Numpy Project

Overview

This project demonstrates the usage of NumPy, a fundamental package for scientific computing with Python. NumPy provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

Features

• Multi-dimensional array creation and manipulation
• Vectorized mathematical operations
• Linear algebra, statistics, and Fourier transform support
• Efficient broadcasting and slicing operations
• Integration with other Python libraries like Pandas, Matplotlib, and SciPy

Installation

You can install NumPy using pip:

pip install numpy

Usage

Here are some basic examples of NumPy usage:

Importing NumPy

import numpy as np

Creating Arrays

# 1D array
arr1 = np.array([1, 2, 3, 4, 5])

# 2D array
arr2 = np.array([[1, 2], [3, 4], [5, 6]])

Array Operations

# Element-wise addition
arr_sum = arr1 + 10

# Multiplication
arr_mul = arr1 * 2

# Dot product
result = np.dot(arr1, arr1)

Array Slicing and Indexing

# Accessing elements
print(arr2[0, 1])  # Output: 2

# Slicing
print(arr1[1:4])  # Output: [2 3 4]

Array Functions

# Statistical operations
mean_val = np.mean(arr1)
max_val = np.max(arr1)
min_val = np.min(arr1)

# Reshape
reshaped = arr2.reshape(2, 3)

Advantages of NumPy

• Speed: Efficient operations on large datasets using optimized C code.
• Memory Efficiency: Uses less memory than Python lists.
• Vectorization: Eliminates the need for explicit loops, improving performance.
• Community Support: Widely used in data science, machine learning, and scientific computing.

References

• NumPy Official Documentation
• NumPy Tutorial

License

This project is open-source and available under the MIT License.