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NumPy (pronounced / ˈ n ʌ m p aɪ / NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. [3]
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3] CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU.
Both libraries were merged into a new array package by Travis Oliphant in 2005–2006, creating NumPy, now the de facto standard for numerical data handling in Python. [10] In the following years the existing software packages maintained by STScI as part of their stsci_python suite were ported to NumPy as well.
# imports from jax import grad import jax.numpy as jnp # define the logistic function def logistic (x): return jnp. exp (x) / (jnp. exp (x) + 1) # obtain the gradient function of the logistic function grad_logistic = grad (logistic) # evaluate the gradient of the logistic function at x = 1 grad_log_out = grad_logistic (1.0) print (grad_log_out)
By default, a Pandas index is a series of integers ascending from 0, similar to the indices of Python arrays. However, indices can use any NumPy data type, including floating point, timestamps, or strings. [4]: 112 Pandas' syntax for mapping index values to relevant data is the same syntax Python uses to map dictionary keys to values.
Python: 0 no no checked array of array [23] yes no [46 ... is how one would use Fortran to create arrays from the even and odd entries of an array. Another common use ...
Due to Python’s Global Interpreter Lock, local threads provide parallelism only when the computation is primarily non-Python code, which is the case for Pandas DataFrame, Numpy arrays or other Python/C/C++ based projects. Local process A multiprocessing scheduler leverages Python’s concurrent.futures.ProcessPoolExecutor to execute computations.
In Python, the ellipsis is a nullary expression that represents the Ellipsis singleton. It's used particularly in NumPy, where an ellipsis is used for slicing an arbitrary number of dimensions for a high-dimensional array: [10]