Understanding Interval Merging in Pandas DataFrames

Introduction

When working with datasets, it’s common to encounter situations where you want to merge two dataframes based on certain conditions. In this blog post, we’ll explore how to perform an interval merge using pandas in Python.

An interval merge is a type of merge where the values in one column are within a specific range of another column. For example, if you’re merging zip codes from two datasets, you might want to consider two zip codes as “nearby” if they’re within 15 units of each other. This approach allows you to create more meaningful matches between data points.

The Problem with Traditional Left Joins

In the provided Stack Overflow question, the user explains that traditional left joins won’t meet their requirements because they don’t enforce an interval range for matching values.

To illustrate this issue, consider the following example:

Suppose we have two datasets:

data1 = [[10001, 'NY'], [10007, 'NY'], [10013, 'NY'], [90011, 'CA'], [91331, 'CA'], [90650, 'CA']]
df_left = pd.DataFrame(data1, columns=['Zip', 'State'])

and

data2 = [[10003, 'NY', 1200], [10008, 'NY', 1460], [10010, 'NY', 1900], [90011, 'CA', 850], [91315, 'CA', 1700], [90645, 'CA',2300]]
df_right = pd.DataFrame(data2, columns=['Zip', 'State', 'Average_Rent'])

If we perform a traditional left join using merge with the default behavior:

df_merge = df_left.merge(df_right, left_on='Zip', right_on='Zip', how='left')
print(df_merge)

We get the following result:

As you can see, the values don’t match up as closely as desired.

Interval Merging with Pandas

To achieve interval merging using pandas, we need to use a combination of functions from the pandas library.

Here’s the modified code:

# Cross merge dataframes
merged = df_left.merge(df_right, how='cross', suffixes=('_left', '_right'))

# Filter rows where states match
merged = merged[merged['State_left']==merged['State_right']]

# Calculate absolute difference between zip codes
merged['Diff'] = merged['Zip_left'].sub(merged['Zip_right']).abs()

# Find rows where the difference is closest for each 'Zip_left'
merged = merged[merged.groupby('Zip_left')['Diff'].transform('min') == merged['Diff']]

# Mask rows where difference is greater than 15
cols = merged.columns[~merged.columns.str.endswith('left')]
merged[cols] = merged[cols].mask(merged['Diff']>15)

# Select desired columns and drop NaN values
out = merged.drop(columns=['State_right','Diff']).rename(columns={'State_left':'State'}).reset_index(drop=True)
print(out)

The output will be:

In this example, the interval merging produces a more meaningful match between zip codes.

Conclusion

Performing an interval merge in pandas dataframes allows you to create more accurate matches based on specific conditions. By combining functions from the pandas library and using some clever indexing techniques, we can easily implement this type of merging in your own projects.

Remember to carefully consider the requirements for your interval merging needs, as different approaches may yield better results depending on your dataset.

Last modified on 2025-04-19