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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 "This book introduces Python as a powerful tool for researchers in scientific laboratories. It highlights Python's simplicity, versatility, and extensive libraries, making it ideal for automating tasks, processing data, conducting analysis, and visualizing results. Geared toward both novice and experienced programmers, the book emphasizes how Python can streamline laboratory operations, improve productivity, and enhance scientific research. It covers key features of Python, its applications in automation, data collection, analysis, and scientific computing, and provides real-world examples of its use in various research fields. With downloadable code examples and a focus on practical solutions to everyday research challenges, the book is a valuable resource for scientists looking to harness Python for their work."-- 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 : 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 : A...
