Pandas rolling apply using multiple columns

Question

I am trying to use a pandas.DataFrame.rolling.apply() rolling function on multiple columns. Python version is 3.7, pandas is 1.0.2.

import pandas as pd

#function to calculate
def masscenter(x):
    print(x); # for debug purposes
    return 0;

#simple DF creation routine
df = pd.DataFrame( [['02:59:47.000282', 87.60, 739],
                    ['03:00:01.042391', 87.51, 10],
                    ['03:00:01.630182', 87.51, 10],
                    ['03:00:01.635150', 88.00, 792],
                    ['03:00:01.914104', 88.00, 10]], 
                   columns=['stamp', 'price','nQty'])
df['stamp'] = pd.to_datetime(df2['stamp'], format='%H:%M:%S.%f')
df.set_index('stamp', inplace=True, drop=True)

'stamp' is monotonic and unique, 'price' is double and contains no NaNs, 'nQty' is integer and also contains no NaNs.

So, I need to calculate rolling 'center of mass', i.e. sum(price*nQty)/sum(nQty).

What I tried so far:

df.apply(masscenter, axis = 1)

masscenter is be called 5 times with a single row and the output will be like

price     87.6
nQty     739.0
Name: 1900-01-01 02:59:47.000282, dtype: float64

It is desired input to a masscenter, because I can easily access price and nQty using x[0], x[1]. However, I stuck with rolling.apply() Reading the docs DataFrame.rolling() and rolling.apply() I supposed that using 'axis' in rolling() and 'raw' in apply one achieves similiar behaviour. A naive approach

rol = df.rolling(window=2)
rol.apply(masscenter)

prints row by row (increasing number of rows up to window size)

stamp
1900-01-01 02:59:47.000282    87.60
1900-01-01 03:00:01.042391    87.51
dtype: float64

then

stamp
1900-01-01 02:59:47.000282    739.0
1900-01-01 03:00:01.042391     10.0
dtype: float64

So, columns is passed to masscenter separately (expected).

Sadly, in the docs there is barely any info about 'axis'. However the next variant was, obviously

rol = df.rolling(window=2, axis = 1)
rol.apply(masscenter)

Never calls masscenter and raises ValueError in rol.apply(..)

> Length of passed values is 1, index implies 5

I admit that I'm not sure about 'axis' parameter and how it works due to lack of documentation. It is the first part of the question: What is going on here? How to use 'axis' properly? What it is designed for?

Of course, there were answers previously, namely:

How-to-apply-a-function-to-two-columns-of-pandas-dataframe
It works for the whole DataFrame, not Rolling.

How-to-invoke-pandas-rolling-apply-with-parameters-from-multiple-column
The answer suggests to write my own roll function, but the culprit for me is the same as asked in comments: what if one needs to use offset window size (e.g. '1T') for non-uniform timestamps?
I don't like the idea to reinvent the wheel from scratch. Also I'd like to use pandas for everything to prevent inconsistency between sets obtained from pandas and 'self-made roll'. There is another answer to that question, suggessting to populate dataframe separately and calculate whatever I need, but it will not work: the size of stored data will be enormous. The same idea presented here:
Apply-rolling-function-on-pandas-dataframe-with-multiple-arguments

Another Q & A posted here
Pandas-using-rolling-on-multiple-columns
It is good and the closest to my problem, but again, there is no possibility to use offset window sizes (window = '1T').

Some of the answers were asked before pandas 1.0 came out, and given that docs could be much better, I hope it is possible to roll over multiple columns simultaneously now.

The second part of the question is: Is there any possibility to roll over multiple columns simultaneously using pandas 1.0.x with offset window size?

Thank you very much.

Answer 1

How about this:

def masscenter(ser):
    print(df.loc[ser.index])
    return 0

rol = df.price.rolling(window=2)
rol.apply(masscenter, raw=False)

It uses the rolling logic to get subsets from an arbitrary column. The raw=False option provides you with index values for those subsets (which are given to you as Series), then you use those index values to get multi-column slices from your original DataFrame.