计算DataFrame的所有列和另DataFrame的所有列:间的相关性?
问题说明
我有一个充满股票收益的DataFrame对象stocks.我还有另一个充满行业回报的DataFrame对象industries.我想找到每种股票与每个行业的相关性.
I have a DataFrame object stocks filled with stock returns. I have another DataFrame object industries filled with industry returns. I want to find each stock's correlation with each industry.
import numpy as np
np.random.seed(123)
df1=pd.DataFrame( {'s1':np.random.randn(10000), 's2':np.random.randn(10000) } )
df2=pd.DataFrame( {'i1':np.random.randn(10000), 'i2':np.random.randn(10000) } )
执行此操作的昂贵方法是合并两个DataFrame对象，计算相关性，然后丢弃所有库存与库存之间以及行业与行业之间的相关性.有没有更有效的方法可以做到这一点?
The expensive way to do this is to merge the two DataFrame objects, calculate correlation, and then throw out all the stock to stock and industry to industry correlations. Is there a more efficient way to do this?
正确答案
这是一种单列代码，它在列上使用apply并避免了嵌套的for循环.主要优点是apply将结果构建在DataFrame中.
And here's a one-liner that uses apply on the columns and avoids the nested for loops. The main benefit is that apply builds the result in a DataFrame.
df1.apply(lambda s: df2.corrwith(s))
这篇好文章是转载于：学新通技术网
