How can I "merge" rows by same value in a column in Pandas with aggregation functions?

I would like to group rows in a dataframe, given one column. Then I would like to receive an edited dataframe for which I can decide which aggregation function makes sense. The default should be just the value of the first entry in the group.

(it would be nice if the solution also worked for a combination of two columns)

Example

#!/usr/bin/env python """Test data frame grouping.""" # 3rd party modules import pandas as pd df = pd.DataFrame([{'id': 1, 'price': 123, 'name': 'anna', 'amount': 1}, {'id': 1, 'price': 7, 'name': 'anna', 'amount': 2}, {'id': 2, 'price': 42, 'name': 'bob', 'amount': 30}, {'id': 3, 'price': 1, 'name': 'charlie', 'amount': 10}, {'id': 3, 'price': 2, 'name': 'david', 'amount': 100}]) print(df) 

gives the dataframe:

 amount id name price 0 1 1 anna 123 1 2 1 anna 7 2 30 2 bob 42 3 10 3 charlie 1 4 100 3 david 2 

And I would like to get:

amount id name price 3 1 anna 130 30 2 bob 42 110 3 charlie 3 

So:

• Entries with the same value in the id column belong together. After that operation, there should still be an id column, but it should have only unique values.
• All values in amount and price which have the same id get summed up
• For name, just the first one (by the current order of the dataframe) is taken.

Is this possible with Pandas?

2 Answers

You are looking for

aggregation_functions = {'price': 'sum', 'amount': 'sum', 'name': 'first'} df_new = df.groupby(df['id']).aggregate(aggregation_functions) 

which gives

 price name amount id 1 130 anna 3 2 42 bob 30 3 3 charlie 110 

For same columns ordering is necessary add reindex, because aggregate by dict:

d = {'price': 'sum', 'name': 'first', 'amount': 'sum'} df_new = df.groupby('id', as_index=False).aggregate(d).reindex(columns=df.columns) print (df_new) amount id name price 0 3 1 anna 130 1 30 2 bob 42 2 110 3 charlie 3