Create numpy matrix filled with NaNs

Question

I have the following code   r   numpy zeros shape    width  height  9     It creates a width x height x 9 matrix filled with zeros  Instead  I d like to know if there s a function or way to initialize them instead to NaNs in an easy way

User · Answer

Yet another possibility not yet mentioned here is to use NumPy tile:

a = numpy.tile(numpy.nan, (3, 3))

Also gives

array([[ NaN,  NaN,  NaN],
       [ NaN,  NaN,  NaN],
       [ NaN,  NaN,  NaN]])

I don't know about speed comparison.

User · Answer

Another alternative is numpy broadcast to val n  which returns in constant time regardless of the size and is also the most memory efficient  it returns a view of the repeated element   The caveat is that the returned value is read-only   Below is a comparison of the performances of all the other methods that have been proposed using the same benchmark as in Nico Schl  mer s answer

User · Answer

As said  numpy empty   is the way to go  However  for objects  fill   might not do exactly what you think it does   In 36   a   numpy empty 5 dtype object  In 37   a fill     In 38   a Out 38   array                       dtype object  In 39   a 0  append 4  In 40   a Out 40   array   4    4    4    4    4    dtype object    One way around can be e g    In 41   a   numpy empty 5 dtype object  In 42   a          for x in range 5   In 43   a 0  append 4  In 44   a Out 44   array   4                    dtype object

User · Answer

Are you familiar with numpy nan   You can create your own method such as   def nans shape  dtype float       a   numpy empty shape  dtype      a fill numpy nan      return a   Then  nans  3 4     would output  array    NaN   NaN   NaN   NaN            NaN   NaN   NaN   NaN            NaN   NaN   NaN   NaN      I found this code in a mailing list thread

User · Answer

Another option is to use numpy full  an option available in NumPy 1 8   a   np full  height  width  9   np nan    This is pretty flexible and you can fill it with any other number that you want

User · Answer

You can always use multiplication if you don t immediately recall the  empty or  full methods    gt  gt  gt  np nan   np ones shape  3 2   array    nan   nan            nan   nan            nan   nan      Of course it works with any other numerical value as well    gt  gt  gt  42   np ones shape  3 2   array    42   42            42   42            42  42      But the  u0b34a0f6ae s accepted answer is 3x faster  CPU cycles  not brain cycles to remember numpy syntax        python -mtimeit  import numpy as np  X   np empty  100 100      X      np nan   100000 loops  best of 3  8 9 usec per loop  predict laneh predict   src predict predict webapp  master   python -mtimeit  import numpy as np  X   np ones  100 100      X    np nan   10000 loops  best of 3  24 9 usec per loop

User · Answer

I compared the suggested alternatives for speed and found that  for large enough vectors matrices to fill  all alternatives except val   ones and array n    val   are equally fast       Code to reproduce the plot   import numpy import perfplot  val   42 0   def fill n       a   numpy empty n      a fill val      return a   def colon n       a   numpy empty n      a      val     return a   def full n       return numpy full n  val    def ones times n       return val   numpy ones n    def list n       return numpy array n    val     perfplot show      setup lambda n  n      kernels  fill  colon  full  ones times  list       n range  2    k for k in range 20        logx True      logy True      xlabel  len a

User · Answer

You rarely need loops for vector operations in numpy  You can create an uninitialized array and assign to all entries at once    gt  gt  gt  a   numpy empty  3 3     gt  gt  gt  a      numpy nan  gt  gt  gt  a array    NaN   NaN   NaN            NaN   NaN   NaN            NaN   NaN   NaN        I have timed the alternatives a      numpy nan here and a fill numpy nan  as posted by Blaenk     python -mtimeit  import numpy as np  a   np empty  100 100      a fill np nan   10000 loops  best of 3  54 3 usec per loop   python -mtimeit  import numpy as np  a   np empty  100 100      a      np nan   10000 loops  best of 3  88 8 usec per loop   The timings show a preference for ndarray fill     as the faster alternative  OTOH  I like numpy s convenience implementation where you can assign values to whole slices at the time  the code s intention is very clear   Note that ndarray fill performs its operation in-place  so numpy empty  3 3    fill numpy nan  will instead return None

[python] Create numpy matrix filled with NaNs

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