How to do a custom Group By?

If I understand correctly, there is only one unique non-missing value in each of the val2 and val3 columns per group. Otherwise your question does not make much sense, because you did not specify how to decide which value to take from these columns.

Given these constraints, you can use:

result = df.groupby('Name', as_index=False).agg({'Val1': 'sum', 'val2': 'first', 'val3': 'first'})

To group by one or multiple columns while aggregating others you can use groupBy followed by aggregate (or its alias agg).

Example

Given input:

  Waiter  Revenue  Hours   Tables Gender
0  Alice     3000    3.0  Outside      f
1    Bob     2000    4.0   Inside      m
2   Alex     1000    2.0   Inside      d
3   Alex      500    0.5  Outside   None

Expected output:

        Revenue  Hours             Tables Gender
Waiter                                          
Alex       1500   1.25  {Inside, Outside}      d
Alice      3000   3.00          {Outside}      f
Bob        2000   4.00           {Inside}      m

Grouped by Waiter shows:

• the sum of Revenue
• the mean of worked Hours
• the set of unique Tables served
• the first (non undefined) value for Gender

How to group-by with specific aggregation

Code:

import pandas as pd

df = pd.DataFrame({'Waiter': ['Alice','Bob','Alex', 'Alex'], 'Revenue': [3000, 2000, 1000, 500], 'Hours': [3, 4, 2, 0.5], 'Tables': ['Outside', 'Inside', 'Inside', 'Outside'], 'Gender': ['f', 'm', 'd', None]})

df.groupby(['Waiter']).agg({'Revenue': 'sum', 'Hours': 'mean', 'Tables': lambda x: set(x), 'Gender': 'first'})

Explained:

1. groupby can be done be multiple of a single column, here only Waiter
2. agg (or aggregate) by a dict defining the aggregation for each column. The column is specified as key like Revenue with the aggregate function specified either as function name 'sum' (in quotes or as reference like list) or as lambda like lambda x: set(x)

Note: to get the list of Tables we could also define following value as aggregation function:

• 'unique' produces a list with unique values (see Series.unique)
• set for a set as we did with the lambda
• list for a list (which may contain duplicates)

See also

• pandas groupby + multiple aggregate/apply with multiple columns
• How to get unique values from multiple columns in a pandas groupby
• How to Group By Multiple Columns in Pandas