Dividing by zero follows the IEEE 754 standard in floating point operation. Dividing positive numbers by zero gives Infinity, dividing negative numbers by zero gives -Infinity, dividing zero gives NaN; 2. NaN represents an invalid numerical operation, which is not available == judgment. It should be recognized by special functions such as math.isnan() or Number.isNaN(); 3. Although Infinity is legal, it may indicate an error, and it needs to be checked through isFinite() and formatted or downgraded; 4. It is recommended to check whether the denominator is zero before division, clean out the outliers when data is input and output, and log the exception location to improve the robustness of the code. Complete practice can effectively avoid hidden errors caused by NaN or Infinity.

Edge cases like NaN (Not a Number), Infinity , and dividing by zero are often overlooked when dealing with numerical calculations, but they can cause programs to experience difficult-to-trace errors or inconsistent behavior. Correctly understanding and handling these special situations is the key to writing robust numerical code.

1. Understand the result of dividing by zero: When Infinity or NaN is generated

In most programming languages (such as JavaScript, Python, Java, etc.), the behavior of dividing floating point numbers by zero follows the IEEE 754 standard:

• Positive number divided by zero → Infinity
• Negative number divided by zero → -Infinity
• Zero divided by zero → NaN

For example in JavaScript:

 console.log(1 / 0); // Infinity
console.log(-1 / 0); // -Infinity
console.log(0 / 0); // NaN

Using floating point numbers in Python also has similar behavior:

 print(1.0 / 0.0) # Will raise ZeroDivisionError? No! Integer division only will error # In fact, floating point division by zero will error unless you use the math module or a special way to import math
print(math.inf) # You can use infinity directly

?? Note: Python 1.0 / 0.0 will throw ZeroDivisionError , but you can explicitly create infinity via float('inf') or math.inf .

suggestion :

• Check whether the denominator is zero before doing division, especially the user input or dynamically calculated values.
• If infinity is allowed to make sense (such as limit calculations), Infinity scenario should be handled explicitly.

 def safe_divide(a, b):
    if b == 0:
        return float(&#39;inf&#39;) if a > 0 else float(&#39;-inf&#39;) if a < 0 else float(&#39;nan&#39;)
    return a / b

2. Correctly identify and process NaNs

NaN is a special floating point value that represents the result of "invalid numerical operation", such as 0/0 , sqrt(-1) (in the real range), inf - inf , etc.

Key Features:

• NaN returns False when compared to any value (including itself)
• That is: NaN == NaN → False
• So it cannot be used == to determine whether it is NaN

? Correct way to judge:

• JavaScript: isNaN(value) or more secure Number.isNaN()
• Python: math.isnan(x)
• Java: Double.isNaN(x)

Example (Python):

 import math

x = 0.0 / 0.0 # In fact, Python will report an error, use it instead:
x = float('nan')

print(x != x) # True! This is a kind of "black technology" to judge NaN
print(math.isnan(x)) # Recommended method, clear and safe

Common Traps :

• In data processing (such as Pandas), NaN causes the aggregate function to skip or return NaN
• Unprocessed NaN in machine learning propagate, causing model training to fail

suggestion :

• Actively check and process NaN during data cleaning phase
• Use pandas.dropna() or fillna() to handle missing values
• Avoid using NaN for control flow judgment (such as if x == nan: )

3. Infinity impact and processing

Although Infinity is legal, it may be an error signal in some scenarios:

• Indicates a numerical overflow or divided by zero
• May not be supported in the database
• It's not friendly to show "Infinity" in the UI

Example question:

 const score = 100 / 0; // Infinity
console.log(`score: ${score}`); // Output "score: Infinity" - User confusion

Handling suggestions :

• Setting up upper and lower bound checks on key business values
• Convert Infinity to meaningful default or error prompt

 function formatScore(value) {
    if (!isFinite(value)) {
        return 'Data exception';
    }
    return value.toFixed(2);
}

Using math.isfinite() in Python:

 import math

if math.isfinite(result):
    print(result)
else:
    print("Result out of range")

Summary of key practices

To deal with these numerical edge cases, the core is to prevent explicit inspections and reasonable degradation in advance :

• ? Use isFinite() to check whether the value is normal
• ? Use isNaN() instead of == to judge NaN
• ? Check whether the denominator is zero before division (especially when floating point numbers are close to zero)
• ? Clean and format the data input/output layer
• ? Record the location of Infinity or NaN in the log for easy debugging

Basically that's it. It may seem simple, but in a real project, an unprocessed NaN may allow you to debug all day.

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