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Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python.
Item #:
177626428
Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter
Item #:
177626428
€ 74
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What Stands Out
Comprehensive Content
This book covers extensive data analysis techniques using popular libraries like pandas and NumPy, making it ideal for data enthusiasts looking to enhance their skills in data wrangling.
Practical Examples
Filled with real-world examples, it enables readers to grasp complex concepts and apply data analysis effectively, addressing common challenges faced by data professionals.
User-Friendly Approach
Designed for both beginners and experienced users, the integration with Jupyter enhances interactivity, ensuring readers can easily follow along and apply techniques in their projects.
Product Details
- Definitive handbook for manipulating, processing, and cleaning datasets in Python
- Updated for Python 3.10 and pandas 1.4, with practical case studies
- Written by Wes McKinney, creator of the Python pandas project
- Ideal for analysts new to Python and Python programmers new to data science
- Covers Jupyter notebook, IPython shell, NumPy, pandas, and matplotlib
- Thorough examples for solving real-world data analysis problems
| 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 |
Product Description
Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter
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Pros
- Comprehensive data analysis guide
- Easy-to-follow examples
- Great for beginners and experts
- Covers key Python libraries
- Well-structured and organized
Cons
- Some topics could be more detailed.
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€ 74
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Features & Benefits
- 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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