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Python Regex: The 5 Exclusive Examples

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 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 = re.search(pattern, text) if result:     print("Pattern found:", result.group()) 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 re.search(pattern, 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
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Tableau 2 Top Books to Read

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In this post, I have given two top data visualization in Tableau books. These books helpful for your self-study.  


Tableau 2 Top Books to Read

Top Tableau Course Contents


  • Introduction to Tableau 
  • Connecting to data 
  • Visual analytics 
  • How to design dashboard 
  • Mapping 
  • Calculations 
  • How to publish and Manage with Tableau 
  • Security with Tableau .....Read more.

Two Best books on Tableau to Learn and Visualize the Data.


#1. Learning Tableau

  • Learn how to create effective data visualizations with Tableau and unlock a smarter approach to business analytics. 
  • It might just transform your organization About This Book
  • Create stylish visualizations and dashboards that explain complexity with clarity.
  • Learn effective data storytelling to transform how your business uses ideas and makes decisions
  • Explore all the new features in Tableau 10 and start to redefine what business. analytics means to your organization.

#2. How to create Data Visualization in Tableau 

  • Tableau Your Data! is a comprehensive guide to the effective use of the Tableau Software toolset for data visualization.
  • It covers the core feature set for data analytics, illustrating best practices for creating and sharing effective visualizations that support timely business decision-making.
  • The author also covers little-known as well as more advanced features and techniques, illustrating them using detailed, real-world case studies that the author has developed as part of his consulting and training.

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