# 5 Best Ways to Remove Tuples of Length K in Python

π‘ Problem Formulation: Suppose we have a list of tuples, and we want to remove all the tuples which have a specific length `k`. For example, given `[(1, 2), (3,), (4, 5, 6), (7, 8)]` and `k=2`, we want the output to be `[(3,), (4, 5, 6)]`.

## Method 1: Using List Comprehension

List comprehension provides a concise syntax for creating lists. To remove tuples of length `k`, we can use list comprehension to iterate over the list and only include tuples that do not match the specified length.

Here’s an example:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.
```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in input_list if len(tup) != k]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This code snippet creates a new list `filtered_list` by iterating over `input_list` and including only those tuples whose length is not equal to `k`. This method is both efficient and easy to read.

## Method 2: Using the filter Function

The `filter` function in Python can be used to include only elements that satisfy a specific condition. By defining a lambda function that returns `True` when the tuple length isn’t `k`, we can filter out tuples of the specified length.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(filter(lambda tup: len(tup) != k, input_list))
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This approach uses a lambda function as the first argument to `filter`, with the second being the `input_list`. The `filter` function iterates over each element and applies the lambda condition, creating an iterator of elements that match the condition. We then convert this iterator back into a list.

## Method 3: Using a For Loop

A traditional `for` loop along with the `append` method allows precise control when removing items from a list. This method entails iterating over the original list and appending to a new list only those tuples whose length is not `k`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = []
for tup in input_list:
if len(tup) != k:
filtered_list.append(tup)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

In this snippet, `filtered_list` is initialized as an empty list. As the `for` loop executes, each tuple from `input_list` is checked, and if its length doesn’t match `k`, it’s added to `filtered_list`. This method is more verbose but can be easier for beginners to understand.

## Method 4: Using List Comprehension with `filter` and `lambda`

Combining list comprehension, `filter`, and `lambda` functions enables a compact method for removing tuples of a certain length. This approach merges the readability of list comprehension with the functionality of `filter`.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = [tup for tup in filter(lambda x: len(x) != k, input_list)]
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This method first applies `filter` to create a filtered iterator, then uses a list comprehension to convert this iterator back into a list. This approach makes the code compact while maintaining legibility.

## Bonus One-Liner Method 5: Using a Generator Expression

Generator expressions offer an even more succinct syntax for filtering lists. Like list comprehensions, they can also be used for filtering but without allocating memory for the whole list at once, which can be memory-efficient for large lists.

Here’s an example:

```input_list = [(1, 2), (3,), (4, 5, 6), (7, 8)]
k = 2
filtered_list = list(tup for tup in input_list if len(tup) != k)
print(filtered_list)```

The output of this code snippet is:

`[(3,), (4, 5, 6)]`

This generator expression filters `input_list`, yielding tuples of lengths that don’t equal `k`. The `list` constructor is then used to create a list from the generator expression. This approach provides a balance between readability and performance for large datasets.

## Summary/Discussion

• Method 1: List Comprehension. Offers a clear and Pythonic way to filter lists. Not always the most efficient for very large lists.
• Method 2: Using `filter` Function. Utilizes functional programming concepts, can be efficient, but it may be less intuitive for those unfamiliar with lambda functions.
• Method 3: Using a For Loop. Perfect for beginners and those who prefer traditional iteration methods. Tends to be verbose and less Pythonic.
• Method 4: Combining List Comprehension, `filter`, and `lambda`. Blends readability with functional programming. Syntactic sugar that might confuse beginners.
• Method 5: Using a Generator Expression. Ideal for large lists due to its memory-efficient nature. Balances brevity and clarity, but might be slower for small lists.