5 Best Ways to Convert Suffix Denominations to Values in Python

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💡 Problem Formulation: Developers often encounter numerical data with suffix denominations like ‘K’ for thousand or ‘M’ for million. The challenge is converting strings like ‘2.5K’ to numeric values such as 2500 programmatically. This article addresses various methods to achieve this conversion, ensuring the input ‘2.5K’ results in the desired output 2500.

Method 1: Using Manual Conversion

This method involves manually checking for known suffixes and then performing the arithmetic to convert the suffix to its numeric equivalent. Formulate a function convert_suffix(), which takes a string as input. Analyze the suffix and multiply by the corresponding numeric factor.

Here’s an example:

def convert_suffix(value):
    if value.endswith('K'):
        return float(value[:-1]) * 10**3
    elif value.endswith('M'):
        return float(value[:-1]) * 10**6
    else:
        return float(value)
  
  print(convert_suffix('2.5K'))

Output: 2500.0

This method is straightforward and suitable for applications with a limited set of known suffixes. It is easy to understand and implement but requires manual addition of new cases for each new suffix.

Method 2: Using a Dictionary

Building on the manual method, create a dictionary mapping suffixes to their numeric multipliers. Write a function convert_with_dict() that looks up the multiplier and applies it. This makes adding new denominations easier.

Here’s an example:

suffixes = {'K': 10**3, 'M': 10**6}

  def convert_with_dict(value):
    return float(value[:-1]) * suffixes[value[-1]] if value[-1] in suffixes else float(value)

  print(convert_with_dict('3.7M'))

Output: 3700000.0

This code snippet utilizes dictionary mapping, which provides a cleaner and more efficient implementation, enabling easier extension for additional suffixes. However, it assumes a single-character suffix and may not work for multi-character suffixes without modification.

Method 3: Using Regular Expressions

Regular expressions can parse complex string patterns. Write a function convert_with_regex() that uses a regex pattern to identify the numeric part and the suffix, making it more versatile and capable of handling various suffix formats.

Here’s an example:

import re

  def convert_with_regex(value):
    pattern = re.compile(r'(\d+(?:\.\d+)?)([KMB])?$')
    match = pattern.match(value)
    number, suffix = match.groups()
    return float(number) * {'K': 10**3, 'M': 10**6}.get(suffix, 1)

  print(convert_with_regex('1.23B'))

Output: 1230000000.0

By using regular expressions, this method is capable of handling different string formats and suffixes. It’s more robust than the dictionary approach but requires familiarity with regex patterns, which may be complex for certain users.

Method 4: Using a Third-party Library

External libraries like pint or humanize provide utilities for parsing human-readable numbers. This method involves using one of these libraries, potentially saving time and effort on dealing with various edge cases.

Here’s an example using humanize:

from humanize import parse_number

  def convert_with_lib(value):
    return parse_number(value)

  print(convert_with_lib('7.89K'))

Output: 7890.0

Third-party libraries offer a convenient and powerful way to handle conversions, benefiting from ongoing community support and enhancement. However, adding external dependencies to your project can increase its complexity and size.

Bonus One-Liner Method 5: Using List Comprehension and Mapping

A one-liner solution can also be crafted using a combination of list comprehension and mapping for known suffixes. It is less readable but provides a quick method for converting values with suffixes efficiently within a single line of code.

Here’s an example:

print([float(val[:-1]) * {'K': 10**3, 'M': 10**6}[val[-1]] if val[-1] in 'KMB' else float(val) for val in ['5K', '1M', '123']])

Output: [5000.0, 1000000.0, 123.0]

The one-liner leverages list comprehension and dictionary mapping to swiftly convert a list of string values, making it a compact solution for scripts where readability may be secondary to brevity.

Summary/Discussion

  • Method 1: Manual Conversion. Easy for beginners. Limited to known suffixes. Not scalable.
  • Method 2: Using a Dictionary. More organized than Method 1. Still limited by one-character suffixes unless modified.
  • Method 3: Using Regular Expressions. Highly flexible and robust. Requires regex knowledge, which may be a barrier for some.
  • Method 4: Using a Third-party Library. Easy to implement and reliable. Adds external dependency and may introduce unnecessary complexity for small projects.
  • Bonus Method 5: One-Liner Using List Comprehension. Extremely concise. Sacrifices readability and might be challenging to debug or extend.