Python Tools for Data Scientists Pocket Primer / / Oswald Campesato.
As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to numerous Python tools for data scientists. The book covers features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. It includes separate chapte...
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| Superior document: | Title is part of eBook package: De Gruyter MLI AI COLLECTION |
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| Place / Publishing House: | Dulles, VA : : Mercury Learning and Information, , [2022] ©2022 |
| Year of Publication: | 2022 |
| Language: | English |
| Series: | Pocket Primer
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| Online Access: | |
| Physical Description: | 1 online resource (300 p.) |
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| 100 | 1 | |a Campesato, Oswald, |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Python Tools for Data Scientists Pocket Primer / |c Oswald Campesato. |
| 264 | 1 | |a Dulles, VA : |b Mercury Learning and Information, |c [2022] | |
| 264 | 4 | |c ©2022 | |
| 300 | |a 1 online resource (300 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 490 | 0 | |a Pocket Primer | |
| 505 | 0 | 0 | |t Frontmatter -- |t Contents -- |t Preface -- |t Chapter 1: Introduction to Python -- |t Chapter 2: Introduction to NumPy -- |t Chapter 3: Introduction to Pandas -- |t Chapter 4: Working with Sklearn and Scipy -- |t Chapter 5: Data Cleaning Tasks -- |t Chapter 6: Data Visualization -- |t Appendix A: Working with Data -- |t Appendix B: Working with awk -- |t Index |
| 506 | 0 | |a restricted access |u http://purl.org/coar/access_right/c_16ec |f online access with authorization |2 star | |
| 520 | |a As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to numerous Python tools for data scientists. The book covers features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. It includes separate chapters on data visualization and working with Sklearn and SciPy. Companion files with source code are available. Features: Introduces Python, NumPy, Sklearn, SciPy, and awk Covers data cleaning tasks and data visualization Features numerous code samples throughout Includes companion files with source code | ||
| 530 | |a Issued also in print. | ||
| 538 | |a Mode of access: Internet via World Wide Web. | ||
| 546 | |a In English. | ||
| 588 | 0 | |a Description based on online resource; title from PDF title page (publisher's Web site, viewed 02. Jun 2024) | |
| 650 | 4 | |a Data. | |
| 650 | 4 | |a Programming. | |
| 650 | 7 | |a COMPUTERS / Programming Languages / Python. |2 bisacsh | |
| 653 | |a NumPy. | ||
| 653 | |a Python. | ||
| 653 | |a SciPy. | ||
| 653 | |a Sklearn. | ||
| 653 | |a awk. | ||
| 653 | |a data cleaning. | ||
| 653 | |a data science. | ||
| 653 | |a data visualization. | ||
| 653 | |a programming. | ||
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| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t MLI and ITGP STEM IT PACKAGE |z 9783111574073 |
| 773 | 0 | 8 | |i Title is part of eBook package: |d De Gruyter |t Sciendo All Ebooks Trial Collection 2024 |z 9783111502496 |
| 776 | 0 | |c EPUB |z 9781683928218 | |
| 776 | 0 | |c print |z 9781683928232 | |
| 856 | 4 | 0 | |u https://doi.org/10.1515/9781683928225 |
| 856 | 4 | 0 | |u https://www.degruyter.com/isbn/9781683928225 |
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