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Description

Fast and adaptable, the concepts of vectorization, indexing, and broadcasting in NumPy have become the benchmark for array computation in the present day. This powerful library provides an extensive array of mathematical functions, random number generators, linear algebra capabilities, Fourier transforms, and beyond. NumPy is compatible with a diverse array of hardware and computing environments, seamlessly integrating with distributed systems, GPU libraries, and sparse array frameworks. At its core, NumPy is built upon highly optimized C code, which allows users to experience the speed associated with compiled languages while enjoying the flexibility inherent to Python. The high-level syntax of NumPy makes it user-friendly and efficient for programmers across various backgrounds and skill levels. By combining the computational efficiency of languages like C and Fortran with the accessibility of Python, NumPy simplifies complex tasks, resulting in clear and elegant solutions. Ultimately, this library empowers users to tackle a wide range of numerical problems with confidence and ease.

Description

All components of a URL, including scheme, user, password, host, port, path, query, and fragment, can be accessed through their respective properties. Every manipulation of a URL results in a newly generated URL object, and the strings provided to the constructor or modification functions are automatically encoded to yield a canonical format. While standard properties return percent-decoded values, the raw_ variants should be used to obtain encoded strings. A human-readable version of the URL can be accessed using the .human_repr() method. Binary wheels for yarl are available on PyPI for operating systems such as Linux, Windows, and MacOS. In cases where you wish to install yarl on different systems like Alpine Linux—which does not comply with manylinux standards due to the absence of glibc—you will need to compile the library from the source using the provided tarball. This process necessitates having a C compiler and the necessary Python headers installed on your machine. It is important to remember that the uncompiled, pure-Python version is significantly slower. Nevertheless, PyPy consistently employs a pure-Python implementation, thus remaining unaffected by performance variations. Additionally, this means that regardless of the environment, PyPy users can expect consistent behavior from the library.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

3LC Yes 
Avanzai Yes 
Codédex Yes 
Coiled Yes 
Cython Yes 
Dash Yes 
Flower Yes 
Gensim Yes 
JAX Yes 
PaizaCloud Yes 
PyCharm Yes 
Python No 
Spyder Yes 
Train in Data Yes 
Unify AI Yes 
Visual Studio Code Yes 
Yamak.ai Yes 
h5py Yes 
imageio Yes 
scikit-learn Yes 

Integrations

3LC No 
Avanzai No 
Codédex No 
Coiled No 
Cython No 
Dash No 
Flower No 
Gensim No 
JAX No 
PaizaCloud No 
PyCharm No 
Python Yes 
Spyder No 
Train in Data No 
Unify AI No 
Visual Studio Code No 
Yamak.ai No 
h5py No 
imageio No 
scikit-learn No 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Pricing Details

Free
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

NumPy

Website

numpy.org

Vendor Details

Company Name

Python Software Foundation

Country

United States

Website

pypi.org/project/yarl/

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