Numpy - add row to array

Question

How does one add rows to a numpy array   I have an array A   A   array   0  1  2    0  2  0      I wish to add rows to this array from another array X if the first element of each row in X meets a specific condition   Numpy arrays do not have a method  append  like that of lists  or so it seems   If A and X were lists I would merely do   for i in X      if i 0   lt  3          A append i    Is there a numpythonic way to do the equivalent   Thanks  S  -

User · Answer

I use  np vstack  which is faster  EX    import numpy as np  input array np array  1 2 3   new row  np array  4 5 6    new array np vstack  input array  new row

User · Answer

I use numpy insert arr  i  the object to be added  axis  in order to insert  object to be added at the i th row axis 0  or column axis 1  import numpy as np  a   np array   1  2  3    5  4  6      array   1  2  3             5  4  6     np insert a  1   55  66   axis 1    array    1  55   2   3              5  66   4   6     np insert a  2   50  60  70   axis 0    array    1   2   3              5   4   6             50  60  70     Too old discussion  but I hope it helps someone

User · Answer

If no calculations are necessary after every row  it s much quicker to add rows in python  then convert to numpy  Here are timing tests using python 3 6 vs  numpy 1 14  adding 100 rows  one at a time   import numpy as np  from time import perf counter  sleep  def time it          Compare performance of two methods for adding rows to numpy array     py array     0  1  2    0  2  0       py row    4  5  6      numpy array   np array py array      numpy row   np array  4 5 6       n loops   100      start clock   perf counter       for count in range 0  n loops          numpy array   np vstack  numpy array  numpy row     5 8 micros     duration   perf counter   - start clock     print  numpy 1 14 takes    3f  micros per row  format duration   1e6   n loops        start clock   perf counter       for count in range 0  n loops           py array append py row     15 micros     numpy array   np array py array    43 9 micros            duration   perf counter   - start clock     print  python 3 6 takes    3f  micros per row  format duration   1e6   n loops       sleep 15    time it   prints   numpy 1 14 takes 5 971 micros per row python 3 6 takes 0 694 micros per row   So  the simple solution to the original question  from seven years ago  is to use vstack   to add a new row after converting the row to a numpy array  But a more realistic solution should consider vstack s poor performance under those circumstances  If you don t need to run data analysis on the array after every addition  it is better to buffer the new rows to a python list of rows  a list of lists  really   and add them as a group to the numpy array using vstack   before doing any data analysis

User · Answer

What is X  If it is a 2D-array  how can you then compare its row to a number  i  lt  3   EDIT after OP s comment   A   array   0  1  2    0  2  0    X   array   0  1  2    1  2  0    2  1  2    3  2  0      add to A all rows from X where the first element  lt  3   import numpy as np A   np vstack  A  X X   0   lt  3       returns   array   0  1  2           0  2  0           0  1  2           1  2  0           2  1  2

User · Answer

import numpy as np array    np array   1 2 3    add row   np array   4 5 6     array    np concatenate  array   add row   axis 0

User · Answer

well u can do this      newrow    1 2 3    A   numpy vstack  A  newrow

User · Answer

You can also do this   newrow    1 2 3  A   numpy concatenate  A newrow

User · Answer

You can use numpy append   to append a row to numpty array and reshape to a matrix later on   import numpy as np a   np array  1 2   a   np append a   3 4   print a    1 2 3 4    in your example A    1 2  for row in X      A   np append A  row

User · Answer

If you can do the construction in a single operation  then something like the vstack-with-fancy-indexing answer is a fine approach  But if your condition is more complicated or your rows come in on the fly  you may want to grow the array  In fact the numpythonic way to do something like this - dynamically grow an array - is to dynamically grow a list   A   np array   1 2 3   4 5 6    Alist    r for r in A  for i in range 100       newrow   np arange 3  i     if i 5          Alist append newrow  A   np array Alist  del Alist   Lists are highly optimized for this kind of access pattern  you don t have convenient numpy multidimensional indexing while in list form  but for as long as you re appending it s hard to do better than a list of row arrays

User · Answer

As this question is been 7 years before  in the latest version which I am using is numpy version 1 13  and python3  I am doing the same thing with adding a row to a matrix  remember to put a double bracket to the second argument  otherwise  it will raise dimension error    In here I am adding on matrix A  1 2 3 4 5 6   with a row   7 8 9   same usage in np r   A    1  2  3    4  5  6   np append A    7  8  9    axis 0        gt  gt  array   1  2  3                  4  5  6                  7  8  9     or  np r  A   7 8 9      Just to someone s intersted  if you would like to add a column    array   np c  A np zeros  A s row size    following what we did before on matrix A  adding a column to it  np c  A   2 8     gt  gt  array   1  2  3  2              4  5  6  8

[python] Numpy - add row to array

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