Informationen zum Autor Mark F. Russo, PhD is currently on the faculty in the Department of Computer Science at The College of New Jersey. Previously, he had a multi-decade professional career in biotech and pharma with a focus on scientific computing, automation, and scientific data. William Neil is currently at Bristol Myers Squibb and has been working in the pharmaceutical industry since 1995. Klappentext Enables scientists and researchers to efficiently use one of the most popular programming languages in their day-to-day work Streamlining Your Research Laboratory with Python covers the Python programming language and its ecosystem of tools applied to tasks encountered by laboratory scientists and technicians working in the life sciences. After opening with the basics of Python, the chapters move through working with and analyzing data, generating reports, and automating the lab environment. The book includes example processes within chapters and code listings on nearly every page along with schematics and plots that can clearly illustrate Python at work in the lab. The book also explores some real-world examples of Python's application in research settings, demonstrating its potential to streamline processes, improve productivity, and foster innovation. Streamlining Your Research Laboratory with Python includes information on: Language basics including the interactive console, data types, variables and literals, strings, and expressions using operatorsCustom functions and exceptions such as arguments and parameters, names and scope, and decoratorsConditional and repeated execution as methods to control the flow of a programTools such as JupyterLab, Matplotlib, NumPy, pandas DataFrame, and SciPyReport generation in Microsoft Word and PowerPoint, PDF report generation, and serving results through HTTP and email automatically Whether you are a biologist analyzing genetic data, a chemist scouting synthesis routes, an engineer optimizing machine parameters, or a social scientist studying human behavior, Streamlining Your Research Laboratory with Python serves as a logical and practical guide to add Python to your research toolkit. Inhaltsverzeichnis Contents Dedication. 1 Contents. 2 Preface. 10 Chapter 1 : Introduction. 11 Section 1.1 : Python Implementations. 11 Section 1.2 : Installing the Python Toolkit. 12 Section 1.3 : Python 3 vs. Python 2. 13 Section 1.4 : Python Package Index. 13 Section 1.5 : Programming Editors. 14 Section 1.6 : Notebook Editors. 15 Section 1.7 : Using the Jupyter Notebook Interface. 17 Section 1.8 : JupyterLite. 18 Section 1.9 : Things Change. 20 Section 1.10 : Key Takeaways. 20 Chapter 2 : Language Basics. 21 Section 2.1 : Python Interactive Console. 21 Section 2.2 : Data Types. 22 Section 2.3 : Variables and Literals. 24 Section 2.4 : Strings. 26 2.4.1 : Simple Strings. 26 2.4.2 : Multi-line Strings. 26 2.4.3 : Escape Characters in a String. 26 2.4.4 : Raw Strings. 28 2.4.5 : Formatted Strings. 28 2.4.6 : Strings as Objects. 30 2.4.7 : Characters and Encodings. 31 Section 2.5 : Expressions using Operators. 32 2.5.1 : Arithmetic Operators. 33 2.5.2 : Assignment Operators. 35 2.5.3 : Comparison Operators. 36 2.5.4 : Boolean Operators. 37 2.5.5 : Chaining Comparisons. 39 2.5.6 : Comparing Floating Point Numbers. 39 Section 2.6 : Functions and How to Use Them.. 40 2.6.1 : Invoking Functions. 40 2.6.2 : Built-in Functions. 41 2.6.3 : The math Module for Additional Mathematical Functions. 42 2.6.4 : The random Module for Pseudo-random Number Generation. 44 2.6.5 : The time and datetime Modules for Handling Dates and Times. 47 2.6.6 ...

Autorentext

Mark F. Russo, PhD is currently on the faculty in the Department of Computer Science at The College of New Jersey. Previously, he had a multi-decade professional career in biotech and pharma with a focus on scientific computing, automation, and scientific data. William Neil is currently at Bristol Myers Squibb and has been working in the pharmaceutical industry since 1995.

Klappentext

Enables scientists and researchers to efficiently use one of the most popular programming languages in their day-to-day work Streamlining Your Research Laboratory with Python covers the Python programming language and its ecosystem of tools applied to tasks encountered by laboratory scientists and technicians working in the life sciences. After opening with the basics of Python, the chapters move through working with and analyzing data, generating reports, and automating the lab environment. The book includes example processes within chapters and code listings on nearly every page along with schematics and plots that can clearly illustrate Python at work in the lab. The book also explores some real-world examples of Python's application in research settings, demonstrating its potential to streamline processes, improve productivity, and foster innovation. Streamlining Your Research Laboratory with Python includes information on:

• Language basics including the interactive console, data types, variables and literals, strings, and expressions using operators
• Custom functions and exceptions such as arguments and parameters, names and scope, and decorators
• Conditional and repeated execution as methods to control the flow of a program
• Tools such as JupyterLab, Matplotlib, NumPy, pandas DataFrame, and SciPy
• Report generation in Microsoft Word and PowerPoint, PDF report generation, and serving results through HTTP and email automatically Whether you are a biologist analyzing genetic data, a chemist scouting synthesis routes, an engineer optimizing machine parameters, or a social scientist studying human behavior, Streamlining Your Research Laboratory with Python serves as a logical and practical guide to add Python to your research toolkit.

Inhalt

Contents

Dedication. 1

Contents. 2

Preface. 10

Chapter 1 : Introduction. 11

Section 1.1 : Python Implementations. 11

Section 1.2 : Installing the Python Toolkit. 12

Section 1.3 : Python 3 vs. Python 2. 13

Section 1.4 : Python Package Index. 13

Section 1.5 : Programming Editors. 14

Section 1.6 : Notebook Editors. 15

Section 1.7 : Using the Jupyter Notebook Interface. 17

Section 1.8 : JupyterLite. 18

Section 1.9 : Things Change. 20

Section 1.10 : Key Takeaways. 20

Chapter 2 : Language Basics. 21

Section 2.1 : Python Interactive Console. 21

Section 2.2 : Data Types. 22

Section 2.3 : Variables and Literals. 24

Section 2.4 : Strings. 26

2.4.1 : Simple Strings. 26

2.4.2 : Multi-line Strings. 26

2.4.3 : Escape Characters in a String. 26

2.4.4 : Raw Strings. 28

2.4.5 : Formatted Strings. 28

2.4.6 : Strings as Objects. 30

2.4.7 : Characters and Encodings. 31

Section 2.5 : Expressions using Operators. 32

2.5.1 : Arithmetic Operators. 33

2.5.2 : Assignment Operators. 35

2.5.3 : Comparison Operators. 36

2.5.4 : Boolean Operators. 37

2.5.5 : Chaining Comparisons. 39

2.5.6 : Comparing Floating Point Numbers. 39

Section 2.6 : Functions and How to Use Them.. 40

2.6.1 : Invoking Functions. 40

2.6.2 : Built-in Functions. 41

2.6.3 : The math Module for Additional Mathematical Functions. 42

2.6.4 : The random Module for Pseudo-random Number Generation. 44

2.6.5 : The time and datetime Modules for Handling Dates and Times. 47

2.6.6 : The sys Module for System Interactions. 50

2.6.7 : Scope and Namespace. 51

Section 2.7 : Your First Python Program.. 52

Section 2.8 : Key Takeaways. 53

Chapter 3 : Data Structures. 55

Section 3.1 : Lists. 55

3.1.1 : Introducing Lists. 55

3.1.2 : Global Functions that Operate on Lists. 55

3.1.3 : Accessing List Elements. 56

3.1.4 : Slicing Lists. 57

3.1.5 : Lists Operators. 59

3.1.6 : Lists as Objects. 61

Section 3.2 : Tuples. 65

3.2.1 : Introducing Tuples. 65

Section 3.3 : Dictionaries. 66

3.3.1 : Introducing Dictionaries. 66

3.3.2 : Global Functions that Operate on Di…