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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...

Python map() and lambda() Use Cases and Examples

 In Python, map() and lambda functions are often used together for functional programming. Here are some examples to illustrate how they work.

Python map and lambda


Python map and lambda top use cases

1. Using map() with lambda

The map() function applies a given function to all items in an iterable (like a list) and returns a map object (which can be converted to a list).

Example: Doubling Numbers


numbers = [1, 2, 3, 4, 5] doubled = list(map(lambda x: x * 2, numbers)) print(doubled) # Output: [2, 4, 6, 8, 10]

2. Using map() to Convert Data Types

Example: Converting Strings to Integers


string_numbers = ["1", "2", "3", "4", "5"] integers = list(map(lambda x: int(x), string_numbers)) print(integers) # Output: [1, 2, 3, 4, 5]

3. Using map() with Multiple Iterables

You can also use map() with more than one iterable. The lambda function can take multiple arguments.

Example: Adding Two Lists Element-wise


list1 = [1, 2, 3] list2 = [4, 5, 6] summed = list(map(lambda x, y: x + y, list1, list2)) print(summed) # Output: [5, 7, 9]

4. Using map() with Custom Functions

You can define a regular function and use it with map().

Example: Squaring Numbers


def square(x): return x ** 2 numbers = [1, 2, 3, 4, 5] squared = list(map(square, numbers)) print(squared) # Output: [1, 4, 9, 16, 25]

5. Combining filter() and map()

You can combine filter() and map() to process data in a pipeline.

Example: Squaring Even Numbers


numbers = [1, 2, 3, 4, 5] squared_evens = list(map(lambda x: x ** 2, filter(lambda x: x % 2 == 0, numbers))) print(squared_evens) # Output: [4, 16]

Summary

  • map() applies a function to each item in an iterable.
  • lambda allows you to define small, anonymous functions in line.
  • They can be combined for concise and expressive transformations of data.

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