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)?
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
To add, it seems the example provided in the question also works now (as of version 1.5.3).
import numpy as np
df = pd.DataFrame(np.random.random((10,2)), columns=['A','B'])
df.apply({'A':np.mean, 'B':np.sum}, axis=0)
A 0.495771
B 5.939556
dtype: float64