Python float() is a built-in function that converts numbers or strings into decimal (floating-point) values. It plays an important role when you need to work with precise values, such as 3.14 or 2.0, in your calculations. The float() function can turn whole numbers or numeric strings like “3.14” and “2” into decimals. This makes it easier to handle fractions, perform accurate math operations, and avoid common rounding errors. In this blog, you will explore how the float() function works in Python with examples, real-life applications, and practical use cases.
Table of Contents:
What is the float() Function in Python
The Python float() function is an inbuilt function that converts a number or string into a floating-point number, allowing it to return a float. The string must contain a numeric value. It takes an optional parameter (argument), either an integer, a float, or a string that may consist of digits with a decimal, or it can take no parameter. The default value is 0.0. Then it either returns a float or raises a value error if it cannot be parsed to a valid float. The float() function is useful in apps that work with decimals, like in science or money. It helps make calculations more accurate
float() Function Syntax in Python
The Python float() function follows a straightforward syntax that is easy to use and takes a number or a string of numeric values and converts it to a floating-point number. It has an optional argument, which can be an integer, a floating-point number, or even a string representing a numeric value. It returns 0.0 if no argument is passed.
Syntax of float()
float([x])
Example:
Output:
Explanation: Here, the float() function converts the numeric string “3.14” to the floating-point number 3.14, and the integer 5 to the floating-point number 5.0.
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Parameters of the float() Function in Python
The float() function in Python takes a single optional parameter, usually denoted as x. This parameter can be a number or a numeric string that you want to convert into a floating-point number. If no argument is provided, it returns 0.0.
x (What You’re Checking):
This x is the value you give to float(). It can be a number (like 5), a string containing a number (like “3.14”), or even an expression.
- If x is already a number or a numeric string, float() will convert it to a floating-point number (like 5.0 or 3.14).
- If x isn’t provided (you leave the parentheses empty), float() returns 0.0 by default.
Return Value of float() Function in Python
When you use the float() function, it gives back a floating-point number. This is essentially a number that includes a decimal point, even if it’s a whole number. If the value you gave it, is a valid number or numeric string, float() returns that number in floating-point form (like 5.0 or 3.14). If no argument is provided, float() returns 0.0 by default.
How the float() Function Works with Different Data Types in Python
The float() function in Python converts multiple data types into float values. It converts integers, strings, infinity, and NaN into float values. Let’s learn how it is converting with the examples.
1. Using float() with Integer Values in Python
The float() function in Python converts integer values into float values. The float() function accepts integer values and converts them into a float value. It is useful when you need to perform mathematical operations like division or scientific calculations that require decimal points. The following example shows how the float() function converts an integer value to a float value.
Example:
Output:
Explanation: Here, the integer 90 is passed to the float() function, which returns the value of 90.0. It converts just the data type, not the value.
2. Using float() with Strings in Python
The float() function in Python converts strings into float values. It accepts a string format and converts it into a float value. For example, if the input string is “90.5” in string format, it will be converted into a 90.5 float value. The string must contain only the numeric characters and decimal points. The following example shows how the float() function converts strings to float values.
Example:
Output:
Explanation: Here, the strings “80” and “-18.88” are both valid numeric values. So the float() function converts them to float values, 80.0 and -18.88.
3. Using float() with Lists, Tuples, and Dictionaries
The float() function in Python can convert special string values like “inf” or “NaN” into a float data type. It is useful in complicated mathematical calculations and data-related tasks. The example below shows how float() helps simplify working with these data types.
Example:
Output:
Explanation: Here, the float() function interprets “InF” and “InFiNiTy” as positive infinity, and “nan” or “NaN” as not-a-number. These values are returned as special floating-point constants representing undefined or unbounded numeric results.
The float() function in Python can convert many input datatypes like integers, decimals, number-like strings, “inf” or “NaN”. It is useful for tasks like cleaning the data, user input handling, and converting values from files or forms. Each example below shows how float() helps simplify working with numbers.
Example:
Output:
Explanation: Here, the float() function handles various valid input numbers, numeric strings, and even strings with whitespace or special float constants and consistently returns correct floating-point values.
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Built-in Float Methods in Python
Python has a few built-in methods for the float() function that help in dealing with multiple mathematical calculations to produce the result accurately. This method includes a float.as_integer_ratio(), float.is_integer(), float.hex(), and float.fromhex(). Learning these built-in functions will help you code precisely.
1. float.as_integer_ratio()
This float.as_integer_ratio() method in the Python float() function gives you two whole numbers that, when divided, will be equal to the float value the user has given. In simple terms, it turns the float into a fraction in its simplest form.
Example:
Output:
Explanation: Here, the output (15, 4) shows that the float value 3.75 is equal to the fractional value 15/4.
2. float.is_integer()
The float.is_integer() method in the Python float() function helps to check if a float has no decimal part or not. If the decimal part is zero, it returns True; otherwise, it returns False.
Example:
Output:
Explanation: Here, the first output True means 5.0 has no decimal fraction, that is, its value after the decimal point is zero, and is considered an integer, while the second output False indicates that 4.75 has a fractional part.
3. float.hex()
The float.hex() method in the Python float() function helps to convert a floating-point number into its hexadecimal string form. It is useful in low-level programming or to check how a float is stored in memory at the binary level.
Example:
Output:
Explanation: Here, the output is the hexadecimal format representing the decimal number 10.5.
4. float.fromhex()
The float.fromhex() method in the Python float function helps to convert a hexadecimal string back into a floating-point number. It reverses the process that float.hex() does, allowing you to move between hexadecimal and decimal representations of floats.
Example:
Output:
Explanation: Here, the hexadecimal string is converted back to the decimal floating-point number 10.5, showing that the float.fromhex() method reverses the hex() conversion.
Difference Between float() and the decimal Module
| Feature |
float() Function |
decimal Module |
| Purpose |
Converts a value to a floating-point number |
Provides decimal floating-point arithmetic with exact precision |
| Precision |
Limited precision (approx. 15–17 decimal digits) |
Arbitrary precision, user-configurable, ideal for financial calculations |
| Type of Operation |
Type conversion to built-in float type |
Creates Decimal objects for precise decimal arithmetic |
| Input |
Integers, floats, numeric strings, or special float strings like “inf”, or “nan” |
Strings, integers, floats, or tuples representing decimal values |
| Output |
A built-in float object (binary floating-point) |
A Decimal object with exact decimal representation |
| Use Case |
General numerical computations need speed and simplicity |
When exact decimal precision is critical, such as currency calculations |
Common Errors in Python float() and How to Avoid Them
When using Python’s float() function, some errors and exceptions commonly occur. Knowing these common errors helps in writing error-free code and saves you time to debug the code.
1. ValueError: could not convert string to float
The Value error occurs when you try to convert a string into a float value using the float() function in Python, which is not a valid number. The float() function works with strings that are valid numeric digits or special float strings, not with alphabetic characters like “inf” or “nan”.
Example:
Output:
Explanation: Here, in this output, “hello” is not a valid number, so float() returns a ValueError.
Solution: Before transforming it, make sure that the string contains a valid numeric value. You can handle errors with the help of a try-except block.
2. OverflowError: int too large to convert to float
The Overflow error takes place when you convert an integer value that is too large into a float value with the help of the float() function.
Example:
Output:
Explanation: Here, in this output, the integer cannot be represented as a floating-point number as it is very large, so a message of OverflowError is displayed in the output.
Solution: You can try using some other data types or some other Python libraries that will be used to handle very large numbers. Try to avoid converting huge integers into floats.
3. TypeError: float() argument must be a string or a number
A TypeError happens if you use float() on a list, None, or other non-number types.
The float() function only accepts two data types, integer and string, with a numerical value.
Example:
Output:
Explanation: Here, float() can only convert strings or numeric types, so we get a TypeError if we pass in None or other types.
Solutions: Validate your input data to ensure that it is a valid string or number before trying to convert.
Common Use Cases of float() in Python
1. Convert Numeric Strings to Floats: When working with data from users, files, or web forms, numeric values returned are often in the form of strings. The float() function is used to convert these numeric strings to float values for calculations.
2. User Input to Calculations: Since user input is usually a string, you can use float() to convert it into a decimal number for calculations.
3. Converting Scientific Notation or special float values: The float() function can also convert strings representing scientific notation (like “1.23e4”) or special float values such as “inf” (infinity), or “nan” (not a number), to their respective equivalent floating-point representation.
Best Practices for Using float() in Python
1. The method float() is usually for converting integers, strings, or other numeric types to decimal precision, and is useful in calculations.
2. When doing monetary or financial calculations, you receive float decimals, and do not expect float() or anything else to do very precise calculations. You should use Python’s decimal module to avoid rounding errors.
3. Using == to compare floating-point numbers can give incorrect results, because of small rounding errors in calculations. Instead of checking if two floats are exactly equal, it’s better to check if their difference is very small. Python’s math.isclose() function helps handle this and avoids floating-point precision issues.
4. Consider that float() will accept special values as input, like “inf”, “-inf”, and “nan”. If you are going to use those, think very carefully about unintended usage, bringing unexpected behaviours in calculations.
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Conclusion
In conclusion, the float() function in Python is valuable for converting values from other data types to float values, particularly for integers and strings that contain integer values. Because floating-point math has an inherent level of precision, avoid using float() for a reproduction of a decimal value, especially for financial applications, which use the decimal module. Always validate user input to avoid bugs, and be careful when comparing floating-point numbers, as small rounding errors can affect the results. Understanding how float() deals with special values such as inf and nan will improve how you can manage edge cases well.
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Python float() Function – FAQs
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