# Python Create List of the Same Value (5 Best Methods)

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β Problem Formulation: How to initialize a list with the same value multiple times, which can be used for fixed-size arrays, placeholder content before populating with actual data, or manipulating data in bulk. Suppose you wanted to create a list of length 10, where every element is the integer 5. The expected output should be `[5, 5, 5, 5, 5, 5, 5, 5, 5, 5]`.

## Method 1: Using the Multiplication Operator

Python lists can be initialized with the same value by utilizing the multiplication (*) operator. This operator allows the repetition of list elements, effectively duplicating them a specified number of times, hence creating a new list containing only duplicates of the initial value.

Here’s an example:

```n = 10
value = 5
my_list = [value] * n
print(my_list)```

In this example, a list containing just the item `5` is multiplied by `10`, resulting in a new list `my_list` where `5` is repeated 10 times. This is a very straightforward and pythonic way to create a list with identical values.

## Method 2: Using List Comprehension

List comprehension offers a concise way to generate lists in Python. It can be utilized to create a list of the same value by iterating through a range object that defines the desired number of elements.

Here’s an example:

```n = 10
value = 5
my_list = [value for _ in range(n)]
print(my_list)```

In this snippet, the list comprehension iterates over a range of 10, disregarding the actual index (using `_` as a dummy variable), and inserts the fixed `value` of `5` for each iteration, effectively creating a list of 10 elements, all set to `5`.

## Method 3: Using the itertools.repeat Function

The `itertools` module in Python provides a function called `repeat`, which can generate an iterator that returns the same value each time it’s called. To create a list, one can couple this with `itertools.islice` to cut the infinite sequence to the desired length.

Here’s an example:

```from itertools import repeat, islice
n = 10
value = 5
my_list = list(islice(repeat(value), n))
print(my_list)```

This code utilizes `repeat` to generate an iterator that always yields `5` and then slices this iterator to a length of `10` using `islice`, finally converting the sliced iterator into a list with the `list()` constructor. This method is especially useful when dealing with very large lists, as it does not instantiate the list elements until necessary.

## Method 4: Using a For Loop

While not as succinct as other methods, using a simple for loop can create a list with the same value by appending to an initially empty list in each iteration.

Here’s an example:

```n = 10
value = 5
my_list = []
for _ in range(n):
my_list.append(value)
print(my_list)```

Here, we start with an empty list `my_list` and loop over a range of `10`, where in every iteration, we append the `value` `5` to `my_list`. This is a more verbose method but can be helpful for those who prefer or are accustomed to a traditional looping approach.

## Bonus One-Liner Method 5: Using collections.defaultdict

For a creative one-liner solution, `collections.defaultdict` can be used to create a dictionary that defaults to a particular value, then we can generate a list from the dictionary values.

Here’s an example:

```from collections import defaultdict
n = 10
value = 5
my_list = list(defaultdict(lambda: value, zip(range(n), [value]*n)).values())
print(my_list)```

This snippet creates a `defaultdict` that would return `5` for any missing keys, but we immediately populate it with `10` keys, each with the `value` of `5`. Then, we collect the values into a list. This method may seem overkill for such a simple task, but demonstrates the flexibility of Python’s data structures.

## Summary/Discussion

• Method 1:
• Strength: Extremely concise and straightforward.
• Weakness: Might not indicate the intent clearly in complex scripts.
• Method 2:
• Strength: Readily comprehensible and Pythonic.
• Weakness: Slightly more verbose compared to multiplication operator.
• Method 3:
• Strength: Efficient for large lists as elements are generated on-the-fly.
• Weakness: Requires additional import and is a bit more complex to understand.
• Method 4:
• Strength: Explicit and clear to most programmers, good for beginners.
• Weakness: More verbose and slower than other methods.
• Bonus Method 5:
• Strength: Showcases Python’s powerful standard library and is flexible.
• Weakness: Unnecessarily complex and less performant for the given task.

While methods 1 and 2 are generally preferable for their conciseness and clarity, methods 3 and 4 can be beneficial in specific contexts or for educational purposes. The bonus method is more of an intellectual exercise rather than a practical solution.

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