This article is from 2023, and some product details in it may be out of date. Saturn Cloud now offers dedicated inference endpoints built with NVIDIA DSX, running on private GPU capacity we secure through our neocloud partners.
The Problem
Suppose we have a DataFrame df that consists of multiple rows for each unique value in the id column:
| employee_id | employee_name | sales |
|---|---|---|
| 1 | Carlos | 4 |
| 1 | Carlos | 1 |
| 2 | Dan | 3 |
| 3 | Samuel | 2 |
| 3 | Samuel | 5 |
| 3 | Samuel | 3 |
We want to combine the rows for each unique value in the id column into a single row, resulting in a DataFrame that looks like this:
| employee_id | employee_name | sales |
|---|---|---|
| 1 | Carlos | 5 |
| 2 | Dan | 3 |
| 3 | Samuel | 10 |
Creating the dataFrame
First let’s create the dataframe we have above so we can apply the solutions on it:
# create dataFrame
df = pd.DataFrame({'employee_id': [1, 1, 2, 3, 3, 3],
'employee_name': ['Carlos', 'Carlos', 'Dan', 'Samuel', 'Samuel', 'Samuel'],
'sales': [4, 1, 3, 2, 5, 3],})
# view DataFrame
print(df)
The output should be the following:
employee_id employee_name sales
0 1 Carlos 4
1 1 Carlos 1
2 2 Dan 3
3 3 Samuel 2
4 3 Samuel 5
5 3 Samuel 3
Solution 1: Using groupby and aggregate
One way to combine multiple rows into a single row is to use the groupby function to group the DataFrame by the id column and then use the aggregate function to apply an aggregation function to each group.
#define how to aggregate various fields
agg_functions = {'employee_name': 'first', 'sales': 'sum', }
#create new DataFrame by combining rows with same id values
df_new = df.groupby(df['employee_id']).aggregate(agg_functions)
#Display the new data frame
print(df_new)
Now the new dataFrame should look like this:
employee_id employee_name sales
1 Carlos 5
2 Dan 3
3 Samuel 10
The new DataFrame combined all of the rows in the previous DataFrame that had the same value in the employee_id and employee_name column and then calculated the sum of the values in the sales column.
Conclusion
In this article, we have explored how to combine multiple rows into a single row using pandas. By understanding these techniques, you can efficiently manipulate and analyze data in pandas, and be better equipped to tackle real-world data science and engineering challenges.
