NumPy, short for Numerical Python, is a powerful library in Python used for numerical computing. It provides support for arrays, matrices, mathematical functions, and high-level mathematical operations, making it essential for data manipulation, scientific computing, and machine learning. In this crash course, we’ll explore the fundamentals of NumPy and how to leverage its capabilities for various numerical tasks.

I have created an Awesome Numpy Tutorial on Youtube. Excuse me for the noise, as it was in the middle of heavy rain. I ensure that the course content will not waste your time.

## Course Content

- What is NumPy? (00:00:00)
- Why Learn NumPy?(00:01:30
- Anaconda and Jutyper Notebook setup (00:03:17)
- How to create your first Jupyter Notebook (00:08:24)
- Introduction to NumPy – calculate BMI (00:11:17)
- Introduction to NumPy Array – (00:22:20)
- – Use the type()
- – Fetch the first element of NumPy Array
- – Fetch the last element of NumPy Array
- – Fetch the last 5 elements of NumPy Array
- – Convert 1D array to 2D array

- Attributes of NumPy Array (00:28:30)
- – shape() – to find the array dimensions
- size – total number of elements
- -dtype – Data types of the array elements
- – ndim – Number of dimensions of an array

- DIfferent methods to create Arrays in NumPy (00:32:38)
- – array() – Create numpy array from List, tuples and dictionaries
- – ones() – Create an array of 1s.
- – zeros() – Create an array of 0s.
- – arange() – Create an array with the continuous integers.
- – linspace() – Generate values which are equally spaced from each other.
- – random.random() – Create array of random numbers

- Indexing in NumPy – Array (00:52:54)
- – Access the First element
- – Access the Last element
- – Get a row from a [m, n] array – Grid
- – Get a column from a [m, n] array – Grid
- – Get a small matrix – [a, b] from an array [m, n] – Obtain specific indices from an [m, n] array

- Broadcasting in NumPy – (01:01:02)
- Vectorization in NumPy – (01:07:45) – Compare the NumPy inbuilt methods vs custom functions performance
- Common mathematical and algebraic operations in NumPy (1:11:58)
- Reshape arrays – np.reshape()
- Stacking Arrays
- Combining arrays – np.vstack() and np.hstack()
- Basic Arithmetic operations like: Addition: np.add() Multiplication: np.multiply() Power – np.power() Absolute value – np.absolute() Trigonometric Operations Aggregate functions – reduce() and aggregate()
- Basic Linear Algebra Matrix (arrays) multiplications Determinant of an Array The rank of a matrix The inverse of a Matrix ✓

- Basic Performance comparison between NumPy Array and List (01:30:25)

## Conclusion:

NumPy is a fundamental library for numerical computing in Python, providing powerful tools for array manipulation, mathematical operations, and linear algebra. This crash course has covered the basics of NumPy, from array creation and manipulation to advanced mathematical functions and linear algebra operations. By mastering NumPy, developers can streamline their numerical computing tasks and unlock the full potential of Python for scientific computing and data analysis.