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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

The h5py library serves as a user-friendly interface for the HDF5 binary data format in Python. It allows users to handle vast quantities of numerical data and efficiently work with it alongside NumPy. For instance, you can access and manipulate multi-terabyte datasets stored on your disk as if they were standard NumPy arrays. You can organize thousands of datasets within a single file, applying your own categorization and tagging methods. H5py embraces familiar NumPy and Python concepts, such as dictionary and array syntax. For example, it enables you to loop through datasets in a file or examine the .shape and .dtype properties of those datasets. Getting started with h5py requires no prior knowledge of HDF5, making it accessible for newcomers. Besides its intuitive high-level interface, h5py is built on an object-oriented Cython wrapper for the HDF5 C API, ensuring that nearly any operation possible in C with HDF5 can also be performed using h5py. This combination of simplicity and power makes it a popular choice for data handling in the scientific community.

API Access

Has API

API Access

Has API

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Screenshots View All

Integrations

3LC
Avanzai
Coiled
Dash
Flower
JAX
MPI for Python (mpi4py)
NVIDIA FLARE
NumPy
PaizaCloud
PyCharm
Python
Spyder
Unify AI
Visual Studio
Visual Studio Code
Yamak.ai
Yandex Data Proc
h5py
scikit-learn

Integrations

3LC
Avanzai
Coiled
Dash
Flower
JAX
MPI for Python (mpi4py)
NVIDIA FLARE
NumPy
PaizaCloud
PyCharm
Python
Spyder
Unify AI
Visual Studio
Visual Studio Code
Yamak.ai
Yandex Data Proc
h5py
scikit-learn

Pricing Details

Free
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

NumPy

Website

numpy.org

Vendor Details

Company Name

HDF5

Website

www.h5py.org

Product Features

Product Features

Alternatives

h5py Reviews

h5py

HDF5

Alternatives