DataTypes in Python

"Data types classify data to inform the compiler or interpreter how the programmer intends to use the data. They determine the operations that can be performed on the data, the values that the data can take, and the amount of memory needed to store the data."

Photo by Markus Spiske on Unsplash

some basic datatypes in python

2. Primitive Data Types

Integers (int)

Integers are whole numbers, positive or negative, without a decimal point.

a = 10
b = -5

Floating Point Numbers (float)

Floats represent real numbers with a decimal point.

pi = 3.14
gravity = 9.81

Strings (str)

Strings are sequences of characters, enclosed in single, double, or triple quotes.

greeting = "Hello, World!"
multiline_string = """This is a
multiline string."""

Booleans (bool)

Booleans represent one of two values: True or False

is_active = True
is_closed = False

3. Compound Data Types

Lists (list)

Lists are ordered collections of items, which can be of different types. Lists are mutable, meaning you can change their contents.

fruits = ["apple", "banana", "cherry"]
numbers = [1, 2, 3, 4.5, 6]
mixed = [1, "hello", 3.14, True]

Tuples (tuple)

Tuples are ordered collections of items, similar to lists, but they are immutable.

coordinates = (10.0, 20.0)
colors = ("red", "green", "blue")

Dictionaries (dict)

Dictionaries are collections of key-value pairs. Keys must be unique and immutable (like strings, numbers, or tuples), while values can be of any type.

person = {
    "name": "Alice",
    "age": 25,
    "city": "New York"
}

Sets (set)

Sets are unordered collections of unique items.

unique_numbers = {1, 2, 3, 4, 4, 5}

4. Special Data Types

NoneType (None)

None represents the absence of a value or a null value.

result = None

5. Type Conversion

Python allows you to convert one data type to another. This process is called type casting.

# Converting integer to float
a = 10
b = float(a)  # 10.0

# Converting float to integer
c = 3.14
d = int(c)  # 3
# Converting integer to string
e = 100
f = str(e)  # "100"
# Converting string to integer
g = "50"
h = int(g)  # 50

6. Conclusion

Understanding data types is fundamental to mastering Python programming. They are the building blocks of data manipulation and provide the necessary structure for writing efficient and effective code. With this knowledge, you are now better equipped to handle different types of data in Python, making your programs more robust and versatile.