NumPy 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.

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broot Reviews
broot
The ROOT data analysis framework is widely utilized in High Energy Physics (HEP) and features its own file output format (.root). It seamlessly integrates with software developed in C++, while for Python users, there is an interface called pyROOT. However, pyROOT has compatibility issues with python3.4. To address this, broot is a compact library designed to transform data stored in Python's numpy ndarrays into ROOT files, structuring them with a branch for each array. This library aims to offer a standardized approach for exporting Python numpy data structures into ROOT files. Furthermore, it is designed to be portable and compatible with both Python2 and Python3, as well as ROOT versions 5 and 6, without necessitating changes to the ROOT components themselves—only a standard installation is needed. Users should find that installing the library requires minimal effort, as they only need to compile the library once or choose to install it as a Python package, making it a convenient tool for data analysis. Additionally, this ease of use encourages more researchers to adopt ROOT in their workflows.
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h5py Reviews
h5py
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.
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Pricing

Pricing Starts At:
Free
Free Version:
Yes

Integrations

API:
Yes, NumPy has an API
View Integrations

Reviews - 1 Verified Review

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Company Details

Company:
NumPy
Website:
numpy.org

Media

NumPy Screenshot 1
NumPy Screenshot 1
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Product Details

Platforms
Windows
Mac
Linux
Types of Training
Training Docs
Training Videos
Customer Support
Online Support

NumPy Features and Options

NumPy User Reviews

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  • Name: Anonymous (Verified)
    Job Title: Principal Software Engineer
    Length of product use: 6-12 Months
    Used How Often?: Daily
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
    Likelihood to Recommend to Others
    1 2 3 4 5 6 7 8 9 10

    Excellent Python math library

    Date: Aug 03 2022

    Summary: NumPy is an essential part of the Python ecosystem. It provides a huge variety of mathematical functions in a very performant library for free.

    Positive: - used for scientific computing
    - huge variety of mathematical functions, random number generators, etc.
    - supports a wide variety of hardware and GPU acceleration
    - very fast code that runs in C, despite working with Python
    - simple syntax makes it easy to learn
    - good documentation
    - free and open source

    Negative: - there aren't many cons to using NumPy; it's a mainstay of the Python computing community for a reason

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