# 5 Best Ways to Check if a String Can Be Repeated to Make Another String in Python

π‘ Problem Formulation: Suppose you have two strings, `str1` and `str2`. The challenge is to check whether `str1` can be repeated some number of times to exactly match `str2`. For example, if `str1 = "ab"` and `str2 = "ababab"`, the answer should be True because repeating “ab” three times gives “ababab”, which is equal to `str2`.

## Method 1: Using Division and Multiplication

This method checks if the length of `str2` is a multiple of `str1`‘s length. If it is, it repeats `str1` the appropriate number of times and compares it to `str2`.

Here’s an example:

```str1 = "abc"
str2 = "abcabcabc"
def can_string_be_repeated(str1, str2):
if len(str2) % len(str1) == 0:
return str1 * (len(str2) // len(str1)) == str2
return False

print(can_string_be_repeated(str1, str2))```

Output: `True`

This code defines a function `can_string_be_repeated` that first checks if the length of `str2` is divisible by the length of `str1`. If the check passes, it then multiplies `str1` by the division result and compares the new string to `str2`.

## Method 2: Using the `str.find()` Method

This approach utilizes the built-in `str.find()` method to look for `str1` inside `str2`. After confirming the size relationship as in Method 1, it seeks to match the beginning of `str1` throughout the entire `str2`. If `str1` is found at multiples of its length, then it can be concluded that `str2` is a repetition of `str1`.

Here’s an example:

```str1 = "ab"
str2 = "ababab"
def can_string_be_repeated(str1, str2):
if len(str2) % len(str1) != 0:
return False
return all(str2.find(str1, i) == i for i in range(0, len(str2), len(str1)))

print(can_string_be_repeated(str1, str2))```

Output: `True`

This snippet checks for the occurrence of `str1` at every index that it should appear if `str2` is made of repeated `str1` patterns. It uses a generator expression within the `all()` function to ensure every required index matches.

## Method 3: Using Regular Expressions

By leveraging Pythonβs regular expressions module `re`, this method crafts a pattern to match `str1` repeated consecutively and then tries to match this pattern against `str2`.

Here’s an example:

```import re
str1 = "ab"
str2 = "abab"
pattern = f"({str1})+"
match = re.fullmatch(pattern, str2)

print(bool(match))```

Output: `True`

In the code, a regular expression pattern is created that represents `str1` repeated one or more times. The `re.fullmatch()` function is then used to determine if the entire string `str2` conforms to this pattern, yielding a boolean result.

## Method 4: Using String Slicing

This method attempts to solve the problem by slicing `str2` into chunks the size of `str1` and then checking if every slice equals `str1`.

Here’s an example:

```str1 = "xyz"
str2 = "xyzxyzxyzxyz"
def can_string_be_repeated(str1, str2):
len_str1 = len(str1)
return all(str2[i:i+len_str1] == str1 for i in range(0, len(str2), len_str1))

print(can_string_be_repeated(str1, str2))```

Output: `True`

The function divides `str2` into smaller strings of the same length as `str1` and uses a generator expression with the `all()` function to check if each substring is equal to `str1`.

## Bonus One-Liner Method 5: Using the `in` Operator

A clever one-liner leverages the fact that if `str1` can be repeated to form `str2`, then `str2` should be contained in any string formed by repeating `str1` at least twice.

Here’s an example:

```str1 = "a"
str2 = "aaaa"
print(str2 in str1 * 2 * (len(str2) // len(str1)))```

Output: `True`

This one-liner multiplies `str1` by twice the quotient of the lengths of `str2` and `str1` and checks if `str2` is a substring of the result using the `in` operator.

## Summary/Discussion

• Method 1: Division and Multiplication. Strengths: Simple and fast for short strings. Weaknesses: Performance may degrade with very long strings due to the multiplication operation.
• Method 2: `str.find()` Method. Strengths: Does not create new strings, saving memory. Weaknesses: Slightly more complex and potentially slower due to repetitive finding operation.
• Method 3: Regular Expressions. Strengths: Very powerful and can be adapted to more complex patterns. Weaknesses: Can be slower and less readable for those not familiar with regex syntax.
• Method 4: String Slicing. Strengths: Memory efficient as it does not create a new string. Weaknesses: Could be slower than other methods for very large strings.
• Method 5: Using the `in` Operator. Strengths: Extremely concise. Weaknesses: May not be immediately clear to readers how it works, and potential for excessive memory usage in some cases.