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How to compress columns of dataframe by function
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()
2 answers
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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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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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