Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter 3rd Edition ISBN: 9781098104030
Original price was: $64.95.$44.99Current price is: $44.99.
Product Details
- Condition: New
- Publisher: O’Reilly Media
- Language: English
- Paperback: 579 pages
- ISBN: 9781098104030
- Item Weight: 1.95 pounds
- Dimensions: 7 x 1.5 x 9 inches
Description
Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, and Jupyter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
- Use the Jupyter notebook and IPython shell for exploratory computing
- Learn basic and advanced features in NumPy
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas groupby facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples
Returns & Exchange:
– Please refer to our Return and Exchange Policy for more details.
| Weight | 1.95 lbs |
|---|---|
| Dimensions | 17.78 × 3.81 × 3.81 in |
Zachary Coleman –
Working in New York, USA, this book gave me a solid understanding of data wrangling using pandas and NumPy. The practical coding examples made complex data transformations much easier to implement. It significantly improved my workflow efficiency only.
Trevor Simmons –
Based in San Jose, USA, I found the Jupyter Notebook examples extremely helpful for hands-on practice. The real datasets and step-by-step explanations strengthened my data analysis skills only.
Miles Donovan –
Preparing in Austin, USA, the clear breakdown of indexing, grouping, and merging operations made working with large datasets far more manageable only.
Evan Caldwell –
From Seattle, USA, the updated content for modern Python tools and libraries keeps this edition highly relevant for real-world analytics projects only.
Connor MacLeod –
Studying in Toronto, Canada, the explanations of NumPy arrays and vectorized operations helped me optimize performance in my scripts only.
Liam Prescott –
Based in Vancouver, Canada, the book’s focus on practical data cleaning and reshaping techniques made it an essential reference for my daily analysis tasks only.
Brady Lawson –
Working in Chicago, USA, I appreciated how clearly the author explains time series analysis and data visualization basics using pandas only.
Owen Fletcher –
From Calgary, Canada, the real-world examples and performance tips made this a strong resource for intermediate and advanced learners only.
Kelsey Morgan –
Studying in Boston, USA, the structured explanations and clear code snippets helped bridge the gap between theory and hands-on data work only.
Madeline Brooks –
Based in Ottawa, Canada, the chapters on data aggregation and transformation were especially useful for managing complex datasets efficiently only.
Hailey Thornton –
Preparing in Denver, USA, the modern approach to data analysis with pandas and Jupyter made learning both practical and engaging only.
Savannah Pierce –
From Montreal, Canada, this edition provides a comprehensive and practical guide for mastering Python-based data analysis workflows only.