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   "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/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "# Chapter 7: Sequential Data (Appendix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "In the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Tuples-are-like-\"Immutable-Lists\") of the chapter, we proposed the idea that `tuple` objects are like \"immutable lists.\" Often, however, we use `tuple` objects to represent a **record** of related **fields**. Then, each element has a *semantic* meaning (i.e., a descriptive name).\n",
    "\n",
    "As an example, think of a spreadsheet with information on students in a course. Each row represents a record and holds all the data associated with an individual student. The columns (e.g., matriculation number, first name, last name) are the fields that may come as *different* data types (e.g., `int` for the matriculation number, `str` for the names).\n",
    "\n",
    "A simple way of modeling a single student is as a `tuple` object, for example, `(123456, \"John\", \"Doe\")`. A disadvantage of this approach is that we must remember the order and meaning of the elements/fields in the `tuple` object.\n",
    "\n",
    "An example from a different domain is the representation of $(x, y)$-points in the $x$-$y$-plane. Again, we could use a `tuple` object like `current_position` below to model the point $(4, 2)$."
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  {
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   "source": [
    "current_position = (4, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "We implicitly assume that the first element represents the $x$ and the second the $y$ coordinate. While that follows intuitively from convention in math, we should at least add comments somewhere in the code to document this assumption."
   ]
  },
  {
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   "source": [
    "## The `namedtuple` Type"
   ]
  },
  {
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   "source": [
    "A better way is to create a *custom* data type. While that is covered in depth 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/develop/11_classes/00_content.ipynb), the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
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  {
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   "execution_count": 2,
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   "source": [
    "from collections import namedtuple"
   ]
  },
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   "source": [
    "[namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) takes two arguments. The first argument is the name of the data type. That could be different from the variable `Point` we use to refer to the new type, but in most cases it is best to keep them in sync. The second argument is a sequence with the field names as `str` objects. The names' order corresponds to the one assumed in `current_position`."
   ]
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  {
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   "source": [
    "Point = namedtuple(\"Point\", [\"x\", \"y\"])"
   ]
  },
  {
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   "source": [
    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`. In a way, [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) gives us a way to create our own custom **constructors**."
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   "cell_type": "code",
   "execution_count": 4,
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     "data": {
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       "94457911453856"
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     "execution_count": 4,
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   "source": [
    "id(Point)"
   ]
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   "cell_type": "code",
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     "data": {
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       "type"
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     "execution_count": 5,
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   "source": [
    "type(Point)"
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   "source": [
    "The value of `Point` is just itself in a *literal notation*."
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
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    {
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       "__main__.Point"
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     "execution_count": 6,
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    "Point"
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    "We write `Point(4, 2)` to create a *new* object of type `Point`."
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   "source": [
    "current_position = Point(4, 2)"
   ]
  },
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   "source": [
    "Now, `current_position` has a somewhat nicer representation. In particular, the coordinates are named `x` and `y`."
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  {
   "cell_type": "code",
   "execution_count": 8,
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       "Point(x=4, y=2)"
      ]
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     "execution_count": 8,
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    "current_position"
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  {
   "cell_type": "markdown",
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   "source": [
    "It is *not* a `tuple` any more but an object of type `Point`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
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    {
     "data": {
      "text/plain": [
       "140376178109184"
      ]
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     "execution_count": 9,
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   "source": [
    "id(current_position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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     "slide_type": "fragment"
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    {
     "data": {
      "text/plain": [
       "__main__.Point"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "type(current_position)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "We use the dot operator `.` to access the defined attributes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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   "source": [
    "current_position.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "current_position.y"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "As before, we get an `AttributeError` if we try to access an undefined attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Point' object has no attribute 'z'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
     ]
    }
   ],
   "source": [
    "current_position.z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "`current_position` continues to work like a `tuple` object! That is why we can use `namedtuple` as a replacement for `tuple`. The underlying implementations exhibit the *same* computational efficiencies and memory usages.\n",
    "\n",
    "For example, we can index into or loop over `current_position` as it is still a sequence with the familiar four properties."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
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     "slide_type": "slide"
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    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "current_position[0]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "current_position[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "for number in current_position:\n",
    "    print(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for number in reversed(current_position):\n",
    "    print(number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(current_position)"
   ]
  }
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