5 Best Ways to Extract Elements with Equal Frequency as Value in Python

💡 Problem Formulation: The task is to extract elements from a list in Python where each element’s frequency is equal to the element’s value itself. For instance, given an input array [3, 3, 3, 4, 4], the desired output is [3], since the number 3 appears exactly three times in the array.

Method 1: Using Counter from collections

This method utilizes the Counter class from Python’s collections module to tally the frequency of each element. Then it filters those elements whose frequencies match their values. It’s efficient for dealing with large datasets, as Counter is optimized for counting hashable objects.

Here’s an example:

from collections import Counter

def extract_elements(arr):
    count = Counter(arr)
    return [item for item, freq in count.items() if item == freq]

print(extract_elements([3, 3, 3, 4, 4]))

Output:

[3]

This snippet defines a function extract_elements which first creates a frequency map of the list using Counter. It then uses a list comprehension to filter and return those elements whose value is equal to their frequency in the array.

Method 2: Using defaultdict from collections

The defaultdict from the collections module can also be used to create a frequency map by incrementing the count of elements as they appear. This method gives the developer finer control over the counting process than Counter.

Here’s an example:

from collections import defaultdict

def extract_elements(arr):
    freq_map = defaultdict(int)
    for elem in arr:
        freq_map[elem] += 1
    return [elem for elem, freq in freq_map.items() if elem == freq]

print(extract_elements([1, 2, 2, 3, 3, 3]))

Output:

[1, 3]

Here, extract_elements creates a defaultdict to store the frequencies, incremented upon each encounter of an element. The resulting list is obtained by filtering items in the frequency map.

Method 3: Traditional Looping and Dictionaries

For those who prefer the traditional approach, a simple dictionary can be used along with looping to track the frequency of each item and then filter accordingly. This method does not require any special imports but can be less efficient for larger data.

Here’s an example:

def extract_elements(arr):
    freq_map = {}
    for elem in arr:
        if elem in freq_map:
            freq_map[elem] += 1
        else:
            freq_map[elem] = 1
    return [elem for elem, freq in freq_map.items() if elem == freq]

print(extract_elements([2, 2, 2, 3, 3, 4, 4, 4, 4]))

Output:

[2, 4]

Using for loops, the function extract_elements creates a frequency dictionary by iterating over the list. It then returns the desired list by comparing the keys to their corresponding values.

Method 4: Using Counter and Filter

Combining Counter with filter can provide a more functional programming flavored solution. The filter function can offer a more concise code but may be less intuitive for those unfamiliar with functional programming concepts.

Here’s an example:

from collections import Counter

def extract_elements(arr):
    count = Counter(arr)
    return list(filter(lambda x: count[x] == x, count))

print(extract_elements([5, 1, 2, 2]))

Output:

[1, 2]

The function creates a Counter, then filters the keys (unique elements) based on whether their count matches their value. The filter returns an iterable which is then converted to a list.

Bonus One-Liner Method 5: Using Comprehension with Counter

A one-liner method combines the power of list comprehensions with Counter, resulting in a very concise expression. It’s excellent for shorter code but may compromise readability for those who are not used to one-liners.

Here’s an example:

from collections import Counter

print([elem for elem, cnt in Counter([3, 3, 1, 2, 2]).items() if elem == cnt])

Output:

[1, 2]

This single line of code defines no functions. Instead, it directly uses a list comprehension to iterate over the items of a Counter constructed from the list, filtering as it goes to produce the output.

Summary/Discussion

• Method 1: Counter from collections. Efficient for large datasets with hashable items. May not be as transparent for Python beginners.
• Method 2: defaultdict from collections. Offers control over counting with a clean API. Requires understanding of defaultdict.
• Method 3: Traditional Looping and Dictionaries. Easy to understand for beginners. Potentially less performant with large datasets.
• Method 4: Counter and Filter. Leverages functional programming concepts for brevity. Can be obscure for some users.
• Method 5: Comprehension with Counter. Provides a neat one-liner. Could sacrifice readability for succinctness.