如何拆分文本数据并计算熊猫数据框中的出现次数？

Question

我在数据框中有以下格式的数据：

df=pd.DataFrame([
    [42,{"tags":["illustration","logo","design","ui"]}],
    [81,{"tags":["typography","icon","vector","ux"]}],
    [98,{"tags":["branding","app"]}],
    [52,{"tags":["animation","web","flat"]}],
    [17,{"tags":["type","lettering"]}],
    [37,{"tags":["illustration","typography","branding","typography","branding"]}],
    [63,{"tags":["logo","icon","app","web","lettering"]}],
    [47,{"tags":["ui","ux"]}],
    [6,{"tags":["design","vector","icon","flat","lettering","branding","app"]}],
    [53,{"tags":["ui","ux","lettering","branding","app","animation","web","flat"]}],
    [64,{"tags":["branding","app","typography","branding"]}],
    [89,{"tags":["typography","branding","ux","lettering","branding"]}]
],columns=["_id","tags"])

我想用特定数量的标签计算“id”的数量（这个数字的分布），所以对于上面的数据，它会是：

Number of posts    Number of tags 
     3                 2
     1                 3
     3                 4 
     3                 5
     1                 7

我应该如何处理这个任务给定格式的文本标签？

谢谢

Answer 1

使用DataFrame构造函数+Counter和list理解每个tags的计数长度为lists：

from collections import Counter

c = Counter([len(x['tags']) for x in df['tags']])

df = pd.DataFrame({'Number of posts':list(c.values()), ' Number of tags ': list(c.keys())})
print (df)
   Number of posts   Number of tags 
0                3                 4
1                3                 2
2                1                 3
3                3                 5
4                1                 7
5                1                 8

或将apply 与value_counts 一起使用：

df = (df['tags'].apply(lambda x: len(x['tags']))
                .value_counts()
                .rename_axis('Number of tags')
                .reset_index(name='Number of posts')
                [['Number of posts','Number of tags']])
print (df)
   Number of posts  Number of tags
0                3               5
1                3               4
2                3               2
3                1               8
4                1               7
5                1               3