NumPy is a fundamental package for scientific computing with Python.
In this article, I compare the randomness of secret keys generated using Django’s built-in key generator and my custom JavaScript method. Using statistical tests like Chi-square, KS test, runs test, and entropy calculation, I analyze the randomness and security of the keys. Both methods show high levels of randomness, with Django’s keys slightly outperforming in some tests. ( read the article )
Categories - Development , Django , SciPy , NumPy
NumPy, short for Numerical Python, is a core library for scientific computing in Python. It provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is widely used in data analysis, machine learning, and scientific research due to its performance and ease of use. Its integration with other scientific libraries like SciPy and pandas makes it an indispensable tool in the Python ecosystem. Whether you're performing complex numerical computations or simple array manipulations, NumPy offers the versatility and power needed to handle large datasets and perform high-level mathematical operations.