Can we have a faster way to create an array?
Is there a faster way to create the following array? I need to create an array for further calculation. It takes a long time to start up the array. Basically I need to create a series or array with the values of the norm.ppf function of size 4000.
nrow = 4000
lts = pandas.Series(numpy.zeros(nrow))
lts = lts.apply(lambda x : norm.ppf(random.random(),10),5))
lts = np.asarray(lts, int)
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You are not using Pandas for any purpose other than Series being a container, so it would be faster to cut Pandas out of this calculation.
Presumably norm
- scipy.stats.norm
. If so, the norm.ppf
first argument could be a numpy array. So it's much faster to call norm.ppf
once on a 4000 NumPy array than to call norm.ppf
4000 times on a float:
lts = norm.ppf(np.random.random(4000), 10, 5).astype('int')
In [120]: %timeit lts = norm.ppf(np.random.random(4000), 10, 5).astype('int')
100 loops, best of 3: 2.51 ms per loop
In [121]: %%timeit
.....: lts_orig = pd.Series(np.zeros(nrow))
.....: lts_orig = lts_orig.apply(lambda x : norm.ppf(random.random(),10, 5))
.....: lts_orig = np.asarray(lts_orig, int)
.....:
1 loops, best of 3: 572 ms per loop
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