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Code Crafter IT Innovation

Introduction to NumPy

What is NumPy?

NumPy stands for Numerical Python.
It is a powerful Python library for mathematics, arrays, and scientific computing.
It provides a special data structure called ndarray (n-dimensional array).
It is the foundation of most Python data science libraries like Pandas, Matplotlib, and TensorFlow.

Why Use NumPy?

Speed: Much faster than Python lists.
Efficiency: Uses less memory.
Convenience: Supports vectorized operations (perform math on the whole array at once).
Mathematics: Provides functions for statistics, algebra, trigonometry, and probability.

Behind the Scene

Normal Python lists store data as objects, which makes them slow.
NumPy arrays are stored in a continuous block of memory, like C arrays.
This allows fast execution and efficient operations.
That’s why NumPy is the backbone of scientific computing in Python.

Uses of NumPy

Data Science: Handling and transforming large datasets.
Machine Learning: Preparing input data for ML models.
Image Processing: Images are stored as arrays of pixels.
Mathematics & Statistics: Probability, linear algebra, and statistical analysis.
Engineering & Physics: Simulations and matrix computations.