Pandas objects can be split into groups based on any of their column values using the groupby() method. Let us now see how the grouping objects can be applied to the Pandas DataFrame using the groupby() method.

March 6, 2025 - That’s exactly what groupby() combined with basic aggregation functions like sum(), mean(), and max() can do. Here’s how you do it: import pandas as pd # Sample data data = { 'Department': ['HR', 'HR', 'IT', 'IT', 'Finance', 'Finance'], 'Employee': ['John', 'Emma', 'Steve', 'Mia', 'Tom', 'Sophia'], 'Salary': [50000, 60000, 75000, 80000, 65000, 70000] } df = pd.DataFrame(data) # Sum of salaries by department print(df.groupby('Department')['Salary'].sum()) # Mean salary by department print(df.groupby('Department')['Salary'].mean()) What you’ll get: Department Finance 135000 HR 110000 IT 155000 Name: Salary, dtype: int64 Department Finance 67500.0 HR 55000.0 IT 77500.0 Name: Salary, dtype: float64 ·

Group DataFrame using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results.

Group DataFrame using a mapper or by a Series of columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results.

An aggregation is a GroupBy operation that reduces the dimension of the grouping object. The result of an aggregation is, or at least is treated as, a scalar value for each column in a group. For example, producing the sum of each column in a group of values.

An aggregation is a GroupBy operation that reduces the dimension of the grouping object. The result of an aggregation is, or at least is treated as, a scalar value for each column in a group. For example, producing the sum of each column in a group of values.

import pandas as pd data = { 'co2': [95, 90, 99, 104, 105, 94, 99, 104], 'model': ['Citigo', 'Fabia', 'Fiesta', 'Rapid', 'Focus', 'Mondeo', 'Octavia', 'B-Max'], 'car': ['Skoda', 'Skoda', 'Ford', 'Skoda', 'Ford', 'Ford', 'Skoda', 'Ford'] } df = pd.DataFrame(data) print(df.groupby(["car"]).mean()) Try it Yourself » ·

pandas.api.typing.DataFrameGroupBy and pandas.api.typing.SeriesGroupBy instances are returned by groupby calls pandas.DataFrame.groupby() and pandas.Series.groupby() respectively.

January 25, 2023 - In this article, you will learn about the Pandas groupby function, how to aggregate data, and group Pandas DataFrames with multiple columns using the groupby method.

June 16, 2025 - With the Python pandas DataFrame.groupby() function, you can group data based on specific criteria and perform various ag­gre­ga­tions and trans­for­ma­tions to the data.

Group DataFrame or Series using one or more columns. A groupby operation involves some combination of splitting the object, applying a function, and combining the results.

January 19, 2025 - The pandas .groupby() method allows you to efficiently analyze and transform datasets when working with data in Python. With df.groupby(), you can split a DataFrame into groups based on column values, apply functions to each group, and combine ...

September 22, 2025 - Pandas takes the original DataFrame and partitions it into smaller DataFrames based on the criteria you provide in the by parameter. Each of these smaller DataFrames contains rows that share the same value for the specified key(s). For our example df, if we group by the 'Team' column ...

>>> df = pd.DataFrame({'A': [1, 1, 2, 1, 2], ... 'B': [np.nan, 2, 3, 4, 5], ... 'C': [1, 2, 1, 1, 2]}, columns=['A', 'B', 'C']) Groupby one column and return the mean of the remaining columns in each group.

June 26, 2025 - groupby() is a powerful function in pandas that is used for grouping data based on some criteria. It enables you to split a DataFrame into groups based on one or more columns and then apply a function (such as aggregation, transformation, or ...

Apply a function groupby to each row or column of a DataFrame. pandas.core.groupby.DataFrameGroupBy.last · Compute the last non-null entry of each column. pandas.core.groupby.DataFrameGroupBy.nth · Take the nth row from each group. Examples · >>> df = pd.DataFrame(dict(A=[1, 1, 3], B=[None, 5, 6], C=[1, 2, 3], ...

Pandas groupby splits all the records from your data set into different categories or groups so that you can analyze the data by these groups. When you use the .groupby() function on any categorical column of DataFrame, it returns a GroupBy ...