根据从另一个数据框构建的条件列表，选择一个熊猫数据框的子集

Question

Suppose we have the following DataFrame

>>> import pandas as pd

>>> df_org = pd.DataFrame({'A' : [1,2,3,4,5,6], 
                           'B' : [1,1,1,1,2,2],
                           'C' : [1,2,3,4,1,2]})
   A  B  C
0  1  1  1
1  2  1  2
2  3  1  3
3  4  1  4
4  5  2  1
5  6  2  2

And this another one, df_criteria , that has some of the columns of df_org and from which we will build our criteria. For instance:

>>> df_criteria = pd.DataFrame({'B' : [1,2], 
                                'C' : [1,1]}) 

   B  C
0  1  1
1  2  1

I'd like to be able to fetch the value of A in the df_org DataFrame for which the corresponding values of the B and C match the ones listed in the df_criteria DataFrame. In this examples, I would like to have a subset of df_org that contains its rows '0' and '4', like so:

   A  B  C
0  1  1  1
4  5  2  1

Being a newbie in pandas, the way I've implemented this is using the for -loop mindset: by iterating over the rows of df_criteria and querying df_org for each row. However, this is very slow and I have the impression that there must be a more pythonic (and faster) way that does not make use of for -loops. I've also explored the use of DataFrame.lookup , however it is not useful in my case because the indices in df_criteria and df_org do not necessarily match.

Any suggestion would be very much appreciated. Many thanks!

Answer 1

A simple inner merge would work:

In [285]:

df_org.merge(df_criteria, on=['B','C'])
Out[285]:
   A  B  C
0  1  1  1
1  5  2  1