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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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2 Top Skills You Need For E-commerce

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The following skills are must for every data analytics engineer to be successful in e-commerce companies. Many software engineers are struggling to get this information. I am giving here both technical and general skills.

skills e-commerce

1# Technical Skills for Analytics Career

What skills they need to be successful in their analytics career. The skills required to get an entry into the Analytics job are here for your reference.

I have told in my previous posts that there are many branches in analytics. You need to apply domesticated techniques to extract actionable knowledge.

2# General Skills

I have selected the following mindset and skills that you need to get a job in data analytics. These are proven skills. Set a clear goal to acquire these skills.
  1. Strong interpersonal, oral and written communication and presentation skills; 
  2. Ability to communicate complex findings and ideas in plain language 
  3. Being able to work in teams towards a shared goal; 
  4. Ability to change direction quickly based on data analysis; 
  5. Enjoying discovering and solving problems; 
  6. Proactively seeking clarification of requirements and direction; take responsibility when needed; 
  7. Being able to work in a stressful situation when insights in (new) data sets are required quickly.

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