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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]} ...
#1: Initial revision
by
mcp
·
2023-02-13T20:27:44Z (almost 2 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()
```