000			03946nam a22004095i 4500
999			_c362019 _d362019 _x1
001			362019
003			ES-MaUEC
005			20230102121458.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			220205s2021 sz | s |||| 0|eng d
020			_a9783030689520
024	7		_a10.1007/978-3-030-68952-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.73.P98 _b2021 EB
100			_aUnpingco, José. _eautor _4 _4http://id.loc.gov/vocabulary/relators/aut _998936
245	1	0	_aPython Programming for Data Analysis _cby José Unpingco.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XII, 263 páginas) _b 134 ilustraciones, 123 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aEngineering (SpringerNature-11647)
490	0		_aEngineering (R0) (SpringerNature-43712)
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	3		_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.
988			_aSpringer_Engineering_2021
650		7	_2embne _9161036 _aPython (Lenguaje de programación)
776	0	8	_iPrinted edition: _z9783030689513
776	0	8	_iPrinted edition: _z9783030689537
776	0	8	_iPrinted edition: _z9783030689544
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-68952-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eIG _zSI