Scientific Data: A 50 Steps Guide using Python (De Gruyter Textbook)

Acknowledgements
Contents
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
List of Figures
Index