Welcome back, everyone! In the previous video, we discussed what variables are and the various data types supported by Python. Understanding them is crucial to writing effective Python code. If you missed the last video, make sure to check it out for the complete story. Link to that will be shown above and also shared in description.
Now, we'll dive deeper into the Numerical Data Type, which is one of the various data types supported by Python. Python offers a variety of numerical data types that we'll explore together, along with how to use them in your code.
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Now, let's dive deep into the art of handling numbers in Python with practical examples and helpful tips!
Python Numerical Data Type
In this video, the focus is on numerical data types, which are used for mathematical operations like addition, subtraction, multiplication, and division. Python supports various numerical data types such as integers, floating-point numbers, and complex numbers.
The video starts with an introduction to numeric data types, followed by an explanation of integers, floating-point numbers, and complex numbers. It discusses how to create variables of these data types and perform mathematical operations with them.
The video also covers the Decimal data type, which is used for high-precision calculations and is not a default data type in Python. The Decimal module is used to work with the Decimal data type.
The video concludes with an overview of type conversion, which is the process of converting one data type to another, and a discussion of the Boolean data type, which is used to represent true or false values.
Overall, this video provides a comprehensive overview of Python numerical data types and how to work with them effectively.
- Integer An integer is a whole number that can be positive, negative, or zero. In Python, to declare an integer simply assign an integer value to a variable. For example:
1x = 10 2y = -5 3z = 0
Now you can perform numerical operations on these variables.
- Floating-point A floating-point number is a number with a decimal point. Assigning a floating value to a variable will declare a float data type. For example:
1a = 3.14 2b = -2.5 3c = 0.0
- Complex A complex number is a number with a real and imaginary part. Let’s see how to declare a complex data type:
1d = 2 + 3j 2e = -4j 3f = 1.5 - 2j
Now, let's try to use one of the many operations supported by complex numbers. Suppose we want the .conjugate() method, which flips the sign of the imaginary part:
1a = 2 + 3j 2a.conjugate() #Output: 2-3j
Same way there are many othe operations that you can perform on complex numbers.
💡 In case you don't know, Python complex numbers are used in lots of mathematical and scientific calculations. Here are some common Python uses for complex numbers:
In signal processing complex numbers are used to represent signals with both amplitude and phase components.
Control systems use complex numbers to represent systems' transfer functions.
Quantum mechanics uses complex numbers to represent particle wave functions.
Etc.
Decimal
There's one more type, and it's decimal.
This isn't Python's default data type. To use it you need to invoke the respective package.
Decimals are a type of number that can be used to store and calculate values with more precision than regular floats. This can be useful for situations where a higher degree of accuracy is required, such as in financial calculations.
Now I know both of these concepts might be new to many. Let’s discuss them one by one.
💡 First is package. You might be wondering what this package thing is all about.
Then, let's first get an overview of what is a Python package. We'll go through these in depth in future videos.
Consider a package to be a collection of code written by someone else that you can use in your own programs. Now, if you want to use that feature, you don’t have to write that code again. You can use that package in your code.
If it is still somewhat confusing, then let’s understand with an example.
Suppose you want to add a feature to your program that requires working with dates and times, then you can choose the datetime package. To use this Python package in your program by importing it:import datetime
A sample program could be written like this:1from datetime import datetime 2 3# get current date 4current_date = datetime.date.today() 5 6print(current_date) #Output: 2022-12-27More on this will come later like how to write a package, install it, and use it. For now, this is enough.
Precision
Second is precision. Precision refers to the level of detail or accuracy with which a value is represented or measured. To understand it more clearly, let’s understand the difference between float vs Decimal data types.
Float vs Decimal
Ofcourse, Python uses floats and decimals to represent decimal numbers. Then we must ask a question why do we need Decimal when floats are already there? The answer is that they differ in precision and rounding behavior.
Floats are the default data type used to represent decimal numbers in Python. Float data type in Python has a lower precision, which means that they can only be used to represent a limited number of decimal places accurately. This can result in rounding errors and inaccuracies when performing calculations with very large or very small decimal numbers.
For example, consider Python's float value 0.1. Although it may appear a simple and exact value, it is an approximation. When performing calculations with this value, rounding errors can occur, as the actual value may be slightly different from what is displayed. For example, let's say we want to perform a simple division operation in Python:
For example, let's say we want to perform a simple division operation in Python:
1a = 1 2b = 3 3c = a / b 4print(c)
This will output 0.3333333333333333, which is a floating value with limited precision due to the way floating-point numbers are stored. This is where we can use the decimal module:
Decimals are a specialized data type that provides a higher level of precision and control over rounding behavior. Hence, they can represent a much larger range of decimal values with greater accuracy. Because of this high accuracy, they are used a lot in financial and scientific applications where precision is critical, and rounding errors must be minimized.
1from decimal import Decimal 2a = Decimal('1') 3b = Decimal('3') 4c = a / b 5print(c)
This will now output 0.3333333333333333333333333333, which is also a decimal value but with higher precision. Of course, it's not equal to 1/3 but it's a much closer approximation to 1/3 than what we get from floating point division.
Overall, the choice between floats or decimals in Python depends on the specific needs of the application. Floats are faster and more memory-efficient than decimals, but they have lower precision and can introduce rounding errors. Decimals offer enhanced precision and control over rounding behavior but are slower and require more memory.
Python's website has an interesting article regarding this. Link to that is given in the description:
Floating point issue and Limitations: https://docs.python.org/3/tutorial/floatingpoint.html
Type Conversion
We’ll not be discussing this topic right now. But just to give you an overview that numeric data types can be converted to other data types using functions like int(), float(), and complex(). This is known as type conversion. We'll discuss this in the coming sections.
Boolean data type:
Lastly, we will talk about boolean data types in today's video. Although, It isn't a numerical data type, but I thought it would be a good idea to discuss it here. They are also very simple types of data, unlike the ones we'll discuss in our future videos. Boolean data types represent true or false values. Python represents boolean values by the bool data type. For example:
1g = True 2h = False
- Boolean expressions can be evaluated using logical operators like
and,or, andnot. - Boolean values can be converted to other data types like
intandfloat. - Boolean values are often used in control statements like
if,while, andforloops. - Boolean values can be combined with comparison operators like
==,!=,<,>,<=, and>=.
Conclusion
Now let's wrap up our topic for today. As we discussed, Python supports various numerical data types used to represent different types of numerical values. These data types are int, float, complex, bool, and decimal. Understanding these data types is essential for writing efficient and accurate Python programs.
And that wraps up today's video. There's more to it. That we'll cover in our next video. Be sure to tune in to the next video and hit that subscribe button to stay updated!