Unlock NumPy: Comprehensive Guide to Array Iteration, Reshaping, and Advanced Operations

01 Array Iteration

NumPy provides the multi‑dimensional iterator numpy.nditer which follows the standard Python iterator protocol, allowing element‑wise traversal of an array.

import numpy as np
a = np.arange(0, 60, 5)
a = a.reshape(3, 4)
print(a)
for x in np.nditer(a):
    print(x)

If two arrays are broadcastable, np.nditer can iterate over them simultaneously, broadcasting the smaller array to the shape of the larger one.

import numpy as np
a = np.arange(0, 60, 5).reshape(3, 4)
print(a)
b = np.array([1, 2, 3, 4], dtype=int)
print(b)
for x, y in np.nditer([a, b]):
    print(x, y)

02 Array Shape‑Modification Functions

ndarray.reshape

Returns a new view of the array with a different shape without changing its data.

import numpy as np
a = np.arange(8)
print(a)
b = a.reshape(4, 2)
print(b)

ndarray.flat

Provides a 1‑D iterator over the array, similar to Python's built‑in iterator.

import numpy as np
a = np.arange(0, 16, 2).reshape(2, 4)
print(a)
print(list(a.flat))

ndarray.flatten

Returns a copy of the array collapsed into one dimension. The optional order argument can be 'C', 'F', 'A', or 'K'.

import numpy as np
a = np.arange(8).reshape(2, 4)
print(a)
print(a.flatten())
print(a.flatten(order='F'))

03 Array Transposition Functions

numpy.transpose

Permutes the axes of an array, returning a view when possible.

import numpy as np
a = np.arange(24).reshape(2, 3, 4)
print(a)
b = np.transpose(a)
print(b)
print(b.shape)
# custom axes order
c = np.transpose(a, (1, 0, 2))
print(c)

ndarray.T

Convenient shortcut for numpy.transpose on a 2‑D array.

import numpy as np
a = np.arange(12).reshape(3, 4)
print(a)
print(a.T)

numpy.swapaxes

Swaps two axes of an array.

import numpy as np
a = np.arange(8).reshape(2, 2, 2)
print(a)
print(np.swapaxes(a, 2, 0))

numpy.rollaxis

Rolls the specified axis backwards until it reaches a given position.

import numpy as np
a = np.arange(8).reshape(2, 2, 2)
print(a)
print(np.rollaxis(a, 2))
print(np.rollaxis(a, 2, 1))

04 Array Broadcasting and Dimension‑Adding Functions

numpy.broadcast_to

Broadcasts an array to a new shape, returning a read‑only view.

import numpy as np
a = np.arange(4).reshape(1, 4)
print(a)
print(np.broadcast_to(a, (4, 4)))

numpy.expand_dims

Inserts a new axis at the specified position.

import numpy as np
x = np.array([[1, 2], [3, 4]])
print(x)
y = np.expand_dims(x, axis=0)
print(y)
y = np.expand_dims(x, axis=1)
print(y)

numpy.squeeze

Removes axes of length one from the shape of an array.

import numpy as np
x = np.arange(9).reshape(1, 3, 3)
print(x)
y = np.squeeze(x)
print(y)

05 Array Stacking and Concatenation

NumPy provides four main functions for joining arrays: concatenate, stack, hstack, and vstack.

numpy.stack

import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
print(np.stack((a,b), 0))
print(np.stack((a,b), 1))

numpy.hstack

import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
print('Horizontal stack:')
print(np.hstack((a, b)))

numpy.vstack

import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
print('Vertical stack:')
print(np.vstack((a, b)))

numpy.concatenate

import numpy as np
a = np.array([[1,2],[3,4]])
b = np.array([[5,6],[7,8]])
print(np.concatenate((a, b)))
print(np.concatenate((a, b), axis=1))

06 Array Splitting Functions

Key splitting utilities are split, hsplit, and vsplit.

numpy.split

import numpy as np
a = np.arange(9)
print(a)
print('Three equal parts:')
print(np.split(a, 3))
print('Split at indices 4 and 7:')
print(np.split(a, [4, 7]))

numpy.hsplit

import numpy as np
a = np.arange(16).reshape(4,4)
print(a)
print('Horizontal split:')
print(np.hsplit(a, 2))

numpy.vsplit

import numpy as np
a = np.arange(16).reshape(4,4)
print(a)
print('Vertical split:')
print(np.vsplit(a, 2))

07 Array Element Operations

Functions include resize, append, insert, delete, and unique.

numpy.resize

import numpy as np
a = np.array([[1,2,3],[4,5,6]])
print(a)
print(a.shape)
print(np.resize(a, (3,2)))
print(np.resize(a, (3,3)))
print(np.resize(a, (2,2)))

numpy.append

import numpy as np
a = np.array([[1,2,3],[4,5,6]])
print(np.append(a, [[7,8,9]], axis=0))
print(np.append(a, [[5,5,5],[7,8,9]], axis=1))

numpy.insert

import numpy as np
a = np.array([[1,2],[3,4],[5,6]])
print(np.insert(a, 3, [11,12]))
print(np.insert(a, 1, [11], axis=0))
print(np.insert(a, 1, [11], axis=1))

numpy.delete

import numpy as np
a = np.array([[1,2],[3,4],[5,6]])
print(np.delete(a, 5))
print(np.delete(a, 1, axis=1))

numpy.unique

import numpy as np
a = np.array([5,2,6,2,7,5,6,8,2,9])
print(np.unique(a))
print(np.unique(a, return_index=True))
print(np.unique(a, return_inverse=True))
print(np.unique(a, return_counts=True))

08 NumPy String Functions

Vectorised string operations are available via numpy.char.

import numpy as np
print(np.char.add(['hello'], [' xyz']))
print(np.char.multiply('Hello ', 3))
print(np.char.center('hello', 20, fillchar='*'))
print(np.char.capitalize('hello world'))
print(np.char.title('hello how are you?'))
print(np.char.lower(['HELLO','WORLD']))
print(np.char.upper('hello'))
print(np.char.split('hello how are you?'))
print(np.char.split('YiibaiPoint,Hyderabad,Telangana', sep=','))
print(np.char.strip('ashok arora', 'a'))
print(np.char.join(':', 'dmy'))
print(np.char.replace('He is a good boy', 'is', 'was'))

09 NumPy Arithmetic Functions

Includes trigonometric, rounding, and basic arithmetic utilities.

import numpy as np
a = np.array([0,30,45,60,90])
print(np.sin(a*np.pi/180))
print(np.cos(a*np.pi/180))
print(np.tan(a*np.pi/180))

10 Sorting, Searching and Counting

import numpy as np
a = np.array([[3,7,3,1],[9,7,8,7]])
print(np.sort(a))
print(np.argsort(a))
print(np.argmax(a))
print(np.argmin(a))
print(np.nonzero(a))
print(np.where(a > 3))

11 IO File Operations

import numpy as np
a = np.array([1,2,3,4,5])
np.save('outfile', a)
b = np.load('outfile.npy')
print(b)
np.savetxt('out.txt', a)
c = np.loadtxt('out.txt')
print(c)