Numpy - add row to array

How does one add rows to a numpy array?

I开发者_JAVA百科 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] < 3:
        A.append(i)

Is there a numpythonic way to do the equivalent?

Thanks, S ;-)

You can do this:

newrow = [1, 2, 3]
A = numpy.vstack([A, newrow])

What is X? If it is a 2D-array, how can you then compare its row to a number: i < 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 < 3:

import numpy as np
A = np.vstack((A, X[X[:,0] < 3]))

# returns: 
array([[0, 1, 2],
       [0, 2, 0],
       [0, 1, 2],
       [1, 2, 0],
       [2, 1, 2]])

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)

    >> 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]]

>> array([[1, 2, 3, 2],
          [4, 5, 6, 8]])

If you want to prepend, you can just flip the order of the arguments, i.e.:

np.r_([[7, 8, 9]], A)

    >> array([[7, 8, 9],
             [1, 2, 3],
             [4, 5, 6]])

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.

You can also do this:

newrow = [1,2,3]
A = numpy.concatenate((A,newrow))

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)

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])

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.

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.

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)