Python being an awesome higher level language, provides us many functionalities for programmer's comfort. But sometimes, the outcomes may not seem obvious to a normal Python user at the first sight.
Here's an attempt to collect such classic and tricky examples of unexpected behaviors in Python and see what exactly is happening under the hood! While some of the examples may not be WTFs in truest sense, but they'll definitely reveal some of the interesting parts of Pyhton. Anyways, I find it a nice way to learn internals of a language and I think you'll like them as well!
- If you're an beginner to intermdediate level Python programmer, I'd personally recommend you to go through all of the examples below, as being aware about such pitfalls may be able to save a lot of debugging time in your future.
- If you're an experienced Python programmer, you might be familiar with most of these examples, and I might be able to revive some nice old memories of yours being bitten by these gotchas.
- [The function inside loop magic](#the-function-inside-loop-magic)
- [Explaination](#explaination)
- [Loop variables leaking out of local scope!](#loop-variables-leaking-out-of-local-scope)
- [Explanation](#explanation-1)
- [A tic-tac-toe where X wins in first attempt!](#a-tic-tac-toe-where-x-wins-in-first-attempt)
- [Explanation](#explanation-2)
- [Beware of default mutable arguments!](#beware-of-default-mutable-arguments)
- [Explanation](#explanation-3)
- [You can't change the values contained in tuples because they're immutable.. Oh really?](#you-cant-change-the-values-contained-in-tuples-because-theyre-immutable-oh-really)
- [Explanation](#explanation-4)
- [Using a varibale not defined in scope](#using-a-varibale-not-defined-in-scope)
A good way to go through these examples in my opinion will be to just to read them chronologically, and for every example:
- Carefully read the initial code for setting up the example. If you're an experienced Python programmer, 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.
+ You know the exact reason behind the output being the way it is. If no, read the explaination (and if you still don't understand, shout out and create an issue [here]()). If yes, give a gentle pat on your back and you may skip to the next example.
## `datetime.time` object is considered to be false if it represented midnight in UTC
```py
from datetime import datetime
midnight = datetime(2018, 1, 1, 0, 0)
midnight_time = midnight.time()
noon = datetime(2018, 1, 1, 12, 0)
noon_time = noon.time()
if midnight_time:
print("Time at midnight is", midnight_time)
if noon_time:
print("Time at noon is", noon_time)
```
**Output:**
```sh
('Time at noon is', datetime.time(12, 0))
```
### Explanation
Before Python 3.5, a 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".
## `is` is not what it is!
```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 if the `is` operator returns `True` then the equality is definitely `True`, but the opposite may or may not be True.
**`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 to just 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 actually just get back a reference to the existing object. So it should be possible to change the value of 1. I suspect the behaviour of Python in this case is undefined. :-)
Here the integer isn't smart enough while executing `y = 257` to recongnize 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 same object, when initialized with same value in same line**
* When a and b are set to `257` in the same line, the Python interpretor creates new object, then references the second variable at the same time. If you do it in separate lines, it doesn't "know" that there's already `257` as an object.
* It's a compiler optimization and specifically applies to interactive environment. When you do 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.
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 varibable to the function which will define the variable again within the function's scope.
But `x` was never defined ourtside 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."
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`)
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. Examlple:
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 changed; however, the collection of objects directly referenced by an immutable object cannot change.)
* 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 awesome guide to learn more about how namespaces and scope resolution works in Python.
* To actually modify the outer scope variable `a` in `another_func`, use `global` keyword.
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 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.
**Output (Python 2.x):**
```py
>>> e
Exception()
>>> print e
# Nothing is printed!
```
## Return in both `try` and `finally` clauses
```py
def some_func():
try:
return 'from_try'
finally:
return 'from_finally'
```
**Output:**
```py
>>> some_func()
'from_finally'
```
### Explanation
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.
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 backwards compatibility, they couldn't make `True` and `False` constants- they just were built-in variables.
Python 3 was backwards-incompatible, so it was now finally possible to fix that, and so this example wont't work with Python 3.x.
- In a generator 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`.
As per https://docs.python.org/2/reference/expressions.html#not-in
> 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.
*`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
* 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.
A raw string literal, where the backslash doesn't have the special meaning, as indicated by the prefix r. What it 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.
* Iteration over a dictionary that you edit at the same time is not supported.
* It runs 8 times because that's the point at which the dictionary resizes to hold more keys (we have 8 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.
Most methods that modiy 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))
* 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.
* **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. Similar thing will happen with every alternate element in the list sequence.
* See this 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.
c = float('-iNf') #These strings are case-insensitive
d = float('nan')
```
**Output:**
```py
>>> a
inf
>>> b
nan
>>> c
-inf
>>> float('some_other_string')
ValueError: could not convert string to float: some_other_string
>>> a == -c #inf==inf
True
>>> b == d #but nan!=nan
False
>>> 50/a
0
>>> a/a
nan
>>> 23 + b
nan
```
### Explanation
`'inf'` and `'nan'` are special strings (case-insensitieve), which when explicitly type casted to `float` type, are used to represent mathematical "infinity" and "not a number" respectively.
## Well, something is fishy...
```py
def square(x):
sum_so_far = 0
for counter in range(x):
sum_so_far = sum_so_far + x
return sum_so_far
print(square(10))
```
**Output (Python 2.x):**
(After pasting the above snippet in the interactive Python interpreter)
```py
10
```
**Output (Python 3.x):**
```py
TabError: inconsistent use of tabs and spaces in indentation
```
**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.
### Explaination
* **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.
> 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 replace with 8 spaces and it gets into the loop.
>>> another_obj.another_list is SomeClass.another_list
True
>>> another_obj.another_list is some_obj.another_list
True
```
### Explaination:
* 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 current class, the parent classes are searched for it.
* The `+=` operator modifies the mutable object in-place without creating a new object.
* 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,
This is not a WTF, but just a nice thing 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
```
### Explanination
- 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.
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.
Also, it's string specific, and it sounds wrong to put a string-specific method on a generic list.
* 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 iterable in Python.
+ `3 --0-- 5 == 8` and `--5 == 5` are both semantically correct statments and evalute to `True`.
* Booleans are a subclass of `int`
```py
>>> isinstance(True, int)
True
>>> isinstance(False, float)
True
```
* 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!):
Such an `else` clause is also called "completion clause" as reaching the `else` clause in a `try` statement means that the try block actually completed successfully.
+ `+=` 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)
This contains some of the potential bugs in you code that are very common but hard to detect.
### Initializing a tuple containing single element
```py
t = ('one', 'two')
for i in t:
print(i)
t = ('one')
for i in t:
print(i)
t = ()
print(t)
```
**Output:**
```py
one
two
o
n
e
tuple()
```
#### Explanation
* 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`.
Trying to comeup with an example that combines multiple examples discussed above, making it difficult for the reader to guess the output correctly :sweat_smile:.
