04073nam a22005775i 4500 ssj0002240293 WaSeSS 20220420150929.0 m d cr n 190906s2019 xxu| o |||| 0|eng d 9781484250006 (print) 10.1007/978-1-4842-5001-3 doi eng WaSeSS AIMIT LIBRARY UMX bicssc COM051360 bisacsh UMX thema 005.1372 1 BETR EBOOK Betancourt, Randy. 30611 Python for SAS Users : sas oriented introduction to python / By Randy Betancourt and Sarah Chen. 1st ed. New York : Apress , 2022. Berkeley, CA : Apress : Imprint: Apress, 2019. xvii, 434p. ; online resource. 25.5 cm. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 1: Why Python -- Chapter 2: Python Types and Formatting -- Chapter 3: pandas Library -- Chapter 4: Indexing and GroupBy -- Chapter 5: Data Management -- Chapter 6: pandas Readers and Writers -- Chapter 7: Date and Time -- Chapter 8: saspy Module -- Appendix A: Generating the Tickets DataFrame -- Appendix B: Many-to-Many Use Case -- . Requires an SPL library card. Business users familiar with Base SAS programming can now learn Python by example. You will learn via examples that map SAS programming constructs and coding patterns into their Python equivalents. Your primary focus will be on pandas and data management issues related to analysis of data. It is estimated that there are three million or more SAS users worldwide today. As the data science landscape shifts from using SAS to open source software such as Python, many users will feel the need to update their skills. Most users are not formally trained in computer science and have likely acquired their skills programming SAS as part of their job. As a result, the current documentation and plethora of books and websites for learning Python are technical and not geared for most SAS users. Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs to the Python scripts. The first chapters are more Python-centric, while the remaining chapters illustrate SAS and corresponding Python examples to solve common data analysis tasks such as reading multiple input sources, missing value detection, imputation, merging/combining data, and producing output. This book is an indispensable guide for integrating SAS and Python workflows. What You'll Learn: Quickly master Python for data analysis without using a trial-and-error approach Understand the similarities and differences between Base SAS and Python Better determine which language to use, depending on your needs Obtain quick results. Mode of access: World Wide Web. Python (Computer program language) 30612 Python types and formatting 30613 Pandas library 30614 Data management 30615 Pandas readers and writers 30616 Electronic books. local 30617 Chen, Sarah. 30618 SpringerLink (Online service) 30619 O'Reilly (Firm) 30620 Serials Solutions 30621 Springer eBooks Printed edition: 9781484250006 Printed edition: 9781484250020 View this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484250013/?ar An e-book available through full-text database. ddc BK 1st 005.1372 BETR 222516 222516 03659437 0 0 ddc 0 0 MCA AIMIT AIMIT 2022-03-24 1699.00 Bill no:6623; Bill dt:2022-03-23 0 005.1372 BETR MCA17080 2022-04-20 00:00:00 1359.20 2022-04-20 BK