Merge pandas DataFrame on column of float values

Question

I have two data frames that I am trying to merge.

Dataframe A:

    col1    col2    sub    grade
0   1       34.32   x       a 
1   1       34.32   x          


        

Answer 1

You can use a little hack - multiple float columns by some constant like 100, 1000..., convert column to int, merge and last divide by constant:

N = 100
#thank you koalo for comment
A.col2 = np.round(A.col2*N).astype(int) 
B.col2 = np.round(B.col2*N).astype(int) 
df = pd.merge(A, B, how = 'outer', on = ['col1', 'col2'])
df.col2 = df.col2 / N
print (df)
   col1   col2  sub grade group ID
0     1  34.32    x     a     t  z
1     1  34.32    x     b     t  z
2     1  34.33    y     c     r  z
3     2  10.14    z     b     q  z
4     3  33.01    z     a     q  e
5     1  54.32  NaN   NaN     s  w

Answer 2

I had a similar problem where I needed to identify matching rows with thousands of float columns and no identifier. This case is difficult because values can vary slightly due to rounding.

In this case, I used scipy.spatial.distance.cosine to get the cosine similarity between rows.

from scipy import distance

threshold = 0.99999
similarity = 1 - spatial.distance.cosine(row1, row2)

if similarity >= threshold:
    # it's a match
else:
    # loop and check another row pair

This won't work if you have duplicate or very similar rows, but when you have a large number of float columns and not too many of rows, it works well.