Learn Python
- Python basic
- Introduction to File Handling
- Basics of List Comprehension
- Introduction to Matplotlib
- Classes and Objects
- Introduction to Functions
- Python Numbers
- Creating Basic Plots
- Opening and closing files
- Function parameters and arguments
- Advanced Techniques
- Attributes and Methods
- Python Strings
- Scope and lifetime of variables
- Advanced Plotting
- Reading from files
- Performance and Limitations
- Encapsulation
- Python List
- Specialized Plots
- Writing to files
- Return statement and output
- Inheritance
- Python Tuple
- Advanced Customization
- Working with different file formats
- Lambda Functions
- Polymorphism
- Practical Applications
- Higher-Order Functions
Python Numbers
Exploring floating numbers
1. Representing decimal numbers in Python
Unlike integers, floats (short for floating-point numbers) are used to represent decimal numbers in Python. Examples include 3.14159 (pi), 1.234, and -98.7.
Python uses a binary floating-point system to store these numbers internally. This means they are represented using a combination of zeros and ones, similar to how integers are stored, but with a special allocation for the decimal part.
2. Understanding floating-point precision limitations
It's important to understand that computers can't represent all possible decimal numbers perfectly using a finite number of bits. This can lead to slight inaccuracies in calculations with floats. Here's why:
The number of bits used to store the decimal part determines the level of precision. While Python uses double-precision floats offering a good range and precision, some decimal numbers might be approximated slightly.
The number of bits used to store the decimal part determines the level of precision. While Python uses double-precision floats offering a good range and precision, some decimal numbers might be approximated slightly.
3. Performing arithmetic operations with floats
Python supports most arithmetic operations with floats, similar to integers:
- Addition (+), Subtraction (-), Multiplication (*): These work as expected, but keep in mind potential precision limitations.
- Division (/): This usually results in a float, even if dividing two integers.
Example:
radius = 10.0 pi = 3.14159 # Slight precision loss might occur here due to float representation area = pi * radius ** 2 print(area) # This might not print exactly 314.159... due to precision limitations
4. Common functions for working with floats
- round(number, ndigits=0): Rounds a float to a specified number of decimal places (ndigits). Useful for controlling the displayed precision.
- abs(number): Returns the absolute value (positive version) of a float.
Example:
# Rounding to 2 decimal places rounded_area = round(area, 2) print(rounded_area) # This might print 314.16 (more precise display) # Getting absolute value distance = abs(-5.2) print(distance) # distance will be 5.2
Remember: While floating-point precision limitations exist, they are usually negligible for most everyday calculations. However, if you require very high precision, consider using libraries like decimal that offer more accurate decimal number handling.
It's time to take a quiz!
Test your knowledge and see what you've just learned.
Which of the following is a valid float representation in Python?
Dall of the above
A3.14159
B1.234
C-98.7
Check Answer
What can cause inaccuracies in floating-point calculations in Python?
AFinite number of bits used to store decimal numbers
BAll decimals can be represented exactly
CInteger division only
DNone of the above
Check Answer
What is the result of the following operation: 10.0 / 2?
D10
A5.0
B5
C2
Check Answer
What does the round() function do in Python?
AConverts float to int
BRounds a float to a specified number of decimal places
CReturns the float's absolute value
DNone of the above
Check Answer
What will abs(-5.2) return in Python?
C5.2
DNone
A5.2
B5.0
Check Answer