Pandas groupby: efficiently chain several functions

Question

I need to group a DataFrame and apply several chained functions on each group.

My problem is basically the same as in pandas - Groupby two functions: apply cumsum then shift on each group.

There are answers there on how to obtain a correct result, however they seem to have a suboptimal performance. My specific question is thus: is there a more efficient way than the ones I describe below?

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First here is some large testing data:

from string import ascii_lowercase

import numpy as np
import pandas as pd


n = 100_000_000
np.random.seed(0)
df = pd.DataFrame(
    {
        "x": np.random.choice(np.array([*ascii_lowercase]), size=n),
        "y": np.random.normal(size=n),
    }
)

Below is the performance of each function:

%timeit df.groupby("x")["y"].cumsum()
4.65 s ± 71 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
%timeit df.groupby("x")["y"].shift()
5.29 s ± 54.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

A basic solution is to group twice. It seems suboptimal since grouping is a large part of the total runtime and should only be done once.

%timeit df.groupby("x")["y"].cumsum().groupby(df["x"]).shift()
10.1 s ± 63.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

The accepted answer to the aforementioned question suggests to use apply with a custom function to avoid this issue. However for some reason it is actually performing much worse than the previous solution.

def cumsum_shift(s):
    return s.cumsum().shift()

%timeit df.groupby("x")["y"].apply(cumsum_shift)
27.8 s ± 858 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

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Do you have any idea how to optimize this code? Especially in a case where I'd like to chain more than two functions, performance gains can become quite significant.