Numpy Tutorial

Creating NumPy Array

NumPy Array Manipulation

Matrix in NumPy

Operations on NumPy Array

Reshaping NumPy Array

Indexing NumPy Array

Arithmetic operations on NumPy Array

Linear Algebra in NumPy Array

NumPy and Random Data

Sorting and Searching in NumPy Array

Universal Functions

Working With Images

Projects and Applications with NumPy

Resize the shape of the given matrix in Numpy

In NumPy, while the reshape() method is commonly used to change the shape of an array without altering its data, the resize() function is used to change the size and shape of the array, potentially introducing or removing elements. This tutorial will explore how to use the resize() function in NumPy.

1. Setup

First, ensure that you have NumPy installed:

pip install numpy

Then, in your Python script or notebook:

import numpy as np

2. Basic Usage of resize()

When you use resize(), if the new shape requires more elements than the original array has, the new array will be filled with repeated copies of the original array. Conversely, if the new shape requires fewer elements, the new array will be a truncated version of the original array.

arr = np.array([1, 2, 3, 4])
resized_arr = np.resize(arr, (2, 3))

print(resized_arr)

Output:

[[1 2 3]
 [4 1 2]]

Note that the values from the original array are repeated to fill the new size.

3. Reducing the Size

When reducing the size, elements from the original are truncated:

arr = np.array([1, 2, 3, 4, 5, 6])
reduced_arr = np.resize(arr, (2, 2))

print(reduced_arr)

Output:

[[1 2]
 [3 4]]

4. In-place Resize

The resize() function from the NumPy module returns a new array with the specified size. If you want to modify the original array in-place, you can use the resize() method of the ndarray object. However, when using the method version, you can't specify a shape that will increase the total size of the array:

arr = np.array([1, 2, 3, 4, 5, 6])
arr.resize((2, 2))

print(arr)

Output:

[[1 2]
 [3 4]]

5. Differences Between resize() and reshape()

It's important to understand the differences between resize() and reshape():

  • resize() can change the total number of elements in the array, repeating or truncating the original data as necessary. reshape() retains the original data and requires the total number of elements to remain the same.

  • resize() has both a function version (from the NumPy module) and a method version (of the ndarray object). The function version can return a larger array than the original, while the method version cannot.

  • reshape() only has a method version, and it never changes the original data.

Conclusion

The resize() function in NumPy is a versatile tool that can help you adjust the size and shape of arrays. This capability is useful in situations where the exact size and shape of your data matter, such as in image processing or when preparing data for specific machine learning algorithms. Always remember to choose between resize() and reshape() based on your specific needs.

1. Resize a matrix in Python with Numpy:

Resizing a matrix involves changing its dimensions, potentially repeating or truncating elements.

import numpy as np

# Create a 2D NumPy array (matrix)
matrix = np.array([[1, 2, 3],
                   [4, 5, 6],
                   [7, 8, 9]])

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)

2. How to use numpy.resize for matrix resizing:

Use numpy.resize() to change the dimensions of a matrix in NumPy.

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)

3. Numpy matrix resizing example code:

Example code demonstrating the resizing of a matrix using NumPy's numpy.resize().

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)

4. Python numpy resize matrix dimensions:

Change the dimensions of a matrix in Python using the numpy.resize() function.

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)

5. Sample code for resizing matrices in numpy:

Sample code illustrating the resizing of matrices using NumPy's numpy.resize().

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)

6. Resizing multi-dimensional arrays with numpy:

Extend the concept of resizing to multi-dimensional arrays using NumPy.

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)

7. Resizing vs reshaping in numpy for matrices:

Understand the differences between resizing and reshaping matrices in NumPy.

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

# Reshape the matrix using numpy.reshape()
reshaped_matrix = np.reshape(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)
print("\nReshaped Matrix:")
print(reshaped_matrix)

8. Python numpy resize usage for matrix manipulation:

Use the numpy.resize() function in NumPy for manipulating the dimensions of a matrix.

# Assuming 'matrix' is already defined

# Resize the matrix using numpy.resize()
resized_matrix = np.resize(matrix, (2, 5))

print("Original Matrix:")
print(matrix)
print("\nResized Matrix:")
print(resized_matrix)
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