Powering Numerical Computing in Python: NumPy:

Powering Numerical Computing in Python: NumPy:

• 1.
  NumPy: Powering Numerical Computingin Python
• 2.
  Agenda • 1. Introductionto 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.
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  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 ofNumPy • 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.
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  NumPy Arrays • NumPyarrays 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.
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  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.
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  Summary • NumPy isthe foundation of the Python data science ecosystem. • It provides high-performance array operations. • Understanding NumPy is essential for advanced Python programming.