Python DataTypes Part 2 :python| float datatypes |datatypes
Characteristics of float in Python:
---------------------------------------------------------
Syntax: A float is written with a decimal point, even if it is a whole number (e.g., 1.0 instead of 1).
Precision: Floats in Python are typically represented in 64-bit double precision, meaning they have around 15–17 significant decimal digits of precision.
Range: Floats can represent very large or very small numbers, but they are limited by the underlying hardware’s floating-point arithmetic (IEEE 754 standard).
Scientific Notation: Floats can also be written in scientific notation, such as 3.5e5 (which means
3.5
×
1
0
5
3.5×10
5
).
Example of Float Declaration
a = 3.14 # a float with a decimal point
b = -0.456 # a negative float
c = 1.0 # whole number represented as a float
d = 4.2e3 # scientific notation, equivalent to 4200.0
Common Operations with Float
Floats support all standard arithmetic operations: addition, subtraction, multiplication, and division.
x = 5.5
y = 2.0
Addition
result_add = x + y # Output: 7.5
Subtraction
result_sub = x - y # Output: 3.5
Multiplication
result_mul = x * y # Output: 11.0
Division
result_div = x / y # Output: 2.75
Exponentiation
result_exp = x ** y # Output: 30.25
Converting Other Types to Float
You can convert other types like integers or strings (if they contain valid numeric data) to floats using the float() function.
Integer to float
int_num = 10
float_num = float(int_num) # Converts 10 to 10.0
String to float
str_num = "123.45"
float_num = float(str_num) # Converts "123.45" to 123.45
Invalid conversion
invalid_str = "abc"
float(invalid_str) # Raises ValueError because "abc" is not a number
Rounding Floats
Since floats can be imprecise when performing operations, you can use the round() function to round them to a specific number of decimal places.
pi = 3.1415926535
rounded_pi = round(pi, 2) # Output: 3.14
Handling Float Precision Issues
Due to the limitations of binary floating-point representation, some decimal numbers cannot be represented exactly. This can lead to small precision errors.
print(0.1 + 0.2) # Output: 0.30000000000000004 (not exactly 0.3)
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