PERF: faster constructors from ea scalars

[lukemanley]

• Tests added and passed if fixing a bug or adding a new feature
• All code checks passed.
• Added an entry in the latest doc/source/whatsnew/vX.X.X.rst file if fixing a bug or adding a new feature.

Perf improvement when constructing a DataFrame/Series from a scalar EA value.

Examples:

import pandas as pd

N = 1_000_000


%timeit pd.DataFrame({"A": pd.NA, "B": 1.0}, index=range(N), dtype=pd.Float64Dtype())
2.18 s ± 214 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)       <- main
22.4 ms ± 2.81 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)   <- PR


%timeit pd.Series(pd.NA, index=range(N), dtype=pd.Float64Dtype())
1.62 s ± 50.3 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)      <- main
7.33 ms ± 1.05 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)  <- PR


%timeit pd.Series(1, index=range(N), dtype='Int64')
242 ms ± 21.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)      <- main
11.6 ms ± 231 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)   <- PR

[jbrockmendel]

what happens if you don't special case Categorical?

[lukemanley]

Calling take on the empty categorical would error because the categories were not defined.

I just pushed another commit which first sets the categories. Categorical now has a similar improvement:

import pandas as pd

N = 1_000_000

%timeit pd.Series(1, index=range(N), dtype='category')
415 ms ± 91.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)     <- main
4.5 ms ± 1.57 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)  <- PR

[jbrockmendel]

Calling take on the empty categorical would error because the categories were not defined.

Hmm i think this might be a problem for other not-fully-initialized dtype objects. IIRC IntervalDtype without 'closed' set is another one, not sure if we have a way to detect the general case

[lukemanley]

latest update uses .repeat per @jreback's suggestion below.

[jreback]

can we just use .repeat()? (I think we define that generally on EA)

[lukemanley]

Yes, that's even better - just updated. Thanks for pointing it out.

[jreback]

very nice @lukemanley keep em coming!