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JoiningArrays.md

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Joining in Arrays

  • Joining = putting contents of 2/more arrays in a single array.
  • NumPy arrays are joined by axes.
  • Sequence of arrays needed to be joined is passed through "concatenate()", along with the axis.
  • If the axis isn't explicitly passed, it's taken as '0'.

Joining 2 Arrays

import numpy as np
ar1 = np.array([1,2,3])
ar2 = np.array([4,5,6])
arr = np.concatenate((ar1, ar2))
print(arr)


Output :

[1 2 3 4 5 6]


Joining 2D arrays along rows (axis = 1)

ar1 = np.array([[1,2], [3,4]])
ar2 = np.array([[5,6], [7,8]])
arr = np.concatenate((ar1, ar2), axis = 1)
print(arr)


Output:

[[1 2 5 6] [3 4 7 8]]

Joining Arrays Using Stack Functions

  • Stacking is the same as concatenation.
  • The only difference between stacking & concatenation is that it's performed along a new axis.
  • Concatenating 2 1D arrays along the 2nd axis results in putting them one over the other, i.e., stacking.
  • Sequence of arrays needed to be joined is passed through "stack()" along with the axis. If axis isn't explicitly defined, it's taken as 0.

ar1 = np.array([1,2,3])
ar2 = np.array([4,5,6])
arr = np.stack((ar1, ar2), axis = 1)
print(arr)


Output:

[[1 3] [2 5] [3 6]]

Stacking along rows

  • "hstack()" is used to stack along rows.

np.hstack((ar1, ar2))

Output:

[1 2 3 4 5 6]

Stacking along columns

  • "vstack()" is used to stack along columns.

np.vstack((ar1, ar2))

Output:

[[1 2 3] [4 5 6]]

Stacking along height (depth)

  • "dstack()" is used to stack along height.

np.dstack((ar1, ar2))

Output:

[[[1 4] [2 5] [3 6]]]


Splitting NumPy Arrays

  • Splitting is the reverse of NumPy Joining
  • The array we want to split and the number of splits are passed through "array-split()".

Splitting the array into 3 parts:

aer = np.array([1,2,3,4,5,6])
new_aer = np.array_split(aer, 3)
print(new_aer)


Output:

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


NOTE: If the array has fewer elements than required for splitting, it'll adjust from the end accordingly.
split() is also available in Python but, it won't adjust the elements, when elements are less while splitting. array_split() works properly and better as compared to split().

Splitting Into Arrays

  • Return value of the array_split() is an array containing each of the splits as an array.
  • If we split an array into 3 arrays, we can access them from the result similar to any array element.

print(new_aer[0])
print(new_aer[1])


Output:

[1 2]
[3 4]


Specifying axis of the split in 2D arrays

abc = np.array([[1,2,3], [4,5,6], [7,8,9], [10,11,12]])
new_abc = np.array_split(abc, 3, axis = 1)
print(new_abc)


Output:

[array([[1], [4], [7], [10]]), array([[2], [5], [8], [11]]), array([[3], [6], [9], [12]])]


NOTE: We can also use np.hsplit(abc, 3) to get the same result.

Splitting using np.hstack()

print(np.hstack(abc))

Output:

[1 2 3 4 5 6 7 8 9 10 11 12]


NOTE: Similarly, we can also split using vstack(), dstack() and vsplit().