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 to a regular user at first sight.
Here is a fun project attempting to collect such classic & tricky examples of unexpected behaviors and lesser known features in Python, and discuss what exactly is happening under the hood!
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 think you'll find them interesting as well!
If you're an experienced Python programmer, you can take it as a challenge to get most of them right in first attempt. You may be already familiar with some of these examples, and I might be able to revive sweet old memories of yours being bitten by these gotchas :sweat_smile: And if you're a returning reader, you can learn about the new modifications [here](https://github.com/satwikkansal/wtfpython/releases/).
**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 in the description.
A nice way to get the most out of these examples, in my opinion, will be just to read the examples chronologically, and for every example:
- Carefully read the initial code for setting up the example. If you're an experienced Python programmer, most of the times you will successfully anticipate what's going to happen next.
+ Make sure if you know the exact reason behind the output being the way it is.
- If no, take a deep breath, and read the explanation (and if you still don't understand, shout out! and create an issue [here](https://github.com/satwikkansal/wtfPython)).
The built-in `ord()` function returns a character's Unicode [code point](https://en.wikipedia.org/wiki/Code_point), and different code positions of Cyrillic 'e' and Latin 'e' justify the behavior of the above example.
---
### ▶ Teleportation *
```py
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:**
```py
>>> energy_send(123.456)
>>> energy_receive()
123.456
```
Where's the Nobel Prize?
#### 💡 Explanation:
* 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).
**Note:** If you're not able to reproduce this, try running the file [mixed_tabs_and_spaces.py](/mixed_tabs_and_spaces.py) via the shell.
#### 💡 Explanation
* **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.
+ `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](http://xkcd.com/353/) about Python.
+ Well, there's more to it. There's **another easter egg inside the easter egg**. If look at the [code](https://github.com/python/cpython/blob/master/Lib/antigravity.py#L7-L17), there's a function defined that purports to implement the [XKCD's geohashing algorithm](https://xkcd.com/426/).
goto .breakout # breaking out from a deeply nested loop
label .breakout
print("Freedom!")
```
**Output (Python 2.3):**
```py
I'm trapped, please rescue!
I'm trapped, please rescue!
Freedom!
```
#### 💡 Explanation:
- A working version of `goto` in Python was [announced](https://mail.python.org/pipermail/python-announce-list/2004-April/002982.html) 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](https://docs.python.org/3/faq/design.html#why-is-there-no-goto) to why `goto` is not present in Python.
- This is relevant to [PEP-401](https://www.python.org/dev/peps/pep-0401/) 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](https://www.python.org/dev/peps/pep-0401/).
---
### ▶ Even Python understands that love is complicated *
*`this` module in Python is an easter egg for The Zen Of Python ([PEP 20](https://www.python.org/dev/peps/pep-0020)).
* And if you think that's already interesting enough, check out the implementation of [this.py](https://hg.python.org/cpython/file/c3896275c0f6/Lib/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.
- The `else` clause after a loop is executed only when there's no explicit `break` after all the iterations.
-`else` clause after try block is also called "completion clause" as reaching the `else` clause in a `try` statement means that the try block actually completed successfully.
* [Name Mangling](https://en.wikipedia.org/wiki/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) and not ending with more than one trailing underscore by adding `_NameOfTheClass` in front.
* So, to access `__honey` attribute, we are required to append `_Yo` to the front which would prevent conflicts with the same name attribute defined in any other class.
>>> id("some" + "_" + "string") # Notice that both the ids are same.
140420665652016
```
2\.
```py
>>> a = "wtf"
>>> b = "wtf"
>>> a is b
True
>>> a = "wtf!"
>>> b = "wtf!"
>>> a is b
False
>>> a, b = "wtf!", "wtf!"
>>> a is b
True
```
3\.
```py
>>> 'a' * 20 is 'aaaaaaaaaaaaaaaaaaaa'
True
>>> 'a' * 21 is 'aaaaaaaaaaaaaaaaaaaaa'
False
```
Makes sense, right?
#### 💡 Explanation:
+ Such behavior is due to CPython optimization (called string interning) that tries to use existing immutable objects in some cases rather than creating a new object every time.
+ Note that the keyword `is` is used for _reference_ equality (unlike `==` which is used for _value_ equality); this means that `a is b` is the same as `id(a) == id(b)`, and therefore it should not be used for string comparison. It is only used here to illustrate the effects of string interning.
+ In the snippets above, strings are implicitly interned. The decision of when to implicitly intern a string is implementation dependent. There are some facts that can be used to guess if a string will be interned or not:
+ When `a` and `b` 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 compiler optimization and specifically applies to the interactive environment.
* Python dictionaries check for equality and compare the hash value to determine if two keys are the same.
* Immutable objects with same value always have the same hash in Python.
```py
>>> 5 == 5.0
True
>>> hash(5) == hash(5.0)
True
```
**Note:** Objects with different values may also have same hash (known as hash collision).
* When the statement `some_dict[5] = "Python"` is executed, the existing value "JavaScript" is overwritten with "Python" because Python recongnizes `5` and `5.0` as the same keys of the dictionary `some_dict`.
* This StackOverflow [answer](https://stackoverflow.com/a/32211042/4354153) explains beautifully the rationale behind it.
- When a `return`, `break` or `continue` statement is executed in the `try` suite of a "try…finally" statement, the `finally` clause is also executed ‘on the way out.
- The return value of a function is determined by the last `return` statement executed. Since the `finally` clause always executes, a `return` statement executed in the `finally` clause will always be the last one executed.
>>> 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
```
#### 💡 Explanation:
* When `id` was called, Python created a `WTF` class object and passed it to the `id` function. The `id` function takes its `id` (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, 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 to `False`? Let's see with this snippet.
* A `for` statement is defined in the [Python grammar](https://docs.python.org/3/reference/grammar.html) 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:
```py
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)` this case) is unpacked and assigned the target list variables (`i` in this case).
* The `enumerate(some_string)` function yields a new value `i` (A counter going up) and a character from the `some_string` in each iteration. It then sets the (just assigned) `i` key of the dictionary `some_dict` to that character. The unrolling of the loop can be simplified as:
- In a [generator](https://wiki.python.org/moin/Generators) 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 of `1`, `8` and `15`, only the count of `8` is greater than `0`, the generator only yields `8`.
*`==` 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,
```py
>>> [] == []
True
>>> [] is [] # These are two empty lists 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. :-)
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.
* 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 already `257` 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.
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`)
- 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. So all of the functions use the latest value assigned to the variable for computation.
- To get the desired behavior you can pass in the loop variable as a named variable to the function. **Why this works?** Because this will define the variable again within the function's scope.
- 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](https://bugs.python.org/issue9232). The remarks in [this](https://bugs.python.org/issue9232#msg248399) post discuss in brief different usages of trailing commas in Python.
- In a raw string literal, as indicated by the prefix `r`, the backslash doesn't have the special meaning.
```py
>>> print(repr(r"wt\"f"))
'wt\\"f'
```
- What the interpreter actually does, though, is simply change the behavior of backslashes, so they pass themselves and the following character through. That's why backslashes don't work at the end of a raw string.
* Operator precedence affects how an expression is evaluated, and `==` operator has higher precedence than `not` operator in Python.
* So `not x == y` is equivalent to `not (x == y)` which is equivalent to `not (True == False)` finally evaluating to `True`.
* But `x == not y` raises a `SyntaxError` because it can be thought of being equivalent to `(x == not) y` and not `x == (not y)` which you might have expected at first sight.
* The parser expected the `not` token to be a part of the `not in` operator (because both `==` and `not in` operators have the same precedence), but after not being able to find an `in` token following the `not` token, it raises a `SyntaxError`.
>>> # The following statements raise `SyntaxError`
>>> # print('''wtfpython')
>>> # print("""wtfpython")
```
#### 💡 Explanation:
+ Python supports implicit [string literal concatenation](https://docs.python.org/2/reference/lexical_analysis.html#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.
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."
* 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.
---
### ▶ yielding None
```py
some_iterable = ('a', 'b')
def some_func(val):
return "something"
```
**Output:**
```py
>>> [x for x in some_iterable]
['a', 'b']
>>> [(yield x) for x in some_iterable]
<generatorobject<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']
```
#### 💡 Explanation:
- Source and explanation can be found here: https://stackoverflow.com/questions/32139885/yield-in-list-comprehensions-and-generator-expressions
- Related bug report: http://bugs.python.org/issue10544
---
### ▶ Mutating the immutable!
```py
some_tuple = ("A", "tuple", "with", "values")
another_tuple = ([1, 2], [3, 4], [5, 6])
```
**Output:**
```py
>>> 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...
#### 💡 Explanation:
* Quoting from https://docs.python.org/2/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.
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 the `except` clause. The same is not the case with functions which have their separate inner-scopes. The example below illustrates this:
```py
def f(x):
del(x)
print(x)
x = 5
y = [5, 4, 3]
```
**Output:**
```py
>>>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 to `Exception()` instance, so when you try to print, it prints nothing.
- Initially, Python used to have no `bool` type (people used 0 for false and non-zero value like 1 for true). Then they added `True`, `False`, and a `bool` type, but, for backward compatibility, they couldn't make `True` and `False` constants- they just were built-in variables.
Most methods that modify the items of sequence/mapping objects like `list.append`, `dict.update`, `list.sort`, etc. modify the objects in-place and return `None`. 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](http://docs.python.org/2/faq/design.html#why-doesn-t-list-sort-return-the-sorted-list))
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 in `cls` or anything it inherits from.
* Since `object` is hashable, but `list` is non-hashable, it breaks the transitivity relation.
* More detailed explanation can be found [here](https://www.naftaliharris.com/blog/python-subclass-intransitivity/).
* According to [Python language reference](https://docs.python.org/2/reference/simple_stmts.html#assignment-statements), assignment statements have the form
> 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 are `a, b` and `a[b]` (note the expression list is exactly one, which in our case is `{}, 5`).
* After the expression list is evaluated, it's value is unpacked to the target lists from **left to right**. So, in our case, first the `{}, 5` tuple is unpacked to `a, b` and we now have `a = {}` and `b = 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 both `a` and `b` have not been defined in the statements before. But remember, we just assigned `a` to `{}` and `b` to `5`).
* 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 that `a` is already referencing). Another simpler example of circular reference could be
* 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.
* For more information, you may refer to this StackOverflow [thread](https://stackoverflow.com/questions/44763802/bug-in-python-dict) explaining a similar example in detail.
Phew, deleted at last. You might have guessed what saved from `__del__` being called in our first attempt to delete `x`. Let's add more twist to the example.
2\.
```py
>>> x = SomeClass()
>>> y = x
>>> del x
>>> y # check if y exists
<__main__.SomeClassinstanceat0x7f98a1a67fc8>
>>> del y # Like previously, this should print "Deleted!"
>>> globals() # oh, it didn't. Let's check all our global variables and confirm
+ Whenever `del x` is encountered, Python decrements the reference count for `x` by one, and `x.__del__()` when x’s reference count reaches zero.
+ In the second output snippet, `y.__del__()` was not called because the previous statement (`>>> y`) in the interactive interpreter created another reference to the same object, thus preventing the reference count to reach zero when `del y` was encountered.
+ Calling `globals` caused the existing reference to be destroyed and hence we can see "Deleted!" being printed (finally!).
* 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.
>>> 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 the `var_name` from the local or global namespace (That's why the `list_1` is unaffected).
*`remove` removes the first matching value, not a specific index, raises `ValueError` if the value is not found.
*`pop` removes the element at a specific index and returns it, raises `IndexError` 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` from `list_2` or `list_4`, the contents of the lists are now `[2, 3, 4]`. The remaining elements are shifted down, i.e., `2` is at index 0, and `3` is at index 1. Since the next iteration is going to look at index 1 (which is the `3`), the `2` gets skipped entirely. A similar thing will happen with every alternate element in the list sequence.
* Refer to this StackOverflow [thread](https://stackoverflow.com/questions/45946228/what-happens-when-you-try-to-delete-a-list-element-while-iterating-over-it) explaining the example
* See also this nice StackOverflow [thread](https://stackoverflow.com/questions/45877614/how-to-change-all-the-dictionary-keys-in-a-for-loop-with-d-items) for a similar example related to dictionaries in Python.
- 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](https://docs.python.org/3/whatsnew/3.0.html) documentation:
> "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 a `list()` constructor, and in particular the loop control variables are no longer leaked into the surrounding scope."
- 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 `[]` to `some_func` as the argument, the default value of the `default_arg` variable was not used, so the function returned as expected.
- 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:
* 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,
*`a += b` doesn't always behave the same way as `a = a + b`. Classes *may* implement the *`op=`* operators differently, and lists do this.
* The expression `a = a + [5,6,7,8]` generates a new list and sets `a`'s reference to that new list, leaving `b` unchanged.
* The expression `a + =[5,6,7,8]` is actually mapped to an "extend" function that operates on the list such that `a` and `b` still point to the same list that has been modified in-place.
* When you make an assignment to a variable in scope, it becomes local to that scope. So `a` becomes local to the scope of `another_func`, but it has not been initialized previously in the same scope which throws an error.
* Read [this](http://sebastianraschka.com/Articles/2014_python_scope_and_namespaces.html) short but an awesome guide to learn more about how namespaces and scope resolution works in Python.
* To modify the outer scope variable `a` in `another_func`, use `global` keyword.
> 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 to `True is False and False == False` and since the first part of the statement (`True is False`) evaluates to `False`, the overall expression evaluates to `False`.
*`1 > 0 < 1` is equivalent to `1 > 0 and 0 < 1` which evaluates to `True`.
* The expression `(1 > 0) < 1` is equivalent to `True < 1` and
```py
>>> int(True)
1
>>> True + 1 #not relevant for this example, but just for fun
* 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',)` or `t = 'one',` (missing comma) otherwise the interpreter considers `t` to be a `str` and iterates over it character by character.
*`()` is a special token and denotes empty `tuple`.
+ `+=` is faster than `+` for concatenating more than two strings because the first string (example, `s1` for `s1 += s2 + s3`) is not destroyed while calculating the complete string.
- You can read more about [timeit](https://docs.python.org/3/library/timeit.html) from here. It is generally used to measure the execution time of snippets.
- 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 of `add_bytes_with_plus` function)
- Therefore, it's advised to use `.format.` or `%` syntax (however, they are slightly slower than `+` for 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.
-`add_string_with_plus` didn't show a quadratic increase in execution time unlike `add_bytes_with_plus` because of the `+=` optimizations discussed in the previous example. Had the statement been `s = s + "x" + "y" + "z"` instead of `s += "xyz"`, the increase would have been quadratic.
`'inf'` and `'nan'` are special strings (case-insensitive), which when explicitly typecasted to `float` type, are used to represent mathematical "infinity" and "not a number" respectively.
*`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 generic `list` object API.
* Few weird looking but semantically correct statements:
+ `[] = ()` is a semantically correct statement (unpacking an empty `tuple` into an empty `list`)
+ `'a'[0][0][0][0][0]` is also a semantically correct statement as strings are [sequences](https://docs.python.org/3/glossary.html#term-sequence)(iterables supporting element access using integer indices) in Python.
+ `3 --0-- 5 == 8` and `--5 == 5` are both semantically correct statements and evaluate to `True`.
* 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.
+ There is no `++` operator in Python grammar. It is actually two `+` operators.
+ `++a` parses as `+(+a)` which translates to `a`. Similarly, the output of the statement `--a` can be justified.
+ This StackOverflow [thread](https://stackoverflow.com/questions/3654830/why-are-there-no-and-operators-in-python) discusses the rationale behind the absence of increment and decrement operators in Python.
* 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!):
```py
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](https://wiki.python.org/moin/GlobalInterpreterLock) 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](https://docs.python.org/2/library/multiprocessing.html) module.
* List slicing with out of the bounds indices throws no errors
```py
>>> some_list = [1, 2, 3, 4, 5]
>>> some_list[111:]
[]
```
*`int('١٢٣٤٥٦٧٨٩')` returns `123456789` 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](http://chris.improbable.org/2014/8/25/adventures-in-unicode-digits/) related to this behavior of Python.
*`'abc'.count('') == 4`. Here's an approximate implementation of `count` method, which would make the things more clear
```py
def 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.
For discussions, you can either create a new [issue](https://github.com/satwikkansal/wtfpython/issues/new) or ping on the Gitter [channel](https://gitter.im/wtfpython/Lobby)
The idea and design for this collection were initially inspired by Denys Dovhan's awesome project [wtfjs](https://github.com/denysdovhan/wtfjs). The overwhelming support by the community gave it the shape it is in right now.
I'm looking for full-time opportunities. I'd highly appreciate if you could do me a small favor by letting me know about open positions around you. You can find more about me [here](https://satwikkansal.xyz).
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