Handling large files

Objective
By the end of this lesson, students will understand efficient techniques for handling large files in Python. They will learn how to read files in manageable chunks rather than loading the entire file into memory, which is crucial for conserving resources when working with large datasets.


1. Introduction to large file handling:

Reading large files all at once can be inefficient and may lead to memory issues. In Python, several methods allow for reading large files in chunks, making it possible to process data without overloading system memory.


2. Reading files in chunks with read():

The read(size) method allows you to read a specified number of bytes (or characters) at a time. This technique is ideal for files too large to fit into memory.

with open("largefile.txt", "r") as file:
    chunk_size = 1024  # Read in 1KB chunks
    chunk = file.read(chunk_size)
    while chunk:
        print(chunk)
        chunk = file.read(chunk_size)

In this example, each chunk of 1KB is processed, avoiding memory overload.


3. Using iter() with a sentinel value for chunked reading:

The iter() function with a sentinel value can create an iterator that reads a file in chunks until a specific condition is met, such as reaching the end of the file.

chunk_size = 1024
with open("largefile.txt", "r") as file:
    for chunk in iter(lambda: file.read(chunk_size), ''):
        print(chunk)

This method reads each chunk until file.read(chunk_size) returns an empty string, signaling the end of the file.


4. Using readlines() with a limit:

The readlines() method, combined with a limit, can read a specific number of lines at a time. This approach is useful for line-based files.

with open("largefile.txt", "r") as file:
    lines = file.readlines(100)  # Read 100 characters at a time
    while lines:
        for line in lines:
            print(line)
        lines = file.readlines(100)

By limiting each read, this approach conserves memory, making it effective for processing structured text files.


5. Practical tips for large file handling:

6. Practical examples and exercises:

Exercise 1: Read in chunks
1. Open a large text file and read it in 512-byte chunks, printing each chunk.

Exercise 2: Count occurrences in chunks
1. Open a file and read it in 1KB chunks.
2. Count and print the occurrences of a specific word or character across all chunks.

Exercise 3: Chunk-based line processing
1. Use readlines() with a limit to process each line in a large file without loading the entire file into memory.
2. Print lines that contain a keyword and ignore others.

Exercise 4: Saving processed Chunks
1. Open a large file, read it in chunks, and save each processed chunk to a new file.


Conclusion
In this lesson, students learned several efficient techniques for handling large files, including reading in chunks with read(), using iter() with a sentinel value, and limiting reads with readlines(). These methods help manage memory efficiently and avoid performance issues when working with substantial datasets in Python.