import pandas as pd
from pandas._libs.arrays import NDArrayBacked as cls
dti = pd.date_range("2016-01-01", periods=3) dta = dti._data arr = dta._ndarray
obj = cls._simple_new(arr, arr.dtype)
# for foo in [arr, dta, obj]: ...
%timeit foo.copy() 299 ns ± 30 ns per loop # <-- arr underlying ndarray (for reference) 530 ns ± 9.24 ns per loop # <-- dta with cython NDArrayBacked 1.66 µs ± 46.3 ns per loop # <-- dta without cython NDArrayBacked 328 ns ± 5.29 ns per loop # <-- obj with NDArrayBacked.__cinit__ 371 ns ± 6.97 ns per loop # <-- obj with NDArrayBacked._simple_new
%timeit foo.T 125 ns ± 6.27 ns per loop # <-- arr underlying ndarray (for reference) 226 ns ± 7.66 ns per loop # <-- dta with cython NDArrayBacked 911 ns ± 16.6 ns per loop # <-- dta without cython NDArrayBacked 215 ns ± 4.54 ns per loop # <-- obj with NDArrayBacked._simple_new
Implementing these methods in cython improves performance quite a bit.
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