Skip to main content

seek(), tell() and other functions

 


Learning Sections          show

seek(), tell() and Other Functions in Python

Python provides several functions to manipulate the file pointer while reading or writing files. Here are some commonly used functions:

1. seek()

The seek() method sets the file's current position at the offset. The position is calculated from the beginning of the file, unless the optional whence parameter is provided. The whence parameter can take the following values:

  • 0: default, the beginning of the file
  • 1: current file position
  • 2: end of the file

# Open the file in read mode
with open('example.txt', 'r') as file:
    # Move the file pointer to the beginning of the file
    file.seek(0)
    
    # Read the first 10 characters
    content = file.read(10)
    print(content)
    

2. tell()

The tell() method returns the current position of the file pointer within the file.


# Open the file in read mode
with open('example.txt', 'r') as file:
    # Read the first 10 characters
    content = file.read(10)
    print(content)
    
    # Get the current position of the file pointer
    position = file.tell()
    print(position)
    

3. truncate()

The truncate() method resizes the file to the given number of bytes. If the size is not specified, it resizes the file to the current file pointer position.


# Open the file in write mode
with open('example.txt', 'w') as file:
    # Write some content to the file
    file.write('Hello, World!')
    
    # Resize the file to the first 5 bytes
    file.truncate(5)
    

4. flush()

The flush() method flushes the internal buffer, ensuring that all data is written to the file.


# Open the file in write mode
with open('example.txt', 'w') as file:
    # Write some content to the file
    file.write('Hello, World!')
    
    # Flush the internal buffer
    file.flush()
    

5. fileno()

The fileno() method returns the file descriptor, which is a unique integer handle assigned to the file by the operating system.


# Open the file in read mode
with open('example.txt', 'r') as file:
    # Get the file descriptor
    file_descriptor = file.fileno()
    print(file_descriptor)
    

Popular posts from this blog

Learn Python

  Learning Sections Introduction to Python Comment, escape sequence and print statement in Python Variables and Data Types in Python Typecasting in Python User input in Python String slicing and operations on string in Python String methods in Python If else conditional statements in Python Match case statement in Python For loops in Python While loops in Python Break and continue statement in Python Functions in Python Function Arguments in Python introduction to lists in Python List methods in Python Tuples in Python Operations on tuple in Python f strings in Python Docstrings in Python Recursion in Python Sets in Python Set methods in Python Dictionaries in Python for Loop with else in Python Exception Handling in Python Finally keyword in Python Raising custom errors in Python Short hand if else statements Enumerate Function in Python Virtual Environment in Python How import works in Python if __nam...

MultiProcessing in Python

  Learning Sections          show MultiProcessing in Python Multiprocessing in Python involves using the multiprocessing module to run multiple processes concurrently, taking advantage of multiple CPU cores. This module provides a higher level of concurrency than threading and is especially useful for CPU-bound tasks. Creating Processes You can create and start a new process by using the multiprocessing module: import multiprocessing def print_numbers (): for i in range ( 10 ): print ( i ) p1 = multiprocessing.Process ( target = print_numbers ) p1 . start () p1 . join () # Wait for the process to complete Using Process Pools The multiprocessing module provides a Pool class, which allows you to manage a pool of worker processes: from multiprocessing import Pool def square ( n ): return n * n with Pool ( 4 ) as pool : result = pool.map ( square , range (...

Conclusion and where to go after this

  Conclusion and Where to Go After This Congratulations on completing your Python learning journey! You've covered a wide array of topics, from the basics of syntax and data types to advanced concepts like multithreading, multiprocessing, and decorators. But learning doesn't stop here. Python is a versatile language with many specialized fields where you can apply your skills. Here are some potential paths you can explore next: Machine Learning Machine Learning (ML) is one of the most exciting fields you can dive into. Python's libraries like TensorFlow, Keras, scikit-learn, and PyTorch make it an ideal language for building ML models. You'll learn about supervised and unsupervised learning, deep learning, neural networks, and more. Start with the basics of linear regression and classification, then move on to more complex models like convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Data Structures and Algorithms (DSA)...