5 Best Ways to Filter Rows with Only Alphabets from a List of Lists in Python

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💡 Problem Formulation: You often encounter situations where you need to clean input data for processing. Specifically, you might have a list of lists containing strings and want to remove any rows that include non-alphabetical characters. For instance, if your input is [['apple'], ['banana1'], ['cherry!'], ['date'], ['elderberry']], the desired output would be [['apple'], ['date'], ['elderberry']]. In this article, we’ll look at multiple methods to achieve that.

Method 1: Using List Comprehension and isalpha()

This method filters out rows in a list of lists that include only alphabet characters by using list comprehension and the isalpha() string method. isalpha() returns True if all characters in the string are letters. This is both concise and efficient for filtering data.

Here’s an example:

input_data = [['apple'], ['banana1'], ['cherry!'], ['date'], ['elderberry']]
filtered_data = [row for row in input_data if all(elem.isalpha() for elem in row for elem in elem)]
print(filtered_data)

Output:

[['apple'], ['date'], ['elderberry']]

This code snippet creates a new list filtered_data that includes only the sublists from input_data whose elements are purely alphabetical. It achieves this by utilizing nested list comprehension to iterate over each element of each sublist and checking with isalpha().

Method 2: Using the filter() Function and a Lambda

The filter() function is designed to construct an iterator from elements of an iterable for which a function returns true. In conjunction with a lambda function, it can succinctly apply a test to each row of a list of lists to keep only those with alphabetic characters.

Here’s an example:

input_data = [['apple'], ['banana1'], ['cherry!'], ['date'], ['elderberry']]
is_alphabetic = lambda row: all(elem.isalpha() for elem in row for elem in elem)
filtered_data = list(filter(is_alphabetic, input_data))
print(filtered_data)

Output:

[['apple'], ['date'], ['elderberry']]

This code defines a lambda function is_alphabetic that uses all() and isalpha() to test each row. The filter() function then applies this lambda to each element (row) in input_data and constructs a filtered list.

Method 3: Using Regular Expressions

Regular expressions (regex) provide a powerful method for matching patterns in strings. In Python, the re module allows for sophisticated string matching and can be used to filter out rows that do not match the regex pattern for all alphabetic strings.

Here’s an example:

import re
input_data = [['apple'], ['banana1'], ['cherry!'], ['date'], ['elderberry']]
filtered_data = [row for row in input_data if all(re.match("^[A-Za-z]+$", elem) for elem in row for elem in elem)]
print(filtered_data)

Output:

[['apple'], ['date'], ['elderberry']]

This script uses a list comprehension with a regex pattern that matches strings containing only letters. The re.match() function checks each element of each sublist against this pattern, filtering the list accordingly.

Method 4: Custom Filtering Function

For more control and clarity, you can write a custom filtering function that explicitly verifies whether a string contains only alphabet characters. This approach makes the code more readable and can be easily extended or modified for more complex conditions.

Here’s an example:

def is_only_alphabets(lst):
    return all(c.isalpha() for row in lst for c in row)

input_data = [['apple'], ['banana1'], ['cherry!'], ['date'], ['elderberry']]
filtered_data = [row for row in input_data if is_only_alphabets(row)]
print(filtered_data)

Output:

[['apple'], ['date'], ['elderberry']]

The custom function is_only_alphabets checks each character in each element of the sublist to ensure all are alphabetic. The list comprehension then uses this function to filter out any rows that don’t meet the criteria.

Bonus One-Liner Method 5: Compact List Comprehension with str.isalpha()

This one-liner method employs a highly condensed list comprehension expression that leverages str.isalpha() to filter the necessary rows. This expression is for those who prefer concise code and are comfortable with Python’s advanced list comprehension capabilities.

Here’s an example:

input_data = [['apple'], ['banana1'], ['cherry!'], ['date'], ['elderberry']]
filtered_data = [row for row in input_data if ''.join(row).isalpha()]
print(filtered_data)

Output:

[['apple'], ['date'], ['elderberry']]

This one-liner combines each sublist into a single string with ''.join(row) and then checks if this combined string is alphabetical with isalpha(). The result is a simplified, albeit less explicit, filtering of rows.

Summary/Discussion

  • Method 1: List Comprehension with isalpha(). Strengths: straight-forward and readable. Weaknesses: could be less efficient with very large data sets due to the level of nesting.
  • Method 2: filter() Function and Lambda. Strengths: clean and functional. Weaknesses: some find lambda expressions less readable than a declarative approach.
  • Method 3: Regular Expressions. Strengths: extremely powerful for pattern matching. Weaknesses: regex can be difficult to maintain and is slower than direct string methods.
  • Method 4: Custom Filtering Function. Strengths: clear and easily customizable. Weaknesses: potentially less concise if only used for a simple condition.
  • Method 5: Compact List Comprehension. Strengths: very concise. Weaknesses: could be less readable due to compaction and assumes all elements are strings (joins without checking type).