pandas Python中Dataframe中每一行之间的余弦相似度
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Cosine similarity between each row in a Dataframe in Python
提问by Jayanth Prakash Kulkarni
I have a DataFrame containing multiple vectors each having 3 entries. Each row is a vector in my representation. I needed to calculate the cosine similarity between each of these vectors. Converting this to a matrix representation is better or is there a cleaner approach in DataFrame itself?
我有一个包含多个向量的 DataFrame，每个向量有 3 个条目。在我的表示中，每一行都是一个向量。我需要计算每个向量之间的余弦相似度。将其转换为矩阵表示更好还是 DataFrame 本身有更简洁的方法？
Here is the code that I have tried.
这是我尝试过的代码。
import pandas as pd
from scipy import spatial
df = pd.DataFrame([X,Y,Z]).T
similarities = df.values.tolist()
for x in similarities:
for y in similarities:
result = 1 - spatial.distance.cosine(x, y)
回答by miradulo
You can directly just use sklearn.metrics.pairwise.cosine_similarity.
您可以直接使用sklearn.metrics.pairwise.cosine_similarity.
Demo
演示
import numpy as np; import pandas as pd
from sklearn.metrics.pairwise import cosine_similarity
df = pd.DataFrame(np.random.randint(0, 2, (3, 5)))
df
## 0 1 2 3 4
## 0 1 1 1 0 0
## 1 0 0 1 1 1
## 2 0 1 0 1 0
cosine_similarity(df)
## array([[ 1. , 0.33333333, 0.40824829],
## [ 0.33333333, 1. , 0.40824829],
## [ 0.40824829, 0.40824829, 1. ]])
