How do you read a file into a list in Python?

python

file-reading

memory-management

list-comprehensions

byAnton Shumikhin·Jan 6, 2025

To convert a file into a list of lines in Python, check this code snippet:

with open('filename.txt') as file:
    lines = file.read().splitlines()

This will generate a lines list, where each item is a line from filename.txt, excluding newlines.

When your file path contains escape characters like backslashes (\), remember to double them (\\), or prefix the string with r to preserve the original string:

with open('C:\\path\\to\\filename.txt') as file:
    lines = file.read().splitlines()
# The Batman's lair? Nope, just your ordinary file path.

Efficient read for large files

For large files, consider memory efficiency and process lines one by one:

lines = []
with open('large_file.txt', 'r') as file:
    for line in file:
        lines.append(line.strip())
# Your memory thanked you.

Dealing with numbers

For numeric calculations, convert string lines to integers using int():

with open('numbers.txt', 'r') as file:
    # Casting a spell here ... and poof! Strings to integers!
    numbers = [int(line.strip()) for line in file]

Special cases and exceptions

Confirm your file path and name

Make sure that the file path is accurate and accessible. A FileNotFoundError can be quite unforgiving.

Versions and `with` statement

In Python 2.5, the with statement requires an import:

from __future__ import with_statement
# Because "with it" is the new cool.

Encoding - the silent disruptor

Be wary of file encoding when opening text files, especially those with non-ASCII characters:

with open('filename.txt', encoding='utf-8') as file:
    lines = file.read().splitlines()
# Speaking the same language.

Memory management

Memory management is essential with massive files. To combat this, use generator expressions or libraries like pandas for working with large datasets.

Harnessing list comprehensions

A touch of type conversion

Files with each line representing different data types, like integer or float, can be neatly handled via type conversion:

with open('numeric_data.txt', 'r') as file:
    # Turning lead(lines) into gold(numbers).
    numbers = [float(line.strip()) for line in file if line]

Apply some transformations

Incorporate simple data filtering or transformations straight at the list comprehension:

with open('log.txt', 'r') as file:
    # Spotting warnings like a hawk.
    warnings = [line.strip() for line in file if 'WARNING' in line]

Going beyond basic file reading

Preserving newlines with `readlines()`

Use readlines() if you need to preserve newlines or are not particularly concerned with memory usage:

with open('poem.txt', 'r') as file:
    lines = file.readlines()
# Every /n is a dramatic pause in this poem.

Custom line processing

If each line requires a more sophisticated processing, define your custom function:

def process_line(line):
    # Cast your complex processing spell here
    return line.strip()

with open('script.txt', 'r') as file:
    # File reading... now in custom flavor.
    lines = [process_line(line) for line in file]

Reading lazily with generators

With enormous files, use a generator function to read and process lines lazily (i.e., on-demand):

def read_file_line_by_line(file_path):
    with open(file_path, 'r') as file:
        for line in file:
            yield line.strip()
    # Slight delay, massive memory savings.

lines = read_file_line_by_line('massive_file.txt')