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 b
2 1 34.33 y c
3 2 10.14 z b
4 3 33.01 z a
Dataframe B:
col1 col2 group ID
0 1 34.32 t z
1 1 54.32 s w
2 1 34.33 r z
3 2 10.14 q z
4 3 33.01 q e
I want to merge on col1 and col2. I've been pd.merge with the following syntax:
pd.merge(A, B, how = 'outer', on = ['col1', 'col2'])
However, I think I am running into issues joining on the float values of col2 since many rows are being dropped. Is there any way to use np.isclose to match the values of col2? When I reference the index of a particular value of col2 in either dataframe, the value has many more decimal places than what is displayed in the dataframe.
I would like the result to be:
col1 col2 sub grade group ID
0 1 34.32 x a t z
1 1 34.32 x b s w
2 1 54.32 s w NaN NaN
3 1 34.33 y c r z
4 2 10.14 z b q z
5 3 33.01 z a q e
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