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- [Structure of the Examples](#structure-of-the-examples)

- [Some fancy Title](#some-fancy-title)

- [💡 Explanation:](#-explanation)

- [Usage](#usage)

- [👀 Examples](#-examples)

- [Skipping lines?](#skipping-lines)

- [💡 Explanation](#-explanation)

- [Well, something is fishy...](#well-something-is-fishy)

- [💡 Explanation](#-explanation-1)

- [Time for some hash brownies!](#time-for-some-hash-brownies)

- [💡 Explanation](#-explanation-2)

- [Evaluation time discrepancy](#evaluation-time-discrepancy)

- [💡 Explanation](#-explanation-3)

- [Modifying a dictionary while iterating over it](#modifying-a-dictionary-while-iterating-over-it)

- [💡 Explanation:](#-explanation-1)

- [Deleting a list item while iterating over it](#deleting-a-list-item-while-iterating-over-it)

- [💡 Explanation:](#-explanation-2)

- [Backslashes at the end of string](#backslashes-at-the-end-of-string)

- [💡 Explanation](#-explanation-4)

- [Let's make a giant string!](#lets-make-a-giant-string)

- [💡 Explanation](#-explanation-5)

- [String concatenation interpreter optimizations.](#string-concatenation-interpreter-optimizations)

- [💡 Explanation:](#-explanation-3)

- [Yes, it exists!](#yes-it-exists)

- [💡 Explanation:](#-explanation-4)

- [`is` is not what it is!](#is-is-not-what-it-is)

- [💡 Explanation:](#-explanation-5)

- [`is not ...` is different from `is (not ...)`](#is-not--is-different-from-is-not-)

- [💡 Explanation](#-explanation-6)

- [The function inside loop sticks to the same output](#the-function-inside-loop-sticks-to-the-same-output)

- [💡 Explanation](#-explanation-7)

- [Loop variables leaking out of local scope!](#loop-variables-leaking-out-of-local-scope)

- [💡 Explanation:](#-explanation-6)

- [A tic-tac-toe where X wins in the first attempt!](#a-tic-tac-toe-where-x-wins-in-the-first-attempt)

- [💡 Explanation:](#-explanation-7)

- [Beware of default mutable arguments!](#beware-of-default-mutable-arguments)

- [💡 Explanation:](#-explanation-8)

- [Same operands, different story!](#same-operands-different-story)

- [💡 Explanation:](#-explanation-9)

- [Mutating the immutable!](#mutating-the-immutable)

- [💡 Explanation:](#-explanation-10)

- [Using a variable not defined in scope](#using-a-variable-not-defined-in-scope)

- [💡 Explanation:](#-explanation-11)

- [The disappearing variable from outer scope](#the-disappearing-variable-from-outer-scope)

- [💡 Explanation:](#-explanation-12)

- [Return return everywhere!](#return-return-everywhere)

- [💡 Explanation:](#-explanation-13)

- [When True is actually False](#when-true-is-actually-false)

- [💡 Explanation:](#-explanation-14)

- [Be careful with chained operations](#be-careful-with-chained-operations)

- [💡 Explanation:](#-explanation-15)

- [Name resolution ignoring class scope](#name-resolution-ignoring-class-scope)

- [💡 Explanation](#-explanation-8)

- [From filled to None in one instruction...](#from-filled-to-none-in-one-instruction)

- [💡 Explanation](#-explanation-9)

- [Explicit typecast of strings](#explicit-typecast-of-strings)

- [💡 Explanation:](#-explanation-16)

- [Class attributes and instance attributes](#class-attributes-and-instance-attributes)

- [💡 Explanation:](#-explanation-17)

- [Catching the Exceptions!](#catching-the-exceptions)

- [💡 Explanation](#-explanation-10)

- [Midnight time doesn't exist?](#midnight-time-doesnt-exist)

- [💡 Explanation:](#-explanation-18)

- [Counting the booleans](#counting-the-booleans)

- [💡 Explanation:](#-explanation-19)

- [Needle in a Haystack](#needle-in-a-haystack)

- [💡 Explanation:](#-explanation-20)

- [Loop variable resilient to changes](#loop-variable-resilient-to-changes)

- [💡 Explanation:](#-explanation-21)

- [Let's see if you can guess this?](#lets-see-if-you-can-guess-this)

- [💡 Explanation:](#-explanation-22)

- [Minor Ones](#minor-ones)

- [TODO: Hell of an example!](#todo-hell-of-an-example)

- [Contributing](#contributing)

- [Acknowledgements](#acknowledgements)

- [Some nice Links!](#some-nice-links)

- [🎓 License](#-license)

<!-- END doctoc generated TOC please keep comment here to allow auto update -->

**Note:** All the examples mentioned below are tested on Python 3.5.2 interactive interpreter, and they should work for all the Python versions unless explicitly specified in the example description.

All the examples are structured like below:

```py

```

**Output (Python version):**

```py

>>> triggering_statement

Probably unexpected output

```

(Optional): One line describing the unexpected output.

* Brief explanation of what's happening and why is it happening.

```py

Setting up examples for clarification (if necessary)

```

**Output:**

```py

>>> trigger # some example that makes it easy to unveil the magic

# some justified output

```

A good 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 gonna happen next.

- Read the output snippets and

+ Check if the outputs are the same as you'd expect.

+ Make sure know the exact reason behind the output being the way it is.

- If no, read the explanation (and if you still don't understand, shout out! and create an issue [here](https://github.com/satwikkansal/wtfPython)).

- If yes, give a gentle pat on your back, and you may skip to the next example.

PS: You can also read these examples at the command line. First install the npm package `wtfpython`,

```sh

$ npm install -g wtfpython

Now, just run `wtfpython` at the command line which will open this collection in your selected `$PAGER`.

**Output:**

```py

>>> value = 11

>>> valuе = 32

>>> value

```

Wut?

**Note:** The easiest way to reproduce this is to simply copy the statements from the above snippet and paste them into your file/shell.

Some Unicode characters look identical to ASCII ones, but are considered distinct by the interpreter.

```py

>>> value = 42 #ascii e

>>> valuе = 23 #cyrillic e, Python 2.x interpreter would raise a `SyntaxError` here

>>> value

```

```py

def square(x):

"""

sum_so_far = 0

for counter in range(x):

sum_so_far = sum_so_far + x

return sum_so_far

```

**Output (Python 2.x):**

```py

>>> square(10)

```

Shouldn't that be 100?

**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.

* **Don't mix tabs and spaces!** The character just preceding return is a "tab", and the code is indented by multiple of "4 spaces" elsewhere in the example.

* This is how Python handles tabs:

> First, tabs are replaced (from left to right) by one to eight spaces such that the total number of characters up to and including the replacement is a multiple of eight <...>

* So the "tab" at the last line of `square` function is replaced with eight spaces, and it gets into the loop.

* Python 3 is nice enough to automatically throw an error for such cases.

**Output (Python 3.x):**

```py

TabError: inconsistent use of tabs and spaces in indentation

```

```py

some_dict = {}

some_dict[5.5] = "Ruby"

some_dict[5.0] = "JavaScript"

some_dict[5] = "Python"

```

**Output:**

```py

>>> some_dict[5.5]

"Ruby"

>>> some_dict[5.0]

"Python"

>>> some_dict[5]

"Python"

```

"Python" destroyed the existence of "JavaScript"?

#### 💡 Explanation

* `5` (an `int` type) is implicitly converted to `5.0` (a `float` type) before calculating the hash in Python.

```py

>>> hash(5) == hash(5.0)

True

```

* This StackOverflow [answer](https://stackoverflow.com/a/32211042/4354153) explains beautifully the rationale behind it.

---

### Evaluation time discrepancy

```py

array = [1, 8, 15]

g = (x for x in array if array.count(x) > 0)

array = [2, 8, 22]

```

**Output:**

```py

>>> print(list(g))

[8]

```

#### 💡 Explanation

- 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 run time.

- So before run time, `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`.

---

### Modifying a dictionary while iterating over it

```py

x = {0: None}

for i in x:

del x[i]

x[i+1] = None

print(i)

```

**Output:**

```

0

1

2

3

4

5

6

7

```

Yes, it runs for exactly **eight** times and stops.

#### 💡 Explanation:

* 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.

* Refer to this StackOverflow [thread](https://stackoverflow.com/questions/44763802/bug-in-python-dict) explaining a similar example.

---

### Deleting a list item while iterating over it

```py

list_1 = [1, 2, 3, 4]

list_2 = [1, 2, 3, 4]

list_3 = [1, 2, 3, 4]

list_4 = [1, 2, 3, 4]

for idx, item in enumerate(list_1):

del item

for idx, item in enumerate(list_2):

list_2.remove(item)

for idx, item in enumerate(list_3[:]):

list_3.remove(item)

for idx, item in enumerate(list_4):

list_4.pop(idx)

```

**Output:**

```py

>>> 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]`?

#### 💡 Explanation:

* 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.

```py

>>> some_list = [1, 2, 3, 4]

>>> id(some_list)

139798789457608

>>> id(some_list[:]) # Notice that python creates new object for sliced list.

139798779601192

```

**Difference between `del`, `remove`, and `pop`:**

* `remove` removes the first matching value, not a specific index, raises `ValueError` if the value is not found.

* `del` removes a specific index (That's why first `list_1` was unaffected), raises `IndexError` if an invalid index is specified.

* `pop` removes 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.

* See 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.

---

### Backslashes at the end of string

**Output:**

```

>>> print("\\ some string \\")

>>> print(r"\ some string")

>>> print(r"\ some string \")

File "<stdin>", line 1

print(r"\ some string \")

^

SyntaxError: EOL while scanning string literal

```

#### 💡 Explanation

- In a raw string literal, as indicated by the prefix `r`, the backslash doesn't have the special meaning.

- 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.

---

### Let's make a giant string!

This is not a WTF at all, just some nice things to be aware of :)

```py

def add_string_with_plus(iters):

s = ""

for i in range(iters):

s += "xyz"

assert len(s) == 3*iters

def add_string_with_format(iters):

fs = "{}"*iters

s = fs.format(*(["xyz"]*iters))

assert len(s) == 3*iters

def add_string_with_join(iters):

l = []

for i in range(iters):

l.append("xyz")

s = "".join(l)

assert len(s) == 3*iters

def convert_list_to_string(l, iters):

s = "".join(l)

assert len(s) == 3*iters

```

**Output:**

```py

>>> timeit(add_string_with_plus(10000))

100 loops, best of 3: 9.73 ms per loop

>>> timeit(add_string_with_format(10000))

100 loops, best of 3: 5.47 ms per loop

>>> timeit(add_string_with_join(10000))

100 loops, best of 3: 10.1 ms per loop

>>> l = ["xyz"]*10000

>>> timeit(convert_list_to_string(l, 10000))

10000 loops, best of 3: 75.3 µs per loop

```

#### 💡 Explanation

- 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.

- 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.

---

### String concatenation interpreter optimizations.

```py

>>> a = "some_string"

>>> id(a)

140420665652016

>>> id("some" + "_" + "string") # Notice that both the ids are same.

140420665652016

# using "+", three strings:

>>> timeit.timeit("s1 = s1 + s2 + s3", setup="s1 = ' ' * 100000; s2 = ' ' * 100000; s3 = ' ' * 100000", number=100)

0.25748300552368164

# using "+=", three strings:

>>> timeit.timeit("s1 += s2 + s3", setup="s1 = ' ' * 100000; s2 = ' ' * 100000; s3 = ' ' * 100000", number=100)

0.012188911437988281

```

#### 💡 Explanation:

+ `+=` 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.

+ Both the strings refer to the same object because of CPython optimization hat tries to use existing immutable objects in some cases (implementation specific) rather than creating a new object every time. You can read more about this [here](https://stackoverflow.com/questions/24245324/about-the-changing-id-of-an-immutable-string)

---

### Yes, it exists!

**The `else` clause for loops.** One typical example might be:

```py

def does_exists_num(l, to_find):

for num in l:

if num == to_find:

print("Exists!")

break

else:

print("Does not exist")

```

**Output:**

```py

>>> some_list = [1, 2, 3, 4, 5]

>>> does_exists_num(some_list, 4)

Exists!

>>> does_exists_num(some_list, -1)

Does not exist

```

**The `else` clause in exception handling.** An example,

```py

try:

pass

except:

print("Exception occurred!!!")

else:

print("Try block executed successfully...")

```

**Output:**

```py

Try block executed successfully...

```

#### 💡 Explanation:

- 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.

---

### `is` is not what it is!

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

```py

>>> a = 256

>>> b = 256

>>> a is b

True

>>> a = 257

>>> b = 257

>>> a is b

False

>>> a = 257; b = 257

>>> a is b

True

```

#### 💡 Explanation:

**The difference between `is` and `==`**

* `is` operator checks if both the operands refer to the same object (i.e. it checks if the identity of the operands matches or not).

* `==` operator compares the values of both the operands and checks if they are the same.

* So `is` is for reference equality and `==` is for value equality. An example to clear things up,

```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. :-)

```py

>>> id(256)

10922528

>>> a = 256

>>> b = 256

>>> id(a)

10922528

>>> id(b)

10922528

>>> id(257)

140084850247312

>>> x = 257

>>> y = 257

>>> id(x)

140084850247440

>>> id(y)

140084850247344

```

Here the interpreter isn't smart enough while executing `y = 257` to recognize that we've already created an integer of the value `257` and so it goes on to create another object in the memory.

**Both `a` and `b` refer to the same object, when initialized with same value in the same line.**

```py

>>> a, b = 257, 257

>>> id(a)

140640774013296

>>> id(b)

140640774013296

>>> a = 257

>>> b = 257

>>> id(a)

140640774013392

>>> id(b)

140640774013488

```

* When a and b are set to `257` in the same line, the Python interpreter creates a new object, then references the second variable at the same time. If you do it on separate lines, it doesn't "know" that there's 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.

---

### `is not ...` is different from `is (not ...)`

```py

>>> 'something' is not None

True

>>> 'something' is (not None)

False

```

#### 💡 Explanation

- `is not` is a single binary operator, and has behavior different than using `is` and `not` separated.

- `is not` evaluates to `False` if the variables on either side of the operator point to the same object and `True` otherwise.

---

### The function inside loop sticks to the same output

```py

funcs = []

results = []

for x in range(7):

def some_func():

return x

funcs.append(some_func)

results.append(some_func())

funcs_results = [func() for func in funcs]

```

**Output:**

```py

>>> results

[0, 1, 2, 3, 4, 5, 6]

>>> funcs_results

[6, 6, 6, 6, 6, 6, 6]

```

Even when the values of `x` were different in every iteration prior to appending `some_func` to `funcs`, all the functions return 6.

//OR

```py

>>> powers_of_x = [lambda x: x**i for i in range(10)]

>>> [f(2) for f in powers_of_x]

[512, 512, 512, 512, 512, 512, 512, 512, 512, 512]

```

#### 💡 Explanation

- 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.

```py

funcs = []

for x in range(7):

def some_func(x=x):

return x

funcs.append(some_func)

```

**Output:**

```py

>>> funcs_results = [func() for func in funcs]

>>> funcs_results

[0, 1, 2, 3, 4, 5, 6]

```

---

### Loop variables leaking out of local scope!

1\.

```py

for x in range(7):

if x == 6:

print(x, ': for x inside loop')

print(x, ': x in global')

```

**Output:**

```py

6 : for x inside loop

6 : x in global

```

But `x` was never defined outside the scope of for loop...

2\.

```py

# This time let's initialize x first

x = -1

for x in range(7):

if x == 6:

print(x, ': for x inside loop')

print(x, ': x in global')

```

**Output:**

```py

6 : for x inside loop

6 : x in global

```

3\.

```

x = 1

print([x for x in range(5)])

print(x, ': x in global')

```

**Output (on Python 2.x):**

```

[0, 1, 2, 3, 4]

(4, ': x in global')

```

**Output (on Python 3.x):**

```

[0, 1, 2, 3, 4]

1 : x in global

```

#### 💡 Explanation:

- 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."

---

### A tic-tac-toe where X wins in the first attempt!

```py

# Let's initialize a row

row = [""]*3 #row i['', '', '']

# Let's make a board

board = [row]*3

```

**Output:**

```py

>>> board

[['', '', ''], ['', '', ''], ['', '', '']]

>>> board[0]

['', '', '']

>>> board[0][0]

''

>>> board[0][0] = "X"

>>> board

[['X', '', ''], ['X', '', ''], ['X', '', '']]

```

We didn't assigned 3 "X"s or did we?

#### 💡 Explanation:

When we initialize `row` variable, this visualization explains what happens in the memory


And when the `board` is initialized by multiplying the `row`, this is what happens inside the memory (each of the elements `board[0]`, `board[1]` and `board[2]` is a reference to the same list referred by `row`)


---

### Beware of default mutable arguments!

```py

def some_func(default_arg=[]):

default_arg.append("some_string")

return default_arg

```

**Output:**

```py

>>> some_func()

['some_string']

>>> some_func()

['some_string', 'some_string']

>>> some_func([])

['some_string']

>>> some_func()

['some_string', 'some_string', 'some_string']

```

#### 💡 Explanation:

- 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.

```py

def some_func(default_arg=[]):

default_arg.append("some_string")

return default_arg

```

**Output:**

```py

>>> some_func.__defaults__ #This will show the default argument values for the function

([],)

>>> some_func()

>>> some_func.__defaults__

(['some_string'],)

>>> some_func()

>>> some_func.__defaults__

(['some_string', 'some_string'],)

>>> some_func([])

>>> some_func.__defaults__

(['some_string', 'some_string'],)

```

- A common practice to avoid bugs due to mutable arguments is to assign `None` as the default value and later check if any value is passed to the function corresponding to that argument. Example:

```py

def some_func(default_arg=None):

if not default_arg:

default_arg = []

default_arg.append("some_string")

return default_arg

```

---

### Same operands, different story!

1\.

```py

a = [1, 2, 3, 4]

b = a

a = a + [5, 6, 7, 8]

```

**Output:**

```py

>>> a

[1, 2, 3, 4, 5, 6, 7, 8]

>>> b

[1, 2, 3, 4]

```

2\.

```py

a = [1, 2, 3, 4]

b = a

a += [5, 6, 7, 8]

```

**Output:**

```py

>>> a

[1, 2, 3, 4, 5, 6, 7, 8]

>>> b

[1, 2, 3, 4, 5, 6, 7, 8]

```

#### 💡 Explanation:

* a += b doesn't behave the same way as a = a + b

* The expression `a = a + [5,6,7,8]` generates a new object and sets `a`'s reference to that new object, leaving `b` unchanged.

* The expression `a + =[5,6,7,8]` is actually mapped to an "extend" function that operates on the object such that `a` and `b` still point to the same object that has been modified in-place.

---

### 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.

---

### Using a variable not defined in scope

```py

a = 1

def some_func():

return a

def another_func():

a += 1

return a

```

**Output:**

```py

>>> some_func()

1

>>> another_func()

UnboundLocalError: local variable 'a' referenced before assignment

```

#### 💡 Explanation:

* When you make an assignment to a variable in a 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.

```py

def another_func()

global a

a += 1

return a

```

**Output:**

```py

>>> another_func()

2

```

---

### The disappearing variable from outer scope

```py

e = 7

try:

raise Exception()

except Exception as e:

pass

```

**Output (Python 2.x):**

```py

>>> print(e)

# prints nothing

```

**Output (Python 3.x):**

```py

>>> print(e)

NameError: name 'e' is not defined

```

#### 💡 Explanation:

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

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

```py

except E as N:

foo

```

was translated to

```py

except E as N:

try:

foo

finally:

del N

```

This means the exception must be assigned to a different name to be able to refer to it after the except clause. Exceptions are cleared because, with the traceback attached to them, they form a reference cycle with the stack frame, keeping all locals in that frame alive until the next garbage collection occurs.

* The clauses are not scoped in Python. Everything in the example is present in the same scope, and the variable `e` got removed due to the execution of 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]

```