How to use pandas rolling apply with a simple custom function?

Question

I have a function func that I want to apply to consecutive rows of a pandas dataframe. However, I get a ValueError: when I try to do it as below.

import numpy as np
import pandas as pd

def func(a: np.ndarray, b: np.ndarray) -> float:
    """Return the sum of sum of vectors a and b"""
    return np.sum(a) + np.sum(b)

df = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [10, 11, 12, 13, 14]})
df.rolling(window=2, axis=1).apply(func)
>>>
ValueError: Length of passed values is 2, index implies 5.

All I want to do is apply func on a rolling basis to consecutive rows (which is why I chose window=2 above). The snippet below is a manual implementation of this.

func(df.iloc[0, :].values, df.iloc[1, :].values)
>>> 24
func(df.iloc[1, :].values, df.iloc[2, :].values)
>>> 28

and so on.

Note that the example I gave for func is just for illustrative purposes - I know that that you could use a simple df.sum(axis=1) + df.shift(-1).sum(axis=1) in this case. What I want to know is how you use rolling apply for custom functions in the general case.

Answer 1

I guess this can be done with a few lines of code and an intermediate dataframe:

import numpy as np
import pandas as pd

def func(a: np.ndarray) -> float:
    return np.sum(a)

df = pd.DataFrame({"a": [1, 2, 3, 4, 5], "b": [10, 11, 12, 13, 14]})
df_rolled = df.rolling(window=2).apply(func)
df["ab_rolled"] = [func([df_rolled["a"][i], df_rolled["b"][i]])
                   for i in range(0, len(df_rolled["a"]))]

print(df)

outputs:

   a   b  ab_rolled
0  1  10        NaN
1  2  11       24.0
2  3  12       28.0
3  4  13       32.0
4  5  14       36.0

This well could be an ugly code though. Sorry, it's the first time I use pandas.