Introduction to NumPy

What is NumPy?

NumPy, or Numerical Python, is a fundamental package for scientific computing in Python. It provides support for arrays, matrices, and a large collection of mathematical functions to operate on these data structures.

Installation

NumPy can be installed using pip. Open your terminal or command prompt and run:

pip install numpy

Basic Operations

NumPy allows for various operations on arrays. Here are some of the basic operations:

import numpy as np

# 1D array
array_1d = np.array([1, 2, 3, 4, 5])
print(array_1d)

# 2D array
array_2d = np.array([[1, 2, 3], [4, 5, 6]])
print(array_2d)

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

# Addition
result = a + b
print(result)  # Output: [5 7 9]

array = np.array([10, 20, 30, 40, 50])

# Indexing
print(array[0])  # Output: 10

# Slicing
print(array[1:4])  # Output: [20 30 40]

Advanced Features

NumPy provides several advanced features such as:

• Linear algebra operations
• Random number generation
• Fourier transforms
• Statistical operations

from numpy.linalg import inv

# Matrix inversion
matrix = np.array([[1, 2], [3, 4]])
inverse_matrix = inv(matrix)
print(inverse_matrix)

Best Practices

• Use vectorized operations instead of loops for improved performance.
• Leverage broadcasting to perform operations on arrays of different shapes.
• Employ NumPy’s built-in functions for mathematical operations to take advantage of optimizations.

FAQ

NumPy Workflow

graph TD;
                A[Start] --> B{Data Type?};
                B -->|Array| C[Use NumPy Array];
                B -->|List| D[Convert to NumPy Array];
                C --> E[Perform Operations];
                D --> E;
                E --> F[End];