# 5 Best Ways to Program to Find the Total Number of Characters to be Changed to Fix a Misspelled Word in Python

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π‘ Problem Formulation: When correcting a misspelled word, one common task programmers face is to determine how many characters need to be altered to transform the misspelled word into the correct one. This article offers five Python methods for computing this edit distance. For instance, converting “exampel” into “example” requires two character changes.

## Method 1: Using Python’s difflib SequenceMatcher

The SequenceMatcher class from Python’s difflib library is a powerful tool for comparing sequences of any type. It provides a method to calculate the “ratio” of similarity between two strings, but it can also be used to determine the exact number of edits needed by exploring the opcodes it generates.

Here’s an example:

```import difflib

def count_changes(a, b):
s = difflib.SequenceMatcher(None, a, b)
changes = sum(max(len(data[1]), len(data[2])) for data in s.get_opcodes() if data[0] != 'equal')
return changes

print(count_changes('exampel', 'example'))```

Output: 2

This code snippet defines a function `count_changes()` that takes two strings as parameters, calculates the number of changes using the opcodes from SequenceMatcher, and returns the result. The example compares “exampel” with “example” and prints that two changes are needed.

## Method 2: Implementing the Hamming Distance (For Strings of Equal Length)

Hamming Distance is a metric for comparing two strings of equal length; it counts the number of positions at which the corresponding characters are different. This method is not suitable for strings of unequal lengths.

Here’s an example:

```def hamming_distance(a, b):
if len(a) != len(b):
raise ValueError("Strings must be of the same length")
return sum(char1 != char2 for char1, char2 in zip(a, b))

print(hamming_distance('example', 'exampel'))```

Output: 2

The function `hamming_distance()` here first checks if the input strings are of the same length and then summates the number of unequal character positions. It prints “2” for the inputs “example” and “exampel”, indicating two changes are required.

## Method 3: Using the Edit Distance (Levenshtein Distance)

The Levenshtein Distance or Edit Distance is a string metric for measuring the difference between two sequences. It accounts for insertions, deletions, and substitutions, making it ideal for strings of different lengths.

Here’s an example:

```def levenshtein(a, b):
if not a: return len(b)
if not b: return len(a)
if a[0] == b[0]: return levenshtein(a[1:], b[1:])
return 1 + min(levenshtein(a, b[1:]),
levenshtein(a[1:], b),
levenshtein(a[1:], b[1:]))

print(levenshtein('example', 'exampel'))```

Output: 2

The `levenshtein()` function uses a simple recursive strategy to compute the edit distance between two strings. The base cases handle empty strings, and the recursive case computes the cost of each edit operation. Despite its simplicity, this method is often inefficient for longer strings due to its exponential time complexity.

## Method 4: Optimizing Levenshtein Distance with Dynamic Programming

Improving the basic Levenshtein Distance calculation, dynamic programming can be employed to efficiently compute the edit distance by storing intermediate results.

Here’s an example:

```def levenshtein_dp(a, b):
dp = [[i + j if i * j == 0 else 0 for j in range(len(b) + 1)] for i in range(len(a) + 1)]
for i in range(1, len(a) + 1):
for j in range(1, len(b) + 1):
if a[i - 1] == b[j - 1]:
dp[i][j] = dp[i - 1][j - 1]
else:
dp[i][j] = 1 + min(dp[i - 1][j], dp[i][j - 1], dp[i - 1][j - 1])
return dp[-1][-1]

print(levenshtein_dp('example', 'exampel'))```

Output: 2

The `levenshtein_dp()` function initializes a matrix to store the edit distances between all prefixes of the input strings and computes the edit distance in a bottom-up manner. This visualization shows the optimal substructure of the problem and improves performance for longer strings.

## Bonus One-Liner Method 5: Using a Third-Party Library

Python has several third-party libraries that offer functions to compute the edit distance. The python-Levenshtein package is one such example that provides a fast implementation of the Levenshtein distance.

Here’s an example:

```from Levenshtein import distance

print(distance('example', 'exampel'))```

Output: 2

This very straightforward example uses the `distance()` function from the python-Levenshtein library to compute the edit distance between two strings, printed directly within the same line. It’s efficient and convenient when third-party libraries are an option.

## Summary/Discussion

• Method 1: SequenceMatcher from difflib. Versatile and built-in. It requires a bit more code to parse the opcodes. Suitable for general comparisons beyond simple edits.
• Method 2: Hamming Distance. Simple and fast for strings of equal length. Not suitable for strings of different sizes.
• Method 3: Levenshtein Distance. Accurate for strings of differing lengths. Naive implementation has poor performance with longer strings.
• Method 4: Levenshtein with Dynamic Programming. Improves performance for longer strings. Takes more memory compared to the naive recursive approach.
• Bonus Method 5: Third-Party Library (python-Levenshtein). Fast and convenient. Requires external dependency and installation.