APPLICATION OF PYTHON LANGUAGE IN SEARCH ENGINE OPTIMIZATION: EXPLORING ITS CONTRIBUTION TO DATA ANALYSIS

Felipe Ivo da Silva; Gustavo Camossi; Marilde Terezinha Prado Santos; Cecílio Merlotti Rodas

doi:10.1590/SciELOPreprints.14513

article.authors6a08bfa723385

Felipe Ivo da Silva Universidade Federal de São Carlos https://orcid.org/0009-0005-1379-4692
- Conceptualization
- Data Curation
- Formal Analysis
- Funding Acquisition
- Investigation
- Methodology
- Project Administration
- Resources
- Supervision
- Validation
- Visualization
- Writing – Original Draft Preparation
- Writing – Review & Editing
Gustavo Camossi Unesp de Marília https://orcid.org/0000-0002-1553-1053
- Conceptualization
- Data Curation
- Formal Analysis
- Funding Acquisition
- Investigation
- Methodology
- Project Administration
- Resources
- Supervision
- Validation
- Visualization
- Writing – Original Draft Preparation
- Writing – Review & Editing
Marilde Terezinha Prado Santos Universidade Federal de São Carlos https://orcid.org/0000-0001-5572-2009
- Conceptualization
- Data Curation
- Formal Analysis
- Funding Acquisition
- Investigation
- Methodology
- Project Administration
- Resources
- Supervision
- Validation
- Visualization
- Writing – Original Draft Preparation
- Writing – Review & Editing
Cecílio Merlotti Rodas Unesp de Marília https://orcid.org/0000-0002-4856-066X
- Conceptualization
- Data Curation
- Formal Analysis
- Funding Acquisition
- Investigation
- Methodology
- Project Administration
- Resources
- Supervision
- Validation
- Visualization
- Writing – Original Draft Preparation
- Writing – Review & Editing

DOI:

https://doi.org/10.1590/SciELOPreprints.14513

Palavras-chave:

Python, Search Engine Optimization, Data analysis, Automation

Resumo

This study investigates the contribution of the Python programming language to data analysis and optimization for Search Engine Optimization (SEO), with an emphasis on information organization and retrieval. A theoretical-exploratory approach was adopted, based on a bibliographic review in recognized databases such as Scopus and Web of Science, in addition to the analysis of specialized SEO tools. The study identified that Python, through libraries such as Pandas and NumPy, enables the automation of essential processes, including data extraction, keyword analysis, and predictive modeling of indexing patterns. The results indicate that applying these tools enhances the efficiency of SEO strategies, making them more precise and data-driven. Furthermore, the intersection between
Information Science and Computer Science is highlighted, demonstrating how programming can contribute to the semantic structuring and organization of digital content. It is concluded that Python provides effective solutions for automation and predictive analysis in SEO, increasing visibility and information retrieval in search engines. Future research should focus on experimental studies to validate the practical application of these techniques in real-world scenarios.