Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter
HKD 641
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Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python.
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What Stands Out
產品詳情
- Definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python
- Updated for Python 3.10 and pandas 1.4
- Packed with practical case studies for effective data analysis
- Written by Wes McKinney, creator of the Python pandas project
- Ideal for analysts new to Python and programmers new to data science
- Includes data files and related material on GitHub
| Publishers | O'Reilly Media |
| Date of publication | 26 August 2022 |
| Prints | 3. |
| Language | English |
| Print Length | 550 pages |
| ISBN-10 | 109810403X |
| ISBN-13 | 978-1098104030 |
| Dimensions | 17.78 x 3.81 x 22.86 cm |
產品敘述
Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter
客戶問題與解答
-
題:
Who is the author of this book?
回答: The author of the book is Wes McKinney, the creator of the Python pandas project. -
題:
Is this book useful for analysts and programmers new to data science?
回答: Yes, the book is ideal for analysts new to Python and for Python programmers new to data science and scientific computing. -
題:
Are there practical examples in the book?
回答: Yes, the book is packed with practical case studies and real-world examples to demonstrate key concepts.
İngilizce Baskı Wes McKinney Format: Kağıt Kapak Editorial Review
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優點
- Comprehensive data analysis guide
- Easy-to-follow examples
- Great for beginners and experts
- Covers key Python libraries
- Well-structured and organized
缺點
- Some topics could be more detailed.
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特色和優勢
- Practical guide for solving data analysis problems in Python with latest versions of pandas, NumPy, and Jupyter
- Ideal for analysts new to Python and for Python programmers new to data science and scientific computing
- Includes practical case studies and real-world examples to demonstrate key concepts
- Learn how to manipulate, process, clean, and crunch datasets with flexible tools
- Create informative visualizations with matplotlib and analyze time series data
- Data files and related material available on GitHub
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