Mastering NumPy: 100 Tips and Strategies For Efficient Data Manipulation and Analysis in Python

NumPy (Numerical Python) is a powerful open-source library for numerical and mathematical operations in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these elements efficiently. NumPy is a foundational library for scientific computing in Python and serves as the basis for many other libraries in the Python data science ecosystem. Here’s an overview of key features and functionalities of NumPy:

1. Arrays:

• numpy.ndarray: The primary data structure in NumPy is the n-dimensional array, or ndarray. It is a homogeneous array of fixed-size elements.
• Array Creation: Arrays can be created using functions like numpy.array(), numpy.zeros(), numpy.ones(), numpy.arange(), etc.
• Array Operations: NumPy supports element-wise operations and broadcasting, making it efficient for numerical computations.

2. Array Indexing and Slicing:

• Indexing: Accessing elements in an array using integer indices.
• Slicing: Extracting portions of an array using slicing notation.

3. Array Operations: