Python Pandas 过滤和 groupby

Question

我将其作为 csv 在 pandas 中工作 - 前十行

print frame1.head(10)

      alert         Subject    filetype type      country   status
0  33965790    44676 aba     Attachment  doc  RU,RU,RU,RU  deleted
1  33965786    44676 rcrump  Attachment  zip          NaN  deleted
2  33965771            3aba  Attachment  zip          NaN  deleted
3  33965770             NaN  Attachment   js           ,,  deleted
4  33965766             NaN  Attachment   js           ,,  deleted
5  33965761             NaN  Attachment  zip          NaN  deleted
6  33965760             NaN  Attachment  zip          NaN  deleted
7  33965757             NaN  Attachment  zip          NaN  deleted
8  33965751  35200     3aba  Attachment  doc     RU,RU,RU  deleted
9  33965747  35200   INVaba  Attachment  zip          NaN  deleted

我需要获取主题列并计算所有以“aba”作为子字符串的行。

Occurrences of aba- 512

甚至是这样的结果

aba    12
3aba   5
INVaba 2

这是我的代码-

targeted = frame1[frame1['Subject'].str.contains('aba', case=False , na=False)].groupby('Subject')
print (targeted.to_string(header=False))

获取错误 - AttributeError:无法访问“DataFrameGroupBy”对象的可调用属性“to_string”，请尝试使用“apply”方法

*****注意:我早些时候让这个工作用于不同文件类型的计数，这个工作 -

filetype = frame1.groupby('filetype').size()
###clean up the printing
print "Delivered in Email"
print (filetype.to_string(header=False))

然后给我 -

Delivered in Email
Attachment    32647
Header          131
URL            9236

Answer 1

要获得完整计数，只需使用 str.contains其次是 count .

>>> df.Subject.str.contains('aba', case=False, na=False).count()
10

然后要获取包含 'aba' 的唯一字符串的计数，您可以访问由 contains 找到的那些值，然后使用 value_counts .

>>> df.loc[df.Subject.str.contains('aba', case=False, na=False), 'Subject'].value_counts()

3aba      1
INVaba    1
aba       1
Name: Subject, dtype: int64