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How to compress columns of dataframe by function

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Problem

How can I compress each column of a dataframe to the output of a function (i.e., mean), preserving columns?

MWE

import pandas as pd


data = {"A": [1, 2, 3, 4], "B": [5, 6, 7, 8]}

df = pd.DataFrame(data)
   A  B
0  1  5
1  2  6
2  3  7
3  4  8

Desired Output

     A    B
0  2.5  6.5

Tried

I was thinking one of the apply() or aggregate() functions would work. apply has a results_type field, but none of them produced the desired output.

Workarounds

These are workarounds I figured out that produce the desired outcome, but I find them cumbersome and un-intuitive, and feel there must be a simpler way I have not discovered.

Repetitive, cumbersome, and not scalable:

df = pd.DataFrame({"A": [df["A"].mean()], "B": [df["B"].mean()]})

Un-intuitive and long:

df.mean().to_frame().transpose()
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2 comment threads

How would that simpler way look like? (2 comments)
I'd say the alledged „Un-intuitive and long“ ”workaround” is the solution. (1 comment)

2 answers

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Conceptually, applying a function along an axis of a DataFrame (i.e., applying it to each row or column) inherently produces a Series: a two-dimensional result is collapsed to a one-dimensional result, because one-dimensional "lines" of data are fed into a function that produces a scalar value.

Such a series can be appended as a row to an existing DataFrame, if the labels are compatible - such as with the original DataFrame:

>>> df.loc['avg'] = df.mean()
>>> df
       A    B
0    1.0  5.0
1    2.0  6.0
2    3.0  7.0
3    4.0  8.0
avg  2.5  6.5

However, creating a row by itself - i.e., in a new DataFrame - either requires an existing DataFrame with those labels:

>>> x = df.mean()
>>> y = pd.DataFrame(columns=x.index)
>>> y.loc[0] = x
>>> y
     A    B
0  2.5  6.5

or creating it as you have tried already. As a hint, the T property saves some typing:

>>> df.mean().to_frame().T
     A    B
0  2.5  6.5

However, there is not an option for converting a Series directly to a single-row DataFrame; it converts to a column regardless.

Reference: https://stackoverflow.com/questions/59406045

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Since pandas uses numpy for these computations under the hood, I would have suggested to use df.mean(keepdims=True), but apparently this has been explicitly disabled by pandas.

However, after looking into the docs, I noticed you should be able to get the desired result as follows (note the []):

>>> df.agg(["mean"])
        A    B
mean  2.5  6.5

The list can also contain functions and/or more operations. Note that this will introduce a row (with index) for each function.

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