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020 _a9783030689520
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024 7 _a10.1007/978-3-030-68952-0
_2doi
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_2bicssc
072 7 _aTEC041000
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082 0 4 _a621.382
_223
100 1 _aUnpingco, José.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_949691
245 1 0 _aPython Programming for Data Analysis
_h[electronic resource] /
_cby José Unpingco.
250 _a1st ed. 2021.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2021.
300 _aXII, 263 p. 134 illus., 123 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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_2rda
505 0 _aIntroduction -- Basic Language -- Basic Data Structures -- Basic Programming -- File Input/Output -- Dealing with Errors -- Power Python Features to Master -- Advanced Language Features -- Using modules -- Object oriented programming -- Debugging from Python -- Using Numpy – Numerical Arrays in Python -- Data Visualization Using Python -- Bokeh for Web-based Visualization -- Getting Started with Pandas -- Some Useful Python-Fu -- Conclusion.
520 _aThis textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
650 0 _aTelecommunication.
_910437
650 0 _aComputer science—Mathematics.
_931682
650 0 _aMathematical statistics.
_99597
650 0 _aQuantitative research.
_94633
650 0 _aSignal processing.
_94052
650 0 _aStatistics .
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650 0 _aData mining.
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650 1 4 _aCommunications Engineering, Networks.
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650 2 4 _aProbability and Statistics in Computer Science.
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650 2 4 _aData Analysis and Big Data.
_949692
650 2 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_931790
650 2 4 _aData Mining and Knowledge Discovery.
_949693
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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856 4 0 _uhttps://doi.org/10.1007/978-3-030-68952-0
912 _aZDB-2-ENG
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