Handling large files efficiently in Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Reading a large file line by line

What is the output of this code snippet when reading a large file line by line?

Python

count = 0
with open('large_file.txt', 'r') as f:
    for line in f:
        count += 1
print(count)

A Prints the total number of characters in the file

B Prints the first line of the file

C Prints the total number of lines in the file

D Raises a FileNotFoundError if the file does not exist

💡 Hint

Think about what the loop is counting.

The code reads the file line by line and increments count for each line, so it prints the total number of lines.

❓ Predict Output

intermediate

Using read() vs readlines() for large files

What will happen if you use readlines() on a very large file compared to using a for loop over the file object?

Python

with open('large_file.txt', 'r') as f:
    lines = f.readlines()
print(len(lines))

A Reads the entire file into memory at once, which can cause high memory usage

B Reads the file line by line efficiently without high memory usage

C Raises a SyntaxError

D Prints the first line only

💡 Hint

Consider how readlines() works internally.

readlines() loads all lines into a list in memory, which can be inefficient for very large files.

🔧 Debug

advanced

Fixing memory error when processing large file

This code tries to read a large file and process each line, but it causes a MemoryError. Which option fixes the problem?

Python

with open('large_file.txt', 'r') as f:
    data = f.read()
    for line in data.split('\n'):
        process(line)

A Increase system memory

B Use f.readlines() instead of f.read()

C Use f.read(1024) to read 1024 bytes only

D Replace f.read() with a for loop: for line in f: process(line)

💡 Hint

Think about how to avoid loading the whole file at once.

Reading the file line by line with a for loop avoids loading the entire file into memory, preventing MemoryError.

🧠 Conceptual

advanced

Why use buffered reading for large files?

Why is buffered reading important when handling large files in Python?

A It converts the file to binary format

B It reduces the number of system calls and improves performance

C It encrypts the file contents automatically

D It loads the entire file into memory for faster access

💡 Hint

Think about how reading in chunks affects system resources.

Buffered reading reads data in chunks, reducing system calls and improving efficiency when processing large files.

❓ Predict Output

expert

Output of generator-based file processing

What is the output of this code snippet?

Python

def read_chunks(file_path, chunk_size=4):
    with open(file_path, 'r') as f:
        while chunk := f.read(chunk_size):
            yield chunk

result = []
for part in read_chunks('test.txt'):
    result.append(part)
print(result)

A A list of strings, each string is a chunk of 4 characters from the file

B A list of lines from the file

C A single string with the entire file content

D Raises a SyntaxError due to walrus operator

💡 Hint

Look at how the file is read in chunks and yielded.

The function reads the file in 4-character chunks and yields each chunk, so the result is a list of these chunks.