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Python Data Analysis - Second Edition
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Learn how to apply powerful data analysis techniques with popular open source Python modules
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Product Details
| 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?
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Data Analysts
This edition provides insights into data manipulation techniques, ideal for professionals looking to enhance their analytical skills.
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Students Learning Python
Perfect for students new to Python who want to focus on practical data analysis applications alongside programming fundamentals.
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Business Intelligence Professionals
Offers practical tools and techniques for extracting actionable insights from data to support business decision-making.
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Complete Beginners
Individuals with no programming background may find the content too advanced or challenging to follow effectively.
Product Description
Python Data Analysis - Second Edition
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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. **
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Pros
- 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.
Cons
- Some chapters may feel densely packed with information, potentially requiring further exploration.
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AUD 125
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Features & Benefits
- 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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