import narwhals as nw
from narwhals.typing import IntoFrameT

def agnostic_function(df_native: IntoFrameT, date_column: str, price_column: str) -> IntoFrameT:
    return (
        nw.from_native(df_native)
        .group_by(nw.col(date_column).dt.truncate("1mo"))
        .agg(nw.col(price_column).mean())
        .sort(date_column)
        .to_native()
    )

import pandas as pd
from datetime import datetime

data = {
    "date": [datetime(2020, 1, 1), datetime(2020, 1, 8), datetime(2020, 2, 3)],
    "price": [1, 4, 3],
}
print(agnostic_function(pd.DataFrame(data), "date", "price"))

import polars as pl

print(agnostic_function(pl.DataFrame(data), "date", "price"))

def f(x: np.ndarray): ...

from numpy import ndarray, dtype, uint8, bool_, float64

def process(
    x: ndarray[tuple[int], dtype[bool_]],
    y: ndarray[tuple[int, int, int], dtype[uint8]],
) -> ndarray[tuple[int], dtype[float64]]:
    ...

import static_frame as sf

@sf.CallGuard.check
def f(x: ndarray[tuple[int], dtype[np.number]]): ...

# Вместо apply
df["log"] = np.log(df["x"])

df["sum"] = df["a"] + df["b"]

df["country"] = df["country"].astype("category")

pd.read_csv("big.csv", usecols=["col1", "col2"], nrows=100_000)

df.to_csv("result.csv", index=False)

df.memory_usage(deep=True)