Grouping by Two Columns and Printing Rows with Minimum Value in the Third Column

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When working with dataframes, it’s not uncommon to need to group by multiple columns and perform operations based on the values in those columns. In this article, we’ll explore a common use case: grouping by two columns and printing out rows corresponding to the minimum value on the third column.

Introduction

Let’s start with an example of two dataframes in pandas:

df = pd.DataFrame({0: [1.5, 2, 2.3, 4], 1: [3, 6, 1, 0]})
df2 = pd.DataFrame({0: [1.7, 4.05, 2.1, 2.99], 1: [1, 3, 1, 7]})

df.columns = ["x1", "y1"]
df2.columns = ["x2", "y2"]

Merging the Dataframes

We’ll merge these two dataframes on the first column (x1 and x2) to create a new dataframe with both columns:

merged1 = df.merge(df2, how='cross')

This creates a cross-join of the two dataframes based on their common column (x1).

Calculating the Difference Between Columns

Next, we’ll calculate the absolute difference between the values in x1 and x2 columns:

merged1['diff'] = (merged1['x1'].sub(merged1['x2'])).abs()

This creates a new column (diff) with the absolute differences.

Grouping by One Column and Printing Minimum Values

Now, we’ll group the dataframe by the y2 column and print out the rows corresponding to the minimum value in the diff column:

out1 = (merged1.loc[merged1.groupby(df.columns.tolist())['diff'].idxmin().to_numpy()])

This code groups the dataframe by all columns (x1, y1) and then selects the row with the minimum difference.

The Problem

The problem arises when trying to print out only the rows corresponding to the minimum value in the diff column, grouped by the y2 column. Instead of getting the desired output, we get all rows with the smallest differences for each group.

out1.groupby(["y2"])['diff'].min()

This code groups the dataframe by the y2 column and calculates the minimum difference for each group. However, it returns a Series with the minimum value for each group, not the corresponding rows.

The Solution

To fix this issue, we need to use the loc method to select only the rows that correspond to the minimum value in the diff column, grouped by the y2 column:

out1.loc[out1.groupby(["y2"])['diff'].idxmin()]

This code groups the dataframe by the y2 column and then selects only the row with the index corresponding to the minimum difference.

Alternative Solutions Using pandas.merge_asof

There are two alternative solutions using pandas.merge_asof that can achieve this result in a single step:

(pd
 .merge_asof(df, df2.sort_values(by='x2'),
             left_on='x1', right_on='x2', direction='nearest')
 .loc[lambda d: d['x1'].sub(d['x2']).abs().groupby(d['y2']).idxmin()]
)

or,

(pd
 .merge_asof(df, df2.sort_values(by='x2'),
             left_on='x1', right_on='x2', direction='nearest')
 .assign(diff=lambda d: d['x1'].sub(d['x2']).abs())
 .loc[lambda d: d.groupby('y2')['diff'].idxmin()]
)

The first solution merges the dataframes on a sorted x2 column and then selects only the rows with the nearest value. The second solution assigns a new column for the difference, groups by y2, and then selects only the row with the minimum difference.

Conclusion

Grouping by two columns and printing out rows corresponding to the minimum value on the third column can be achieved using various methods in pandas. By understanding how to group and select data from dataframes, we can solve a range of problems involving multiple columns and conditional logic. In this article, we’ve explored alternative solutions using pandas.merge_asof that can simplify these operations.

Last modified on 2025-03-25