Python data science handbook : Essential tools for working with data
By: VanderPlas, Jake
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Publisher: Sebastopol O'Reilly Media, Inc. 20017Edition: 1st.Description: xvi, 529p. | Binding - Paperback | 23.3*17.6 cm.ISBN: 9789352134915.Subject(s): Computer Engineering | PANDAS | Data Manipulation | NumPy | MatplotlibDDC classification: 006.312 Summary: For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allÃIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
| Item type | Current location | Collection | Call number | Status | Date due | Barcode | Item holds |
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Text Books
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School of Engineering & Technology Reference Section | Reference | 006.312 VAN (Browse shelf) | Not For Loan | E14609 | ||
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Text Books
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School of Engineering & Technology General Stacks | Circulation | 006.312 VAN (Browse shelf) | Available | E14610 |
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them allÃIPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.
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