How to Create High Precision Data Types

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Problem Formulation and Solution Overview

In this article, you’ll learn how to create high-precision data types in Python.

💡 Definition: High-precision data types are numeric data types, such as integers, or floats, that use additional memory when complex mathematical calculations require extreme accuracy.

💬 Question: How would we write Python code to create high-precision data types?

We can accomplish this task by one of the following options:


Method 1: Use the Math Library sqrt()

This example calls Python’s built-in math library and uses the sqrt() function from the same.

from math import sqrt
print(sqrt(3)) 

This code will always return the result in a float64 format with a precision of up to 16 decimal places.

1.7320508075688772
Python Math Module [Ultimate Guide]

Method 2: Use NumPy sqrt()

If you prefer to select either a float32 or a float64 return format, use NumPy’s sqrt() function.

Before moving forward, this library will need to be installed. Click here for installation instructions.

import numpy as np 
print(np.sqrt(3, dtype=np.float64)) 

NumPy’s sqrt() function, by default, assumes the dtype is float64, so there is no need to add this argument. However, for this example, it was added.

1.7320508075688772

To return a float as a float32, change the dtype below and run. This returns a float with a precision of up to seven (7) decimal places.

import numpy as np 
print(np.sqrt(3, dtype=np.float32)) 
1.7320508
NumPy Tutorial - Everything You Need to Know to Get Started

Method 3: Use Mpmath Library

If you require accurate precision to a more significant degree, the mpmath library is your go-to! This library breaks out of the traditional 32/64 restrictions.

Before moving forward, this library will need to be installed. Navigate to a terminal and enter the following at the command prompt:

pip install mpmath

If successful, you now have access to this amazing library!

Let’s test the precision.

import mpmath as mp
from mpmath import *

mp.dps = 20
print(mpf('5') ** mpf('1.1'))

Above, the mpath library is called, and all of its functions are imported.

For this example, we set the number of decimal places to 20 (mp.dps = 20).

Then, mpf('5') is called, which instantiates a real floating-point number.
A mathematical computation is declared (**), and another call to mpf('1.1') is made. The calculation is done and output to the terminal.

💡Note: To achieve accurate precision, pass the arguments to mpf() as Strings.

5.8730947154400950296

Method 4: Use format()

This method uses Python’s format() function, where you can specify the precise number of decimal places.

num = 22.9379999999
res = float("{:.5f}".format(num))
print(res)

Above assigns a floating point with ten decimal places and saves to num. Then this number is formatted to five (5) places and saved to res. The results are output to the terminal.

22.938

💡Note: Notice all numbers are counted. In this case, two (2) before the decimal and three (3) after adding up to five (5).

Python format() Function: No-BS Guide by Example

Method 5: Use round()

Python’s round() function rounds down a number to a specified number of decimal places.

num = 4.986578934
print(round(num, ndigits=5))

Above assigns a floating-point number to num.

Next, round() is called and num is passed as an argument, as well as the number of digits desired (ndigits=5). The result is output to the terminal.

4.98658
Python round() — A Helpful Interactive Guide

Summary

These methods of creating high-precision data types should give you enough information to select the best one for your coding requirements.

Good Luck & Happy Coding!


Programmer Humor

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