How to Keep Other Columns While Finding Minimums in Grouped Pandas DataFrames?

Keeping Other Columns During Grouped Minimum Operation in Pandas DataFrame

When using the groupby function to group data by a specific column and perform aggregation operations like finding the minimum, other columns in theDataFrame may be inadvertently dropped.

To retain additional columns while performing a minimum operation on a grouped column, consider the following methods:

Method 1: Using idxmin()

idxmin() returns the indices of the minimum values within each group. By utilizing this, you can select only the desired rows:

<code class="python">result = df.loc[df.groupby("item")["diff"].idxmin()]</code>

Method 2: Sorting and Getting the First Element

Alternatively, you can sort the dataframe by the minimum column before performing the groupby operation and extracting the first row in each group:

<code class="python">result = df.sort_values("diff").groupby("item", as_index=False).first()</code>

Both methods will produce the desired output, retaining the otherstuff column while filtering rows based on the minimum diff value:

   item  diff  otherstuff
0     1     1           2
1     2    -6           2
2     3     0           0

Note that the resulting indices may vary between the two methods, although the row content remains the same.

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