Test if numpy array contains only zeros

Question

We initialize a numpy array with zeros as bellow   np zeros  N N 1     But how do we check whether all elements in a given n n numpy array matrix is zero  The method just need to return a True if all the values are indeed zero

User · Answer

I d use np all here  if you have an array a    gt  gt  gt  np all a  0

User · Answer

The other answers posted here will work  but the clearest and most efficient function to use is numpy any      gt  gt  gt  all zeros   not np any a    or   gt  gt  gt  all zeros   not a any      This is preferred over numpy all a  0  because it uses less RAM   It does not require the temporary array created by the a  0 term   Also  it is faster than numpy count nonzero a  because it can return immediately when the first nonzero element has been found    Edit  As  Rachel pointed out in the comments  np any   no longer uses  short-circuit  logic  so you won t see a speed benefit for small arrays

User · Answer

This will work  def check arr       if np all arr    0           return True     return False

User · Answer

Check out numpy count nonzero    gt  gt  gt  np count nonzero np eye 4   4  gt  gt  gt  np count nonzero   0 1 7 0 0   3 0 0 2 19    5

User · Answer

As another answer says  you can take advantage of truthy falsy evaluations if you know that 0 is the only falsy element possibly in your array  All elements in an array are falsy iff there are not any truthy elements in it     gt  gt  gt  a   np zeros 10   gt  gt  gt  not np any a  True   However  the answer claimed that any was faster than other options due partly to short-circuiting  As of 2018  Numpy s all and any do not short-circuit    If you do this kind of thing often  it s very easy to make your own short-circuiting versions using numba   import numba as nb    short-circuiting replacement for np any    nb jit nopython True  def sc any array       for x in array flat          if x              return True     return False    short-circuiting replacement for np all    nb jit nopython True  def sc all array       for x in array flat          if not x              return False     return True   These tend to be faster than Numpy s versions even when not short-circuiting  count nonzero is the slowest    Some input to check performance   import numpy as np  n   10  8 middle   n  2 all 0   np zeros n  dtype int  all 1   np ones n  dtype int  mid 0   np ones n  dtype int  mid 1   np zeros n  dtype int  np put mid 0  middle  0  np put mid 1  middle  1    mid 0    1 1 1     1 0 1     1 1 1    mid 1    0 0 0     0 1 0     0 0 0    Check      count nonzero  timeit np count nonzero all 0     220 ms    8 73 ms per loop  mean    std  dev  of 7 runs  1 loop each   timeit np count nonzero all 1    150 ms    4 56 ms per loop  mean    std  dev  of 7 runs  10 loops each       all   np all  timeit np all all 1   timeit np all mid 0   timeit np all all 0    56 8 ms    3 41 ms per loop  mean    std  dev  of 7 runs  10 loops each    57 4 ms    1 76 ms per loop  mean    std  dev  of 7 runs  10 loops each    55 9 ms    2 13 ms per loop  mean    std  dev  of 7 runs  10 loops each     sc all  timeit sc all all 1   timeit sc all mid 0   timeit sc all all 0    44 4 ms    2 49 ms per loop  mean    std  dev  of 7 runs  10 loops each    22 7 ms    599   s per loop  mean    std  dev  of 7 runs  10 loops each    288 ns    6 36 ns per loop  mean    std  dev  of 7 runs  1000000 loops each       any   np any  timeit np any all 0   timeit np any mid 1   timeit np any all 1    60 7 ms    1 38 ms per loop  mean    std  dev  of 7 runs  10 loops each    60 ms    287   s per loop  mean    std  dev  of 7 runs  10 loops each    57 7 ms    1 12 ms per loop  mean    std  dev  of 7 runs  10 loops each     sc any  timeit sc any all 0   timeit sc any mid 1   timeit sc any all 1    41 7 ms    1 24 ms per loop  mean    std  dev  of 7 runs  10 loops each    22 4 ms    1 51 ms per loop  mean    std  dev  of 7 runs  10 loops each    287 ns    12 7 ns per loop  mean    std  dev  of 7 runs  1000000 loops each      Helpful all and any equivalences   np all a     np logical not np any np logical not a    np any a     np logical not np all np logical not a    not np all a     np any np logical not a   not np any a     np all np logical not a

User · Answer

If you re testing for all zeros to avoid a warning on another numpy function then wrapping the line in a try  except block will save having to do the test for zeros before the operation you re interested in i e    try    removes output noise for empty slice      mean   np mean array  except      mean   0

[python] Test if numpy array contains only zeros

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