如何执行分类列之间的关联

问题描述

我在数据帧df1中有一组列(col1，col2，col3)我在数据框df2中有另一组列(col4，col5，col6)假设这两个数据帧具有相同的行数.

I have a set of columns (col1,col2,col3) in dataframe df1I have another set of columns (col4,col5,col6) in dataframe df2Assume this two dataframes has the same number of rows.

如何生成在df1和df2之间进行成对相关的相关表?

How do I generate a correlation table that do pairwise correlation between df1 and df2?

表格看起来像

    col1 col2 col3
col4 ..   ..   ..
col5 ..   ..   ..
col6 ..   ..   ..

我使用df1.corrwith(df2)，它似乎没有按照要求生成表.

I use df1.corrwith(df2), it does not seem to generate the table as required.

我在这里问了类似的问题:如何在具有不同列的两个数据框之间执行关联名称但是现在我正在处理分类列.

I have a asked a similar question here:How to perform Correlation between two dataframes with different column namesbut now I am dealing with categorical columns.

如果不能直接比较，是否有标准方法使其可比较(例如使用get_dummies)?并且这是一种自动处理所有字段(假设所有字段都是分类的)并计算其相关性的更快方法吗?

If it is not comparable directly, is there a standard way to make it comparable (like using get_dummies)? and is that a faster way to automatically process all fields (assume all are categorical) and calculate their correlation?

推荐答案

您正确地需要pd.get_dummies来获得相关性.在下面，我将使用两个分类列创建一些虚假数据，然后使用corrwith

You are correct that pd.get_dummies would be needed to get the correlation. Below, I will create some fake data with two categorical columns and then use corrwith

df = pd.DataFrame({'col1':np.random.choice(list('abcde'),100),
                  'col2':np.random.choice(list('xyz'),100)}, dtype='category')
df1 = pd.DataFrame({'col1':np.random.choice(list('abcde'),100),
                   'col2':np.random.choice(list('xyz'),100)}, dtype='category')

dfa = pd.get_dummies(df)
dfb = pd.get_dummies(df1)
dfa.corrwith(dfb)

col1_a   -0.057735
col1_b    0.002513
col1_c    0.137956
col1_d   -0.095050
col1_e   -0.114022
col2_x    0.022568
col2_y   -0.081699
col2_z   -0.128350

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