WHY PYTHON PROGRAMMING IN PHARMACEUTICAL SCIENCE?

The rapid digital transformation of the pharmaceutical sector has created a growing demand for professionals who can combine pharmaceutical knowledge with computational and data-analytic skills. Python programming has emerged as a valuable educational and professional tool because of its simple syntax, open-source nature, extensive scientific libraries, and applicability in data analysis, visualization, automation, pharmacokinetics, pharmacovigilance, formulation research, clinical studies, bioinformatics, quality control, and artificial intelligence-assisted drug development. The revised B.Pharm curriculum introduced by the Pharmacy Council of India under the NEP 2020 framework recognizes this changing professional environment by including Basics of Python Programming for Pharmaceutical Sciences at the undergraduate level. This curricular development represents an important shift from conventional computer literacy toward structured programming, pharmaceutical data handling, scientific visualization, and computational problem-solving. The present review examines the rationale for incorporating Python programming into pharmaceutical education and correlates the revised curriculum with emerging requirements of the pharmaceutical industry. Particular emphasis is placed on the relevance of NumPy, Pandas, Matplotlib, data cleaning, CSV/Excel processing, pharmacokinetic datasets, adverse drug reaction analysis, dissolution-profile visualization, research data management, and introductory pathways toward machine learning and artificial intelligence. The review further discusses the potential contribution of Python competency to pharmaceutical research and development, quality assurance, manufacturing, regulatory science, clinical research, pharmacovigilance, and Pharma 4.0. Challenges related to faculty training, infrastructure, limited instructional hours, data integrity, validation, and responsible use of artificial intelligence are also considered. It is concluded that Python should not be regarded merely as an additional programming subject in the pharmacy curriculum, but as a foundational digital competency capable of improving data literacy, research capability, interdisciplinary collaboration, and industrial readiness among future pharmacy graduates. This article cover subject wisr role of python programming in pharmaceutical industries.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-01
DOI
https://doi.org/10.5281/zenodo.23037327
Primary Topic
Genetics, Bioinformatics, and Biomedical Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

WHY PYTHON PROGRAMMING IN PHARMACEUTICAL SCIENCE?

Dr. Surendra Pardhi1*, Mr. Gajanand Dashahare2, Mr. Govind Kirar3, Dr. Vaibhav Solanki4
Zenodo (CERN European Organization for Nuclear Research)
Genetics, Bioinformatics, and Biomedical Research
article

WHY PYTHON PROGRAMMING IN PHARMACEUTICAL SCIENCE?

Dr. Surendra Pardhi1*, Mr. Gajanand Dashahare2, Mr. Govind Kirar3, Dr. Vaibhav Solanki4
article en

Abstract

The rapid digital transformation of the pharmaceutical sector has created a growing demand for professionals who can combine pharmaceutical knowledge with computational and data-analytic skills. Python programming has emerged as a valuable educational and professional tool because of its simple syntax, open-source nature, extensive scientific libraries, and applicability in data analysis, visualization, automation, pharmacokinetics, pharmacovigilance, formulation research, clinical studies, bioinformatics, quality control, and artificial intelligence-assisted drug development. The revised B.Pharm curriculum introduced by the Pharmacy Council of India under the NEP 2020 framework recognizes this changing professional environment by including Basics of Python Programming for Pharmaceutical Sciences at the undergraduate level. This curricular development represents an important shift from conventional computer literacy toward structured programming, pharmaceutical data handling, scientific visualization, and computational problem-solving. The present review examines the rationale for incorporating Python programming into pharmaceutical education and correlates the revised curriculum with emerging requirements of the pharmaceutical industry. Particular emphasis is placed on the relevance of NumPy, Pandas, Matplotlib, data cleaning, CSV/Excel processing, pharmacokinetic datasets, adverse drug reaction analysis, dissolution-profile visualization, research data management, and introductory pathways toward machine learning and artificial intelligence. The review further discusses the potential contribution of Python competency to pharmaceutical research and development, quality assurance, manufacturing, regulatory science, clinical research, pharmacovigilance, and Pharma 4.0. Challenges related to faculty training, infrastructure, limited instructional hours, data integrity, validation, and responsible use of artificial intelligence are also considered. It is concluded that Python should not be regarded merely as an additional programming subject in the pharmacy curriculum, but as a foundational digital competency capable of improving data literacy, research capability, interdisciplinary collaboration, and industrial readiness among future pharmacy graduates. This article cover subject wisr role of python programming in pharmaceutical industries.

Zenodo (CERN European Organization for Nuclear Research)
Quality Education
Openalex Percentile: Top 20%
Genetics, Bioinformatics, and Biomedical Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.