Comparison and use of read, readline and readlines methods in Python

1. Method Overview

1. read() method

read()Methods are used to read a specified number of bytes or characters from a file (in text mode) and read the entire file contents if no parameter is specified or the parameter is negative.

file = open('', 'r')
content = ()  # Read the entire file content()

2. readline() method

readline()Methods are used to read a line from a file, including line breaks at the end of the line (if present).

file = open('', 'r')
line = ()  # Read the first line()

3. readlines() method

readlines()The method reads all lines of the file and returns a list where each element is a line of the file (including line breaks at the end of the line).

file = open('', 'r')
lines = ()  # Get a list containing all rows()

2. Detailed comparison

1. Return value type

2. Memory usage

• read(): Load the entire file into memory at one time, with the maximum memory consumption
• readlines(): Also loads all content at once, but is stored in list form, and the memory consumption is equivalent to read()
• readline(): Only one line is read at a time, with the highest memory efficiency and suitable for large file processing

3. Performance characteristics

• Small file: The performance differences between the three methods are not big
• Large file:
  • read()andreadlines()It will consume a lot of memory because it loads all content at once
  • readline()Or iterate the file object is the best choice

4. Use scenarios

• read():

  • When the file content needs to be processed as a whole
  • When the file size is controllable and the memory is sufficient
  • When you need to quickly access all content

• readline():

  • When processing large files line by line
  • Just check the beginning of the file
  • When fine control of the reading process is required

• readlines():

  • When you need to access multiple lines of a file randomly
  • The file size is moderate and can be loaded safely into memory
  • When you need to get all rows and perform list operations

3. In-depth use examples

1. Advanced usage of read()

# Read large files in chunksdef read_in_chunks(file_path, chunk_size=1024):
    with open(file_path, 'r') as file:
        while True:
            chunk = (chunk_size)
            if not chunk:
                break
            yield chunk

# Use generator to process large files block by blockfor chunk in read_in_chunks('large_file.txt'):
    process(chunk)  # Process each block

2. Loop reading of readline()

# Use readline() to traverse fileswith open('', 'r') as file:
    while True:
        line = ()
        if not line:  # Arrive at the end of the file            break
        print(line, end='')  # Remove the line wraps that come with print
# The more Pythonic way is to iterate over the file object directlywith open('', 'r') as file:
    for line in file:
        print(line, end='')

3. Advanced application of readlines()

# Use list comprehension to process all rowswith open('', 'r') as file:
    lines = [() for line in ()]
    # or more efficient writing    lines = [() for line in file]  # Iterate directly into the file object
# Random access to file lineswith open('', 'r') as file:
    lines = ()
    print(lines[10])  # Visit Line 11    print(lines[-1])  # Access the last line

4. Performance comparison test

Let's compare the performance differences between the three methods through actual testing:

import time

def test_read(filename):
    start = ()
    with open(filename, 'r') as file:
        content = ()
    return () - start

def test_readline(filename):
    start = ()
    with open(filename, 'r') as file:
        while ():
            pass
    return () - start

def test_readlines(filename):
    start = ()
    with open(filename, 'r') as file:
        lines = ()
    return () - start

def test_iter(filename):
    start = ()
    with open(filename, 'r') as file:
        for line in file:
            pass
    return () - start

filename = 'large_file.txt'  # Suppose this is a 100MB file
print(f"read() time: {test_read(filename):.4f} seconds")
print(f"readline() time: {test_readline(filename):.4f} seconds")
print(f"readlines() time: {test_readlines(filename):.4f} seconds")
print(f"file iteration time: {test_iter(filename):.4f} seconds")

Typical results may be as follows (depending on hardware and file size):

read() time: 0.1254 seconds
readline() time: 0.2345 seconds
readlines() time: 0.1321 seconds
file iteration time: 0.1208 seconds

From the test, we can see:

• read()andreadlines()Because it loads everything at once, it's faster
• readline()Because of multiple I/O operations, the speed is slow
• Iterating file objects directly is the fastest way, and it is also recommended by Python

5. Best Practice Suggestions

1. When processing large files：

   • usefor line in file:Iteration method (the highest memory efficiency)
   • Avoid usingread()andreadlines()
   • If you need a specific row, consider usingreadline()

2. When processing small files：

   • useread()Get all the content for overall processing
   • usereadlines()If a row list is required for random access

3. General recommendations：

   • Always usewithStatement to ensure the file is closed correctly
   • Consider using a generator to handle large files
   • Pay attention to the difference in line breaks under different operating systems
   • Used when processing binary files'rb'model

4. Alternatives：

   • For very large files, consider usingmmapModule
   • For structured data, consider usingcsvModules or special parsing libraries

6. FAQs

Q1: Why is iterating file objects directly faster than readline()?

A: Python's file object implements the iterator protocol and is optimized internally. Direct iteration avoids the overhead caused by repeated calls to methods.

Q2: Will read() and readlines() ignore line breaks?

A: No. Both methods retain line breaks at the end of the line. If you need to remove it, you can call it manuallystrip()orrstrip()。

Q3: How to efficiently read the last few lines of a file?

A: For large files, reverse reading is more efficient:

def tail(filename, n=10):
    with open(filename, 'rb') as file:
        # Move to the first 1024 bytes at the end of the file        (-1024, 2)
        lines = ()
        return [() for line in lines[-n:]]

Q4: What are the differences between these three methods in binary mode?

A: In binary mode ('rb')Down:

• read()Return bytes object
• readline()Returns a bytes object containing a row of data
• readlines()Returns a list containing the bytes object

Q5: How to deal with files with different encodings?

A: Specify the correct encoding method:

with open('', 'r', encoding='utf-8') as file:
    content = ()

7. Summary

read()、readline()andreadlines()Each has its own applicable scenarios:

1. read(): Suitable for small files or scenarios that require overall processing, simple and direct, but high memory consumption.
2. readline(): Suitable for processing large files line by line, memory friendly but slightly slower.
3. readlines(): Suitable for scenarios where random access to rows or row list operations are required, but also consumes memory.

Best PracticesYes: For most cases, especially when dealing with large files, use them directlyfor line in file:The iterative method is the most efficient and Pythonic. These three methods are considered only when it is clear that all content or specific features are needed.

Understanding the differences and applicable scenarios of these methods will help you write more efficient and robust Python file processing code.

The above is the detailed explanation of the comparison and use of read(), readline() and readlines() methods in Python. For more information about the Python read(), readline() and readlines() methods, please follow my other related articles!