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Item #:
64339843
Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition
88% of respondents would recommend this to a friend
Item #:
64339843
AUD 85
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What Stands Out
Comprehensive Recipes
Offers over 80 diverse recipes that cover various aspects of financial data analysis, helping users to efficiently solve real-world financial problems with practical coding examples.
Updated Content
The 2nd edition includes revised recipes that reflect the latest trends in Python programming and financial analytics, ensuring users have access to current tools and methods.
User-Friendly Structure
Organized layout with clear instructions helps both beginners and experienced users quickly implement solutions, making financial data analysis more accessible and manageable.
Product Details
| Publisher | Packt Publishing |
| Publication date | December 30, 2022 |
| Edition | 2nd ed. |
| Language | English |
| Print length | 740 pages |
| ISBN-10 | 1803243198 |
| ISBN-13 | 978-1803243191 |
| Item Weight | 2.75 pounds (1.25 kg) |
| Dimensions | 7.5 x 1.67 x 9.25 inches (19.1 x 4.2 x 23.5 cm) |
Who Should Buy?
-
Aspiring Data Analysts
Those looking to develop skills in analyzing financial data through practical, hands-on programming examples in Python.
-
Finance Professionals
Current finance practitioners seeking to automate analysis and enhance their data handling capabilities using Python.
-
Students Learning Finance
Students interested in finance can benefit from a practical approach to financial data analysis utilizing Python recipes.
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Complete Beginners
Individuals with no programming experience may struggle to follow the book's recipes effectively without foundational knowledge.
Product Description
Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition
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Data Modeling & Design Editorial Review
Python for Finance Cookbook: Over 80 powerful recipes for effective financial data analysis, 2nd Edition is an exceptional resource for anyone looking to delve into financial data analysis using Python. This extensively updated edition, published by Packt Publishing, offers practical examples that highlight various Python libraries, making it straightforward to download and analyze financial time series data. Readers praise its clear and systematic explanations, which greatly enhance the learning experience. However, while many find the recipes beneficial, some readers express disappointment with certain sections, such as the model factor section, citing confusion in its application. Overall, it serves as a solid platform for beginners entering the realm of financial analysis.
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Pros
- Comprehensive Python libraries for financial analysis
- Clear explanations enhance learning experience
- Useful for both study and practical applications
- Detailed code examples available online
- Effective at analyzing financial time series data
Cons
- Some sections may be confusing for readers
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