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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
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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
Product Details
- 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.
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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.
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Pros
- 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
Cons
- A second edition would be beneficial for updates
Product Price History
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Features & Benefits
- 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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