Detect if a NumPy array contains at least one non-numeric value

Question

I need to write a function which will detect if the input contains at least one value which is non-numeric  If a non-numeric value is found I will raise an error  because the calculation should only return a numeric value   The number of dimensions of the input array is not known in advance - the function should give the correct value regardless of ndim  As an extra complication the input could be a single float or numpy float64 or even something oddball like a zero-dimensional array    The obvious way to solve this is to write a recursive function which iterates over every iterable object in the array until it finds a non-iterabe  It will apply the numpy isnan   function over every non-iterable object  If at least one non-numeric value is found then the function will return False immediately  Otherwise if all the values in the iterable are numeric it will eventually return True    That works just fine  but it s pretty slow and I expect that NumPy has a much better way to do it  What is an alternative that is faster and more numpyish   Here s my mockup   def contains nan  myarray                 param myarray   An n-dimensional array or a single float      type myarray   numpy ndarray  numpy array  float      returns  bool     Returns true if myarray is numeric or only contains numeric values      Returns false if at least one non-numeric value exists     Not-A-Number is given by the numpy isnan   function              return True

User · Answer

np where np isnan A    0  shape 0  will be greater than 0 if A contains at least one element of nan  A could be an n x m matrix   Example   import numpy as np  A   np array  1 2 4 np nan    if  np where np isnan A    0  shape 0        print  A contains nan  else      print  A does not contain nan

User · Answer

With numpy 1 3 or svn you can do this  In  1   a   arange 10000   reshape 100 100   In  3   isnan a max    Out 3   False  In  4   a 50 50    nan  In  5   isnan a max    Out 5   True  In  6   timeit isnan a max    10000 loops  best of 3  66 3   s per loop   The treatment of nans in comparisons was not consistent in earlier versions

User · Answer

This should be faster than iterating and will work regardless of shape   numpy isnan myarray  any     Edit  30x faster   import timeit s    import numpy a   numpy arange 10000   reshape  100 100   a 10 10  numpy nan  ms          numpy isnan a  any          any numpy isnan x  for x in a flatten      for m in ms      print      2f s    timeit Timer m  s  timeit 1000   m   Results     0 11 s numpy isnan a  any     3 75 s any numpy isnan x  for x in a flatten      Bonus  it works fine for non-array NumPy types    gt  gt  gt  a   numpy float64 42    gt  gt  gt  numpy isnan a  any   False  gt  gt  gt  a   numpy float64 numpy nan   gt  gt  gt  numpy isnan a  any   True

User · Answer

Pfft  Microseconds  Never solve a problem in microseconds that can be solved in nanoseconds   Note that the accepted answer    iterates over the whole data  regardless of whether a nan is found creates a temporary array of size N  which is redundant    A better solution is to return True immediately when NAN is found   import numba import numpy as np  NAN   float  nan     numba njit nogil True  def  any nans a       for x in a          if np isnan x   return True     return False   numba jit def any nans a       if not a dtype kind   f   return False     return  any nans a flat   array1M   np random rand 1000000  assert any nans array1M   False  timeit any nans array1M     573us  array1M 0    NAN assert any nans array1M   True  timeit any nans array1M     774ns    nanoseconds    and works for n-dimensions   array1M nd   array1M reshape  len array1M  2  2   assert any nans array1M nd   True  timeit any nans array1M nd     774ns   Compare this to the numpy native solution   def any nans a       if not a dtype kind   f   return False     return np isnan a  any    array1M   np random rand 1000000  assert any nans array1M   False  timeit any nans array1M     456us  array1M 0    NAN assert any nans array1M   True  timeit any nans array1M     470us   timeit np isnan array1M  any      532us   The early-exit method is 3 orders or magnitude speedup  in some cases   Not too shabby for a simple annotation

User · Answer

If infinity is a possible value  I would use numpy isfinite  numpy isfinite myarray  all     If the above evaluates to True  then myarray contains no  numpy nan  numpy inf or -numpy inf values   numpy nan will be OK with numpy inf values  for example   In  11   import numpy as np  In  12   b   np array   4  np inf   np nan  -np inf     In  13   np isnan b  Out 13    array   False  False            True  False    dtype bool   In  14   np isfinite b  Out 14    array    True  False           False  False    dtype bool

[python] Detect if a NumPy array contains at least one non-numeric value?

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