Python Pandas: How to merge based on an “OR” condition?

Question

Let\'s say I have two dataframes, and the column names for both are:

table 1 columns:
[ShipNumber, TrackNumber, ShipDate, Quantity, Weight]
table 2 columns:
[Shi         


        

Answer 1

Use merge() and concat(). Then drop any duplicate cases where both A and B match (thanks @Scott Boston for that final step).

df1 = pd.DataFrame({'A':[3,2,1,4], 'B':[7,8,9,5]})
df2 = pd.DataFrame({'A':[1,5,6,4], 'B':[4,1,8,5]})

df1         df2
   A  B        A  B
0  1  7     0  1  4
1  2  8     1  5  1
2  3  9     2  6  8
3  4  5     3  4  5

With these data frames we should see:

• df1.loc[0] matches A on df2.loc[0]
• df1.loc[1] matches B on df2.loc[2]
• df1.loc[3] matches both A and B on df2.loc[3]

We'll use suffixes to keep track of what matched where:

suff_A = ['_on_A_match_1', '_on_A_match_2']
suff_B = ['_on_B_match_1', '_on_B_match_2']

pd.concat([df1.merge(df2, on='A', suffixes=suff_A), 
           df1.merge(df2, on='B', suffixes=suff_B)])

     A  A_on_B_match_1  A_on_B_match_2    B  B_on_A_match_1  B_on_A_match_2
0  1.0             NaN             NaN  NaN             9.0             4.0
1  4.0             NaN             NaN  NaN             5.0             5.0
0  NaN             2.0             6.0  8.0             NaN             NaN
1  NaN             4.0             4.0  5.0             NaN             NaN

Note that the second and fourth rows are duplicate matches (for both data frames, A = 4 and B = 5). We need to remove one of those sets.

dupes = (df.B_on_A_match_1 == df.B_on_A_match_2) # also could remove A_on_B_match
df.loc[~dupes]

     A  A_on_B_match_1  A_on_B_match_2    B  B_on_A_match_1  B_on_A_match_2
0  1.0             NaN             NaN  NaN             9.0             4.0
0  NaN             2.0             6.0  8.0             NaN             NaN
1  NaN             4.0             4.0  5.0             NaN             NaN