pbreach pbreach - 1 year ago 144
Python Question

Pandas: apply different functions to different columns

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.

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')

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)?

Answer Source

You can try a closure:

def multi_func(functions):
    def f(col):
        return functions[col.name](col)
    return f

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