How to Use Double Precision Floating Values in Python


By squashlabs, Last Updated: November 2, 2023

How to Use Double Precision Floating Values in Python

Double precision floating-point numbers, also known as doubles, are a common data type used in Python to represent decimal numbers with a high degree of precision. In this guide, we will explore how to use double precision floating values in Python and cover various aspects of working with doubles.

1. Initializing Double Precision Floating Values

In Python, you can initialize a double precision floating value by assigning a decimal number to a variable. The built-in float class in Python represents double precision floating-point numbers. Here’s an example:

my_double = 3.14159

In this example, my_double is assigned the value of pi, which is a decimal number. Python automatically assigns the appropriate type based on the value provided.

Related Article: How To Limit Floats To Two Decimal Points In Python

2. Performing Arithmetic Operations with Double Precision Floating Values

Python provides various arithmetic operations that can be performed on double precision floating values. These operations include addition, subtraction, multiplication, and division. Here are some examples:

# Addition
result = 1.2 + 2.3

# Subtraction
result = 5.6 - 3.4

# Multiplication
result = 2.5 * 4.5

# Division
result = 10.0 / 3.0

In the above examples, the arithmetic operations are performed on double precision floating values, and the results are also double precision floating values.

3. Formatting Double Precision Floating Values

Python provides several ways to format double precision floating values for display. One common method is to use the format function or the f-string syntax introduced in Python 3.6. Here’s an example:

# Using the format function
value = 3.14159
formatted = format(value, ".2f")
print(formatted)  # Output: 3.14

# Using f-string syntax
value = 3.14159
formatted = f"{value:.2f}"
print(formatted)  # Output: 3.14

In both examples, the format function and f-string syntax allow you to specify the number of decimal places to display. In this case, we are formatting the value to two decimal places.

4. Comparing Double Precision Floating Values

When comparing double precision floating values, it’s important to consider the potential for rounding errors. Due to the limited precision of floating-point numbers, two values that are theoretically equal may not be exactly equal in practice. To compare double precision floating values, you can use the math.isclose function or a tolerance value. Here’s an example:

import math

value1 = 1.23456789
value2 = 1.23456788

# Using math.isclose
if math.isclose(value1, value2, rel_tol=1e-9):
    print("The values are close.")
    print("The values are not close.")

# Using a tolerance value
tolerance = 1e-9
if abs(value1 - value2) < tolerance:
    print("The values are close.")
    print("The values are not close.")

In this example, we compare two double precision floating values, value1 and value2, using both math.isclose and a tolerance value. The rel_tol parameter in math.isclose specifies the relative tolerance, which is the maximum allowed difference between the two values.

Related Article: How To Rename A File With Python

5. Best Practices for Working with Double Precision Floating Values

To ensure accurate and reliable results when working with double precision floating values in Python, consider the following best practices:

– Avoid comparing floating-point values for exact equality. Use tolerance values or the math.isclose function instead.
– Be aware of the potential for rounding errors and precision loss. Consider using the decimal module for applications that require exact decimal arithmetic.
– Use appropriate formatting when displaying double precision floating values to avoid excessive decimal places or scientific notation.
– Be mindful of the range and magnitude of the values you are working with. Double precision floating values have a limited range, and very large or very small values may result in precision loss.

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