initialize a numpy array

Question

Is there way to initialize a numpy array of a shape and add to it  I will explain what I need with a list example  If I want to create a list of objects generated in a loop  I can do   a      for i in range 5       a append i    I want to do something similar with a numpy array  I know about vstack  concatenate etc  However  it seems these require two numpy arrays as inputs  What I need is   big array   Initially empty  This is where I don t know what to specify for i in range 5       array i of shape    2 4  created      add to big array   The big array should have a shape  10 4   How to do this     EDIT   I want to add the following clarification  I am aware that I can define big array   numpy zeros  10 4   and then fill it up  However  this requires specifying the size of big array in advance  I know the size in this case  but what if I do not  When we use the  append function for extending the list in python  we don t need to know its final size in advance  I am wondering if something similar exists for creating a bigger array from smaller arrays  starting with an empty array

User · Answer

Array analogue for the python s  a      for i in range 5       a append i    is   import numpy as np  a   np empty  0   for i in range 5       a   np append a  i

User · Answer

numpy zeros      Return a new array of given shape and   type  filled with zeros    or     numpy ones      Return a new array of given shape and   type  filled with ones    or     numpy empty      Return a new array of given shape and   type  without initializing entries      However  the mentality in which we construct an array by appending elements to a list is not much used in numpy  because it s less efficient  numpy datatypes are much closer to the underlying C arrays   Instead  you should preallocate the array to the size that you need it to be  and then fill in the rows  You can use numpy append if you must  though

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Maybe something like this will fit your needs    import numpy as np  N   5 res       for i in range N       res append np cumsum np ones shape  2 4      res   np array res  reshape  10  4   print res    Which produces the following output     1   2   3   4      5   6   7   8      1   2   3   4      5   6   7   8      1   2   3   4      5   6   7   8      1   2   3   4      5   6   7   8      1   2   3   4      5   6   7   8

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I d suggest defining shape first   Then iterate over it to insert values   big array  np zeros shape     6  2    for it in range 6       big array it     it it    For example   gt  gt  gt big array  array    0    0             1    1             2    2             3    3             4    4             5    5

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numpy fromiter   is what you are looking for   big array   numpy fromiter xrange 5   dtype  int     It also works with generator expressions  e g    big array   numpy fromiter   i  i 1  2 for i in xrange 5    dtype  int      If you know the length of the array in advance  you can specify it with an optional  count  argument

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You do want to avoid explicit loops as much as possible when doing array computing  as that reduces the speed gain from that form of computing  There are multiple ways to initialize a numpy array  If you want it filled with zeros  do as katrielalex said   big array   numpy zeros  10 4    EDIT  What sort of sequence is it you re making  You should check out the different numpy functions that create arrays  like numpy linspace start  stop  size   equally spaced number   or numpy arange start  stop  inc   Where possible  these functions will make arrays substantially faster than doing the same work in explicit loops

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The way I usually do that is by creating a regular list  then append my stuff into it  and finally transform the list to a numpy array as follows    import numpy as np big array         empty regular list for i in range 5       arr   i np ones  2 4     for instance     big array append arr  big np array   np array big array     transformed to a numpy array   of course your final object takes twice the space in the memory at the creation step  but appending on python list is very fast  and creation using np array   also

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For your first array example use   a   numpy arange 5    To initialize big array  use  big array   numpy zeros  10 4     This assumes you want to initialize with zeros  which is pretty typical  but there are many other ways to initialize an array in numpy   Edit  If you don t know the size of big array in advance  it s generally best to first build a Python list using append  and when you have everything collected in the list  convert this list to a numpy array using numpy array mylist    The reason for this is that lists are meant to grow very efficiently and quickly  whereas numpy concatenate would be very inefficient since numpy arrays don t change size easily   But once everything is collected in a list  and you know the final array size  a numpy array can be efficiently constructed

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Introduced in numpy 1 8      numpy full      Return a new array of given shape and type  filled with fill value    Examples    gt  gt  gt  import numpy as np  gt  gt  gt  np full  2  2   np inf  array    inf   inf            inf   inf     gt  gt  gt  np full  2  2   10  array   10  10           10  10

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Whenever you are in the following situation   a      for i in range 5       a append i    and you want something similar in numpy  several previous answers have pointed out ways to do it  but as  katrielalex pointed out these methods are not efficient  The efficient way to do this is to build a long list and then reshape it the way you want after you have a long list  For example  let s say I am reading some lines from a file and each row has a list of numbers and I want to build a numpy array of shape  number of lines read  length of vector in each row   Here is how I would do it more efficiently   long list      counter   0 with open  filename    r   as f      for row in f          row list   row split           long list extend row list          counter      now we have a long list and we are ready to reshape result   np array long list  reshape counter  len row list      desired numpy array

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To initialize a numpy array with a specific matrix   import numpy as np  mat   np array   1  1  0  0  0                    0  1  0  0  1                    1  0  0  1  1                    0  0  0  0  0                    1  0  1  0  1     print mat shape print mat   output    5  5    1 1 0 0 0    0 1 0 0 1    1 0 0 1 1    0 0 0 0 0    1 0 1 0 1

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I realize that this is a bit late  but I did not notice any of the other answers mentioning indexing into the empty array   big array   numpy empty 10  4  for i in range 5       array i   numpy random random 2  4      big array 2   i 2    i   1        array i   This way  you preallocate the entire result array with numpy empty and fill in the rows as you go using indexed assignment   It is perfectly safe to preallocate with empty instead of zeros in the example you gave since you are guaranteeing that the entire array will be filled with the chunks you generate

[python] initialize a numpy array

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