Match rows in one Pandas dataframe to another based on three columns

Question

I have two Pandas dataframes, one quite large (30000+ rows) and one a lot smaller (100+ rows).

The dfA looks something like:

      X     Y    ONSET_TIME          


        

Answer 1

Use merge() - it works like JOIN in SQL - and you have first part done.

d1 = '''      X     Y    ONSET_TIME    COLOUR 
   104    78          1083         6    
   172    78          1083        16
   240    78          1083        15 
   308    78          1083         8
   376    78          1083         8
   444    78          1083        14
   512    78          1083        14
   308    78          3000        14
   308    78          2000        14''' 


d2 = '''    TIME     X     Y
      7   512   350 
   1722   512   214 
   1906   376   214 
   2095   376   146 
   2234   308    78 
   2406   172   146'''

import pandas as pd
from StringIO import StringIO

dfA = pd.DataFrame.from_csv(StringIO(d1), sep='\s+', index_col=None)
#print dfA

dfB = pd.DataFrame.from_csv(StringIO(d2), sep='\s+', index_col=None)
#print dfB

df1 =  pd.merge(dfA, dfB, on=['X','Y'])
print df1

result:

     X   Y  ONSET_TIME  COLOUR  TIME
0  308  78        1083       8  2234
1  308  78        3000      14  2234
2  308  78        2000      14  2234

Then you can use it to filter results.

df2 = df1[ df1['ONSET_TIME'] < df1['TIME'] ]
print df2

result:

     X   Y  ONSET_TIME  COLOUR  TIME
0  308  78        1083       8  2234
2  308  78        2000      14  2234

Answer 2

There is probably an even more efficient way to do this, but here is a method without those slow for loops:

import pandas as pd

dfB = pd.DataFrame({'X':[1,2,3],'Y':[1,2,3], 'Time':[10,20,30]})
dfA = pd.DataFrame({'X':[1,1,2,2,2,3],'Y':[1,1,2,2,2,3], 'ONSET_TIME':[5,7,9,16,22,28],'COLOR': ['Red','Blue','Blue','red','Green','Orange']})

#create one single table
mergeDf = pd.merge(dfA, dfB, left_on = ['X','Y'], right_on = ['X','Y'])
#remove rows where time is less than onset time
filteredDf = mergeDf[mergeDf['ONSET_TIME'] < mergeDf['Time']]
#take min time (closest to onset time)
groupedDf = filteredDf.groupby(['X','Y']).max()

print filteredDf

 COLOR  ONSET_TIME  X  Y  Time
0     Red           5  1  1    10
1    Blue           7  1  1    10
2    Blue           9  2  2    20
3     red          16  2  2    20
5  Orange          28  3  3    30


print groupedDf

COLOR  ONSET_TIME  Time
X Y                          
1 1     Red           7    10
2 2     red          16    20
3 3  Orange          28    30

The basic idea is to merge the two tables so you have the times together in one table. Then I filtered on the recs that are the largest (closest to the time on your dfB). Let me know if you have questions about this.