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Item #:
62606761
Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Jupyter, (Paperback)
80% of respondents would recommend this to a friend
Item #:
62606761
CRC 32510
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Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python.
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What Stands Out
Comprehensive Guide
This book offers an in-depth exploration of data analysis techniques using Python, making it ideal for beginners and professionals looking to enhance their skills.
Practical Examples
With hands-on tutorials that utilize Pandas, Numpy, and Jupyter, readers gain practical experience, facilitating easier application of concepts in real-world data scenarios.
User-Friendly Approach
The clear and structured layout empowers readers without requiring extensive prior knowledge, ensuring they can effectively learn and apply data wrangling skills.
Product Details
- Updated handbook for manipulating, processing, and crunching datasets in Python
- Includes practical case studies to solve data analysis problems effectively
- Covers the latest versions of pandas, NumPy, and Jupyter
- Written by Wes McKinney, the creator of the Python pandas project
- Ideal for analysts new to Python and Python programmers new to data science
- Provides data files and related material on GitHub for practical application
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Publication date | September, 2022 |
| Pages | 579 |
| Reading level | General |
| Subgenre | Data Science |
| Series title | No Series |
| Edition | 3rd Edition |
| Publisher | O'Reilly Media |
| Original languages | English |
| Language | English |
| Is collectible | N |
| Editor | Fu-Chan Wei |
| Recording time | 0 min |
| Retail packaging | Single Piece |
| Assembled product dimensions (l x w x h) | 9.10 x 1.20 x 6.90 in (23.1 x 3 x 17.5 cm) |
| Assembled product weight | 2.07 lb (940 grams) |
| Bisac subject heading | Computers |
Who Should Buy?
-
Data Analysts
Ideal for professionals looking to enhance their data manipulation skills with practical examples and exercises.
-
Students
Perfect for university students studying data science or analytics seeking foundational knowledge in Python libraries.
-
Self-learners
Great for individuals eager to learn data analysis independently, offering hands-on tutorials and comprehensive explanations.
-
Complete Beginners
Not suitable for those without prior programming knowledge, as some foundational concepts may be challenging.
Product Description
Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Jupyter, (Paperback)
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Wes McKinney All Books Editorial Review
Python for Data Analysis: Data Wrangling with Pandas, Numpy, and Jupyter is an essential non-fiction book for those interested in computing and the internet, particularly in the realm of data science. This 3rd edition, published by O'Reilly Media in September 2022, spans 579 pages and is designed for a general reading level. It offers practical insights into data wrangling techniques with the help of popular libraries like Pandas and Numpy, making it suitable for beginners and professionals alike. The book is written in English and aims to enhance readers' understanding of data analysis with Jupyter as a key tool, giving readers hands-on experience in working with real data sets.
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Pros
- Comprehensive guide to data wrangling techniques
- Covers popular libraries: Pandas and Numpy
- Updated 3rd edition offers the latest information
- Written for a general audience, easy to understand
- Practical examples enhance learning experience
Cons
- Weight may make it slightly less portable for some readers
Product Price History
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CRC 32510
Order now and get it around Friday, October 23
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Features & Benefits
- Updated for Python 3.10 and pandas 1.4
- Packed with practical case studies
- Ideal for analysts new to Python and for Python programmers new to data science
- Learn basic and advanced features in NumPy
- Create informative visualizations with matplotlib
- Solve real-world data analysis problems with thorough, detailed examples
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