000 02381nam a2200337 i 4500
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008 180709s2018 xxu s 00| 0 eng d
020 _a9781491957639
040 _aUBU.BC
_bspa
_cES-MaUEC
_dES-MaUEC
050 4 _aQA76.73 .P98
_b2018 EB
100 1 _aMcKinney, Wes
_9682658
_eautor
245 1 0 _aPython for Data Analysis :
_bData Wrangling with Pandas, NumPy, and IPython
_cWes McKinney
250 _aSecond edition
264 1 _aSebastopol
_bO'Really
_c2018
300 _a1 recurso en línea (529 páginas)
336 _2rdacontent
_aTexto
_btxt
337 _2rdamedia
_aelectrónico
_bc
338 _2rdacarrier
_arecurso electrónico
_bcr
520 3 _aGet complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process.Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It's ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.Use the IPython shell and Jupyter notebook for exploratory computingLearn basic and advanced features in NumPy (Numerical Python)Get started with data analysis tools in the pandas libraryUse flexible tools to load, clean, transform, merge, and reshape dataCreate informative visualizations with matplotlibApply the pandas groupby facility to slice, dice, and summarize datasetsAnalyze and manipulate regular and irregular time series dataLearn how to solve real-world data analysis problems with thorough, detailed examples.
650 7 _aPython (Lenguaje de programación)
_2embne
_9161036
650 7 _aData mining
_2embne
_9162648
856 4 0 _uhttp://search.ebscohost.com//login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=1605925
_zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942 _2lcc
_cLE
998 _b03/2022
_dz
_eu
_zSI