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Pandas for Everyone: Python Data Analysis: Python Data Analysis (Addison-Wesley Data & Analytics Series)
85% of respondents would recommend this to a friend
商品＃:
90353655
TWD 1763
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立即訂購並活動它 週三, 十月 07
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Pandas for Everyone brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis
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What Stands Out
Comprehensive Guide
Pandas for Everyone offers a complete introduction to data analysis using Python, ensuring readers gain a robust understanding of pandas library functionalities and real-world application for all skill levels.
Practical Examples
The book includes practical examples that illustrate concepts in real-time, making it easier for readers to grasp data manipulation and analysis techniques, effectively bridging theory with hands-on experience.
Accessible Language
Written in clear, approachable language, the book is designed for non-programmers and beginners, eliminating jargon while encouraging engagement and confidence in using Python for data analysis.
產品詳情
- An example-rich introduction to Pandas data analysis in Python
- Allows automation of data analysis tasks regardless of size or complexity
- Covers data cleaning, manipulation, reshaping, and filtering
- Provides guidance on fitting models, clustering, and exploration
- Includes tips on performance, scalability, and using other Python data analysis tools
- Updated second edition available with code and library updates to Python 3.9
| Publisher | Addison-Wesley Professional |
| Publication date | 26 Dec. 2017 |
| Edition | 1st |
| Language | English |
| Print length | 412 pages |
| ISBN-10 | 0134546938 |
| ISBN-13 | 978-0134546933 |
| Item weight | 680 g |
| Dimensions | 17.81 x 2.36 x 23.19 cm |
| Part of series | Addison-Wesley Data & Analytics |
Who Should Buy?
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Data Science Beginners
Ideal for individuals new to data science who want to learn essential Pandas skills hands-on.
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Analysts and Researchers
Suitable for professionals who perform data analysis regularly and need an easy-to-follow, practical guide.
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Self-Learners
Great for those who prefer self-study and interactive learning experiences with real-world data examples.
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Advanced Users
Experienced data professionals may find the content basic and lacking in depth on advanced functionalities.
產品描述
有疑問？ 和我們聊天
客戶問題與解答
-
問題:
Do I need programming experience to understand the book?
Answer: Yes, some programming experience is recommended. -
問題:
What kind of datasets are used in the book?
Answer: The book uses realistic datasets that cover a wide range of industries and applications. -
問題:
Is the book suitable for advanced Python users?
Answer: No, the book is aimed at beginners and intermediate Python users who want to learn data analysis with Pandas.
Data Mining Editorial Review
Pandas For Everyone: Python Data Analysis is an insightful guide published by Addison-Wesley Professional on December 26, 2017. This 412-page book thoroughly covers data analysis using Python and the Pandas library, making it a valuable resource for both beginners and experienced users. Readers appreciate the clear explanations and real-world examples that enhance the learning experience, often mentioning how it complements existing works like McKinney's book. One welcoming feature is its accessibility, as no prior knowledge of GitHub is required; necessary files are provided for hands-on practice. It's a highly recommended addition for anyone interested in mastering data analysis with Python, especially for university courses.
Customer Reviews & Ratings
310 客戶評價
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優點
- Clear explanations on data manipulation
- Excellent real-world examples provided
- No prior GitHub knowledge needed
- Files for practice are included
- Highly recommended for students and professionals
缺點
- A second edition would be beneficial for updates
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TWD 1763
立即訂購並活動它 週三, 十月 07
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特色和優勢
- Learn how to use the open source Pandas library for Python data analysis
- Solve real-world problems incrementally with practical examples
- Clean and transform datasets for effective decision-making
- Fit models for prediction, inference, and exploration
- Improve performance and scalability with advanced techniques
- Compare and select the best models for your data
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