Python sample code for performe array manipulation using Numpy|S-Logix

How to Perform Array Manipulation Using NumPy in Python

Array Manipulation Using NumPy

Condition for Array Manipulation Using NumPy in Python

Description:
NumPy is a powerful library in Python used for numerical computations and provides support for large, multi-dimensional arrays and matrices.

Array manipulation refers to operations that modify or access elements, change the shape, and perform mathematical operations on arrays.

Step-by-Step Process

Import NumPy:
Import the NumPy library.
Create an Array:
Use numpy.array() to create arrays or other functions like numpy.arange(), numpy.zeros(), numpy.ones(), and numpy.linspace() for specific needs.
Array Indexing and Slicing:
Use slicing and indexing to access or modify elements in the array.
Reshaping Arrays:
Use functions like reshape(), flatten(), and transpose() to modify the shape of the array.
Element-wise Operations:
Perform mathematical operations on arrays, such as addition, multiplication, or applying a function element-wise.
Concatenation and Splitting:
Use concatenate(), hstack(), vstack() to join arrays, and split() to divide arrays.
Broadcasting:
Perform operations between arrays of different shapes.

Sample Source Code

# Code for array manipulation

import numpy as np

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

print("Original Array:", arr)

# Accessing an element by index
print("\nElement at index 2:", arr[2])

# Slicing the array
print("\nSliced Array (from index 1 to 3):", arr[1:4])

# Reshaping Arrays
arr_2d = arr.reshape(1, -1) # Reshape to 2D
print("\nReshaped Array (1 row, 5 columns):\n", arr_2d)

# Flattening the array (Converting 2D to 1D)
flattened_arr = arr_2d.flatten()
print("\nFlattened Array:", flattened_arr)

# Element-wise Operations
arr_add = arr + 10 # Add 10 to each element
print("\nArray after adding 10:", arr_add)

arr_multiply = arr * 2 # Multiply each element by 2
print("\nArray after multiplying by 2:", arr_multiply)

arr_square = np.square(arr) # Square each element
print("\nArray after squaring elements:", arr_square)

# Concatenation
arr2 = np.array([6, 7, 8])
concatenated_arr = np.concatenate((arr, arr2))
print("\nConcatenated Array:", concatenated_arr)

# Array Splitting
split_arr = np.split(arr, 5) # Split array into 5 parts
print("\nArray Split into 5 parts:", split_arr)

# Broadcasting (Adding a scalar to an array)
broadcasted_arr = arr + 3 # Add 3 to each element
print("\nArray after broadcasting with 3:", broadcasted_arr)

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