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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 Differences Automation Vs Internet of Things

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Five reasons why IoT automation provides opportunities to deliver better product or Services. The data from sensors is a golden asset to derive benefits and to apply in products or services.

Automation and IoT both are different 

Automation

The automation is based on the data collected from various devices and make it happen when something goes wrong you can say as automation.

The best example is based on sensor generated data the automation tool take corrective action during course of flying from one country to other.

  Internet of Things

  1. More mobile phones than fixed
  2. New architecture models (ex: Cloud computing)
  3. The new protocol (Ipv6)
  4. Everything is Sensor-laden
  5. More machines than people

The Growth of Internet Usage

The internet will be double in size every 5.32 years. More devices can be connected to the internet through IP. The internet limitation in IPv4 is 4 billion addresses.

But, the internet limitation for IPv6 is 2^128. The total IP traffic over the internet is 1 ZettaByte as of 2011.

Data process
Wisdom from Data

Data 

  1. Information-It is the data after you did clean the raw data.
  2. Knowledge-The ideas or patterns you obtain from cleaned data.
  3. Wisdom-Building models and you can make automate the certain task.

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