Data Types in Python
Python Datatypes are examined with examples in this post, aiming to fill learning gaps.
Built-in Data Types
It is important to understand the concept of data type in programming.
There are different types of Python datatypes. Each type can store different types of data, and each type can be applied to various things if we want them to do so.
The following data types are built-in by default in Python:
| Text Type: | str |
| Numeric Types: | int, float, complex |
| Sequence Types: | list, tuple, range |
| Mapping Type: | dict |
| Set Types: | set, frozenset |
| Boolean Type: | bool |
| Binary Types: | bytes, bytearray,memoryview |
Get Data Type
Datatypes in Python are represented by the type() function, which can be utilized to determine the data type of any object:
The data type of the variable a should be displayed as follows:
Example
Set Data Type
The type of a variable is determined by the value that you assign to it when you utilize Python Datatypes:
| Example | Data Type | Try it |
|---|---|---|
| a = “This is String” | str | Execute |
| a = 9 | int | Execute |
| a = 9.7 | float | Execute |
| a = 9ex | complex | Execute |
| a = [“Tesla”, “spaceX”, “Twiter”] | list | Execute |
| a = (“Microsoft”, “Apple”, “Facebook”) | tuple | Execute |
| a = range(9) | range | Execute |
| a = {“owner” : “Elon Musk”, “age” : 51} | dict | Execute |
| a = {“Past”, “Present”, “Future”} | set | Execute |
| a = frozenset({“United”, “Arab”, “Emirates”}) | frozenset | Execute |
| a = True | bool | Execute |
| a = b”bytes” | bytes | Execute |
| a = bytearray(9) | bytearray | Execute |
| a = memoryview(bytes(9)) | memoryview | Execute |
Set up a particular Data Type
In Python Datatypes, you can declare the data type using the constructor functions:
| Example | Data Type | Try it |
|---|---|---|
| b = str(“This is a string value”) | str | Execute |
| b = int(6) | int | Execute |
| b = float(6.6) | float | Execute |
| b = complex(6mx) | complex | Execute |
| b = list((“valueone”, “valuetwo”, “valuethree”)) | list | Execute |
| b = tuple((“Elon”, “Reeve”, “Musk”)) | tuple | Execute |
| b = range(9) | range | Execute |
| b = dict(name=”Musk”, age=51) | dict | Execute |
| b = set((“World”, “Health”, “Organization”)) | set | Execute |
| b = frozenset((“USA”, “Canada”, “Germany”)) | frozenset | Execute |
| b = bool(6) | bool | Execute |
| b = bytes(7) | bytes | Execute |
| b = bytearray(8) | bytearray | Execute |
| b = memoryview(bytes(9)) | memoryview | Execute |
Python Data Types Usage
Understanding and effectively using data types in Python is important for several reasons:
- Data types define how data is represented and stored in memory. Each data type has a specific structure and behavior, allowing you to accurately represent and manipulate different kinds of data, such as numbers, text, collections, and more.
- By using appropriate data types, you can ensure the integrity and validity of your data. Data types enforce constraints on the values that can be assigned to variables, helping prevent unintended data manipulation or corruption. For example, using a numeric data type ensures that a variable can only hold numerical values, while using a string data type ensures that a variable stores text.
- Different data types have different memory requirements. Using the appropriate data type for your data can optimize memory usage and reduce storage overhead. For instance, using integer data types for whole numbers requires less memory compared to using float data types for the same values with decimal points.
- Data types determine the operations and computations you can perform on the data. Each data type comes with built-in methods and functions that enable specific operations. For example, numeric data types support mathematical calculations, string data types provide string manipulation functions, and set data types support set operations.
- Proper use of data types contributes to code correctness and reliability. By assigning and manipulating data using the correct data types, you can avoid type errors, such as trying to perform incompatible operations or comparisons between incompatible data types. This helps ensure that your code behaves as expected and produces accurate results.
