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Append an element to Array of Arrays

Suppose I have an array of arrays.
import numpy as np
x = np.array([ [1, 2], [3, 4], [5, 6]])
I want to add 10 as the first element of each of those arrays without running for loop. Result should look like
array([[10, 1, 2],
[10, 3, 4],
[10, 5, 6]])
Plain append does not work.
np.append(10, x)
array([10, 1, 2, 3, 4, 5, 6])
My original problem has 100K arrays. So I need to find an efficient way to do this.

You are looking for np.insert.
https://numpy.org/doc/stable/reference/generated/numpy.insert.html
np.insert(x, 0, [10,10,10], axis=1)

np.insert is your choice
>>> import numpy as np
x = np.array([ [1, 2], [3, 4], [5, 6]])
>>> x
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.insert(x, 0, 10, axis=1)
array([[10, 1, 2],
[10, 3, 4],
[10, 5, 6]])
you also can insert different values
>>> np.insert(x, 0, [10,11,12] , axis=1)
array([[10, 1, 2],
[11, 3, 4],
[12, 5, 6]])

Concatenate indices of each Numpy Array in a Matrix

So I have a Numpy Array with a bunch of numpy arrays inside of them. I want to group them based on the position in their individual array.
For Example:
If Matrix is:
[[1, 2], [2, 3], [4, 5], [6, 7]]
Then the code should return:
[[1, 2, 4, 6], [2, 3, 5, 7]]
This is becuase 1, 2, 4, 6 are all the first elements in their individual arrays, and 2, 3, 5, 7 are the second elements in their individual arrays.
Anyone know some function that could do this. Thanks.
Answer in Python.

Using numpy transpose should do the trick:
a = np.array([[1, 2], [2, 3], [4, 5], [6, 7]])
a_t = a.T
print(a_t)
array([[1, 2, 4, 6],
[2, 3, 5, 7]])

Your data as a list:
In [101]: alist = [[1, 2], [2, 3], [4, 5], [6, 7]]
In [102]: alist
Out[102]: [[1, 2], [2, 3], [4, 5], [6, 7]]
and as a numpy array:
In [103]: arr = np.array(alist)
In [104]: arr
Out[104]:
array([[1, 2],
[2, 3],
[4, 5],
[6, 7]])
A standard idiom for 'transposing' lists is:
In [105]: list(zip(*alist))
Out[105]: [(1, 2, 4, 6), (2, 3, 5, 7)]
with arrays, there's a transpose method:
In [106]: arr.transpose()
Out[106]:
array([[1, 2, 4, 6],
[2, 3, 5, 7]])
The first array is (4,2) shape; its transpose is (2,4).

argmax on 2 axis for 3-d numpy array

I'd like to obtain a 1D array of indexes from a 3D matrix.
For instance given x = np.random.randint(10, size=(10,3,3)), I'd like to do something like np.argmax(x, axis=(1,2)) just like you can do with np.max, that is, obtain a 1D array of length 10 containing the indexes (0 to 8) of the maximums of each submatrix of size (3,3).
I have not found anything helpful so far and I want to avoid looping on the first dimension (and use np.argmax(x)) as it is quite big.
Cheers!

Reshape to merge those last two axes and then use np.argmax -
idx = x.reshape(x.shape[0],-1).argmax(-1)
out = np.unravel_index(idx, x.shape[-2:])
Sample run -
In [263]: x = np.random.randint(10, size=(4,3,3))
In [264]: x
Out[264]:
array([[[0, 9, 2],
[7, 7, 8],
[2, 5, 9]],
[[1, 7, 2],
[8, 9, 0],
[2, 8, 3]],
[[7, 5, 0],
[7, 1, 6],
[5, 1, 1]],
[[0, 7, 3],
[5, 4, 1],
[9, 8, 9]]])
In [265]: idx = x.reshape(x.shape[0],-1).argmax(-1)
In [266]: np.unravel_index(idx, x.shape[-2:])
Out[266]: (array([0, 1, 0, 2]), array([1, 1, 0, 0]))
If you meant getting the merged index, then its simpler -
x.reshape(x.shape[0],-1).argmax(1)
Sample run -
In [283]: x
Out[283]:
array([[[2, 3, 7],
[8, 1, 0],
[3, 6, 9]],
[[8, 0, 5],
[2, 2, 9],
[9, 0, 9]],
[[1, 9, 2],
[5, 0, 3],
[7, 2, 1]],
[[1, 6, 5],
[2, 3, 7],
[7, 4, 6]]])
In [284]: x.reshape(x.shape[0],-1).argmax(1)
Out[284]: array([8, 5, 1, 5])

Replicating elements in numpy array

I have a numpy array say
a = array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
I have an array 'replication' of the same size where replication[i,j](>=0) denotes how many times a[i][j] should be repeated along the row. Obiviously, replication array follows the invariant that np.sum(replication[i]) have the same value for all i.
For example, if
replication = array([[1, 2, 1],
[1, 1, 2],
[2, 1, 1]])
then the final array after replicating is:
new_a = array([[1, 2, 2, 3],
[4, 5, 6, 6],
[7, 7, 8, 9]])
Presently, I am doing this to create new_a:
##allocate new_a
h = a.shape[0]
w = a.shape[1]
for row in range(h):
ll = [[a[row][j]]*replicate[row][j] for j in range(w)]
new_a[row] = np.array([item for sublist in ll for item in sublist])
However, this seems to be too slow as it involves using lists. Can I do the intended entirely in numpy, without the use of python lists?

You can flatten out your replication array, then use the .repeat() method of a:
import numpy as np
a = array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
replication = array([[1, 2, 1],
[1, 1, 2],
[2, 1, 1]])
new_a = a.repeat(replication.ravel()).reshape(a.shape[0], -1)
print(repr(new_a))
# array([[1, 2, 2, 3],
# [4, 5, 6, 6],
# [7, 7, 8, 9]])

Slicing a 2D numpy array in python

What's wrong with the code below?
arr=numpy.empty((2,2))
arr[0:,0:]=1
print(arr[1:,1:])
arr=([ [1, 2, 3], [ 4, 5, 6], [ 7, 8, 9] ])
print(arr[1:2, 1])
I am getting the following error and not able to slice the array( fifth line). Please help me with this.
TypeError: list indices must be integers, not tuple.

You rebind the name arr to point to a Python list in your fourth line, and so your question title doesn't quite fit: you're not slicing a 2d numpy array. lists can't be sliced the way that numpy arrays can. Compare:
>>> arr= numpy.array([ [1, 2, 3], [ 4, 5, 6], [ 7, 8, 9] ])
>>> arr
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
>>> arr[1:2, 1]
array([5])
but
>>> arr.tolist()
[[1, 2, 3], [4, 5, 6], [7, 8, 9]]
>>> arr.tolist()[1:2, 1]
Traceback (most recent call last):
File "<ipython-input-23-4a441cf2eaa9>", line 1, in <module>
arr.tolist()[1:2, 1]
TypeError: list indices must be integers, not tuple

arr=([ [1, 2, 3], [ 4, 5, 6], [ 7, 8, 9] ]) is a python list,not a numpy array.
You reassign arr with arr=([ [1, 2, 3], [ 4, 5, 6], [ 7, 8, 9] ]) to a list.
Make it a numpy array:
In [37]: arr = numpy.array([ [1, 2, 3], [ 4, 5, 6], [ 7, 8, 9] ])
In [38]: arr
Out[38]:
array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
In [39]: (arr[1:2, 1])
Out[39]: array([5])