You can use the following basic syntax to combine rows with the same column values in a pandas DataFrame:

#define how to aggregate various fields
agg_functions = {'field1': 'first', 'field2': 'sum', 'field': 'sum'}

#create new DataFrame by combining rows with same id values
df_new = df.groupby(df['id']).aggregate(agg_functions)

The following example shows how to use this syntax in practice.

Example: Combine Rows with Same Column Values in Pandas

Suppose we have the following pandas DataFrame that contains information about sales and returns made by various employees at a company:

import pandas as pd

#create dataFrame
df = pd.DataFrame({'id': [101, 101, 102, 103, 103, 103],
                   'employee': ['Dan', 'Dan', 'Rick', 'Ken', 'Ken', 'Ken'],
                   'sales': [4, 1, 3, 2, 5, 3],
                   'returns': [1, 2, 2, 1, 3, 2]})

#view DataFrame
print(df)

    id employee  sales  returns
0  101      Dan      4        1
1  101      Dan      1        2
2  102     Rick      3        2
3  103      Ken      2        1
4  103      Ken      5        3
5  103      Ken      3        2

We can use the following syntax to combine rows that have the same value in the id column and then aggregate the remaining columns:

#define how to aggregate various fields
agg_functions = {'employee': 'first', 'sales': 'sum', 'returns': 'sum'}

#create new DataFrame by combining rows with same id values
df_new = df.groupby(df['id']).aggregate(agg_functions)

#view new DataFrame
print(df_new)

    employee  sales  returns
id                          
101      Dan      5        3
102     Rick      3        2
103      Ken     10        6

The new DataFrame combined all of the rows in the previous DataFrame that had the same value in the id column and then calculated the sum of the values in the sales and returns columns.

Note: Refer to the for a complete list of aggregations available to use with the GroupBy() function.

The following tutorials explain how to perform other common tasks in pandas: