How To Truncate a Float in Python

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In the realm of programming, precision is paramount. However, there are times when we need to simplify our data for various reasons, such as improving readability or ensuring compatibility with certain systems. One common operation in this regard is truncating a floating-point number. Truncation involves removing the decimal portion of a float, effectively converting it into an integer. In Python, a language renowned for its simplicity and versatility, there are several methods to achieve this. Whether you’re working on data analysis, game development, or any other domain, understanding how to truncate floats can be a valuable skill. In this tutorial, we will delve into the various techniques to truncate a float in Python, discussing their advantages and potential pitfalls.

  1. Understanding Floats and Truncation
  2. The Basic int() Method
  3. Using the math.trunc() Function
  4. The Floor and Ceiling Approach with math.floor() and math.ceil()
  5. Truncating to Specific Decimal Places
  6. Potential Pitfalls and Precision Issues
  7. Real-world Applications of Float Truncation
  8. Alternatives to Truncation: Rounding and Formatting
  9. Best Practices and Recommendations

Understanding Floats and Truncation

In Python, numbers with decimal points are represented as floats. For instance, 3.14, 0.001, and -45.67 are all float values. Floats allow for decimal precision, making them indispensable for tasks like scientific calculations and financial operations.

However, there are scenarios where you might want to discard the decimal part and retain only the whole number. This process is known as truncation. Truncating a float essentially means cutting off its decimal tail, leaving you with an integer.

Why truncate?
Truncation is often used when:

  • You need whole numbers for specific calculations.
  • You’re aiming for data simplification.
  • You want to improve data readability.

Difference between Truncation and Rounding:
It’s crucial to differentiate between truncation and rounding. While both methods modify the original float, their outcomes can be different.

OperationOriginal FloatResult

As seen in the table, truncation removes the decimal part, whereas rounding modifies the float to the nearest whole number. In the upcoming sections, we’ll explore various methods to truncate floats in Python, ensuring you have the tools and knowledge to apply truncation effectively in your projects.

The Basic int() Method

One of the simplest ways to truncate a float in Python is by using the built-in int() function. This method is straightforward and doesn’t require any additional libraries or modules.

How does it work?

When you pass a float to the int() function, it discards the decimal portion and returns the whole number part. It’s important to note that this method doesn’t round the number; it merely chops off the decimal.


float_number = 12.78
truncated_number = int(float_number)
print(truncated_number)  # Outputs: 12

In the example above, the decimal .78 is removed, and the result is the integer 12.


  • Simplicity: No need for external libraries or complex logic.
  • Speed: Being a built-in function, it’s optimized and fast.


  • It only works with floats. Passing a string or other non-numeric type will raise a TypeError.
  • It always truncates towards zero. For negative floats like -12.78, it will return -12.

The int() method is a quick and easy way to truncate floats in Python. While it’s suitable for many scenarios, it’s essential to be aware of its limitations, especially when working with negative floats or non-numeric data types.

Using the math.trunc() Function

Diving a bit deeper into Python’s toolkit, we find the math.trunc() function. Part of the math module, this function offers a more explicit way to truncate floats compared to the basic int() method.

How does it work?

The math.trunc() function operates similarly to the int() function: it returns the integer portion of a float, discarding the decimal part. The primary difference is that math.trunc() is explicitly designed for truncation, making its intent clearer in your code.


import math

float_number = -12.78
truncated_number = math.trunc(float_number)
print(truncated_number)  # Outputs: -12

As shown, the function truncates the float -12.78 to -12.


  • Clarity: Using math.trunc() makes the code’s intention clear, signaling that truncation is deliberate.
  • Consistency: Like the int() method, it truncates towards zero for both positive and negative floats.


  • Requires importing the math module, adding a slight overhead.
  • As with int(), passing non-numeric data types will result in errors.

While the math.trunc() function performs a similar operation to the int() method, its explicit nature can enhance code readability. It’s especially useful when you want to make the truncation operation evident to anyone reading or maintaining the code.

The Floor and Ceiling Approach with math.floor() and math.ceil()

Beyond simple truncation, Python’s math module offers two other functions that can modify floats: math.floor() and math.ceil(). While they don’t truncate in the traditional sense, understanding their behavior is crucial, especially when comparing them to truncation methods.

How do they work?

  • math.floor(): This function returns the largest integer less than or equal to the float. In other words, it “rounds down” the number.
  • math.ceil(): Conversely, this function returns the smallest integer greater than or equal to the float, effectively “rounding up” the number.


import math

float_number = 12.78
floored_number = math.floor(float_number)
ceiled_number = math.ceil(float_number)

print(floored_number)  # Outputs: 12
print(ceiled_number)   # Outputs: 13

For negative numbers, the behavior is slightly different:

negative_float = -12.78
floored_negative = math.floor(negative_float)
ceiled_negative = math.ceil(negative_float)

print(floored_negative)  # Outputs: -13
print(ceiled_negative)   # Outputs: -12


  • Flexibility: Allows for rounding up or down based on the specific needs of your application.
  • Precision: Can be useful when you need to ensure values always round in a particular direction.


  • Not true truncation: These methods round the float either up or down, rather than simply removing the decimal part.
  • Requires importing the math module.

While math.floor() and math.ceil() don’t truncate floats in the traditional sense, they offer valuable rounding capabilities. Recognizing the difference between truncation and these methods ensures you choose the right tool for your specific task.

Truncating to Specific Decimal Places

Sometimes, truncating a float doesn’t mean removing all decimal places. Instead, you might want to retain a specific number of decimal points for precision while discarding the rest. This approach is particularly useful in scenarios like financial calculations or data visualization.

How does it work?

To truncate a float to a specific number of decimal places, you can multiply the float by a power of 10, truncate the result, and then divide back by the same power of 10.


To truncate 12.7896 to two decimal places:

float_number = 12.7896
multiplier = 10 ** 2  # for two decimal places

truncated_temp = int(float_number * multiplier)
truncated_result = truncated_temp / multiplier

print(truncated_result)  # Outputs: 12.78


  • Precision Control: Allows you to specify the exact number of decimal places you want to retain.
  • Versatility: Can be adapted for various applications, from finance to engineering.


  • Slightly more complex than basic truncation methods.
  • Potential for floating-point precision errors, especially with very large or small numbers.

Truncating to specific decimal places provides a middle ground between full truncation and retaining the entire float. By understanding and implementing this method, you can achieve a balance between precision and simplicity in your applications.

Potential Pitfalls and Precision Issues

While truncating floats in Python is generally straightforward, it’s essential to be aware of potential pitfalls, especially concerning precision. Floating-point numbers in computers have inherent limitations that can lead to unexpected results.

Floating-Point Representation:

Computers represent floats using the binary floating-point format. Due to this representation, some decimal numbers can’t be precisely stored, leading to tiny errors. This phenomenon is not exclusive to Python but is a characteristic of floating-point arithmetic in digital computers.


result = 0.1 + 0.2
print(result)  # Might output: 0.30000000000000004

Truncation Errors:

When truncating, especially to specific decimal places, these tiny precision errors can become more pronounced.


If you want to truncate the number 0.29999999999999999 to two decimal places, you might expect 0.29. However, due to floating-point representation, the result might differ.

Solutions and Precautions:

  1. Use the decimal module: Python’s decimal module provides arbitrary precision arithmetic, making it suitable for financial and monetary calculations.
  2. Rounding: Sometimes, rounding the number before truncating can mitigate precision issues.
  3. Awareness: Simply being aware of these issues can help you make informed decisions about when and how to truncate.

Real-world Applications of Float Truncation

Truncating floats isn’t just a theoretical exercise; it has practical applications in various domains. Understanding where and why float truncation is used can provide context and appreciation for its utility.

1. Financial Systems:

In the world of finance, precision is paramount. Whether it’s for calculating interest, processing transactions, or determining tax amounts, truncating floats helps ensure that only the relevant decimal places are considered, preventing minuscule errors from accumulating.

2. Graphics and Game Development:

In graphics rendering or game physics, sometimes only the whole number part of a coordinate is required. Truncation can help in simplifying calculations and improving performance.

3. Data Storage and Compression:

Storing long floating-point numbers can be inefficient, especially when dealing with large datasets. Truncating these numbers can save storage space and speed up data retrieval.

4. User Interface Design:

For better readability, user interfaces might display only whole numbers or limit the number of decimal places. Truncation ensures that the displayed values are concise and user-friendly.

5. Scientific Research:

In experiments where measurements are taken up to a certain precision, truncating ensures that data is consistent and within the desired accuracy range.

6. Manufacturing and Engineering:

When producing parts or components, measurements might be truncated to fit machine precision or to standardize dimensions.

Alternatives to Truncation: Rounding and Formatting

While truncation is a valuable tool in a developer’s arsenal, it’s not always the best fit for every scenario. Sometimes, other methods like rounding or formatting can offer more suitable solutions. Let’s delve into these alternatives and understand when to use them.

1. Rounding:

Rounding is the process of adjusting a number to the nearest value based on specific criteria. Python’s built-in round() function can be used for this purpose.


rounded_number = round(12.78)
print(rounded_number)  # Outputs: 13

You can also round to a specific number of decimal places:

rounded_decimal = round(12.7896, 2)
print(rounded_decimal)  # Outputs: 12.79

When to use: Rounding is ideal when you want to approximate a value rather than just discard the decimal portion. It’s commonly used in financial calculations, statistics, and data analysis.

2. Formatting:

Formatting doesn’t alter the actual number but changes its representation. This is particularly useful for displaying data without modifying the underlying value.


formatted_number = "{:.2f}".format(12.7896)
print(formatted_number)  # Outputs: 12.79

With Python 3.6+, you can also use f-strings:

formatted_number = f"{12.7896:.2f}"
print(formatted_number)  # Outputs: 12.79

When to use: Formatting is perfect for situations where you want to display data in a specific manner without altering the original value, such as in user interfaces or reports.

Best Practices and Recommendations

Truncating floats, like any other operation in programming, comes with its set of best practices. Adhering to these can ensure accuracy, maintainability, and performance in your applications.

1. Be Clear About Intent:

If you’re truncating a float, make sure it’s clear in the code why you’re doing so. Using explicit functions like math.trunc() can help convey intent better than the more general int().

2. Beware of Floating-Point Precision:

Always remember that floating-point numbers in computers have inherent precision issues. Before truncating, understand the potential implications and errors that might arise.

3. Consider Alternatives:

Before truncating, evaluate if rounding or formatting might be more appropriate for your specific use-case.

4. Use the decimal Module for Financial Calculations:

For monetary and financial operations, consider using Python’s decimal module, which offers better precision than standard floats.

5. Test Extensively:

Especially when dealing with critical data, always test your truncation logic with a variety of inputs to ensure accuracy and reliability.

6. Document Your Decisions:

If you’re truncating for a specific reason, document it. This can help other developers (or future you) understand the rationale behind the decision.

7. Stay Updated:

Python, like all languages, evolves. New functions and methods might be introduced, or existing ones might be deprecated. Stay updated with the latest best practices and recommendations.

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