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Manage and Automate Data Analysis with Pandas in Python Today, 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.
Nº de artículo:
73518765
Addison-Wesley Data & Analytics Pandas for Everyone: Python Data Analysis, Paperback
Nº de artículo:
73518765
ARS 132372
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Manage and Automate Data Analysis with Pandas in Python Today, 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.
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What Stands Out
Comprehensive Introduction
Covers the fundamentals of data analysis using Python and Pandas, making it ideal for beginners and professionals looking to enhance their data manipulation skills.
Practical Examples
Includes real-world examples and exercises that help readers apply theoretical concepts, ensuring a hands-on understanding of data analytics and boosting retention.
Accessible Language
Written in an easy-to-understand style, it simplifies complex topics, making it approachable even for those without a technical background, facilitating learning for a broader audience.
Detalles de producto
- Manage and automate data analysis with Pandas in Python
- Learn practical knowledge and insight for solving real problems with Pandas
- Updated coverage of plotting and the seaborn data visualization library
- Includes examples, resources, and online bonus material
- Covers data cleaning techniques, model fitting, and performance tips
- Explore advanced features like linear modeling and unsupervised machine learning
| Language | English |
| Series Title | Addison-Wesley Data & Analytics |
| Publisher | Pearson Education |
| Book Format | Paperback |
| Original Languages | English |
| Number of Pages | 512 |
| Author | Addison-Wesley Professional |
| Is Large Print | N |
| Title | Pandas for Everyone: Python Data Analysis |
| ISBN-13 | 9780137891153 |
| Publication Date | December, 2022 |
| ISBN-10 | 0137891156 |
| Book format | Paperback |
| Fiction/nonfiction | Non-Fiction |
| Genre | Computing & Internet |
| Pages | 512 |
| Reading level | Professional and Scholarly |
| Subgenre | Languages |
| Edition | 2nd Edition |
| Original languages | English |
| Item Weight | 1.5 lbs (680 grams) |
Who Should Buy?
-
Beginner Programmers
Ideal for individuals new to programming, offering a clear introduction to Python and data analysis with practical examples.
-
Data Analysts
Great for aspiring data analysts looking to enhance their skills in data manipulation and analysis using the Pandas library.
-
Academic Students
Well-suited for students studying data science or analytics courses, providing foundational knowledge essential for academic success.
-
Advanced Users
Not ideal for experienced data scientists who seek advanced techniques or complex data analysis beyond basic Pandas functionalities.
DESCRIPCIÓN DEL PRODUCTO
Addison-Wesley Data & Analytics Pandas for Everyone: Python Data Analysis, Paperback
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Preguntas y respuestas de los clientes
-
Pregunta:
What key concepts are introduced in this book?
Respuesta: Key concepts such as using regularization to prevent data overfitting and unsupervised machine learning methods are introduced. -
Pregunta:
What are some new features in the second edition of the book?
Respuesta: Extended coverage of plotting, expanded examples, and resources, updated Python 3.9 code, and packages coverage. -
Pregunta:
What are some techniques covered for data cleaning in this book?
Respuesta: From basic string manipulation to applying functions across dataframes, powerful data cleaning techniques are demonstrated.
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características y beneficios
- Automate data analysis tasks using Pandas in Python
- Suitable for managing data of high variety, velocity, and volume
- Can handle large and complex data analysis tasks
- Ensure data veracity and visualize it effectively for decision-making
- Reproducibility across multiple data sets
- Practical guide for solving real-world data science problems
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