python - 在 pandas Dataframe 的列上运行函数的有效方法？

Question

我想在 Pandas Dataframe 的列上运行一个函数。语料库是一个pd.Dataframe

import pandas as pd 
import numpy as np
from scipy.spatial.distance import cosine

corpus = pd.DataFrame([[3,1,1,1,1,60],[2,2,0,2,0,20], [0,2,1,1,0,0], [0,0,2,1,0,1],[0,0,0,0,1,0]],index=["stark","groß","schwach","klein", "dick"],columns=["d1", "d2", "d3","d4","d5","d6"])

我有疑问。查询是 Pandas 系列。

query = pd.Series([1,1,0,0,0], index=["stark","groß","schwach","klein", "dick"])

现在我想对语料库和查询中的每一列运行余弦函数。

for column in corpus:
print("Similarity of Documents", column," and query: \n" ,1-cosine(query, corpus[column]))

是否有更好的方法对列运行余弦函数？也许是某种获取列并在每一列上运行该函数的方法。我想避免 for 循环。

Answer 1

你可以使用 scipy.spatial.distance.cdist's 'cosine' 向量化解决方案的功能，就像这样 -

from scipy.spatial.distance import cdist

out = 1-cdist(query.values[None], corpus.values.T, 'cosine')

sample 运行-

In [192]: corpus
Out[192]: 
         d1  d2  d3  d4  d5  d6
stark     3   1   1   1   1  60
groß      2   2   0   2   0  20
schwach   0   2   1   1   0   0
klein     0   0   2   1   0   1
dick      0   0   0   0   1   0

In [193]: query
Out[193]: 
stark      1
groß       1
schwach    0
klein      0
dick       0
dtype: int64

In [194]: from scipy.spatial.distance import cosine

In [195]: for column in corpus:
     ...:     print(1-cosine(query, corpus[column]))
     ...:     
0.980580675691
0.707106781187
0.288675134595
0.801783725737
0.5
0.89431540856

In [196]: 1-cdist(query.values[None], corpus.values.T, 'cosine')
Out[196]: array([[ 0.98058,  0.70711,  0.28868,  0.80178,  0.5    ,  0.89432]])

运行时测试-

In [225]: corpus = pd.DataFrame(np.random.rand(100,10000))

In [226]: query = pd.Series(np.random.rand(100))

# @C.Square's apply based soln
In [227]: %timeit corpus.apply(lambda x:1-cosine(query, x), axis=0)
1 loop, best of 3: 352 ms per loop

 # Proposed in this post using cdist()
In [228]: %timeit 1-cdist(query.values[None], corpus.values.T, 'cosine')
100 loops, best of 3: 3.2 ms per loop