variable.py

"""
Variables allow us to store values in named records that can be used in
a program. This module shows how to define variables and make assertions
about the state of each defined variable.
"""

import math


def main() -> None:
 # Here are the main literal types to be aware of
 a = 1
 b = 2.0
 c = True
 d = "hello"

 # Notice that each type is a class. Each of the variables above refers
 # to an instance of the class it belongs to
 a_type = type(a)
 b_type = type(b)
 c_type = type(c)
 d_type = type(d)

 # Also, say hello to the `assert` keyword! This is a debugging aid that
 # we will use to validate the code as we progress through each `main`
 # function. These statements are used to validate the correctness of
 # the data and to reduce the amount of output sent to the screen
 assert a_type is int
 assert b_type is float
 assert c_type is bool
 assert d_type is str

 # Everything is an object in Python. That means instances are objects
 # and classes are objects as well
 assert isinstance(a, object) and isinstance(a_type, object)
 assert isinstance(b, object) and isinstance(b_type, object)
 assert isinstance(c, object) and isinstance(c_type, object)
 assert isinstance(d, object) and isinstance(d_type, object)

 # We can represent integer literals in Python using 4 bases: decimal,
 # hexadecimal, octal, and binary. Decimal literals do not require any
 # prefix while other bases require prefixes:
 # - `0x` for hexadecimal
 # - `0o` for octal
 # - `0b` for binary
 assert 100 == 0x64 == 0o144 == 0b1100100

 # We can use underscores (literal `_`) to separate digit groups in
 # integer literals
 assert 10_000 == 10000
 assert 0x01_0F_2C == 69_420
 assert math.isclose(3.456_290e-1, 0.3_456_290)

 # There is also a special literal called None. This literal is used to
 # point that a particular variable or object is not created
 e = None
 e_type = type(e)
 assert e_type is type(None)
 assert isinstance(e, object) and isinstance(e_type, object)


if __name__ == "__main__":
 main()