{
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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 ![](\"../static/link/to_mb.png\")
](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/05_appendix.ipynb)."
]
},
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"source": [
"# Chapter 7: Sequential Data (Appendix)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
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"source": [
"In the [third part ![](\"../static/link/to_nb.png\")
](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/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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"source": [
"current_position = (4, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"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."
]
},
{
"cell_type": "markdown",
"metadata": {
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"slide_type": "slide"
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"source": [
"## The `namedtuple` Type"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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},
"source": [
"A better way is to create a *custom* data type. While that is covered in depth in [Chapter 11 ](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb), the [collections ![](\"../static/link/to_py.png\")
](https://docs.python.org/3/library/collections.html) module in the [standard library ](https://docs.python.org/3/library/index.html) provides a [namedtuple() ](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."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
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"outputs": [],
"source": [
"from collections import namedtuple"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"[namedtuple() ](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`."
]
},
{
"cell_type": "code",
"execution_count": 3,
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"source": [
"Point = namedtuple(\"Point\", [\"x\", \"y\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"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() ](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": {
"text/plain": [
"94457911453856"
]
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"source": [
"id(Point)"
]
},
{
"cell_type": "code",
"execution_count": 5,
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"slide_type": "fragment"
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{
"data": {
"text/plain": [
"type"
]
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"source": [
"type(Point)"
]
},
{
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"slide_type": "skip"
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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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"slide_type": "fragment"
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"text/plain": [
"__main__.Point"
]
},
"execution_count": 6,
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}
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"Point"
]
},
{
"cell_type": "markdown",
"metadata": {
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"slide_type": "skip"
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"source": [
"We write `Point(4, 2)` to create a *new* object of type `Point`."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"slideshow": {
"slide_type": "slide"
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"source": [
"current_position = Point(4, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"Now, `current_position` has a somewhat nicer representation. In particular, the coordinates are named `x` and `y`."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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{
"data": {
"text/plain": [
"Point(x=4, y=2)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"current_position"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
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"source": [
"It is *not* a `tuple` any more but an object of type `Point`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"slideshow": {
"slide_type": "fragment"
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},
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{
"data": {
"text/plain": [
"140376178109184"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"id(current_position)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
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{
"data": {
"text/plain": [
"__main__.Point"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(current_position)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"We use the dot operator `.` to access the defined attributes."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"current_position.x"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"current_position.y"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"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"
}
},
"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)",
"Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcurrent_position\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mz\u001b[49m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
]
}
],
"source": [
"current_position.z"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"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": {
"slideshow": {
"slide_type": "slide"
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{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"current_position[0]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"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"
}
},
"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"
}
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(current_position)"
]
}
],
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