💡 Problem Formulation: You need to create a nested list in Python with sublists containing repeated elements. Each sublist’s length should correspond to a specific count. For example, from input [1, 2, 3], the desired output for a count of 2 would be [[1, 1], [2, 2], [3, 3]].
Method 1: Using a For Loop
An intuitive way to create nested lists in Python with repeated counts is by employing a basic for loop. Here, you iterate through each item in the original list and multiply it by the count, thus creating a new list.
Here’s an example:
input_list = [1, 2, 3] count = 2 nested_list = [[item] * count for item in input_list]
The output will be:
[[1, 1], [2, 2], [3, 3]]
This method multiplies the element by the count inside a list comprehension, effectively duplicating the item and nesting it inside a new sublist.
Method 2: Using map and lambda functions
The map function can generate the same nested list structure by applying a lambda function to each element of the original list. This is a functional programming approach to the problem.
Here’s an example:
input_list = [1, 2, 3] count = 2 nested_list = list(map(lambda x: [x]*count, input_list))
The output will be:
[[1, 1], [2, 2], [3, 3]]
This code uses map to apply a lambda function that multiplies each item by the count to form a new sublist. The entire mapping is then converted to a list.
Method 3: Using itertools.repeat
With itertools.repeat, you can efficiently repeat each element of the input list a specified number of times and then encapsulate these repetitions into separate lists.
Here’s an example:
from itertools import repeat input_list = [1, 2, 3] count = 2 nested_list = [list(repeat(item, count)) for item in input_list]
The output will be:
[[1, 1], [2, 2], [3, 3]]
This snippet utilizes itertools.repeat in a list comprehension to repeat each item the required number of times before converting the iterator to a list.
Method 4: Using NumPy
If you are working with numerical data, NumPy offers optimized and concise ways to create repeated patterns. Using NumPy’s repeat function can be advantageous for performance with large datasets.
Here’s an example:
import numpy as np input_list = [1, 2, 3] count = 2 nested_list = np.repeat(input_list, count).reshape(len(input_list), count).tolist()
The output will be:
[[1, 1], [2, 2], [3, 3]]
This code uses the NumPy library’s repeat function to duplicate the items and then reshape to format the flat array into the nested structure, finally converting it back to a Python list with tolist().
Bonus One-Liner Method 5: Using the zip function
Python’s zip function can be cleverly used to pair each element with itself, and then construct the nested list with a list comprehension.
Here’s an example:
input_list = [1, 2, 3] count = 2 nested_list = [list(zip(*([item]*count))) for item in input_list]
The output will be:
[[1, 1], [2, 2], [3, 3]]
This one-liner uses zip to create pairs of the same element and then unpacks the tuples into lists within a list comprehension.
Summary/Discussion
- Method 1: For Loop. Easy to understand. Best for those familiar with basic Python syntax. Not as concise as other methods available.
- Method 2: map and lambda functions. More Pythonic, with a functional programming twist. Can be less readable for those unfamiliar with such paradigms.
- Method 3: itertools.repeat. Good for large datasets when performance matters. Requires importing an additional module.
- Method 4: Using NumPy. Highly efficient with numeric data. Overkill for simple or non-numeric tasks, as it introduces a heavy dependency.
- Bonus Method 5: zip function. Clever and compact. May be confusing as it uses advanced unpacking. More Pythonic but potentially less readable.