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Beschreibung
An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration.
Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data.
Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms.
You’ll also learn how to:
Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.
[Mehr]
Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data.
Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms.
You’ll also learn how to:
- Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features
- Use Python’s built-in data types, write custom functions and classes, and document your code
- Represent data with the essential NumPy, Matplotlib, and pandas libraries
- Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations
Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.
An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration.
Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data.
Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms.
You’ll also learn how to:
Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.
Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You’ll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data.
Once you’ve built an optimal programming environment with Anaconda, you’ll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book’s fast-paced Python primer, you’ll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms.
You’ll also learn how to:
- Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development features
- Use Python’s built-in data types, write custom functions and classes, and document your code
- Represent data with the essential NumPy, Matplotlib, and pandas libraries
- Use Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations
Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.
Über den Autor
Lee Vaughan
Inhaltsverzeichnis
Introduction
Part 1: Setting up for ScienceChapter 1: Installing Anaconda and Launching Navigator
Chapter 2: Keeping Organized with Conda Environments
Chapter 3: Simple Scripting in Jupyter Qt Console
Chapter 4: Serious Scripting with Spyder
Chapter 5: Jupyter Notebook: An Interactive Journal for Computational Research
Chapter 6: JupyterLab: Your Center for Science
Part 2: Python PrimerChapter 7: Integers, Floats, and Strings
Chapter 8: Variables
Chapter 9: The Container Data Types
Chapter 10: Flow Control
Chapter 11: Functions and Modules
Chapter 12: Files and Folders
Chapter 13: Object Oriented Programming
Chapter 14: Documenting your Work
Part 3: The Scientific and Visualization LibrariesChapter 15: The Scientific Libraries
Chapter 16: The InfoVis and SciVis Visualization Libraries
Chapter 17: The GeoVis Libraries
Part 4: The Essential LibrariesChapter 18: Numpy: Numerical Python
Chapter 19: Demystifying Matplotlib
Chapter 20: Pandas, Seaborn, and Scikit-learn
Chapter 21: Managing Dates and Times with Python and Pandas
Appendix A: Answers to the "Test your Knowledge" Challenges
[Mehr]
Part 1: Setting up for ScienceChapter 1: Installing Anaconda and Launching Navigator
Chapter 2: Keeping Organized with Conda Environments
Chapter 3: Simple Scripting in Jupyter Qt Console
Chapter 4: Serious Scripting with Spyder
Chapter 5: Jupyter Notebook: An Interactive Journal for Computational Research
Chapter 6: JupyterLab: Your Center for Science
Part 2: Python PrimerChapter 7: Integers, Floats, and Strings
Chapter 8: Variables
Chapter 9: The Container Data Types
Chapter 10: Flow Control
Chapter 11: Functions and Modules
Chapter 12: Files and Folders
Chapter 13: Object Oriented Programming
Chapter 14: Documenting your Work
Part 3: The Scientific and Visualization LibrariesChapter 15: The Scientific Libraries
Chapter 16: The InfoVis and SciVis Visualization Libraries
Chapter 17: The GeoVis Libraries
Part 4: The Essential LibrariesChapter 18: Numpy: Numerical Python
Chapter 19: Demystifying Matplotlib
Chapter 20: Pandas, Seaborn, and Scikit-learn
Chapter 21: Managing Dates and Times with Python and Pandas
Appendix A: Answers to the "Test your Knowledge" Challenges
Details
Erscheinungsjahr: | 2023 |
---|---|
Genre: | Importe, Technik allg. |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: | Einband - flex.(Paperback) |
ISBN-13: | 9781718502666 |
ISBN-10: | 1718502664 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Vaughan, Lee |
Hersteller: |
Random House LLC US
No Starch Press |
Verantwortliche Person für die EU: | Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, D-65189 Wiesbaden, info@bod.de |
Maße: | 233 x 181 x 37 mm |
Von/Mit: | Lee Vaughan |
Erscheinungsdatum: | 17.01.2023 |
Gewicht: | 1,153 kg |
Über den Autor
Lee Vaughan
Inhaltsverzeichnis
Introduction
Part 1: Setting up for ScienceChapter 1: Installing Anaconda and Launching Navigator
Chapter 2: Keeping Organized with Conda Environments
Chapter 3: Simple Scripting in Jupyter Qt Console
Chapter 4: Serious Scripting with Spyder
Chapter 5: Jupyter Notebook: An Interactive Journal for Computational Research
Chapter 6: JupyterLab: Your Center for Science
Part 2: Python PrimerChapter 7: Integers, Floats, and Strings
Chapter 8: Variables
Chapter 9: The Container Data Types
Chapter 10: Flow Control
Chapter 11: Functions and Modules
Chapter 12: Files and Folders
Chapter 13: Object Oriented Programming
Chapter 14: Documenting your Work
Part 3: The Scientific and Visualization LibrariesChapter 15: The Scientific Libraries
Chapter 16: The InfoVis and SciVis Visualization Libraries
Chapter 17: The GeoVis Libraries
Part 4: The Essential LibrariesChapter 18: Numpy: Numerical Python
Chapter 19: Demystifying Matplotlib
Chapter 20: Pandas, Seaborn, and Scikit-learn
Chapter 21: Managing Dates and Times with Python and Pandas
Appendix A: Answers to the "Test your Knowledge" Challenges
Part 1: Setting up for ScienceChapter 1: Installing Anaconda and Launching Navigator
Chapter 2: Keeping Organized with Conda Environments
Chapter 3: Simple Scripting in Jupyter Qt Console
Chapter 4: Serious Scripting with Spyder
Chapter 5: Jupyter Notebook: An Interactive Journal for Computational Research
Chapter 6: JupyterLab: Your Center for Science
Part 2: Python PrimerChapter 7: Integers, Floats, and Strings
Chapter 8: Variables
Chapter 9: The Container Data Types
Chapter 10: Flow Control
Chapter 11: Functions and Modules
Chapter 12: Files and Folders
Chapter 13: Object Oriented Programming
Chapter 14: Documenting your Work
Part 3: The Scientific and Visualization LibrariesChapter 15: The Scientific Libraries
Chapter 16: The InfoVis and SciVis Visualization Libraries
Chapter 17: The GeoVis Libraries
Part 4: The Essential LibrariesChapter 18: Numpy: Numerical Python
Chapter 19: Demystifying Matplotlib
Chapter 20: Pandas, Seaborn, and Scikit-learn
Chapter 21: Managing Dates and Times with Python and Pandas
Appendix A: Answers to the "Test your Knowledge" Challenges
Details
Erscheinungsjahr: | 2023 |
---|---|
Genre: | Importe, Technik allg. |
Rubrik: | Naturwissenschaften & Technik |
Medium: | Taschenbuch |
Inhalt: | Einband - flex.(Paperback) |
ISBN-13: | 9781718502666 |
ISBN-10: | 1718502664 |
Sprache: | Englisch |
Einband: | Kartoniert / Broschiert |
Autor: | Vaughan, Lee |
Hersteller: |
Random House LLC US
No Starch Press |
Verantwortliche Person für die EU: | Springer Fachmedien Wiesbaden GmbH, Postfach:15 46, D-65189 Wiesbaden, info@bod.de |
Maße: | 233 x 181 x 37 mm |
Von/Mit: | Lee Vaughan |
Erscheinungsdatum: | 17.01.2023 |
Gewicht: | 1,153 kg |
Sicherheitshinweis