Python Built-in Methods – Python enumerate() Function

Hits: 0

Python enumerate() Function

Adds a counter to an iterable

Usage

The enumerate() function adds a counter to an iterable and returns it as an enumerate object.

By default, enumerate() starts counting at 0 but if you add a second argument start, it’ll start from that number instead.

Syntax

enumerate(iterable,start)

Parameter Condition Description
iterable Required An iterable (e.g. list, tuple, string etc.)
start Optional A number to start counting from.
Default is 0.

Basic Example

# Create a list that can be enumerated
L = ['red', 'green', 'blue']
x = list(enumerate(L))
print(x)
# Prints [(0, 'red'), (1, 'green'), (2, 'blue')]

Specify Different Start

By default, enumerate() starts counting at 0 but if you add a second argument start, it’ll start from that number instead.

# Start counter from 10
L = ['red', 'green', 'blue']
x = list(enumerate(L, 10))
print(x)
# Prints [(10, 'red'), (11, 'green'), (12, 'blue')]

Iterate Enumerate Object

When you iterate an enumerate object, you get a tuple containing (counter, item)

L = ['red', 'green', 'blue']
for pair in enumerate(L):
    print(pair)
# Prints (0, 'red')
# Prints (1, 'green')
# Prints (2, 'blue')

You can unpack the tuple into multiple variables as well.

L = ['red', 'green', 'blue']
for index, item in enumerate(L):
    print(index, item)
# Prints 0 red
# Prints 1 green
# Prints 2 blue

 

Python Example for Beginners

Two Machine Learning Fields

There are two sides to machine learning:

  • Practical Machine Learning:This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.
  • Theoretical Machine Learning: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes

Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

Applied Statistics with R for Beginners and Business Professionals

Data Science and Machine Learning Projects in Python: Tabular Data Analytics

Data Science and Machine Learning Projects in R: Tabular Data Analytics

Python Machine Learning & Data Science Recipes: Learn by Coding

R Machine Learning & Data Science Recipes: Learn by Coding

Comparing Different Machine Learning Algorithms in Python for Classification (FREE)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.