Programming for Data Science, Lec 3: NumPy ndarray, np.arange, np.random, np.linalg, np.concatenate
#python #datascience #machinelearning
Jupyter Notebook: https://github.com/farhad-pourkamali/...
NumPy (https://numpy.org/) is the fundamental library for scientific computing and data science with Python.
It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions, random number generators, linear algebra routines, and more.
The fundamental data structure in NumPy is the `ndarray`, which stands for N-dimensional array.
`ndarrays` can have multiple dimensions, allowing for the representation of matrices, tensors, and other multi-dimensional data structures.
`ndarrays` are more memory-efficient and faster than built-in Python lists, especially for large data sets. This efficiency is crucial for numerical and scientific computing.
Many other libraries in the Python ecosystem, such as pandas, scikit-learn, and TensorFlow, build upon NumPy.