切割 pandas 时间序列的最快方法

Question

Looking for the fastest way to cut a timeseries... for example just taking the values that are more recent than a certain index.

I've found two commonly used methods:

df = original_series.truncate(before=example_time)

and

df = original_series[example_time:]

Which one is faster (for large time-series > 10**6 values)?

Answer 1

This usually depends on what your dataframe index is, throwing a random DataFrame of 10^7 values into timeit we get the following.

From a performance standpoint in truncation more inefficient as pandas is optimized for integer based indexing via numpy.

Truncate: 
62.6 ms ± 3.63 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Bracket Indexing:
54.1 µs ± 4.41 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

ILoc:
69.5 µs ± 4.52 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Loc:
92 µs ± 5.09 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Ix (which is deprecated):
110 µs ± 8.44 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

EDIT This is all on pandas 0.24.2, back in the 0.14-0.18 versions loc performance was much much worse