"If you want to learn more, please read the [original blog](http://python-history.blogspot.ru/2010/06/method-resolution-order.html) post by Guido van Rossum.\n",
"Here, class `D` searches in `B` first, which in turn inherits from `A` (note that class `C` also inherits from `A`, but has its own `.foo()` method) so that we come up with the search order: `D, B, C, A`. "
"Python `list`s are mutable objects as we all know. So, if we are using the `+=` operator on `list`s, we extend the `list` by directly modifying the object directly. \n",
"\"It often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that, unlike any other time value, midnight (i.e. `datetime.time(0,0,0)`) is False. A long discussion on the python-ideas mailing list shows that, while surprising, that behavior is desirable\u2014at least in some quarters.\" \n",
"This oddity occurs, because Python keeps an array of small integer objects (i.e., integers between -5 and 256, [see the doc](https://docs.python.org/2/c-api/int.html#PyInt_FromLong))."
"(*I received a comment that this is in fact a CPython artefact and **must not necessarily be true** in all implementations of Python!*)\n",
"\n",
"So the take home message is: always use \"==\" for equality, \"is\" for identity!\n",
"\n",
"Here is a [nice article](http://python.net/%7Egoodger/projects/pycon/2007/idiomatic/handout.html#other-languages-have-variables) explaining it by comparing \"boxes\" (C language) with \"name tags\" (Python)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example demonstrates that this applies indeed for integers in the range in -5 to 256:"
"If we use the assignment operator to assign one list to another list, we just create a new name reference to the original list. If we want to create a new list object, we have to make a copy of the original list. This can be done via `a_list[:]` of `a_list.copy()`."
"As we have seen above, a shallow copy works fine if we want to create a new list with contents of the original list which we want to modify independently. \n",
"\n",
"However, if we are dealing with compound objects (e.g., lists that contain other lists, [read here](https://docs.python.org/2/library/copy.html) for more information) it becomes a little trickier.\n",
"\n",
"In the case of compound objects, a shallow copy would create a new compound object, but it would just insert the references to the contained objects into the new compound object. In contrast, a deep copy would go \"deeper\" and create also new objects \n",
"for the objects found in the original compound object. \n",
"If you follow the code, the concept should become more clear:"
"- If both values in `or` expressions are `True`, Python will select the first value (e.g., select `\"a\"` in `\"a\" or \"b\"`), and the second one in `and` expressions. \n",
"This is also called **short-circuiting** - we already know that the logical `or` must be `True` if the first value is `True` and therefore can omit the evaluation of the second value.\n",
"Don't use mutable objects (e.g., dictionaries, lists, sets, etc.) as default arguments for functions! You might expect that a new list is created every time when we call the function without providing an argument for the default parameter, but this is not the case: **Python will create the mutable object (default parameter) the first time the function is defined - not when it is called**, see the following code:\n",
"Be aware of what is happening when combining \"`in`\" checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
"Chicken or egg? In the history of Python (Python 2.2 to be specific) truth values were implemented via 1 and 0 (similar to the old C). In order to avoid syntax errors in old (but perfectly working) Python code, `bool` was added as a subclass of `int` in Python 2.3.\n",
"Remember the section about the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n",
"In the first example below, we call a `lambda` function in a list comprehension, and the value `i` will be dereferenced every time we call `lambda` within the scope of the list comprehension. Since the list is already constructed when we `for-loop` through the list, it will be set to the last value 4."
"And if you are really keen on using lists, there is a nifty trick that circumvents this problem as a reader nicely pointed out in the comments: We can simply pass the loop variable `i` as a default argument to the lambdas."
"There is nothing particularly surprising about Python's LEGB scope resolution (Local -> Enclosed -> Global -> Built-in), but it is still useful to take a look at some examples!"
"According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` `local`ly in the function's scope, it won't have an effect on the `global` `x`."
"If we want to modify the `global` x via a function, we can simply use the `global` keyword to import the variable into the function's scope:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = 0\n",
"def in_func():\n",
" global x\n",
" x = 1\n",
" print('in_func:', x)\n",
" \n",
"in_func()\n",
"print('global:', x)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"in_func: 1\n",
"global: 1\n"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `local` vs. `enclosed`\n",
"\n",
"Now, let us take a look at `local` vs. `enclosed`. Here, we set the variable `x = 1` in the `outer` function and set `x = 1` in the enclosed function `inner`. Since `inner` looks in the local scope first, it won't modify `outer`'s `x`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def outer():\n",
" x = 1\n",
" print('outer before:', x)\n",
" def inner():\n",
" x = 2\n",
" print(\"inner:\", x)\n",
" inner()\n",
" print(\"outer after:\", x)\n",
"outer()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"outer before: 1\n",
"inner: 2\n",
"outer after: 1\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is where the `nonlocal` keyword comes in handy - it allows us to modify the `x` variable in the `enclosed` scope:"
"However, **there are ways** to modify the mutable contents of the tuple without raising the `TypeError`, the solution is the `.extend()` method, or alternatively `.append()` (for lists):"
"**A. Jesse Jiryu Davis** has a nice explanation for this phenomenon (Original source: [http://emptysqua.re/blog/python-increment-is-weird-part-ii/](http://emptysqua.re/blog/python-increment-is-weird-part-ii/))\n",
"If we try to extend the list via `+=` *\"then the statement executes `STORE_SUBSCR`, which calls the C function `PyObject_SetItem`, which checks if the object supports item assignment. In our case the object is a tuple, so `PyObject_SetItem` throws the `TypeError`. Mystery solved.\"*"
"#### One more note about the `immutable` status of tuples. Tuples are famous for being immutable. However, how comes that this code works?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_tup = (1,)\n",
"my_tup += (4,)\n",
"my_tup = my_tup + (5,)\n",
"print(my_tup)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"(1, 4, 5)\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What happens \"behind\" the curtains is that the tuple is not modified, but every time a new object is generated, which will inherit the old \"name tag\":"
"\"List comprehensions are fast, but generators are faster!?\" - No, not really (or significantly, see the benchmarks below). So what's the reason to prefer one over the other?\n",
"- use lists if you want to use the plethora of list methods \n",
"Who has not stumbled across this quote \"we are all consenting adults here\" in the Python community, yet? Unlike in other languages like C++ (sorry, there are many more, but that's one I am most familiar with), we can't really protect class methods from being used outside the class. \n",
"All we can do is to indicate methods as private to make clear that they are better not used outside the class, but it is really up to the class user, since \"we are all consenting adults here\"! \n",
"So, when we want to \"make\" class methods private, we just put a double-underscore in front of it (same with other class members), which invokes some name mangling if we want to acess the private class member outside the class! \n",
"This doesn't prevent the class user to access this class member though, but he has to know the trick and also knows that it his own risk...\n",
"\n",
"Let the following example illustrate what I mean:"
"Look at the following examples, and for a fun exercise: try to figure out what is going on before you skip to the solution!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = [1, 2, 3, 4, 5]\n",
"for i in a:\n",
" if not i % 2:\n",
" a.remove(i)\n",
"print(a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1, 3, 5]\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = [2, 4, 5, 6]\n",
"for i in b:\n",
" if not i % 2:\n",
" b.remove(i)\n",
"print(b)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[4, 5]\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"**The solution** is that we are iterating through the list index by index, and if we remove one of the items in-between, we inevitably mess around with the indexing, look at the following example, and it will become clear:"
"Usually, it is no problem to access global variables in the local scope of a function:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func():\n",
" print(var)\n",
"\n",
"var = 'global'\n",
"my_func()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"global\n"
]
}
],
"prompt_number": 37
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And is also no problem to use the same variable name in the local scope without affecting the local counterpart: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func():\n",
" var = 'locally changed'\n",
"\n",
"var = 'global'\n",
"my_func()\n",
"print(var)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"global\n"
]
}
],
"prompt_number": 38
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But we have to be careful if we use a variable name that occurs in the global scope, and we want to access it in the local function scope if we want to reuse this name:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func():\n",
" print(var) # want to access global variable\n",
" var = 'locally changed' # but Python thinks we forgot to define the local variable!\n",
" \n",
"var = 'global'\n",
"my_func()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "UnboundLocalError",
"evalue": "local variable 'var' referenced before assignment",
"Let's assume a scenario where we want to duplicate sub`list`s of values stored in another list. If we want to create independent sub`list` object, using the arithmetic multiplication operator could lead to rather unexpected (or undesired) results:"
"But it might be still worthwhile, especially for Python newcomers, to take a look at some of those!\n",
"(Note: the the code was executed in Python 3.4.0 and Python 2.7.5 and copied from interactive shell sessions.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### Unicode...\n",
"####- Python 2: \n",
"We have ASCII `str()` types, separate `unicode()`, but no `byte` type\n",
"####- Python 3: \n",
"Now, we finally have Unicode (utf-8) `str`ings, and 2 byte classes: `byte` and `bytearray`s"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#############\n",
"# Python 2\n",
"#############\n",
"\n",
">>> type(unicode('is like a python3 str()'))\n",
"<type 'unicode'>\n",
"\n",
">>> type(b'byte type does not exist')\n",
"<type 'str'>\n",
"\n",
">>> 'they are really' + b' the same'\n",
"'they are really the same'\n",
"\n",
">>> type(bytearray(b'bytearray oddly does exist though'))\n",
"<type 'bytearray'>\n",
"\n",
"#############\n",
"# Python 3\n",
"#############\n",
"\n",
">>> print('strings are now utf-8 \\u03BCnico\\u0394\u00e9!')\n",
"strings are now utf-8 \u03bcnico\u0394\u00e9!\n",
"\n",
"\n",
">>> type(b' and we have byte types for storing data')\n",
"<class 'bytes'>\n",
"\n",
">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n",
"<class 'bytearray'>\n",
"\n",
">>> 'string' + b'bytes for data'\n",
"Traceback (most recent call last):s\n",
" File \"<stdin>\", line 1, in <module>\n",
"TypeError: Can't convert 'bytes' object to str implicitly"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The print statement\n",
"Very trivial, but this change makes sense, Python 3 now only accepts `print`s with proper parentheses - just like the other function calls ..."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
">>> print 'Hello, World!'\n",
"Hello, World!\n",
">>> print('Hello, World!')\n",
"Hello, World!\n",
"\n",
"# Python 3\n",
">>> print('Hello, World!')\n",
"Hello, World!\n",
">>> print 'Hello, World!'\n",
" File \"<stdin>\", line 1\n",
" print 'Hello, World!'\n",
" ^\n",
"SyntaxError: invalid syntax"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And if we want to print the output of 2 consecutive print functions on the same line, you would use a comma in Python 2, and a `end=\"\"` in Python 3:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
">>> print \"line 1\", ; print 'same line'\n",
"line 1 same line\n",
"\n",
"# Python 3\n",
">>> print(\"line 1\", end=\"\") ; print (\" same line\")\n",
"line 1 same line"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Integer division\n",
"This is a pretty dangerous thing if you are porting code, or executing Python 3 code in Python 2 since the change in integer-division behavior can often go unnoticed. \n",
"So, I still tend to use a `float(3/2)` or `3/2.0` instead of a `3/2` in my Python 3 scripts to save the Python 2 guys some trouble ... (PS: and vice versa, you can `from __future__ import division` in your Python 2 scripts)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
">>> 3 / 2\n",
"1\n",
">>> 3 // 2\n",
"1\n",
">>> 3 / 2.0\n",
"1.5\n",
">>> 3 // 2.0\n",
"1.0\n",
"\n",
"# Python 3\n",
">>> 3 / 2\n",
"1.5\n",
">>> 3 // 2\n",
"1\n",
">>> 3 / 2.0\n",
"1.5\n",
">>> 3 // 2.0\n",
"1.0"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### `xrange()` \n",
"`xrange()` was pretty popular in Python 2.x if you wanted to create an iterable object. The behavior was quite similar to a generator ('lazy evaluation'), but you could iterate over it infinitely. The advantage was that it was generally faster than `range()` (e.g., in a for-loop) - not if you had to iterate over the list multiple times, since the generation happens every time from scratch! \n",
"In Python 3, the `range()` was implemented like the `xrange()` function so that a dedicated `xrange()` function does not exist anymore."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
"> python -m timeit 'for i in range(1000000):' ' pass'\n",
"10 loops, best of 3: 66 msec per loop\n",
"\n",
" > python -m timeit 'for i in xrange(1000000):' ' pass'\n",
"10 loops, best of 3: 27.8 msec per loop\n",
"\n",
"# Python 3\n",
"> python3 -m timeit 'for i in range(1000000):' ' pass'\n",
"10 loops, best of 3: 51.1 msec per loop\n",
"\n",
"> python3 -m timeit 'for i in xrange(1000000):' ' pass'\n",
"Traceback (most recent call last):\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 292, in main\n",
" x = t.timeit(number)\n",
" File \"/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/timeit.py\", line 178, in timeit\n",
" timing = self.inner(it, self.timer)\n",
" File \"<timeit-src>\", line 6, in inner\n",
" for i in xrange(1000000):\n",
"NameError: name 'xrange' is not defined"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Raising exceptions\n",
"\n",
"Where Python 2 accepts both notations, the 'old' and the 'new' way, Python 3 chokes (and raises a `SyntaxError` in turn) if we don't enclose the exception argument in parentheses:"
"You probably will never make use of them (or at least very rarely). Usually, we write good function documentations below the function as a docstring - or at least this is how I would do it (okay this case is a little bit extreme, I have to admit):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def is_palindrome(a):\n",
" \"\"\"\n",
" Case-and punctuation insensitive check if a string is a palindrom.\n",
" \n",
" Keyword arguments:\n",
" a (str): The string to be checked if it is a palindrome.\n",
" \n",
" Returns `True` if input string is a palindrome, else False.\n",
" \n",
" \"\"\"\n",
" stripped_str = [l for l in my_str.lower() if l.isalpha()]\n",
" return stripped_str == stripped_str[::-1]\n",
" "
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"However, function annotations can be useful to indicate that work is still in progress in some cases. But they are optional and I see them very very rarely.\n",
"\n",
"As it is stated in [PEP3107](http://legacy.python.org/dev/peps/pep-3107/#fundamentals-of-function-annotations):\n",
"\n",
"1. Function annotations, both for parameters and return values, are completely optional.\n",
"\n",
"2. Function annotations are nothing more than a way of associating arbitrary Python expressions with various parts of a function at compile-time.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The nice thing about function annotations is their `__annotations__` attribute, which is dictionary of all the parameters and/or the `return` value you annotated."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo1.__annotations__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"{'y': 'insert x^2 here', 'x': 'insert x here'}"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo2.__annotations__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"{'return': 'Hi!'}"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**When are they useful?**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Function annotations can be useful for a couple of things \n",
"Python's `try-except-finally` blocks are very handy for catching and handling errors. The `finally` block is always executed whether an `exception` has been raised or not as illustrated in the following example."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def try_finally1():\n",
" try:\n",
" print('in try:')\n",
" print('do some stuff')\n",
" float('abc')\n",
" except ValueError:\n",
" print('an error occurred')\n",
" else:\n",
" print('no error occurred')\n",
" finally:\n",
" print('always execute finally')\n",
" \n",
"try_finally1()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"in try:\n",
"do some stuff\n",
"an error occurred\n",
"always execute finally\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"<br>\n",
"But can you also guess what will be printed in the next code cell?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def try_finally2():\n",
" try:\n",
" print(\"do some stuff in try block\")\n",
" return \"return from try block\"\n",
" finally:\n",
" print(\"do some stuff in finally block\")\n",
" return \"always execute finally\"\n",
" \n",
"print(try_finally2())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"do some stuff in try block\n",
"do some stuff in finally block\n",
"always execute finally\n"
]
}
],
"prompt_number": 21
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br>\n",
"Here, the abortive `return` statement in the `finally` block simply overrules the `return` in the `try` block, since **`finally` is guaranteed to always be executed.** So, be careful using abortive statements in `finally` blocks!"
