Pandas Groupby Problems with Calculating Column-Wise Quantiles with "quantile" - pandas

Question

i need to compute quantiles for a large DF across columns or column-wise along rows or "months" in my case. Apparently, the quantile function applied on just a df works using the key word "axis" but if you try and apply quantile using a groupby, it is rejected with an error:
TypeError: quantile() got an unexpected keyword argument 'axis'
Here is the situation that the quantile works with data like this:
Num Num Num Quantile 0.5
5 6 4 5
4 1 2 2
3 9 7 7
7 2 8 7
5 5 4 5
But, if I add more columns with a groupby statement to find the same quantile(0.5, axis=1), then I get the error shown above. Please help and thank you. My actual data looks like this below:
site month Num Num Num Quantile 0.5
0 A 8 5 6 4 5
1 A 9 4 1 2 2
2 A 10 3 9 7 7
3 A 11 7 2 8 7
4 A 12 5 5 4 5
5 B 8 3 7 5 5
6 B 9 6 9 0 6
7 B 10 4 1 3 3
8 B 11 8 3 0 3
9 B 12 5 6 8 6

Answer 1

The confusion arises from the fact that pd.DataFrame.quantile and DataFrameGroupBy.quantile are not the same functions. The first one has an axis parameter, the second one does not. Hence the error.
When you think about it, it is perfectly logical that the second function does not have this option. Suppose we do:
groups = df.groupby('site')
for group in groups:
print(group[1])
site month Num Num.1 Num.2
0 A 8 5 6 4
1 A 9 4 1 2
2 A 10 3 9 7
3 A 11 7 2 8
4 A 12 5 5 4
site month Num Num.1 Num.2
5 B 8 3 7 5
6 B 9 6 9 0
7 B 10 4 1 3
8 B 11 8 3 0
9 B 12 5 6 8
Now ask yourself the question which axis could generate a qauntile that is meaningfully related to A | B. The answer surely is column-wise. I could get a quantile of Num for A, or Num.1. E.g.:
print(groups.quantile())
month Num Num.1 Num.2
site
A 10.0 5.0 5.0 4.0
B 10.0 5.0 6.0 3.0
It wouldn't make sense to say, let's get the quantile row-wise for A at row 0 (and pretend that this has anything to do with A as a grouped value as distinct from B). Indeed, you don't need a groupby for that at all.
Sidenote: you will have noticed that your columns Num, Num, Num have turned into Num, Num.1, Num.2 in my examples. This conversion takes place automatically when you read from the clipboard (pd.read_clipboard). In general, having multiple columns with duplicate names is very bad practice and might get you into all sorts of problems with various operators. So, I strongly advice you to rename them.