NumPy: Powering Numerical Computing in Python

NumPy is the foundation of the Python data
science ecosystem. It provides high-performance array
operations.

1. NumPy: Powering Numerical
Computing in PythonComputing in Python

2. Agenda
•1. Introduction to NumPy
•2. Why Use NumPy?
•3. Core Features
•4. NumPy Arrays•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.•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 •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.•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.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().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).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.operations.
•Understanding NumPy is essential for
advanced Python programming.