Python '!=' Is Not 'is not': Comparing Objects in Python
In this tutorial, you’ll learn:
• What the difference is between object equality and identity
• When to use equality and identity operators to compare objects
• What these Python operators do under the hood
• Why using is and is not to compare values leads to unexpected behavior
• How to write a custom
https://realpython.com/python-is-identity-vs-equality/
In this tutorial, you’ll learn:
• What the difference is between object equality and identity
• When to use equality and identity operators to compare objects
• What these Python operators do under the hood
• Why using is and is not to compare values leads to unexpected behavior
• How to write a custom
__eq__() class method to define equality operator behaviorhttps://realpython.com/python-is-identity-vs-equality/
Realpython
Python != Is Not is not: Comparing Objects in Python – Real Python
In this quick and practical tutorial, you'll learn when to use the Python is, is not, == and != operators. You'll see what these comparison operators do under the hood, dive into some quirks of object identity and interning, and define a custom class.
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None is evaluated as False so you can write
>>> results = None
>>> print(results or "No results")# "is" vs "=="
True
True
True
False
# • "is" expressions evaluate to True if two
# variables point to the same object
# • "==" evaluates to True if the objects
# referred to by the variables are equal
>>> a = [1, 2, 3]
>>> b = a
>>> a is bTrue
>>> a == bTrue
>>> c = list(a)
>>> a == cTrue
>>> a is cFalse
# • "is" expressions evaluate to True if two
# variables point to the same object
# • "==" evaluates to True if the objects
# referred to by the variables are equal
# Functions are first-class citizens in Python:
# They can be passed as arguments to other functions,
# returned as values from other functions, and
# assigned to variables and stored in data structures.
<function myfunc at 0x107012230>
5
# They can be passed as arguments to other functions,
# returned as values from other functions, and
# assigned to variables and stored in data structures.
>>> def myfunc(a, b):
... return a + b
...
>>> funcs = [myfunc]
>>> funcs[0]<function myfunc at 0x107012230>
>>> funcs[0](2, 3)5
No switch case in Python. However, we can emulate it with the help of first class function feature of Python.
# Because Python has first-class functions they can
# be used to emulate switch/case statements
16
16
None
None
# be used to emulate switch/case statements
def dispatch_if(operator, x, y):
if operator == 'add':
return x + y
elif operator == 'sub':
return x - y
elif operator == 'mul':
return x * y
elif operator == 'div':
return x / y
else:
return None
def dispatch_dict(operator, x, y):
return {
'add': lambda: x + y,
'sub': lambda: x - y,
'mul': lambda: x * y,
'div': lambda: x / y,
}.get(operator, lambda: None)()
>>> dispatch_if('mul', 2, 8)16
>>> dispatch_dict('mul', 2, 8)16
>>> dispatch_if('unknown', 2, 8)None
>>> dispatch_dict('unknown', 2, 8)None
# Python's list slice syntax can be used without indices
# for a few fun and useful things:
# You can clear all elements from a list:
[]
# You can replace all elements of a list
# without creating a new list object:
[7, 8, 9]
[7, 8, 9]
True
# You can also create a (shallow) copy of a list:
[7, 8, 9]
False
# for a few fun and useful things:
# You can clear all elements from a list:
>>> lst = [1, 2, 3, 4, 5]
>>> del lst[:]
>>> lst[]
# You can replace all elements of a list
# without creating a new list object:
>>> a = lst
>>> lst[:] = [7, 8, 9]
>>> lst[7, 8, 9]
>>> a[7, 8, 9]
>>> a is lstTrue
# You can also create a (shallow) copy of a list:
>>> b = lst[:]
>>> b[7, 8, 9]
>>> b is lstFalse
# Python's list comprehensions are awesome.
# This is equivalent to:
# Example:
[0, 4, 16, 36, 64]
vals = [expression
for value in collection
if condition]# This is equivalent to:
vals = []
for value in collection:
if condition:
vals.append(expression)
# Example:
>>> even_squares = [x * x for x in range(10) if not x % 2]
>>> even_squares[0, 4, 16, 36, 64]
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# Python has a HTTP server built into the
# standard library. This is super handy for
# previewing websites.
# Python 3.x
# Python 2.x
# (This will serve the current directory at
# http://localhost:8000)
# standard library. This is super handy for
# previewing websites.
# Python 3.x
$ python3 -m http.server# Python 2.x
$ python -m SimpleHTTPServer 8000# (This will serve the current directory at
# http://localhost:8000)
# Python 3.5+ supports 'type
# annotations' that can be
# used with tools like Mypy
# to write statically typed Python:
# annotations' that can be
# used with tools like Mypy
# to write statically typed Python:
def my_add(a: int, b: int) -> int:
return a + b# itertools.permutations() generates
# permutations for an iterable.
# Time to brute-force those passwords
('A', 'B', 'C', 'D')
('A', 'B', 'D', 'C')
('A', 'C', 'B', 'D')
('A', 'C', 'D', 'B')
('A', 'D', 'B', 'C')
('A', 'D', 'C', 'B')
('B', 'A', 'C', 'D')
('B', 'A', 'D', 'C')
('B', 'C', 'A', 'D')
('B', 'C', 'D', 'A')
('B', 'D', 'A', 'C')
('B', 'D', 'C', 'A')
('C', 'A', 'B', 'D')
('C', 'A', 'D', 'B')
('C', 'B', 'A', 'D')
('C', 'B', 'D', 'A')
('C', 'D', 'A', 'B')
('C', 'D', 'B', 'A')
('D', 'A', 'B', 'C')
('D', 'A', 'C', 'B')
('D', 'B', 'A', 'C')
('D', 'B', 'C', 'A')
('D', 'C', 'A', 'B')
('D', 'C', 'B', 'A')
# permutations for an iterable.
# Time to brute-force those passwords
>>> import itertools
>>> for p in itertools.permutations('ABCD'):
... print(p)('A', 'B', 'C', 'D')
('A', 'B', 'D', 'C')
('A', 'C', 'B', 'D')
('A', 'C', 'D', 'B')
('A', 'D', 'B', 'C')
('A', 'D', 'C', 'B')
('B', 'A', 'C', 'D')
('B', 'A', 'D', 'C')
('B', 'C', 'A', 'D')
('B', 'C', 'D', 'A')
('B', 'D', 'A', 'C')
('B', 'D', 'C', 'A')
('C', 'A', 'B', 'D')
('C', 'A', 'D', 'B')
('C', 'B', 'A', 'D')
('C', 'B', 'D', 'A')
('C', 'D', 'A', 'B')
('C', 'D', 'B', 'A')
('D', 'A', 'B', 'C')
('D', 'A', 'C', 'B')
('D', 'B', 'A', 'C')
('D', 'B', 'C', 'A')
('D', 'C', 'A', 'B')
('D', 'C', 'B', 'A')
# When To Use repr vs str?
# Emulate what the std lib does:
# Result of str should be readable:
# Result of repr should be unambiguous:
# Python interpreter sessions use
# repr to inspect objects:
# Emulate what the std lib does:
>>> import datetime
>>> today = datetime.date.today()
# Result of str should be readable:
>>> str(today)
'2020-04-27’# Result of repr should be unambiguous:
>>> repr(today)
'datetime.date(2020,4,27)’# Python interpreter sessions use
# repr to inspect objects:
>>> today
datetime.date(2020,4,27)