng)分"),
16????????????legend_opts=opts.LegendOpts(
17????????????????orient="vertical",?pos_top="5%",?pos_left="2%"??#?左面比例尺
18????????????),
19????????)
20????????.set_series_opts(label_opts=opts.LabelOpts(formatter="{b}:{c}"))
21????)
22c.render_notebook()

 1from?pyecharts.charts?import?Bar,Pie
 2from?pyecharts?import?options?as?opts
 3df1?=?df.groupby('菜系')['用料數(shù)'].mean()?#按菜系分組，對(duì)評(píng)分計(jì)數(shù)
 4df1?=?df1.sort_values(ascending=False)?#降序
 5df1?=?df1.round(0)
 6print(df1)
 7bar?=?Bar()
 8bar.add_xaxis(df1.index.to_list())
 9bar.add_yaxis("用料數(shù)量",df1.to_list())
10bar.set_global_opts(title_opts=opts.TitleOpts(title="各菜系用料數(shù)量",subtitle="數(shù)據(jù)來(lái)源：豆果美食"))
11bar.render_notebook()

1#?繪制詞云圖
2text1?=?get_cut_words(content_series=df[df['菜系']=='川菜']['用料'])
3stylecloud.gen_stylecloud(text='?'.join(text1),?max_words=1000,
4??????????????????????????collocations=False,
5??????????????????????????font_path='字酷堂清楷體.ttf',
6??????????????????????????icon_name='fas?fa-thumbs-up',
7??????????????????????????size=653,
8??????????????????????????output_name='./川菜.png')
9Image(filename='./川菜.png')?