Unsuccessful append to an empty NumPy array

Question

I am trying to fill an empty not np empty   array with values using append but I am gettin error   My code is as follows   import numpy as np result np asarray  np asarray     np asarray       result 0  np append  result 0    1 2     And I am getting   ValueError  could not broadcast input array from shape  2  into shape  0

User · Answer

Here s the result of running your code in Ipython   Note that result is a  2 0  array  2 rows  0 columns  0 elements   The append produces a  2   array   result 0  is  0   array   Your error message has to do with trying to assign that 2 item array into a size 0 slot   Since result is dtype float64  only scalars can be assigned to its elements   In  65   result np asarray  np asarray     np asarray        In  66   result Out 66   array     shape  2  0   dtype float64   In  67   result 0  Out 67   array     dtype float64   In  68   np append result 0   1 2   Out 68   array   1    2      np array is not a Python list   All elements of an array are the same type  as specified by the dtype    Notice also that result is not an array of arrays   Result could also have been built as  ll           result   np array ll    while  ll 0     1 2    ll     1 2        the same is not true for result   np zeros  2 0   also produces your result   Actually there s another quirk to result   result 0    1   does not change the values of result   It accepts the assignment  but since it has 0 columns  there is no place to put the 1   This assignment would work in result was created as np zeros  2 1     But that still can t accept a list   But if result has 2 columns  then you can assign a 2 element list to one of its rows   result   np zeros  2 2   result 0        0 0  result 0     1 2    What exactly do you want result to look like after the append operation

User · Answer

numpy append is pretty different from list append in python  I know that s thrown off a few programers new to numpy  numpy append is more like concatenate  it makes a new array and fills it with the values from the old array and the new value s  to be appended  For example   import numpy  old   numpy array  1  2  3  4   new   numpy append old  5  print old    1  2  3  4  print new    1  2  3  4  5  new   numpy append new   6  7   print new    1  2  3  4  5  6  7    I think you might be able to achieve your goal by doing something like   result   numpy zeros  10    result 0 2     1  2     Or result   numpy zeros  10  2   result 0        1  2    Update   If you need to create a numpy array using loop  and you don t know ahead of time what the final size of the array will be  you can do something like   import numpy as np  a   np array  0   1    b   np array  2   3     temp      while True      rnd   random randint 0  100      if rnd  gt  50          temp append a      else          temp append b      if rnd    0           break   result   np array temp    In my example result will be an  N  2  array  where N is the number of times the loop ran  but obviously  you can adjust it to your needs   new update  The error you re seeing has nothing to do with types  it has to do with the shape of the numpy arrays you re trying to concatenate  If  you do np append a  b   the shapes of a and b need to match  If you append an  2  n   and  n   you ll get a  3  n  array  Your code is trying to append a  1  0  to a  2     Those shapes don t match so you get an error

User · Answer

This error arise from the fact that you are trying to define an object of shape  0   as an object of shape  2    If you append what you want without forcing it to be equal to result 0  there is no any issue   b   np append  result 0     1 2     But when you define result 0    b you are equating objects of different shapes  and you can not do this  What are you trying to do

User · Answer

I might understand the question incorrectly  but if you want to declare an array of a certain shape but with nothing inside  the following might be helpful   Initialise empty array    gt  gt  gt  a   np zeros  0 3    or np empty  0 3   or np array     reshape 0 3   gt  gt  gt  a array     shape  0  3   dtype float64    Now you can use this array to append rows of similar shape to it  Remember that a numpy array is immutable  so a new array is created for each iteration    gt  gt  gt  for i in range 3           a   np vstack  a   i i i         gt  gt  gt  a array    0    0    0             1    1    1             2    2    2       np vstack and np hstack is the most common method for combining numpy arrays  but coming from Matlab I prefer np r  and np c    Concatenate 1d    gt  gt  gt  a   np zeros 0   gt  gt  gt  for i in range 3           a   np r  a   i  i  i        gt  gt  gt  a array   0    0    0    1    1    1    2    2    2      Concatenate rows    gt  gt  gt  a   np zeros  0 3    gt  gt  gt  for i in range 3           a   np r  a    i i i         gt  gt  gt  a array    0    0    0             1    1    1             2    2    2       Concatenate columns    gt  gt  gt  a   np zeros  3 0    gt  gt  gt  for i in range 3           a   np c  a    i   i   i         gt  gt  gt  a array    0    1    2             0    1    2             0    1    2

User · Answer

SO thread  Multiply two arrays element wise  where one of the arrays has arrays as elements   has an example of constructing an array from arrays   If the subarrays are the same size  numpy makes a 2d array   But if they differ in length  it makes an array with dtype object  and the subarrays retain their identity   Following that  you could do something like this   In  5   result np array  np zeros  1   np zeros  2      In  6   result Out 6   array  array   0     array   0    0      dtype object   In  7   np append  result 0    1 2   Out 7   array   0    1    2     In  8   result 0  Out 8   array   0     In  9   result 0  np append  result 0    1 2    In  10   result Out 10   array  array   0    1    2     array   0    0      dtype object    However  I don t offhand see what advantages this has over a pure Python list or lists   It does not work like a 2d array   For example I have to use result 0  1   not result 0 1    If the subarrays are all the same length  I have to use np array result tolist    to produce a 2d array

User · Answer

numpy append always copies the array before appending the new values   Your code is equivalent to the following   import numpy as np result   np zeros  2 0   new result   np append  result 0    1 2   result 0    new result   ERROR  has shape  2 0   new result has shape  2     Perhaps you mean to do this   import numpy as np result   np zeros  2 0   result   np append  result 0    1 2

[python] Unsuccessful append to an empty NumPy array

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