Python Pandas max value in a group as a new column

by

Use groupby + transform:

df['max'] = df.groupby('group')['odds'].transform('max')

This is equivalent to the verbose:

maxima = df.groupby('group')['odds'].max()
df['max'] = df['group'].map(maxima)

The transform method aligns the groupby result to the groupby indexer, so no explicit mapping is required.

More Related Contents:

  1. Get statistics for each group (such as count, mean, etc) using pandas GroupBy?
  2. Aggregation in Pandas
  3. Groupby value counts on the dataframe pandas
  4. Converting a Pandas GroupBy output from Series to DataFrame
  5. Multiple aggregations of the same column using pandas GroupBy.agg()
  6. Pandas groupby mean – into a dataframe?
  7. How to loop over grouped Pandas dataframe?
  8. How to access pandas groupby dataframe by key
  9. Pandas Groupby and Sum Only One Column
  10. How to drop duplicates based on two or more subsets criteria in Pandas data-frame
  11. Python Pandas Group by date using datetime data
  12. Pandas get frequency of item occurrences in a column as percentage [duplicate]
  13. Pandas – dataframe groupby – how to get sum of multiple columns
  14. Group dataframe and get sum AND count?
  15. How to summarize on different groupby combinations?
  16. get first and last values in a groupby
  17. Pandas groupby and aggregation output should include all the original columns (including the ones not aggregated on)
  18. Get the row corresponding to the max in pandas GroupBy [duplicate]
  19. How to get number of groups in a groupby object in pandas?
  20. When is it appropriate to use df.value_counts() vs df.groupby(‘…’).count()?
  21. How to deal with SettingWithCopyWarning in Pandas
  22. Pandas split column into multiple columns by comma
  23. Cumsum as a new column in an existing Pandas data
  24. Rolling Mean on pandas on a specific column
  25. Pandas expand rows from list data available in column
  26. Selecting/excluding sets of columns in pandas [duplicate]
  27. Reverse a get_dummies encoding in pandas
  28. pandas divide row value by aggregated sum with a condition set by other cell
  29. Updating a value in a pandas dataframe in an iterrows loop
  30. Python: Adding hours to pandas timestamp

Leave a Comment