5 Best Ways to Check if All Occurrences of Every Value Are Unique in Python

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def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.

💡 Problem Formulation: You want to check whether all values in a given list or collection have unique occurrence counts in Python. For instance, given an input list like [1, 2, 2, 3, 3, 3], the program should confirm that values 1, 2, and 3 have unique counts of 1, 2, and 3, respectively. On the other hand, a list like [1, 2, 2, 3, 3, 3, 3] would fail this check, as values 2 and 3 both occur 3 times.

Method 1: Using a Counter

This method utilizes the collections.Counter module to count the occurrences of each value in the list. Then it creates a set from these counts to check if the length of this set matches the length of the original count. This method is straightforward and typically efficient.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.
from collections import Counter

def unique_occurrences(arr):
    counts = Counter(arr)
    return len(counts) == len(set(counts.values()))

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

The output of this code snippet will be:

True

The function unique_occurrences() computes the occurrence of each item using Counter, then checks if the number of unique counts (using set) is equal to the number of unique elements. This indicates whether each element has a unique occurrence count.

Method 2: Manual Count with Dictionary

A manual method involves iterating over the list to create a dictionary that maps each value to its count, then another loop to ensure each count is unique. This approach does not require additional modules and allows for customized counting behavior if needed.

Here’s an example:

def unique_occurrences(arr):
    counts = {}
    for val in arr:
        counts[val] = counts.get(val, 0) + 1
    unique_counts = set()
    for count in counts.values():
        if count in unique_counts:
            return False
        unique_counts.add(count)
    return True

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

The output of this code snippet will be:

True

This code defines a function unique_occurrences() that first counts the occurrences of each item manually and then checks for uniqueness by using a set to store unique counts, returning False as soon as a duplicate count is detected.

Method 3: Using a List Comprehension and Count Function

With list comprehension and the count function, one can create a list of counts and then apply the set theory to find if there are any duplicates. This method is concise but may not be the most efficient for large lists due to repeated calling of count().

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set([arr.count(x) for x in set(arr)]))

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

The output of this code snippet will be:

True

The function unique_occurrences() first creates a set of unique items and then generates another set from a list comprehension that counts the occurrences of each unique item. If the lengths match, it indicates uniqueness.

Method 4: Using Lambda Function and Map

This method employs a lambda function within a map to apply counting over the set of unique elements. Then, similar to other methods, it compares the length of this mapping to the set’s length. This is a functional approach and can be concise but may be less readable for those not familiar with lambda and map functions.

Here’s an example:

def unique_occurrences(arr):
    return len(set(arr)) == len(set(map(lambda x: arr.count(x), set(arr))))

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

The output of this code snippet will be:

True

This snippet defines a function unique_occurrences(), which uses map to apply the count function to each unique item and checks the uniqueness of occurrence counts in a similar manner to the list comprehension method.

Bonus One-Liner Method 5: Using all() and List Comprehension

This one-liner utilizes the all() function with a list comprehension that compares the count of each unique occurrence to ensure all are unique. Although concise and elegant, this one-liner can be hard to understand for beginners.

Here’s an example:

def unique_occurrences(arr):
    return all(arr.count(x) == arr.count(y) for x in set(arr) for y in set(arr) if x != y)

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

The output of this code snippet will be:

True

This one-liner compares the count of each unique element against every other to confirm their uniqueness. The all() function ensures that if any comparison is False, the overall result is False.

Summary/Discussion

  • Method 1: Using a Counter. Fast and easy to understand. However, relies on importing the Counter class from collections.
  • Method 2: Manual Count with Dictionary. Provides more control and doesn’t require additional imports. It might be slower for larger data sets.
  • Method 3: Using List Comprehension and Count Function. Concise but can have poor performance on large data sets due to the repeated calling of count().
  • Method 4: Using Lambda Function and Map. Functional and concise. It may not be as readable for beginners or those not comfortable with functional programming concepts.
  • Bonus Method 5: Using all() and List Comprehension. Elegant one-liner but potentially confusing for novices, and like Method 3 can suffer from performance issues.