How to Check if a Float Variable is Empty in Python

In my ten years of architecting Python solutions for data-heavy firms in Chicago, I have seen developers stumble over one specific concept: the “empty” number.

In Python, strings can be empty (“”) and lists can be empty ([]), but a float is a numeric type. It always holds a value, even if that value represents “nothing.”

I remember working on a high-frequency trading algorithm where a “missing” price could crash the whole system if not handled correctly.

When people ask how to check if a float is empty in Python, they usually mean checking if the variable is None, if it is NaN (Not a Number), or if it is zero.

In this tutorial, I will show you the exact methods I use to validate float variables in professional Python environments.

What Does an “Empty” Float Mean in Python?

Technically, a float cannot be “empty” in the same way a container can. It is a scalar value.

However, in the real world—like when you are pulling mortgage rates from a US banking API—you often encounter missing data.

This missing data usually appears in Python in three forms:

None: The variable was never assigned a number.
NaN (Not a Number): A mathematical undefined result (like 0.0/0.0).
Zero: In some business contexts, 0.0 is treated as “empty.”

Method 1: Check for None Using the ‘is’ Operator

This is the most common check I perform. If you are fetching a float from a database, the field might be null.

In Python, a null database entry usually translates to the None keyword. I always use the is operator for this check.

# Monthly rainfall in Phoenix, Arizona (Missing data)
rainfall_inches = None

# Checking if the float variable is None
if rainfall_inches is None:
    print("The rainfall data is missing or empty.")
else:
    print(f"Recorded rainfall: {rainfall_inches} inches.")

# Output: The rainfall data is missing or empty.

You can see the output in the screenshot below.

In Python, is checks for identity, while == checks for equality. Using is None is the PEP 8 standard and is faster and safer for this specific Python check.

Method 2: Check for NaN Using the math Module

When working with scientific data or the Python Pandas library, you will frequently encounter NaN.

NaN is a special float value that represents an undefined numeric result. A tricky part about NaN is that it does not equal itself (NaN == NaN is False).

import math

# Dividend yield for a new US startup (Undefined)
dividend_yield = float('nan')

# Checking if the float is NaN in Python
if math.isnan(dividend_yield):
    print("The dividend yield is not a number (NaN).")
else:
    print(f"The yield is {dividend_yield}%")

# Output: The dividend yield is not a number (NaN).

You can see the output in the screenshot below.

I rely on math.isnan() whenever I am processing CSV files containing US economic indicators, as empty cells often import as NaN in Python.

Method 3: Check if a Float is Zero (Truthiness)

In some scenarios, a zero value is considered “empty.” For example, if a user hasn’t entered their annual income in a tax form, the system might default to 0.0.

In Python, numeric types have “truthy” and “falsy” values. The number 0.0 is considered False, while any other number is True.

# Annual bonus for a consultant in Seattle
annual_bonus = 0.0

# Using Python truthiness to check if the value is 0.0
if not annual_bonus:
    print("The bonus variable is zero or empty.")
else:
    print(f"Your bonus is ${annual_bonus:,.2f}")

# Output: The bonus variable is zero or empty.

You can see the output in the screenshot below.

Be careful with this method. If 0.0 is a valid, meaningful piece of data (like a 0.0% interest rate), this check will incorrectly flag it as “empty.”

Method 4: The Robust “Is Valid” Check

In professional Python development, I usually combine these checks into a single validation function.

This is especially helpful when building robust APIs for US clients where you cannot trust the input data quality.

import math

def is_float_empty(value):
    """
    Checks if a float variable is None, NaN, or 0.0.
    """
    if value is None:
        return True
    if math.isnan(value):
        return True
    if value == 0.0:
        return True
    return False

# Example: Checking a list of temperatures in Fahrenheit
sensor_readings = [72.5, None, float('nan'), 0.0, 68.2]

for i, reading in enumerate(sensor_readings):
    if is_float_empty(reading):
        print(f"Reading {i}: This variable is empty or invalid.")
    else:
        print(f"Reading {i}: Valid data found -> {reading}")

You can see the output in the screenshot below.

Method 5: Use NumPy for Batch Processing

If you are a Data Scientist in the USA, you likely handle thousands of float variables at once using NumPy.

Checking each variable individually in a loop is too slow. Instead, I use NumPy’s vectorized functions to find “empty” or missing floats in an entire array.

import numpy as np

# Portfolio values for a group of investors
portfolio_values = np.array([15000.50, np.nan, 22000.00, 0.0, None], dtype=object)

# Check for NaN or None in the array
is_missing = pd.isna(portfolio_values) # Using pandas logic often easier for mixed types

print(f"Missing values detected at indices: {np.where(is_missing)[0]}")

Using these optimized Python libraries is essential when your dataset grows beyond a few hundred rows.

Handle Floating Point Precision

One thing I have learned over the last decade is that floats are rarely “exactly” zero.

Due to precision issues, a calculation might result in 0.0000000000000001. If you check if value == 0.0, it will return False.

In my financial scripts, I often check if a value is “effectively” empty by checking if it falls within a tiny threshold (called epsilon).

# Epsilon check for 'near-zero' floats
epsilon = 1e-9
value = 0.00000000000005

if abs(value) < epsilon:
    print("This float is effectively empty.")

Summary of Python Float Check Methods

Method	What it catches	Best Use Case
`val is None`	Only `None`	Database nulls
`math.isnan(val)`	Only `NaN`	Scientific/Calculation errors
`if not val:`	`None`, `0.0`, `NaN` (some cases)	Quick, simple checks
`abs(val) < eps`	Very small numbers	Engineering/Financial apps

Checking if a float variable is empty in Python depends entirely on what you consider “empty” in your specific project. In most cases, you will likely be looking for None or NaN.

I have found that being explicit with your checks—using is None and math.isnan()—makes your code much easier for other developers to read and maintain. It prevents those subtle bugs that occur when a 0.0 is accidentally treated as missing data.

If you are working with large data sets, always opt for the vectorized methods in NumPy or Pandas to keep your Python code performing at its best.

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Bijay Kumar

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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