Python itertools.groupby Function

The itertools.groupby function in Python's itertools module returns consecutive keys and groups from the input iterable. The elements are grouped based on the value of a specified key function. It is useful for grouping elements in an iterable that share a common property.

Table of Contents

1. Introduction
2. itertools.groupby Function Syntax
3. Examples
   • Basic Usage
   • Grouping by Length of Words
   • Grouping with a Custom Key Function
4. Real-World Use Case
5. Conclusion

Introduction

The itertools.groupby function creates an iterator that returns consecutive keys and groups from the input iterable. The key function specifies a function of one argument that is used to extract the grouping key for each element. By default, it groups by the identity function, i.e., it groups consecutive identical elements.

itertools.groupby Function Syntax

Here is how you use the itertools.groupby function:

import itertools

iterator = itertools.groupby(iterable, key=None)

Parameters:

• iterable: The input iterable whose elements are to be grouped.
• key: Optional. A function that computes a key value for each element. If not specified, the identity function is used, meaning the elements are grouped by themselves.

Returns:

• An iterator that produces pairs of a key and a group iterator for consecutive keys and their groups from the input iterable.

Examples

Basic Usage

Group consecutive identical elements in a list.

Example

import itertools

data = [1, 1, 2, 2, 3, 3, 3]
grouped = itertools.groupby(data)

for key, group in grouped:
    print(key, list(group))

Output:

1 [1, 1]
2 [2, 2]
3 [3, 3, 3]

Grouping by Length of Words

Group words by their length.

Example

import itertools

words = ['cat', 'dog', 'apple', 'banana', 'kiwi', 'pear']
grouped = itertools.groupby(sorted(words, key=len), key=len)

for key, group in grouped:
    print(key, list(group))

Output:

3 ['cat', 'dog']
4 ['kiwi', 'pear']
5 ['apple']
6 ['banana']

Grouping with a Custom Key Function

Group numbers by their parity (even or odd).

Example

import itertools

data = [1, 2, 3, 4, 5, 6, 7, 8, 9]
grouped = itertools.groupby(data, key=lambda x: x % 2)

for key, group in grouped:
    parity = 'even' if key == 0 else 'odd'
    print(parity, list(group))

Output:

odd [1]
even [2]
odd [3]
even [4]
odd [5]
even [6]
odd [7]
even [8]
odd [9]

Real-World Use Case

Grouping Transactions by Date

Group transactions by their transaction date.

Example

import itertools
from datetime import datetime

transactions = [
    {'date': '2023-07-21', 'amount': 100},
    {'date': '2023-07-21', 'amount': 150},
    {'date': '2023-07-22', 'amount': 200},
    {'date': '2023-07-23', 'amount': 50},
    {'date': '2023-07-23', 'amount': 75},
]

# Convert date strings to datetime objects for correct sorting and grouping
transactions.sort(key=lambda x: datetime.strptime(x['date'], '%Y-%m-%d'))
grouped = itertools.groupby(transactions, key=lambda x: x['date'])

for date, group in grouped:
    print(date, list(group))

Output:

2023-07-21 [{'date': '2023-07-21', 'amount': 100}, {'date': '2023-07-21', 'amount': 150}]
2023-07-22 [{'date': '2023-07-22', 'amount': 200}]
2023-07-23 [{'date': '2023-07-23', 'amount': 50}, {'date': '2023-07-23', 'amount': 75}]

Conclusion

The itertools.groupby function is used for grouping elements in an iterable based on a specified key function. It provides an efficient way to process and organize data that shares common properties, making it a valuable addition to your Python toolkit for data analysis and manipulation.