Python: Read Text File into List of Words

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βœ… Problem Formulation: When working with file input/output in Python, one common requirement is to read a text file and store its contents as a list of words. This could be needed for tasks such as word frequency analysis, text processing, or simply pre-processing for other computational tasks.

For example, given an input file containing the text “Hello, world! This is a test.”, the desired output would be a list like ['Hello', 'world', 'This', 'is', 'a', 'test'].

Method 1: Using read() and split()

To tackle the task of reading a text file and converting its contents into a list of words, one straightforward approach is to read the entire contents of the file into a single string and then use the split() method. This method splits the string into a list at each space, effectively breaking it up into individual words.

Here’s an example:

with open('example.txt', 'r') as file:
    words = file.read().split()

print(words)

In this code snippet, the open() function is used with the context manager with to ensure proper handling of the file. Using file.read(), we read the entire file content into a single string, and split() creates a list of words based on whitespace separation.

πŸ‘‰ Python Read Text File into List of Strings

Method 2: Using readlines() and List Comprehension

Another method involves reading each line of the file using readlines(), then using list comprehension to split each line into words and flatten the resulting list of lists into a single list.

Here’s an example:

with open('example.txt', 'r') as file:
    words = [word for line in file.readlines() for word in line.split()]

print(words)

This code snippet reads the file line by line, splits each line into words, and flattens the list of lists using list comprehension, which is an elegant and concise way to process lists in Python.

Method 3: Using file object iteration and extend()

Alternatively, you can iterate over the file object directly and use the extend() method to add the words of each line into a single list, minimizing the memory overhead of readlines().

Here’s an example:

words = []
with open('example.txt', 'r') as file:
    for line in file:
        words.extend(line.split())

print(words)

This code snippet loops over each line in the file, splits the line into words, and then extends the list words with the words from that line. This method works well for large files as it processes one line at a time.

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Method 4: Using the re module for Regular Expressions

When you need to get more specific about what constitutes a “word” (for example, removing punctuation), you can use Python’s re module to split the text based on a regular expression pattern.

Here’s an example:

import re

with open('example.txt', 'r') as file:
    text = file.read()
    words = re.findall(r'\b\w+\b', text)

print(words)

The re.findall() function is used here to find all substrings that match the regex pattern \b\w+\b, which corresponds to sequences of word characters (letters, digits, underscores) that are bounded by word boundaries (such as spaces or punctuation). This is a powerful method for more complex word extraction tasks.

Bonus One-Liner Method 5: Using read() with split() in a One-Line Statement

For a quick and concise one-liner solution, you can read the file and create the list of words using a combination of open(), read(), and split() directly in a single statement.

Here’s an example:

words = open('example.txt', 'r').read().split()
print(words)

This compact code is a one-liner version of Method 1, performing the read and split operations directly after opening the file. It demonstrates Python’s ability to chain methods for a more concise expression of the task.

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Summary and Discussion

  • Method 1:
    • Strength: Straightforward and easy to understand.
    • Weakness: Reads entire file into memory.
  • Method 2:
    • Strength: Efficient for large files due to line-by-line processing.
    • Weakness: Slightly less readable due to double list comprehension.
  • Method 3:
    • Strength: Processes file line by line, good for very large files.
    • Weakness: Slightly more verbose.
  • Method 4:
    • Strength: Highly adaptable to complex word definitions.
    • Weakness: Requires understanding of regular expressions.
  • Method 5:
    • Strength: Extremely concise.
    • Weakness: Lacks explicit file closure, which may lead to resource leaks.

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