我想在 Pandas Dataframe 的列上运行一个函数。 Corpus 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。查询是一个 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]))
是否有更好的方法在列上运行余弦函数?也许有某种方法可以获取列并在每列上运行该函数。我想避免 for 循环。
最佳答案
您可以使用scipy.spatial.distance.cdist's
矢量化解决方案的'cosine'
功能,就像这样 -
from scipy.spatial.distance import cdist
out = 1-cdist(query.values[None], corpus.values.T, 'cosine')
示例运行 -
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
关于python - 在 pandas Dataframe 的列上运行函数的有效方法?,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/44973484/