How to Create High Precision Data Types

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()
• Method 2: Use NumPy Library sqrt()
• Method 3: Use Mpmath Library
• Method 4: Use format()
• Method 5: Use round()

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.

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.

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)) 

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.

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.

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

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.

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!

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