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How do you get the magnitude of a vector in Numpy?
...
In [4]: %timeit np.sqrt((a*a).sum(axis=1))
100000 loops, best of 3: 15.6 µs per loop
In [5]: %timeit np.sqrt(np.einsum('ij,ij->i',a,a))
100000 loops, best of 3: 8.71 µs per loop
or vectors:
In [5]: a = np.arange(100000)
In [6]: %timeit np.sqrt(a.dot(a))
10000 loops, best of 3: 80.8 µs p...
How to access the ith column of a NumPy multidimensional array?
.... Using the copied version is much faster:
%timeit A_c1_ref.sum() # ~248 µs
%timeit A_c1_copy.sum() # ~12.8 µs
This is due to the different number of strides mentioned before:
A_c1_ref.strides[0] # 40000 bytes
A_c1_copy.strides[0] # 4 bytes
Although it might seem that using column copies...
Rank items in an array using Python/NumPy, without sorting array twice
...)
%%timeit -n10000 -r5
x = (rankdata(l) - 1).astype(int)
>>> 128 µs ± 2.72 µs per loop (mean ± std. dev. of 5 runs, 10000 loops each)
%%timeit -n10000 -r5
a = np.array(l)
r = a.argsort().argsort()
>>> 69.1 µs ± 464 ns per loop (mean ± std. dev. of 5 runs, 10000 loops each)...
Python, compute list difference
...ange(10)
b = range(10/2)
timeit[diff(a, b)]
100000 loops, best of 3: 1.97 µs per loop
timeit[set_diff(a, b)]
100000 loops, best of 3: 2.71 µs per loop
timeit[diff_lamb_hension(a, b)]
100000 loops, best of 3: 2.1 µs per loop
timeit[diff_lamb_filter(a, b)]
100000 loops, best of 3: 3.58 µs per ...
How do I get user IP address in django?
... answered May 12 '11 at 9:38
SævarSævar
1,38788 silver badges1212 bronze badges
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What is the most efficient string concatenation method in python?
...ng/path/'
The contenders are
f'http://{domain}/{lang}/{path}' - 0.151 µs
'http://%s/%s/%s' % (domain, lang, path) - 0.321 µs
'http://' + domain + '/' + lang + '/' + path - 0.356 µs
''.join(('http://', domain, '/', lang, '/', path)) - 0.249 µs (notice that building a constant-length tuple i...
Drop rows with all zeros in pandas data frame
...,0,1]})
In [91]: %timeit df[(df.T != 0).any()]
1000 loops, best of 3: 686 µs per loop
In [92]: df[(df.sum(axis=1) != 0)]
Out[92]:
a b
1 0 1
2 1 0
3 1 1
In [95]: %timeit df[(df.sum(axis=1) != 0)]
1000 loops, best of 3: 495 µs per loop
In [96]: %timeit df[df.values.sum(axis=1) != 0]
1...
Numpy argsort - what is it doing?
...*2)
In [81]: %timeit using_argsort_twice(x)
100000 loops, best of 3: 3.45 µs per loop
In [79]: %timeit using_indexed_assignment(x)
100000 loops, best of 3: 4.78 µs per loop
In [80]: %timeit using_rankdata(x)
100000 loops, best of 3: 19 µs per loop
In [82]: %timeit using_digitize(x)
10000 loop...
How to truncate milliseconds off of a .NET DateTime
...nce is between 50% and about 100% depending on the runtime; net 4.7.2: 0.35µs vs 0.62 µs and core 3.1: 0.18 µs vs 0.12 µs that's micro-seconds (10^-6 seconds)
– juwens
Feb 3 at 15:55
...
How to find the statistical mode?
...ered Dec 14 '12 at 8:00
Rasmus BååthRasmus Bååth
3,62222 gold badges2121 silver badges2525 bronze badges
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