Keep other columns when doing groupby

by

Method #1: use idxmin() to get the indices of the elements of minimum diff, and then select those:

>>> df.loc[df.groupby("item")["diff"].idxmin()]
   item  diff  otherstuff
1     1     1           2
6     2    -6           2
7     3     0           0

[3 rows x 3 columns]

Method #2: sort by diff, and then take the first element in each item group:

>>> df.sort_values("diff").groupby("item", as_index=False).first()
   item  diff  otherstuff
0     1     1           2
1     2    -6           2
2     3     0           0

[3 rows x 3 columns]

Note that the resulting indices are different even though the row content is the same.

More Related Contents:

  1. How to group dataframe rows into list in pandas groupby
  2. Multiple aggregations of the same column using pandas GroupBy.agg()
  3. How can I pivot a dataframe?
  4. How do I Pandas group-by to get sum?
  5. Concatenate strings from several rows using Pandas groupby
  6. Groupby value counts on the dataframe pandas
  7. Split pandas dataframe based on groupby
  8. GroupBy pandas DataFrame and select most common value
  9. Converting a Pandas GroupBy output from Series to DataFrame
  10. pandas GroupBy columns with NaN (missing) values
  11. Count unique values per groups with Pandas [duplicate]
  12. Pandas groupby cumulative sum
  13. Python Pandas max value in a group as a new column
  14. pandas dataframe groupby datetime month
  15. How to loop over grouped Pandas dataframe?
  16. How to move pandas data from index to column after multiple groupby
  17. Python – rolling functions for GroupBy object
  18. Pandas sum by groupby, but exclude certain columns
  19. Use pandas.shift() within a group
  20. Rename result columns from Pandas aggregation (“FutureWarning: using a dict with renaming is deprecated”)
  21. Python Pandas Conditional Sum with Groupby
  22. Identifying statistical outliers with pandas: groupby and reduce rows into different dataframe
  23. Sample each group after pandas groupby
  24. Pandas groupby with categories with redundant nan
  25. pandas groupby where you get the max of one column and the min of another column
  26. Bar graph from dataframe groupby
  27. pandas group by and assign a group id then ungroup
  28. Pandas groupby and aggregation output should include all the original columns (including the ones not aggregated on)
  29. Get the row corresponding to the max in pandas GroupBy [duplicate]
  30. When is it appropriate to use df.value_counts() vs df.groupby(‘…’).count()?

Leave a Comment