Python for SAS Users: A Sas-Oriented Introduction to Python

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

Who This Book Is For

SAS users, SAS programmers, data scientists, data scientist leaders, and Python users who need to work with SAS

熟悉Base SAS程式設計的商業使用者現在可以透過實例學習Python。您將透過實例學習將SAS程式設計結構和編碼模式映射到Python的等效部分。您的主要重點將放在pandas和與數據分析相關的數據管理問題上。

目前估計全球有三百萬或更多的SAS使用者。隨著數據科學領域從使用SAS轉向使用Python等開源軟體，許多使用者將感到有必要更新他們的技能。大多數使用者並沒有接受過正式的計算機科學培訓，他們可能是在工作中通過編寫SAS程式來獲得技能。

因此，目前的文件和大量用於學習Python的書籍和網站都是針對技術人員而非大多數SAS使用者。《Python for SAS Users》提供了目前最全面的實例集。它包含了超過200個Python腳本和大約75個與Python腳本相對應的SAS程式。前幾章更偏向於Python，而後面的章節則展示了SAS和相應的Python實例來解決常見的數據分析任務，例如讀取多個輸入源、檢測缺失值、填充、合併/結合數據和生成輸出。這本書是整合SAS和Python工作流程的不可或缺的指南。

您將學到什麼：

- 不使用試誤法快速掌握Python進行數據分析
- 理解Base SAS和Python之間的相似性和差異
- 根據需求更好地決定使用哪種語言
- 獲得快速結果

適合閱讀對象：

- SAS使用者、SAS程式設計師、數據科學家、數據科學家領導者以及需要與SAS一起工作的Python使用者。

Randy Betancourt's professional career has been in and around data analysis. His journey began by managing a technical support group supporting over 2,000 technical research analysts and scientists from the US Environmental Protection Agency at one of the largest mainframe complexes run by the federal government. He moved to Duke University, working for the administration, to analyze staff resource utilization and costs. There, he was introduced to the politics of data access as the medical school had most of the data and computer resources.

He spent the majority of his career at SAS Institute Inc. in numerous roles, starting in marketing and later moving into field enablement and product management. He subsequently developed the role for Office of the CTO consultant.

Randy traveled the globe meeting with IT and business leaders discussing the impact of data analysis to drive their business. And they also discussed challenges they faced. At the same time, he talked to end users, wanting to hear their perspective. Together, these experiences shaped his understanding of trade-offs that businesses make allocating scarce resources to data collection, analysis, and deployment of models.

More recently, he has worked as an independent consultant for firms, including the International Institute of Analytics, Microsoft's SQL Server group, and Accenture's Applied Intelligence platform.

Sarah Chen has 12 years of analytics experience in banking and insurance, including personal auto pricing, compliance, surveillance, fraud analytics, sales analytics, credit risk modeling for business, and regulatory stress testing. She is a Fellow of both the Casualty Actuarial Society and the Society of Actuaries (FCAS, FSA), an actuary, data scientist, and innovator.

Sarah's career began with five and a half years at Verisk Analytics in the Personal Auto Actuarial division, building predictive models for various ISO products. At Verisk, she learned and honed core skills in data analysis and data management. Her skills and domain expertise were broadened when she moved to KPMG, working with leading insurers, banks, and large online platforms on diverse business and risk management problems.

Randy Betancourt的職業生涯一直與數據分析相關。他的旅程始於管理一個技術支援團隊，為美國環境保護署的2000多名技術研究分析師和科學家提供支援，該團隊運營著聯邦政府運行的最大的主機集群之一。他轉到杜克大學，在行政部門工作，分析人員的資源利用和成本。在那裡，他接觸到了數據訪問的政治問題，因為醫學院擁有大部分數據和計算機資源。

他在SAS Institute Inc.度過了大部分職業生涯，擔任多個職位，從市場營銷開始，後來轉入現場支援和產品管理。隨後，他開發了首席技術官顧問的角色。

Randy遊歷世界各地，與IT和業務領導人會面，討論數據分析對他們業務的影響，同時也討論他們面臨的挑戰。同時，他也與最終用戶交談，希望聽取他們的觀點。這些經歷共同塑造了他對企業在數據收集、分析和模型部署中分配有限資源所做取捨的理解。

最近，他作為獨立顧問為多家公司工作，包括國際分析學院、微軟的SQL Server團隊和埃森哲的應用智能平台。

Sarah Chen在銀行和保險業擁有12年的分析經驗，包括個人汽車定價、合規性、監控、欺詐分析、銷售分析、商業信用風險建模和監管壓力測試。她是財產事故精算師學會和精算師學會的會士（FCAS，FSA），是一位精算師、數據科學家和創新者。

Sarah的職業生涯始於在Verisk Analytics的個人汽車精算部門工作了五年半，為各種ISO產品建立預測模型。在Verisk，她學習並磨練了數據分析和數據管理的核心技能。當她轉到KPMG時，她的技能和領域專業知識得到了擴展，她與領先的保險公司、銀行和大型在線平台合作解決各種業務和風險管理問題。