NumPy

• What it is: A fundamental library for scientific computing in Python that provides powerful multi-dimensional array objects and tools to work with them.
• Core functionality: Optimized for numerical operations, enabling fast and efficient calculations on large datasets.
• Primary use case: Performing mathematical and logical operations on arrays and matrices, making it ideal for scientific and engineering applications.
• Data structure: Uses a single, uniform type for all elements in an array. 

pandas

• What it is: A library built on top of NumPy that provides high-performance, easy-to-use data structures and data analysis tools.
• Core functionality: Enables efficient manipulation of structured, tabular data.
• Primary use case: Data analysis tasks like cleaning, transforming, merging, and analyzing datasets that have different data types.
• Data structure: Includes two main structures:
  • Series: A one-dimensional labeled array.
  • DataFrame: A two-dimensional, size-mutable, tabular data structure with labeled axes (rows and columns). 

NUMPY

BLAS– Basic Linear Algebra SubProgram

LAPACK– Linear Algrbra Package

PANDAS

Tabulea

Data Analysis