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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
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Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Item #: 8326834

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

85% of respondents would recommend this to a friend

Item #: 8326834

LSL 1775

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What Stands Out

Comprehensive Coverage
This edition provides an in-depth exploration of data manipulation techniques using Pandas and NumPy, addressing essential data wrangling needs for analysts and data scientists alike.
Practical Examples
Includes numerous real-world examples and case studies, enabling users to apply learned concepts effectively and enhance their understanding of data analysis workflows.
Updated Tools
Covers the latest advancements in IPython and related libraries, ensuring users are equipped with up-to-date tools and techniques for modern data analysis challenges.

Product Details

Shop the latest edition of Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython at Ubuy Lesotho. Improve your data analysis skills with Python.
  • Complete instructions for manipulating, processing, cleaning, and crunching datasets in Python
  • Practical case studies demonstrating effective solutions for data analysis problems
  • Covers the latest versions of pandas, NumPy, IPython, and Jupiter
  • Written by Wes McKinney, the creator of the Python pandas project
  • Ideal for analysts and Python programmers new to data science and scientific computing
  • Includes detailed examples for solving real-world data analysis problems
Publisher O'Reilly Media
Publication date November 14, 2017
Edition 2nd
Language English
Print length 550 pages
ISBN-10 1491957662
ISBN-13 978-1491957660
Item Weight 1.85 pounds (840 grams)
Dimensions 7.25 x 1 x 9.5 inches (18.4 x 2.5 x 24.1 cm)

Who Should Buy?

Suitable For
  • Data Analysts

    Ideal for data analysts looking to enhance their data manipulation and analysis skills using Python libraries.

  • Students

    Students in data science or programming courses who need a practical guide to data handling techniques.

  • Professionals

    Professionals using data-driven decision-making who wish to leverage Python for efficient data processing.

Not Suitable For
  • Complete Beginners

    Not suitable for those with no prior programming or data analysis experience, as foundational knowledge is assumed.

Product Description

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Customer Questions & Answers

  • Question: What is the focus of 'Python for Data Analysis' 2nd Edition?

    Answer: The book primarily focuses on data wrangling techniques using Pandas, NumPy, and IPython. It provides comprehensive insights into manipulating, processing, cleaning, and analyzing data effectively. Understanding these techniques is crucial for data analysts and scientists as it allows them to derive meaningful insights from large datasets, enhancing their analytical skills for real-world data scenarios.
  • Question: Who should read 'Python for Data Analysis' 2nd Edition?

    Answer: This book is ideal for data analysts, scientists, and anyone interested in exploring data analysis using Python. Whether you’re a beginner or an experienced programmer, the book caters to various skill levels, offering practical examples and case studies to bridge the gap between theory and application. It empowers readers to utilize data effectively in their chosen fields, from finance to academia.
  • Question: What programming knowledge is required to benefit from this book?

    Answer: A basic understanding of Python programming fundamentals is recommended. Familiarity with Python constructs such as variables, loops, and functions will greatly enhance your reading experience and enable you to grasp more complex concepts. This foundational knowledge ensures smooth progression through the advanced techniques in data analysis and manipulation covered in the book.
  • Question: Does the book cover data visualization techniques?

    Answer: Yes, 'Python for Data Analysis' includes chapters on data visualization using libraries like Matplotlib and Seaborn. Visualization is crucial in data analysis as it helps to present findings in an understandable format. Examples provided in the book illustrate how to create effective visualizations that reveal patterns and insights, making your analysis more impactful.
  • Question: What are some practical applications of the skills learned in this book?

    Answer: The skills gained from this book can be applied in numerous fields such as finance, marketing, healthcare, and academia. For instance, data analysts can utilize the techniques to clean and prepare customer data for insights to drive sales strategies. Similarly, researchers can analyze survey data to identify trends and inform their studies, making the knowledge gained from this book versatile and valuable.
  • Question: Is this book suitable for learning about machine learning?

    Answer: While the book focuses on data analysis, it provides a solid foundation for understanding data preprocessing, an essential step in machine learning. Knowledge of data manipulation and analysis is vital before diving into machine learning algorithms, ensuring that your data is clean and structured. This makes it a beneficial prerequisite for those looking to further explore machine learning concepts.
  • Question: How does this edition differ from the first edition?

    Answer: The 2nd Edition of 'Python for Data Analysis' incorporates updates reflecting the latest developments in Python libraries and tools, enhancing clarity and usability. It features improved examples, better illustrations, and additional content on newer features of Pandas and NumPy. This ensures readers are equipped with current knowledge and techniques essential for contemporary data analysis practices.
  • Question: What is the role of IPython covered in the book?

    Answer: IPython, now known as Jupyter Notebook, plays a significant role in the book as it provides an interactive environment for data analysis and visualization. It allows users to write code, visualize outputs, and document their processes in one place. This integration is particularly useful for experimenting and iterating on analyses, making it an invaluable tool for data scientists.
  • Question: Can I use this book for real-world data analysis projects?

    Answer: Absolutely! The practical examples and exercises incorporated throughout the book are designed to reflect real-world scenarios. Each chapter not only teaches you the theory but also provides hands-on experience in tackling projects, ensuring that you can apply what you've learned effectively in your own data analysis endeavors, enhancing your problem-solving capabilities.
  • Question: Where can I buy Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition in Lesotho?

    Answer: You can purchase 'Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition' on Ubuy. They offer a convenient platform for finding a wide selection of books and resources, ensuring you get this key resource delivered directly to you, equipped with the right tools for your data analysis journey.

Data Modeling & Design Editorial Review

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython 2nd Edition is a well-written book that provides clear and concise examples for learning data analysis in Python. The author, Wes McKinney, the creator of Pandas, demonstrates deep understanding of the topic and uses code snippets to illustrate his points effectively. However, some readers found the book challenging to understand, especially for beginners without programming background. The book covers a range of topics, but some readers felt that the topics were random and lacked coherence. Overall, it is a useful resource for learning data analysis in Python, but may require persistence and additional resources for beginners.

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Pros

  • Well-written by the creator of Pandas
  • Clear and concise examples
  • Useful in terms of getting data into and out of Python from other sources
  • Written by the creator of Pandas
  • Demonstrates deep understanding of the topic

Cons

  • Challenging for beginners without programming background

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Features & Benefits

  • Learn data analysis with Python 3.6
  • Hands-on guide with practical case studies
  • Ideal for beginners in Python and data science
  • GitHub access to data files and materials
  • Solve real-world data analysis problems
  • Thorough and detailed examples

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