Scientific Data: A 50 Steps Guide using Python

Matthias Josef Hofmann

Book

Scientific Data: A 50 Steps Guide using Python

Matthias Josef Hofmann

Language: English

Published/Copyright: 2024

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About this book

"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. Topics covered include instructions for setting up a workspace, guidelines for structuring data, examples for interfacing with results files and suggestions for drawing scientific conclusions therefrom. Additionally, concepts for designing experiments and visualizing the corresponding results are highlighted next to ways of extracting meaningful characteristics and leveraging those in terms of multi-objective optimizations.

The concise problem-solution-discussion structure used throughout supported by Python code snippets emphasizes the work’s focus on practitioners. This guide will provide you with a solid understanding of how to process and understand experimental data within a natural scientific context while ensuring sustainable use of your findings and processing as seen through a programmer’s eyes.

Guide for

designing experiments.
interfacing with results files.
drawing scientific conclusions.

Author / Editor information

Matthias Hofmann holds a Ph.D. in Physical Chemistry from the University of Regensburg. At Albert Invent, Matthias continues to contribute to innovative methods in natural science research and accelerating R&D through a data-driven approach.

He is the author of "Data Management for Natural Scientists - A Practical Guide to Data Extraction and Storage Using Python".

Topics

Frontmatter Publicly Available Download PDF	I
Acknowledgements Publicly Available Download PDF	VII
Contents Publicly Available Download PDF	XIII
Introduction and challenge Requires Authentication Unlicensed Licensed	1
Basics Requires Authentication Unlicensed Licensed	3
1 Getting hands on Python Requires Authentication Unlicensed Licensed	4
2 Using virtual environments Requires Authentication Unlicensed Licensed	6
3 Configuring your integrated development environment Requires Authentication Unlicensed Licensed	9
4 Having a GitHub account Requires Authentication Unlicensed Licensed	12
5 Creating repositories for dedicated projects Requires Authentication Unlicensed Licensed	14
6 Synchronizing GitHub desktop Requires Authentication Unlicensed Licensed	16
7 Knowing basic markdown Requires Authentication Unlicensed Licensed	19
Organization Requires Authentication Unlicensed Licensed	21
8 Having the overall concept sketch in mind Requires Authentication Unlicensed Licensed	25
9 Initializing a project with poetry Requires Authentication Unlicensed Licensed	27
10 Tracking the environment Requires Authentication Unlicensed Licensed	30
11 Getting your paths right Requires Authentication Unlicensed Licensed	32
12 Preparing to share Requires Authentication Unlicensed Licensed	35
13 Writing convenience functions Requires Authentication Unlicensed Licensed	38
14 Using TOML files for configuration Requires Authentication Unlicensed Licensed	41
15 Getting used to testing Requires Authentication Unlicensed Licensed	43
Interfacing with common data formats Requires Authentication Unlicensed Licensed	47
16 Reading Excel files Requires Authentication Unlicensed Licensed	48
17 Reading text files Requires Authentication Unlicensed Licensed	51
18 Reading text from Word files Requires Authentication Unlicensed Licensed	54
19 Reading tables from Word files Requires Authentication Unlicensed Licensed	57
20 Reading PDF files Requires Authentication Unlicensed Licensed	59
21 Parsing website contents Requires Authentication Unlicensed Licensed	61
22 Leveraging regular expressions Requires Authentication Unlicensed Licensed	64
23 Writing to a database Requires Authentication Unlicensed Licensed	67
24 Reading from a database Requires Authentication Unlicensed Licensed	71
Planning experiments and/or building on legacy data/information Requires Authentication Unlicensed Licensed	77
25 Leveraging existing experiments Requires Authentication Unlicensed Licensed	78
26 Planning experiments Requires Authentication Unlicensed Licensed	81
27 Using legacy and planned experiments hand in hand Requires Authentication Unlicensed Licensed	87
Collecting experimental data / lab work phase Requires Authentication Unlicensed Licensed	93
28 Using dedicated modules – use what’s available Requires Authentication Unlicensed Licensed	94
29 Using dedicated modules – build what’s missing Requires Authentication Unlicensed Licensed	99
Visualization of experimental results Requires Authentication Unlicensed Licensed	103
30 Simplicity of matplotlib Requires Authentication Unlicensed Licensed	105
31 Creating a custom matplotlib style Requires Authentication Unlicensed Licensed	109
32 Convenience of seaborn Requires Authentication Unlicensed Licensed	112
33 Interactivity of plotly Requires Authentication Unlicensed Licensed	115
34 Representing multidimensional data Requires Authentication Unlicensed Licensed	118
35 Representing multidimensional data in a funny way Requires Authentication Unlicensed Licensed	124
Approaching the scientific questions (modeling and recommendation) Requires Authentication Unlicensed Licensed	131
36 Picking relevant data and information Requires Authentication Unlicensed Licensed	132
37 Building a model with gplearn Requires Authentication Unlicensed Licensed	138
38 Playing with the model or “what if” Requires Authentication Unlicensed Licensed	145
39 Playing with the model or – jupyter notebook Requires Authentication Unlicensed Licensed	153
40 Playing with the model or – voila Requires Authentication Unlicensed Licensed	157
41 Playing with the model or – streamlit Requires Authentication Unlicensed Licensed	160
42 Dealing with too few experiments Requires Authentication Unlicensed Licensed	166
43 Solving the reverse problem applying multiobjective optimization Requires Authentication Unlicensed Licensed	173
44 Ensuring the envisioned causality Requires Authentication Unlicensed Licensed	180
Sharing the project Requires Authentication Unlicensed Licensed	187
45 Building files for distribution Requires Authentication Unlicensed Licensed	188
46 Pushing to package indices Requires Authentication Unlicensed Licensed	190
47 Sharing streamlit applications Requires Authentication Unlicensed Licensed	193
Further reading Requires Authentication Unlicensed Licensed	197
48 Ensuring code styling via black Requires Authentication Unlicensed Licensed	198
49 Configuring pre-commit Requires Authentication Unlicensed Licensed	201
50 Building standalone solutions via PyQt Requires Authentication Unlicensed Licensed	204
Concluding remarks Requires Authentication Unlicensed Licensed	207
List of Figures Requires Authentication Unlicensed Licensed	211
Index Requires Authentication Unlicensed Licensed	215

Publishing information

Pages and Images/Illustrations in book

eBook published on:

October 7, 2024

eBook ISBN:

9783111334608

Paperback published on:

October 7, 2024

Paperback ISBN:

9783111334578

Pages and Images/Illustrations in book

Front matter:

16

Main content:

218

Coloured Illustrations:

85

https://doi.org/10.1515/9783111334608

eBook ISBN: 9783111334608

Paperback ISBN: 9783111334578

Keywords for this book

Scientific experiments; Modeling experiments; Data analysis; Experiments evaluation; Programming language Python

Audience(s) for this book

Graduate students of the natural sciences, researchers and professionals.

Safety & product resources

Manufacturer information:
Walter de Gruyter GmbH
Genthiner Straße 13
10785 Berlin
productsafety@degruyterbrill.com

Scientific Data: A 50 Steps Guide using Python

Overview

About this book

Author / Editor information

Topics

Table of contents

Bibliographic data