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An output of pip install virtualenv. Pip's command-line interface allows the install of Python software packages by issuing a command: pip install some-package-name. Users can also remove the package by issuing a command: pip uninstall some-package-name. pip has a feature to manage full lists of packages and corresponding version numbers ...
To avoid installing the large SciPy package just to get an array object, this new package was separated and called NumPy. Support for Python 3 was added in 2011 with NumPy version 1.5.0. [15] In 2011, PyPy started development on an implementation of the NumPy API for PyPy. [16] As of 2023, it is not yet fully compatible with NumPy. [17]
Some package managers, including pip, use PyPI as the default source for packages and their dependencies. [6] [7] As of 6 May 2024, more than 530,000 Python packages are available. PyPI primarily hosts Python packages in the form of source archives, called "sdists", or of "wheels" [8] that may contain binary modules from a compiled language.
It is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language.Spyder integrates with a number of prominent packages in the scientific Python stack, including NumPy, SciPy, Matplotlib, pandas, IPython, SymPy and Cython, as well as other open-source software.
NumPy, a BSD-licensed library that adds support for the manipulation of large, multi-dimensional arrays and matrices; it also includes a large collection of high-level mathematical functions. NumPy serves as the backbone for a number of other numerical libraries, notably SciPy. De facto standard for matrix/tensor operations in Python.
TensorFlow is Google Brain's second-generation system. Version 1.0.0 was released on February 11, 2017. [17] While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA and SYCL extensions for general-purpose computing on graphics processing units). [18]
CuPy is a part of the NumPy ecosystem array libraries [7] and is widely adopted to utilize GPU with Python, [8] especially in high-performance computing environments such as Summit, [9] Perlmutter, [10] EULER, [11] and ABCI.
Conda was developed to address dependency conflicts native to the pip package manager, which would automatically install any dependent Python packages without checking for conflicts with previously installed packages [39] (until its version 20.3, which later implemented consistent dependency resolution). [40]