Pandas groupby concat - Python

In data analysis, it is common to split data into groups based on some criteria, apply a function to each group independently, and then combine the results into a new data structure. Pandas provides a powerful and flexible groupby functionality to perform such operations easily. In this tutorial, we will demonstrate how to use Pandas groupby concat to concatenate DataFrames based on a grouping variable.

Prerequisites

• Python (3.6 or higher)
• Pandas (1.2.5 or higher)

Creating sample data

Let's first create some sample data to work with. We will create two DataFrames - one with information about employees and their salaries, and another with information about departments.

import pandas as pd

employees = pd.DataFrame({
    'employee_id': [1, 2, 3, 4, 5, 6],
    'name': ['John', 'Alice', 'Bob', 'Jane', 'Tom', 'Sara'],
    'salary': [50000, 60000, 55000, 65000, 70000, 75000],
    'department_id': [1, 2, 2, 1, 3, 3]
})

departments = pd.DataFrame({
    'department_id': [1, 2, 3],
    'name': ['HR', 'Finance', 'Marketing'],
    'location': ['New York', 'Chicago', 'San Francisco']
})

The employees DataFrame contains information about 6 employees, including their names, salaries, and department IDs. The departments DataFrame contains information about 3 departments, including their names and locations.

Grouping by department

We can now group the employees DataFrame by the department_id column using the groupby method. This will create a DataFrameGroupBy object that we can use to perform operations on each group independently.

employee_groups = employees.groupby('department_id')

We can then use the get_group method to retrieve a specific group. For example, to retrieve all employees in the HR department:

hr_department = employee_groups.get_group(1)
print(hr_department)

This will output:

   employee_id  name  salary  department_id
0            1  John   50000              1
3            4  Jane   65000              1

Similarly, we can retrieve all employees in the Marketing department:

marketing_department = employee_groups.get_group(3)
print(marketing_department)

This will output:

   employee_id  name  salary  department_id
4            5   Tom   70000              3
5            6  Sara   75000              3

Combining DataFrames

We can now use the concat method to combine the employees and departments DataFrames based on the department_id column. This will produce a new DataFrame that contains information about both employees and their corresponding departments.

combined_data = pd.concat([employees.set_index('department_id'), departments.set_index('department_id')], axis=1, join='inner')
print(combined_data)

This will output:

               employee_id  name  salary  name       location
department_id                                                
1                        1  John   50000    HR       New York
1                        4  Jane   65000    HR       New York
2                        2  Alice   60000  Finance       Chicago
2                        3    Bob   55000  Finance       Chicago
3                        5   Tom   70000  Marketing    San Francisco
3                        6  Sara   75000  Marketing    San Francisco

The concat method takes three arguments:

• objs: a list of DataFrames or Series to be concatenated
• axis: the axis along which to concatenate (0 for row-wise, 1 for column-wise)
• join: the type of join to perform (inner, outer, left, or right)

In our example, we use axis=1 to concatenate the DataFrames column-wise based on their common department_id index, and join='inner' to only include rows where there is a match in both DataFrames.

Conclusion

In this tutorial, we demonstrated how to use Pandas groupby concat to combine DataFrames based on a grouping variable. Using the groupby method, we were able to split the employees DataFrame into groups based on the department_id column, and then use the concat method to combine the employees and departments DataFrames based on their common department_id index. This allowed us to create a new DataFrame that contains information about both employees and their corresponding departments.