A comparison operator in Python (also called a Python relational operator), looks at the values of two operands and returns a boolean True
or False
if the comparison
condition is or is not met.
The table below shows the most common Python comparison operators:
Operator  Operation  Description 

> 
"greater than" 
a > b is True if a is strictly greater in value than b

< 
"less than" 
a < b is True if a is strictly less in value than b

== 
"equal to" 
a == b is True if a is strictly equal to b in value 
>= 
"greater than or equal to" 
a >= b is True if a > b OR a == b in value 
<= 
"less than or equal to" 
a <= b is True if a < b or a == b in value 
!= 
"not equal to" 
a != b is True if a == b is False

is 
"identity" 
a is b is True if and only if a and b are the same object

is not 
"negated identity" 
a is not b is True if a and b are not the same object

in 
"containment test" 
a in b is True if a is member, subset, or element of b

not in 
"negated containment test" 
a not in b is True if a is not a member, subset, or element of b

They all have the same priority (which is higher than that of Boolean operations, but lower than that of arithmetic or bitwise operations).
Objects that are different types (except numeric types) never compare equal by default.
Nonidentical instances of a class
will also not compare as equal unless the class
defines special rich comparison methods that customize the default object
comparison behavior.
For (much) more detail, see Value comparisons in the Python documentation.
Numeric types are (mostly) an exception to this type matching rule.
An integer
can be considered equal to a float
(or an octal
equal to a hexadecimal
), as long as the types can be implicitly converted for comparison.
For the other numeric types (complex, decimal, fractions), comparison operators are defined where they "make sense" (where implicit conversion does not change the outcome), but throw a TypeError
if the underlying objects cannot be accurately converted for comparison.
For more information on the rules that Python uses for numeric conversion, see arithmetic conversions in the Python documentation.
>>> import fractions
# A string cannot be converted to an int.
>>> 17 == '17'
False
# An int can be converted to float for comparison.
>>> 17 == 17.0
True
# The fraction 6/3 can be converted to the int 2
# The int 2 can be converted to 0b10 in binary.
>>> 6/3 == 0b10
True
# An int can be converted to a complex
# number with a 0 imaginary part.
>>> 17 == complex(17)
True
# The fraction 2/5 can be converted to the float 0.4
>>> 0.4 == 2/5
True
>>> complex(2/5, 1/2) == complex(0.4, 0.5)
True
Any ordered comparison of a number to a NaN
(not a number) type is False
.
A confusing side effect of Python's NaN
definition is that NaN
never compares equal to NaN
.
If you are curious as to why NaN
was defined this way in Python, this Stack Overflow Post on NaN around the setting of the international standard is an interesting read.
>>> x = float('NaN')
>>> 3 < x
False
>>> x < 3
False
# NaN never compares equal to NaN
>>> x == x
False
Strings (str
) are compared lexicographically, using their individual Unicode code points (the result of passing each code point in the str
to the builtin function ord()
, which returns an int
).
If all code points in both strings match and are in the same order, the two strings are considered equal.
This comparison is done in a 'pairwise' fashion  firsttofirst, secondtosecond, etc.
In Python 3.x, str
and bytes
cannot be directly coerced/compared.
>>> 'Python' > 'Rust'
False
>>> 'Python' > 'JavaScript'
True
# Examples with Mandarin.
# hello < goodbye
>>> '你好' < '再见'
True
# ord() of first characters
>>> ord('你'), ord('再')
(20320, 20877)
# ord() of second characters
>>> ord('好'), ord('见')
(22909, 35265)
# And with Korean words.
# Pretty < beautiful.
>>> '예쁜' < '아름다운'
False
>>> ord('예'), ord('아')
(50696, 50500)
Container data types (lists
, tuples
, sets
, dicts
, etc.) also compare lexicographically  they are equal if both containers have the same data and the same data types (in the case of lists
and tuples
, they must also have the same ordering), unequal otherwise.
>>> [1, 2] == [1, 2]
True
# But if the data is not in the same order, they are not equal.
>>> [2, 1] == [1, 2]
False
# The same holds true for tuples
>>> (3,4,5) == (5,4,3)
False
# Length is also compared
>>> [1, 2] < [1, 2, 3]
True
# Comparing dicts
>>> {'name': 'John', 'age': 19} == {'name': 'John', 'age': 18}
False
>>> {'name': 'John', 'age': 19} == {'name': 'John', 'age': 19}
True
Comparison operators can be chained arbitrarily.
Note that the evaluation of an expression takes place from left
to right
.
For example, x < y <= z
is equivalent to x < y
and
y <= z
, except that y
is evaluated only once.
In both cases, z
is not evaluated at all when x < y
is found to be False
.
This is often called shortcircuit evaluation
 the evaluation stops if the truth value of the expression has already been determined.
Short circuiting
is supported by various boolean operators, functions, and also by comparison chaining in Python.
Unlike many other programming languages, including C
, C++
, C#
, and Java
, chained expressions like a < b < c
in Python have a conventional mathematical interpretation and precedence.
>>> x = 2
>>> y = 5
>>> z = 10
>>> x < y < z
True
>>> x < y > z
False
>>> x > y < z
False
The operators is
and is not
test for object identity, as opposed to object value.
An object's identity never changes after creation and can be found by using the id()
function.
<apple> is <orange>
evaluates to True
if and only if id(<apple>)
== id(<orange>)
.
<apple> is not <orange>
yields the inverse.
Due to their singleton status, None
and NotImplemented
should always be compared to items using is
and is not
.
See the Python reference docs on value comparisons and PEP8 for more details on this convention.
>>>
# A list of favorite numbers.
>>> my_fav_numbers = [1, 2, 3]
>>> your_fav_numbers = my_fav_numbers
>>> my_fav_numbers is your_fav_numbers
True
# The returned id will differ by system and Python version.
>>> id(my_fav_numbers)
4517478208
# your_fav_numbers is only an alias pointing to the original my_fav_numbers object.
# Assigning a new name does not create a new object.
>>> id(your_fav_numbers)
4517478208
>>> my_fav_numbers is not your_fav_numbers
False
>>> my_fav_numbers is not None
True
>>> my_fav_numbers is NotImplemented
False
The operators in
and not in
test for membership.
<fish> in <soup>
evaluates to True
if <fish>
is a member of <soup>
(if <fish>
is a subset of or is contained within <soup>
), and evaluates False
otherwise.
<fish> not in <soup>
returns the negation, or opposite of <fish> in <soup>
.
For string and bytes types, <name> in <fullname>
is True
if and only if <name>
is a substring of <fullname>
.
>>>
# A set of lucky numbers.
>>> lucky_numbers = {11, 22, 33}
>>> 22 in lucky_numbers
True
>>> 44 in lucky_numbers
False
# A dictionary of employee information.
>>> employee = {'name': 'John Doe', 'id': 67826, 'age': 33, 'title': 'ceo'}
# Checking for the membership of certain keys.
>>> 'age' in employee
True
>>> 33 in employee
False
>>> 'lastname' not in employee
True
# Checking for substring membership
>>> name = 'Super Batman'
>>> 'Bat' in name
True
>>> 'Batwoman' in name
False
Comparison behavior for objects can be customized through the implementation of rich comparison methods
.
For more information, see Python Tutorial: classes, Python Classes and Magic Methods (Dan Bader), and Special method names.