pandas :将不同的功能应用于不同的列 [英] Pandas: apply different functions to different columns

问题描述

使用df.mean()时，得到的结果是给出每一列的平均值.现在，假设我要第一列的均值和第二列的总和.有没有办法做到这一点?我不想拆卸并重新组装DataFrame.

When using df.mean() I get a result where the mean for each column is given. Now let's say I want the mean of the first column, and the sum of the second. Is there a way to do this? I don't want to have to disassemble and reassemble the DataFrame.

我最初的想法是像pandas.groupby.agg()那样做一些事情:

My initial idea was to do something along the lines of pandas.groupby.agg() like so:

df = pd.DataFrame(np.random.random((10,2)), columns=['A','B'])
df.apply({'A':np.mean, 'B':np.sum}, axis=0)

Traceback (most recent call last):

  File "<ipython-input-81-265d3e797682>", line 1, in <module>
    df.apply({'A':np.mean, 'B':np.sum}, axis=0)

  File "C:\Users\Patrick\Anaconda\lib\site-packages\pandas\core\frame.py", line 3471, in apply
    return self._apply_standard(f, axis, reduce=reduce)

  File "C:\Users\Patrick\Anaconda\lib\site-packages\pandas\core\frame.py", line 3560, in _apply_standard
    results[i] = func(v)

TypeError: ("'dict' object is not callable", u'occurred at index A')

但是显然这是行不通的.似乎通过dict是这样做的一种直观方式，但是还有另一种方式(再次无需拆卸和重新组装DataFrame)?

But clearly this doesn't work. It seems like passing a dict would be an intuitive way of doing this, but is there another way (again without disassembling and reassembling the DataFrame)?

推荐答案

我认为您可以将agg方法与字典一起用作参数.例如:

I think you can use the agg method with a dictionary as the argument. For example:

df = pd.DataFrame({'A': [0, 1, 2], 'B': [3, 4, 5]})

df =
A   B
0   0   3
1   1   4
2   2   5

df.agg({'A': 'mean', 'B': sum})

A     1.0
B    12.0
dtype: float64

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