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Useful Numpy Functions: reshape() and nditer()

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In the last article, we have learned about Numpy module in Python and also have seen the concept of N-Dimensional Array with examples. It is now time to see some more useful Numpy functions. This time we will focus on another two important functions - reshape() and nditer(). Both of the functions are widely used by Python Programmers and Developers across all the platforms and hence it is important to understand the usage of these functions with the help of examples. More on Numpy official documentation.

Useful Numpy Functions: reshape() and nditer()

Useful Numpy Functions: reshape() and nditer()

reshape() -> Numpy reshape() function shapes an array without modifying the actual elements of   array. Like, 1D array can be shaped into  any other dimensional array and vice-versa. Let's understand by following example.

Example

import numpy

array = numpy.arange(1, 9, 1)
print(f"1D Array : {array}")
t_array = array.reshape(4, 2)
print(f"Reshaped in 2D-Array:\n {t_array}")
m_array = array.reshape(2,2,2)
print(f"Reshaped in 3D-Array:\n {m_array}")

Output

1D Array : [1 2 3 4 5 6 7 8]

Reshaped in 2D-Array:
[[1 2]
[3 4]
[5 6]
[7 8]]

Reshaped in 3D-Array:
[[[1 2]
[3 4]]
[[5 6]
[7 8]]]

Similarly, an array can be reshaped into any dimension. Just make sure the number of elements in an array to be reshaped is equal in both shapes. Like, in above example, 8 element of 1D-array can be reshaped into 4 rows of 2 elements but it can't be reshaped into 3 rows of 3 elements as it would require 9 elements in 1D-array.

Multi-dimensional array can also be converted back into 1D-array by passing -1 as value to the function reshape().

Example

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import numpy

array = numpy.array([[1,2], [3,4], [5,6]])
print(f"2D-Array:\n{array}")
print(f"Converted 1D-Array: \n {array.reshape(-1)}")

Output

Actual Array:

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

1D-Array:

[1 2 3 4 5 6]

nditer() -> numpy.nditer is an iterator object provided by python Numpy package. It is used to iterate over an array in efficient and organized  manner. Let's define and iterate an array using below example to understand the concept. There are 3 orders in which iteration can be performed. By default the order is 'K' which matches the memory layout of defined array without considering any particular order. Other 2 orders are 'C' and 'F'. There comes time when iteration needs to be done in a particular order without considering memory layout of defined array. In such cases, an extra parameter is passed to the nditer() function to provide the order of iteration.

Example1: Using Default Order

import numpy

array = numpy.arange(1, 10)
m_array = array.reshape(3, 3)
print(f"Actual Array:\n{array}")
print(f"Reshaped Array:\n{m_array}")
print("Iterated Array:\n")
for i in numpy.nditer(m_array):
print(i)

Output

Actual Array:

[1 2 3 4 5 6 7 8 9]

Reshaped Array:

[[1 2 3]
[4 5 6]
[7 8 9]]

Iterated Array:

1 2 3 4 5 6 7 8 9

Example 2: Using 'C' Order

import numpy

array = numpy.arange(1, 10)
m_array = array.reshape(3, 3)
print(f"Actual Array:\n{array}")
print(f"Reshaped Array:\n{m_array}")
print("Iterated Array:\n")
for i in numpy.nditer(m_array, order = 'C'):
print(i)

Output

Actual Array:

[1 2 3 4 5 6 7 8 9]

Reshaped Array:

[[1 2 3]
[4 5 6]
[7 8 9]]

Iterated Array:

1 2 3 4 5 6 7 8 9

Example 3: Using 'F' Order

import numpy

array = numpy.arange(1, 10)
m_array = array.reshape(3, 3)
print(f"Actual Array:\n{array}")
print(f"Reshaped Array:\n{m_array}")
print("Iterated Array:\n")
for i in numpy.nditer(m_array, order = 'F'):
print(i)

Output

Actual Array:

[1 2 3 4 5 6 7 8 9]

Reshaped Array:
[[1 2 3]
[4 5 6]
[7 8 9]]

Iterated Array:

1 4 7 2 5 8 3 6 9

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