Exploring and understanding Python through surprising snippets.
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Python, being a beautifully designed high-level and interpreter-based programming language, provides us with many features for the programmer's comfort. But sometimes, the outcomes of a Python snippet may not seem obvious at first sight.
Here's a fun project attempting to explain what exactly is happening under the hood for some counter-intuitive snippets and lesser-known features in Python.
While some of the examples you see below may not be WTFs in the truest sense, but they'll reveal some of the interesting parts of Python that you might be unaware of. I find it a nice way to learn the internals of a programming language, and I believe that you'll find it interesting too!
If you're an experienced Python programmer, you can take it as a challenge to get most of them right in the first attempt. You may have already experienced some of them before, and I might be able to revive sweet old memories of yours! 😅
PS: If you're a returning reader, you can learn about the new modifications here (the examples marked with asterisk are the ones added in the latest major revision).
So, here we go...
- Structure of the Examples
- Usage
- 👀 Examples
- Section: Strain your brain!
- ▶ First things first! *
- ▶ Strings can be tricky sometimes
- ▶ Be careful with chained operations
- ▶ How not to use
is
operator - ▶ Hash brownies
- ▶ Deep down, we're all the same.
- ▶ Disorder within order *
- ▶ Keep trying... *
- ▶ For what?
- ▶ Evaluation time discrepancy
- ▶
is not ...
is notis (not ...)
- ▶ A tic-tac-toe where X wins in the first attempt!
- ▶ Schrödinger's variable
- ▶ The chicken-egg problem *
- ▶ Subclass relationships
- ▶ Methods equality and identity
- ▶ All-true-ation *
- ▶ The surprising comma
- ▶ Strings and the backslashes
- ▶ not knot!
- ▶ Half triple-quoted strings
- ▶ What's wrong with booleans?
- ▶ Class attributes and instance attributes
- ▶ yielding None
- ▶ Yielding from... return! *
- ▶ Nan-reflexivity *
- ▶ Mutating the immutable!
- ▶ The disappearing variable from outer scope
- ▶ The mysterious key type conversion
- ▶ Let's see if you can guess this?
- Section: Slippery Slopes
- ▶ Modifying a dictionary while iterating over it
- ▶ Stubborn
del
operation - ▶ The out of scope variable
- ▶ Deleting a list item while iterating
- ▶ Lossy zip of iterators *
- ▶ Loop variables leaking out!
- ▶ Beware of default mutable arguments!
- ▶ Catching the Exceptions
- ▶ Same operands, different story!
- ▶ Name resolution ignoring class scope
- ▶ Rounding like a banker *
- ▶ Needles in a Haystack *
- ▶ Splitsies *
- ▶ Wild imports *
- ▶ All sorted? *
- ▶ Midnight time doesn't exist?
- Section: The Hidden treasures!
- Section: Appearances are deceptive!
- Section: Miscellaneous
- Section: Strain your brain!
- Contributing
- Acknowledgements
- 🎓 License
All the examples are structured like below:
# Set up the code. # Preparation for the magic...Output (Python version(s)):
>>> triggering_statement Some unexpected output(Optional): One line describing the unexpected output.
- Brief explanation of what's happening and why is it happening.
# Set up code # More examples for further clarification (if necessary)Output (Python version(s)):
>>> trigger # some example that makes it easy to unveil the magic # some justified output
Note: All the examples are tested on Python 3.5.2 interactive interpreter, and they should work for all the Python versions unless explicitly specified before the output.
A nice way to get the most out of these examples, in my opinion, is to read them chronologically, and for every example:
- Carefully read the initial code for setting up the example. If you're an experienced Python programmer, you'll successfully anticipate what's going to happen next most of the time.
- Read the output snippets and,
- Check if the outputs are the same as you'd expect.
- Make sure if you know the exact reason behind the output being the way it is.
- If the answer is no (which is perfectly okay), take a deep breath, and read the explanation (and if you still don't understand, shout out! and create an issue here).
- If yes, give a gentle pat on your back, and you may skip to the next example.
PS: You can also read WTFPython at the command line using the pypi package,
$ pip install wtfpython -U
$ wtfpython
For some reason, the Python 3.8's "Walrus" operator (:=
) has become quite popular. Let's check it out,
1.
# Python version 3.8+
>>> a = "wtf_walrus"
>>> a
'wtf_walrus'
>>> a := "wtf_walrus"
File "<stdin>", line 1
a := "wtf_walrus"
^
SyntaxError: invalid syntax
>>> (a := "wtf_walrus") # This works though
'wtf_walrus'
>>> a
'wtf_walrus'
2 .
# Python version 3.8+
>>> a = 6, 9
>>> a
(6, 9)
>>> (a := 6, 9)
(6, 9)
>>> a
6
>>> a, b = 6, 9 # Typical unpacking
>>> a, b
(6, 9)
>>> (a, b = 16, 19) # Oops
File "<stdin>", line 1
(a, b = 16, 19)
^
SyntaxError: invalid syntax
>>> (a, b := 16, 19) # This prints out a weird 3-tuple
(6, 16, 19)
>>> a # a is still unchanged?
6
>>> b
16
Quick walrus operator refresher
The Walrus operator (:=
) was introduced in Python 3.8, it can be useful in situations where you'd want to assign values to variables within an expression.
def some_func():
# Assume some expensive computation here
# time.sleep(1000)
return 5
# So instead of,
if some_func():
print(some_func()) # Which is bad practice since computation is happening twice
# or
a = some_func()
if a:
print(a)
# Now you can concisely write
if a := some_func():
print(a)
Output (> 3.8):
5
5
5
This saved one line of code, and implicitly prevented invoking some_func
twice.
-
Unparenthesized "assignment expression" (use of walrus operator), is restricted at the top level, hence the
SyntaxError
in thea := "wtf_walrus"
statement of the first snippet. Parenthesizing it worked as expected and assigneda
. -
As usual, parenthesizing of an expression containing
=
operator is not allowed. Hence the syntax error in(a, b = 6, 9)
. -
The syntax of the Walrus operator is of the form
NAME:= expr
, whereNAME
is a valid identifier, andexpr
is a valid expression. Hence, iterable packing and unpacking are not supported which means,-
(a := 6, 9)
is equivalent to((a := 6), 9)
and ultimately(a, 9)
(wherea
's value is 6')>>> (a := 6, 9) == ((a := 6), 9) True >>> x = (a := 696, 9) >>> x (696, 9) >>> x[0] is a # Both reference same memory location True
-
Similarly,
(a, b := 16, 19)
is equivalent to(a, (b := 16), 19)
which is nothing but a 3-tuple.
-
1.
>>> a = "some_string"
>>> id(a)
140420665652016
>>> id("some" + "_" + "string") # Notice that both the ids are same.
140420665652016
2.
>>> a = "wtf"
>>> b = "wtf"
>>> a is b
True
>>> a = "wtf!"
>>> b = "wtf!"
>>> a is b
False
3.
>>> a, b = "wtf!", "wtf!"
>>> a is b # All versions except 3.7.x
True
>>> a = "wtf!"; b = "wtf!"
>>> a is b # This will print True or False depending on where you're invoking it (python shell / ipython / as a script)
False
# This time in file some_file.py
a = "wtf!"
b = "wtf!"
print(a is b)
# prints True when the module is invoked!
4.
Output (< Python3.7 )
>>> 'a' * 20 is 'aaaaaaaaaaaaaaaaaaaa'
True
>>> 'a' * 21 is 'aaaaaaaaaaaaaaaaaaaaa'
False
Makes sense, right?
- The behavior in first and second snippets is due to a CPython optimization (called string interning) that tries to use existing immutable objects in some cases rather than creating a new object every time.
- After being "interned," many variables may reference the same string object in memory (saving memory thereby).
- In the snippets above, strings are implicitly interned. The decision of when to implicitly intern a string is implementation-dependent. There are some rules that can be used to guess if a string will be interned or not:
- All length 0 and length 1 strings are interned.
- Strings are interned at compile time (
'wtf'
will be interned but''.join(['w', 't', 'f'])
will not be interned) - Strings that are not composed of ASCII letters, digits or underscores, are not interned. This explains why
'wtf!'
was not interned due to!
. CPython implementation of this rule can be found here
- When
a
andb
are set to"wtf!"
in the same line, the Python interpreter creates a new object, then references the second variable at the same time. If you do it on separate lines, it doesn't "know" that there's already"wtf!"
as an object (because"wtf!"
is not implicitly interned as per the facts mentioned above). It's a compile-time optimization. This optimization doesn't apply to 3.7.x versions of CPython (check this issue for more discussion). - A compile unit in an interactive environment like IPython consists of a single statement, whereas it consists of the entire module in case of modules.
a, b = "wtf!", "wtf!"
is single statement, whereasa = "wtf!"; b = "wtf!"
are two statements in a single line. This explains why the identities are different ina = "wtf!"; b = "wtf!"
, and also explain why they are same when invoked insome_file.py
- The abrupt change in the output of the fourth snippet is due to a peephole optimization technique known as Constant folding. This means the expression
'a'*20
is replaced by'aaaaaaaaaaaaaaaaaaaa'
during compilation to save a few clock cycles during runtime. Constant folding only occurs for strings having a length of less than 21. (Why? Imagine the size of.pyc
file generated as a result of the expression'a'*10**10
). Here's the implementation source for the same. - Note: In Python 3.7, Constant folding was moved out from peephole optimizer to the new AST optimizer with some change in logic as well, so the fourth snippet doesn't work for Python 3.7. You can read more about the change here.
>>> (False == False) in [False] # makes sense
False
>>> False == (False in [False]) # makes sense
False
>>> False == False in [False] # now what?
True
>>> True is False == False
False
>>> False is False is False
True
>>> 1 > 0 < 1
True
>>> (1 > 0) < 1
False
>>> 1 > (0 < 1)
False
As per https://docs.python.org/3/reference/expressions.html#membership-test-operations
Formally, if a, b, c, ..., y, z are expressions and op1, op2, ..., opN are comparison operators, then a op1 b op2 c ... y opN z is equivalent to a op1 b and b op2 c and ... y opN z, except that each expression is evaluated at most once.
While such behavior might seem silly to you in the above examples, it's fantastic with stuff like a == b == c
and 0 <= x <= 100
.
False is False is False
is equivalent to(False is False) and (False is False)
True is False == False
is equivalent toTrue is False and False == False
and since the first part of the statement (True is False
) evaluates toFalse
, the overall expression evaluates toFalse
.1 > 0 < 1
is equivalent to1 > 0 and 0 < 1
which evaluates toTrue
.- The expression
(1 > 0) < 1
is equivalent toTrue < 1
andSo,>>> int(True) 1 >>> True + 1 #not relevant for this example, but just for fun 2
1 < 1
evaluates toFalse
The following is a very famous example present all over the internet.
1.
>>> a = 256
>>> b = 256
>>> a is b
True
>>> a = 257
>>> b = 257
>>> a is b
False
2.
>>> a = []
>>> b = []
>>> a is b
False
>>> a = tuple()
>>> b = tuple()
>>> a is b
True
3. Output
>>> a, b = 257, 257
>>> a is b
True
Output (Python 3.7.x specifically)
>>> a, b = 257, 257
>> a is b
False
The difference between is
and ==
is
operator checks if both the operands refer to the same object (i.e., it checks if the identity of the operands matches or not).==
operator compares the values of both the operands and checks if they are the same.- So
is
is for reference equality and==
is for value equality. An example to clear things up,>>> class A: pass >>> A() is A() # These are two empty objects at two different memory locations. False
256
is an existing object but 257
isn't
When you start up python the numbers from -5
to 256
will be allocated. These numbers are used a lot, so it makes sense just to have them ready.
Quoting from https://docs.python.org/3/c-api/long.html
The current implementation keeps an array of integer objects for all integers between -5 and 256, when you create an int in that range you just get back a reference to the existing object. So it should be possible to change the value of 1. I suspect the behavior of Python, in this case, is undefined. :-)
>>> id(256)
10922528
>>> a = 256
>>> b = 256
>>> id(a)
10922528
>>> id(b)
10922528
>>> id(257)
140084850247312
>>> x = 257
>>> y = 257
>>> id(x)
140084850247440
>>> id(y)
140084850247344
Here the interpreter isn't smart enough while executing y = 257
to recognize that we've already created an integer of the value 257,
and so it goes on to create another object in the memory.
Similar optimization applies to other immutable objects like empty tuples as well. Since lists are mutable, that's why [] is []
will return False
and () is ()
will return True
. This explains our second snippet. Let's move on to the third one,
Both a
and b
refer to the same object when initialized with same value in the same line.
Output
>>> a, b = 257, 257
>>> id(a)
140640774013296
>>> id(b)
140640774013296
>>> a = 257
>>> b = 257
>>> id(a)
140640774013392
>>> id(b)
140640774013488
-
When a and b are set to
257
in the same line, the Python interpreter creates a new object, then references the second variable at the same time. If you do it on separate lines, it doesn't "know" that there's already257
as an object. -
It's a compiler optimization and specifically applies to the interactive environment. When you enter two lines in a live interpreter, they're compiled separately, therefore optimized separately. If you were to try this example in a
.py
file, you would not see the same behavior, because the file is compiled all at once. This optimization is not limited to integers, it works for other immutable data types like strings (check the "Strings are tricky example") and floats as well,>>> a, b = 257.0, 257.0 >>> a is b True
-
Why didn't this work for Python 3.7? The abstract reason is because such compiler optimizations are implementation specific (i.e. may change with version, OS, etc). I'm still figuring out what exact implementation change cause the issue, you can check out this issue for updates.
1.
some_dict = {}
some_dict[5.5] = "JavaScript"
some_dict[5.0] = "Ruby"
some_dict[5] = "Python"
Output:
>>> some_dict[5.5]
"JavaScript"
>>> some_dict[5.0] # "Python" destroyed the existence of "Ruby"?
"Python"
>>> some_dict[5]
"Python"
>>> complex_five = 5 + 0j
>>> type(complex_five)
complex
>>> some_dict[complex_five]
"Python"
So, why is Python all over the place?
-
Uniqueness of keys in a Python dictionary is by equivalence, not identity. So even though
5
,5.0
, and5 + 0j
are distinct objects of different types, since they're equal, they can't both be in the samedict
(orset
). As soon as you insert any one of them, attempting to look up any distinct but equivalent key will succeed with the original mapped value (rather than failing with aKeyError
):>>> 5 == 5.0 == 5 + 0j True >>> 5 is not 5.0 is not 5 + 0j True >>> some_dict = {} >>> some_dict[5.0] = "Ruby" >>> 5.0 in some_dict True >>> (5 in some_dict) and (5 + 0j in some_dict) True
-
This applies when setting an item as well. So when you do
some_dict[5] = "Python"
, Python finds the existing item with equivalent key5.0 -> "Ruby"
, overwrites its value in place, and leaves the original key alone.>>> some_dict {5.0: 'Ruby'} >>> some_dict[5] = "Python" >>> some_dict {5.0: 'Python'}
-
So how can we update the key to
5
(instead of5.0
)? We can't actually do this update in place, but what we can do is first delete the key (del some_dict[5.0]
), and then set it (some_dict[5]
) to get the integer5
as the key instead of floating5.0
, though this should be needed in rare cases. -
How did Python find
5
in a dictionary containing5.0
? Python does this in constant time without having to scan through every item by using hash functions. When Python looks up a keyfoo
in a dict, it first computeshash(foo)
(which runs in constant-time). Since in Python it is required that objects that compare equal also have the same hash value (docs here),5
,5.0
, and5 + 0j
have the same hash value.>>> 5 == 5.0 == 5 + 0j True >>> hash(5) == hash(5.0) == hash(5 + 0j) True
Note: The inverse is not necessarily true: Objects with equal hash values may themselves be unequal. (This causes what's known as a hash collision, and degrades the constant-time performance that hashing usually provides.)
class WTF:
pass
Output:
>>> WTF() == WTF() # two different instances can't be equal
False
>>> WTF() is WTF() # identities are also different
False
>>> hash(WTF()) == hash(WTF()) # hashes _should_ be different as well
True
>>> id(WTF()) == id(WTF())
True
-
When
id
was called, Python created aWTF
class object and passed it to theid
function. Theid
function takes itsid
(its memory location), and throws away the object. The object is destroyed. -
When we do this twice in succession, Python allocates the same memory location to this second object as well. Since (in CPython)
id
uses the memory location as the object id, the id of the two objects is the same. -
So, the object's id is unique only for the lifetime of the object. After the object is destroyed, or before it is created, something else can have the same id.
-
But why did the
is
operator evaluated toFalse
? Let's see with this snippet.class WTF(object): def __init__(self): print("I") def __del__(self): print("D")
Output:
>>> WTF() is WTF() I I D D False >>> id(WTF()) == id(WTF()) I D I D True
As you may observe, the order in which the objects are destroyed is what made all the difference here.
from collections import OrderedDict
dictionary = dict()
dictionary[1] = 'a'; dictionary[2] = 'b';
ordered_dict = OrderedDict()
ordered_dict[1] = 'a'; ordered_dict[2] = 'b';
another_ordered_dict = OrderedDict()
another_ordered_dict[2] = 'b'; another_ordered_dict[1] = 'a';
class DictWithHash(dict):
"""
A dict that also implements __hash__ magic.
"""
__hash__ = lambda self: 0
class OrderedDictWithHash(OrderedDict):
"""
An OrderedDict that also implements __hash__ magic.
"""
__hash__ = lambda self: 0
Output
>>> dictionary == ordered_dict # If a == b
True
>>> dictionary == another_ordered_dict # and b == c
True
>>> ordered_dict == another_ordered_dict # then why isn't c == a ??
False
# We all know that a set consists of only unique elements,
# let's try making a set of these dictionaries and see what happens...
>>> len({dictionary, ordered_dict, another_ordered_dict})
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unhashable type: 'dict'
# Makes sense since dict don't have __hash__ implemented, let's use
# our wrapper classes.
>>> dictionary = DictWithHash()
>>> dictionary[1] = 'a'; dictionary[2] = 'b';
>>> ordered_dict = OrderedDictWithHash()
>>> ordered_dict[1] = 'a'; ordered_dict[2] = 'b';
>>> another_ordered_dict = OrderedDictWithHash()
>>> another_ordered_dict[2] = 'b'; another_ordered_dict[1] = 'a';
>>> len({dictionary, ordered_dict, another_ordered_dict})
1
>>> len({ordered_dict, another_ordered_dict, dictionary}) # changing the order
2
What is going on here?
-
The reason why intransitive equality didn't hold among
dictionary
,ordered_dict
andanother_ordered_dict
is because of the way__eq__
method is implemented inOrderedDict
class. From the docsEquality tests between OrderedDict objects are order-sensitive and are implemented as
list(od1.items())==list(od2.items())
. Equality tests betweenOrderedDict
objects and other Mapping objects are order-insensitive like regular dictionaries. -
The reason for this equality in behavior is that it allows
OrderedDict
objects to be directly substituted anywhere a regular dictionary is used. -
Okay, so why did changing the order affect the length of the generated
set
object? The answer is the lack of intransitive equality only. Since sets are "unordered" collections of unique elements, the order in which elements are inserted shouldn't matter. But in this case, it does matter. Let's break it down a bit,>>> some_set = set() >>> some_set.add(dictionary) # these are the mapping objects from the snippets above >>> ordered_dict in some_set True >>> some_set.add(ordered_dict) >>> len(some_set) 1 >>> another_ordered_dict in some_set True >>> some_set.add(another_ordered_dict) >>> len(some_set) 1 >>> another_set = set() >>> another_set.add(ordered_dict) >>> another_ordered_dict in another_set False >>> another_set.add(another_ordered_dict) >>> len(another_set) 2 >>> dictionary in another_set True >>> another_set.add(another_ordered_dict) >>> len(another_set) 2
So the inconsistency is due to
another_ordered_dict in another_set
beingFalse
becauseordered_dict
was already present inanother_set
and as observed before,ordered_dict == another_ordered_dict
isFalse
.
def some_func():
try:
return 'from_try'
finally:
return 'from_finally'
def another_func():
for _ in range(3):
try:
continue
finally:
print("Finally!")
def one_more_func(): # A gotcha!
try:
for i in range(3):
try:
1 / i
except ZeroDivisionError:
# Let's throw it here and handle it outside for loop
raise ZeroDivisionError("A trivial divide by zero error")
finally:
print("Iteration", i)
break
except ZeroDivisionError as e:
print("Zero division error occurred", e)
Output:
>>> some_func()
'from_finally'
>>> another_func()
Finally!
Finally!
Finally!
>>> 1 / 0
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ZeroDivisionError: division by zero
>>> one_more_func()
Iteration 0
- When a
return
,break
orcontinue
statement is executed in thetry
suite of a "try…finally" statement, thefinally
clause is also executed on the way out. - The return value of a function is determined by the last
return
statement executed. Since thefinally
clause always executes, areturn
statement executed in thefinally
clause will always be the last one executed. - The caveat here is, if the finally clause executes a
return
orbreak
statement, the temporarily saved exception is discarded.
some_string = "wtf"
some_dict = {}
for i, some_dict[i] in enumerate(some_string):
i = 10
Output:
>>> some_dict # An indexed dict appears.
{0: 'w', 1: 't', 2: 'f'}
-
A
for
statement is defined in the Python grammar as:for_stmt: 'for' exprlist 'in' testlist ':' suite ['else' ':' suite]
Where
exprlist
is the assignment target. This means that the equivalent of{exprlist} = {next_value}
is executed for each item in the iterable. An interesting example that illustrates this:for i in range(4): print(i) i = 10
Output:
0 1 2 3
Did you expect the loop to run just once?
💡 Explanation:
- The assignment statement
i = 10
never affects the iterations of the loop because of the way for loops work in Python. Before the beginning of every iteration, the next item provided by the iterator (range(4)
in this case) is unpacked and assigned the target list variables (i
in this case).
- The assignment statement
-
The
enumerate(some_string)
function yields a new valuei
(a counter going up) and a character from thesome_string
in each iteration. It then sets the (just assigned)i
key of the dictionarysome_dict
to that character. The unrolling of the loop can be simplified as:>>> i, some_dict[i] = (0, 'w') >>> i, some_dict[i] = (1, 't') >>> i, some_dict[i] = (2, 'f') >>> some_dict
1.
array = [1, 8, 15]
# A typical generator expression
gen = (x for x in array if array.count(x) > 0)
array = [2, 8, 22]
Output:
>>> print(list(gen)) # Where did the other values go?
[8]
2.
array_1 = [1,2,3,4]
gen_1 = (x for x in array_1)
array_1 = [1,2,3,4,5]
array_2 = [1,2,3,4]
gen_2 = (x for x in array_2)
array_2[:] = [1,2,3,4,5]
Output:
>>> print(list(gen_1))
[1, 2, 3, 4]
>>> print(list(gen_2))
[1, 2, 3, 4, 5]
3.
array_3 = [1, 2, 3]
array_4 = [10, 20, 30]
gen = (i + j for i in array_3 for j in array_4)
array_3 = [4, 5, 6]
array_4 = [400, 500, 600]
Output:
>>> print(list(gen))
[401, 501, 601, 402, 502, 602, 403, 503, 603]
-
In a generator expression, the
in
clause is evaluated at declaration time, but the conditional clause is evaluated at runtime. -
So before runtime,
array
is re-assigned to the list[2, 8, 22]
, and since out of1
,8
and15
, only the count of8
is greater than0
, the generator only yields8
. -
The differences in the output of
g1
andg2
in the second part is due the way variablesarray_1
andarray_2
are re-assigned values. -
In the first case,
array_1
is binded to the new object[1,2,3,4,5]
and since thein
clause is evaluated at the declaration time it still refers to the old object[1,2,3,4]
(which is not destroyed). -
In the second case, the slice assignment to
array_2
updates the same old object[1,2,3,4]
to[1,2,3,4,5]
. Hence both theg2
andarray_2
still have reference to the same object (which has now been updated to[1,2,3,4,5]
). -
Okay, going by the logic discussed so far, shouldn't be the value of
list(gen)
in the third snippet be[11, 21, 31, 12, 22, 32, 13, 23, 33]
? (becausearray_3
andarray_4
are going to behave just likearray_1
). The reason why (only)array_4
values got updated is explained in PEP-289Only the outermost for-expression is evaluated immediately, the other expressions are deferred until the generator is run.
>>> 'something' is not None
True
>>> 'something' is (not None)
False
is not
is a single binary operator, and has behavior different than usingis
andnot
separated.is not
evaluates toFalse
if the variables on either side of the operator point to the same object andTrue
otherwise.- In the example,
(not None)
evaluates toTrue
since the valueNone
isFalse
in a boolean context, so the expression becomes'something' is True
.
# Let's initialize a row
row = [""] * 3 #row i['', '', '']
# Let's make a board
board = [row] * 3
Output:
>>> board
[['', '', ''], ['', '', ''], ['', '', '']]
>>> board[0]
['', '', '']
>>> board[0][0]
''
>>> board[0][0] = "X"
>>> board
[['X', '', ''], ['X', '', ''], ['X', '', '']]
We didn't assign three "X"
s, did we?
When we initialize row
variable, this visualization explains what happens in the memory
And when the board
is initialized by multiplying the row
, this is what happens inside the memory (each of the elements board[0]
, board[1]
and board[2]
is a reference to the same list referred by row
)
We can avoid this scenario here by not using row
variable to generate board
. (Asked in this issue).
>>> board = [['']*3 for _ in range(3)]
>>> board[0][0] = "X"
>>> board
[['X', '', ''], ['', '', ''], ['', '', '']]
funcs = []
results = []
for x in range(7):
def some_func():
return x
funcs.append(some_func)
results.append(some_func()) # note the function call here
funcs_results = [func() for func in funcs]
Output (Python version):
>>> results
[0, 1, 2, 3, 4, 5, 6]
>>> funcs_results
[6, 6, 6, 6, 6, 6, 6]
The values of x
were different in every iteration prior to appending some_func
to funcs
, but all the functions return 6 when they're evaluated after the loop completes.
>>> powers_of_x = [lambda x: x**i for i in range(10)]
>>> [f(2) for f in powers_of_x]
[512, 512, 512, 512, 512, 512, 512, 512, 512, 512]
- When defining a function inside a loop that uses the loop variable in its body, the loop function's closure is bound to the variable, not its value. The function looks up
x
in the surrounding context, rather than using the value ofx
at the time the function is created. So all of the functions use the latest value assigned to the variable for computation. We can see that it's using thex
from the surrounding context (i.e. not a local variable) with:
>>> import inspect
>>> inspect.getclosurevars(funcs[0])
ClosureVars(nonlocals={}, globals={'x': 6}, builtins={}, unbound=set())
Since x
is a global value, we can change the value that the funcs
will lookup and return by updating x
:
>>> x = 42
>>> [func() for func in funcs]
[42, 42, 42, 42, 42, 42, 42]
- To get the desired behavior you can pass in the loop variable as a named variable to the function. Why does this work? Because this will define the variable inside the function's scope. It will no longer go to the surrounding (global) scope to look up the variables value but will create a local variable that stores the value of
x
at that point in time.
funcs = []
for x in range(7):
def some_func(x=x):
return x
funcs.append(some_func)
Output:
>>> funcs_results = [func() for func in funcs]
>>> funcs_results
[0, 1, 2, 3, 4, 5, 6]
It is not longer using the x
in the global scope:
>>> inspect.getclosurevars(funcs[0])
ClosureVars(nonlocals={}, globals={}, builtins={}, unbound=set())
1.
>>> isinstance(3, int)
True
>>> isinstance(type, object)
True
>>> isinstance(object, type)
True
So which is the "ultimate" base class? There's more to the confusion by the way,
2.
>>> class A: pass
>>> isinstance(A, A)
False
>>> isinstance(type, type)
True
>>> isinstance(object, object)
True
3.
>>> issubclass(int, object)
True
>>> issubclass(type, object)
True
>>> issubclass(object, type)
False
type
is a metaclass in Python.- Everything is an
object
in Python, which includes classes as well as their objects (instances). - class
type
is the metaclass of classobject
, and every class (includingtype
) has inherited directly or indirectly fromobject
. - There is no real base class among
object
andtype
. The confusion in the above snippets is arising because we're thinking about these relationships (issubclass
andisinstance
) in terms of Python classes. The relationship betweenobject
andtype
can't be reproduced in pure python. To be more precise the following relationships can't be reproduced in pure Python,- class A is an instance of class B, and class B is an instance of class A.
- class A is an instance of itself.
- These relationships between
object
andtype
(both being instances of each other as well as themselves) exist in Python because of "cheating" at the implementation level.
Output:
>>> from collections import Hashable
>>> issubclass(list, object)
True
>>> issubclass(object, Hashable)
True
>>> issubclass(list, Hashable)
False
The Subclass relationships were expected to be transitive, right? (i.e., if A
is a subclass of B
, and B
is a subclass of C
, the A
should a subclass of C
)
- Subclass relationships are not necessarily transitive in Python. Anyone is allowed to define their own, arbitrary
__subclasscheck__
in a metaclass. - When
issubclass(cls, Hashable)
is called, it simply looks for non-Falsey "__hash__
" method incls
or anything it inherits from. - Since
object
is hashable, butlist
is non-hashable, it breaks the transitivity relation. - More detailed explanation can be found here.
class SomeClass:
def method(self):
pass
@classmethod
def classm(cls):
pass
@staticmethod
def staticm():
pass
Output:
>>> print(SomeClass.method is SomeClass.method)
True
>>> print(SomeClass.classm is SomeClass.classm)
False
>>> print(SomeClass.classm == SomeClass.classm)
True
>>> print(SomeClass.staticm is SomeClass.staticm)
True
Accessing classm
twice, we get an equal object, but not the same one? Let's see what happens
with instances of SomeClass
:
o1 = SomeClass()
o2 = SomeClass()
Output:
>>> print(o1.method == o2.method)
False
>>> print(o1.method == o1.method)
True
>>> print(o1.method is o1.method)
False
>>> print(o1.classm is o1.classm)
False
>>> print(o1.classm == o1.classm == o2.classm == SomeClass.classm)
True
>>> print(o1.staticm is o1.staticm is o2.staticm is SomeClass.staticm)
True
Accessing classm
or method
twice, creates equal but not same objects for the same instance of SomeClass
.
- Functions are descriptors. Whenever a function is accessed as an
attribute, the descriptor is invoked, creating a method object which "binds" the function with the object owning the
attribute. If called, the method calls the function, implicitly passing the bound object as the first argument
(this is how we get
self
as the first argument, despite not passing it explicitly).
>>> o1.method
<bound method SomeClass.method of <__main__.SomeClass object at ...>>
- Accessing the attribute multiple times creates a method object every time! Therefore
o1.method is o1.method
is never truthy. Accessing functions as class attributes (as opposed to instance) does not create methods, however; soSomeClass.method is SomeClass.method
is truthy.
>>> SomeClass.method
<function SomeClass.method at ...>
classmethod
transforms functions into class methods. Class methods are descriptors that, when accessed, create a method object which binds the class (type) of the object, instead of the object itself.
>>> o1.classm
<bound method SomeClass.classm of <class '__main__.SomeClass'>>
- Unlike functions,
classmethod
s will create a method also when accessed as class attributes (in which case they bind the class, not to the type of it). SoSomeClass.classm is SomeClass.classm
is falsy.
>>> SomeClass.classm
<bound method SomeClass.classm of <class '__main__.SomeClass'>>
- A method object compares equal when both the functions are equal, and the bound objects are the same. So
o1.method == o1.method
is truthy, although not the same object in memory. staticmethod
transforms functions into a "no-op" descriptor, which returns the function as-is. No method objects are ever created, so comparison withis
is truthy.
>>> o1.staticm
<function SomeClass.staticm at ...>
>>> SomeClass.staticm
<function SomeClass.staticm at ...>
- Having to create new "method" objects every time Python calls instance methods and having to modify the arguments
every time in order to insert
self
affected performance badly. CPython 3.7 solved it by introducing new opcodes that deal with calling methods without creating the temporary method objects. This is used only when the accessed function is actually called, so the snippets here are not affected, and still generate methods :)
>>> all([True, True, True])
True
>>> all([True, True, False])
False
>>> all([])
True
>>> all([[]])
False
>>> all([[[]]])
True
Why's this True-False alteration?
-
The implementation of
all
function is equivalent to -
def all(iterable): for element in iterable: if not element: return False return True
-
all([])
returnsTrue
since the iterable is empty. -
all([[]])
returnsFalse
because the passed array has one element,[]
, and in python, an empty list is falsy. -
all([[[]]])
and higher recursive variants are alwaysTrue
. This is because the passed array's single element ([[...]]
) is no longer empty, and lists with values are truthy.
Output (< 3.6):
>>> def f(x, y,):
... print(x, y)
...
>>> def g(x=4, y=5,):
... print(x, y)
...
>>> def h(x, **kwargs,):
File "<stdin>", line 1
def h(x, **kwargs,):
^
SyntaxError: invalid syntax
>>> def h(*args,):
File "<stdin>", line 1
def h(*args,):
^
SyntaxError: invalid syntax
- Trailing comma is not always legal in formal parameters list of a Python function.
- In Python, the argument list is defined partially with leading commas and partially with trailing commas. This conflict causes situations where a comma is trapped in the middle, and no rule accepts it.
- Note: The trailing comma problem is fixed in Python 3.6. The remarks in this post discuss in brief different usages of trailing commas in Python.
Output:
>>> print("\"")
"
>>> print(r"\"")
\"
>>> print(r"\")
File "<stdin>", line 1
print(r"\")
^
SyntaxError: EOL while scanning string literal
>>> r'\'' == "\\'"
True
- In a usual python string, the backslash is used to escape characters that may have a special meaning (like single-quote, double-quote, and the backslash itself).
>>> "wt\"f" 'wt"f'
- In a raw string literal (as indicated by the prefix
r
), the backslashes pass themselves as is along with the behavior of escaping the following character.>>> r'wt\"f' == 'wt\\"f' True >>> print(repr(r'wt\"f') 'wt\\"f' >>> print("\n") >>> print(r"\\n") '\\n'
- This means when a parser encounters a backslash in a raw string, it expects another character following it. And in our case (
print(r"\")
), the backslash escaped the trailing quote, leaving the parser without a terminating quote (hence theSyntaxError
). That's why backslashes don't work at the end of a raw string.
x = True
y = False
Output:
>>> not x == y
True
>>> x == not y
File "<input>", line 1
x == not y
^
SyntaxError: invalid syntax
- Operator precedence affects how an expression is evaluated, and
==
operator has higher precedence thannot
operator in Python. - So
not x == y
is equivalent tonot (x == y)
which is equivalent tonot (True == False)
finally evaluating toTrue
. - But
x == not y
raises aSyntaxError
because it can be thought of being equivalent to(x == not) y
and notx == (not y)
which you might have expected at first sight. - The parser expected the
not
token to be a part of thenot in
operator (because both==
andnot in
operators have the same precedence), but after not being able to find anin
token following thenot
token, it raises aSyntaxError
.
Output:
>>> print('wtfpython''')
wtfpython
>>> print("wtfpython""")
wtfpython
>>> # The following statements raise `SyntaxError`
>>> # print('''wtfpython')
>>> # print("""wtfpython")
File "<input>", line 3
print("""wtfpython")
^
SyntaxError: EOF while scanning triple-quoted string literal
- Python supports implicit string literal concatenation, Example,
>>> print("wtf" "python") wtfpython >>> print("wtf" "") # or "wtf""" wtf
'''
and"""
are also string delimiters in Python which causes a SyntaxError because the Python interpreter was expecting a terminating triple quote as delimiter while scanning the currently encountered triple quoted string literal.
1.
# A simple example to count the number of booleans and
# integers in an iterable of mixed data types.
mixed_list = [False, 1.0, "some_string", 3, True, [], False]
integers_found_so_far = 0
booleans_found_so_far = 0
for item in mixed_list:
if isinstance(item, int):
integers_found_so_far += 1
elif isinstance(item, bool):
booleans_found_so_far += 1
Output:
>>> integers_found_so_far
4
>>> booleans_found_so_far
0
2.
>>> some_bool = True
>>> "wtf" * some_bool
'wtf'
>>> some_bool = False
>>> "wtf" * some_bool
''
3.
def tell_truth():
True = False
if True == False:
print("I have lost faith in truth!")
Output (< 3.x):
>>> tell_truth()
I have lost faith in truth!
-
bool
is a subclass ofint
in Python>>> issubclass(bool, int) True >>> issubclass(int, bool) False
-
And thus,
True
andFalse
are instances ofint
>>> isinstance(True, int) True >>> isinstance(False, int) True
-
The integer value of
True
is1
and that ofFalse
is0
.>>> int(True) 1 >>> int(False) 0
-
See this StackOverflow answer for the rationale behind it.
-
Initially, Python used to have no
bool
type (people used 0 for false and non-zero value like 1 for true).True
,False
, and abool
type was added in 2.x versions, but, for backward compatibility,True
andFalse
couldn't be made constants. They just were built-in variables, and it was possible to reassign them -
Python 3 was backward-incompatible, the issue was finally fixed, and thus the last snippet won't work with Python 3.x!
1.
class A:
x = 1
class B(A):
pass
class C(A):
pass
Output:
>>> A.x, B.x, C.x
(1, 1, 1)
>>> B.x = 2
>>> A.x, B.x, C.x
(1, 2, 1)
>>> A.x = 3
>>> A.x, B.x, C.x # C.x changed, but B.x didn't
(3, 2, 3)
>>> a = A()
>>> a.x, A.x
(3, 3)
>>> a.x += 1
>>> a.x, A.x
(4, 3)
2.
class SomeClass:
some_var = 15
some_list = [5]
another_list = [5]
def __init__(self, x):
self.some_var = x + 1
self.some_list = self.some_list + [x]
self.another_list += [x]
Output:
>>> some_obj = SomeClass(420)
>>> some_obj.some_list
[5, 420]
>>> some_obj.another_list
[5, 420]
>>> another_obj = SomeClass(111)
>>> another_obj.some_list
[5, 111]
>>> another_obj.another_list
[5, 420, 111]
>>> another_obj.another_list is SomeClass.another_list
True
>>> another_obj.another_list is some_obj.another_list
True
- Class variables and variables in class instances are internally handled as dictionaries of a class object. If a variable name is not found in the dictionary of the current class, the parent classes are searched for it.
- The
+=
operator modifies the mutable object in-place without creating a new object. So changing the attribute of one instance affects the other instances and the class attribute as well.
some_iterable = ('a', 'b')
def some_func(val):
return "something"
Output (<= 3.7.x):
>>> [x for x in some_iterable]
['a', 'b']
>>> [(yield x) for x in some_iterable]
<generator object <listcomp> at 0x7f70b0a4ad58>
>>> list([(yield x) for x in some_iterable])
['a', 'b']
>>> list((yield x) for x in some_iterable)
['a', None, 'b', None]
>>> list(some_func((yield x)) for x in some_iterable)
['a', 'something', 'b', 'something']
- This is a bug in CPython's handling of
yield
in generators and comprehensions. - Source and explanation can be found here: https://stackoverflow.com/questions/32139885/yield-in-list-comprehensions-and-generator-expressions
- Related bug report: https://bugs.python.org/issue10544
- Python 3.8+ no longer allows
yield
inside list comprehension and will throw aSyntaxError
.
1.
def some_func(x):
if x == 3:
return ["wtf"]
else:
yield from range(x)
Output (> 3.3):
>>> list(some_func(3))
[]
Where did the "wtf"
go? Is it due to some special effect of yield from
? Let's validate that,
2.
def some_func(x):
if x == 3:
return ["wtf"]
else:
for i in range(x):
yield i
Output:
>>> list(some_func(3))
[]
The same result, this didn't work either.
- From Python 3.3 onwards, it became possible to use
return
statement with values inside generators (See PEP380). The official docs say that,
"...
return expr
in a generator causesStopIteration(expr)
to be raised upon exit from the generator."
-
In the case of
some_func(3)
,StopIteration
is raised at the beginning because ofreturn
statement. TheStopIteration
exception is automatically caught inside thelist(...)
wrapper and thefor
loop. Therefore, the above two snippets result in an empty list. -
To get
["wtf"]
from the generatorsome_func
we need to catch theStopIteration
exception,try: next(some_func(3)) except StopIteration as e: some_string = e.value
>>> some_string ["wtf"]
1.
a = float('inf')
b = float('nan')
c = float('-iNf') # These strings are case-insensitive
d = float('nan')
Output:
>>> a
inf
>>> b
nan
>>> c
-inf
>>> float('some_other_string')
ValueError: could not convert string to float: some_other_string
>>> a == -c # inf==inf
True
>>> None == None # None == None
True
>>> b == d # but nan!=nan
False
>>> 50 / a
0.0
>>> a / a
nan
>>> 23 + b
nan
2.
>>> x = float('nan')
>>> y = x / x
>>> y is y # identity holds
True
>>> y == y # equality fails of y
False
>>> [y] == [y] # but the equality succeeds for the list containing y
True
-
'inf'
and'nan'
are special strings (case-insensitive), which, when explicitly typecast-ed tofloat
type, are used to represent mathematical "infinity" and "not a number" respectively. -
Since according to IEEE standards
NaN != NaN
, obeying this rule breaks the reflexivity assumption of a collection element in Python i.e. ifx
is a part of a collection likelist
, the implementations like comparison are based on the assumption thatx == x
. Because of this assumption, the identity is compared first (since it's faster) while comparing two elements, and the values are compared only when the identities mismatch. The following snippet will make things clearer,>>> x = float('nan') >>> x == x, [x] == [x] (False, True) >>> y = float('nan') >>> y == y, [y] == [y] (False, True) >>> x == y, [x] == [y] (False, False)
Since the identities of
x
andy
are different, the values are considered, which are also different; hence the comparison returnsFalse
this time. -
Interesting read: Reflexivity, and other pillars of civilization
This might seem trivial if you know how references work in Python.
some_tuple = ("A", "tuple", "with", "values")
another_tuple = ([1, 2], [3, 4], [5, 6])
Output:
>>> some_tuple[2] = "change this"
TypeError: 'tuple' object does not support item assignment
>>> another_tuple[2].append(1000) #This throws no error
>>> another_tuple
([1, 2], [3, 4], [5, 6, 1000])
>>> another_tuple[2] += [99, 999]
TypeError: 'tuple' object does not support item assignment
>>> another_tuple
([1, 2], [3, 4], [5, 6, 1000, 99, 999])
But I thought tuples were immutable...
-
Quoting from https://docs.python.org/3/reference/datamodel.html
Immutable sequences An object of an immutable sequence type cannot change once it is created. (If the object contains references to other objects, these other objects may be mutable and may be modified; however, the collection of objects directly referenced by an immutable object cannot change.)
-
+=
operator changes the list in-place. The item assignment doesn't work, but when the exception occurs, the item has already been changed in place. -
There's also an explanation in official Python FAQ.
e = 7
try:
raise Exception()
except Exception as e:
pass
Output (Python 2.x):
>>> print(e)
# prints nothing
Output (Python 3.x):
>>> print(e)
NameError: name 'e' is not defined
-
Source: https://docs.python.org/3/reference/compound_stmts.html#except
When an exception has been assigned using
as
target, it is cleared at the end of theexcept
clause. This is as ifexcept E as N: foo
was translated into
except E as N: try: foo finally: del N
This means the exception must be assigned to a different name to be able to refer to it after the except clause. Exceptions are cleared because, with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs.
-
The clauses are not scoped in Python. Everything in the example is present in the same scope, and the variable
e
got removed due to the execution of theexcept
clause. The same is not the case with functions that have their separate inner-scopes. The example below illustrates this:def f(x): del(x) print(x) x = 5 y = [5, 4, 3]
Output:
>>>f(x) UnboundLocalError: local variable 'x' referenced before assignment >>>f(y) UnboundLocalError: local variable 'x' referenced before assignment >>> x 5 >>> y [5, 4, 3]
-
In Python 2.x, the variable name
e
gets assigned toException()
instance, so when you try to print, it prints nothing.Output (Python 2.x):
>>> e Exception() >>> print e # Nothing is printed!
class SomeClass(str):
pass
some_dict = {'s': 42}
Output:
>>> type(list(some_dict.keys())[0])
str
>>> s = SomeClass('s')
>>> some_dict[s] = 40
>>> some_dict # expected: Two different keys-value pairs
{'s': 40}
>>> type(list(some_dict.keys())[0])
str
-
Both the object
s
and the string"s"
hash to the same value becauseSomeClass
inherits the__hash__
method ofstr
class. -
SomeClass("s") == "s"
evaluates toTrue
becauseSomeClass
also inherits__eq__
method fromstr
class. -
Since both the objects hash to the same value and are equal, they are represented by the same key in the dictionary.
-
For the desired behavior, we can redefine the
__eq__
method inSomeClass
class SomeClass(str): def __eq__(self, other): return ( type(self) is SomeClass and type(other) is SomeClass and super().__eq__(other) ) # When we define a custom __eq__, Python stops automatically inheriting the # __hash__ method, so we need to define it as well __hash__ = str.__hash__ some_dict = {'s':42}
Output:
>>> s = SomeClass('s') >>> some_dict[s] = 40 >>> some_dict {'s': 40, 's': 42} >>> keys = list(some_dict.keys()) >>> type(keys[0]), type(keys[1]) (__main__.SomeClass, str)
a, b = a[b] = {}, 5
Output:
>>> a
{5: ({...}, 5)}
- According to Python language reference, assignment statements have the form
and
(target_list "=")+ (expression_list | yield_expression)
An assignment statement evaluates the expression list (remember that this can be a single expression or a comma-separated list, the latter yielding a tuple) and assigns the single resulting object to each of the target lists, from left to right.
-
The
+
in(target_list "=")+
means there can be one or more target lists. In this case, target lists area, b
anda[b]
(note the expression list is exactly one, which in our case is{}, 5
). -
After the expression list is evaluated, its value is unpacked to the target lists from left to right. So, in our case, first the
{}, 5
tuple is unpacked toa, b
and we now havea = {}
andb = 5
. -
a
is now assigned to{}
, which is a mutable object. -
The second target list is
a[b]
(you may expect this to throw an error because botha
andb
have not been defined in the statements before. But remember, we just assigneda
to{}
andb
to5
). -
Now, we are setting the key
5
in the dictionary to the tuple({}, 5)
creating a circular reference (the{...}
in the output refers to the same object thata
is already referencing). Another simpler example of circular reference could be>>> some_list = some_list[0] = [0] >>> some_list [[...]] >>> some_list[0] [[...]] >>> some_list is some_list[0] True >>> some_list[0][0][0][0][0][0] == some_list True
Similar is the case in our example (
a[b][0]
is the same object asa
) -
So to sum it up, you can break the example down to
a, b = {}, 5 a[b] = a, b
And the circular reference can be justified by the fact that
a[b][0]
is the same object asa
>>> a[b][0] is a True
x = {0: None}
for i in x:
del x[i]
x[i+1] = None
print(i)
Output (Python 2.7- Python 3.5):
0
1
2
3
4
5
6
7
Yes, it runs for exactly eight times and stops.
- Iteration over a dictionary that you edit at the same time is not supported.
- It runs eight times because that's the point at which the dictionary resizes to hold more keys (we have eight deletion entries, so a resize is needed). This is actually an implementation detail.
- How deleted keys are handled and when the resize occurs might be different for different Python implementations.
- So for Python versions other than Python 2.7 - Python 3.5, the count might be different from 8 (but whatever the count is, it's going to be the same every time you run it). You can find some discussion around this here or in this StackOverflow thread.
- Python 3.7.6 onwards, you'll see
RuntimeError: dictionary keys changed during iteration
exception if you try to do this.
class SomeClass:
def __del__(self):
print("Deleted!")
Output: 1.
>>> x = SomeClass()
>>> y = x
>>> del x # this should print "Deleted!"
>>> del y
Deleted!
Phew, deleted at last. You might have guessed what saved __del__
from being called in our first attempt to delete x
. Let's add more twists to the example.
2.
>>> x = SomeClass()
>>> y = x
>>> del x
>>> y # check if y exists
<__main__.SomeClass instance at 0x7f98a1a67fc8>
>>> del y # Like previously, this should print "Deleted!"
>>> globals() # oh, it didn't. Let's check all our global variables and confirm
Deleted!
{'__builtins__': <module '__builtin__' (built-in)>, 'SomeClass': <class __main__.SomeClass at 0x7f98a1a5f668>, '__package__': None, '__name__': '__main__', '__doc__': None}
Okay, now it's deleted 😕
del x
doesn’t directly callx.__del__()
.- When
del x
is encountered, Python deletes the namex
from current scope and decrements by 1 the reference count of the objectx
referenced.__del__()
is called only when the object's reference count reaches zero. - In the second output snippet,
__del__()
was not called because the previous statement (>>> y
) in the interactive interpreter created another reference to the same object (specifically, the_
magic variable which references the result value of the last nonNone
expression on the REPL), thus preventing the reference count from reaching zero whendel y
was encountered. - Calling
globals
(or really, executing anything that will have a nonNone
result) caused_
to reference the new result, dropping the existing reference. Now the reference count reached 0 and we can see "Deleted!" being printed (finally!).
1.
a = 1
def some_func():
return a
def another_func():
a += 1
return a
2.
def some_closure_func():
a = 1
def some_inner_func():
return a
return some_inner_func()
def another_closure_func():
a = 1
def another_inner_func():
a += 1
return a
return another_inner_func()
Output:
>>> some_func()
1
>>> another_func()
UnboundLocalError: local variable 'a' referenced before assignment
>>> some_closure_func()
1
>>> another_closure_func()
UnboundLocalError: local variable 'a' referenced before assignment
-
When you make an assignment to a variable in scope, it becomes local to that scope. So
a
becomes local to the scope ofanother_func
, but it has not been initialized previously in the same scope, which throws an error. -
To modify the outer scope variable
a
inanother_func
, we have to use theglobal
keyword.def another_func() global a a += 1 return a
Output:
>>> another_func() 2
-
In
another_closure_func
,a
becomes local to the scope ofanother_inner_func
, but it has not been initialized previously in the same scope, which is why it throws an error. -
To modify the outer scope variable
a
inanother_inner_func
, use thenonlocal
keyword. The nonlocal statement is used to refer to variables defined in the nearest outer (excluding the global) scope.def another_func(): a = 1 def another_inner_func(): nonlocal a a += 1 return a return another_inner_func()
Output:
>>> another_func() 2
-
The keywords
global
andnonlocal
tell the python interpreter to not declare new variables and look them up in the corresponding outer scopes. -
Read this short but an awesome guide to learn more about how namespaces and scope resolution works in Python.
list_1 = [1, 2, 3, 4]
list_2 = [1, 2, 3, 4]
list_3 = [1, 2, 3, 4]
list_4 = [1, 2, 3, 4]
for idx, item in enumerate(list_1):
del item
for idx, item in enumerate(list_2):
list_2.remove(item)
for idx, item in enumerate(list_3[:]):
list_3.remove(item)
for idx, item in enumerate(list_4):
list_4.pop(idx)
Output:
>>> list_1
[1, 2, 3, 4]
>>> list_2
[2, 4]
>>> list_3
[]
>>> list_4
[2, 4]
Can you guess why the output is [2, 4]
?
-
It's never a good idea to change the object you're iterating over. The correct way to do so is to iterate over a copy of the object instead, and
list_3[:]
does just that.>>> some_list = [1, 2, 3, 4] >>> id(some_list) 139798789457608 >>> id(some_list[:]) # Notice that python creates new object for sliced list. 139798779601192
Difference between del
, remove
, and pop
:
del var_name
just removes the binding of thevar_name
from the local or global namespace (That's why thelist_1
is unaffected).remove
removes the first matching value, not a specific index, raisesValueError
if the value is not found.pop
removes the element at a specific index and returns it, raisesIndexError
if an invalid index is specified.
Why the output is [2, 4]
?
- The list iteration is done index by index, and when we remove
1
fromlist_2
orlist_4
, the contents of the lists are now[2, 3, 4]
. The remaining elements are shifted down, i.e.,2
is at index 0, and3
is at index 1. Since the next iteration is going to look at index 1 (which is the3
), the2
gets skipped entirely. A similar thing will happen with every alternate element in the list sequence.
- Refer to this StackOverflow thread explaining the example
- See also this nice StackOverflow thread for a similar example related to dictionaries in Python.
>>> numbers = list(range(7))
>>> numbers
[0, 1, 2, 3, 4, 5, 6]
>>> first_three, remaining = numbers[:3], numbers[3:]
>>> first_three, remaining
([0, 1, 2], [3, 4, 5, 6])
>>> numbers_iter = iter(numbers)
>>> list(zip(numbers_iter, first_three))
[(0, 0), (1, 1), (2, 2)]
# so far so good, let's zip the remaining
>>> list(zip(numbers_iter, remaining))
[(4, 3), (5, 4), (6, 5)]
Where did element 3
go from the numbers
list?
- From Python docs, here's an approximate implementation of zip function,
def zip(*iterables): sentinel = object() iterators = [iter(it) for it in iterables] while iterators: result = [] for it in iterators: elem = next(it, sentinel) if elem is sentinel: return result.append(elem) yield tuple(result)
- So the function takes in arbitrary number of iterable objects, adds each of their items to the
result
list by calling thenext
function on them, and stops whenever any of the iterable is exhausted. - The caveat here is when any iterable is exhausted, the existing elements in the
result
list are discarded. That's what happened with3
in thenumbers_iter
. - The correct way to do the above using
zip
would be,The first argument of zip should be the one with fewest elements.>>> numbers = list(range(7)) >>> numbers_iter = iter(numbers) >>> list(zip(first_three, numbers_iter)) [(0, 0), (1, 1), (2, 2)] >>> list(zip(remaining, numbers_iter)) [(3, 3), (4, 4), (5, 5), (6, 6)]
1.
for x in range(7):
if x == 6:
print(x, ': for x inside loop')
print(x, ': x in global')
Output:
6 : for x inside loop
6 : x in global
But x
was never defined outside the scope of for loop...
2.
# This time let's initialize x first
x = -1
for x in range(7):
if x == 6:
print(x, ': for x inside loop')
print(x, ': x in global')
Output:
6 : for x inside loop
6 : x in global
3.
Output (Python 2.x):
>>> x = 1
>>> print([x for x in range(5)])
[0, 1, 2, 3, 4]
>>> print(x)
4
Output (Python 3.x):
>>> x = 1
>>> print([x for x in range(5)])
[0, 1, 2, 3, 4]
>>> print(x)
1
-
In Python, for-loops use the scope they exist in and leave their defined loop-variable behind. This also applies if we explicitly defined the for-loop variable in the global namespace before. In this case, it will rebind the existing variable.
-
The differences in the output of Python 2.x and Python 3.x interpreters for list comprehension example can be explained by following change documented in What’s New In Python 3.0 changelog:
"List comprehensions no longer support the syntactic form
[... for var in item1, item2, ...]
. Use[... for var in (item1, item2, ...)]
instead. Also, note that list comprehensions have different semantics: they are closer to syntactic sugar for a generator expression inside alist()
constructor, and in particular, the loop control variables are no longer leaked into the surrounding scope."
def some_func(default_arg=[]):
default_arg.append("some_string")
return default_arg
Output:
>>> some_func()
['some_string']
>>> some_func()
['some_string', 'some_string']
>>> some_func([])
['some_string']
>>> some_func()
['some_string', 'some_string', 'some_string']
-
The default mutable arguments of functions in Python aren't really initialized every time you call the function. Instead, the recently assigned value to them is used as the default value. When we explicitly passed
[]
tosome_func
as the argument, the default value of thedefault_arg
variable was not used, so the function returned as expected.def some_func(default_arg=[]): default_arg.append("some_string") return default_arg
Output:
>>> some_func.__defaults__ #This will show the default argument values for the function ([],) >>> some_func() >>> some_func.__defaults__ (['some_string'],) >>> some_func() >>> some_func.__defaults__ (['some_string', 'some_string'],) >>> some_func([]) >>> some_func.__defaults__ (['some_string', 'some_string'],)
-
A common practice to avoid bugs due to mutable arguments is to assign
None
as the default value and later check if any value is passed to the function corresponding to that argument. Example:def some_func(default_arg=None): if default_arg is None: default_arg = [] default_arg.append("some_string") return default_arg
some_list = [1, 2, 3]
try:
# This should raise an ``IndexError``
print(some_list[4])
except IndexError, ValueError:
print("Caught!")
try:
# This should raise a ``ValueError``
some_list.remove(4)
except IndexError, ValueError:
print("Caught again!")
Output (Python 2.x):
Caught!
ValueError: list.remove(x): x not in list
Output (Python 3.x):
File "<input>", line 3
except IndexError, ValueError:
^
SyntaxError: invalid syntax
-
To add multiple Exceptions to the except clause, you need to pass them as parenthesized tuple as the first argument. The second argument is an optional name, which when supplied will bind the Exception instance that has been raised. Example,
some_list = [1, 2, 3] try: # This should raise a ``ValueError`` some_list.remove(4) except (IndexError, ValueError), e: print("Caught again!") print(e)
Output (Python 2.x):
Caught again! list.remove(x): x not in list
Output (Python 3.x):
File "<input>", line 4 except (IndexError, ValueError), e: ^ IndentationError: unindent does not match any outer indentation level
-
Separating the exception from the variable with a comma is deprecated and does not work in Python 3; the correct way is to use
as
. Example,some_list = [1, 2, 3] try: some_list.remove(4) except (IndexError, ValueError) as e: print("Caught again!") print(e)
Output:
Caught again! list.remove(x): x not in list
1.
a = [1, 2, 3, 4]
b = a
a = a + [5, 6, 7, 8]
Output:
>>> a
[1, 2, 3, 4, 5, 6, 7, 8]
>>> b
[1, 2, 3, 4]
2.
a = [1, 2, 3, 4]
b = a
a += [5, 6, 7, 8]
Output:
>>> a
[1, 2, 3, 4, 5, 6, 7, 8]
>>> b
[1, 2, 3, 4, 5, 6, 7, 8]
-
a += b
doesn't always behave the same way asa = a + b
. Classes may implement theop=
operators differently, and lists do this. -
The expression
a = a + [5,6,7,8]
generates a new list and setsa
's reference to that new list, leavingb
unchanged. -
The expression
a += [5,6,7,8]
is actually mapped to an "extend" function that operates on the list such thata
andb
still point to the same list that has been modified in-place.
1.
x = 5
class SomeClass:
x = 17
y = (x for i in range(10))
Output:
>>> list(SomeClass.y)[0]
5
2.
x = 5
class SomeClass:
x = 17
y = [x for i in range(10)]
Output (Python 2.x):
>>> SomeClass.y[0]
17
Output (Python 3.x):
>>> SomeClass.y[0]
5
- Scopes nested inside class definition ignore names bound at the class level.
- A generator expression has its own scope.
- Starting from Python 3.X, list comprehensions also have their own scope.
Let's implement a naive function to get the middle element of a list:
def get_middle(some_list):
mid_index = round(len(some_list) / 2)
return some_list[mid_index - 1]
Python 3.x:
>>> get_middle([1]) # looks good
1
>>> get_middle([1,2,3]) # looks good
2
>>> get_middle([1,2,3,4,5]) # huh?
2
>>> len([1,2,3,4,5]) / 2 # good
2.5
>>> round(len([1,2,3,4,5]) / 2) # why?
2
It seems as though Python rounded 2.5 to 2.
- This is not a float precision error, in fact, this behavior is intentional. Since Python 3.0,
round()
uses banker's rounding where .5 fractions are rounded to the nearest even number:
>>> round(0.5)
0
>>> round(1.5)
2
>>> round(2.5)
2
>>> import numpy # numpy does the same
>>> numpy.round(0.5)
0.0
>>> numpy.round(1.5)
2.0
>>> numpy.round(2.5)
2.0
- This is the recommended way to round .5 fractions as described in IEEE 754. However, the other way (round away from zero) is taught in school most of the time, so banker's rounding is likely not that well known. Furthermore, some of the most popular programming languages (for example: JavaScript, Java, C/C++, Ruby, Rust) do not use banker's rounding either. Therefore, this is still quite special to Python and may result in confusion when rounding fractions.
- See the round() docs or this stackoverflow thread for more information.
- Note that
get_middle([1])
only returned 1 because the index wasround(0.5) - 1 = 0 - 1 = -1
, returning the last element in the list.
I haven't met even a single experience Pythonist till date who has not come across one or more of the following scenarios,
1.
x, y = (0, 1) if True else None, None
Output:
>>> x, y # expected (0, 1)
((0, 1), None)
2.
t = ('one', 'two')
for i in t:
print(i)
t = ('one')
for i in t:
print(i)
t = ()
print(t)
Output:
one
two
o
n
e
tuple()
3.
ten_words_list = [
"some",
"very",
"big",
"list",
"that"
"consists",
"of",
"exactly",
"ten",
"words"
]
Output
>>> len(ten_words_list)
9
4. Not asserting strongly enough
a = "python"
b = "javascript"
Output:
# An assert statement with an assertion failure message.
>>> assert(a == b, "Both languages are different")
# No AssertionError is raised
5.
some_list = [1, 2, 3]
some_dict = {
"key_1": 1,
"key_2": 2,
"key_3": 3
}
some_list = some_list.append(4)
some_dict = some_dict.update({"key_4": 4})
Output:
>>> print(some_list)
None
>>> print(some_dict)
None
6.
def some_recursive_func(a):
if a[0] == 0:
return
a[0] -= 1
some_recursive_func(a)
return a
def similar_recursive_func(a):
if a == 0:
return a
a -= 1
similar_recursive_func(a)
return a
Output:
>>> some_recursive_func([5, 0])
[0, 0]
>>> similar_recursive_func(5)
4
-
For 1, the correct statement for expected behavior is
x, y = (0, 1) if True else (None, None)
. -
For 2, the correct statement for expected behavior is
t = ('one',)
ort = 'one',
(missing comma) otherwise the interpreter considerst
to be astr
and iterates over it character by character. -
()
is a special token and denotes emptytuple
. -
In 3, as you might have already figured out, there's a missing comma after 5th element (
"that"
) in the list. So by implicit string literal concatenation,>>> ten_words_list ['some', 'very', 'big', 'list', 'thatconsists', 'of', 'exactly', 'ten', 'words']
-
No
AssertionError
was raised in 4th snippet because instead of asserting the individual expressiona == b
, we're asserting entire tuple. The following snippet will clear things up,>>> a = "python" >>> b = "javascript" >>> assert a == b Traceback (most recent call last): File "<stdin>", line 1, in <module> AssertionError >>> assert (a == b, "Values are not equal") <stdin>:1: SyntaxWarning: assertion is always true, perhaps remove parentheses? >>> assert a == b, "Values are not equal" Traceback (most recent call last): File "<stdin>", line 1, in <module> AssertionError: Values are not equal
-
As for the fifth snippet, most methods that modify the items of sequence/mapping objects like
list.append
,dict.update
,list.sort
, etc. modify the objects in-place and returnNone
. The rationale behind this is to improve performance by avoiding making a copy of the object if the operation can be done in-place (Referred from here). -
Last one should be fairly obvious, mutable object (like
list
) can be altered in the function, and the reassignation of an immutable (a -= 1
) is not an alteration of the value. -
Being aware of these nitpicks can save you hours of debugging effort in the long run.
>>> 'a'.split()
['a']
# is same as
>>> 'a'.split(' ')
['a']
# but
>>> len(''.split())
0
# isn't the same as
>>> len(''.split(' '))
1
- It might appear at first that the default separator for split is a single space
' '
, but as per the docsIf sep is not specified or is
None
, a different splitting algorithm is applied: runs of consecutive whitespace are regarded as a single separator, and the result will contain no empty strings at the start or end if the string has leading or trailing whitespace. Consequently, splitting an empty string or a string consisting of just whitespace with a None separator returns[]
. If sep is given, consecutive delimiters are not grouped together and are deemed to delimit empty strings (for example,'1,,2'.split(',')
returns['1', '', '2']
). Splitting an empty string with a specified separator returns['']
. - Noticing how the leading and trailing whitespaces are handled in the following snippet will make things clear,
>>> ' a '.split(' ') ['', 'a', ''] >>> ' a '.split() ['a'] >>> ''.split(' ') ['']
# File: module.py
def some_weird_name_func_():
print("works!")
def _another_weird_name_func():
print("works!")
Output
>>> from module import *
>>> some_weird_name_func_()
"works!"
>>> _another_weird_name_func()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name '_another_weird_name_func' is not defined
-
It is often advisable to not use wildcard imports. The first obvious reason for this is, in wildcard imports, the names with a leading underscore don't get imported. This may lead to errors during runtime.
-
Had we used
from ... import a, b, c
syntax, the aboveNameError
wouldn't have occurred.>>> from module import some_weird_name_func_, _another_weird_name_func >>> _another_weird_name_func() works!
-
If you really want to use wildcard imports, then you'd have to define the list
__all__
in your module that will contain a list of public objects that'll be available when we do wildcard imports.__all__ = ['_another_weird_name_func'] def some_weird_name_func_(): print("works!") def _another_weird_name_func(): print("works!")
Output
>>> _another_weird_name_func() "works!" >>> some_weird_name_func_() Traceback (most recent call last): File "<stdin>", line 1, in <module> NameError: name 'some_weird_name_func_' is not defined
>>> x = 7, 8, 9
>>> sorted(x) == x
False
>>> sorted(x) == sorted(x)
True
>>> y = reversed(x)
>>> sorted(y) == sorted(y)
False
-
The
sorted
method always returns a list, and comparing lists and tuples always returnsFalse
in Python. -
>>> [] == tuple() False >>> x = 7, 8, 9 >>> type(x), type(sorted(x)) (tuple, list)
-
Unlike
sorted
, thereversed
method returns an iterator. Why? Because sorting requires the iterator to be either modified in-place or use an extra container (a list), whereas reversing can simply work by iterating from the last index to the first. -
So during comparison
sorted(y) == sorted(y)
, the first call tosorted()
will consume the iteratory
, and the next call will just return an empty list.>>> x = 7, 8, 9 >>> y = reversed(x) >>> sorted(y), sorted(y) ([7, 8, 9], [])
from datetime import datetime
midnight = datetime(2018, 1, 1, 0, 0)
midnight_time = midnight.time()
noon = datetime(2018, 1, 1, 12, 0)
noon_time = noon.time()
if midnight_time:
print("Time at midnight is", midnight_time)
if noon_time:
print("Time at noon is", noon_time)
Output (< 3.5):
('Time at noon is', datetime.time(12, 0))
The midnight time is not printed.
Before Python 3.5, the boolean value for datetime.time
object was considered to be False
if it represented midnight in UTC. It is error-prone when using the if obj:
syntax to check if the obj
is null or some equivalent of "empty."
Section: The Hidden treasures!
This section contains a few lesser-known and interesting things about Python that most beginners like me are unaware of (well, not anymore).
Well, here you go
import antigravity
Output: Sshh... It's a super-secret.
antigravity
module is one of the few easter eggs released by Python developers.import antigravity
opens up a web browser pointing to the classic XKCD comic about Python.- Well, there's more to it. There's another easter egg inside the easter egg. If you look at the code, there's a function defined that purports to implement the XKCD's geohashing algorithm.
from goto import goto, label
for i in range(9):
for j in range(9):
for k in range(9):
print("I am trapped, please rescue!")
if k == 2:
goto .breakout # breaking out from a deeply nested loop
label .breakout
print("Freedom!")
Output (Python 2.3):
I am trapped, please rescue!
I am trapped, please rescue!
Freedom!
- A working version of
goto
in Python was announced as an April Fool's joke on 1st April 2004. - Current versions of Python do not have this module.
- Although it works, but please don't use it. Here's the reason to why
goto
is not present in Python.
If you are one of the people who doesn't like using whitespace in Python to denote scopes, you can use the C-style {} by importing,
from __future__ import braces
Output:
File "some_file.py", line 1
from __future__ import braces
SyntaxError: not a chance
Braces? No way! If you think that's disappointing, use Java. Okay, another surprising thing, can you find where's the SyntaxError
raised in __future__
module code?
- The
__future__
module is normally used to provide features from future versions of Python. The "future" in this specific context is however, ironic. - This is an easter egg concerned with the community's feelings on this issue.
- The code is actually present here in
future.c
file. - When the CPython compiler encounters a future statement, it first runs the appropriate code in
future.c
before treating it as a normal import statement.
Output (Python 3.x)
>>> from __future__ import barry_as_FLUFL
>>> "Ruby" != "Python" # there's no doubt about it
File "some_file.py", line 1
"Ruby" != "Python"
^
SyntaxError: invalid syntax
>>> "Ruby" <> "Python"
True
There we go.
-
This is relevant to PEP-401 released on April 1, 2009 (now you know, what it means).
-
Quoting from the PEP-401
Recognized that the != inequality operator in Python 3.0 was a horrible, finger-pain inducing mistake, the FLUFL reinstates the <> diamond operator as the sole spelling.
-
There were more things that Uncle Barry had to share in the PEP; you can read them here.
-
It works well in an interactive environment, but it will raise a
SyntaxError
when you run via python file (see this issue). However, you can wrap the statement inside aneval
orcompile
to get it working,from __future__ import barry_as_FLUFL print(eval('"Ruby" <> "Python"'))
import this
Wait, what's this? this
is love ❤️
Output:
The Zen of Python, by Tim Peters
Beautiful is better than ugly.
Explicit is better than implicit.
Simple is better than complex.
Complex is better than complicated.
Flat is better than nested.
Sparse is better than dense.
Readability counts.
Special cases aren't special enough to break the rules.
Although practicality beats purity.
Errors should never pass silently.
Unless explicitly silenced.
In the face of ambiguity, refuse the temptation to guess.
There should be one-- and preferably only one --obvious way to do it.
Although that way may not be obvious at first unless you're Dutch.
Now is better than never.
Although never is often better than *right* now.
If the implementation is hard to explain, it's a bad idea.
If the implementation is easy to explain, it may be a good idea.
Namespaces are one honking great idea -- let's do more of those!
It's the Zen of Python!
>>> love = this
>>> this is love
True
>>> love is True
False
>>> love is False
False
>>> love is not True or False
True
>>> love is not True or False; love is love # Love is complicated
True
this
module in Python is an easter egg for The Zen Of Python (PEP 20).- And if you think that's already interesting enough, check out the implementation of this.py. Interestingly, the code for the Zen violates itself (and that's probably the only place where this happens).
- Regarding the statement
love is not True or False; love is love
, ironic but it's self-explanatory (if not, please see the examples related tois
andis not
operators).
The else
clause for loops. One typical example might be:
def does_exists_num(l, to_find):
for num in l:
if num == to_find:
print("Exists!")
break
else:
print("Does not exist")
Output:
>>> some_list = [1, 2, 3, 4, 5]
>>> does_exists_num(some_list, 4)
Exists!
>>> does_exists_num(some_list, -1)
Does not exist
The else
clause in exception handling. An example,
try:
pass
except:
print("Exception occurred!!!")
else:
print("Try block executed successfully...")
Output:
Try block executed successfully...
- The
else
clause after a loop is executed only when there's no explicitbreak
after all the iterations. You can think of it as a "nobreak" clause. else
clause after a try block is also called "completion clause" as reaching theelse
clause in atry
statement means that the try block actually completed successfully.
def some_func():
Ellipsis
Output
>>> some_func()
# No output, No Error
>>> SomeRandomString
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
NameError: name 'SomeRandomString' is not defined
>>> Ellipsis
Ellipsis
- In Python,
Ellipsis
is a globally available built-in object which is equivalent to...
.>>> ... Ellipsis
- Ellipsis can be used for several purposes,
- As a placeholder for code that hasn't been written yet (just like
pass
statement) - In slicing syntax to represent the full slices in remaining direction
So our>>> import numpy as np >>> three_dimensional_array = np.arange(8).reshape(2, 2, 2) array([ [ [0, 1], [2, 3] ], [ [4, 5], [6, 7] ] ])
three_dimensional_array
is an array of array of arrays. Let's say we want to print the second element (index1
) of all the innermost arrays, we can use Ellipsis to bypass all the preceding dimensionsNote: this will work for any number of dimensions. You can even select slice in first and last dimension and ignore the middle ones this way (>>> three_dimensional_array[:,:,1] array([[1, 3], [5, 7]]) >>> three_dimensional_array[..., 1] # using Ellipsis. array([[1, 3], [5, 7]])
n_dimensional_array[firs_dim_slice, ..., last_dim_slice]
)- In type hinting to indicate only a part of the type (like
(Callable[..., int]
orTuple[str, ...]
)) - You may also use Ellipsis as a default function argument (in the cases when you want to differentiate between the "no argument passed" and "None value passed" scenarios).
- As a placeholder for code that hasn't been written yet (just like
The spelling is intended. Please, don't submit a patch for this.
Output (Python 3.x):
>>> infinity = float('infinity')
>>> hash(infinity)
314159
>>> hash(float('-inf'))
-314159
- Hash of infinity is 10⁵ x π.
- Interestingly, the hash of
float('-inf')
is "-10⁵ x π" in Python 3, whereas "-10⁵ x e" in Python 2.
1.
class Yo(object):
def __init__(self):
self.__honey = True
self.bro = True
Output:
>>> Yo().bro
True
>>> Yo().__honey
AttributeError: 'Yo' object has no attribute '__honey'
>>> Yo()._Yo__honey
True
2.
class Yo(object):
def __init__(self):
# Let's try something symmetrical this time
self.__honey__ = True
self.bro = True
Output:
>>> Yo().bro
True
>>> Yo()._Yo__honey__
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'Yo' object has no attribute '_Yo__honey__'
Why did Yo()._Yo__honey
work?
3.
_A__variable = "Some value"
class A(object):
def some_func(self):
return __variable # not initialized anywhere yet
Output:
>>> A().__variable
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: 'A' object has no attribute '__variable'
>>> A().some_func()
'Some value'
- Name Mangling is used to avoid naming collisions between different namespaces.
- In Python, the interpreter modifies (mangles) the class member names starting with
__
(double underscore a.k.a "dunder") and not ending with more than one trailing underscore by adding_NameOfTheClass
in front. - So, to access
__honey
attribute in the first snippet, we had to append_Yo
to the front, which would prevent conflicts with the same name attribute defined in any other class. - But then why didn't it work in the second snippet? Because name mangling excludes the names ending with double underscores.
- The third snippet was also a consequence of name mangling. The name
__variable
in the statementreturn __variable
was mangled to_A__variable
, which also happens to be the name of the variable we declared in the outer scope. - Also, if the mangled name is longer than 255 characters, truncation will happen.
Output:
>>> value = 11
>>> valuе = 32
>>> value
11
Wut?
Note: The easiest way to reproduce this is to simply copy the statements from the above snippet and paste them into your file/shell.
Some non-Western characters look identical to letters in the English alphabet but are considered distinct by the interpreter.
>>> ord('е') # cyrillic 'e' (Ye)
1077
>>> ord('e') # latin 'e', as used in English and typed using standard keyboard
101
>>> 'е' == 'e'
False
>>> value = 42 # latin e
>>> valuе = 23 # cyrillic 'e', Python 2.x interpreter would raise a `SyntaxError` here
>>> value
42
The built-in ord()
function returns a character's Unicode code point, and different code positions of Cyrillic 'e' and Latin 'e' justify the behavior of the above example.
# `pip install numpy` first.
import numpy as np
def energy_send(x):
# Initializing a numpy array
np.array([float(x)])
def energy_receive():
# Return an empty numpy array
return np.empty((), dtype=np.float).tolist()
Output:
>>> energy_send(123.456)
>>> energy_receive()
123.456
Where's the Nobel Prize?
- Notice that the numpy array created in the
energy_send
function is not returned, so that memory space is free to reallocate. numpy.empty()
returns the next free memory slot without reinitializing it. This memory spot just happens to be the same one that was just freed (usually, but not always).
def square(x):
"""
A simple function to calculate the square of a number by addition.
"""
sum_so_far = 0
for counter in range(x):
sum_so_far = sum_so_far + x
return sum_so_far
Output (Python 2.x):
>>> square(10)
10
Shouldn't that be 100?
Note: If you're not able to reproduce this, try running the file mixed_tabs_and_spaces.py via the shell.
-
Don't mix tabs and spaces! The character just preceding return is a "tab", and the code is indented by multiple of "4 spaces" elsewhere in the example.
-
This is how Python handles tabs:
First, tabs are replaced (from left to right) by one to eight spaces such that the total number of characters up to and including the replacement is a multiple of eight <...>
-
So the "tab" at the last line of
square
function is replaced with eight spaces, and it gets into the loop. -
Python 3 is kind enough to throw an error for such cases automatically.
Output (Python 3.x):
TabError: inconsistent use of tabs and spaces in indentation
# using "+", three strings:
>>> timeit.timeit("s1 = s1 + s2 + s3", setup="s1 = ' ' * 100000; s2 = ' ' * 100000; s3 = ' ' * 100000", number=100)
0.25748300552368164
# using "+=", three strings:
>>> timeit.timeit("s1 += s2 + s3", setup="s1 = ' ' * 100000; s2 = ' ' * 100000; s3 = ' ' * 100000", number=100)
0.012188911437988281
+=
is faster than+
for concatenating more than two strings because the first string (example,s1
fors1 += s2 + s3
) is not destroyed while calculating the complete string.
def add_string_with_plus(iters):
s = ""
for i in range(iters):
s += "xyz"
assert len(s) == 3*iters
def add_bytes_with_plus(iters):
s = b""
for i in range(iters):
s += b"xyz"
assert len(s) == 3*iters
def add_string_with_format(iters):
fs = "{}"*iters
s = fs.format(*(["xyz"]*iters))
assert len(s) == 3*iters
def add_string_with_join(iters):
l = []
for i in range(iters):
l.append("xyz")
s = "".join(l)
assert len(s) == 3*iters
def convert_list_to_string(l, iters):
s = "".join(l)
assert len(s) == 3*iters
Output:
# Executed in ipython shell using %timeit for better readability of results.
# You can also use the timeit module in normal python shell/scriptm=, example usage below
# timeit.timeit('add_string_with_plus(10000)', number=1000, globals=globals())
>>> NUM_ITERS = 1000
>>> %timeit -n1000 add_string_with_plus(NUM_ITERS)
124 µs ± 4.73 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
>>> %timeit -n1000 add_bytes_with_plus(NUM_ITERS)
211 µs ± 10.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit -n1000 add_string_with_format(NUM_ITERS)
61 µs ± 2.18 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit -n1000 add_string_with_join(NUM_ITERS)
117 µs ± 3.21 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> l = ["xyz"]*NUM_ITERS
>>> %timeit -n1000 convert_list_to_string(l, NUM_ITERS)
10.1 µs ± 1.06 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
Let's increase the number of iterations by a factor of 10.
>>> NUM_ITERS = 10000
>>> %timeit -n1000 add_string_with_plus(NUM_ITERS) # Linear increase in execution time
1.26 ms ± 76.8 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit -n1000 add_bytes_with_plus(NUM_ITERS) # Quadratic increase
6.82 ms ± 134 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit -n1000 add_string_with_format(NUM_ITERS) # Linear increase
645 µs ± 24.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> %timeit -n1000 add_string_with_join(NUM_ITERS) # Linear increase
1.17 ms ± 7.25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
>>> l = ["xyz"]*NUM_ITERS
>>> %timeit -n1000 convert_list_to_string(l, NUM_ITERS) # Linear increase
86.3 µs ± 2 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
-
You can read more about timeit or %timeit on these links. They are used to measure the execution time of code pieces.
-
Don't use
+
for generating long strings — In Python,str
is immutable, so the left and right strings have to be copied into the new string for every pair of concatenations. If you concatenate four strings of length 10, you'll be copying (10+10) + ((10+10)+10) + (((10+10)+10)+10) = 90 characters instead of just 40 characters. Things get quadratically worse as the number and size of the string increases (justified with the execution times ofadd_bytes_with_plus
function) -
Therefore, it's advised to use
.format.
or%
syntax (however, they are slightly slower than+
for very short strings). -
Or better, if already you've contents available in the form of an iterable object, then use
''.join(iterable_object)
which is much faster. -
Unlike
add_bytes_with_plus
because of the+=
optimizations discussed in the previous example,add_string_with_plus
didn't show a quadratic increase in execution time. Had the statement beens = s + "x" + "y" + "z"
instead ofs += "xyz"
, the increase would have been quadratic.def add_string_with_plus(iters): s = "" for i in range(iters): s = s + "x" + "y" + "z" assert len(s) == 3*iters >>> %timeit -n100 add_string_with_plus(1000) 388 µs ± 22.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) >>> %timeit -n100 add_string_with_plus(10000) # Quadratic increase in execution time 9 ms ± 298 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
-
So many ways to format and create a giant string are somewhat in contrast to the Zen of Python, according to which,
There should be one-- and preferably only one --obvious way to do it.
some_dict = {str(i): 1 for i in range(1_000_000)}
another_dict = {str(i): 1 for i in range(1_000_000)}
Output:
>>> %timeit some_dict['5']
28.6 ns ± 0.115 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
>>> some_dict[1] = 1
>>> %timeit some_dict['5']
37.2 ns ± 0.265 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
>>> %timeit another_dict['5']
28.5 ns ± 0.142 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
>>> another_dict[1] # Trying to access a key that doesn't exist
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
KeyError: 1
>>> %timeit another_dict['5']
38.5 ns ± 0.0913 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
Why are same lookups becoming slower?
- CPython has a generic dictionary lookup function that handles all types of keys (
str
,int
, any object ...), and a specialized one for the common case of dictionaries composed ofstr
-only keys. - The specialized function (named
lookdict_unicode
in CPython's source) knows all existing keys (including the looked-up key) are strings, and uses the faster & simpler string comparison to compare keys, instead of calling the__eq__
method. - The first time a
dict
instance is accessed with a non-str
key, it's modified so future lookups use the generic function. - This process is not reversible for the particular
dict
instance, and the key doesn't even have to exist in the dictionary. That's why attempting a failed lookup has the same effect.
import sys
class SomeClass:
def __init__(self):
self.some_attr1 = 1
self.some_attr2 = 2
self.some_attr3 = 3
self.some_attr4 = 4
def dict_size(o):
return sys.getsizeof(o.__dict__)
Output: (Python 3.8, other Python 3 versions may vary a little)
>>> o1 = SomeClass()
>>> o2 = SomeClass()
>>> dict_size(o1)
104
>>> dict_size(o2)
104
>>> del o1.some_attr1
>>> o3 = SomeClass()
>>> dict_size(o3)
232
>>> dict_size(o1)
232
Let's try again... In a new interpreter:
>>> o1 = SomeClass()
>>> o2 = SomeClass()
>>> dict_size(o1)
104 # as expected
>>> o1.some_attr5 = 5
>>> o1.some_attr6 = 6
>>> dict_size(o1)
360
>>> dict_size(o2)
272
>>> o3 = SomeClass()
>>> dict_size(o3)
232
What makes those dictionaries become bloated? And why are newly created objects bloated as well?
- CPython is able to reuse the same "keys" object in multiple dictionaries. This was added in PEP 412 with the motivation to reduce memory usage, specifically in dictionaries of instances - where keys (instance attributes) tend to be common to all instances.
- This optimization is entirely seamless for instance dictionaries, but it is disabled if certain assumptions are broken.
- Key-sharing dictionaries do not support deletion; if an instance attribute is deleted, the dictionary is "unshared", and key-sharing is disabled for all future instances of the same class.
- Additionaly, if the dictionary keys have be resized (because new keys are inserted), they are kept shared only if they are used by a exactly single dictionary (this allows adding many attributes in the
__init__
of the very first created instance, without causing an "unshare"). If multiple instances exist when a resize happens, key-sharing is disabled for all future instances of the same class: CPython can't tell if your instances are using the same set of attributes anymore, and decides to bail out on attempting to share their keys. - A small tip, if you aim to lower your program's memory footprint: don't delete instance attributes, and make sure to initialize all attributes in your
__init__
!
-
join()
is a string operation instead of list operation. (sort of counter-intuitive at first usage)💡 Explanation: If
join()
is a method on a string, then it can operate on any iterable (list, tuple, iterators). If it were a method on a list, it'd have to be implemented separately by every type. Also, it doesn't make much sense to put a string-specific method on a genericlist
object API. -
Few weird looking but semantically correct statements:
[] = ()
is a semantically correct statement (unpacking an emptytuple
into an emptylist
)'a'[0][0][0][0][0]
is also a semantically correct statement as strings are sequences(iterables supporting element access using integer indices) in Python.3 --0-- 5 == 8
and--5 == 5
are both semantically correct statements and evaluate toTrue
.
-
Given that
a
is a number,++a
and--a
are both valid Python statements but don't behave the same way as compared with similar statements in languages like C, C++, or Java.>>> a = 5 >>> a 5 >>> ++a 5 >>> --a 5
💡 Explanation:
- There is no
++
operator in Python grammar. It is actually two+
operators. ++a
parses as+(+a)
which translates toa
. Similarly, the output of the statement--a
can be justified.- This StackOverflow thread discusses the rationale behind the absence of increment and decrement operators in Python.
- There is no
-
You must be aware of the Walrus operator in Python. But have you ever heard about the space-invader operator?
>>> a = 42 >>> a -=- 1 >>> a 43
It is used as an alternative incrementation operator, together with another one
>>> a +=+ 1 >>> a >>> 44
💡 Explanation: This prank comes from Raymond Hettinger's tweet. The space invader operator is actually just a malformatted
a -= (-1)
. Which is equivalent toa = a - (- 1)
. Similar for thea += (+ 1)
case. -
Python has an undocumented converse implication operator.
>>> False ** False == True True >>> False ** True == False True >>> True ** False == True True >>> True ** True == True True
💡 Explanation: If you replace
False
andTrue
by 0 and 1 and do the maths, the truth table is equivalent to a converse implication operator. (Source) -
Since we are talking operators, there's also
@
operator for matrix multiplication (don't worry, this time it's for real).>>> import numpy as np >>> np.array([2, 2, 2]) @ np.array([7, 8, 8]) 46
💡 Explanation: The
@
operator was added in Python 3.5 keeping the scientific community in mind. Any object can overload__matmul__
magic method to define behavior for this operator. -
From Python 3.8 onwards you can use a typical f-string syntax like
f'{some_var=}
for quick debugging. Example,>>> some_string = "wtfpython" >>> f'{some_string=}' "some_string='wtfpython'"
-
Python uses 2 bytes for local variable storage in functions. In theory, this means that only 65536 variables can be defined in a function. However, python has a handy solution built in that can be used to store more than 2^16 variable names. The following code demonstrates what happens in the stack when more than 65536 local variables are defined (Warning: This code prints around 2^18 lines of text, so be prepared!):
import dis exec(""" def f(): """ + """ """.join(["X" + str(x) + "=" + str(x) for x in range(65539)])) f() print(dis.dis(f))
-
Multiple Python threads won't run your Python code concurrently (yes, you heard it right!). It may seem intuitive to spawn several threads and let them execute your Python code concurrently, but, because of the Global Interpreter Lock in Python, all you're doing is making your threads execute on the same core turn by turn. Python threads are good for IO-bound tasks, but to achieve actual parallelization in Python for CPU-bound tasks, you might want to use the Python multiprocessing module.
-
Sometimes, the
print
method might not print values immediately. For example,# File some_file.py import time print("wtfpython", end="_") time.sleep(3)
This will print the
wtfpython
after 3 seconds due to theend
argument because the output buffer is flushed either after encountering\n
or when the program finishes execution. We can force the buffer to flush by passingflush=True
argument. -
List slicing with out of the bounds indices throws no errors
>>> some_list = [1, 2, 3, 4, 5] >>> some_list[111:] []
-
Slicing an iterable not always creates a new object. For example,
>>> some_str = "wtfpython" >>> some_list = ['w', 't', 'f', 'p', 'y', 't', 'h', 'o', 'n'] >>> some_list is some_list[:] # False expected because a new object is created. False >>> some_str is some_str[:] # True because strings are immutable, so making a new object is of not much use. True
-
int('١٢٣٤٥٦٧٨٩')
returns123456789
in Python 3. In Python, Decimal characters include digit characters, and all characters that can be used to form decimal-radix numbers, e.g. U+0660, ARABIC-INDIC DIGIT ZERO. Here's an interesting story related to this behavior of Python. -
You can separate numeric literals with underscores (for better readability) from Python 3 onwards.
>>> six_million = 6_000_000 >>> six_million 6000000 >>> hex_address = 0xF00D_CAFE >>> hex_address 4027435774
-
'abc'.count('') == 4
. Here's an approximate implementation ofcount
method, which would make the things more cleardef count(s, sub): result = 0 for i in range(len(s) + 1 - len(sub)): result += (s[i:i + len(sub)] == sub) return result
The behavior is due to the matching of empty substring(
''
) with slices of length 0 in the original string.
A few ways in which you can contribute to wtfpython,
- Suggesting new examples
- Helping with translation (See issues labeled translation)
- Minor corrections like pointing out outdated snippets, typos, formatting errors, etc.
- Identifying gaps (things like inadequate explanation, redundant examples, etc.)
- Any creative suggestions to make this project more fun and useful
Please see CONTRIBUTING.md for more details. Feel free to create a new issue to discuss things.
PS: Please don't reach out with backlinking requests, no links will be added unless they're highly relevant to the project.
The idea and design for this collection were initially inspired by Denys Dovhan's awesome project wtfjs. The overwhelming support by Pythonistas gave it the shape it is in right now.
- https://www.youtube.com/watch?v=sH4XF6pKKmk
- https://www.reddit.com/r/Python/comments/3cu6ej/what_are_some_wtf_things_about_python
- https://sopython.com/wiki/Common_Gotchas_In_Python
- https://stackoverflow.com/questions/530530/python-2-x-gotchas-and-landmines
- https://stackoverflow.com/questions/1011431/common-pitfalls-in-python
- https://www.python.org/doc/humor/
- https://github.com/cosmologicon/pywat#the-undocumented-converse-implication-operator
- https://www.codementor.io/satwikkansal/python-practices-for-efficient-code-performance-memory-and-usability-aze6oiq65
- https://github.com/wemake-services/wemake-python-styleguide/search?q=wtfpython&type=Issues
- WFTPython discussion threads on Hacker News and Reddit.
If you like wtfpython, you can use these quick links to share it with your friends,
I've received a few requests for the pdf (and epub) version of wtfpython. You can add your details here to get them as soon as they are finished.
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