Positives Many reviewers commend this book for its comprehensive and detailed exploration of Python's pandas and NumPy libraries, written by the creator of pandas himself, Wes McKinney. Readers found it to be a very practical resource, filled with numerous examples that make the concepts easy to grasp and apply. It is praised for clearly explaining the inner workings of these essential data manipulation tools, covering fundamental operations such as reading, cleaning, filtering, and grouping data. For those already familiar with Python, the book offers significant improvements in workflow and handling real-world messy datasets, even for experienced users of related libraries. It is considered an excellent, organized reference that integrates various components of the Python data science stack, going beyond typical documentation to teach effective technical computing workflows.
Negatives Despite its strengths, several readers felt the book's title, "Python for Data Analysis," was misleading, as it focuses almost exclusively on pandas and NumPy, with only superficial coverage of other libraries like Matplotlib. A common criticism is the heavy reliance on randomly generated or "made-up" datasets rather than meaningful real-world case studies, which some found made the examples less engaging and stripped of practical context. This approach led some to describe the book as a "tiresome parade" of features or an expanded version of official documentation, lacking the motivation of problem-solving with actual data. Additionally, some reviewers noted that parts of the book might be out-of-date, with deprecated functions, and that its dense, tutorial-like style can be challenging to read cover-to-cover, especially for beginners.
Conclusion In conclusion, this book is highly recommended as a thorough and essential reference for anyone looking to master the pandas and NumPy libraries for data wrangling in Python. It is particularly well-suited for individuals who are already familiar with basic Python or data science methods and are seeking in-depth knowledge of these specific tools, whether for scientific computing, engineering, finance, or machine learning. However, if you are seeking a broad introduction to the entire field of data analysis with Python, or prefer learning through extensive real-world case studies, this book may not meet those expectations. It serves as an invaluable resource for those committed to leveraging Python as their primary analytical tool, often best utilized alongside hands-on practice or as a comprehensive reference guide.
