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Q&A 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]} ...

2 answers  ·  posted 1y ago by mcp‭  ·  last activity 8mo ago by mr Tsjolder‭

Question python pandas
#1: Initial revision by user avatar mcp‭ · 2023-02-13T20:27:44Z (about 1 year 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()
```