Python, being awesome by design 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 and tricky examples of unexpected behaviors 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 might be familiar with most of these examples, and I might be able to revive some sweet old memories of yours being bitten by these gotchas :sweat_smile:
- 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.
**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.
* Python 3 is nice enough to automatically throw an error for such cases.
- 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
* 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.
* 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.
- 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.
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.
+ `+=` 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)
-`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.
> 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.
- 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.
- 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:
* 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.
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.)
* 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.
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:
- 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 backward compatibility, they couldn't make `True` and `False` constants- they just were built-in variables.
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
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))
`'inf'` and `'nan'` are special strings (case-insensitive), which when explicitly type casted to `float` type, are used to represent mathematical "infinity" and "not a number" respectively.
* 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.
* 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,
Before Python 3.5, the boolean value fo `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."
* 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`.
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.
* 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!):
* Multiple Python threads don't run concurrently (yes you heard it right!). It may seem intuitive to spawn several threads and let them execute concurrently, but, because of the Global Interpreter Lock in Python, all you're doing is making your threads execute on the same core turn by turn. To achieve actual parallelization in Python, you might want to use the Python [multiprocessing](https://docs.python.org/2/library/multiprocessing.html) module.
Trying to come up with an example that combines multiple examples discussed above, making it difficult for the reader to guess the output correctly :sweat_smile:.
