Communities

Writing
Writing
Codidact Meta
Codidact Meta
The Great Outdoors
The Great Outdoors
Photography & Video
Photography & Video
Scientific Speculation
Scientific Speculation
Cooking
Cooking
Electrical Engineering
Electrical Engineering
Judaism
Judaism
Languages & Linguistics
Languages & Linguistics
Software Development
Software Development
Mathematics
Mathematics
Christianity
Christianity
Code Golf
Code Golf
Music
Music
Physics
Physics
Linux Systems
Linux Systems
Power Users
Power Users
Tabletop RPGs
Tabletop RPGs
Community Proposals
Community Proposals
tag:snake search within a tag
answers:0 unanswered questions
user:xxxx search by author id
score:0.5 posts with 0.5+ score
"snake oil" exact phrase
votes:4 posts with 4+ votes
created:<1w created < 1 week ago
post_type:xxxx type of post
Search help
Notifications
Mark all as read See all your notifications »
Q&A

Welcome to Software Development on Codidact!

Will you help us build our independent community of developers helping developers? We're small and trying to grow. We welcome questions about all aspects of software development, from design to code to QA and more. Got questions? Got answers? Got code you'd like someone to review? Please join us.

How to compress columns of dataframe by function

+3
−0

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()
History
Why does this post require moderator attention?
You might want to add some details to your flag.
Why should this post be closed?

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

+0
−0

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

History
Why does this post require moderator attention?
You might want to add some details to your flag.

0 comment threads

+0
−0

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.

History
Why does this post require moderator attention?
You might want to add some details to your flag.

0 comment threads

Sign up to answer this question »