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Item #:
17783700
Geoprocessing with Python
85% of respondents would recommend this to a friend
Item #:
17783700
MUR 2537
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Take on professional geoprocessing tasks without investing in expensive proprietary packages.
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What Stands Out
Advanced Analysis
Leverage Python's powerful libraries for geospatial analysis, enabling users to conduct complex processes with greater accuracy and efficiency compared to traditional methods.
Custom Automation
Automate repetitive geoprocessing tasks seamlessly, saving time for professionals while reducing human error and ensuring consistent results across various projects.
Cross-Platform Support
Compatible with various operating systems and GIS software, allowing users to integrate geoprocessing capabilities into their existing workflows effortlessly.
Product Details
| Publisher | Manning |
| Publication date | May 23, 2016 |
| Edition | First Edition |
| Language | English |
| Print length | 360 pages |
| ISBN-10 | 1617292141 |
| ISBN-13 | 978-1617292149 |
| Item Weight | 1.34 pounds (610 grams) |
| Dimensions | 7.38 x 1 x 9.25 inches (18.7 x 2.5 x 23.5 cm) |
Who Should Buy?
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GIS Analysts
Ideal for GIS analysts looking to automate workflows and enhance geospatial data processing using Python scripting.
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Data Scientists
Data scientists interested in geospatial analysis can leverage Python's libraries to analyze and manipulate geographic information efficiently.
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Environmental Researchers
Perfect for environmental researchers who require detailed geoprocessing capabilities to analyze spatial data for their studies.
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Beginners in Programming
Not suitable for beginners unfamiliar with programming concepts, as it assumes foundational knowledge of Python and geospatial analysis.
Product Description
Geoprocessing with Python
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Software Development Editorial Review
** Geoprocessing with Python** "Geoprocessing with Python" has garnered an enthusiastic reception from readers, particularly those engaged in geospatial analysis and GIS (Geographic Information Systems). The author’s ability to simplify the complexities of geoprocessing through a clear and direct style resonates with users, making advanced concepts accessible to those at various levels of expertise. Consistently praised for its well-structured chapters, the book builds foundational knowledge effectively, allowing readers to gain a profound understanding of Python programming within a geospatial context. It provides detailed, step-by-step Python code examples that facilitate comprehension and practical application. The text includes a diverse range of examples from vector and raster data, enabling readers to familiarize themselves with different geospatial formats. Another highlight of the book is its thorough guidance on setting up the Python programming environment, which many readers found clearer and more straightforward compared to other resources. This approach not only enhances the overall learning experience but also equips practitioners with skills that can extend beyond the book’s content, applicable even with other programming languages and libraries. Readers appreciate the extensive information compiled in one place, which would often require extensive time and effort to source individually online. Many Consider this book an essential resource for anyone aiming to delve into geospatial problems, regardless of their prior experience with Python. In summary, "Geoprocessing with Python" stands out for its clarity, comprehensive content, and practical approach, making it an excellent recommendation for GIS professionals and Python developers alike. **
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Pros
- Clear and straightforward explanations
- Well-structured chapters building on previous concepts
- Detailed step-by-step Python code examples
- Comprehensive coverage of GDAL and other geospatial libraries
- Practical applications relevant to GIS professionals
- Clear guidance on setting up the Python programming environment
Cons
- Limited to GDAL as the primary focus, which may not align with those preferring newer libraries exclusively.
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MUR 2537
Order now and get it around Wednesday, November 04
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Features & Benefits
- Learn to read, write, and process geospatial data using Python.
- Utilize free open source tools like GDAL, NumPy, and matplotlib.
- Master core practices with hands-on examples and custom geoprocessing tools.
- Handle multiple vector file formats and perform basic analyses on vector data.
- Manipulate and analyze raster data including aerial photographs and elevation models.
- Includes a free eBook with your print book purchase.
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