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intro-to-python/07_sequences/03_content.ipynb
Alexander Hess 94e5112f10
Release 0.1.0 
After refurbishing the project we prepare a new relaease.
There are no changes with respect to the contents as compared to v0.0.0
that are noteworthy release notes.
2024-04-08 22:13:31 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/03_content.ipynb)."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Chapter 7: Sequential Data (continued)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"In this third part of the chapter, we first look at a major implication of the `list` type's mutability. Then, we see how its close relative, the `tuple` type, can mitigate this. Lastly, we see how Python's syntax assumes sequential data at various places: for example, when unpacking iterables during a `for`-loop or an assignment, or when working with `function` objects."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Modifiers vs. Pure Functions"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"As `list` objects are mutable, the caller of a function can see the changes made to a `list` object passed to the function as an argument. That is often a surprising *side effect* and should be avoided.\n",
"\n",
"As an example, consider the `add_xyz()` function."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"letters = [\"a\", \"b\", \"c\"]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def add_xyz(arg):\n",
" \"\"\"Append letters to a list.\"\"\"\n",
" arg.extend([\"x\", \"y\", \"z\"])\n",
" return arg"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, reference the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
"\n",
"So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"letters_with_xyz = add_xyz(letters)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'x', 'y', 'z']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters_with_xyz"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'x', 'y', 'z']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"A better practice is to first create a copy of `arg` within the function that is then modified and returned. If we are sure that `arg` contains immutable elements only, we get away with a shallow copy. The downside of this approach is the higher amount of memory necessary.\n",
"\n",
"The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
"\n",
"For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"letters = [\"a\", \"b\", \"c\"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def add_xyz(arg):\n",
" \"\"\"Create a new list from an existing one.\"\"\"\n",
" new_arg = arg[:]\n",
" new_arg.extend([\"x\", \"y\", \"z\"])\n",
" return new_arg"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"letters_with_xyz = add_xyz(letters)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": true,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'x', 'y', 'z']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters_with_xyz"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c']"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"letters = [\"a\", \"b\", \"c\"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"def add_xyz(arg):\n",
" \"\"\"Append letters to a list.\"\"\"\n",
" arg.extend([\"x\", \"y\", \"z\"])\n",
" return # None"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"add_xyz(letters)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'x', 'y', 'z']"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"If we call `add_xyz()` with `letters` as the argument again, we end up with an even longer `list` object."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"add_xyz(letters)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"letters"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Functions that only work on the argument passed in are called **modifiers**. Their primary purpose is to change the **state** of the argument. On the contrary, functions that have *no* side effects on the arguments are said to be **pure**."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## The `tuple` Type"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"To create a `tuple` object, we can use the same literal notation as for `list` objects *without* the brackets and list all elements."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"numbers = 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"However, to be clearer, many Pythonistas write out the optional parentheses `(` and `)`."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"numbers = (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"As before, `numbers` is an object on its own."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"140248673535456"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"id(numbers)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"tuple"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"While we could use empty parentheses `()` to create an empty `tuple` object ..."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"empty_tuple = ()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"()"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"empty_tuple"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"tuple"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(empty_tuple)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"... we must use a *trailing comma* to create a `tuple` object holding one element. If we forget the comma, the parentheses are interpreted as the grouping operator and effectively useless!"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"one_tuple = (1,) # we could ommit the parentheses but not the comma"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1,)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"one_tuple"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"tuple"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(one_tuple)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"no_tuple = (1)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_tuple"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"int"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(no_tuple)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Alternatively, we may use the [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) built-in that takes any iterable as its argument and creates a new `tuple` from its elements."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1,)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tuple([1])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"('i', 't', 'e', 'r', 'a', 'b', 'l', 'e')"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tuple(\"iterable\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Tuples are like \"Immutable Lists\""
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Most operations involving `tuple` objects work in the same way as with `list` objects. The main difference is that `tuple` objects are *immutable*. So, if our program does not depend on mutability, we may and should use `tuple` and not `list` objects to model sequential data. That way, we avoid the pitfalls seen above.\n",
"\n",
"`tuple` objects are *sequences* exhibiting the familiar *four* behaviors. So, `numbers` holds a *finite* number of elements ..."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(numbers)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"... that we can obtain individually by looping over it in a predictable *forward* or *reverse* order."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7 11 8 5 3 12 2 6 9 10 1 4 "
]
}
],
"source": [
"for number in numbers:\n",
" print(number, end=\" \")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"4 1 10 9 6 2 12 3 5 8 11 7 "
]
}
],
"source": [
"for number in reversed(numbers):\n",
" print(number, end=\" \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"To check if a given object is *contained* in `numbers`, we use the `in` operator and conduct a linear search."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"0 in numbers"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1 in numbers"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1.0 in numbers # in relies on == behind the scenes"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"We may index and slice with the `[]` operator. The latter returns *new* `tuple` objects."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers[0]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers[-1]"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(2, 6, 9, 10, 1, 4)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers[6:]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Index assignment does *not* work as tuples are *immutable* and results in a `TypeError`."
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnumbers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m99\u001b[39m\n",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"source": [
"numbers[-1] = 99"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"The `+` and `*` operators work with `tuple` objects as well: They always create *new* `tuple` objects."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 99)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers + (99,)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2 * numbers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Being immutable, `tuple` objects only provide the `.count()` and `.index()` methods of `Sequence` types. The `.append()`, `.extend()`, `.insert()`, `.reverse()`, `.pop()`, and `.remove()` methods of `MutableSequence` types are *not* available. The same holds for the `list`-specific `.sort()`, `.copy()`, and `.clear()` methods."
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers.count(0)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers.index(1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"The relational operators work in the *same* way as for `list` objects."
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers == (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers != (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"numbers < (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains references to mutable objects, the entire nested structure is *not* immutable as a whole!\n",
"\n",
"Consider the following stylized example `not_immutable`: It contains *three* elements, `1`, `[2, ..., 11]`, and `12`, and the elements of the nested `list` object may be changed. While it is not practical to mix data types in a `tuple` object that is used as an \"immutable list,\" we want to make the point that the mere usage of the `tuple` type does *not* guarantee a nested object to be immutable as a whole."
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"not_immutable = (1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"not_immutable"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"not_immutable[1][:] = [99, 99, 99]"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1, [99, 99, 99], 12)"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"not_immutable"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Packing & Unpacking"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"In the \"*List Operations*\" section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb#List-Operations) of this chapter, the `*` symbol **unpacks** the elements of a `list` object into another one. This idea of *iterable unpacking* is built into Python at various places, even *without* the `*` symbol.\n",
"\n",
"For example, we may write variables on the left-hand side of a `=` statement in a literal `tuple` style. Then, any *finite* iterable on the right-hand side is unpacked. So, `numbers` is unpacked into *twelve* variables below."
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12 = numbers"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n1"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"11"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n2"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n3"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Having to type twelve variables on the left is already tedious. Furthermore, if the iterable on the right yields a number of elements *different* from the number of variables, we get a `ValueError`."
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"ename": "ValueError",
"evalue": "too many values to unpack (expected 11)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[60], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11 \u001b[38;5;241m=\u001b[39m numbers\n",
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
]
}
],
"source": [
"n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11 = numbers"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"ename": "ValueError",
"evalue": "not enough values to unpack (expected 13, got 12)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12, n13 \u001b[38;5;241m=\u001b[39m numbers\n",
"\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
]
}
],
"source": [
"n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12, n13 = numbers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"So, to make iterable unpacking useful, we prepend the `*` symbol to *one* of the variables on the left: That variable then becomes a `list` object holding the elements not captured by the other variables. We say that the excess elements from the iterable are **packed** into this variable.\n",
"\n",
"For example, let's get the `first` and `last` element of `numbers` and collect the rest in `middle`."
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"first, *middle, last = numbers"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"7"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"first"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"middle # always a list!"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"last"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"We already used unpacking before this section without knowing it. Whenever we write a `for`-loop over the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in, that generates a new `tuple` object in each iteration that we unpack by listing several loop variables.\n",
"\n",
"So, the `name, position` below acts like a left-hand side of an `=` statement and unpacks the `tuple` objects generated from \"zipping\" the `names` list and the `positions` tuple together."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"names = [\"Berthold\", \"Oliver\", \"Carl\"]"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [],
"source": [
"positions = (\"goalkeeper\", \"defender\", \"midfielder\", \"striker\", \"coach\")"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Berthold is a goalkeeper\n",
"Oliver is a defender\n",
"Carl is a midfielder\n"
]
}
],
"source": [
"for name, position in zip(names, positions):\n",
" print(name, \"is a\", position)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Without unpacking, [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) generates a series of `tuple` objects."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'tuple'> ('Berthold', 'goalkeeper')\n",
"<class 'tuple'> ('Oliver', 'defender')\n",
"<class 'tuple'> ('Carl', 'midfielder')\n"
]
}
],
"source": [
"for pair in zip(names, positions):\n",
" print(type(pair), pair, sep=\" \")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Unpacking also works for nested objects. Below, we wrap [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `number` inside the `for`-loop. In each iteration, a `tuple` object consisting of `number` and another `tuple` object is created. The inner one then holds the `name` and `position`."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Berthold (jersey #1) is a goalkeeper\n",
"Oliver (jersey #2) is a defender\n",
"Carl (jersey #3) is a midfielder\n"
]
}
],
"source": [
"for number, (name, position) in enumerate(zip(names, positions), start=1):\n",
" print(f\"{name} (jersey #{number}) is a {position}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### Swapping Variables"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"A popular use case of unpacking is **swapping** two variables.\n",
"\n",
"Consider `a` and `b` below."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"a = 0\n",
"b = 1"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Without unpacking, we must use a temporary variable `temp` to swap `a` and `b`."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"temp = a\n",
"a = b\n",
"b = temp\n",
"\n",
"del temp"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"With unpacking, the solution is more elegant. *All* expressions on the right-hand side are evaluated *before* any assignment takes place."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [],
"source": [
"a, b = 0, 1"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": [
"a, b = b, a"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"(1, 0)"
]
},
"execution_count": 77,
"metadata": {},
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"#### Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
]
},
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"source": [
"Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
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"def fibonacci(i):\n",
" \"\"\"Calculate the ith Fibonacci number.\n",
"\n",
" Args:\n",
" i (int): index of the Fibonacci number to calculate\n",
"\n",
" Returns:\n",
" ith_fibonacci (int)\n",
" \"\"\"\n",
" a, b = 0, 1\n",
"\n",
" for _ in range(i - 1):\n",
" a, b = b, a + b\n",
"\n",
" return b"
]
},
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"cell_type": "code",
"execution_count": 79,
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"data": {
"text/plain": [
"144"
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"execution_count": 79,
"metadata": {},
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"fibonacci(12)"
]
},
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"source": [
"## Function Definitions & Calls"
]
},
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"The concepts of packing and unpacking are also helpful when writing and using functions.\n",
"\n",
"For example, let's look at the `product()` function below. Its implementation suggests that `args` must be a sequence type. Otherwise, it would not make sense to index into it with `[0]` or take a slice with `[1:]`. In line with the function's name, the `for`-loop multiplies all elements of the `args` sequence. So, what does the `*` do in the header line, and what is the exact data type of `args`?\n",
"\n",
"The `*` is again *not* an operator in this context but a special syntax that makes Python *pack* all *positional* arguments passed to `product()` into a single `tuple` object called `args`."
]
},
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"def product(*args):\n",
" \"\"\"Multiply all arguments.\"\"\"\n",
" result = args[0]\n",
"\n",
" for arg in args[1:]:\n",
" result *= arg\n",
"\n",
" return result"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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"source": [
"So, we can pass an *arbitrary* (i.e., also none) number of *positional* arguments to `product()`.\n",
"\n",
"The product of just one number is the number itself."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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"outputs": [
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"data": {
"text/plain": [
"42"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product(42)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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},
"source": [
"Passing in several numbers works as expected."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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},
"outputs": [
{
"data": {
"text/plain": [
"100"
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},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product(2, 5, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"However, this implementation of `product()` needs *at least* one argument passed in due to the expression `args[0]` used internally. Otherwise, we see a *runtime* error, namely an `IndexError`. We emphasize that this error is *not* caused in the header line."
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"ename": "IndexError",
"evalue": "tuple index out of range",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"Cell \u001b[0;32mIn[80], line 3\u001b[0m, in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mproduct\u001b[39m(\u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m 2\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Multiply all arguments.\"\"\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m 6\u001b[0m result \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m arg\n",
"\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
]
}
],
"source": [
"product()"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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},
"source": [
"Another downside of this implementation is that we can easily generate *semantic* errors: For example, if we pass in an iterable object like the `one_hundred` list, *no* exception is raised. However, the return value is also not a numeric object as we expect. The reason for this is that during the function call, `args` becomes a `tuple` object holding *one* element, which is `one_hundred`, a `list` object. So, we created a nested structure by accident."
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"one_hundred = [2, 5, 10]"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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},
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{
"data": {
"text/plain": [
"[2, 5, 10]"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product(one_hundred) # a semantic error!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"This error does not occur if we unpack `one_hundred` upon passing it as the argument."
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
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"outputs": [
{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product(*one_hundred)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"That is the equivalent of writing out the following tedious expression. Yet, that does *not* scale for iterables with many elements in them."
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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{
"data": {
"text/plain": [
"100"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"product(one_hundred[0], one_hundred[1], one_hundred[2])"
]
},
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"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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"source": [
"In the \"*Packing & Unpacking with Functions*\" [exercise <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/04_exercises.ipynb), we look at `product()` in more detail.\n",
"\n",
"While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general. For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2, 5, 10]\n"
]
}
],
"source": [
"print(one_hundred) # prints the tuple; we do not want that"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 5 10 "
]
}
],
"source": [
"for number in one_hundred:\n",
" print(number, end=\" \")"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2 5 10\n"
]
}
],
"source": [
"print(*one_hundred) # replaces the for-loop"
]
}
],
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