NumPy array initialization fill with identical values

Question

I need to create a NumPy array of length n  each element of which is v   Is there anything better than   a   empty n  for i in range n       a i    v   I know zeros and ones would work for v   0  1  I could use v   ones n   but it won t work when v is None  and also would be much slower

User · Answer

without numpy   gt  gt  gt  2  3  2  2  2

User · Answer

Updated for Numpy 1 7 0  Hat-tip to  Rolf Bartstra    a np empty n   a fill 5  is fastest   In descending speed order    timeit a np empty 1e4   a fill 5  100000 loops  best of 3  5 85 us per loop   timeit a np empty 1e4   a    5  100000 loops  best of 3  7 15 us per loop   timeit a np ones 1e4  5 10000 loops  best of 3  22 9 us per loop   timeit a np repeat 5  1e4   10000 loops  best of 3  81 7 us per loop   timeit a np tile 5  1e4   10000 loops  best of 3  82 9 us per loop

User · Answer

You can use numpy tile  e g     v   7 rows   3 cols   5 a   numpy tile v   rows cols   a Out 1    array   7  7  7  7  7           7  7  7  7  7           7  7  7  7  7      Although tile is meant to  tile  an array  instead of a scalar  as in this case   it will do the job  creating pre-filled arrays of any size and dimension

User · Answer

Apparently  not only the absolute speeds but also the speed order  as reported by user1579844  are machine dependent  here s what I found   a np empty 1e4   a fill 5  is fastest   In descending speed order   timeit a np empty 1e4   a fill 5     100000 loops  best of 3  10 2 us per loop timeit a np empty 1e4   a    5   100000 loops  best of 3  16 9 us per loop timeit a np ones 1e4  5   100000 loops  best of 3  32 2 us per loop timeit a np tile 5  1e4     10000 loops  best of 3  90 9 us per loop timeit a np repeat 5  1e4     10000 loops  best of 3  98 3 us per loop timeit a np array  5  int 1e4     1000 loops  best of 3  1 69 ms per loop  slowest BY FAR     So  try and find out  and use what s fastest on your platform

User · Answer

I had   numpy array n    value     in mind  but apparently that is slower than all other suggestions for large enough n   Here is full comparison with perfplot  a pet project of mine      The two empty alternatives are still the fastest  with NumPy 1 12 1   full catches up for large arrays     Code to generate the plot   import numpy as np import perfplot   def empty fill n       a   np empty n      a fill 3 14      return a   def empty colon n       a   np empty n      a      3 14     return a   def ones times n       return 3 14   np ones n    def repeat n       return np repeat 3 14   n     def tile n       return np repeat 3 14   n     def full n       return np full  n   3 14    def list to array n       return np array n    3 14     perfplot show      setup lambda n  n      kernels  empty fill  empty colon  ones times  repeat  tile  full  list to array       n range  2    k for k in range 27        xlabel  len a        logx True      logy True

User · Answer

I believe fill is the fastest way to do this   a   np empty 10  a fill 7    You should also always avoid iterating like you are doing in your example   A simple a      v will accomplish what your iteration does using numpy broadcasting

User · Answer

NumPy  1 8 introduced np full    which is a more direct method than empty   followed by fill   for creating an array filled with a certain value    gt  gt  gt  np full  3  5   7  array    7    7    7    7    7             7    7    7    7    7             7    7    7    7    7       gt  gt  gt  np full  3  5   7  dtype int  array   7  7  7  7  7           7  7  7  7  7           7  7  7  7  7      This is arguably the way of creating an array filled with certain values  because it explicitly describes what is being achieved  and it can in principle be very efficient since it performs a very specific task

[python] NumPy array initialization (fill with identical values)

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