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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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6 Exclusive List and Tuple Differences in Python

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Here're quick differences between List and Tuple


Here're the quick differences between Tuple and List in Python. These are helpful for interviews and your project.

Tuple and List differences

List

  • Comma-separated elements inside a square bracket [] make a list.
  • The elements are indexed, which starts from '0'
  • These you need to enclose in a single quote and separate by a comma.
  • It can contain another list, which is called a NESTED list.
  • Use type() function to get the type of data it is.
  • The list is mutable (you can change the data). The objects (elements) can be of different data types. Here're examples on the List.

Tuple

  • The elements comma-separated and enclosed in parenthesis () 
  • The elements are indexed, which starts from '0'
  • It can have heterogeneous data (integer, float, string, list, etc.)
  • It is immutable. So you can't change the elements.
  • Use the type() function to get the type of data it is. 
  • Here're examples of Tuple.

List Example

#Illustration of creating a list 
new_list=[1, 2, 3, 4] 
print(new_list) 


# Homogeneous data elements 
new_list1=[1, "John", 55.5] 
print(new_list1) 


# Heterogeneous data elements 
new_list2=[111, [1, "Clara", 75.5]] 
# Nested list 
print(new_list2)


Output



[1, 2, 3, 4]
[1, ‘John’, 55.5]
[111, [1, ‘Clara’, 75.5]]



Tuple Example


#Illustration of unpacking a tuple 
 new_tuple2=(111, [1, "Clara", 75.5], (2, "Simon", 80.5)) 

# Nested tuple 
print(new_tuple2) x, y, z=new_tuple2 
print(x) 
print(y) 
print(z) 


Output



111
[1, ‘Clara’, 75.5]
(2, ‘Simon’, 80.5)

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