|
|
|
|
| LEADER |
03567nam a2200445Ia 4500 |
| 001 |
99841526504151 |
| 005 |
20241112161454.0 |
| 006 |
m o d |
| 007 |
cr#cn||||||||| |
| 008 |
210701s2018 caua o 001 0 eng d |
| 020 |
|
|
|a 9781491957639
|q (electrónico)
|
| 020 |
|
|
|a 1491957638
|q (electrónico)
|
| 020 |
|
|
|a 1491957662
|q (impreso)
|
| 020 |
|
|
|a 9781491957660
|q (impreso)
|
| 035 |
|
|
|a (OCoLC)1258366511
|
| 035 |
|
|
|a (OCoLC)on1258366511
|
| 040 |
|
|
|a U@S
|b spa
|c U@S
|
| 049 |
|
|
|a U@SA
|
| 050 |
|
4 |
|a QA76.73.P98
|b M35 2018eb
|
| 099 |
|
|
|a Recurso electrónico en INTERNET
|
| 100 |
1 |
|
|a McKinney, Wes.
|
| 245 |
1 |
0 |
|a Python for data analysis
|h [recurso electrónico] :
|b data wrangling with pandas, NumPy, and IPython /
|c Wes McKinney.
|
| 246 |
3 |
0 |
|a Data wrangling with pandas, NumPy, and IPython
|
| 250 |
|
|
|a 2nd ed.
|
| 260 |
|
|
|a Sebastopol, CA :
|b O'Reilly Media,
|c c2018.
|
| 300 |
|
|
|a 1 recurso en línea (xvi, 528 p.) :
|b il.
|
| 516 |
|
|
|a Libro electrónico.
|
| 500 |
|
|
|a Editado originalmente en 2012.
|
| 500 |
|
|
|a Título tomado de la pantalla de presentación (visto 1 de julio de 2021)
|
| 500 |
|
|
|a Incluye índice.
|
| 538 |
|
|
|a Modo de acceso: Disponible en línea a través de Internet.
|
| 505 |
0 |
|
|a Preliminaries -- Python language basics, IPython, and Jupyter notebooks -- Built-in data structures, functions, and files -- NumPy basics: arrays and vectorized computation -- Getting started with pandas -- Data loading, storage, and file formats -- Data cleaning and preparation -- Data wrangling: join, combine, and reshape -- Plotting and visualization -- Data aggregation and group operations -- Time series -- Advanced pandas -- Introduction to modeling libraies in Python -- Data analysis examples -- Appendix A. Advanced NumPy -- Appendix B. More on the IPython system.
|
| 520 |
|
|
|a "Get 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 computing Learn basic and advanced features in NumPy (Numerical Python). Get started with data analysis tools in the pandas library. Use flexible tools to load, clean, transform, merge, and reshape data. Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets. Analyze and manipulate regular and irregular time series data. Learn how to solve real-world data analysis problems with thorough, detailed examples." --Descripción del editor.
|
| 650 |
|
0 |
|a Python (Computer program language)
|
| 650 |
|
0 |
|a Programming languages (Electronic computers)
|
| 650 |
|
0 |
|a Data mining.
|
| 650 |
|
7 |
|a Python (Lenguaje de programación (Computadoras))
|2 UDESA
|
| 650 |
|
7 |
|a Lenguajes de programación (Computadoras)
|2 UDESA
|
| 650 |
|
7 |
|a Minería de datos.
|2 UDESA
|
| 856 |
4 |
0 |
|z Solo para usuarios autorizados, varios accesos simultáneos
|u https://ebookcentral.proquest.com/lib/sanandres/detail.action?docID=5061179
|