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Comments on How to compress columns of dataframe by function
Parent
How to compress columns of dataframe by function
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 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:
pd.DataFrame({"A": [df["A"].mean()], "B": [df["B"].mean()]})
Un-intuitive and long:
df.mean().to_frame().transpose()
Post
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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