Basics of Python Programming for Pharmaceutical Sciences | As per PCI BP101T under NEP 2020. Questo articolo non è disponibile.
Lingua: inglese
Editore: Notion Press, 2026
- Rilegato
- Nuovo

Condizione: Nuovo
EUR 89,60
Descrizione dell’articolo da parte del venditore
Basics of Python Programming for Pharmaceutical Sciences | As per PCI BP101T under NEP 2020 | B. Tech Gade Pradeep Kumar Reddy | Buch | Englisch | 2026 | Notion Press | EAN 9798907630499 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Codice articolo 138067765
- Titolo
- Basics of Python Programming for Pharmaceutical Sciences | As per PCI BP101T under NEP 2020
- Autore
- B. Tech Gade Pradeep Kumar Reddy
- Editore
- Notion Press
- Anno di pubblicazione
- 2026
- Condizione
- Neu
- Rilegatura
- Buch
- Lingua
- inglese
- ISBN 13
- 9798907630499
- Peso dell'articolo
- 1040 grammi
- Dimensioni
- 235 x 157 x 31 mm
- Cataloghi dei venditori
- Bücher
Written by Gade Pradeep Kumar Reddy, who holds a B.Tech. in Information Technology from the National Institute of Technology (NIT), Raipur, Chhattisgarh, the book establishes a foundation for further pharmaceutical study and the informed use of computational methods in laboratory data handling and research.
Reliable pharmaceutical data analysis begins with clear computational thinking. Basics of Python Programming for Pharmaceutical Sciences develops these foundations for first-year B.Pharm students, with coverage aligned to PCI BP101T under NEP 2020.
The text progresses from variables, decision-making, loops and functions to data structures and file handling. NumPy, pandas and Matplotlib extend these skills to numerical operations, data cleaning, analysis and scientific visualisation. Pharmaceutical examples connect code with calculations, assay results, dissolution profiles and pharmacokinetic observations.
Worked programs link problems to code, explanations, output and pharmaceutical interpretation, making the reasoning behind each solution explicit. Common-error explanations, practice exercises and revision questions support independent learning. Attention to units, data quality, privacy and responsible interpretation places computational results within their scientific context.
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