A floating-point number, commonly called a float, is a number that contains a decimal point.

Python uses the built-in float data type to represent decimal values.

Floating-point numbers are commonly used for measurements, prices, percentages, scientific calculations, and any situation where fractional values are required.

Unlike integers, floating-point numbers have limited precision because they are stored using the IEEE 754 floating-point representation.

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Syntax #

variable_name = floating_point_value

Components #

Component	Description
variable_name	Name of the variable.
=	Assignment operator.
floating_point_value	A number containing a decimal point or written in scientific notation.

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Example 1: Positive Floating-Point Number #

main.py

price = 99.95

print(price)
print(type(price))

Output #

99.95
<class 'float'>

Explanation #

• 99.95 contains a decimal point.
• Python automatically stores it as a floating-point number.
• The type() function confirms that the value belongs to the float data type.

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Example 2: Negative Floating-Point Number #

main.py

temperature = -18.5

print(temperature)
print(type(temperature))

Output #

-18.5
<class 'float'>

Explanation #

• Floating-point numbers can be positive or negative.
• The minus (-) sign indicates a negative value.
• The variable stores a value of type float.

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Example 3: Zero as a Float #

main.py

balance = 0.0

print(balance)
print(type(balance))

Output #

0.0
<class 'float'>

Explanation #

• 0.0 represents zero as a floating-point number.
• Although its value is zero, its data type is float, not int.

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Example 4: Arithmetic with Floats #

main.py

a = 5.5
b = 2.5

print(a + b)
print(a - b)
print(a * b)
print(a / b)

Output #

8.0
3.0
13.75
2.2

Explanation #

• Floating-point numbers support all arithmetic operations.
• The results are also floating-point numbers.
• Decimal values are preserved during calculations.

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Example 5: Reading a Float from the User #

main.py

height = float(input("Enter your height: "))

print(height)
print(type(height))

Sample Input #

5.8

Output #

5.8
<class 'float'>

Explanation #

• input() always returns a string.
• The float() function converts the string into a floating-point number.
• The variable stores a value of type float.

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Example 6: Integer and Float Together #

main.py

marks = 90
bonus = 2.5

total = marks + bonus

print(total)
print(type(total))

Output #

92.5
<class 'float'>

Explanation #

• One operand is an integer.
• The other operand is a floating-point number.
• Python automatically converts the integer to a float before performing the addition.
• The result is a floating-point number.

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Example 7: Floating-Point Precision #

main.py

print(0.1 + 0.2)

Output #

0.30000000000000004

Explanation #

• Many decimal numbers cannot be represented exactly in binary.
• Therefore, very small rounding errors may occur.
• This is a normal characteristic of floating-point arithmetic and not a Python error.

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Example 8: Comparing Floating-Point Numbers #

main.py

a = 5.75
b = 3.25

print(a > b)
print(a == b)

Output #

True
False

Explanation #

• Floating-point numbers can be compared using comparison operators.
• Since 5.75 is greater than 3.25, the first expression returns True.
• The two values are different, so the equality comparison returns False.

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Example 9: Large Floating-Point Number #

main.py

distance = 9876543210.12345

print(distance)
print(type(distance))

Output #

9876543210.12345
<class 'float'>

Explanation #

• Floating-point numbers can represent very large values.
• However, unlike integers, they have limited precision.
• Extremely large or highly precise decimal values may lose some accuracy.

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Example 10: Float Without Leading Zero #

main.py

value = .75

print(value)
print(type(value))

Output #

0.75
<class 'float'>

Explanation #

• Python allows decimal numbers to be written without a leading zero.
• Internally, .75 is treated as 0.75.
• For better readability, many programmers prefer writing 0.75.

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Common Mistakes #

1. Forgetting to Convert User Input #

Incorrect

price = input("Enter price: ")

print(price + 5)

Output #

TypeError: can only concatenate str (not "int") to str

Reason

input() returns a string.

Correct

price = float(input("Enter price: "))

print(price + 5)

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2. Expecting Exact Decimal Precision #

Incorrect

print(0.1 + 0.2 == 0.3)

Output #

False

Reason

Floating-point numbers may contain tiny rounding errors due to their binary representation.

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3. Assuming 5 and 5.0 Have the Same Data Type #

Incorrect Assumption

print(type(5))
print(type(5.0))

Output #

<class 'int'>
<class 'float'>

Reason

5 is an integer, while 5.0 is a floating-point number.

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4. Using Commas in Numeric Values #

Incorrect

price = 1,234.56

Reason

Python interprets this as a tuple, not as a floating-point number.

Correct

price = 1234.56

or

price = 1_234.56

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Best Practices #

• Use float whenever decimal values are required.
• Convert user input using float() before performing calculations.
• Avoid comparing floating-point numbers directly when exact precision is required.
• Use a leading zero before the decimal point (for example, 0.75 instead of .75) to improve readability.
• Use numeric underscores to improve the readability of large floating-point values.

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Key Points to Remember #

• Floating-point numbers are represented by the float data type.
• Floats store numbers with decimal points.
• Floats support all arithmetic operations.
• Python automatically converts integers to floats during mixed arithmetic operations.
• Floating-point values have limited precision.
• Small rounding errors are normal in floating-point arithmetic.
• Use float() to convert strings into floating-point numbers.
• Use 0.75 instead of .75 for better code readability.