“Python” papers · AI for Research

In this appendix, we provide a whirlwind tour of the Python programming language.It will in no way be exhaustive and is not intended to negate the need for learning the language more comprehensively using the many excellent resources available teaching Python. 1 Nonetheless, it should be enough to enable a newcomer to the language to get started. A.1 PYTHON INTERPRETER MODESThe Python interpreter can be executed in one of two modes: 'interactive' or 'batch'.When run interactively in a shell or on Windows, at a command prompt, the interpreter will wait for input from the user, and when sufficient input has been made for a statement to be executed will execute that statement immediately and go back to waiting for the next.In batch mode, a complete Python script stored in a file is given to the interpreter and executed, all at the same time. A.1.1 Interactive ModeTo launch an interactive session with the interpreter, from your shell or command prompt, issue the command python -i.All being well, the interpreter should then indicate its willingness to begin processing statements by displaying its primary prompt, which looks like a sideways chevron '>>>'.On Windows, starting a session might look something like this: c: Documents and Settings PythonUser>python -i python -i ActivePython 2.5.1.1 (ActiveState Software Inc.) based on Python 2.5.1 (r251:54863, May 1 2007, 17:47:05) [MSC v.1310 32 bit (Intel)] on win32 Type "help", "copyright", "credits" or "license" for more information.

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Python

Simon Yuill, Harry Halpin · 2006

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PYTHON

Jia, Kan · Figshare · 2025

This study innovatively employs large language model (LLM) technology to construct a public digital procurement (PDP) index and, by utilizing microlevel data from Chinese A-share listed companies (2015-2023), systematically examines the impact of PDP on corporate total factor productivity (TFP) and its underlying mechanisms. The results demonstrate that PDP has a significantly positive effect on corporate TFP, and this conclusion remains robust after endogeneity concerns are addressed and multiple robustness tests are conducted. Mechanism analysis reveals that PDP significantly increases corporate TFP primarily through three pathways: enhancing digital technology innovation, alleviating financial constraints, and improving corporate information disclosure. Furthermore, in the context of industry–university–research (IUR) collaboration, PDP has a more pronounced positive effect on corporate TFP. Additional analysis indicates a synergistic effect between PDP and corporate digital transformation, meaning that firms with a higher level of digital transformation can more effectively leverage PDP to achieve TFP growth. This study provides critical theoretical and empirical evidence for leveraging PDP to improve corporate TFP and offers important references for government departments in optimizing the design of PDP policies and enhancing their implementation effectiveness.

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Python

Yohann Schatz · Open MIND · 2024

Codes développés en utilisant le langage de programmation Python.

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Python

Long, Knox, Knigge, Christian, Sim, Stuart et al. · Zenodo (CERN European Organization for Nuclear Research) · 2024

This is the repository for Python, the radiative transfer code used to model AGN winds.

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Python

F. Miranda, Eduardo, Bortolotto, Marcelo, Cesar, Roney · Zenodo (CERN European Organization for Nuclear Research) · 2024

Sou Roney Cesar, estou cursando Análise e Desenvolvimento em Sistemas pela Faculdade Anhanguera São Paulo Capital. Sou um profissional em transição de carreira da área comercial para tecnologia. Além dos estudos acadêmicos realizo cursos e certificações para aprimorar meus conhecimentos em linguagens como Python, C, Javascript além de banco de dados como MySQL e Oracle. Em paralelo dei início a estudos sobre Salesforce e conhecimento sobre IA no qual vou realizar uma Pós-graduação. Python A pesquisa destaca as vantagens e desvantagens da linguagem Python, apontando sua simplicidade, flexibilidade e escalabilidade, o quanto a comunidade e ativa. è mencionado as desvantagens como por exemplo o desempenho em comparação com linguagens compiladas

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PYTHON

Jia, Kan · Figshare · 2025

This study innovatively employs large language model (LLM) technology to construct a public digital procurement (PDP) index and, by utilizing microlevel data from Chinese A-share listed companies (2015-2023), systematically examines the impact of PDP on corporate total factor productivity (TFP) and its underlying mechanisms. The results demonstrate that PDP has a significantly positive effect on corporate TFP, and this conclusion remains robust after endogeneity concerns are addressed and multiple robustness tests are conducted. Mechanism analysis reveals that PDP significantly increases corporate TFP primarily through three pathways: enhancing digital technology innovation, alleviating financial constraints, and improving corporate information disclosure. Furthermore, in the context of industry–university–research (IUR) collaboration, PDP has a more pronounced positive effect on corporate TFP. Additional analysis indicates a synergistic effect between PDP and corporate digital transformation, meaning that firms with a higher level of digital transformation can more effectively leverage PDP to achieve TFP growth. This study provides critical theoretical and empirical evidence for leveraging PDP to improve corporate TFP and offers important references for government departments in optimizing the design of PDP policies and enhancing their implementation effectiveness.

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Python

Riccardo Rigon · OSF Preprints (OSF Preprints) · 2023

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