Python Data Analysis - Second Edition
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Python Data Analysis - Second Edition
Learn how to apply powerful data analysis techniques with popular open source Python modules
Python Data Analysis - Second Edition
Nº de artículo: 13658994

Python Data Analysis - Second Edition

90% of respondents would recommend this to a friend

Nº de artículo: 13658994

PYG 613558

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What Stands Out

Comprehensive Coverage
This edition thoroughly explores Python tools and libraries for data analysis, ensuring users gain a robust understanding of effective techniques for extracting insights from complex datasets.
Practical Examples
Includes real-world case studies and hands-on projects, helping readers apply concepts immediately, enhancing learning through practice and ensuring better retention of knowledge.
Updated Techniques
Features the latest updates in Python data analysis practices, ensuring users stay ahead with current methodologies and tools, making it invaluable for both beginners and seasoned analysts.

Detalles de producto

Get the revised edition of Python Data Analysis book and analyze data with Python. Order now on Ubuy Paraguay for reliable online shopping.
  • Key FeaturesFind, manipulate, and analyze your data using the Python 3.5 librariesPerform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python codeAn easy-to-follow guide with realistic examples that are frequently used in real-world data analysis projects.Book DescriptionData analysis techniques generate useful insights from small and large volumes of data. Python, with its strong set of libraries, has become a popular platform to conduct various data analysis and predictive modeling tasks.With this book, you will learn how to process and manipulate data with Python for complex analysis and modeling. We learn data manipulations such as aggregating, concatenating, appending, cleaning, and handling missing values, with NumPy and Pandas. The book covers how to store and retrieve data from various data sources such as SQL and NoSQL, CSV fies, and HDF5. We learn how to visualize data using visualization libraries, along with advanced topics such as signal processing, time series, textual data analysis, machine learning, and social media analysis.The book covers a plethora of Python modules, such as matplotlib, statsmodels, scikit-learn, and NLTK. It also covers using Python with external environments such as R, Fortran, C/C++, and Boost libraries.What you will learnInstall open source Python modules such NumPy, SciPy, Pandas, stasmodels, scikit-learn,theano, keras, and tensorflow on various platformsPrepare and clean your data, and use it for exploratory analysisManipulate your data with PandasRetrieve and store your data from RDBMS, NoSQL, and distributed filesystems such as HDFS and HDF5Visualize your data with open source libraries such as matplotlib, bokeh, and plotlyLearn about various machine learning methods such as supervised, unsupervised, probabilistic, and BayesianUnderstand signal processing and time series data analysisGet to grips with graph processing and social network analysisAbout the AuthorArmando Fandango is Chief Data Scientist at Epic Engineering and Consulting Group, and works on confidential projects related to defense and government agencies. Armando is an accomplished technologist with hands-on capabilities and senior executive-level experience with startups and large companies globally. His work spans diverse industries including FinTech, stock exchanges, banking, bioinformatics, genomics, AdTech, infrastructure, transportation, energy, human resources, and entertainment.Armando has worked for more than ten years in projects involving predictive analytics, data science, machine learning, big data, product engineering, high performance computing, and cloud infrastructures. His research interests spans machine learning, deep learning, and scientific computing.Table of ContentsGetting Started with Python LibrariesNumPy ArraysThe Pandas PrimerStatistics and Linear AlgebraRetrieving, Processing, and Storing DataData VisualizationSignal Processing and Time SeriesWorking with DatabasesAnalyzing Textual Data and Social MediaPredictive Analytics and Machine LearningEnvironments Outside the Python Ecosystem and Cloud ComputingPerformance Tuning, Profiling, and ConcurrencyKey ConceptsUseful FunctionsOnline Resources
Publisher Packt Publishing
Publication date March 27, 2017
Edition 2nd Revised edition
Language English
Print length 330 pages
ISBN-10 1787127486
ISBN-13 978-1787127487
Item Weight 1.25 pounds (570 grams)
Dimensions 7.5 x 0.75 x 9.25 inches (19.1 x 1.9 x 23.5 cm)

Who Should Buy?

Suitable For
  • Data Analysts

    This edition provides insights into data manipulation techniques, ideal for professionals looking to enhance their analytical skills.

  • Students Learning Python

    Perfect for students new to Python who want to focus on practical data analysis applications alongside programming fundamentals.

  • Business Intelligence Professionals

    Offers practical tools and techniques for extracting actionable insights from data to support business decision-making.

Not Suitable For
  • Complete Beginners

    Individuals with no programming background may find the content too advanced or challenging to follow effectively.

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Preguntas y respuestas de los clientes

  • Pregunta: What is the focus of 'Python Data Analysis - Second Edition'?

    Respuesta: The book primarily focuses on data manipulation and complex data analysis using Python. It covers essential libraries like Pandas, NumPy, and Matplotlib, providing readers with practical tools to analyze data effectively. By guiding you through real-world examples, the book helps you understand the intricacies of data cleaning, transformation, and visualization, making it easier to derive insightful conclusions from datasets.
  • Pregunta: Who should read 'Python Data Analysis - Second Edition'?

    Respuesta: This book is ideal for data analysts, scientists, and anyone interested in understanding how to manipulate and analyze data using Python. It caters to both beginners who need foundational knowledge and experienced individuals seeking to enhance their skills. With its hands-on approach, readers from various backgrounds, such as finance, marketing, or scientific research, can benefit greatly from its content.
  • Pregunta: What Python libraries are emphasized in this book?

    Respuesta: The book emphasizes several powerful Python libraries, most notably Pandas, NumPy, and Matplotlib. These libraries are crucial for data analysis, allowing you to perform data manipulation tasks, numerical computations, and create visualizations. Understanding how to leverage these libraries enables you to handle large datasets efficiently, making the analytical process much more effective and insightful.
  • Pregunta: How does this book help in practical data analysis scenarios?

    Respuesta: This book uses real-world datasets and practical scenarios to illustrate data analysis techniques. By working through actual data problems, you gain hands-on experience in applying Python code to solve analytical challenges. This practical approach fosters better comprehension and prepares you to tackle similar issues in your own projects, whether they involve sales data, web analytics, or scientific research.
  • Pregunta: Is previous knowledge of Python required to understand this book?

    Respuesta: While some familiarity with Python can be beneficial, the book is structured to accommodate newcomers as well. It introduces Python concepts gradually, starting with essential syntax and progressing to more complex analysis. As such, even those with minimal programming experience can follow along, learn to code in Python, and apply these skills to data analysis effectively.
  • Pregunta: Does the book cover visualization techniques?

    Respuesta: Yes, 'Python Data Analysis - Second Edition' includes a dedicated section on data visualization techniques using Matplotlib and Seaborn. Visual representations are crucial for interpreting data trends and insights, and the book provides step-by-step guidance to create engaging and informative visualizations. This is particularly useful in fields like marketing and research, where data storytelling plays a vital role.
  • Pregunta: Are there exercises or projects included in the book?

    Respuesta: The book features a variety of exercises and projects designed to reinforce learning and apply concepts discussed in each chapter. These practical assignments enable you to experiment with data firsthand and develop your analytical skills. Consequently, you'll gain confidence in implementing methodologies covered in the text, making it easier to apply these techniques in your professional projects.
  • Pregunta: How does this edition differ from the first edition?

    Respuesta: The second edition of 'Python Data Analysis' includes updates to reflect the latest advancements in Python and its libraries. It features comprehensive examples and improved explanations based on reader feedback from the first edition, ensuring clarity and relevance. Enhancements in this edition also address newer data analysis tools, providing a more robust educational resource for users seeking to work with contemporary data science practices.
  • Pregunta: Can this book be used for self-study?

    Respuesta: Absolutely! The book is designed for self-study, featuring clear explanations and structured content that guide you through the learning process. With standalone chapters that build upon one another, you can progress at your own pace. The inclusion of practical exercise sets and coding snippets encourages active engagement, making it a perfect resource for independent learners looking to master data analysis with Python.
  • Pregunta: Where can I buy 'Python Data Analysis - Second Edition' in Paraguay?

    Respuesta: You can purchase 'Python Data Analysis - Second Edition' from Ubuy in Paraguay. Ubuy offers a wide selection of books, including this critical resource for data analysts, ensuring you have access to the latest editions and formats. Their platform makes it easy to find the book, providing additional details and features that can assist you in making an informed decision.

Data Modeling & Design Editorial Review

**** "Python Data Analysis - Second Edition" has garnered significant appreciation from its readers for its comprehensive approach to data manipulation and analysis using Python. Enthusiasts embarking on the journey of data analysis, especially those dealing with geospatial datasets or specific fields like sports analytics, will find the book to be an invaluable resource. Its detailed exploration of diverse data analysis techniques, including an introduction to learning algorithms for tasks like sentiment analysis, positions this book as a strong foundation for newcomers and intermediate users alike. Readers have praised the clarity with which the authors present information about various Python libraries tailored for different data analysis tasks. This organization not only aids comprehension but also enhances practical application, allowing users to quickly find the tools they need for their specific projects. The book does not shy away from addressing the complexities of handling large datasets, equipping readers with strategies to optimize performance as their data needs grow. While some have noted that certain chapters are so rich in content that they could warrant standalone books, this breadth of information ultimately contributes to a well-rounded educational experience. In summary, "Python Data Analysis - Second Edition" stands out as a crucial text for anyone looking to deepen their understanding of data analysis through Python, making it a recommended read for both novices and those looking to refine their skills in the domain. **

Customer Reviews & Ratings

9 valoraciones de los clientes
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  • 4 estrella
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  • 2 estrella
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ventajas

  • Comprehensive foundation for data manipulation and analysis in Python.
  • Strong focus on practical application with various Python libraries categorized by use.
  • Insightful discussions on advanced topics, such as learning algorithms for sentiment analysis.
  • Offers strategies for optimizing performance with large datasets.

Contras

  • Some chapters may feel densely packed with information, potentially requiring further exploration.

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PYG 613558

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características y beneficios

  • Perform advanced, high-performance linear algebra and mathematical calculations with clean and efficient Python code
  • Manipulate and analyze data using Python 3.5 libraries
  • Explore data manipulation techniques such as aggregating, cleaning, handling missing values, and visualizing data
  • Retrieve and store data from various sources such as SQL, NoSQL, CSV files, and HDF5
  • Understand and implement machine learning methods
  • Gain insights into signal processing, time series data analysis, and social media analysis

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