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제품번호 #:
97733425
Python Tools for Scientists: An Introduction to Using Anaconda, JupyterLab, and Python's Scientific Libraries
87% of respondents would recommend this to a friend
제품번호 #:
97733425
KRW 90098
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What Stands Out
Integrated Environment
Utilizes Anaconda and JupyterLab to provide a seamless experience for data scientists, combining library management, coding, and visualization in one platform, which enhances productivity and streamlines workflows.
Comprehensive Libraries
Offers extensive coverage of Python's scientific libraries like NumPy and Pandas, empowering users to perform complex data analysis and scientific computations efficiently, saving time and improving accuracy.
User-Friendly Approach
Designed for beginners and advanced users alike, this guide simplifies the learning curve for Python in scientific contexts, making it accessible and practical for scientists without extensive programming backgrounds.
제품 세부 정보
- 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:Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development featuresUse Python’s built-in data types, write custom functions and classes, and document your codeRepresent data with the essential NumPy, Matplotlib, and pandas librariesUse Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizationsRegardless 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.
| Publisher | No Starch Press |
| Publication date | January 17, 2023 |
| Language | English |
| Print length | 744 pages |
| ISBN-10 | 1718502664 |
| ISBN-13 | 978-1718502666 |
| Item Weight | 13 ounces (368.55 grams) |
| Dimensions | 7 x 1.32 x 9.25 inches (17.8 x 3.4 x 23.5 cm) |
Who Should Buy?
-
Beginner Scientists
This product is ideal for scientists who are new to Python and wish to utilize data analysis tools effectively.
-
Data Analysts
Data analysts looking to enhance their skill set with Python's libraries for data manipulation and visualization will benefit significantly.
-
Academic Researchers
Researchers in academia needing efficient tools for data processing and visualization will find value in this comprehensive guide.
-
Advanced Programmers
Experienced programmers may find the content too basic and not sufficiently challenging or in-depth for their skill level.
제품 설명
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고객 질문 및 답변
-
의문:
What is included in 'Python Tools for Scientists'?
답변: The book covers essential tools such as Anaconda, JupyterLab, and various scientific libraries like NumPy, Pandas, and Matplotlib. Each tool is designed to help scientific computing and data analysis. Anaconda provides a robust environment to manage packages and dependencies, JupyterLab offers an interactive platform for coding, and libraries help perform complex mathematical calculations and data visualizations efficiently. -
의문:
Who is this book intended for?
답변: This book is primarily aimed at scientists, data analysts, and researchers who are new to Python or looking to enhance their skills. It’s suitable for a wide range of disciplines, including biology, chemistry, physics, and engineering, where data analysis and computational tools are increasingly becoming essential. It bridges the gap between theoretical knowledge and practical application in scientific research. -
의문:
How can I benefit from using Anaconda?
답변: Anaconda simplifies package management and deployment for Python. With thousands of scientific packages available, you can quickly set up an environment tailored to your project needs. This is especially valuable when working on collaborative projects, where ensuring environment consistency is crucial. Using Anaconda allows for efficient dependency resolution and package management, streamlining the workflow for data scientists. -
의문:
What are the main advantages of using JupyterLab?
답변: JupyterLab provides an interactive working environment that allows you to write and execute code simultaneously while visualizing results. Its modular interface supports working with notebooks, code editors, text editors, and terminals all in one place. This enhances productivity, making it easier to document your process while experimenting with data, which is invaluable for data exploration and presentation. -
의문:
Can I use this book for learning Python from scratch?
답변: While the book focuses on tools used in scientific applications, it begins with foundational concepts of Python essential for understanding advanced tools. If you are a beginner, it serves as a guided introduction, illustrating how Python is applied within the context of scientific work, which can enhance your learning experience and motivate you to delve deeper into Python programming. -
의문:
What are Python's scientific libraries, and why are they important?
답변: Python's scientific libraries include NumPy, Pandas, SciPy, and Matplotlib, each serving distinct purposes. NumPy provides efficient array computation, Pandas excels in data manipulation and analysis, SciPy focuses on scientific computing, and Matplotlib is great for creating visualizations. These libraries are crucial for conducting data-intensive research, allowing scientists to handle large datasets efficiently and effectively communicate findings. -
의문:
Is this book suitable for advanced users?
답변: While 'Python Tools for Scientists' is designed for those new to the tools, advanced users may still find it beneficial as a reference. It provides insights into best practices and advanced functionalities that may not be immediately obvious. Furthermore, integrating these tools into existing workflows can streamline processes and improve productivity, making it useful for users at any expertise level. -
의문:
What practical projects can I work on after reading this book?
답변: After engaging with the book, you can undertake various projects such as data analysis of scientific datasets, simulations of physical phenomena, or visualizing complex biological data. For example, using Pandas for data cleaning and manipulation followed by Matplotlib for creating insightful visualizations can enhance your research presentations, making your findings more accessible and impactful. -
의문:
How does this book handle complex scientific concepts?
답변: The book simplifies complex scientific concepts by breaking them down into digestible sections that relate directly to practical applications in Python. Each chapter typically presents theoretical explanations followed by hands-on examples or coding exercises that reinforce learning, ensuring that readers not only understand the theory but also how to apply it in real-world scenarios. -
의문:
Where can I buy 'Python Tools for Scientists: An Introduction to Using Anaconda, JupyterLab, and Python's Scientific Libraries'?
답변: You can purchase 'Python Tools for Scientists' from Ubuy in South Korea. Ubuy offers a convenient shopping experience with a wide selection of books, including scientific literature. Furthermore, their platform ensures you have access to reliable product details and customer reviews, enhancing your buying decision.
Data Processing Editorial Review
**** "Python Tools for Scientists" serves as an accessible entry point into the realm of Python programming for individuals venturing into scientific or research work. The book excels in providing a well-organized and methodical introduction, making it particularly valuable for beginners. Reviewers have noted that while the author presents the foundational aspects of Python with clarity, those seeking in-depth mastery of the language will need to look elsewhere. The exhaustive detail regarding setting up the Python development environment is both a strength and a weakness; it's beneficial for newcomers but can feel drawn out for those already familiar with programming. The content is favorably structured, with clear divisions between instructional chapters and reference material, aiding readers in navigating the toolset effectively. Many users have expressed that the book minimizes learning efforts and efficiently equips them to utilize Python's extensive scientific libraries. The inclusion of visualization techniques received positive remarks, particularly for its supportive charts that guide selection of appropriate libraries. However, for more experienced programmers, the book may seem overly focused on basic tutorials, leading to a desire for more complex problem-solving content. Overall, "Python Tools for Scientists" is well-received as a practical guide, especially for those just starting their journey in the Python ecosystem. It provides the essential groundwork and introduces critical libraries, making it a recommended resource for aspiring data scientists and researchers. **
Customer Reviews & Ratings
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- Well-organized and didactic structure
- Detailed introduction to setting up the Python environment
- Useful reference material for Python libraries
- Includes visualization chapters with helpful charts
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- Best suited for beginners; deeper insights into Python may be lacking
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KRW 90098
지금 주문하시면 도착 예정일 화요일, 9월 15
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특징 및 장점
- Comprehensive introduction to Python for scientific applications.
- Covers essential tools like Anaconda, JupyterLab, and key Python libraries.
- Easy-to-follow writing and organized material for effective learning.
- Empowers users to handle data visualization, analysis, and simulations.
- Perfect for beginners aiming to become Python experts.
- Develop practical skills for scientific research and collaboration.
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