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11_classes/00_content.ipynb
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@ -8,7 +8,7 @@
}
},
"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/11_classes/00_content.ipynb)."
"**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/11_classes/00_content.ipynb)."
]
},
{
@ -34,7 +34,7 @@
"\n",
"Classes and instances follow the **[object-oriented programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object-oriented_programming)** (OOP) paradigm where a *large program* is broken down into many *small components* (i.e., the objects) that *reuse* code. This way, a program that is too big for a programmer to fully comprehend as a whole becomes maintainable via its easier to understand individual pieces.\n",
"\n",
"Often, we see the terminology \"classes & objects\" used instead of \"classes & instances\" in Python related texts. In this book, we are more precise as *both* classes and instances are objects as specified already in the \"*Objects vs. Types vs. Values*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values)."
"Often, we see the terminology \"classes & objects\" used instead of \"classes & instances\" in Python related texts. In this book, we are more precise as *both* classes and instances are objects as specified already in the \"*Objects vs. Types vs. Values*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values)."
]
},
{
@ -56,7 +56,7 @@
}
},
"source": [
"Neither core Python nor the standard library offer an implementation of common [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra) functionalities. While we introduce the popular third-party library [numpy](http://www.numpy.org/) in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrarys/00_content.ipynb) as the de-facto standard for that and recommend to use it in real-life projects, we show how one could use Python's object-oriented language features to implement common matrix and vector operations throughout this chapter. Once we have achieved that, we compare our own library with [numpy](http://www.numpy.org/).\n",
"Neither core Python nor the standard library offer an implementation of common [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra) functionalities. While we introduce the popular third-party library [numpy](http://www.numpy.org/) in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/10_arrarys/00_content.ipynb) as the de-facto standard for that and recommend to use it in real-life projects, we show how one could use Python's object-oriented language features to implement common matrix and vector operations throughout this chapter. Once we have achieved that, we compare our own library with [numpy](http://www.numpy.org/).\n",
"\n",
"Without classes, we could model a vector, for example, with a `tuple` or a `list` object, depending on if we want it to be mutable or not.\n",
"\n",
@ -326,7 +326,7 @@
"source": [
"Its type is `type` indicating that it represents a user-defined data type and it evaluates to its fully qualified name (i.e., `__main__` as it is defined in this Jupyter notebook).\n",
"\n",
"We have seen the type `type` before in the \"*Constructors*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Constructors) and also in the \"*The `namedtuple` Type*\" section in [Chapter 7's Appendix <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/05_appendix.ipynb#The-namedtuple-Type). In the latter case, we could also use a `Point` class but the [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) function from 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) is a convenient shortcut to create custom data types that can be derived out of a plain `tuple`.\n",
"We have seen the type `type` before in the \"*Constructors*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Constructors) and also in the \"*The `namedtuple` Type*\" section in [Chapter 7's Appendix <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/05_appendix.ipynb#The-namedtuple-Type). In the latter case, we could also use a `Point` class but the [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) function from 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) is a convenient shortcut to create custom data types that can be derived out of a plain `tuple`.\n",
"\n",
"In all examples, if an object's type is `type`, we can simply view it as a blueprint for a \"family\" of objects."
]
@ -1294,7 +1294,7 @@
}
},
"source": [
"From a theoretical point of view, the text representation provided by `.__repr__()` contains all the information (i.e., the $0$s and $1$s in memory) that is needed to model something in a computer. In a way, it is a natural extension from the binary (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Binary-Representations)), hexadecimal (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Hexadecimal-Representations)), and `bytes` (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb#The-bytes-Type)) representations of information. After all, just like Unicode characters are encoded in `bytes`, the more \"complex\" objects in this chapter are encoded in Unicode characters via their text representations."
"From a theoretical point of view, the text representation provided by `.__repr__()` contains all the information (i.e., the $0$s and $1$s in memory) that is needed to model something in a computer. In a way, it is a natural extension from the binary (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb#Binary-Representations)), hexadecimal (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb#Hexadecimal-Representations)), and `bytes` (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/02_content.ipynb#The-bytes-Type)) representations of information. After all, just like Unicode characters are encoded in `bytes`, the more \"complex\" objects in this chapter are encoded in Unicode characters via their text representations."
]
},
{
@ -1649,7 +1649,7 @@
"\n",
"An example of an instance method from linear algebra is the `.transpose()` method below that switches the rows and columns of an *existing* `Matrix` instance and returns a *new* `Matrix` instance based off that. It is implemented by passing the *iterator* created with 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 as the `data` argument to the `Matrix` constructor: The expression `zip(*self._entries)` may be a bit hard to understand because of the involved unpacking but simply flips a `Matrix`'s rows and columns. The built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor within the `.__init__()` method then materializes the iterator into the `._entries` attribute. Without a concrete `Matrix`'s rows and columns, `.transpose()` does not make sense, conceptually speaking.\n",
"\n",
"Also, we see that it is ok to reference a class from within one of its methods. While this seems trivial to some readers, others may find this confusing. The final versions of the `Vector` and `Matrix` classes in the [fourth 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/11_classes/04_content.ipynb#The-final-Vector-and-Matrix-Classes) of this chapter show how this \"hard coded\" redundancy can be avoided."
"Also, we see that it is ok to reference a class from within one of its methods. While this seems trivial to some readers, others may find this confusing. The final versions of the `Vector` and `Matrix` classes in the [fourth 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/11_classes/04_content.ipynb#The-final-Vector-and-Matrix-Classes) of this chapter show how this \"hard coded\" redundancy can be avoided."
]
},
{