Powering Numerical Computing in Python: NumPy:

Efficient and fast operations on large datasets. Replaces Python lists with powerful
multidimensional arrays.

1. NumPy: Powering Numerical Computing in Python

2. Agenda 1. Introduction to NumPy 2. Why Use NumPy? 3. Core Features 4. NumPy Arrays 5. Operations and Functions 6. Applications 7. Summary & Q&A

3. Introduction to NumPy NumPy stands for Numerical Python. It is a fundamental library for numerical and scientific computing in Python. Developed by Travis Oliphant in 2005.

4. Why Use NumPy? Efficient and fast operations on large datasets. Replaces Python lists with powerful multidimensional arrays. Supports vectorization — eliminating explicit loops. Acts as the foundation for many libraries like Pandas, SciPy, TensorFlow.

5. Core Features of NumPy Multidimensional arrays (ndarray). Mathematical and statistical operations. Broadcasting and vectorized computations. Integration with C/C++ and Fortran code. Random number generation and linear algebra functions.

6. NumPy Arrays NumPy arrays are faster and more memory-efficient than lists. They allow element-wise operations and slicing. Arrays can be created using np.array(), np.zeros(), np.ones(), np.arange(), np.linspace(). Supports reshaping and multidimensional indexing.

7. Operations and Functions Arithmetic operations: +, -, *, / applied element-wise. Aggregate functions: sum(), mean(), std(), var(). Matrix operations: dot(), matmul(), transpose(). Logical operations and comparisons. Broadcasting for operations between different-shaped arrays.

8. Applications of NumPy Data analysis and preprocessing. Scientific computing and research. Machine learning (used in TensorFlow, PyTorch). Image and signal processing. Simulations and mathematical modeling.

9. Summary NumPy is the foundation of the Python data science ecosystem. It provides high-performance array operations. Understanding NumPy is essential for advanced Python programming.