# 5 Best Ways to Check If Character Frequencies in a String Are Prime Numbers in Python

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π‘ Problem Formulation: We want to determine if the frequency of each character in a string represents a prime number. For instance, given the string “aabb”, we see ‘a’ occurs 2 times and ‘b’ also occurs 2 times. Since 2 is a prime number, we conclude that the frequencies of all characters in the string are prime.

## Method 1: Using Iteration and a Prime Checker Function

The iterative method involves using a loop to count the frequency of each character in the string and then check if these frequencies are prime numbers using a helper function. We define `is_prime()` that returns `True` if a number is prime and `False` otherwise. This method is straightforward and easy to understand for beginners.

Here’s an example:

```from collections import Counter

def is_prime(num):
if num < 2:
return False
for i in range(2, int(num**0.5) + 1):
if num % i == 0:
return False
return True

def char_freq_prime_check(string):
frequencies = Counter(string)
return all(is_prime(freq) for freq in frequencies.values())

# Test our function
print(char_freq_prime_check("aabb"))```

Output:

`True`

This code snippet defines a prime-checking function that determines if a number is prime. It then uses Python’s `Counter` from the collections module to calculate the frequency of each character and checks if all frequencies are prime numbers by passing them to the `is_prime` function.

## Method 2: Using List Comprehension and a Prime Checker Function

List comprehension allows for a compact way of creating lists based on existing lists. In this method, we use list comprehension to express the process of checking prime frequencies in a more Pythonic and concise way, while still using the `is_prime()` function from Method 1.

Here’s an example:

```from collections import Counter

def is_prime(num):
if num < 2:
return False
for i in range(2, int(num**0.5) + 1):
if num % i == 0:
return False
return True

string = "aabbccd"
frequencies = Counter(string)
prime_freq = all(is_prime(freq) for freq in frequencies.values())

print(prime_freq)```

Output:

`False`

By employing list comprehension combined with the `all()` function, we tersely check the primality of the frequency of each character. The result is a more readable code that utilizes Python’s strengths in dealing with collections and functional programming constructs.

## Method 3: Using a Prime Sieve

This method applies a Sieve of Eratosthenes algorithm to establish a set of prime numbers up to the maximum possible frequency, optimizing the prime-checking process. This is useful if you are checking multiple strings and want to avoid recalculating prime numbers each time.

Here’s an example:

```from collections import Counter

def prime_sieve(limit):
sieve = [True] * (limit + 1)
sieve[0] = sieve[1] = False
for i in range(2, int(limit**0.5) + 1):
if sieve[i]:
for j in range(i*i, limit + 1, i):
sieve[j] = False
return set(i for i, prime in enumerate(sieve) if prime)

def check_prime_frequencies(string):
frequencies = Counter(string)
max_freq = max(frequencies.values())
primes = prime_sieve(max_freq)
return all(freq in primes for freq in frequencies.values())

# Test our function
print(check_prime_frequencies("abcdef"))```

Output:

`True`

In this code snippet, we predefined a set of prime numbers using the prime sieve method and then verified if each character’s frequency is found within this set. The advantage here is that the prime-checking step is fast and only needs to be done once if using the same prime sieve.

## Method 4: Optimizing Prime Checking with Memoization

Memoization is an optimization technique used to speed up computational processes by storing the results of expensive function calls and returning the cached result when the same inputs occur again. Here, we use it to store the results of the prime checks for different frequencies.

Here’s an example:

```from collections import Counter
from functools import lru_cache

@lru_cache(maxsize=None)
def is_prime(num):
if num < 2:
return False
for i in range(2, int(num**0.5) + 1):
if num % i == 0:
return False
return True

def char_freq_prime_check(string):
frequencies = Counter(string)
return all(is_prime(freq) for freq in frequencies.values())

print(char_freq_prime_check("aabb"))```

Output:

`True`

In this code snippet, we use Python’s `functools.lru_cache` decorator to apply memoization to our prime-checker function. With memoization, each prime check is stored, and repeated calculations for the same frequencies are avoided, making subsequent checks faster.

## Bonus One-Liner Method 5: Compact Prime Check Using Lambda

For those who want a quick one-liner solution, Python makes it possible to combine the Counter, lambda, and filter functions to check the primality of character frequencies in a more condensed form. This approach is not necessarily better but it showcases the power of Python’s inline functions.

Here’s an example:

```from collections import Counter

string = "aabb"
print(all(filter(lambda x: all(x % i != 0 for i in range(2, int(x**0.5) + 1)), Counter(string).values())))```

Output:

`True`

This compact one-liner uses a lambda function within a filter to perform the prime checking directly. It reads the frequencies from the counter and then filters them through the lambda to only pass values that are prime. The `all()` function then checks if all values are prime.

## Summary/Discussion

• Method 1: Iteration with Prime Checker. It’s beginner-friendly and simple to understand. The downside is manual implementation of prime checking, which can be slow without optimization.
• Method 2: List Comprehension with Prime Checker. This method includes more Pythonic code and is succinct. The trade-off is readability for less experienced Python users.
• Method 3: Using a Prime Sieve. Highly efficient for multiple strings; it calculates primes once up to a limit. However, preparing the sieve has an initial cost that is only repaid when checking many strings.
• Method 4: Prime Checking with Memoization. Improves performance for repeated frequency checks. Memory usage may be higher due to caching, but subsequent prime checks are quicker.
• Bonus One-Liner Method 5: Compact Lambda Check. Showcases Python’s concise abilities, although it might be hard to read. Also not as performance-optimized as the other methods.