使用IPython的公司
It provides a rich architecture for interactive computing with a powerful interactive shell, a kernel for Jupyter. It has a support for interactive data visualization and use of GUI toolkits. Flexible, embeddable interpreters to load into your own projects. Easy to use, high performance tools for parallel computing.
使用IPython的公司列表
curl --request POST \
--url "https://api.theirstack.com/v1/companies/search" \
--header "Accept: application/json" \
--header "Content-Type: application/json" \
--header "Authorization: Bearer <api_key>" \
-d "{
\"company_technology_slug_or\": [
\"ipython\"
]
}"Technology
is any of
IPython
员工数16k收入$422MConsumer ServicesIPython高置信度
员工数7.9k收入$1.1BSoftware DevelopmentIPython高置信度
员工数12k收入$1BHospitals and Health CareIPython高置信度
员工数515收入$80MTravel ArrangementsIPython高置信度
员工数3k收入$100MProfessional Training and CoachingIPython高置信度
员工数1.3kSoftware DevelopmentIPython高置信度
员工数530收入$225MAdvertising ServicesIPython高置信度
员工数42ConstructionIPython高置信度
员工数41收入$8.9MSoftware DevelopmentIPython高置信度
员工数750收入$800MEntertainment ProvidersIPython高置信度
我们拥有关于212家公司和用户使用IPython的数据。我们的IPython用户和客户列表可供下载，并附有重要的公司信息，包括行业分类、组织规模、地理位置、融资轮次和收入数据等。
技术使用统计和市场份额
如何定位 IPython 用户
- 如何自定义此列表？
您可以根据地理位置、行业、公司规模、收入、技术使用、职位发布等需求自定义此数据。您可以将数据下载为Excel或CSV格式。
- 如何在公司采用此技术时收到通知？
您可以为此数据设置提醒。您可以通过选择您感兴趣的技术开始，当有新公司使用该技术时，您将收到收件箱中的提醒。
- 如何将此数据导入我的CRM？
您可以将此数据导出为Excel文件，然后导入到您的CRM中。您也可以将数据导出到API。
常见问题
我们的数据来源于从数百万家公司收集的职位发布。我们监控公司网站、招聘网站和其他招聘平台上的这些发布。分析职位发布提供了一种可靠的方法来了解公司正在使用的技术，包括他们使用的内部工具。
我们每天刷新数据，以确保您访问的是最新的信息。这一频繁的更新过程保证了我们的洞察和情报反映行业的最新发展和趋势。
IPython, a powerful interactive computing tool, provides an enhanced environment for data science and scientific computing. Offering a robust interactive shell, a Jupyter notebook interface, and support for multiple programming languages, IPython facilitates seamless integration, data visualization, and parallel computing. Its user-friendly features and extensive capabilities make it a preferred choice among data scientists, researchers, and developers for analyzing, exploring, and sharing computational workflows.
IPython falls under the category of Shells, which are software interfaces that allow users to interact with underlying operating systems. Specifically tailored for Python programming, IPython serves as an advanced Python shell with added functionalities such as code autocompletion, inline plotting, and rich media display. By combining the features of a traditional shell with interactive computing capabilities, IPython elevates the Python coding experience, enabling users to execute code snippets, visualize data, and iterate on algorithms efficiently.
Founded in 2001 by Fernando Perez, IPython originated from Perez's desire to create a more interactive and user-friendly computing environment for scientific research and data analysis. Over the years, the project has evolved into a comprehensive tool for interactive computing, garnering a dedicated user base and community support. With regular updates, enhancements, and contributions from a vibrant developer community, IPython continues to be a prominent player in the data science ecosystem.
In terms of current market share, IPython maintains a significant presence in the data science and scientific computing domains, with a growing user base across academia, industry, and research institutions. As the demand for interactive computing tools and Jupyter-based workflows increases, IPython is poised to expand its market share further. With continuous improvements, compatibility with emerging technologies, and integration with popular data science libraries, IPython is expected to witness a steady growth trajectory in the foreseeable future.