Python Pandas groupby() Method

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The Pandas groupby() method in Python is a powerful tool for data aggregation and analysis. It splits the data into groups, applies a function to each group, and combines the results. This method is essential for data analysis tasks like aggregation, transformations and filtration.

The Pandas groupby() method can be used on both Pandas Series and DataFrame objects, including those with hierarchical indexes. This method is designed to −

• Split data into groups based on specified criteria.

• Apply a function to each group independently.

• Combine the results into a structured format.

Syntax

Following is the syntax of the Python Pandas groupby() method

Series.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=True, observed=<no_default>, dropna=True)

Parameters

The Python Pandas groupby() method accepts the below parameters −

• by: Used to define how to group data. It can be a function, label, Series, or list of labels.

• axis: Determines grouping by rows (0) or columns (1).

• level: Groups by specific levels of a MultiIndex.

• as_index: If True, group labels are used as the index in the result. If False, returns the result with the original index.

• sort: Sort group keys (default is True).

• group_keys: Adds group keys to the index when applying functions. OR Adds group keys to the result if True.

• observed: If True, shows only observed categories when grouping the categorical data.

• dropna: If True, excludes NA values from group keys.

Return Value

The Pandas groupby() method returns a special object depending on the input type. This object is either pandas.api.typing.DataFrameGroupBy or pandas.api.typing.SeriesGroupBy, representing grouped data for further operations.

Example: Grouping a Series by Index Labels

This example demonstrates the basic functionality of the Series.groupby() method by grouping a Pandas Series using index labels.

import pandas as pd

s = pd.Series([1000, 1400, 1000, 900, 1700], index=['BMW', 'Audi', 'Mercedes', 'Audi', 'BMW'], name='Car')

# Display the Input Series
print("Original Series:")
print(s)

# Grouping the Series by Index Labels
result = s.groupby(level=0).sum()

print("\nSeries after Grouping:")
print(result)

When we run above program, it produces following result −

Original Series:
BMW         1000
Audi        1400
Mercedes    1000
Audi         900
BMW         1700
Name: Car, dtype: int64

Series after Grouping:
Audi        2300
BMW         2700
Mercedes    1000
Name: Car, dtype: int64

Example: Grouping a DataFrame Column

The following example demonstrates using the Pandas groupby() method for grouping the DataFrame column.

import pandas as pd

# Create a DataFrame 
df = pd.DataFrame({'Car':['BMW', 'Audi', 'Mercedes', 'Audi', 'BMW'], 'Price':[1000, 1400, 1000, 900, 1700]})

# Display the Input DataFrame
print("Input DataFrame:")
print(df)

# Grouping a DataFrame Column
result = df.groupby("Car").mean()
print("\nDataFrame after Grouping Based on a Column:")
print(result)

While executing the above code we get the following output −

Input DataFrame:

	Car	Price
0	BMW	1000
1	Audi	1400
2	Mercedes	1000
3	Audi	900
4	BMW	1700

DataFrame after Grouping Based on a Column:

	Price
Car
Audi	1150.0
BMW	1350.0
Mercedes	1000.0

Example: Grouping while Handling Missing Values

Handling missing values is a easy task while grouping the Pandas objects using the dropna parameter. The following example sets the dropna=False for including NA values as a separate group.

import pandas as pd
import numpy as np

# Create a DataFrame 
df = pd.DataFrame({'Car':['BMW', 'Audi', np.nan, 'Audi', 'BMW'], 'Price':[1000, 1400, 1000, 900, 1700]})

# Display the Input DataFrame
print("Input DataFrame:")
print(df)

# Including NA as a separate group
result = df.groupby("Car", dropna=False).sum()
print("\nDataFrame after Grouping:")
print(result)

Following is an output of the above code −

Input DataFrame:

	Car	Price
0	BMW	1000
1	Audi	1400
2	NaN	1000
3	Audi	900
4	BMW	1700

DataFrame after Grouping:

	Price
Car
Audi	2300
BMW	2700
Nan	1000

Example: Grouping by Multiple Columns

This example demonstrates grouping a Pandas DataFrame by multiple columns.

import pandas as pd
import numpy as np

# Create a DataFrame 
df = pd.DataFrame({'Car':['BMW', 'Audi', np.nan, 'Audi', 'BMW'], 
'Price':[1000, 1400, 1000, 900, 1700],
'color': ['white', 'black', 'red', 'red', 'white']})

# Display the Input DataFrame
print("Input DataFrame:")
print(df)

# Grouping a DataFrame by multiple columns 
result = df.groupby(["Car", "color"], dropna=False).sum()
print("\nDataFrame after Grouping Based on Multiple Column:")
print(result)

When we run above program, it produces following result −

Input DataFrame:

	Car	Price	color
0	BMW	1000	white
1	Audi	1400	black
2	NaN	1000	red
3	Audi	900	red
4	BMW	1700	white

DataFrame after Grouping Based on Multiple Column:

		Price
Car	color
Audi	black	1400
	red	900
BMW	white	2700
NaN	red	1000

Example: Grouping with Hierarchical Indexes

Grouping of a hierarchical index can be done by using the level parameter of the groupby() method. following example demonstrates the same.

import pandas as pd
import numpy as np

# Create a DataFrame 
data = [['BMW', 'BMW', 'Audi', 'Audi'], 
['white', 'black', 'black', 'white']]

# Create a MultiIndex object
index = pd.MultiIndex.from_arrays(data, names=("car", "color"))

# Creating a MultiIndexed Series
df = pd.DataFrame({'Price': [1000, 1400, 1000, 900]}, index=index)

# Display the input MultiIndexed DataFrame
print("Input MultiIndexed DataFrame:\n")
print(df)

# Grouping MultiIndexed by level name
result = df.groupby("car").sum()
print("\nMultiIndexed DataFrame after Grouping:")
print(result)

Following is an output of the above code −

Input MultiIndexed DataFrame:

		Price
car	color
BMW	white	1000
	black	1400
Audi	black	1000
	white	900

MultiIndexed DataFrame after Grouping:

	Price
car
Audi	1900
BMW	2400