Python Read Text File Into a List of Lists (5 Easy Ways)

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βœ… Problem Formulation: Read a text file and structure its contents into a list of lists, where each inner list represents a line or a set of values from the file. For instance, given a text file with tabulated data, the goal is to transform each line of data into an individual list so that the entire file becomes a list of these lists.

Method 1: Using a for loop with split()

This method involves reading a file line by line using a for loop and then splitting each line into a list using the split() function. This approach is simple and very flexible, as it allows you to specify a delimiter if your data isn’t separated by whitespace.

Here’s an example:

list_of_lists = []
with open('data.txt', 'r') as file:
    for line in file:
        list_of_lists.append(line.strip().split('\t'))  # Assuming tab-delimited data

The code snippet opens a text file named 'data.txt' and reads it line by line. Each line is stripped of leading and trailing whitespaces, then split on tabs (\t), forming an individual list that’s appended to list_of_lists.

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Method 2: List Comprehension with split()

List comprehension is a concise way to create lists in Python. It can be used to read a text file and convert it into a list of lists in a single line of code.

Here’s an example:

with open('data.txt', 'r') as file:
    list_of_lists = [line.strip().split('\t') for line in file]

This snippet uses list comprehension to iterate over each line in ‘data.txt’, strip it of whitespaces, split each line on tabs, and compile the results into list_of_lists.

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Method 3: Using the csv.reader module

The csv module in Python is designed to work with delimited files. The csv.reader function can be utilized to read a file and automatically split each line into a list, thereby producing a list of lists.

Here’s an example:

import csv

with open('data.txt', 'r') as file:
    list_of_lists = list(csv.reader(file, delimiter='\t'))

In this code, csv.reader is fed the file object and the delimiter ('\t' for tab-delimited data). The reader object is converted to a list, assigning it to list_of_lists.

Method 4: Using numpy.loadtxt

For those dealing with numeric data, numpy offers a function called loadtxt which loads data from a text file, with each row in the file becoming a sub-array.

Here’s an example:

import numpy as np

list_of_lists = np.loadtxt('data.txt', delimiter='\t').tolist()

This code reads ‘data.txt’ as an array using numpy.loadtxt, with each line split at tabs. It then converts the array to list_of_lists using the tolist() method.

Bonus One-Liner Method 5: Using map() and split()

A combination of map() and split() within a list comprehension can offer a quick one-liner solution.

Here’s an example:

with open('data.txt', 'r') as file:
    list_of_lists = list(map(lambda line: line.strip().split('\t'), file))

This one-liner uses map() to apply a lambda function that strips and splits each line on tabs, then converts the map object into a list of lists.

Summary/Discussion

  • Method 1: For loop with split()
    • Strength: Highly customizable to different file structures and delimiters.
    • Weakness: More verbose compared to other methods.
  • Method 2: List Comprehension with split()
    • Strength: Offers shorter and cleaner code than a traditional for loop.
    • Weakness: Less readable for beginners or complex operations.
  • Method 3: Using csv.reader
    • Strength: Efficient and built specifically for delimited files, facilitating error handling.
    • Weakness: Overhead of importing an additional module when not already using csv.
  • Method 4: Using numpy.loadtxt
    • Strength: Ideal for numerical data and integrates well with the numpy array operations.
    • Weakness: Not suitable for non-numeric data and adds dependency on numpy.
  • Bonus Method 5: One-liner using map() and split()
    • Strength: Concise one-liner that keeps code compact.
    • Weakness: Can be less intuitive and harder to debug compared to list comprehensions or loops.

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