Question

我对分组“其他”的语法有疑问。例如，

DF

Type  Start  End Count Total
A     x      a   1     3
A     x      b   1     3
A     x      c   1     3
A     y      A   2     4
A     y      b   1     4
A     y      c   1     4
B     x      A   1     6
B     x      b   2     6
B     x      c   3     6
B     y      a   3     6
B     y      b   2     6
B     y      c   1     6

按类型/开始/结束列分组，如果结束不包含“a”或“A”，则将其标记为“其他”

Type  Start  End   Count Total
A     x      a     1     3
A     x      other 2     3
A     y      A     2     4
A     y      other 2     4
B     x      A     1     6
B     x      other 5     6
B     y      a     3     6
B     y      other 3     6

Answer 1

你快到了。 groupby的前两个参数很好，但最后一个需要修改。

f = {'Count': 'sum', 'Total' : 'mean'}   
v = df.End.where(df.End.isin(['a', 'A']), 'other')

df.groupby(['Type', 'Start', v]).agg(f).reset_index()

  Type Start    End  Total  Count
0    A     x      a      3      1
1    A     x  other      3      2
2    A     y      A      4      2
3    A     y  other      4      2
4    B     x      A      6      1
5    B     x  other      6      5
6    B     y      a      6      3
7    B     y  other      6      3

<强>详情

使用where / mask相应地更改df.End的值;

v = df.End.where(df.End.isin(['a', 'A']), 'other')

或者，

v = df.End.mask(~df.End.isin(['a', 'A']), 'other')

v

0         a
1     other
2     other
3         A
4     other
5     other
6         A
7     other
8     other
9         a
10    other
11    other
Name: End, dtype: object

或者，将列小写并进行比较。

v = df.End.where(df.End.str.lower().eq('a'), 'other')

正如他们所说，其余的都是历史。如果您对保留列顺序感兴趣，请在最后reindex来电时拍打。

df.groupby(['Type', 'Start', v])\
  .agg(f)\
  .reset_index()\
  .reindex(columns=df.columns.tolist())

  Type Start    End  Count  Total
0    A     x      a      1      3
1    A     x  other      2      3
2    A     y      A      2      4
3    A     y  other      2      4
4    B     x      A      1      6
5    B     x  other      5      6
6    B     y      a      3      6
7    B     y  other      3      6

Answer 2

我认为您需要将所有未包含a和A的值替换为where other并将isin条件替换为groupby，然后使用s }列和系列s = df['End'].where(df['End'].isin(['a','A']), 'other') print (s) 0 a 1 other 2 other 3 A 4 other 5 other 6 A 7 other 8 other 9 a 10 other 11 other Name: End, dtype: object df = (df.groupby(['Type', 'Start', s]) .agg({'Count':'sum', 'Total':'mean'}) .reset_index())：

End

另一个类似的解决方案是替换列groupby并使用原始解决方案agg + df['End'] = np.where(df['End'].isin(['a','A']), df['End'], 'other') #alternatively #df['End'] = df['End'].where(df['End'].isin(['a','A']), 'other') df = (df.groupby(['Type', 'Start', 'End'], as_index=False) .agg({'Count':'sum', 'Total':'mean'})) print (df) Type Start End Count Total 0 A x a 1 3 1 A x other 2 3 2 A y A 2 4 3 A y other 2 4 4 B x A 1 6 5 B x other 5 6 6 B y a 3 6 7 B y other 3 6：

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Answer 3

您可以更改End中条目的值以反映所需的更改，并使用您已经描述过的groupby。

df.loc[~df.End.isin(['A', 'a']), 'End'] = 'other'
df.groupby(['Type','Start','End'']).agg({'Count':'sum','Tota‌l':'mean'})

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