Converting Python String to Float Dealing with NaN Values

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πŸ’‘ Problem Formulation: Programmers often need to handle strings representing numerical values in Python, and occasionally these strings may contain non-numeric values such as ‘NaN’ (Not a Number). This article explores how to convert such strings to floats, with ‘NaN’ being correctly interpreted as a special floating-point value that indicates an undefined or unrepresentable value. For instance, converting the string ‘nan’ should result in the floating-point NaN value.

Method 1: Using float() and math.isnan()

Conversion of a string that explicitly contains ‘NaN’ to a float can be straightforwardly achieved by the built-in float() function, and the math.isnan() function can be used to check for the resultant NaN value.

Here’s an example:

import math

def string_to_float_nan(value):
    try:
        float_val = float(value)
    except ValueError:
        float_val = float('nan')
    
    return float_val

value = 'nan'
print(string_to_float_nan(value))

The output of this code is:

nan

This code attempts to convert the input string to a float. If it raises a ValueError, indicative of an invalid input for conversion (not a number), we catch the exception and manually return float('nan') instead. If the input is ‘nan’, it is correctly parsed as a NaN value.

Method 2: Using pandas.to_numeric()

The pandas.to_numeric() function is designed to handle strings containing numerical data gracefully, and it automatically converts ‘NaN’ strings to NaN values without additional effort.

Here’s an example:

import pandas as pd

value = 'nan'
float_val = pd.to_numeric(value, errors='coerce')
print(float_val)

The output of this code is:

nan

Here, pd.to_numeric() is used with the errors='coerce' argument which, instead of raising an error, converts the invalid parsing to a NaN value. This method is particularly useful when processing data in bulk, as often done within the pandas framework.

Method 3: Using numpy.float()

The NumPy library provides a numpy.float() function, similar to the built-in float, but is often used within contexts that leverage NumPy arrays.

Here’s an example:

import numpy as np

value = 'nan'
float_val = np.float64(value)
print(float_val)

The output of this code is:

nan

This method shows the direct use of np.float64() to convert a string value to a NumPy float value. This can be especially useful when working with NumPy arrays and expecting NaN values as part of your numeric data.

Method 4: Using ast.literal_eval()

Another technique involves the ast.literal_eval() function, which safely evaluates a string containing a Python literal or container display. It can correctly interpret ‘nan’ as a NaN value.

Here’s an example:

import ast

value = 'nan'
try:
    float_val = ast.literal_eval(value)
except ValueError:
    float_val = float('nan')

print(float_val)

The output of this code is:

nan

With ast.literal_eval(), the string is evaluated as a Python expression. If the evaluation fails (e.g., the string contains something other than a literal), ValueError is caught, and a NaN value is returned.

Bonus One-Liner Method 5: Using a Ternary Operator with float()

A more Pythonic one-liner for checking the string and converting it to a float or NaN makes use of a ternary conditional operator.

Here’s an example:

value = 'nan'
float_val = float(value) if value.lower() == 'nan' else float('nan')
print(float_val)

The output of this code snippet will be:

nan

This one-liner checks if the string value, when converted to lowercase, is ‘nan’ and, if true, converts it to a float. Otherwise, it defaults to float('nan'). It is a very concise way to achieve the conversion.

Summary/Discussion

  • Method 1: Using float() and math.isnan(). Strengths: Simple and uses only the built-in Python library. Weaknesses: Requires explicit exception handling.
  • Method 2: Using pandas.to_numeric(). Strengths: Designed for handling data conversion at scale within pandas. Weakness: Additional dependency on pandas.
  • Method 3: Using numpy.float(). Strengths: Integrates well with NumPy’s numerical computing ecosystem. Weaknesses: Adds dependency on NumPy.
  • Method 4: Using ast.literal_eval(). Strengths: Safe evaluation of strings as Python literals. Weaknesses: Somewhat complex and less performance-efficient.
  • Method 5: One-Liner using Ternary Operator. Strengths: Concise and Pythonic. Weaknesses: May be less readable to beginners.