How to Keep Other Columns When Using GroupBy in Pandas?

Keeping Other Columns When GroupBy

In Pandas dataframes, using groupby to filter rows based on a specific column can result in the loss of other columns in the output. This issue arises when performing group operations like finding the minimum value of a column and excluding rows below a threshold.

To overcome this limitation and retain other columns during groupby, there are a few methods:

Method 1: Using idxmin()

idxmin() returns the indices of rows with the minimum value for a given column. By using this, we can select the specific rows and retain all their columns:

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

Method 2: Sorting and First

Sorting the dataframe by the column to be filtered and then taking the first element of each group will also preserve other columns:

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

Both methods produce the same result, as seen in the example below:

<code class="python">df = pd.DataFrame({"item": [1, 1, 1, 2, 2, 2, 2, 3, 3],
                   "diff": [2, 1, 3, -1, 1, 4, -6, 0, 2],
                   "otherstuff": [1, 2, 7, 0, 3, 9, 2, 0, 9]})

# Method 1
df_filtered1 = df.loc[df.groupby("item")["diff"].idxmin()]

# Method 2
df_filtered2 = df.sort_values("diff").groupby("item", as_index=False).first()

print(df_filtered1)
print(df_filtered2)</code>

Output:

   item  diff  otherstuff
1     1     1           2
6     2    -6           2
7     3     0           0

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

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