NumPy

Free🇺🇸🇺🇸 United States

The fundamental package for scientific computing with Python

Ai Frameworks Open Source Scientific Computing

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Pricing

No pricing plans published yet.

Technical Specs

Inputs

Array, Numerical Data, Matrix

Outputs

Computed Results, Transformations, Scientific Outputs

AI Type

Other

Model Architecture

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Daily Prompts

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Output Quality

Accuracy

80%

Content

80%

Reasoning

80%

Company Profile

Company

NumPy Project

Founded

2005

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Employees

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Total Raised / Total Funding

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Revenue

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ARR

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CEO

Travis Oliphant

Overview

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Total Earnings Till Date

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No monthly delta yet

Market Share

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Current share

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Per active user

Hallucination Rate

80%

Model quality signal

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Monthly paid user trend

No demo video available yet.

Platforms

Web App

Available

API

Available

Performance Metrics

Accuracy

80%

Context

80%

Reasoning

80%

Safety

80%

Benchmarks

No benchmark scores available.

NumPy Models

No model/version data available.

Funding Rounds & Investors

Total Funding

N/A

Rounds

No funding rounds available.

Founders/Team

Travis Oliphant

Founder

Direct competitors

No direct competitors available.

Change Log / Major Updates

2024 · May 20

Added new functions for linear algebra and expanded support for multidimensional arrays.

2024 · Nov 10

Fixed vulnerabilities in the core library and updated documentation.

2025 · Jan 15

Improved array operations and deprecated old modules for better efficiency.

Compliance, Integrations & Support

Industry: Not specified

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Integrations: Not specified

Support:Email

Target audience: Data Scientists, Researchers, Engineers, Analysts, Students, Machine Learning Engineers, Academics

Supported languages: Python

NumPy Acquisitions

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More About NumPy

NumPy, created by Travis Oliphant, is a cornerstone of the Python ecosystem, providing efficient tools for numerical operations and array handling. It serves as a fundamental building block for data science and machine learning applications.

Key capabilities include comprehensive mathematical functions, random number generation, and support for linear algebra routines, Fourier transforms, and more. Its performance optimizations through contiguous memory and vectorized operations make it indispensable for handling large-scale data.

Originating as a community-driven project, NumPy has become a standard in scientific computing, enabling researchers and developers to perform complex calculations with ease. While not focused on generative AI, it underpins many AI frameworks by providing essential numerical capabilities.

"NumPy's design prioritizes speed and usability, making it a go-to library for anyone working with numerical data."

Despite its non-AI focus, NumPy's role in data preparation and analysis highlights its importance in the broader AI landscape.

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