how to add rows to a numpy array

Question

I have a numpy array and want to add a row to it and modify one column. This is my array:

import numpy as np
small_array = np.array ([[[3., 2., 5.],
                          [3., 2., 2.]],
                         [[3., 2., 7.],
                          [3., 2., 5.]]])

Then, firstly I wnat to add a fixed value (e.g. 2.) to last column. I did it this way:

chg = 2.
small_array[:,:,-1] += chg

next thing that I want to do is adding another row to each aubarray. Added row should have the same first and second columns but third column shold be different. This time chg x 2. should be subtracted from the existing value in third column:

big_array = np.array ([[[3., 2., 7.],
                          [3., 2., 4.],
                          [3., 2., 0.]], # this row is added
                         [[3., 2., 9.],
                          [3., 2., 7.],
                          [3., 2., 3.]]]) # this row is added

I very much appreciate any help to do it.

joanis · Accepted Answer · 2022-03-16 13:26:10Z

I believe the operation you are looking for is np.concatenate, which can construct a new array by concatenating two arrays.

Simple example, we can add a row of zeroes like this:

>>> np.concatenate((small_array, np.zeros((2,1,3))), axis=1)
array([[[3., 2., 7.],
        [3., 2., 4.],
        [0., 0., 0.]],

       [[3., 2., 9.],
        [3., 2., 7.],
        [0., 0., 0.]]])

Now, instead of zeros, we can get the values from the first row in each matrix:

>>> np.concatenate((small_array, small_array[:,:1,:]), axis=1)
array([[[3., 2., 7.],
        [3., 2., 4.],
        [3., 2., 7.]],

       [[3., 2., 9.],
        [3., 2., 7.],
        [3., 2., 9.]]])

At this point, you can modify the value in the third column of the new rows as needed.

The axis parameter is important here, it tells concatenate() along which axis I want to concatenate the two input arrays.

Documentation: https://numpy.org/doc/stable/reference/generated/numpy.concatenate.html

apinostomberry · Accepted Answer · 2022-03-16 14:30:20Z

1

have you tried using numpy.resize ?

https://numpy.org/doc/stable/reference/generated/numpy.resize.html

you will have to decide for yourself if this is too computationally expensive, and if a numpy.list makes more sense for your purposes.

for example,

import numpy as np
a = np.array([[1,2], [3,4]])
a = np.resize(a, (3,2))

then use the index to edit the value of the array at the latest position

a[-1]=[8,9]

the final output of this example should be

a
array([[1,2],
      [3,4]
      [8,9]])

edited Mar 16, 2022 at 14:30

answered Mar 16, 2022 at 13:02

apinostomberry

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6 Comments

Link_tester Over a year ago

Dear @apinostomberry, I tried np.resize(small_array,(2,3,3)) but it did not give me what I want.

apinostomberry Over a year ago

I added an example that hopefully illustrates the idea. This is as far as I can take you for now

joanis Over a year ago

When I tested using resize, the original position of the values was changed, so I'm not sure this is going to work.

apinostomberry Over a year ago

well thats just wrong. you must have something else affecting your code. try it with just my example. I just verified my scratch code with repl and posted the result so you can see if you get the same and then isolaate what is causing your position to change. Also I changed answer to use negative indexing, so you shouldnt have to worry about position.

joanis Over a year ago

Well, your array has shape (2,2), while OP's had shape (2,2,3). Your resize inserts one row at the end, but the resize needed should insert one row at the end of each matrix. Please test your code on OP's input or at least on an array of the same shape.

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