Scientific Data : a 50 Steps Guide Using Python / Matthias Josef Hofmann.

Format:

Book

Author/Creator:

Hofmann, Matthias Josef, author.

Series:

De Gruyter textbook.

De Gruyter Textbook Series

Language:

English

Subjects (All):

Python (Computer program language).

Physical Description:

1 online resource (234 pages)

Edition:

First edition.

Place of Publication:

Berlin, Germany : Walter de Gruyter GmbH, [2024]

Summary:

"Scientific Data: A 50 Steps Guide using Python" is your guide towards experimental scientific data.It aims to bridge the gap between classical natural sciences as taught in universities and the ever-growing need for technological/digital capabilities, particularly in industrial research.

Contents:

Intro

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.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

3-11-133460-0

OCLC:

1456761992