Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Python for Data Analysis: Data Wrangling with pandas, NumPy, and Jupyter

Wes McKinney

4.17(2358 readers)
Finding great data analysts is difficult. Despite the explosive growth of data in industries ranging from manufacturing and retail to high technology, finance, and healthcare, learning and accessing data analysis tools has remained a challenge. This pragmatic guide will help train you in one of the most important tools in the field—Python.

Filled with practical case studies, Python for Data Analysis demonstrates the nuts and bolts of manipulating, processing, cleaning, and crunching data with Python. It also serves as a modern introduction to scientific computing in Python for data-intensive applications. Learn about the growing field of data analysis from an expert in the community.

Learn everything you need to start doing real data analysis work with Python

Get the most complete instruction on the basics of the “modern scientific Python platform”

Learn from an insider who builds tools for the scientific stack

Get an excellent introduction for novices and a wealth of advanced methods for experienced analysts

Publisher

O'Reilly Media

Publication Date

9/20/2022

ISBN

9781098104030

Pages

579

Categories

Computer Science

Questions & Answers

The book "Python for Data Analysis" primarily focuses on teaching the use of Python for data manipulation, processing, cleaning, and analysis. It caters to both beginners and experienced programmers by providing a comprehensive guide to Python's data-oriented libraries and tools, such as pandas, NumPy, and Jupyter. For beginners, the book offers foundational knowledge on Python language basics, essential libraries, and interactive computing with IPython and Jupyter notebooks. For experienced programmers, it delves into advanced data manipulation techniques, time series analysis, and real-world data analysis problems, making it a valuable resource for those looking to deepen their skills in data analysis with Python.

The book leverages the latest versions of Python (3.10), NumPy, pandas (1.4), and Jupyter to offer practical, hands-on data analysis guidance. It covers essential Python libraries like NumPy for numerical computations and pandas for data manipulation, ensuring readers are up-to-date with the latest features. The book utilizes Jupyter notebooks for interactive computing and visualization, making it easier to follow along with examples and experiments. It also includes real-world case studies and detailed examples, demonstrating how to solve various data analysis problems effectively using the latest tools and techniques.

The book covers key data manipulation and analysis techniques essential for real-world data analysis. It delves into using pandas for data wrangling, including loading, cleaning, transforming, merging, and reshaping data. It also covers data visualization with matplotlib and seaborn, and time series analysis. Techniques like data aggregation, group operations, and statistical modeling with statsmodels and scikit-learn are also discussed. These techniques are crucial for solving real-world problems, such as analyzing market trends, processing financial data, and understanding user behavior, by enabling efficient data manipulation, insightful analysis, and informative visualization.

The book balances the introduction of new concepts with a strong foundation in Python programming and data analysis by following a structured approach. It starts with essential Python language basics and gradually introduces data analysis tools like NumPy and pandas. This incremental learning process allows readers to build their skills step by step. The book also includes practical case studies and real-world examples, which help readers understand how to apply the concepts they've learned. Additionally, it provides a comprehensive overview of essential Python libraries and tools, ensuring that readers develop a solid foundation before diving into more advanced topics.

The book provides several resources and support for readers to further learn and engage with the Python data analysis community:

  1. Online Version: An open access online version of the book is available on the author's website (https://wesmckinney.com/book) for convenience and updates.
  2. GitHub Repository: A GitHub repository (https://github.com/wesm/pydata-book) contains data files, related material, and code examples for each chapter.
  3. Community and Conferences: The book mentions various Python mailing lists and conferences like PyCon, EuroPython, SciPy, and PyData for connecting with other Python programmers and learning from the community.
  4. Online Learning Platform: O'Reilly Media offers an online learning platform with live training courses, learning paths, interactive coding environments, and a collection of text and video resources.
  5. Acknowledgments: The book acknowledges contributions from the open source scientific Python community, showing appreciation for the collaborative nature of the community.
  6. Technical Reviewers: The book thanks technical reviewers who provided feedback to improve the content's readability and clarity.

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