# Python Built-in Methods – Python int() Function

Converts a string or number to an integer

## Usage

The `int()` function converts the specified value to integer. A value can be a number or a string, except complex numbers.

You can also specify the base (number formats like binary, hex, octal etc.) of the given value.

## Syntax

int(value,base)

 Parameter Condition Description value Optional A number or a string to be converted into an integer. Default is 0. base Optional The number format of specified value. Default is 10. Valid values are 0, 2-36.

## Convert a Number to an Integer

``````x = 4.2
print(int(x))
# Prints 4``````

The `int()` method does not round the number, it just returns integer part of a decimal number.

``````x = 4.99
print(int(x))
# Prints 4``````

If you omit both the arguments, the value is assumed as 0.

``````print(int())
# Prints 0``````

## Convert a String to an Integer

The method can also convert a string to an integer.

``````x = '42'
print(int(x))
# Prints 42

x = '1010'
print(int(x))
# Prints 1010``````

## Specify Base

You can also specify the base of the given value. Valid values are `0` and `2–36`.

If base is specified, then value must be a string.

``````# binary string
x = '1110'
print(int(x, 2))
# Prints 14

x = '0b1110'
print(int(x, 2))
# Prints 14``````
``````# octal string
x = '10'
print(int(x, 8))
# Prints 8

x = '0o10'
print(int(x, 8))
# Prints 8``````
``````# hex string
x = 'F'
print(int(x, 16))
# Prints 15

x = '0xF'
print(int(x, 16))
# Prints 15``````

If the base is `0`, the base used is determined by the format of value.

``````x = '0b1110'
print(int(x, 0))
# Prints 14

x = '0o10'
print(int(x, 0))
# Prints 8

x = '0xF'
print(int(x, 0))
# Prints 15``````

# Special 95% discount

## 2000+ Applied Machine Learning & Data Science Recipes

### Portfolio Projects for Aspiring Data Scientists: Tabular Text & Image Data Analytics as well as Time Series Forecasting in Python & R ## 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.  `