💡 Problem Formulation: This article tackles the challenge of removing elements from one list in Python, based on the indices of duplicate elements found in another list. Suppose you have a list original_list = ['a', 'b', 'c', 'b', 'd'] and an index list duplicate_indices = [1, 3]. The goal is to create a program that produces a new list ['a', 'c', 'd'] where the elements at the duplicate indices are removed.
Method 1: Iterative Removal with List Comprehension
Divulging into the iterative methods, one can utilize a list comprehension combined with an enumeration approach to filter out the elements based on their indices. The Python function enumerate() aids in generating index-element pairs, making it convenient to exclude the elements at indices that exist in the duplicate list.
Here’s an example:
original_list = ['a', 'b', 'c', 'b', 'd'] duplicate_indices = [1, 3] new_list = [element for index, element in enumerate(original_list) if index not in duplicate_indices] print(new_list)
Output:
['a', 'c', 'd']
This snippet generates a new list new_list by iterating over the original_list while keeping the elements whose indices are not present in duplicate_indices. List comprehension offers a concise and understandable way to achieve this.
Method 2: Using a Filter Function
Python’s built-in filter() function can act as a powerful tool to exclude indices. With the help of lambda functions, one can create a predicate that returns False for elements at duplicate indices and True otherwise, resulting in a filtered list.
Here’s an example:
original_list = ['a', 'b', 'c', 'b', 'd'] duplicate_indices = [1, 3] new_list = list(filter(lambda item: original_list.index(item[1]) not in duplicate_indices, enumerate(original_list))) print([item[1] for item in new_list])
Output:
['a', 'c', 'd']
This code creates a new_list that excludes any item whose index is in duplicate_indices. We initially pair each element with its index using enumerate, then filter these pairs, and finally extract the elements creating the filtered list.
Method 3: Deleting Elements in Place
For those who prefer in-place mutation of the original list, we can reverse iterate over the list and delete elements with an index that matches those within the list of duplicate indices. It’s important to iterate in reverse to avoid shifting indices upon deletion.
Here’s an example:
original_list = ['a', 'b', 'c', 'b', 'd']
duplicate_indices = [1, 3]
for index in sorted(duplicate_indices, reverse=True):
del original_list[index]
print(original_list)
Output:
['a', 'c', 'd']
This method directly modifies original_list by iterating over the sorted duplicate_indices in reverse order and deleting the elements at those indices. It ensures that deletion does not affect the remaining indices.
Method 4: Using numpy Library
For large datasets, one can leverage the numpy library which provides efficient array operations. By creating numpy arrays, one can perform set operations to determine indices that are not duplicated and use them to index the original array.
Here’s an example:
import numpy as np original_list = ['a', 'b', 'c', 'b', 'd'] duplicate_indices = [1, 3] # Convert to numpy array for advanced indexing original_array = np.array(original_list) select_indices = np.array([i for i in range(len(original_list)) if i not in duplicate_indices]) # Select the elements that are not at the duplicate indices new_array = original_array[select_indices] print(new_array.tolist())
Output:
['a', 'c', 'd']
In this method, numpy arrays are used to efficiently select and exclude elements based on their indices using array indexing, which often outperforms pure Python operations in terms of speed for larger datasets.
Bonus One-Liner Method 5: Using Comprehension and zip()
Python’s functional tools can succinctly handle this problem in a one-liner. The zip() function can weave together indices from a range object with elements from the original list, followed by a list comprehension to filter.
Here’s an example:
original_list = ['a', 'b', 'c', 'b', 'd'] duplicate_indices = [1, 3] new_list = [element for index, element in zip(range(len(original_list)), original_list) if index not in duplicate_indices] print(new_list)
Output:
['a', 'c', 'd']
This expression takes the original list and pairs each element with its index, using a list comprehension to build a new list that excludes the elements at indices indicated by duplicate_indices.
Summary/Discussion
- Method 1: List Comprehension with enumerate(). Strengths: Simple and Pythonic. Readability and compactness. Weaknesses: Requires understanding of list comprehensions.
- Method 2: Filter Function with lambda. Strengths: Functional programming approach, no in-place modification. Weaknesses: Can be less intuitive and slower for large lists due to the use of
list.index(). - Method 3: Deleting Elements in Place. Strengths: Efficient memory usage by altering the original list in-place. Weaknesses: Modifies the original list, which might not be desired, and requires careful index handling.
- Method 4: Using numpy Library. Strengths: Fastest for large datasets, efficient array manipulation. Weaknesses: Adds third-party dependency, less readable for those not familiar with numpy.
- Bonus Method 5: One-Liner using Comprehension and zip(). Strengths: Extremely concise. Weaknesses: May sacrifice readability for brevity, not necessarily intuitive for beginners.