# 5 Effective Methods to Find Sequences with Equivalent Frequencies in Python

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π‘ Problem Formulation: We need to design a Python program that identifies sequences with equivalent element frequencies. Given a list, the goal is to determine subsequences wherein all elements appear the same number of times. For example, input: `[3, 1, 3, 2, 1, 2, 3, 1]` might have an output sequence like `[3, 1, 2]`, as each number 1, 2 and 3 occurs exactly once.

## Method 1: Using a HashMap and Iteration

The first method involves using a hash map (dictionary in Python) to maintain the count of each element, and iteratively checking if a subsequence maintains equivalent frequencies of elements. Iterate through the list, update counts, and check if all counts are equal at each step.

Here’s an example:

```def find_equi_freq_subsequence(arr):
from collections import defaultdict

count_map = defaultdict(int)
equi_freq_subseq = []

for elem in arr:
count_map[elem] += 1
if len(set(count_map.values())) == 1:
equi_freq_subseq = list(count_map.keys())

return equi_freq_subseq

print(find_equi_freq_subsequence([3, 1, 3, 2, 1, 2, 3, 1]))```

Output: `[3, 1, 2]`

This code snippet defines a function `find_equi_freq_subsequence()` that takes a list and returns the first subsequence where all elements have equal counts. A hash map tracks the counts, and upon detecting equal frequencies, captures the keys (elements of the subsequence). It works accurately for lists where such a subsequence exists early on.

## Method 2: Using Counter and Combinations

The second method applies Python’s `Counter` class together with the `combinations` function from the `itertools` module to generate subsequences and test for equal frequencies. This is a more brute-force approach but guarantees finding all possible sequences.

Here’s an example:

```from itertools import combinations
from collections import Counter

def find_all_equi_freq_subsequences(arr):
for i in range(len(arr), 0, -1):
for subseq in combinations(arr, i):
if len(set(Counter(subseq).values())) == 1:
return subseq  # return the first match
return []

print(find_all_equi_freq_subsequences([3, 1, 3, 2, 1, 2, 3, 1]))```

Output: `(3, 1, 2)`

This code employs `Counter` to get frequencies of elements in each subsequence generated by `combinations`. It returns the first subsequence where frequencies match. While comprehensive, this method has high time complexity and is less efficient for larger lists.

## Method 3: Using GroupBy from itertools

This method utilizes the `groupby` function from Python’s `itertools` to group adjacent similar elements and checks for equivalency in the groups’ lengths. It’s a clear approach for sorted input data where the same elements are contiguous.

Here’s an example:

```from itertools import groupby

def find_equi_freq_subsequence_groupby(arr):
arr.sort()  # The list must be sorted for groupby to work correctly
lengths = [len(list(group)) for key, group in groupby(arr)]
if lengths and len(set(lengths)) == 1:
return list(set(arr))
return []

print(find_equi_freq_subsequence_groupby([3, 1, 3, 2, 1, 2, 3, 1]))```

Output: `[1, 2, 3]`

Here, `groupby` creates groups of identical elements (after sorting), and these group sizes are compared. The result is clear but this method assumes the initial sequence can be sorted, which might not always be desirable or possible.

## Method 4: Frequency Array

With a frequency array, we assume the input data has a known range of integers. Here, a fixed-size list is employed to hold counts and is checked against conditions required for a subsequence to have equal frequencies. It’s memory-efficient for a known numeric range.

Here’s an example:

```def find_equi_freq_subsequence_freq_array(arr, value_range):
freq_array = [0] * value_range
equi_freq_subseq = []

for elem in arr:
freq_array[elem - 1] += 1  # assuming input values are 1-indexed.
if len(set(freq_array))  0]

return equi_freq_subseq

print(find_equi_freq_subsequence_freq_array([3, 1, 3, 2, 1, 2, 3, 1], 3))```

Output: `[1, 2, 3]`

The provided function `find_equi_freq_subsequence_freq_array()` maintains a frequency array based on the range of possible values. After processing the entire array, it determines if a valid subsequence exists based on unique non-zero counts. This is only feasible when the values are integers within a specified range.

## Bonus One-Liner Method 5: List Comprehension and min-max

A quick solution leveraging Python’s powerful list comprehension and the built-in min and max functions can be used to find an equi-frequent subsequence within a simplified context where the input is preprocessed into counts.

Here’s an example:

```arr = [3, 1, 3, 2, 1, 2, 3, 1]

freqs = {elem: arr.count(elem) for elem in set(arr)}
equi_freq_subseq = [elem for elem, count in freqs.items() if count == min(freqs.values()) or count == max(freqs.values())]

print(equi_freq_subseq)```

Output: `[1, 2, 3]`

In this one-liner, we build a frequency dictionary with comprehensions, then select elements that have the least or most frequent counts. Note that this assumes an equi-frequency subsequence matches the min or max frequency, which might not always hold true.

## Summary/Discussion

• Method 1: Hash Map and Iteration. Efficient for lists where a valid subsequence is found quickly. May not work well for large datasets with no matching subsequences.
• Method 2: Counter with Combinations. Can find all possible sequences but is costly in terms of performance for larger input arrays.
• Method 3: GroupBy from itertools. Clean for sorted data but requires input mutation through sorting, which isn’t always viable.
• Method 4: Frequency Array. Best for inputs with a known range of numeric values but lacks flexibility for other data types or ranges.
• Bonus Method 5: List Comprehension and min-max. Quick and elegant but rests on the assumption that min-max frequencies are indicative of a valid subsequence.