In [1]:

import numpy as np
import pandas as pd

In [2]:

pd_series = pd.Series(np.random.rand(10**6))

In [3]:

%%timeit
pd_series.sum()

3.73 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [4]:

%%timeit
np.nansum(pd_series.values)

2.21 ms ± 154 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [5]:

%%timeit
pd_series.sum(skipna=False)

429 µs ± 51.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [6]:

%%timeit
pd_series.values.sum()

382 µs ± 42.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [7]:

%%timeit
np.sum(pd_series.values)

361 µs ± 39.6 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)