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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.Data...
#2: Post edited
by
wjandrea
·
2026-08-24T00:46:44Z (25 days ago)
No need for initial header. H1 should be reserved for question title. Minor code style improvements. Brevity. Don't reassign to `df`, that's confusing.
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- ```py
- 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- ```txt
- A B
- 0 2.5 6.5
- ```
# Tried- I was thinking one of the `apply()` or `aggregate()` functions would
- work.
- [`apply`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.apply)
- has a `results_type` field, but none of them produced the desired
- output.
# WorkaroundsThese 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:
- ```py
df = pd.DataFrame({"A": [df["A"].mean()], "B": [df["B"].mean()]})- ```
- Un-intuitive and long:
- ```py
- df.mean().to_frame().transpose()
- ```
- How can I compress each column of a dataframe to the output of a
- function (i.e., mean), preserving columns?
- ## MWE
- ```py
- 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
- ```txt
- A B
- 0 2.5 6.5
- ```
- ## Tried
- I was thinking one of the `apply()` or `aggregate()` functions would
- work.
- [`apply`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.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:
- ```py
- pd.DataFrame({"A": [df["A"].mean()], "B": [df["B"].mean()]})
- ```
- Un-intuitive and long:
- ```py
- df.mean().to_frame().transpose()
- ```
#1: Initial revision
by
mcp
·
2023-02-13T20:27:44Z (over 3 years ago)
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
```py
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
```txt
A B
0 2.5 6.5
```
# Tried
I was thinking one of the `apply()` or `aggregate()` functions would
work.
[`apply`](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.apply.html#pandas.DataFrame.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:
```py
df = pd.DataFrame({"A": [df["A"].mean()], "B": [df["B"].mean()]})
```
Un-intuitive and long:
```py
df.mean().to_frame().transpose()
```