NumPy Basics
Explore how to efficiently store and manipulate numerical data using NumPy arrays in Python. Understand key attributes, operations, and differences from standard lists to optimize your data science workflows. This lesson provides hands-on practice with industry-standard techniques for array manipulation and linear algebra.
NumPy
Data comes in various forms and needs to be stored and processed in Python programs. NumPy is meant for such purposes and enables a person to operate on data in an optimized and fast way. It handles matrices and multi-dimensional arrays very efficiently. It is used extensively for linear algebra tasks. It comes under the umbrella of modules that are meant for scientific purposes in Python. In this lesson, we’ll understand this using various hands-on exercises and learn how it works.
ndarray data structure
ndarray is short for N-dimensional array. It has a fixed size in memory and contains elements of the same type. We will understand the operations that are extensively used in the industry on NumPy arrays.