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INPUT :

df1

Pg x0 y0 x1 y1 Text
1 521.3 745.92 537.348 754.097 word1
1 538.982 745.92 580.247 754.097 word2
1 527.978 735.253 572.996 747.727 word3
2 268.985 732.36 341.59 746.636 word4
2 344.443 732.36 390.175 746.636 word5

df2

Pg x0 y0 x1 y1 Text T R C
1 507.6 730.8 593.76 754.8 word1 word2 word3 1 1 2
2 334.56 732.36 401.34 746.636 word5 2 3 1

Expected OUTPUT :

Pg x0 y0 x1 y1 Text T R C
1 521.3 745.92 537.348 754.097 word1 1 1 2
1 538.982 745.92 580.247 754.097 word2 1 1 2
1 527.978 735.253 572.996 747.727 word3 1 1 2
2 268.985 732.36 341.59 746.636 word4 
2 344.443 732.36 390.175 746.636 word5 2 3 1

I need to find which all words in df1 are present in df2 on basis of coordinates(overlap) and not Text based approach. After this I need to copy the values of columns [T, R, C] from df2 to df1.

For eg : First row of df2 has coordinates that overlap the coordinates of the word1, word2, word3 of df1. Overlap here means the bbox(x0, y0, x1, y1) of a row in df1 should lie inside the bbox(x0, y0, x1, y1) of a specific row of df2.

My Approach :

I am iterating each row in df2 and then comparing each row coordinate from df1 to find any overlaps and then merging the dataframes.

for i, r in df2.iterrows():
 df1.loc[
 (df1.x0 >= r.x0) &
 (df1.y0 >= r.y0) &
 (df1.x1 <= r.x1) &
 (df1.y1 <= r.y1) , 'flag'] = 1

 df1.loc[df.flag == 1, ['T', 'R', 'C']] = r.T, r.R, r.C

Problem is the whole process is working properly as expected but takes a lot of time to run. It takes around 90 seconds to run df1 = 20,000 rows and df2 = 3500 rows.

You can use apply and masking. Example:

def compare(row):
 mask = df2[
 (df2['x0'] <= row['x0']) &
 (df2['x1'] >= row['x1']) &
 (df2['y0'] <= row['y0']) &
 (df2['y1'] >= row['y1'])
 ]
 if mask.empty:
 return row
 row['T'] = mask['T'].tolist()[0]
 row['R'] = mask['R'].tolist()[0]
 row['C'] = mask['C'].tolist()[0]

return row

result = df1.apply(compare, axis=1)