How to Efficiently Merge Pandas DataFrames Based on a Range Condition?

Join by Range in Pandas

In data manipulation using Pandas, one common task is to merge two dataframes based on a range condition. This involves identifying the rows in dataframe A where the values in a specific column lie within a specified range in dataframe B.

One approach to this task involves creating a dummy column in both dataframes, performing a cross-join using this dummy column, and then filtering out the rows that do not meet the range criteria. However, this method can be inefficient for large datasets.

An alternative solution is to utilize numpy broadcasting, which is a powerful technique for performing element-wise operations on arrays. By converting the relevant columns in dataframes A and B to numpy arrays, we can apply logical operators to compare A_value to the B_low and B_high values to identify the rows that satisfy the range condition.

import numpy as np

# Convert to numpy arrays
a = A.A_value.values
bh = B.B_high.values
bl = B.B_low.values

# Find intersecting indices
i, j = np.where((a[:, None] >= bl) & (a[:, None] <= bh))

# Merge and align dataframes
result = pd.concat([
    A.loc[i, :].reset_index(drop=True),
    B.loc[j, :].reset_index(drop=True)
], axis=1)

print(result)

This method offers greater efficiency for large datasets compared to the dummy column approach. Additionally, it allows for easy implementation of left or right joins by adjusting the logic in the numpy broadcasting step.

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