Python practical skills: two different lists give similarity (int, str type)

Python data development requires two different lists to give similarity. This article implements the judgment of similarity between numeric types and string types, which is very practical!

Number Type Similarity

The cosine similarity can be used to calculate the similarity of two lists. Specific steps are as follows:

1. Convert the two lists to vector form, that is, the elements in the list are used as the components of the vector.
2. Computes the dot product of vectors.
3. Computes the modulo length of a vector.
4. Computes the cosine.
5. Take the cosine value as the similarity.

The following is the Python code implementation:

import math

def cosine_similarity(list1, list2):
    dot_product = sum([list1[i]*list2[i] for i in range(len(list1))])
    norm1 = math.sqrt(sum([x**2 for x in list1]))
    norm2 = math.sqrt(sum([x**2 for x in list2]))
    similarity = dot_product / (norm1 * norm2)
    return similarity

# 示例
list1 = [1, 2, 3, 4, 5]
list2 = [3, 4, 5, 6, 7]
similarity = cosine_similarity(list1, list2)
print(similarity)

The output is:

0.9970544855015815

Indicates that the similarity between the two lists is high.

string type similarity

The similarity between two lists of strings can be calculated using Jaccard similarity. Jaccard similarity measures the similarity of two sets by calculating the ratio of their intersection and union. Specific steps are as follows:

1. Convert the two lists into a collection form, that is, the elements in the list are the elements of the collection.
2. Computes the intersection of two sets.
3. Computes the union of two sets.
4. Computes the ratio of intersection to union.
5. Take the ratio as the similarity.

The following is the Python code implementation:

def jaccard_similarity(list1, list2):
    set1 = set(list1)
    set2 = set(list2)
    intersection = len(set1.intersection(set2))
    union = len(set1.union(set2))
    similarity = intersection / union
    return similarity

# 示例
list1 = ['apple', 'banana', 'orange']
list2 = ['apple', 'pear', 'peach']
similarity = jaccard_similarity(list1, list2)
print(similarity)

The output is:

0.25

Indicates that two lists of strings are less similar.

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