pandas group by and find first non null value for all columns

by

Use GroupBy.first:

df1 = df.groupby('id', as_index=False).first()
print (df1)
   id   age gender country  sales_year
0   1  20.0      M   India        2016
1   2  23.0      F   India        2016
2   3  30.0      M   India        2019
3   4  36.0    NaN   India        2019

If column sales_year is not sorted:

df2 = df.sort_values('sales_year', ascending=False).groupby('id', as_index=False).first()
print (df2)
   id   age gender country  sales_year
0   1  20.0      M   India        2016
1   2  23.0      F   India        2016
2   3  30.0      M   India        2019
3   4  36.0    NaN   India        2019

More Related Contents:

  1. How can I pivot a dataframe?
  2. Get statistics for each group (such as count, mean, etc) using pandas GroupBy?
  3. How do I create a new column from the output of pandas groupby().sum()?
  4. GroupBy pandas DataFrame and select most common value
  5. How to groupby consecutive values in pandas DataFrame
  6. pandas GroupBy columns with NaN (missing) values
  7. Count unique values per groups with Pandas [duplicate]
  8. pandas groupby sort within groups
  9. Pandas ‘count(distinct)’ equivalent
  10. Pandas Groupby Range of Values
  11. Count unique values using pandas groupby
  12. pandas groupby, then sort within groups
  13. Pandas GroupBy and select rows with the minimum value in a specific column
  14. group by in group by and average
  15. Pandas interpolate within a groupby
  16. Pandas: resample timeseries with groupby
  17. pandas – find first occurrence
  18. python pandas groupby() result
  19. Group dataframe and get sum AND count?
  20. Pivot Tables or Group By for Pandas?
  21. Pivot Tables of Counts in Pandas DataFrame
  22. Pandas groupby with categories with redundant nan
  23. Pandas Correlation Groupby
  24. Add a sequence number to each element in a group using python
  25. How to zip two array columns in Spark SQL
  26. Pandas groupby and aggregation output should include all the original columns (including the ones not aggregated on)
  27. Get the row corresponding to the max in pandas GroupBy [duplicate]
  28. Get frequency of item occurrences in a column as percentage [duplicate]
  29. Pandas: groupby forward fill with datetime index
  30. Why do I get a KeyError when using pandas apply?

Leave a Comment