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Step-by-Step Guide to Reading Different Files in Python

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 In the world of data science, automation, and general programming, working with files is unavoidable. Whether you’re dealing with CSV reports, JSON APIs, Excel sheets, or text logs, Python provides rich and easy-to-use libraries for reading different file formats. In this guide, we’ll explore how to read different files in Python , with code examples and best practices. 1. Reading Text Files ( .txt ) Text files are the simplest form of files. Python’s built-in open() function handles them effortlessly. Example: # Open and read a text file with open ( "sample.txt" , "r" ) as file: content = file.read() print (content) Explanation: "r" mode means read . with open() automatically closes the file when done. Best Practice: Always use with to handle files to avoid memory leaks. 2. Reading CSV Files ( .csv ) CSV files are widely used for storing tabular data. Python has a built-in csv module and a powerful pandas library. Using cs...
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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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