Scientific Data: A 50 Steps Guide using Python
eBook - ePub

Scientific Data: A 50 Steps Guide using Python

  1. 234 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Scientific Data: A 50 Steps Guide using Python

About this book

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.

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Yes, you can access Scientific Data: A 50 Steps Guide using Python by Matthias Hofmann,Matthias Josef Hofmann in PDF and/or ePUB format, as well as other popular books in Physical Sciences & Physics. We have over one million books available in our catalogue for you to explore.

Information

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

Table of contents

  1. Title Page
  2. Copyright
  3. Contents
  4. Acknowledgements
  5. Introduction and challenge
  6. Basics
  7. 1 Getting hands on Python
  8. 2 Using virtual environments
  9. 3 Configuring your integrated development environment
  10. 4 Having a GitHub account
  11. 5 Creating repositories for dedicated projects
  12. 6 Synchronizing GitHub desktop
  13. 7 Knowing basic markdown
  14. Organization
  15. 8 Having the overall concept sketch in mind
  16. 9 Initializing a project with poetry
  17. 10 Tracking the environment
  18. 11 Getting your paths right
  19. 12 Preparing to share
  20. 13 Writing convenience functions
  21. 14 Using TOML files for configuration
  22. 15 Getting used to testing
  23. Interfacing with common data formats
  24. 16 Reading Excel files
  25. 17 Reading text files
  26. 18 Reading text from Word files
  27. 19 Reading tables from Word files
  28. 20 Reading PDF files
  29. 21 Parsing website contents
  30. 22 Leveraging regular expressions
  31. 23 Writing to a database
  32. 24 Reading from a database
  33. Planning experiments and/or building on legacy data/information
  34. 25 Leveraging existing experiments
  35. 26 Planning experiments
  36. 27 Using legacy and planned experiments hand in hand
  37. Collecting experimental data / lab work phase
  38. 28 Using dedicated modules – use what’s available
  39. 29 Using dedicated modules – build what’s missing
  40. Visualization of experimental results
  41. 30 Simplicity of matplotlib
  42. 31 Creating a custom matplotlib style
  43. 32 Convenience of seaborn
  44. 33 Interactivity of plotly
  45. 34 Representing multidimensional data
  46. 35 Representing multidimensional data in a funny way
  47. Approaching the scientific questions (modeling and recommendation)
  48. 36 Picking relevant data and information
  49. 37 Building a model with gplearn
  50. 38 Playing with the model or “what if”
  51. 39 Playing with the model or – jupyter notebook
  52. 40 Playing with the model or – voila
  53. 41 Playing with the model or – streamlit
  54. 42 Dealing with too few experiments
  55. 43 Solving the reverse problem applying multiobjective optimization
  56. 44 Ensuring the envisioned causality
  57. Sharing the project
  58. 45 Building files for distribution
  59. 46 Pushing to package indices
  60. 47 Sharing streamlit applications
  61. Further reading
  62. 48 Ensuring code styling via black
  63. 49 Configuring pre-commit
  64. 50 Building standalone solutions via PyQt
  65. Concluding remarks
  66. Subject Index