Showing posts with the label r-vs-sas

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Python Regex: The 5 Exclusive Examples

 Regular expressions (regex) are powerful tools for pattern matching and text manipulation in Python. Here are five Python regex examples with explanations: 01 Matching a Simple Pattern import re text = "Hello, World!" pattern = r"Hello" result =, text) if result:     print("Pattern found:", Output: Output: Pattern found: Hello This example searches for the pattern "Hello" in the text and prints it when found. 02 Matching Multiple Patterns import re text = "The quick brown fox jumps over the lazy dog." patterns = [r"fox", r"dog"] for pattern in patterns:     if, text):         print(f"Pattern '{pattern}' found.") Output: Pattern 'fox' found. Pattern 'dog' found. It searches for both "fox" and "dog" patterns in the text and prints when they are found. 03 Matching Any Digit   import re text = "The price of the

5 Top R Vs SAS Differences

Statistical analysis should know by every software engineer. R is an open source statistical programming language. SAS is licensed analysis suite for statistics. The two are very much popular in Machine learning and data analytics projects. SAS is an Analysis-suite software and R is a programming language. 1. R Language R supports both statistical analysis and Graphics R is an open source project. R is 18th most popular Language R packages are written in C, C++, Java, Python and.Net R is popular in Machine learning, data mining and Statistical analysis projects. a). R Advantages R is flexible since a lot of packages are available. R is best suited for data related projects and  Machine learning . Less cost since it is open source language. R Studio is the best tool to develop R programming modules. Ref: (read more advantages) b). R Disadvantages R language architecture model is out of date. So may not use it for critical applications. R is not suitable for Serve