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15 Python Tips : How to Write Code Effectively

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 Here are some Python tips to keep in mind that will help you write clean, efficient, and bug-free code.     Python Tips for Effective Coding 1. Code Readability and PEP 8  Always aim for clean and readable code by following PEP 8 guidelines.  Use meaningful variable names, avoid excessively long lines (stick to 79 characters), and organize imports properly. 2. Use List Comprehensions List comprehensions are concise and often faster than regular for-loops. Example: squares = [x**2 for x in range(10)] instead of creating an empty list and appending each square value. 3. Take Advantage of Python’s Built-in Libraries  Libraries like itertools, collections, math, and datetime provide powerful functions and data structures that can simplify your code.   For example, collections.Counter can quickly count elements in a list, and itertools.chain can flatten nested lists. 4. Use enumerate Instead of Range     When you need both the index and the value in a loop, enumerate is a more Pyth
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Numpy Array Vs. List: What's the Difference

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Here are the differences between List and NumPy Array. Both store data, but technically these are not the same. You'll find here where they differ from each other.

Python Lists

Here is all about Python lists:

  • Lists can have data of different data types. For instance, data = [3, 3.2, 4.6, 6, 6.8, 9, “hello”, ‘a’]
  • Operations such as subtraction, multiplying, and division allow doing through loops
  • Storage space required is more, as each element is considered an object in Python
  • Execution time is high for large datasets
  • Lists are inbuilt data types


Array vs list


NumPy Arrays

Here is all about NumPy Arrays:
  • Numpy arrays are containers for storing only homogeneous data types. For example: data= [3.2, 4.6, 6.8]; data=[3, 6, 9]; data=[‘hello’, ‘a’]
  • Numpy is designed to do all mathematical operations in parallel and is also simpler than Python
  • Numpy storage space is very much less compared to the list due to the practice of homogeneous data type
  • Execution time is very less for large datasets
  • Numpy is a third-party library that needs to be installed by Conda or PIP


References

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