Setup
Consider the dataframes A and B
A = pd.DataFrame(dict(
A_id=range(10),
A_value=range(5, 105, 10)
))
B = pd.DataFrame(dict(
B_id=range(5),
B_low=[0, 30, 30, 46, 84],
B_high=[10, 40, 50, 54, 84]
))
A
A_id A_value
0 0 5
1 1 15
2 2 25
3 3 35
4 4 45
5 5 55
6 6 65
7 7 75
8 8 85
9 9 95
B
B_high B_id B_low
0 10 0 0
1 40 1 30
2 50 2 30
3 54 3 46
4 84 4 84
numpy
The ✌easiest✌ way is to use numpy broadcasting.
We look for every instance of A_value being greater than or equal to B_low while at the same time A_value is less than or equal to B_high.
a = A.A_value.values
bh = B.B_high.values
bl = B.B_low.values
i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh))
pd.concat([
A.loc[i, :].reset_index(drop=True),
B.loc[j, :].reset_index(drop=True)
], axis=1)
A_id A_value B_high B_id B_low
0 0 5 10 0 0
1 3 35 40 1 30
2 3 35 50 2 30
3 4 45 50 2 30
To address the comments and give something akin to a left join, I appended the part of A that doesn’t match.
pd.concat([
A.loc[i, :].reset_index(drop=True),
B.loc[j, :].reset_index(drop=True)
], axis=1).append(
A[~np.in1d(np.arange(len(A)), np.unique(i))],
ignore_index=True, sort=False
)
A_id A_value B_id B_low B_high
0 0 5 0.0 0.0 10.0
1 3 35 1.0 30.0 40.0
2 3 35 2.0 30.0 50.0
3 4 45 2.0 30.0 50.0
4 1 15 NaN NaN NaN
5 2 25 NaN NaN NaN
6 5 55 NaN NaN NaN
7 6 65 NaN NaN NaN
8 7 75 NaN NaN NaN
9 8 85 NaN NaN NaN
10 9 95 NaN NaN NaN