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91658505
Learning Pandas 2.0: A Comprehensive Guide to Data Manipulation and Analysis for Data Scientists and Machine Learning Professionals
Item #:
91658505
XAF 23068
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What Stands Out
Comprehensive Coverage
This guide extensively covers all aspects of Pandas 2.0, ensuring data scientists and machine learning professionals gain a complete understanding of data manipulation and analysis techniques.
Practical Examples
Filled with real-world examples, the book illustrates complex concepts clearly, enabling readers to grasp and apply Pandas 2.0 strategies effectively in their projects, enhancing learning and retention.
Targeted Audience
Specifically designed for data scientists and machine learning professionals, the guide addresses relevant use cases, ensuring content is tailored to meet the unique demands of these professionals in their data endeavors.
Product Details
| Publisher | GitforGits |
| Publication date | April 10, 2023 |
| Language | English |
| Print length | 175 pages |
| ISBN-10 | 8119177061 |
| ISBN-13 | 978-8119177066 |
| Item Weight | 11 ounces (311.85 grams) |
| Dimensions | 7.5 x 0.4 x 9.25 inches (19.1 x 1 x 23.5 cm) |
Product Description
Learning Pandas 2.0: A Comprehensive Guide to Data Manipulation and Analysis for Data Scientists and Machine Learning Professionals
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Customer Questions & Answers
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Question:
What topics are covered in Learning Pandas 2.0?
Answer: The book covers core concepts of Pandas 2.0, advanced data manipulation techniques, machine learning integration, and specialized data processing needs. -
Question:
Who is this book ideal for?
Answer: This book is ideal for anyone looking to master data manipulation and analysis using Python's Pandas library, from beginners to experienced data scientists. -
Question:
Can I use this book for machine learning projects?
Answer: Yes, it demonstrates how to integrate Pandas 2.0 with popular machine learning libraries for predictive analytics.
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Features & Benefits
- Master core Pandas 2.0 concepts for efficient data manipulation.
- Integrate with leading machine learning libraries like Scikit-learn, TensorFlow, and PyTorch.
- Learn advanced techniques for data transformation, merging, and aggregation.
- Specialized coverage of time series, DateTime, and geospatial data processing.
- Visualize data effectively using tools like Seaborn and Plotly.
- Practical examples to apply skills in real-world scenarios.
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