Data Management for Natural Scientists: A Practical Guide to Data Extraction and Storage Using Python

Matthias Hofmann

Published by De Gruyter 2023

Data Management for Natural Scientists

A Practical Guide to Data Extraction and Storage Using Python

Matthias Hofmann

In the series De Gruyter Textbook

https://doi.org/10.1515/9783110788433

Overview
Contents

About this book

Data Management for Natural Scientists offers a practical guide for scientific processing of data. It covers the way from “getting hands on” experimental results to ensuring their use for addressing various scientific questions. Code snippets are provided in order to introduce the proposed workstream and to demonstrate the adjustability to specific challenges.

Discusses extraction of data and characteristic parameters.
Outlines storage to database(-like) structures.
Details visualization of database(-like) structures.
Material is presented in a consistent manner with relatable examples along the way.

Author / Editor information

Matthias Hofmann

holds a Ph.D. in Physical Chemistry from the University of Regensburg. Currently, he is working in the chemical industry where his focus is on streamlining data processing routines and ensuring the accessibility of experimental data.

Topics

Frontmatter Publicly Available Download PDF	I
Acknowledgment Publicly Available Download PDF	V
Contents Publicly Available Download PDF	XIII
1 Presenting the challenge Requires Authentication Unlicensed Licensed Download PDF	1
2 Python quick start Requires Authentication Unlicensed Licensed Download PDF	4
3 The steps of data processing Requires Authentication Unlicensed Licensed Download PDF	34
4 From experimental files to data Requires Authentication Unlicensed Licensed Download PDF	40
5 From data to information Requires Authentication Unlicensed Licensed Download PDF	73
6 Where to put data and information Requires Authentication Unlicensed Licensed Download PDF	92
7 How to visualize data and information Requires Authentication Unlicensed Licensed Download PDF	119
8 Responding to lessons learned Requires Authentication Unlicensed Licensed Download PDF	142
9 Where to go from here Requires Authentication Unlicensed Licensed Download PDF	154
10 Conclusion Requires Authentication Unlicensed Licensed Download PDF	168
A Packaging a custom module Requires Authentication Unlicensed Licensed Download PDF	173
B Comments to tables and columns via SQLAlchemy Requires Authentication Unlicensed Licensed Download PDF	177
C A word on version control systems Requires Authentication Unlicensed Licensed Download PDF	179
D Overview of utilized Python and package versions Requires Authentication Unlicensed Licensed Download PDF	181
E Extracting data via pd.read_csv Requires Authentication Unlicensed Licensed Download PDF	183
F Installation of an ODBC driver Requires Authentication Unlicensed Licensed Download PDF	187
List of Figures Requires Authentication Unlicensed Licensed Download PDF	189
Bibliography Requires Authentication Unlicensed Licensed Download PDF	195
Index Requires Authentication Unlicensed Licensed Download PDF	197

(Deutsch)

Language: English
Publisher: De Gruyter
Audience: Students, researchers, and laboratory managers in the natural sciences and engineering.
Pages
- Front matter: 16
- Main content: 200
Illustrations
- Illustrations: 1
- Coloured Illustrations: 71
- Tables: 20
Keywords: Data Processing; Python; Data Visualization; Data Structure; Practitioner

eBook

Published: February 20, 2023
ISBN: 9783110788433

Paperback

Published: February 20, 2023
ISBN: 9783110788402