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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 snippetsand 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 attemptYou 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 modificationshere(the examples marked with asterisk are the ones added in the latest major revision).

So, here we go...

• Structure of the Examples
  • ▶ Some fancy Title
• Usage
• 👀 Examples
  • Section: Strain your brain!
    • ▶ First things first! *
    • ▶ Strings can be tricky sometimes
    • ▶ Be careful with chained operations
    • ▶ How not to useis 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?
    • ▶ Exceeds the limit for integer string conversion
  • Section: Slippery Slopes
    • ▶ Modifying a dictionary while iterating over it
    • ▶ Stubborndel 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!
    • ▶ Okay Python, Can you make me fly?
    • ▶goto, but why?
    • ▶ Brace yourself!
    • ▶ Let's meet Friendly Language Uncle For Life
    • ▶ Even Python understands that love is complicated
    • ▶ Yes, it exists!
    • ▶ Ellipsis *
    • ▶ Inpinity
    • ▶ Let's mangle
  • Section: Appearances are deceptive!
    • ▶ Skipping lines?
    • ▶ Teleportation
    • ▶ Well, something is fishy...
  • Section: Miscellaneous
    • ▶+= is faster
    • ▶ Let's make a giant string!
    • ▶ Slowing downdict lookups *
    • ▶ Bloating instancedicts *
    • ▶ Minor Ones *
• Contributing
• Acknowledgements
• 🎓 License
  • Surprise your friends as well!
  • More content like this?

All the examples are structured like below:

Section: (if necessary)

▶ Some fancy Title

# Set up the code.# Preparation for the magic...

Output (Python version(s)):

>>>triggering_statementSomeunexpectedoutput

(Optional): One line describing the unexpected output.

💡 Explanation:

• 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 in sequential order, 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 issuehere).
    • If yes, give a gentle pat on your back, and you may skip to the next example.

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>",line1a:="wtf_walrus"^SyntaxError:invalidsyntax>>> (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)>>>a6>>>a,b=6,9# Typical unpacking>>>a,b(6,9)>>> (a,b=16,19)# OopsFile"<stdin>",line1    (a,b=16,19)^SyntaxError:invalidsyntax>>> (a,b:=16,19)# This prints out a weird 3-tuple(6,16,19)>>>a# a is still unchanged?6>>>b16

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.

defsome_func():# Assume some expensive computation here# time.sleep(1000)return5# So instead of,ifsome_func():print(some_func())# Which is bad practice since computation is happening twice# ora=some_func()ifa:print(a)# Now you can concisely writeifa:=some_func():print(a)

Output (> 3.8):

555

This saved one line of code, and implicitly prevented invokingsome_func twice.

• Unparenthesized "assignment expression" (use of walrus operator), is restricted at the top level,hence theSyntaxError 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 formNAME:= 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]isa# Both reference same memory locationTrue

  • Similarly,(a, b := 16, 19) is equivalent to(a, (b := 16), 19) which is nothing but a 3-tuple.

1. Notice that both the ids are same.

assertid("some_string")==id("some"+"_"+"string")assertid("some_string")==id("some_string")

2.True because it is invoked in script. Might beFalse inpython shell oripython

a="wtf"b="wtf"assertaisba="wtf!"b="wtf!"assertaisb

3.True because it is invoked in script. Might beFalse inpython shell oripython

a,b="wtf!","wtf!"assertaisba="wtf!";b="wtf!"assertaisb

4.Disclaimer - snippet is not relevant in modern Python versions

Output (< Python3.7 )

>>>'a'*20is'aaaaaaaaaaaaaaaaaaaa'True>>>'a'*21is'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 isimplementation-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 foundhere

• Whena 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" thatthere'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 thisissue 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 apeephole optimization technique known as Constant folding.This means the expression'a'*20 is replaced by'aaaaaaaaaaaaaaaaaaaa' during compilation to savea 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 optimizerwith some change in logic as well, so the fourth snippet doesn't work for Python 3.7.You can read more about the changehere.

>>> (False==False)in [False]# makes senseFalse>>>False== (Falsein [False])# makes senseFalse>>>False==Falsein [False]# now what?True>>>TrueisFalse==FalseFalse>>>FalseisFalseisFalseTrue>>>1>0<1True>>> (1>0)<1False>>>1> (0<1)False

As perhttps://docs.python.org/3/reference/expressions.html#comparisons

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 likea == b == c and0 <= x <= 100.

• False is False is False is equivalent to(False is False) and (False is False)

• True is False == False is equivalent to(True 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 to(1 > 0) and (0 < 1) which evaluates toTrue.

• The expression(1 > 0) < 1 is equivalent toTrue < 1 and

  >>>int(True)1>>>True+1# not relevant for this example, but just for fun2

  So,1 < 1 evaluates toFalse

The following is a very famous example present all over the internet.

1.

>>>a=256>>>b=256>>>aisbTrue>>>a=257>>>b=257>>>aisbFalse

2.

>>>a= []>>>b= []>>>aisbFalse>>>a=tuple()>>>b=tuple()>>>aisbTrue

3.Output

>>>a,b=257,257>>>aisbTrue

Output (Python 3.7.x specifically)

>>>a,b=257,257>>>aisbFalse

The difference betweenis 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.

• Sois is for reference equality and== is for value equality. An example to clear things up,

  >>>classA:pass>>>A()isA()# These are two empty objects at two different memory locations.False

256 is an existing object but257 isn't

When you start up python the numbers from-5 to256 will be allocated. These numbers are used a lot, so it makes sense just to have them ready.

Quoting fromhttps://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 executingy = 257 to recognize that we've already created an integer of the value257, and so it goes on to create another object in the memory.

Similar optimization applies to otherimmutable objects like empty tuples as well. Since lists are mutable, that's why[] is [] will returnFalse and() is () will returnTrue. This explains our second snippet. Let's move on to the third one,

Botha andb 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 to257 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>>>aisbTrue

• 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 thisissue 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 byequivalence, not identity. So even though5,5.0, 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+0jTrue>>>5isnot5.0isnot5+0jTrue>>>some_dict= {}>>>some_dict[5.0]="Ruby">>>5.0insome_dictTrue>>> (5insome_dict)and (5+0jinsome_dict)True

• This applies when setting an item as well. So when you dosome_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 to5 (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 find5 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+0jTrue>>>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 ahash collision, and degrades the constant-time performance that hashing usually provides.)

classWTF:pass

Output:

>>>WTF()==WTF()# two different instances can't be equalFalse>>>WTF()isWTF()# identities are also differentFalse>>>hash(WTF())==hash(WTF())# hashes _should_ be different as wellTrue>>>id(WTF())==id(WTF())True

• Whenid 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 theis operator evaluate toFalse? Let's see with this snippet.

  classWTF(object):def__init__(self):print("I")def__del__(self):print("D")

  Output:

  >>>WTF()isWTF()IIDDFalse>>>id(WTF())==id(WTF())IDIDTrue

  As you may observe, the order in which the objects are destroyed is what made all the difference here.

fromcollectionsimportOrderedDictdictionary=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';classDictWithHash(dict):"""    A dict that also implements __hash__ magic.    """__hash__=lambdaself:0classOrderedDictWithHash(OrderedDict):"""    An OrderedDict that also implements __hash__ magic.    """__hash__=lambdaself:0

Output

>>>dictionary==ordered_dict# If a == bTrue>>>dictionary==another_ordered_dict# and b == cTrue>>>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 (mostrecentcalllast):File"<stdin>",line1,in<module>TypeError:unhashabletype:'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 order2

What is going on here?

• The reason why intransitive equality didn't hold amongdictionary,ordered_dict andanother_ordered_dict is because of the way__eq__ method is implemented inOrderedDict class. From thedocs

  Equality tests between OrderedDict objects are order-sensitive and are implemented aslist(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 allowsOrderedDict objects to be directly substituted anywhere a regular dictionary is used.

• Okay, so why did changing the order affect the length of the generatedset 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_dictinsome_setTrue>>>some_set.add(ordered_dict)>>>len(some_set)1>>>another_ordered_dictinsome_setTrue>>>some_set.add(another_ordered_dict)>>>len(some_set)1>>>another_set=set()>>>another_set.add(ordered_dict)>>>another_ordered_dictinanother_setFalse>>>another_set.add(another_ordered_dict)>>>len(another_set)2>>>dictionaryinanother_setTrue>>>another_set.add(another_ordered_dict)>>>len(another_set)2

  So the inconsistency is due toanother_ordered_dict in another_set beingFalse becauseordered_dict was already present inanother_set and as observed before,ordered_dict == another_ordered_dict isFalse.

defsome_func():try:return'from_try'finally:return'from_finally'defanother_func():for_inrange(3):try:continuefinally:print("Finally!")defone_more_func():# A gotcha!try:foriinrange(3):try:1/iexceptZeroDivisionError:# Let's throw it here and handle it outside for loopraiseZeroDivisionError("A trivial divide by zero error")finally:print("Iteration",i)breakexceptZeroDivisionErrorase:print("Zero division error occurred",e)

Output:

>>>some_func()'from_finally'>>>another_func()Finally!Finally!Finally!>>>1/0Traceback (mostrecentcalllast):File"<stdin>",line1,in<module>ZeroDivisionError:divisionbyzero>>>one_more_func()Iteration0

• When areturn,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 lastreturn 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 areturn orbreak statement, the temporarily saved exception is discarded.

some_string="wtf"some_dict= {}fori,some_dict[i]inenumerate(some_string):i=10

Output:

>>>some_dict# An indexed dict appears.{0:'w',1:'t',2:'f'}

• Afor statement is defined in thePython grammar as:

  for_stmt: 'for' exprlist 'in' testlist ':' suite ['else' ':' suite]

  Whereexprlist is the assignment target. This means that the equivalent of{exprlist} = {next_value} isexecuted for each item in the iterable.An interesting example that illustrates this:

  foriinrange(4):print(i)i=10

  Output:

  0123

  Did you expect the loop to run just once?

  💡 Explanation:

  • The assignment statementi = 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).

• Theenumerate(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 expressiongen= (xforxinarrayifarray.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= (xforxinarray_1)array_1= [1,2,3,4,5]array_2= [1,2,3,4]gen_2= (xforxinarray_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+jforiinarray_3forjinarray_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 agenerator expression, thein 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 ofg1 andg2 in the second part is due the way variablesarray_1 andarray_2 are re-assigned values.

• In the first case,array_1 is bound 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 toarray_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 oflist(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 inPEP-289

  Only the outermost for-expression is evaluated immediately, the other expressions are deferred until the generator is run.

>>>'something'isnotNoneTrue>>>'something'is (notNone)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 rowrow= [""]*3#row i['', '', '']# Let's make a boardboard= [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 initializerow variable, this visualization explains what happens in the memory

And when theboard is initialized by multiplying therow, this is what happens inside the memory (each of the elementsboard[0],board[1] andboard[2] is a reference to the same list referred byrow)

We can avoid this scenario here by not usingrow variable to generateboard. (Asked inthis issue).

>>>board= [['']*3for_inrange(3)]>>>board[0][0]="X">>>board[['X','',''], ['','',''], ['','','']]

funcs= []results= []forxinrange(7):defsome_func():returnxfuncs.append(some_func)results.append(some_func())# note the function call herefuncs_results= [func()forfuncinfuncs]

Output (Python version):

>>>results[0,1,2,3,4,5,6]>>>funcs_results[6,6,6,6,6,6,6]

The values ofx were different in every iteration prior to appendingsome_func tofuncs, but all the functions return 6 when they're evaluated after the loop completes.

>>>powers_of_x= [lambdax:x**iforiinrange(10)]>>> [f(2)forfinpowers_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 thevariable, not itsvalue.The function looks upx in the surrounding context, rather than using the value ofx at the timethe 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:

>>>importinspect>>>inspect.getclosurevars(funcs[0])ClosureVars(nonlocals={},globals={'x':6},builtins={},unbound=set())

Sincex is a global value, we can change the value that thefuncs will lookup and return by updatingx:

>>>x=42>>> [func()forfuncinfuncs][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 variableinside 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 ofx at that point in time.

funcs= []forxinrange(7):defsome_func(x=x):returnxfuncs.append(some_func)

Output:

>>>funcs_results= [func()forfuncinfuncs]>>>funcs_results[0,1,2,3,4,5,6]

It is not longer using thex 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.

>>>classA: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 ametaclass in Python.
• Everything is anobject in Python, which includes classes as well as their objects (instances).
• classtype is the metaclass of classobject, and every class (includingtype) has inherited directly or indirectly fromobject.
• There is no real base class amongobject 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 betweenobject andtype (both being instances of each other as well as themselves) exist in Python because of "cheating" at the implementation level.

Output:

>>>fromcollections.abcimportHashable>>>issubclass(list,object)True>>>issubclass(object,Hashable)True>>>issubclass(list,Hashable)False

The Subclass relationships were expected to be transitive, right? (i.e., ifA is a subclass ofB, andB is a subclass ofC, theAshould a subclass ofC)

• Subclass relationships are not necessarily transitive in Python. Anyone is allowed to define their own, arbitrary__subclasscheck__ in a metaclass.
• Whenissubclass(cls, Hashable) is called, it simply looks for non-Falsey "__hash__" method incls or anything it inherits from.
• Sinceobject is hashable, butlist is non-hashable, it breaks the transitivity relation.
• More detailed explanation can be foundhere.

classSomeClass:defmethod(self):pass@classmethoddefclassm(cls):pass@staticmethoddefstaticm():pass

Output:

>>>print(SomeClass.methodisSomeClass.method)True>>>print(SomeClass.classmisSomeClass.classm)False>>>print(SomeClass.classm==SomeClass.classm)True>>>print(SomeClass.staticmisSomeClass.staticm)True

Accessingclassm twice, we get an equal object, but not thesame one? Let's see what happenswith instances ofSomeClass:

o1=SomeClass()o2=SomeClass()

Output:

>>>print(o1.method==o2.method)False>>>print(o1.method==o1.method)True>>>print(o1.methodiso1.method)False>>>print(o1.classmiso1.classm)False>>>print(o1.classm==o1.classm==o2.classm==SomeClass.classm)True>>>print(o1.staticmiso1.staticmiso2.staticmisSomeClass.staticm)True

Accessingclassm ormethod twice, creates equal but notsame objects for the same instance ofSomeClass.

• Functions aredescriptors. Whenever a function is accessed as anattribute, the descriptor is invoked, creating a method object which "binds" the function with the object owning theattribute. If called, the method calls the function, implicitly passing the bound object as the first argument(this is how we getself as the first argument, despite not passing it explicitly).

>>>o1.method<boundmethodSomeClass.methodof<__main__.SomeClassobjectat ...>>

• Accessing the attribute multiple times creates a method object every time! Thereforeo1.method is o1.method isnever truthy. Accessing functions as class attributes (as opposed to instance) does not create methods, however; soSomeClass.method is SomeClass.method is truthy.

>>>SomeClass.method<functionSomeClass.methodat ...>

• classmethod transforms functions into class methods. Class methods are descriptors that, when accessed, createa method object which binds theclass (type) of the object, instead of the object itself.

>>>o1.classm<boundmethodSomeClass.classmof<class'__main__.SomeClass'>>

• Unlike functions,classmethods will create a method also when accessed as class attributes (in which case theybind the class, not to the type of it). SoSomeClass.classm is SomeClass.classm is falsy.

>>>SomeClass.classm<boundmethodSomeClass.classmof<class'__main__.SomeClass'>>

• A method object compares equal when both the functions are equal, and the bound objects are the same. Soo1.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 methodobjects are ever created, so comparison withis is truthy.

>>>o1.staticm<functionSomeClass.staticmat ...>>>>SomeClass.staticm<functionSomeClass.staticmat ...>

• Having to create new "method" objects every time Python calls instance methods and having to modify the argumentsevery time in order to insertself affected performance badly.CPython 3.7solved it by introducing new opcodes that deal with calling methodswithout creating the temporary method objects. This is used only when the accessed function is actually called, so thesnippets 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 ofall function is equivalent to

• defall(iterable):forelementiniterable:ifnotelement:returnFalsereturnTrue

• 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):

>>>deff(x,y,):...print(x,y)...>>>defg(x=4,y=5,):...print(x,y)...>>>defh(x,**kwargs,):File"<stdin>",line1defh(x,**kwargs,):^SyntaxError:invalidsyntax>>>defh(*args,):File"<stdin>",line1defh(*args,):^SyntaxError:invalidsyntax

• 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 isfixed in Python 3.6. The remarks inthis post discuss in brief different usages of trailing commas in Python.

Output:

>>>print("\"")">>>print(r"\"")\">>>print(r"\")File"<stdin>",line1print(r"\")^SyntaxError:EOLwhilescanningstringliteral>>>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 prefixr), 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=Truey=False

Output:

>>>notx==yTrue>>>x==notyFile"<input>",line1x==noty^SyntaxError:invalidsyntax

• Operator precedence affects how an expression is evaluated, and== operator has higher precedence thannot operator in Python.
• Sonot x == y is equivalent tonot (x == y) which is equivalent tonot (True == False) finally evaluating toTrue.
• Butx == 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 thenot 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>",line3print("""wtfpython")                        ^SyntaxError: EOF while scanning triple-quoted string literal

• Python supports implicitstring 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=0booleans_found_so_far=0foriteminmixed_list:ifisinstance(item,int):integers_found_so_far+=1elifisinstance(item,bool):booleans_found_so_far+=1

Output:

>>>integers_found_so_far4>>>booleans_found_so_far0

2.

>>>some_bool=True>>>"wtf"*some_bool'wtf'>>>some_bool=False>>>"wtf"*some_bool''

3.

deftell_truth():True=FalseifTrue==False:print("I have lost faith in truth!")

Output (< 3.x):

>>>tell_truth()Ihavelostfaithintruth!

• 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 ofTrue is1 and that ofFalse is0.

  >>>int(True)1>>>int(False)0

• See this StackOverflowanswer for the rationale behind it.

• Initially, Python used to have nobool 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.

classA:x=1classB(A):passclassC(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.

classSomeClass:some_var=15some_list= [5]another_list= [5]def__init__(self,x):self.some_var=x+1self.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_listisSomeClass.another_listTrue>>>another_obj.another_listissome_obj.another_listTrue

• 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')defsome_func(val):return"something"

Output (<= 3.7.x):

>>> [xforxinsome_iterable]['a','b']>>> [(yieldx)forxinsome_iterable]<generatorobject<listcomp>at0x7f70b0a4ad58>>>>list([(yieldx)forxinsome_iterable])['a','b']>>>list((yieldx)forxinsome_iterable)['a',None,'b',None]>>>list(some_func((yieldx))forxinsome_iterable)['a','something','b','something']

• This is a bug in CPython's handling ofyield 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 allowsyield inside list comprehension and will throw aSyntaxError.

1.

defsome_func(x):ifx==3:return ["wtf"]else:yieldfromrange(x)

Output (> 3.3):

>>>list(some_func(3))[]

Where did the"wtf" go? Is it due to some special effect ofyield from? Let's validate that,

2.

defsome_func(x):ifx==3:return ["wtf"]else:foriinrange(x):yieldi

Output:

>>>list(some_func(3))[]

The same result, this didn't work either.

• From Python 3.3 onwards, it became possible to usereturn statement with values inside generators (SeePEP380). Theofficial docs say that,

"...return expr in a generator causesStopIteration(expr) to be raised upon exit from the generator."

• In the case ofsome_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))exceptStopIterationase:some_string=e.value

  >>>some_string["wtf"]

1.

a=float('inf')b=float('nan')c=float('-iNf')# These strings are case-insensitived=float('nan')

Output:

>>>ainf>>>bnan>>>c-inf>>>float('some_other_string')ValueError:couldnotconvertstringtofloat:some_other_string>>>a==-c# inf==infTrue>>>None==None# None == NoneTrue>>>b==d# but nan!=nanFalse>>>50/a0.0>>>a/anan>>>23+bnan

2.

>>>x=float('nan')>>>y=x/x>>>yisy# identity holdsTrue>>>y==y# equality fails of yFalse>>> [y]== [y]# but the equality succeeds for the list containing yTrue

• '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 standardsNaN != 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 ofx 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'objectdoesnotsupportitemassignment>>>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'objectdoesnotsupportitemassignment>>>another_tuple([1,2], [3,4], [5,6,1000,99,999])

But I thought tuples were immutable...

• Quoting fromhttps://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 inofficial Python FAQ.

e=7try:raiseException()exceptExceptionase:pass

Output (Python 2.x):

>>>print(e)# prints nothing

Output (Python 3.x):

>>>print(e)NameError:name'e'isnotdefined

• Source:https://docs.python.org/3/reference/compound_stmts.html#except

  When an exception has been assigned usingas target, it is cleared at the end of theexcept clause. This is as if

  exceptEasN:foo

  was translated into

  exceptEasN:try:foofinally:delN

  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 variablee 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:

  deff(x):del(x)print(x)x=5y= [5,4,3]

  Output:

  >>>f(x)UnboundLocalError:localvariable'x'referencedbeforeassignment>>>f(y)UnboundLocalError:localvariable'x'referencedbeforeassignment>>>x5>>>y[5,4,3]

• In Python 2.x, the variable namee gets assigned toException() instance, so when you try to print, it prints nothing.

  Output (Python 2.x):

  >>>eException()>>>printe# Nothing is printed!

classSomeClass(str):passsome_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 objects 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

  classSomeClass(str):def__eq__(self,other):return (type(self)isSomeClassandtype(other)isSomeClassandsuper().__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 toPython language reference, assignment statements have the form

  (target_list "=")+ (expression_list | yield_expression)

  and

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 beone 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 fromleft 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 isa[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 key5 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_listissome_list[0]True>>>some_list[0][0][0][0][0][0]==some_listTrue

  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= {},5a[b]=a,b

  And the circular reference can be justified by the fact thata[b][0] is the same object asa

  >>>a[b][0]isaTrue

>>># Python 3.10.6>>>int("2"*5432)>>># Python 3.10.8>>>int("2"*5432)

Output:

>>># Python 3.10.6222222222222222222222222222222222222222222222222222222222222222...>>># Python 3.10.8Traceback (mostrecentcalllast):   ...ValueError:Exceedsthelimit (4300)forintegerstringconversion:valuehas5432digits;usesys.set_int_max_str_digits()toincreasethelimit.

This call toint() works fine in Python 3.10.6 and raises a ValueError in Python 3.10.8. Note that Python can still work with large integers. The error is only raised when converting between integers and strings.

Fortunately, you can increase the limit for the allowed number of digits when you expect an operation to exceed it. To do this, you can use one of the following:

• The -X int_max_str_digits command-line flag
• The set_int_max_str_digits() function from the sys module
• The PYTHONINTMAXSTRDIGITS environment variable

Check the documentation for more details on changing the default limit if you expect your code to exceed this value.

x= {0:None}foriinx:delx[i]x[i+1]=Noneprint(i)

Output (Python 2.7- Python 3.5):

01234567

Yes, it runs for exactlyeight 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 thishere or inthis StackOverflow thread.
• Python 3.7.6 onwards, you'll seeRuntimeError: dictionary keys changed during iteration exception if you try to do this.

classSomeClass:def__del__(self):print("Deleted!")

Output:1.

>>>x=SomeClass()>>>y=x>>>delx# this should print "Deleted!">>>delyDeleted!

Phew, deleted at last. You might have guessed what saved__del__ from being called in our first attempt to deletex. Let's add more twists to the example.

2.

>>>x=SomeClass()>>>y=x>>>delx>>>y# check if y exists<__main__.SomeClassinstanceat0x7f98a1a67fc8>>>>dely# Like previously, this should print "Deleted!">>>globals()# oh, it didn't. Let's check all our global variables and confirmDeleted!{'__builtins__':<module'__builtin__' (built-in)>,'SomeClass':<class__main__.SomeClassat0x7f98a1a5f668>,'__package__':None,'__name__':'__main__','__doc__':None}

Okay, now it's deleted 😕

• del x doesn’t directly callx.__del__().
• Whendel 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.
• Callingglobals (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=1defsome_func():returnadefanother_func():a+=1returna

2.

defsome_closure_func():a=1defsome_inner_func():returnareturnsome_inner_func()defanother_closure_func():a=1defanother_inner_func():a+=1returnareturnanother_inner_func()

Output:

>>>some_func()1>>>another_func()UnboundLocalError:localvariable'a'referencedbeforeassignment>>>some_closure_func()1>>>another_closure_func()UnboundLocalError:localvariable'a'referencedbeforeassignment

• When you make an assignment to a variable in scope, it becomes local to that scope. Soa 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 variablea inanother_func, we have to use theglobal keyword.

  defanother_func()globalaa+=1returna

  Output:

  >>>another_func()2

• Inanother_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 variablea inanother_inner_func, use thenonlocal keyword. The nonlocal statement is used to refer to variables defined in the nearest outer (excluding the global) scope.

  defanother_func():a=1defanother_inner_func():nonlocalaa+=1returnareturnanother_inner_func()

  Output:

  >>>another_func()2

• The keywordsglobal andnonlocal tell the python interpreter to not declare new variables and look them up in the corresponding outer scopes.

• Readthis 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]foridx,iteminenumerate(list_1):delitemforidx,iteminenumerate(list_2):list_2.remove(item)foridx,iteminenumerate(list_3[:]):list_3.remove(item)foridx,iteminenumerate(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, andlist_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 betweendel,remove, andpop:

• 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 remove1 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 StackOverflowthread explaining the example

• See also this nice StackOverflowthread 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 element3 go from thenumbers list?

• From Pythondocs, here's an approximate implementation of zip function,

  defzip(*iterables):sentinel=object()iterators= [iter(it)foritiniterables]whileiterators:result= []foritiniterators:elem=next(it,sentinel)ifelemissentinel:returnresult.append(elem)yieldtuple(result)

• So the function takes in arbitrary number of iterable objects, adds each of their items to theresult 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 theresult list are discarded. That's what happened with3 in thenumbers_iter.

• The correct way to do the above usingzip would be,

  >>>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)]

  The first argument of zip should be the one with fewest elements.

1.

forxinrange(7):ifx==6:print(x,': for x inside loop')print(x,': x in global')

Output:

6 :forxinsideloop6 :xinglobal

Butx was never defined outside the scope of for loop...

2.

# This time let's initialize x firstx=-1forxinrange(7):ifx==6:print(x,': for x inside loop')print(x,': x in global')

Output:

6 :forxinsideloop6 :xinglobal

3.

Output (Python 2.x):

>>>x=1>>>print([xforxinrange(5)])[0,1,2,3,4]>>>print(x)4

Output (Python 3.x):

>>>x=1>>>print([xforxinrange(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 inWhat’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."

defsome_func(default_arg=[]):default_arg.append("some_string")returndefault_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.

  defsome_func(default_arg=[]):default_arg.append("some_string")returndefault_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 assignNone as the default value and later check if any value is passed to the function corresponding to that argument. Example:

  defsome_func(default_arg=None):ifdefault_argisNone:default_arg= []default_arg.append("some_string")returndefault_arg

some_list= [1,2,3]try:# This should raise an ``IndexError``print(some_list[4])exceptIndexError,ValueError:print("Caught!")try:# This should raise a ``ValueError``some_list.remove(4)exceptIndexError,ValueError:print("Caught again!")

Output (Python 2.x):

Caught!ValueError:list.remove(x):xnotinlist

Output (Python 3.x):

File"<input>",line3exceptIndexError,ValueError:^SyntaxError:invalidsyntax

• 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>",line4except (IndexError,ValueError),e:^IndentationError:unindentdoesnotmatchanyouterindentationlevel

• Separating the exception from the variable with a comma is deprecated and does not work in Python 3; the correct way is to useas. Example,

  some_list= [1,2,3]try:some_list.remove(4)except (IndexError,ValueError)ase:print("Caught again!")print(e)

  Output:

  Caught again!list.remove(x): x not in list

1.

a= [1,2,3,4]b=aa=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=aa+= [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. Classesmay implement theop= operators differently, and lists do this.

• The expressiona = a + [5,6,7,8] generates a new list and setsa's reference to that new list, leavingb unchanged.

• The expressiona += [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=5classSomeClass:x=17y= (xforiinrange(10))

Output:

>>>list(SomeClass.y)[0]5

2.

x=5classSomeClass:x=17y= [xforiinrange(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:

defget_middle(some_list):mid_index=round(len(some_list)/2)returnsome_list[mid_index-1]

Python 3.x:

>>>get_middle([1])# looks good1>>>get_middle([1,2,3])# looks good2>>>get_middle([1,2,3,4,5])# huh?2>>>len([1,2,3,4,5])/2# good2.5>>>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() usesbanker's rounding where .5 fractions are rounded to the nearesteven number:

>>>round(0.5)0>>>round(1.5)2>>>round(2.5)2>>>importnumpy# 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 inIEEE 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 theround() docs orthis stackoverflow thread for more information.
• Note thatget_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)ifTrueelseNone,None

Output:

>>>x,y# expected (0, 1)((0,1),None)

2.

t= ('one','two')foriint:print(i)t= ('one')foriint:print(i)t= ()print(t)

Output:

onetwoonetuple()

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.

defsome_recursive_func(a):ifa[0]==0:returna[0]-=1some_recursive_func(a)returnadefsimilar_recursive_func(a):ifa==0:returnaa-=1similar_recursive_func(a)returna

Output:

>>>some_recursive_func([5,0])[0,0]>>>similar_recursive_func(5)4

• For 1, the correct statement for expected behavior isx, y = (0, 1) if True else (None, None).

• For 2, the correct statement for expected behavior ist = ('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']

• NoAssertionError 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">>>asserta==bTraceback (mostrecentcalllast):File"<stdin>",line1,in<module>AssertionError>>>assert (a==b,"Values are not equal")<stdin>:1:SyntaxWarning:assertionisalwaystrue,perhapsremoveparentheses?>>>asserta==b,"Values are not equal"Traceback (mostrecentcalllast):File"<stdin>",line1,in<module>AssertionError:Valuesarenotequal

• As for the fifth snippet, most methods that modify the items of sequence/mapping objects likelist.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 fromhere).

• Last one should be fairly obvious, mutable object (likelist) can be altered in the function, and the reassignment 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 thedocs

  If sep is not specified or isNone, 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.pydefsome_weird_name_func_():print("works!")def_another_weird_name_func():print("works!")

Output

>>>frommoduleimport*>>>some_weird_name_func_()"works!">>>_another_weird_name_func()Traceback (mostrecentcalllast):File"<stdin>",line1,in<module>NameError:name'_another_weird_name_func'isnotdefined

• 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 usedfrom ... import a, b, c syntax, the aboveNameError wouldn't have occurred.

  >>>frommoduleimportsome_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']defsome_weird_name_func_():print("works!")def_another_weird_name_func():print("works!")

  Output

  >>>_another_weird_name_func()"works!">>>some_weird_name_func_()Traceback (mostrecentcalllast):File"<stdin>",line1,in<module>NameError:name'some_weird_name_func_'isnotdefined

>>>x=7,8,9>>>sorted(x)==xFalse>>>sorted(x)==sorted(x)True>>>y=reversed(x)>>>sorted(y)==sorted(y)False

• Thesorted 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)

• Unlikesorted, 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 comparisonsorted(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], [])

fromdatetimeimportdatetimemidnight=datetime(2018,1,1,0,0)midnight_time=midnight.time()noon=datetime(2018,1,1,12,0)noon_time=noon.time()ifmidnight_time:print("Time at midnight is",midnight_time)ifnoon_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 fordatetime.time object was considered to beFalse if it represented midnight in UTC. It is error-prone when using theif obj: syntax to check if theobj is null or some equivalent of "empty."

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

importantigravity

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 theclassic XKCD comic about Python.
• Well, there's more to it. There'sanother easter egg inside the easter egg. If you look at thecode, there's a function defined that purports to implement theXKCD's geohashing algorithm.

fromgotoimportgoto,labelforiinrange(9):forjinrange(9):forkinrange(9):print("I am trapped, please rescue!")ifk==2:goto .breakout# breaking out from a deeply nested looplabel .breakoutprint("Freedom!")

Output (Python 2.3):

Iamtrapped,pleaserescue!Iamtrapped,pleaserescue!Freedom!

• A working version ofgoto in Python wasannounced 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 thereason to whygoto 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__importbraces

Output:

File"some_file.py",line1from __future__importbracesSyntaxError:notachance

Braces? No way! If you think that's disappointing, use Java. Okay, another surprising thing, can you find where's theSyntaxError raised in__future__ modulecode?

• 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 presenthere infuture.c file.
• When the CPython compiler encounters afuture statement, it first runs the appropriate code infuture.c before treating it as a normal import statement.

Output (Python 3.x)

>>>from __future__importbarry_as_FLUFL>>>"Ruby"!="Python"# there's no doubt about itFile"some_file.py",line1"Ruby"!="Python"^SyntaxError:invalidsyntax>>>"Ruby"<>"Python"True

There we go.

• This is relevant toPEP-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 themhere.

• It works well in an interactive environment, but it will raise aSyntaxError when you run via python file (see thisissue). However, you can wrap the statement inside aneval orcompile to get it working,

  from __future__importbarry_as_FLUFLprint(eval('"Ruby" <> "Python"'))

importthis

Wait, what'sthis?this is love ❤️

Output:

The Zen of Python, by Tim PetersBeautiful 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>>>thisisloveTrue>>>loveisTrueFalse>>>loveisFalseFalse>>>loveisnotTrueorFalseTrue>>>loveisnotTrueorFalse;loveislove# Love is complicatedTrue

• 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 ofthis.py. Interestingly,the code for the Zen violates itself (and that's probably the only place where this happens).
• Regarding the statementlove 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).

Theelse clause for loops. One typical example might be:

defdoes_exists_num(l,to_find):fornuminl:ifnum==to_find:print("Exists!")breakelse: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)Doesnotexist

Theelse clause in exception handling. An example,

try:passexcept:print("Exception occurred!!!")else:print("Try block executed successfully...")

Output:

Tryblockexecutedsuccessfully...

• Theelse 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.

defsome_func():Ellipsis

Output

>>>some_func()# No output, No Error>>>SomeRandomStringTraceback (mostrecentcalllast):File"<stdin>",line1,in<module>NameError:name'SomeRandomString'isnotdefined>>>EllipsisEllipsis

• 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 likepass statement)

  • In slicing syntax to represent the full slices in remaining direction

    >>>importnumpyasnp>>>three_dimensional_array=np.arange(8).reshape(2,2,2)array([    [        [0,1],        [2,3]    ],    [        [4,5],        [6,7]    ]])

    So ourthree_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 dimensions

    >>>three_dimensional_array[:,:,1]array([[1,3],   [5,7]])>>>three_dimensional_array[...,1]# using Ellipsis.array([[1,3],   [5,7]])

    Note: 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 (n_dimensional_array[firs_dim_slice, ..., last_dim_slice])

  • Intype 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).