{
"metadata": {
"name": "",
"signature": "sha256:86af4b61efe1215272820c4e70b826e6b19fe283f8025ff1ef099a6b28d46cc3"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sebastian Raschka \n",
"last updated: 04/25/2014\n",
"\n",
"[Link to this IPython Notebook on GitHub](https://github.com/rasbt/python_reference/blob/master/not_so_obvious_python_stuff.ipynb)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### All code was executed in Python 3.4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# A collection of not so obvious Python stuff you should know!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"I am really looking forward to your comments and suggestions to improve and \n",
"extend this little collection! Just send me a quick note \n",
"via Twitter: [@rasbt](https://twitter.com/rasbt) \n",
"or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sections\n",
"- [The C3 class resolution algorithm for multiple class inheritance](#c3_class_res)\n",
"- [The behavior of += for lists](#pm_in_lists)\n",
"- [`True` and `False` in the datetime module](#datetime_module)\n",
"- [Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity](#python_small_int)\n",
"- [Shallow vs. deep copies if list contains other structures and objects](#shallow_vs_deep)\n",
"- [Picking True values from and and or expressions](#false_true_expressions)\n",
"- [Don't use mutable objects as default arguments for functions!](#def_mutable_func)\n",
"- [Be aware of the consuming generator](#consuming_generator)\n",
"- [`bool` is a subclass of `int`](#bool_int)\n",
"- [About lambda-in-closures and-a-loop pitfall](#lambda_closure)\n",
"- [Python's LEGB scope resolution and the keywords `global` and `nonlocal`](#python_legb)\n",
"- [When mutable contents of immutable tuples aren't so mutable](#immutable_tuple)\n",
"- [List comprehensions are fast, but generators are faster!?](#list_generator)\n",
"- [Public vs. private class methods and name mangling](#private_class)\n",
"- [The consequences of modifying a list when looping through it](#looping_pitfall)\n",
"- [Dynamic binding and typos in variable names](#dynamic_binding)\n",
"- [List slicing using indexes that are \"out of range](#out_of_range_slicing)\n",
"- [Reusing global variable names and UnboundLocalErrors](#unboundlocalerror)\n",
"- [Creating copies of mutable objects](#copy_mutable)\n",
"- [Key differences between Python 2 and 3](#python_differences)\n",
"- [Function annotations - What are those `->`'s in my Python code?](#function_annotation)\n",
"- [Abortive statements in `finally` blocks](#finally_blocks)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The C3 class resolution algorithm for multiple class inheritance"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we are dealing with multiple inheritance, according to the newer C3 class resolution algorithm, the following applies: \"class A should be checked before class B\".\n",
"\n",
"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",
"\n",
"(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class A(object):\n",
" def foo(self):\n",
" print(\"class A\")\n",
"\n",
"class B(object):\n",
" def foo(self):\n",
" print(\"class B\")\n",
"\n",
"class C(A, B):\n",
" pass\n",
"\n",
"C().foo()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"class A\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So what actually happened above was that class `C` was looking in the parent class `A` for the method `.foo()` first (and found it)!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I received an email with a nice suggestion using a more nested example to illustrate Guido van Rossum's point a little bit better:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class A(object):\n",
" def foo(self):\n",
" print(\"class A\")\n",
"\n",
"class B(A):\n",
" pass\n",
"\n",
"class C(A):\n",
" def foo(self):\n",
" print(\"class C\")\n",
"\n",
"class D(B,C):\n",
" pass\n",
"\n",
"D().foo()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"class C\n"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"
\n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The behavior of `+=` for lists"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we are using the `+=` operator on lists, we extend the list by modifying the object directly. However, if we use the assigment via `my_list = my_list + ...`, we create a new list object, which can be demonstrated by the following code:\n",
"\n",
"(Original source: [http://stackoverflow.com/questions/2347265/why-does-behave-unexpectedly-on-lists](http://stackoverflow.com/questions/2347265/why-does-behave-unexpectedly-on-lists))"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"list_a = []\n",
"print('ID of list_a', id(list_a))\n",
"list_a += [1]\n",
"print('ID of list_a after `+= [1]`', id(list_a))\n",
"list_a = list_a + [2]\n",
"print('ID of list_a after `list_a = list_a + [2]`', id(list_a))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"ID of list_a 4356429080\n",
"ID of list_a after `+= [1]` 4356429080\n",
"ID of list_a after `list_a = list_a + [2]` 4356453584\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## `True` and `False` in the datetime module\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"it often comes as a big surprise for programmers to find (sometimes by way of a hard-to-reproduce bug) that,\n",
"unlike any other time value, midnight (i.e. datetime.time(0,0,0)) is False.\n",
"A long discussion on the python-ideas mailing list shows that, while surprising,\n",
"that behavior is desirable\u2014at least in some quarters.\"\n",
"\n",
"(Original source: [http://lwn.net/SubscriberLink/590299/bf73fe823974acea/](http://lwn.net/SubscriberLink/590299/bf73fe823974acea/))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import datetime\n",
"\n",
"print('\"datetime.time(0,0,0)\" (Midnight) evaluates to', bool(datetime.time(0,0,0)))\n",
"\n",
"print('\"datetime.time(1,0,0)\" (1 am) evaluates to', bool(datetime.time(1,0,0)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\"datetime.time(0,0,0)\" (Midnight) evaluates to False\n",
"\"datetime.time(1,0,0)\" (1 am) evaluates to True\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Python reuses objects for small integers - always use \"==\" for equality, \"is\" for identity\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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)).\n",
"\n",
"\n",
"(*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 using \"boxes\" (for people with C background) and \"name tags\" (in the case of Python):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 1\n",
"b = 1\n",
"print('a is b', bool(a is b))\n",
"True\n",
"\n",
"a = 999\n",
"b = 999\n",
"print('a is b', bool(a is b))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a is b True\n",
"a is b False\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Another popular example to illustrate the reuse of objects for small integers is:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print(256 is 257 - 1)\n",
"print(257 is 258 - 1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"True\n",
"False\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### And to illustrate the test for equality (`==`) vs. identity (`is`):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = 'hello world!'\n",
"b = 'hello world!'\n",
"print('a is b,', a is b)\n",
"print('a == b,', a == b)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a is b, False\n",
"a == b, True\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### And this example shows when `==` does not necessarilu implies that two objects are the same:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = float('nan')\n",
"print('a == a,', a == a)\n",
"print('a is a,', a is a)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"a == a, False\n",
"a is a, True\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shallow vs. deep copies if list contains other structures and objects\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"List modification of the original list does affect shallow copies, but not deep copies if the list contains compound objects."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from copy import deepcopy\n",
"\n",
"my_first_list = [[1],[2]]\n",
"my_second_list = [[1],[2]]\n",
"print('my_first_list == my_second_list:', my_first_list == my_second_list)\n",
"print('my_first_list is my_second_list:', my_first_list is my_second_list)\n",
"\n",
"my_third_list = my_first_list\n",
"print('my_first_list == my_third_list:', my_first_list == my_third_list)\n",
"print('my_first_list is my_third_list:', my_first_list is my_third_list)\n",
"\n",
"my_shallow_copy = my_first_list[:]\n",
"print('my_first_list == my_shallow_copy:', my_first_list == my_shallow_copy)\n",
"print('my_first_list is my_shallow_copy:', my_first_list is my_shallow_copy)\n",
"\n",
"my_deep_copy = deepcopy(my_first_list)\n",
"print('my_first_list == my_deep_copy:', my_first_list == my_deep_copy)\n",
"print('my_first_list is my_deep_copy:', my_first_list is my_deep_copy)\n",
"\n",
"print('\\nmy_third_list:', my_third_list)\n",
"print('my_shallow_copy:', my_shallow_copy)\n",
"print('my_deep_copy:', my_deep_copy)\n",
"\n",
"my_first_list[0][0] = 2\n",
"print('after setting \"my_first_list[0][0] = 2\"')\n",
"print('my_third_list:', my_third_list)\n",
"print('my_shallow_copy:', my_shallow_copy)\n",
"print('my_deep_copy:', my_deep_copy)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"my_first_list == my_second_list: True\n",
"my_first_list is my_second_list: False\n",
"my_first_list == my_third_list: True\n",
"my_first_list is my_third_list: True\n",
"my_first_list == my_shallow_copy: True\n",
"my_first_list is my_shallow_copy: False\n",
"my_first_list == my_deep_copy: True\n",
"my_first_list is my_deep_copy: False\n",
"\n",
"my_third_list: [[1], [2]]\n",
"my_shallow_copy: [[1], [2]]\n",
"my_deep_copy: [[1], [2]]\n",
"after setting \"my_first_list[0][0] = 2\"\n",
"my_third_list: [[2], [2]]\n",
"my_shallow_copy: [[2], [2]]\n",
"my_deep_copy: [[1], [2]]\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Picking `True` values from `and` and `or` expressions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If both values of in a `or` expression are True, Python will select the first one, and the second one in `and` expressions\n",
"\n",
"Or - as a reader suggested - picture it as \n",
"`a or b == a if a else b` \n",
"`a and b == b if a else a` \n",
"\n",
"(Original source: [http://gistroll.com/rolls/21/horizontal_assessments/new](http://gistroll.com/rolls/21/horizontal_assessments/new))"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"result = (2 or 3) * (5 and 7)\n",
"print('2 * 7 =', result)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2 * 7 = 14\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Don't use mutable objects as default arguments for functions!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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",
"\n",
"(Original source: [http://docs.python-guide.org/en/latest/writing/gotchas/](http://docs.python-guide.org/en/latest/writing/gotchas/)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def append_to_list(value, def_list=[]):\n",
" def_list.append(value)\n",
" return def_list\n",
"\n",
"my_list = append_to_list(1)\n",
"print(my_list)\n",
"\n",
"my_other_list = append_to_list(2)\n",
"print(my_other_list)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[1]\n",
"[1, 2]\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Another good example showing that demonstrates that default arguments are created when the function is created (**and not when it is called!**):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import time\n",
"def report_arg(my_default=time.time()):\n",
" print(my_default)\n",
"\n",
"report_arg()\n",
"\n",
"time.sleep(5)\n",
"\n",
"report_arg()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"1397764090.456688\n",
"1397764090.456688"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Be aware of the consuming generator"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Be aware using `in` checks with generators, since they won't evaluate from the beginning once a position is \"consumed\"."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gen = (i for i in range(5))\n",
"print('2 in gen,', 2 in gen)\n",
"print('3 in gen,', 3 in gen)\n",
"print('1 in gen,', 1 in gen) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2 in gen, True\n",
"3 in gen, True\n",
"1 in gen, False\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**We can circumvent this problem by using a simple list, though:**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"l = [i for i in range(5)]\n",
"print('2 in l,', 2 in l)\n",
"print('3 in l,', 3 in l)\n",
"print('1 in l,', 1 in l) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2 in l, True\n",
"3 in l, True\n",
"1 in l, True\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## `bool` is a subclass of `int`\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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), to avoid syntax error in old (but perfectly working) code, `bool` was added as a subclass of `int` in Python 2.3.\n",
"\n",
"Original source: [http://www.peterbe.com/plog/bool-is-int](http://www.peterbe.com/plog/bool-is-int)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print('isinstance(True, int):', isinstance(True, int))\n",
"print('True + True:', True + True)\n",
"print('3*True:', 3*True)\n",
"print('3*True - False:', 3*True - False)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"isinstance(True, int): True\n",
"True + True: 2\n",
"3*True: 3\n",
"3*True - False: 3\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## About lambda-in-closures-and-a-loop pitfall"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remember the [\"consuming generators\"](consuming_generators)? This example is somewhat related, but the result might still come unexpected. \n",
"\n",
"(Original source: [http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html](http://openhome.cc/eGossip/Blog/UnderstandingLambdaClosure3.html))\n",
"\n",
"In the first example below, where we call a `lambda` function in a list comprehension, the value `i` is 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 is set to the last value 4."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [lambda: i for i in range(5)]\n",
"for l in my_list:\n",
" print(l())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4\n",
"4\n",
"4\n",
"4\n",
"4\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This, however, does not apply to generators:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_gen = (lambda: n for n in range(5))\n",
"for l in my_gen:\n",
" print(l())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python's LEGB scope resolution and the keywords `global` and `nonlocal`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### `global` vs. `local`\n",
"\n",
"According to the LEGB rule, Python will first look for a variable in the local scope. So if we set the variable `x = 1` in the `local`ly in the function's scope, it won't have an effect on the `global` `x`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = 0\n",
"def in_func():\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: 0\n"
]
}
],
"prompt_number": 33
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def outer():\n",
" x = 1\n",
" print('outer before:', x)\n",
" def inner():\n",
" nonlocal x\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: 2\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## When mutable contents of immutable tuples aren't so mutable"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we all know, tuples are immutable objects in Python, right!? But what happens if they contain mutable objects? \n",
"\n",
"First, let us have a look at the expected behavior: a `TypeError` is raised if we try to modify immutable types in a tuple: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tup = (1,)\n",
"tup[0] += 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"prompt_number": 41
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### But what if we put a mutable object into the immutable tuple? Well, modification works, but we **also** get a `TypeError` at the same time."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tup = ([],)\n",
"print('tup before: ', tup)\n",
"tup[0] += [1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"tup before: ([],)\n"
]
},
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtup\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'tup before: '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print('tup after: ', tup)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"tup after: ([1],)\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"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):"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tup = ([],)\n",
"print('tup before: ', tup)\n",
"tup[0].extend([1])\n",
"print('tup after: ', tup)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"tup before: ([],)\n",
"tup after: ([1],)\n"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tup = ([],)\n",
"print('tup before: ', tup)\n",
"tup[0].append(1)\n",
"print('tup after: ', tup)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"tup before: ([],)\n",
"tup after: ([1],)\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Explanation\n",
"\n",
"**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",
"\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.\"*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 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\":"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_tup = (1,)\n",
"print(id(my_tup))\n",
"my_tup += (4,)\n",
"print(id(my_tup))\n",
"my_tup = my_tup + (5,)\n",
"print(id(my_tup))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"4337381840\n",
"4357415496\n",
"4357289952\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## List comprehensions are fast, but generators are faster!?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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 list methods \n",
"- use generators when you are dealing with huge collections to avoid memory issues"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import timeit\n",
"\n",
"def plainlist(n=100000):\n",
" my_list = []\n",
" for i in range(n):\n",
" if i % 5 == 0:\n",
" my_list.append(i)\n",
" return my_list\n",
"\n",
"def listcompr(n=100000):\n",
" my_list = [i for i in range(n) if i % 5 == 0]\n",
" return my_list\n",
"\n",
"def generator(n=100000):\n",
" my_gen = (i for i in range(n) if i % 5 == 0)\n",
" return my_gen\n",
"\n",
"def generator_yield(n=100000):\n",
" for i in range(n):\n",
" if i % 5 == 0:\n",
" yield i"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### To be fair to the list, let us exhaust the generators:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def test_plainlist(plain_list):\n",
" for i in plain_list():\n",
" pass\n",
"\n",
"def test_listcompr(listcompr):\n",
" for i in listcompr():\n",
" pass\n",
"\n",
"def test_generator(generator):\n",
" for i in generator():\n",
" pass\n",
"\n",
"def test_generator_yield(generator_yield):\n",
" for i in generator_yield():\n",
" pass\n",
"\n",
"print('plain_list: ', end = '')\n",
"%timeit test_plainlist(plainlist)\n",
"print('\\nlistcompr: ', end = '')\n",
"%timeit test_listcompr(listcompr)\n",
"print('\\ngenerator: ', end = '')\n",
"%timeit test_generator(generator)\n",
"print('\\ngenerator_yield: ', end = '')\n",
"%timeit test_generator_yield(generator_yield)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"plain_list: 10 loops, best of 3: 22.4 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"listcompr: 10 loops, best of 3: 20.8 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"generator: 10 loops, best of 3: 22 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n",
"\n",
"generator_yield: 10 loops, best of 3: 21.9 ms per loop"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Public vs. private class methods and name mangling\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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 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:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class my_class():\n",
" def public_method(self):\n",
" print('Hello public world!')\n",
" def __private_method(self):\n",
" print('Hello private world!')\n",
" def call_private_method_in_class(self):\n",
" self.__private_method()\n",
" \n",
"my_instance = my_class()\n",
"\n",
"my_instance.public_method()\n",
"my_instance._my_class__private_method()\n",
"my_instance.call_private_method_in_class()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello public world!\n",
"Hello private world!\n",
"Hello private world!\n"
]
}
],
"prompt_number": 28
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The consequences of modifying a list when looping through it"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It can be really dangerous to modify a list when iterating through - it is a very common pitfall that can cause unintended behavior! \n",
"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": [
"
\n",
"
\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:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"b = [2, 4, 5, 6]\n",
"for index, item in enumerate(b):\n",
" print(index, item)\n",
" if not item % 2:\n",
" b.remove(item)\n",
"print(b)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0 2\n",
"1 5\n",
"2 6\n",
"[4, 5]\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dynamic binding and typos in variable names\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Be careful, dynamic binding is convenient, but can also quickly become dangerous!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print('first list:')\n",
"for i in range(3):\n",
" print(i)\n",
" \n",
"print('\\nsecond list:')\n",
"for j in range(3):\n",
" print(i) # I (intentionally) made typo here!"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"first list:\n",
"0\n",
"1\n",
"2\n",
"\n",
"second list:\n",
"2\n",
"2\n",
"2\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## List slicing using indexes that are \"out of range\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As we have all encountered it 1 (x10000) time(s) in our live, the infamous `IndexError`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [1, 2, 3, 4, 5]\n",
"print(my_list[5])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "IndexError",
"evalue": "list index out of range",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmy_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_list\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m: list index out of range"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But suprisingly, it is not raised when we are doing list slicing, which can be a really pain for debugging:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list = [1, 2, 3, 4, 5]\n",
"print(my_list[5:])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[]\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Reusing global variable names and `UnboundLocalErrors`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mUnboundLocalError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m\u001b[0m in \u001b[0;36mmy_func\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmy_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# want to access global variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'locally changed'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mvar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'global'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'var' referenced before assignment"
]
}
],
"prompt_number": 40
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, we have to use the `global` keyword!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def my_func():\n",
" global var\n",
" print(var) # want to access global variable\n",
" var = 'locally changed' # changes the gobal variable\n",
"\n",
"var = 'global'\n",
"\n",
"my_func()\n",
"print(var)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"global\n",
"locally changed\n"
]
}
],
"prompt_number": 43
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating copies of mutable objects\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list1 = [[1, 2, 3]] * 2\n",
"\n",
"print('initially ---> ', my_list1)\n",
"\n",
"# modify the 1st element of the 2nd sublist\n",
"my_list1[1][0] = 'a'\n",
"print(\"after my_list1[1][0] = 'a' ---> \", my_list1)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"initially ---> [[1, 2, 3], [1, 2, 3]]\n",
"after my_list1[1][0] = 'a' ---> [['a', 2, 3], ['a', 2, 3]]\n"
]
}
],
"prompt_number": 24
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
"In this case, we should better create \"new\" objects:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"my_list2 = [[1, 2, 3] for i in range(2)]\n",
"\n",
"print('initially: ---> ', my_list2)\n",
"\n",
"# modify the 1st element of the 2nd sublist\n",
"my_list2[1][0] = 'a'\n",
"print(\"after my_list2[1][0] = 'a': ---> \", my_list2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"initially: ---> [[1, 2, 3], [1, 2, 3]]\n",
"after my_list2[1][0] = 'a': ---> [[1, 2, 3], ['a', 2, 3]]\n"
]
}
],
"prompt_number": 25
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"And here is the proof:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for a,b in zip(my_list1, my_list2):\n",
" print('id my_list1: {}, id my_list2: {}'.format(id(a), id(b)))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"id my_list1: 4350764680, id my_list2: 4350766472\n",
"id my_list1: 4350764680, id my_list2: 4350766664\n"
]
}
],
"prompt_number": 26
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Key differences between Python 2 and 3\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are some good articles already that are summarizing the differences between Python 2 and 3, e.g., \n",
"- [https://wiki.python.org/moin/Python2orPython3](https://wiki.python.org/moin/Python2orPython3)\n",
"- [https://docs.python.org/3.0/whatsnew/3.0.html](https://docs.python.org/3.0/whatsnew/3.0.html)\n",
"- [http://python3porting.com/differences.html](http://python3porting.com/differences.html)\n",
"- [https://docs.python.org/3/howto/pyporting.html](https://docs.python.org/3/howto/pyporting.html) \n",
"etc.\n",
"\n",
"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",
"\n",
"\n",
">>> type(b'byte type does not exist')\n",
"\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",
"\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",
"\n",
"\n",
">>> type(bytearray(b'but also bytearrays for those who prefer them over strings'))\n",
"\n",
"\n",
">>> 'string' + b'bytes for data'\n",
"Traceback (most recent call last):s\n",
" File \"\", line 1, in \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 \"\", 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 \"\", 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:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
">>> raise IOError, \"file error\"\n",
"Traceback (most recent call last):\n",
" File \"\", line 1, in \n",
"IOError: file error\n",
">>> raise IOError(\"file error\")\n",
"Traceback (most recent call last):\n",
" File \"\", line 1, in \n",
"IOError: file error\n",
"\n",
" \n",
"# Python 3 \n",
">>> raise IOError, \"file error\"\n",
" File \"\", line 1\n",
" raise IOError, \"file error\"\n",
" ^\n",
"SyntaxError: invalid syntax\n",
">>> raise IOError(\"file error\")\n",
"Traceback (most recent call last):\n",
" File \"\", line 1, in \n",
"OSError: file error"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Handling exceptions\n",
"\n",
"Also the handling of excecptions has slightly changed in Python 3. Now, we have to use the `as` keyword!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
">>> try:\n",
"... blabla\n",
"... except NameError, err:\n",
"... print err, '--> our error msg'\n",
"... \n",
"name 'blabla' is not defined --> our error msg\n",
"\n",
"# Python 3\n",
">>> try:\n",
"... blabla\n",
"... except NameError as err:\n",
"... print(err, '--> our error msg')\n",
"... \n",
"name 'blabla' is not defined --> our error msg"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The `next()` function and `.next()` method\n",
"\n",
"Where you can use both function and method in Python 2.7.5, the `next()` function is all that remain in Python 3!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Python 2\n",
">>> my_generator = (letter for letter in 'abcdefg')\n",
">>> my_generator.next()\n",
"'a'\n",
">>> next(my_generator)\n",
"'b'\n",
"\n",
"# Python 3\n",
">>> my_generator = (letter for letter in 'abcdefg')\n",
">>> next(my_generator)\n",
"'a'\n",
">>> my_generator.next()\n",
"Traceback (most recent call last):\n",
" File \"\", line 1, in \n",
"AttributeError: 'generator' object has no attribute 'next'"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Function annotations - What are those `->`'s in my Python code?\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[[back to top](#sections)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Have you ever seen any Python code that used colons inside the parantheses of a function definition?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def foo1(x: 'insert x here', y: 'insert x^2 here'):\n",
" print('Hello, World')\n",
" return"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And what about the fancy arrow here?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def foo2(x, y) -> 'Hi!':\n",
" print('Hello, World')\n",
" return"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Q: Is this valid Python syntax? \n",
"A: Yes!\n",
" \n",
" \n",
"Q: So, what happens if I *just call* the function? \n",
"A: Nothing!\n",
" \n",
"Here is the proof!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo1(1,2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello, World\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"foo2(1,2) "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Hello, World\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**So, those are function annotations ... ** \n",
"- the colon for the function parameters \n",
"- the arrow for the return value \n",
"\n",
"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",
"- Documentation in general\n",
"- pre-condition testing\n",
"- [type checking](http://legacy.python.org/dev/peps/pep-0362/#annotation-checker)\n",
" \n",
"..."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
\n",
"
\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Abortive statements in `finally` blocks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": [
"
\n",
"
\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": [
"
\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!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}