Scientific Data: A 50 Steps Guide using Python

Matthias Hofmann, Matthias Josef Hofmann

• Publisher: De Gruyter
• Year: 2024
• eBook ISBN: 9783111334707
• Edition: 0
• Topic: Physical Sciences
• Subtopic: Physics
• Index: Physics

This guide offers a comprehensive understanding of experimental data analysis in the natural sciences while ensuring sustainable processing routines from a programmer's perspective. It applies a concise problem-solution-discussion format, supported by Python code snippets, catering to practitioners.

Table of contents

• Title Page
• Copyright
• Contents
• Acknowledgements
• Introduction and challenge
• Basics
• 1 Getting hands on Python
• 2 Using virtual environments
• 3 Configuring your integrated development environment
• 4 Having a GitHub account
• 5 Creating repositories for dedicated projects
• 6 Synchronizing GitHub desktop
• 7 Knowing basic markdown
• Organization
• 8 Having the overall concept sketch in mind
• 9 Initializing a project with poetry
• 10 Tracking the environment
• 11 Getting your paths right
• 12 Preparing to share
• 13 Writing convenience functions
• 14 Using TOML files for configuration
• 15 Getting used to testing
• Interfacing with common data formats
• 16 Reading Excel files
• 17 Reading text files
• 18 Reading text from Word files
• 19 Reading tables from Word files
• 20 Reading PDF files
• 21 Parsing website contents
• 22 Leveraging regular expressions
• 23 Writing to a database
• 24 Reading from a database
• Planning experiments and/or building on legacy data/information
• 25 Leveraging existing experiments
• 26 Planning experiments
• 27 Using legacy and planned experiments hand in hand
• Collecting experimental data / lab work phase
• 28 Using dedicated modules – use what’s available
• 29 Using dedicated modules – build what’s missing
• Visualization of experimental results
• 30 Simplicity of matplotlib
• 31 Creating a custom matplotlib style
• 32 Convenience of seaborn
• 33 Interactivity of plotly
• 34 Representing multidimensional data
• 35 Representing multidimensional data in a funny way
• Approaching the scientific questions (modeling and recommendation)
• 36 Picking relevant data and information
• 37 Building a model with gplearn
• 38 Playing with the model or “what if”
• 39 Playing with the model or – jupyter notebook
• 40 Playing with the model or – voila
• 41 Playing with the model or – streamlit
• 42 Dealing with too few experiments
• 43 Solving the reverse problem applying multiobjective optimization
• 44 Ensuring the envisioned causality
• Sharing the project
• 45 Building files for distribution
• 46 Pushing to package indices
• 47 Sharing streamlit applications
• Further reading
• 48 Ensuring code styling via black
• 49 Configuring pre-commit
• 50 Building standalone solutions via PyQt
• Concluding remarks
• Subject Index