Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)
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Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)
Best Python/Data Science book I've read in years...Daniel's book is a breath of fresh air. He still manages to educate without getting bogged down in the minutia or the overly verbose theory, math, etc. etc.
Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)
Articol nr.: 63263483

Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series)

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Articol nr.: 63263483

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Ce Iese în evidență

Comprehensive Guide
Offers a complete overview of data analysis with pandas, ensuring readers of all levels can effectively utilize the library for professional and personal projects.
Practical Examples
Includes real-world scenarios and practical exercises that enhance learning, enabling readers to apply their skills in actual data analysis challenges.
Updated Content
The second edition incorporates the latest features and best practices in pandas, ensuring readers are well-informed and competent in contemporary data analysis techniques.

Detalii produs

Shop Pandas for Everyone: Python Data Analysis (Addison-Wesley Data & Analytics Series) online at a best price in România. 0137891156
  • Manage and Automate Data Analysis with Pandas in PythonToday, analysts must manage data characterized by extraordinary variety, velocity, and volume. Using the open source Pandas library, you can use Python to rapidly automate and perform virtually any data analysis task, no matter how large or complex. Pandas can help you ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple data sets.Pandas for Everyone, 2nd Edition, brings together practical knowledge and insight for solving real problems with Pandas, even if you’re new to Python data analysis. Daniel Y. Chen introduces key concepts through simple but practical examples, incrementally building on them to solve more difficult, real-world data science problems such as using regularization to prevent data overfitting, or when to use unsupervised machine learning methods to find the underlying structure in a data set.New features to the second edition include: Extended coverage of plotting and the seaborn data visualization libraryExpanded examples and resourcesUpdated Python 3.9 code and packages coverage, including statsmodels and scikit-learn librariesOnline bonus material on geopandas, Dask, and creating interactive graphics with AltairChen gives you a jumpstart on using Pandas with a realistic data set and covers combining data sets, handling missing data, and structuring data sets for easier analysis and visualization. He demonstrates powerful data cleaning techniques, from basic string manipulation to applying functions simultaneously across dataframes.Once your data is ready, Chen guides you through fitting models for prediction, clustering, inference, and exploration. He provides tips on performance and scalability and introduces you to the wider Python data analysis ecosystem. Work with DataFrames and Series, and import or export dataCreate plots with matplotlib, seaborn, and pandasCombine data sets and handle missing dataReshape, tidy, and clean data sets so they’re easier to work withConvert data types and manipulate text stringsApply functions to scale data manipulationsAggregate, transform, and filter large data sets with groupbyLeverage Pandas’ advanced date and time capabilitiesFit linear models using statsmodels and scikit-learn librariesUse generalized linear modeling to fit models with different response variablesCompare multiple models to select the “best” oneRegularize to overcome overfitting and improve performanceUse clustering in unsupervised machine learning
Publisher Addison-Wesley Professional
Publication date December 20, 2022
Edition 2nd
Language English
Print length 512 pages
ISBN-10 0137891156
ISBN-13 978-0137891153
Item Weight 1.75 pounds (790 grams)
Dimensions 7 x 1.16 x 9.12 inches (17.8 x 2.9 x 23.2 cm)
Part of series Addison-Wesley Data & Analytics

Cine Ar Trebui să Cumpere?

Suitable For
  • Beginners in Data

    Ideal for novice data analysts looking to learn data handling and analysis using Python's Pandas library.

  • Python Enthusiasts

    Great for those familiar with Python who want to enhance their data analysis skills with practical examples.

  • Data Science Students

    Suitable for students studying data science, providing foundational knowledge in data manipulation and analysis techniques.

Not Suitable For
  • Advanced Users

    Experienced analysts may find the content too basic and lacking in advanced data manipulation techniques.

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Întrebări și răspunsuri ale clienților

  • Întrebare: What topics does 'Pandas for Everyone: Python Data Analysis' cover?

    Răspuns: This book provides an extensive introduction to data analysis using Python's Pandas library. It covers fundamental concepts like data structures, data manipulation, merging and joining datasets, and data visualization. Whether you’re a beginner or have prior coding experience, the book guides you through practical use cases such as preparing data for analysis and generating meaningful insights, making it a valuable resource for data enthusiasts.
  • Întrebare: Who is the target audience for this book?

    Răspuns: This book is designed for a variety of readers, from complete beginners in programming to experienced analysts looking to enhance their data manipulation skills with Pandas. It is particularly beneficial for students, researchers, and professionals in fields like data science, statistics, or business intelligence who wish to harness the power of Python for data analysis tasks.
  • Întrebare: Can I use this book as a reference guide?

    Răspuns: Absolutely! 'Pandas for Everyone' serves as an excellent reference guide due to its clear explanations and practical examples. Readers can easily navigate through chapters to find specific information or techniques, whether it’s related to data cleaning, analysis, or visualization. This makes it a handy resource for quick consultations while working on real-world data projects.
  • Întrebare: What programming experience do I need to understand the book?

    Răspuns: A basic understanding of Python is beneficial but not mandatory as the book starts with foundational concepts. It gradually builds up to more complex topics. For those new to programming, introductory sections are included to familiarize you with necessary Python skills. This makes it suitable for both novices and those with some prior experience looking to deepen their knowledge in data analysis.
  • Întrebare: How does this edition differ from the first edition?

    Răspuns: The second edition features updated content reflecting the latest advancements in the Pandas library. This includes new methodologies, enhanced examples, and improved explanations of concepts. It also includes more real-world applications and case studies to better illustrate the practical uses of data analysis. This evolution enhances the reader's learning experience, making the second edition more relevant for current-market applications.
  • Întrebare: What kind of projects are ideal for using Pandas as described in the book?

    Răspuns: Projects that involve data cleaning, analysis, visualization, or any dataset manipulation are ideal for utilizing Python Pandas as described in the book. For instance, analysis of sales data for trend forecasting, evaluating customer feedback across various platforms, or organizing public datasets for research purposes. The practical examples provided in the text assist you in applying what you've learned to real-world situations.
  • Întrebare: Does the book include exercises or practical examples?

    Răspuns: Yes, 'Pandas for Everyone' includes numerous exercises and practical examples throughout the chapters. These exercises are designed to reinforce key concepts and provide hands-on experience in data analysis. You might encounter tasks such as manipulating CSV files, performing group operations, or creating visualizations, which are crucial for mastering the Pandas library.
  • Întrebare: Is it beneficial for learning data visualization techniques?

    Răspuns: Definitely! The book covers essential data visualization techniques that complement data analysis. It introduces you to visualization libraries like Matplotlib and Seaborn, guiding you on how to create informative plots and graphs to represent your findings. These visualization techniques are vital for effectively communicating insights derived from data, making your analysis more impactful in presentations or reports.
  • Întrebare: What is the reading level of 'Pandas for Everyone'?

    Răspuns: The reading level is approachable for most audiences, suitable for both beginners with no programming background and seasoned data analysts. With clear language and step-by-step guidance, the book aims to make complex concepts accessible. Readers with different knowledge levels will find it informative, as it steadily progresses in complexity while incorporating practical examples for enhanced understanding.
  • Întrebare: Where can I buy 'Pandas for Everyone: Python Data Analysis' in Romania?

    Răspuns: You can purchase 'Pandas for Everyone: Python Data Analysis Addison-Wesley Data & Analytics Series 2nd Edition' on Ubuy in Romania. Ubuy is a trusted online marketplace that features a wide range of books, including academic and technical materials like this edition. Enjoy hassle-free navigation and a secure shopping experience while acquiring this essential resource for your data analysis journey.

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Caracteristici și avantaje

  • Manage and automate data analysis with Pandas in Python
  • Practical knowledge and insight for solving real problems with Pandas
  • Extended coverage of plotting and the seaborn data visualization library
  • Updated Python 3.9 code and packages coverage, including statsmodels and scikit-learn libraries
  • Online bonus material on geopandas, Dask, and creating interactive graphics with Altair
  • Tips on performance, scalability, and the wider Python data analysis ecosystem

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