In Python, how do I index a list with another list?

Indexing Python Lists with Another List: Advanced Selection Techniques

Discover various Python techniques for indexing a list using elements from another list, enabling flexible and powerful data selection.

In Python, indexing a list with another list is a common requirement when you need to select multiple elements from a source list based on a collection of indices. Unlike NumPy arrays, standard Python lists do not support 'fancy indexing' directly. This article explores several methods to achieve this, ranging from simple loops to more Pythonic and efficient approaches using list comprehensions, map(), and external libraries like NumPy.

Understanding the Problem: Why Direct Indexing Fails

When you try to use a list of indices directly with a Python list, you'll encounter a TypeError. This is because Python's built-in list indexing expects a single integer or a slice object, not another list. Understanding this limitation is the first step to finding appropriate workarounds.

my_list = ['a', 'b', 'c', 'd', 'e']
indices = [0, 2, 4]

# This will raise a TypeError
# selected_elements = my_list[indices]

Attempting direct list indexing results in a TypeError

flowchart TD
    A[Source List] --> B{Indices List}
    B --> C{Python List Indexing Mechanism}
    C -- Expects single int or slice --> D[TypeError: list indices must be integers or slices, not list]
    D -- Requires alternative methods --> E[Solution Approaches]

Why direct list indexing with another list fails in Python

Method 1: Using a List Comprehension (Recommended)

List comprehensions provide a concise and readable way to create new lists. This is often the most Pythonic and preferred method for selecting elements based on a list of indices.

my_list = ['apple', 'banana', 'cherry', 'date', 'elderberry']
indices = [0, 2, 3]

selected_elements = [my_list[i] for i in indices]
print(selected_elements)
# Output: ['apple', 'cherry', 'date']

Selecting elements using a list comprehension

Method 2: Using map() with operator.itemgetter

The map() function, combined with operator.itemgetter, offers another elegant and often efficient solution. itemgetter creates a callable that fetches items from its operand at the specified indices.

import operator

my_list = ['red', 'green', 'blue', 'yellow', 'purple']
indices = [1, 4, 0]

# itemgetter(1, 4, 0) returns a tuple of elements
selected_elements_tuple = operator.itemgetter(*indices)(my_list)
selected_elements = list(selected_elements_tuple)
print(selected_elements)
# Output: ['green', 'purple', 'red']

Using map() with operator.itemgetter for indexing

Method 3: Using NumPy for Advanced Indexing

If you are working with numerical data or frequently perform advanced array operations, converting your Python list to a NumPy array is a powerful solution. NumPy arrays natively support 'fancy indexing' with lists of integers or boolean arrays.

import numpy as np

my_list = [10, 20, 30, 40, 50]
indices = [0, 3, 1]

np_array = np.array(my_list)
selected_elements_np = np_array[indices]
selected_elements = selected_elements_np.tolist()
print(selected_elements)
# Output: [10, 40, 20]

Indexing a NumPy array with a list of indices