Russo / Neil - Streamlining Your Research Laboratory with Python

Streamlining Your Research Laboratory with Python

Medium: Buch

ISBN: 978-1-394-24988-6

Verlag: Wiley

Erscheinungstermin: 22.07.2025

95,00 € 88,79 € (zzgl. MwSt.)

vorbestellbar, Erscheinungstermin ca. Juli 2025

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.

Produkteigenschaften

Artikelnummer: 9781394249886
Medium: Buch
ISBN: 978-1-394-24988-6
Verlag: Wiley
Erscheinungstermin: 22.07.2025
Sprache(n): Englisch
Auflage: 1. Auflage 2025
Produktform: Gebunden
Seiten: 384
Ausgabetyp: Kein, Unbekannt

Themen

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 Dictionaries. 67

3.3.3: Accessing Dictionary Items. 67

3.3.4: Dictionary Operators. 68

3.3.5: Dictionaries as Objects. 69

Section 3.4: Sets. 71

3.4.1: Introducing Sets. 71

3.4.2: Global Functions that Operate on Sets. 71

3.4.3: Accessing Set Elements. 72

3.4.4: Set Operators. 72

3.4.5: Sets as Objects. 74

Section 3.5: Destructuring Assignment. 75

Section 3.6: Key Takeaways. 76

Chapter 4: Controlling the Flow of a Program. 77

Section 4.1: Conditional Execution. 77

4.1.1: If-statements. 77

4.1.2: if-else Statements. 78

4.1.3: If-elif-else Statements. 80

4.1.4: If-statement Strategies. 82

4.1.5: Truthy and Falsy Values. 85

4.1.6: Conditional Expressions. 86

Section 4.2: Repeated Execution. 88

4.2.1: While-statements. 88

4.2.2: For-statements. 93

4.2.3: For-statements with Range. 95

4.2.4: Break and Continue. 96

4.2.5: Comprehensions. 98

Section 4.3: Key Takeaways. 99

Chapter 5: Custom Functions and Exceptions. 101

Section 5.1: Defining Custom Functions. 101

Section 5.2: Arguments and Parameters. 106

Section 5.3: Names and Scope. 109

5.3.1: Local vs. Global 109

5.3.2: Built-in and Nonlocal Scope. 111

Section 5.4: Scope vs Namespace. 113

Section 5.5: Organizing your Code with Modules. 114

Section 5.6: Decorators. 116

Section 5.7: How Things Go Wrong. 118

Section 5.8: Python Exceptions. 119

Section 5.9: Handling Exceptions. 120

Section 5.10: Raising Your Own Exceptions. 123

Section 5.11: Key Takeaways. 126

Chapter 6: Regular Expressions. 128

Section 6.1: Matching Literal Text. 128

Section 6.2: Alternation. 129

Section 6.3: Defining and Matching Character Classes. 129

Section 6.4: Metaclasses. 130

Section 6.5: Pattern Sequences. 130

Section 6.6: Repeating Patterns with Quantifiers. 130

Section 6.7: Anchors. 132

Section 6.8: Capturing Groups. 133

Section 6.9: Regular Expressions in Python. 135

Section 6.10: Project - A Formula Mass Calculator. 136

Section 6.11: Key Takeaways. 143

Chapter 7: Working with Data. 144

Section 7.1: A File System Primer. 144

Section 7.2: Text Files. 145

Section 7.3: Reading and Writing Text Files. 147

Section 7.4: Working with Comma-Separated Values (CSV) Files. 153

Section 7.5: The csv Module. 157

Section 7.6: Reading and Writing Excel Spreadsheet. 158

7.6.1: openpyxl Workbook Object. 159

7.6.2: openpyxl Worksheet Object. 160

7.6.3: openpyxl Cell Object. 161

Section 7.7: Project – Generate a Random Sample Layout in a Spreadsheet 161

Section 7.8: Project – Forecast Monthly Sample Processing. 163

Section 7.9: Managing the File System. 168

7.9.1: The Path Object. 168

7.9.2: Path Properties. 169

7.9.3: Path Attributes. 170

7.9.4: Operating a Path. 170

7.9.5: Combining Paths. 172

7.9.6: The shutil Module for High-level File Operations. 172

Section 7.10: Walking a File System Tree. 173

Section 7.11: Project – Find Duplicate Files. 174

Section 7.12: Working with Zip Files. 176

7.12.1: ZipFile Object. 177

7.12.2: zipfile.Path Object. 177

7.12.3: Creating Zip Archives. 178

Section 7.13: Working with Standard Data Formats. 179

7.13.1: JSON - JavaScript Object Notation. 179

7.13.2: json Python Module. 180

7.13.3: XML - Extensible Markup Language. 182

7.13.4: Python XML modules. 183

7.13.5: Other Standard Data Formats. 189

Section 7.14: Key Takeaways. 189

Chapter 8: Web Resources. 191

Section 8.1: TCP/IP Networks – What You Need to Know. 191

8.1.1: Internet Protocol (IP). 191

8.1.2: Transmission Control Protocol (TCP). 192

8.1.3: Connections and Ports. 192

8.1.4: Application-layer Protocols. 192

8.1.5: IPv4 vs. IPv6 Addresses. 193

8.1.6: Proxy Servers. 193

Section 8.2: Introduction to Hypertext Transfer Protocol (HTTP). 194

8.2.1: The Uniform Resource Locator (URL). 195

8.2.2: Anatomy of an HTTP Request. 196

8.2.3: Anatomy of an HTTP Response. 198

Section 8.3: Web Services and the Python Requests Module. 199

8.3.1: HTTP GET Requests and the Response Object. 200

8.3.2: HTTP POST Requests. 201

8.3.3: Binary Responses. 202

8.3.4: Customizing the Request Object. 205

8.3.5: Verifying Certificates and Encryption. 205

8.3.6: Other Request Module Options. 206

Section 8.4: Project – Print Weather Forecast for a Location. 207

8.4.1: National Weather Service API Web Service. 207

8.4.2: Getting Forecast URL from Geolocation. 209

8.4.3: Loading and Processing Forecast Data. 209

8.4.4: Completed Program to Generate Temperature Forecast. 210

Section 8.5: Project – Scraping HTML Page Content. 213

Section 8.6: Key Takeaways. 218

Chapter 9: Data Analysis and Visualization. 220

Section 9.1: JupyterLab. 220

Section 9.2: Scientific Plotting with Matplotlib. 222

9.2.1: The pyplot Submodule. 223

9.2.2: The pyplot.plot() function. 223

9.2.3: Customizing a Plot. 224

9.2.4: Multiple Curves on a Single Plot. 225

9.2.5: Additional Plot Types. 227

9.2.6: Multiple Axes on a Single Figure. 227

9.2.7: Other Useful Functions. 229

9.2.8: Project – Plotting Weather Forecast. 229

9.2.9: Project – A Custom Microplate Heat Map. 231

9.2.10: Other Scientific Plotting Libraries. 235

Section 9.3: NumPy – Numerical Python. 236

9.3.1: Creating ndarray Objects. 236

9.3.2: Working with ndarray Objects. 236

9.3.3: Accessing and Updating ndarray Elements. 237

9.3.4: Broadcasting. 239

Section 9.4: pandas DataFrame. 240

9.4.1: Creating and Inspecting DataFrames. 240

9.4.2: Filtering DataFrames. 243

9.4.3: Project – A Screening Experiment. 246

Section 9.5: SciPy – A Library for Mathematics, Science, and Engineering. 254

9.5.1: Descriptive Statistics with SciPy. 254

9.5.2: Hypothesis Testing. 255

9.5.3: Project – Running Hypothesis Tests on Two Samples. 256

9.5.4: Project – Comparing Liquid Handler Syringe Performance. 258

9.5.5: Linear Regression. 260

9.5.6: Fitting Nonlinear Models to Data. 261

9.5.7: Project – Four-Parameter Logistic Regression. 264

Section 9.6: Key Takeaways. 269

Chapter 10: Report Generation. 271

Section 10.1: BytesIO Object. 271

Section 10.2: Generating Reports in Microsoft Word. 272

10.2.1: Document Object. 273

10.2.2: Paragraph Object. 274

10.2.3: Run Object. 275

10.2.4: Picture and InlineShape Objects. 276

10.2.5: Table Object. 277

10.2.6: Project – Generate a Complete Word Report. 279

Section 10.3: Generating Microsoft PowerPoint Presentations. 282

10.3.1: Presentation Object. 283

10.3.2: Slide Objects. 284

10.3.3: SlideShapes Object. 284

10.3.4: Length Objects. 285

10.3.5: Table Object. 287

10.3.6: Project – Generate a PowerPoint Document with Figures and Tables. 289

Section 10.4: Generating PDF File Reports. 293

10.4.1: ReportLab PDF Generation Process. 294

10.4.2: Creating a Canvas Object. 294

10.4.3: Setting Canvas Styles. 294

10.4.4: Managing Text Blocks with PDFTextObjects. 297

10.4.5: Canvas State Stack. 299

10.4.6: Drawing Images. 300

10.4.7: PLATYPUS for Page Layout. 302

10.4.8: Project – Generate a Complete PDF Report. 303

Section 10.5: Sending E-mail Programmatically. 306

10.5.1: Simple Mail Transfer Protocol 306

10.5.2: SMTP Mail Server. 307

10.5.3: Send a Simple Email Message. 307

10.5.4: Sending Email Messages over a Secure Connection. 310

10.5.5: Building an Email Message with Attachments. 311

Section 10.6: Serving Results with an HTTP Server. 314

Section 10.7: Key Takeaways. 315

Chapter 11: Control and Automation. 317

Section 11.1: Concurrency in Python. 317

Section 11.2: Asynchronous Execution. 319

Section 11.3: Concurrent Programs with AsyncIO. 320

Section 11.4: Asynchronous Instrument Control and Coordination. 324

11.4.1: Project – Integrated Laboratory System Control and Coordination. 324

Section 11.5: Communicating over a Serial Port. 332

11.5.1: Reading Barcodes from a Serial Port. 334

11.5.2: Project – Scanning Sample Tasks into a Running Controller. 339

Section 11.6: Execute Remote Commands over HTTP. 341

11.6.1: A Basic HTTP Server with aiohttp. 341

11.6.2: Routing an HTTP Request to a Custom Python Function. 343

Section 11.7: Persistent Network Connections using a WebSocket. 347

11.7.1: A User Interface for an Asynchronous Networked Programs. 348

11.7.2: WebSocketResponse and FileResponse Objects. 348

11.7.3: Project – A Browser-Based WebSocket Message Broadcaster. 349

11.7.4: Project – A Browser UI to Schedule Samples for Analysis. 357

Section 11.8: Responding to File System Changes. 361

11.8.1: Watching a Directory for Changes with watchfiles. 361

11.8.2: File System Monitoring Options. 363

Section 11.9: Executing Tasks on a Schedule. 365

11.9.1: sched Module. 365

11.9.2: Project – Taking and Sending Images on a Schedule. 367

Section 11.10: Key Takeaways. 371

Postface. 373

References. 374

Appendix A: ASCII American Standard Code for Information Interchange. 377

Index. 378

Streamlining Your Research Laboratory with Python

Beschreibung

Autoren/Hrsg.

Inhalt

Produkteigenschaften

Themen

Verlagspartner

Produktsortiment

Fachzeitschriften

Veranstaltungen

Site Breadcrumb

Streamlining Your Research Laboratory with Python

Beschreibung

Autoren/Hrsg.

Inhalt

Produkteigenschaften

Themen