This notebook provides an introduction to some of the basic built-in functions available in the NumPy library for numerical computing in Python.
NumPy is a fundamental package for scientific computing with Python. It provides support for multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
This notebook covers essential NumPy built-in functions, including array creation, manipulation, mathematical operations, and more.
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Array Creation: Introduction to various methods for creating NumPy arrays, including
np.array(),np.zeros(),np.ones(),np.arange(),np.linspace(), and more. -
Array Manipulation: Techniques for reshaping, stacking, splitting, and joining arrays using functions such as
np.reshape(),np.hstack(),np.vstack(),np.concatenate(), etc.
- Linear Algebra Operations: Overview of linear algebra functions for matrix multiplication, matrix inversion, eigenvalue decomposition, and solving linear equations using NumPy.
To run the notebook, you can upload it to an environment that supports Jupyter notebooks, such as Kaggle or Google Colab. Alternatively, you can run it locally on your machine with a Python environment that has NumPy installed.
This notebook does not require any external datasets. All examples and demonstrations use synthetic data generated within the notebook.
Contributions to improve or expand upon this notebook are welcome! If you have any suggestions, bug reports, or feature requests, feel free to open an issue or submit a pull request.