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 Contents......Page 3 Intro......Page 8 1 Why Python......Page 11 Setting Up a Python Environment......Page 12 Anaconda3 Install Process for Windows......Page 13 Troubleshooting Python Installation for Windows......Page 19 Anaconda3 Install Process for Linux......Page 23 Executing a Python Script on Windows......Page 26 Line Continuation Symbol......Page 29 Executing a Python Script on Linux......Page 30 Integrated Development Environment (IDE) for Python......Page 31 Jupyter Notebook......Page 32 Jupyter Notebook for Linux......Page 34 Summary......Page 35 2 Python Types & Formatting......Page 36 Numerics......Page 37 Python Operators......Page 39 Boolean......Page 40 Comparison Operators......Page 41 IN/NOT IN......Page 46 AND/OR/NOT......Page 47 Numerical Precision......Page 49 Strings......Page 53 String Slicing......Page 56 Formatting Strings......Page 60 Formatting Integers......Page 63 Formatting Floats......Page 67 Datetime Formatting......Page 68 Summary......Page 72 3 pandas Library......Page 73 Column Types......Page 75 Series......Page 76 DataFrames......Page 81 DataFrame Validation......Page 83 DataFrame Inspection......Page 86 Missing Data......Page 91 Missing Value Detection......Page 94 isnull() Method......Page 98 Dropping Missing Values......Page 105 Imputation......Page 112 Summary......Page 117 4 Indexing & GroupBy......Page 118 Create Index......Page 119 Return Columns by Position......Page 121 Return Rows by Position......Page 124 Return Rows and Columns by Label......Page 126 Conditionals......Page 130 Updating......Page 133 Return Rows and Columns by Position......Page 135 MultiIndexing......Page 138 Basic Subsets with MultiIndexes......Page 144 Advanced Indexing with MultiIndexes......Page 148 Slicing Rows and Columns......Page 150 Conditional Slicing......Page 153 Cross Sections......Page 155 GroupBy......Page 157 Iteration Over Groups......Page 162 GroupBy Summary Statistics......Page 166 Filtering by Group......Page 168 Group by Column with Continuous Values......Page 169 Transform Based on Group Statistic......Page 172 Pivot......Page 175 Summary......Page 183 5 Data Management......Page 184 SAS Sort/Merge......Page 188 Inner Join......Page 191 Right Join......Page 193 Left Join......Page 196 Outer Join......Page 198 Right Join Unmatched Keys......Page 199 Left Join Unmatched Keys......Page 202 Outer Join Unmatched Keys......Page 204 Validate Keys......Page 207 Joining on an Index......Page 208 Join Key Column with an Index......Page 210 Update......Page 212 Conditional Update......Page 216 Concatenation......Page 220 Finding Column Min and Max Values......Page 229 Sorting......Page 230 Finding Duplicates......Page 234 Dropping Duplicates......Page 235 Sampling......Page 238 Convert Types......Page 241 Map Column Values......Page 242 Transpose......Page 244 Summary......Page 248 6 pandas Readers & Writers......Page 249 Reading .csv Files......Page 250 Date Handling in .csv Files......Page 256 Read .xls Files......Page 259 Write .csv Files......Page 266 Write .xls Files......Page 268 Read JSON......Page 270 Write JSON......Page 274 Read RDBMS Tables......Page 275 Query RDBMS Tables......Page 285 Read SAS Datasets......Page 292 Write RDBMS Tables......Page 295 Summary......Page 300 Date Object......Page 301 Return Today’s Date......Page 302 Date Manipulation......Page 305 Shifting Dates......Page 314 Date Formatting......Page 315 Dates to Strings......Page 319 Strings to Dates......Page 322 Time Object......Page 324 Time of Day......Page 327 Time Formatting......Page 329 Times to Strings......Page 330 Strings to Time......Page 332 Datetime Object......Page 335 Combining Times and Dates......Page 338 Returning Datetime Components......Page 340 Strings to Datetimes......Page 342 Datetimes to Strings......Page 345 Timedelta Object......Page 348 Time zone Object......Page 357 Naïve and Aware Datetimes......Page 358 pytz Library......Page 361 SAS Time zone......Page 369 Summary......Page 378 Install SASPy......Page 379 Set Up the sascfg_personal.py Configuration File......Page 380 Make SAS-Supplied .jar Files Available......Page 382 SASPy Examples......Page 384 Basic Data Wrangling......Page 386 Write DataFrame to SAS Dataset......Page 389 Define the Libref to Python......Page 390 Write the DataFrame to a SAS Dataset......Page 391 Execute SAS Code......Page 397 Write SAS Dataset to DataFrame......Page 399 Passing SAS Macro Variables to Python Objects......Page 403 Prompting......Page 406 Scripting SASPy......Page 407 Datetime Handling......Page 410 Summary......Page 415 Generating the Tickets DataFrame......Page 416 Many-to-Many Use Case......Page 419 Index......Page 428 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 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 anlaysis 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. "Pyhon 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