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Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
4,473
aziende
Abbiamo dati su 4,473 aziende che usano SciPy. La nostra lista di clienti SciPy è disponibile per il download ed è arricchita con specifiche vitali dell'azienda, incluse classificazione industriale, dimensioni organizzative, posizione geografica, round di finanziamenti e cifre di ricavi, tra gli altri.
Azienda | Paese | Settore | Dipendenti | Entrate |
---|---|---|---|---|
Amazon.com Services LLC | Stati Uniti | Retail | 10K | $50M |
Quiet Professionals LLC | Stati Uniti | Defense And Space Manufacturing | 120 | $7M |
Under Armour | Stati Uniti | Retail Apparel And Fashion | 12K | $5.8B |
AssemblyAI | Stati Uniti | Software Development | 113 | |
CEDARS-SINAI | Stati Uniti | Hospitals And Health Care | 14K | $58M |
PriceHubble | Svizzera | Real Estate | 200 | $11M |
Cedars-Sinai | Stati Uniti | Hospitals And Health Care | 15K | |
Fiori Technology Solutions Inc | Stati Uniti | It Services And It Consulting | 16 | |
Magic Leap | Stati Uniti | Computers And Electronics Manufacturing | 1.3K | $185M |
Vicarious Surgical | Stati Uniti | Medical Equipment Manufacturing | 220 | $3M |
SONEM Solutions GmbH | Germania | Appliances, Electrical, And Electronics Manufacturing | 4 | |
Sonalysts, Inc. | Stati Uniti | Defense And Space Manufacturing | 426 | $40M |
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SciPy è utilizzata in 68 paesi
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SciPy is a comprehensive open-source library for scientific computing in Python. It provides a wide range of functionalities for mathematical operations, numerical routines, optimization, linear algebra, integration, interpolation, and more. SciPy is built on top of NumPy, another popular Python library for numerical computing, and together they form a powerful ecosystem for scientific computing and data analysis.
Within the category of Data Science Tools, SciPy plays a crucial role in enabling data scientists, researchers, and engineers to perform complex scientific computations efficiently. Its extensive collection of modules and functions makes it a go-to choice for tasks ranging from simple computations to advanced scientific experiments and simulations. By leveraging SciPy, professionals can streamline their workflows, enhance productivity, and tackle intricate data analysis challenges with ease.
Initially founded in 2001 by Travis Oliphant, SciPy emerged as a response to the need for a robust scientific computing toolset in the Python programming language. Over the years, it has evolved into a cornerstone of the Python ecosystem, attracting a vast community of users and contributors dedicated to advancing scientific computing capabilities. With a strong emphasis on performance, usability, and extensibility, SciPy continues to be a pivotal tool for realizing innovative research projects and data-driven insights.
In terms of current market share within the Data Science Tools category, SciPy holds a significant position due to its widespread adoption and reputation among data science and research communities. As the demand for sophisticated data analysis tools continues to rise, SciPy is expected to maintain its growth trajectory and solidify its presence in the market. With ongoing developments, improvements, and expansions, SciPy is poised to further enhance its capabilities and appeal to a broader audience of data enthusiasts and professionals.
SciPy is a powerful library for scientific computing in Python, widely used by companies to leverage its versatile capabilities in data science projects. With a wide range of functions and tools, SciPy offers a comprehensive environment for data analysis, visualization, and manipulation.
1. Extensive Functionality: SciPy provides a vast array of functions for numerical optimization, linear algebra, integration, interpolation, and more. Unlike other similar technologies, SciPy offers a seamless integration with other popular Python libraries like NumPy and Pandas, making it a preferred choice for data scientists and engineers.
2. Open Source and Community Support: Being an open-source library, SciPy benefits from continuous development and contributions from a vibrant community of developers. This ensures regular updates, bug fixes, and new features, which are crucial for staying ahead in the rapidly evolving field of data science.
3. Performance and Scalability: SciPy is built on top of optimized libraries such as BLAS and LAPACK, enabling high-performance computing for large datasets. Its efficient algorithms and data structures make it a reliable choice for handling complex computational tasks with speed and scalability.
4. Visualization Capabilities: SciPy seamlessly integrates with popular data visualization libraries like Matplotlib and Seaborn, allowing users to create stunning visualizations of their analysis results. This visual appeal not only enhances data interpretation but also aids in presenting findings effectively to stakeholders.
In summary, SciPy stands out in the realm of Data Science Tools for its extensive functionality, community support, performance, scalability, and visualization capabilities, making it an indispensable asset for companies aiming to excel in data-driven decision-making.
SciPy is a popular open-source library in the Python ecosystem that provides efficient numerical routines for scientific computing. Many well-known companies across various industries leverage SciPy to enhance their data analysis and machine learning capabilities. Here are a few case studies highlighting how companies utilize SciPy in their workflow:
1. SpaceX: SpaceX, the aerospace manufacturer and space transportation company founded by Elon Musk, extensively uses SciPy for trajectory optimization and simulation in their rocket launches. By leveraging SciPy's mathematical optimization and integration features, SpaceX engineers can calculate optimal flight paths, minimize fuel consumption, and ensure precise landing of reusable rockets. SpaceX started incorporating SciPy into their computational tools since 2014, leading to improved efficiency and accuracy in their space missions.
2. Netflix: Netflix, the global streaming platform, utilizes SciPy for building recommendation algorithms and analyzing user data to personalize content recommendations. SciPy's capabilities in linear algebra, optimization, and statistics play a crucial role in enhancing Netflix's recommendation engine, ultimately improving user engagement and retention. Netflix integrated SciPy into their data science pipeline back in 2012, enabling them to deliver personalized viewing suggestions to millions of subscribers worldwide accurately.
3. Spotify: Spotify, the popular music streaming service, harnesses SciPy for music recommendation systems and audio signal processing tasks. By leveraging SciPy's signal processing and machine learning functionalities, Spotify can extract valuable insights from audio data, such as genre classification, mood detection, and song similarity analysis. Spotify adopted SciPy as a core component of their data analysis toolkit in 2015, empowering music recommendation algorithms that cater to individual user preferences effectively.
These case studies showcase how prominent companies like SpaceX, Netflix, and Spotify leverage SciPy's capabilities in numerical computing, optimization, and data analysis to drive innovation and enhance user experiences in their respective industries. By incorporating SciPy into their workflows, these companies demonstrate the versatile applications of this powerful data science tool in solving complex real-world challenges.
Puoi accedere a un elenco aggiornato di aziende che utilizzano SciPy visitando TheirStack.com. La nostra piattaforma fornisce un database completo di aziende che utilizzano varie tecnologie e strumenti interni.
Fino ad ora, abbiamo dati su 4,473 aziende che utilizzano SciPy.
SciPy è utilizzato da una vasta gamma di organizzazioni in vari settori, inclusi "Retail", "Defense And Space Manufacturing", "Retail Apparel And Fashion", "Software Development", "Hospitals And Health Care", "Real Estate", "Hospitals And Health Care", "It Services And It Consulting", "Computers And Electronics Manufacturing", "Medical Equipment Manufacturing". Per un elenco completo di tutti i settori che utilizzano SciPy, si prega di visitare TheirStack.com.
Alcune delle aziende che utilizzano SciPy includono Amazon.com Services LLC, Quiet Professionals LLC, Under Armour, AssemblyAI, CEDARS-SINAI, PriceHubble, Cedars-Sinai, Fiori Technology Solutions Inc, Magic Leap, Vicarious Surgical e molte altre. Puoi trovare un elenco completo di 4,473 aziende che utilizzano SciPy su TheirStack.com.
Secondo i nostri dati, SciPy è più popolare in Stati Uniti (1,731 companies), Regno Unito (369 companies), India (151 companies), Canada (142 companies), Germania (136 companies), Francia (124 companies), Spagna (83 companies), Paesi Bassi (52 companies), Svizzera (49 companies), Brasile (44 companies). Tuttavia, è utilizzato da aziende in tutto il mondo.
Puoi trovare aziende che utilizzano SciPy cercandolo su TheirStack.com. Tracciamo le offerte di lavoro di milioni di aziende e le utilizziamo per scoprire quali tecnologie e strumenti interni stanno utilizzando.