Python Pandas：在组内将值向下移动一行

Pandas: Shift down values by one row within a group

提问by jeffalstott

I have a Pandas dataframe, and I want to create a new column whose values are that of another column, shifted down by one row. The last row should show NaN.

我有一个 Pandas 数据框，我想创建一个新列，其值是另一列的值，向下移动一行。最后一行应显示 NaN。

The catch is that I want to do this by group, with the last row of each group showing NaN. NOT have the last row of a group "steal" a value from a group that happens to be adjacent in the dataframe.

问题是我想按组执行此操作，每组的最后一行显示 NaN。不要让组的最后一行从恰好在数据帧中相邻的组中“窃取”一个值。

My attempted implementation is quite shamefully broken, so I'm clearly misunderstanding something fundamental.

我尝试的实现非常可耻地被破坏了，所以我显然误解了一些基本的东西。

df['B_shifted'] = df.groupby(['A'])['B'].transform(lambda x:x.values[1:])

采纳答案by Mike

Shift works on the output of the groupby clause:

Shift 作用于 groupby 子句的输出：

>>> df = pandas.DataFrame(numpy.random.randint(1,3, (10,5)), columns=['a','b','c','d','e'])
>>> df
   a  b  c  d  e
0  2  1  2  1  1
1  2  1  1  1  1
2  1  2  2  1  2
3  1  2  1  1  2
4  2  2  1  1  2
5  2  2  2  2  1
6  2  2  1  1  1
7  2  2  2  1  1
8  2  2  2  2  1
9  2  2  2  2  1


for k, v in df.groupby('a'):
    print k
    print 'normal'
    print v
    print 'shifted'
    print v.shift(1)

1
normal
   a  b  c  d  e
2  1  2  2  1  2
3  1  2  1  1  2
shifted
    a   b   c   d   e
2 NaN NaN NaN NaN NaN
3   1   2   2   1   2
2
normal
   a  b  c  d  e
0  2  1  2  1  1
1  2  1  1  1  1
4  2  2  1  1  2
5  2  2  2  2  1
6  2  2  1  1  1
7  2  2  2  1  1
8  2  2  2  2  1
9  2  2  2  2  1
shifted
    a   b   c   d   e
0 NaN NaN NaN NaN NaN
1   2   1   2   1   1
4   2   1   1   1   1
5   2   2   1   1   2
6   2   2   2   2   1
7   2   2   1   1   1
8   2   2   2   1   1
9   2   2   2   2   1

回答by abeboparebop

@EdChum's comment is a better answer to this question, so I'm posting it here for posterity:

@EdChum 的评论是对这个问题的更好回答，所以我把它贴在这里供后人使用：

df['B_shifted'] = df.groupby(['A'])['B'].transform(lambda x:x.shift())

df['B_shifted'] = df.groupby(['A'])['B'].transform(lambda x:x.shift())

or similarly

或类似

df['B_shifted'] = df.groupby(['A'])['B'].transform('shift').

df['B_shifted'] = df.groupby(['A'])['B'].transform('shift').

The former notation is more flexible, of course (e.g. if you want to shift by 2).

当然，前一种表示法更灵活（例如，如果您想移动 2）。

回答by chrisaycock

Newer versions of pandas can now perform a shifton a group:

较新版本的熊猫现在可以shift对组执行 a ：

df['B_shifted'] = df.groupby(['A'])['B'].shift(1)

Note that when shifting down, it's the firstrow that has NaN.

请注意，向下移动时，它是具有 NaN的第一行。

回答by Kevin Chou

All above answer make a mistake:

以上所有答案都犯了一个错误：

shift(1)is about shift upby one row, which is default behavior;
shift(-1)is really about shift downby one rows.

shift(1)大约向上移动一行，这是默认行为；
shift(-1)真的是向下移动一排。

see pandas documention shiftexample

请参阅熊猫文档shift示例