diff --git a/07_sequences_00_lecture.ipynb b/07_sequences_00_lecture.ipynb index e3887a4..fc97607 100644 --- a/07_sequences_00_lecture.ipynb +++ b/07_sequences_00_lecture.ipynb @@ -1,5 +1,53 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "image/jpeg": 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\n", + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import YouTubeVideo\n", + "YouTubeVideo(\"nx2sCDoeC3I\", width=\"60%\")" + ] + }, { "cell_type": "markdown", "metadata": { @@ -23,7 +71,7 @@ "\n", "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n", "\n", - "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n", + "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n", "\n", "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries." ] @@ -47,16 +95,16 @@ } }, "source": [ - "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n", + "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n", "\n", - "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) according to their behavior in various contexts.\n", + "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n", "\n", "As an illustration, consider `numbers` and `text` below, two objects of *different* types." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "slideshow": { "slide_type": "slide" @@ -65,7 +113,7 @@ "outputs": [], "source": [ "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n", - "text = \"Lorem ipsum dolor sit amet, ...\"" + "text = \"Lorem ipsum dolor sit amet.\"" ] }, { @@ -81,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "slideshow": { "slide_type": "fragment" @@ -103,10 +151,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -114,7 +162,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "L o r e m i p s u m d o l o r s i t a m e t , . . . " + "L o r e m i p s u m d o l o r s i t a m e t . " ] } ], @@ -138,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "slideshow": { "slide_type": "slide" @@ -151,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "slideshow": { "slide_type": "fragment" @@ -164,7 +212,7 @@ "collections.abc.Iterable" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -181,14 +229,14 @@ } }, "source": [ - "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n", + "As seen in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n", "\n", "So, let's \"ask\" Python if it can loop over `numbers` or `text`." ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "slideshow": { "slide_type": "slide" @@ -201,7 +249,7 @@ "True" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -212,10 +260,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -225,7 +273,7 @@ "True" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -247,10 +295,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [ @@ -260,7 +308,7 @@ "False" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -282,7 +330,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "slideshow": { "slide_type": "skip" @@ -296,7 +344,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\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[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable" ] } @@ -324,7 +372,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "slideshow": { "slide_type": "slide" @@ -337,48 +385,13 @@ "False" ] }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "0 in numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "\"l\" in text" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance()](https://docs.python.org/3/library/functions.html#isinstance)." + "0 in numbers" ] }, { @@ -402,7 +415,18 @@ } ], "source": [ - "isinstance(numbers, abc.Container)" + "\"l\" in text" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance()](https://docs.python.org/3/library/functions.html#isinstance)." ] }, { @@ -410,7 +434,7 @@ "execution_count": 13, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -425,6 +449,30 @@ "output_type": "execute_result" } ], + "source": [ + "isinstance(numbers, abc.Container)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "isinstance(text, abc.Container)" ] @@ -442,10 +490,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [ @@ -455,7 +503,7 @@ "False" ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -466,7 +514,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "slideshow": { "slide_type": "skip" @@ -480,7 +528,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\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[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable" ] } @@ -502,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "slideshow": { "slide_type": "slide" @@ -515,37 +563,13 @@ "12" ] }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "31" - ] - }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "len(text)" + "len(numbers)" ] }, { @@ -560,7 +584,7 @@ { "data": { "text/plain": [ - "True" + "27" ] }, "execution_count": 18, @@ -569,7 +593,7 @@ } ], "source": [ - "isinstance(numbers, abc.Sized)" + "len(text)" ] }, { @@ -577,7 +601,7 @@ "execution_count": 19, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -592,6 +616,30 @@ "output_type": "execute_result" } ], + "source": [ + "isinstance(numbers, abc.Sized)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "isinstance(text, abc.Sized)" ] @@ -609,10 +657,10 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [ @@ -622,7 +670,7 @@ "False" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -633,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "slideshow": { "slide_type": "skip" @@ -647,7 +695,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\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[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()" ] } @@ -673,7 +721,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "slideshow": { "slide_type": "slide" @@ -686,7 +734,7 @@ "True" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -697,10 +745,10 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -710,7 +758,7 @@ "True" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -734,7 +782,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "slideshow": { "slide_type": "slide" @@ -756,10 +804,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -767,7 +815,7 @@ "name": "stdout", "output_type": "stream", "text": [ - ". . . , t e m a t i s r o l o d m u s p i m e r o L " + ". t e m a t i s r o l o d m u s p i m e r o L " ] } ], @@ -778,7 +826,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "slideshow": { "slide_type": "fragment" @@ -791,7 +839,7 @@ "True" ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } @@ -802,10 +850,10 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -815,7 +863,7 @@ "True" ] }, - "execution_count": 27, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -837,7 +885,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": { "slideshow": { "slide_type": "slide" @@ -850,7 +898,7 @@ "True" ] }, - "execution_count": 28, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -861,10 +909,10 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -874,7 +922,7 @@ "True" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -895,7 +943,7 @@ "\n", "In Python-related documentations, the terms collection and sequence are heavily used, and the data science practitioner should always think of them in terms of the three or four behaviors they exhibit.\n", "\n", - "Data types that are collections but not sequences are covered in Chapter 8." + "Data types that are collections but not sequences are covered in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb)." ] }, { @@ -922,7 +970,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "slideshow": { "slide_type": "slide" @@ -935,10 +983,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], @@ -959,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": { "slideshow": { "slide_type": "fragment" @@ -983,10 +1031,10 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -996,7 +1044,7 @@ "[[], 10, 20.0, 'Thirty', [40, 50]]" ] }, - "execution_count": 33, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -1018,20 +1066,20 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "skip" } }, "outputs": [ { "data": { "text/plain": [ - "140417317434464" + "139941444092304" ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1042,10 +1090,10 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -1055,7 +1103,7 @@ "list" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1072,14 +1120,14 @@ } }, "source": [ - "Alternatively, we use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n", + "Alternatively, we use the built-in [list()](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n", "\n", "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually." ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": { "slideshow": { "slide_type": "slide" @@ -1092,37 +1140,13 @@ "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]" ] }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(range(1, 13))" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "isinstance(range(1, 13), abc.Iterable)" + "list(range(1, 13))" ] }, { @@ -1233,7 +1257,7 @@ " - supports being looped over\n", " - works with the `for` or `while` statements\n", "- `Reversible`:\n", - " - the elements come in a *predictable* order that we may traverse in a forward or backward fashion\n", + " - the elements come in a *predictable* order that we may loop over in a forward or backward fashion\n", " - works with the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in\n", "- `Sized`:\n", " - the number of elements is finite *and* known in advance\n", @@ -1291,7 +1315,7 @@ "execution_count": 41, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "outputs": [ @@ -1299,17 +1323,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "[] \t140417315676720 \t\n", - "10 \t94860849595456 \t\n", - "20.0 \t140417316476784 \t\n", - "Thirty \t140417315739568 \t\n", - "[40, 50] \t140417315720800 \t\n" + "[] 139941463482272 \n", + "10 94715524076608 \n", + "20.0 139941444613712 \n", + "Thirty 139941444470960 \n", + "[40, 50] 139941444068128 \n" ] } ], "source": [ "for element in nested:\n", - " print(element, id(element), type(element), sep=\" \\t\")" + " print(str(element).ljust(10), str(id(element)).ljust(18), type(element))" ] }, { @@ -1317,7 +1341,7 @@ "execution_count": 42, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "outputs": [ @@ -1409,7 +1433,7 @@ "execution_count": 45, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -1464,7 +1488,7 @@ { "data": { "text/plain": [ - "10" + "[]" ] }, "execution_count": 46, @@ -1473,7 +1497,7 @@ } ], "source": [ - "nested[1]" + "nested[0]" ] }, { @@ -1492,7 +1516,7 @@ "execution_count": 47, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -1552,7 +1576,7 @@ "execution_count": 49, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -1635,7 +1659,7 @@ "execution_count": 51, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -1888,7 +1912,7 @@ "execution_count": 61, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -1949,7 +1973,7 @@ "execution_count": 63, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -1986,7 +2010,7 @@ "execution_count": 64, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [ @@ -2017,7 +2041,7 @@ { "data": { "text/plain": [ - "140417315676720" + "139941463482272" ] }, "execution_count": 65, @@ -2041,7 +2065,7 @@ { "data": { "text/plain": [ - "140417315676720" + "139941463482272" ] }, "execution_count": 66, @@ -2053,30 +2077,6 @@ "id(nested_copy[0])" ] }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "140417315676720" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "id(empty)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -2096,7 +2096,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 67, "metadata": { "slideshow": { "slide_type": "skip" @@ -2109,7 +2109,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 68, "metadata": { "slideshow": { "slide_type": "skip" @@ -2122,7 +2122,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 69, "metadata": { "slideshow": { "slide_type": "skip" @@ -2135,7 +2135,7 @@ "True" ] }, - "execution_count": 70, + "execution_count": 69, "metadata": {}, "output_type": "execute_result" } @@ -2157,7 +2157,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 70, "metadata": { "slideshow": { "slide_type": "skip" @@ -2170,7 +2170,7 @@ "False" ] }, - "execution_count": 71, + "execution_count": 70, "metadata": {}, "output_type": "execute_result" } @@ -2179,6 +2179,30 @@ "nested[0] is nested_deep_copy[0]" ] }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "139941463482272" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id(nested[0])" + ] + }, { "cell_type": "code", "execution_count": 72, @@ -2191,7 +2215,7 @@ { "data": { "text/plain": [ - "140417315676720" + "139941444435792" ] }, "execution_count": 72, @@ -2199,30 +2223,6 @@ "output_type": "execute_result" } ], - "source": [ - "id(nested[0])" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "140417376809600" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ "id(nested_deep_copy[0])" ] @@ -2262,7 +2262,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 73, "metadata": { "slideshow": { "slide_type": "slide" @@ -2275,10 +2275,10 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 74, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2288,7 +2288,7 @@ "[0, 10, 20.0, 'Thirty', [40, 50]]" ] }, - "execution_count": 75, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -2310,7 +2310,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 75, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2318,15 +2318,15 @@ }, "outputs": [], "source": [ - "nested[:4] = [100, 100, 100] # assign three elements where there were four before" + "nested[:4] = [100, 100, 100]" ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 76, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2336,7 +2336,7 @@ "[100, 100, 100, [40, 50]]" ] }, - "execution_count": 77, + "execution_count": 76, "metadata": {}, "output_type": "execute_result" } @@ -2347,10 +2347,10 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 77, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2360,7 +2360,7 @@ "4" ] }, - "execution_count": 78, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -2382,7 +2382,7 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 78, "metadata": { "slideshow": { "slide_type": "skip" @@ -2392,10 +2392,10 @@ { "data": { "text/plain": [ - "140417317434464" + "139941444092304" ] }, - "execution_count": 79, + "execution_count": 78, "metadata": {}, "output_type": "execute_result" } @@ -2417,7 +2417,7 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 79, "metadata": { "slideshow": { "slide_type": "slide" @@ -2430,7 +2430,7 @@ "[[], 10, 20.0, 'Thirty', [40, 50]]" ] }, - "execution_count": 80, + "execution_count": 79, "metadata": {}, "output_type": "execute_result" } @@ -2452,10 +2452,10 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 80, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], @@ -2465,10 +2465,10 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 81, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2478,7 +2478,7 @@ "[[], 10, 20.0, 'Thirty', [1, 2, 3]]" ] }, - "execution_count": 82, + "execution_count": 81, "metadata": {}, "output_type": "execute_result" } @@ -2500,7 +2500,7 @@ }, { "cell_type": "code", - "execution_count": 83, + "execution_count": 82, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2513,7 +2513,7 @@ "[100, 100, 100, [1, 2, 3]]" ] }, - "execution_count": 83, + "execution_count": 82, "metadata": {}, "output_type": "execute_result" } @@ -2537,7 +2537,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 83, "metadata": { "slideshow": { "slide_type": "slide" @@ -2550,10 +2550,10 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 84, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2563,7 +2563,7 @@ "[100, 100, 100]" ] }, - "execution_count": 85, + "execution_count": 84, "metadata": {}, "output_type": "execute_result" } @@ -2585,7 +2585,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 85, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2598,10 +2598,10 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 86, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2611,7 +2611,7 @@ "[]" ] }, - "execution_count": 87, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -2628,33 +2628,7 @@ } }, "source": [ - "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n", - "\n", - "So, we can als \"ask\" Python if `nested` is mutable." - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(nested, abc.MutableSequence)" + "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module." ] }, { @@ -2685,7 +2659,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 87, "metadata": { "slideshow": { "slide_type": "slide" @@ -2709,7 +2683,7 @@ }, { "cell_type": "code", - "execution_count": 90, + "execution_count": 88, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2722,10 +2696,10 @@ }, { "cell_type": "code", - "execution_count": 91, + "execution_count": 89, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2735,7 +2709,7 @@ "['Carl', 'Peter', 'Eckardt']" ] }, - "execution_count": 91, + "execution_count": 89, "metadata": {}, "output_type": "execute_result" } @@ -2757,10 +2731,10 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 90, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "outputs": [], @@ -2770,10 +2744,10 @@ }, { "cell_type": "code", - "execution_count": 93, + "execution_count": 91, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2783,7 +2757,7 @@ "['Carl', 'Peter', 'Eckardt', 'Karl', 'Oliver']" ] }, - "execution_count": 93, + "execution_count": 91, "metadata": {}, "output_type": "execute_result" } @@ -2805,10 +2779,10 @@ }, { "cell_type": "code", - "execution_count": 94, + "execution_count": 92, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "outputs": [], @@ -2816,12 +2790,82 @@ "names.insert(1, \"Berthold\")" ] }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']" + ] + }, + "execution_count": 94, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "source": [ + "As the previous code cell created a *new* `list` object, `names` is still unsorted." + ] + }, { "cell_type": "code", "execution_count": 95, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2840,76 +2884,6 @@ "names" ] }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!" - ] - }, - { - "cell_type": "code", - "execution_count": 96, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']" - ] - }, - "execution_count": 96, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(names)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "As the previous code cell created a *new* `list` object, `names` is still unsorted." - ] - }, - { - "cell_type": "code", - "execution_count": 97, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']" - ] - }, - "execution_count": 97, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names" - ] - }, { "cell_type": "markdown", "metadata": { @@ -2923,7 +2897,7 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 96, "metadata": { "slideshow": { "slide_type": "fragment" @@ -2936,10 +2910,10 @@ }, { "cell_type": "code", - "execution_count": 99, + "execution_count": 97, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2949,7 +2923,7 @@ "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']" ] }, - "execution_count": 99, + "execution_count": 97, "metadata": {}, "output_type": "execute_result" } @@ -2971,7 +2945,7 @@ }, { "cell_type": "code", - "execution_count": 100, + "execution_count": 98, "metadata": { "slideshow": { "slide_type": "slide" @@ -2984,10 +2958,10 @@ }, { "cell_type": "code", - "execution_count": 101, + "execution_count": 99, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -2997,7 +2971,7 @@ "['Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold']" ] }, - "execution_count": 101, + "execution_count": 99, "metadata": {}, "output_type": "execute_result" } @@ -3025,7 +2999,7 @@ }, { "cell_type": "code", - "execution_count": 102, + "execution_count": 100, "metadata": { "slideshow": { "slide_type": "fragment" @@ -3051,10 +3025,10 @@ }, { "cell_type": "code", - "execution_count": 103, + "execution_count": 101, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3064,7 +3038,7 @@ "['Karl', 'Carl', 'Peter', 'Oliver', 'Eckardt', 'Berthold']" ] }, - "execution_count": 103, + "execution_count": 101, "metadata": {}, "output_type": "execute_result" } @@ -3086,10 +3060,10 @@ }, { "cell_type": "code", - "execution_count": 104, + "execution_count": 102, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "outputs": [], @@ -3099,10 +3073,10 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 103, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3112,7 +3086,7 @@ "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl', 'Karl']" ] }, - "execution_count": 105, + "execution_count": 103, "metadata": {}, "output_type": "execute_result" } @@ -3134,7 +3108,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 104, "metadata": { "slideshow": { "slide_type": "slide" @@ -3147,10 +3121,10 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 105, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3160,7 +3134,7 @@ "'Karl'" ] }, - "execution_count": 107, + "execution_count": 105, "metadata": {}, "output_type": "execute_result" } @@ -3171,10 +3145,10 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 106, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3184,7 +3158,7 @@ "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl']" ] }, - "execution_count": 108, + "execution_count": 106, "metadata": {}, "output_type": "execute_result" } @@ -3208,7 +3182,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 107, "metadata": { "slideshow": { "slide_type": "fragment" @@ -3221,10 +3195,10 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 108, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3234,7 +3208,7 @@ "'Eckardt'" ] }, - "execution_count": 110, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -3245,10 +3219,10 @@ }, { "cell_type": "code", - "execution_count": 111, + "execution_count": 109, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3258,7 +3232,7 @@ "['Berthold', 'Oliver', 'Peter', 'Carl']" ] }, - "execution_count": 111, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -3280,7 +3254,7 @@ }, { "cell_type": "code", - "execution_count": 112, + "execution_count": 110, "metadata": { "slideshow": { "slide_type": "slide" @@ -3293,10 +3267,10 @@ }, { "cell_type": "code", - "execution_count": 113, + "execution_count": 111, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3306,7 +3280,7 @@ "['Berthold', 'Oliver', 'Carl']" ] }, - "execution_count": 113, + "execution_count": 111, "metadata": {}, "output_type": "execute_result" } @@ -3328,7 +3302,7 @@ }, { "cell_type": "code", - "execution_count": 114, + "execution_count": 112, "metadata": { "slideshow": { "slide_type": "skip" @@ -3342,7 +3316,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list" ] } @@ -3364,7 +3338,7 @@ }, { "cell_type": "code", - "execution_count": 115, + "execution_count": 113, "metadata": { "slideshow": { "slide_type": "slide" @@ -3377,7 +3351,7 @@ "['Berthold', 'Oliver', 'Carl']" ] }, - "execution_count": 115, + "execution_count": 113, "metadata": {}, "output_type": "execute_result" } @@ -3388,10 +3362,10 @@ }, { "cell_type": "code", - "execution_count": 116, + "execution_count": 114, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3401,7 +3375,7 @@ "1" ] }, - "execution_count": 116, + "execution_count": 114, "metadata": {}, "output_type": "execute_result" } @@ -3412,10 +3386,10 @@ }, { "cell_type": "code", - "execution_count": 117, + "execution_count": 115, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3426,7 +3400,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: 'Karl' is not in list" ] } @@ -3448,7 +3422,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 116, "metadata": { "slideshow": { "slide_type": "fragment" @@ -3461,7 +3435,7 @@ "1" ] }, - "execution_count": 118, + "execution_count": 116, "metadata": {}, "output_type": "execute_result" } @@ -3472,7 +3446,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 117, "metadata": { "slideshow": { "slide_type": "skip" @@ -3485,7 +3459,7 @@ "0" ] }, - "execution_count": 119, + "execution_count": 117, "metadata": {}, "output_type": "execute_result" } @@ -3509,10 +3483,10 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 118, "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "skip" } }, "outputs": [], @@ -3522,10 +3496,10 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 119, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -3535,7 +3509,7 @@ "['Berthold', 'Oliver', 'Carl']" ] }, - "execution_count": 121, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -3557,10 +3531,10 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 120, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [], @@ -3570,10 +3544,10 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 121, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -3583,7 +3557,7 @@ "[]" ] }, - "execution_count": 123, + "execution_count": 121, "metadata": {}, "output_type": "execute_result" } @@ -3629,7 +3603,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 122, "metadata": { "slideshow": { "slide_type": "slide" @@ -3642,7 +3616,7 @@ "['Berthold', 'Oliver', 'Carl']" ] }, - "execution_count": 124, + "execution_count": 122, "metadata": {}, "output_type": "execute_result" } @@ -3653,23 +3627,10 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 123, "metadata": { "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "more_names = [\"Diedrich\", \"Yves\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 126, - "metadata": { - "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3679,21 +3640,21 @@ "['Berthold', 'Oliver', 'Carl', 'Diedrich', 'Yves']" ] }, - "execution_count": 126, + "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "names + more_names" + "names + [\"Diedrich\", \"Yves\"]" ] }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 124, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3703,7 +3664,7 @@ "['Berthold', 'Oliver', 'Carl', 'Berthold', 'Oliver', 'Carl']" ] }, - "execution_count": 127, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -3712,30 +3673,6 @@ "2 * names" ] }, - { - "cell_type": "code", - "execution_count": 128, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['Diedrich', 'Yves', 'Diedrich', 'Yves', 'Diedrich', 'Yves']" - ] - }, - "execution_count": 128, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "more_names * 3" - ] - }, { "cell_type": "markdown", "metadata": { @@ -3751,7 +3688,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 125, "metadata": { "slideshow": { "slide_type": "slide" @@ -3764,7 +3701,7 @@ "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']" ] }, - "execution_count": 129, + "execution_count": 125, "metadata": {}, "output_type": "execute_result" } @@ -3786,7 +3723,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 126, "metadata": { "slideshow": { "slide_type": "fragment" @@ -3799,7 +3736,7 @@ "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']" ] }, - "execution_count": 130, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -3834,7 +3771,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 127, "metadata": { "slideshow": { "slide_type": "slide" @@ -3847,7 +3784,7 @@ "['Berthold', 'Oliver', 'Carl']" ] }, - "execution_count": 131, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -3858,55 +3795,7 @@ }, { "cell_type": "code", - "execution_count": 132, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 132, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names == [\"Berthold\", \"Oliver\", \"Carl\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 133, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names != [\"Berthold\", \"Oliver\", \"Karl\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 134, + "execution_count": 128, "metadata": { "slideshow": { "slide_type": "fragment" @@ -3919,21 +3808,21 @@ "True" ] }, - "execution_count": 134, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "names < [\"Berthold\", \"Oliver\", \"Karl\"]" + "names == [\"Berthold\", \"Oliver\", \"Carl\"]" ] }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 129, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -3943,13 +3832,37 @@ "True" ] }, - "execution_count": 135, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "[\"Achim\", \"Oliver\", \"Carl\"] < names" + "names != [\"Berthold\", \"Oliver\", \"Karl\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 130, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "names < [\"Berthold\", \"Oliver\", \"Karl\"]" ] }, { @@ -3960,15 +3873,15 @@ } }, "source": [ - "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\" That rule is a common cause for *semantic* errors in a program." + "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\"" ] }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 131, "metadata": { "slideshow": { - "slide_type": "skip" + "slide_type": "fragment" } }, "outputs": [ @@ -3978,37 +3891,13 @@ "True" ] }, - "execution_count": 136, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "[\"Berthold\", \"Oliver\"] < names" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 137, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]" + "[\"Berthold\", \"Oliver\"] < names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]" ] }, { @@ -4037,7 +3926,7 @@ }, { "cell_type": "code", - "execution_count": 138, + "execution_count": 132, "metadata": { "slideshow": { "slide_type": "slide" @@ -4050,10 +3939,10 @@ }, { "cell_type": "code", - "execution_count": 139, + "execution_count": 133, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], @@ -4074,12 +3963,12 @@ "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." + "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](http://www.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&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)." ] }, { "cell_type": "code", - "execution_count": 140, + "execution_count": 134, "metadata": { "slideshow": { "slide_type": "fragment" @@ -4092,10 +3981,10 @@ }, { "cell_type": "code", - "execution_count": 141, + "execution_count": 135, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4105,7 +3994,7 @@ "['a', 'b', 'c', 'x', 'y', 'z']" ] }, - "execution_count": 141, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -4116,10 +4005,10 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 136, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4129,7 +4018,7 @@ "['a', 'b', 'c', 'x', 'y', 'z']" ] }, - "execution_count": 142, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -4148,14 +4037,14 @@ "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. This approach is following the **[functional programming](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", + "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor](http://www.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&curInstr=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](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 10." ] }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 137, "metadata": { "slideshow": { "slide_type": "slide" @@ -4168,24 +4057,24 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 138, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], "source": [ "def add_xyz(arg):\n", " \"\"\"Create a new list from an existing one.\"\"\"\n", - " new_arg = arg[:] # a shallow copy is good enough here\n", + " new_arg = arg[:]\n", " new_arg.extend([\"x\", \"y\", \"z\"])\n", " return new_arg" ] }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 139, "metadata": { "slideshow": { "slide_type": "fragment" @@ -4198,11 +4087,11 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 140, "metadata": { "scrolled": true, "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4212,7 +4101,7 @@ "['a', 'b', 'c', 'x', 'y', 'z']" ] }, - "execution_count": 146, + "execution_count": 140, "metadata": {}, "output_type": "execute_result" } @@ -4223,10 +4112,10 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 141, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4236,7 +4125,7 @@ "['a', 'b', 'c']" ] }, - "execution_count": 147, + "execution_count": 141, "metadata": {}, "output_type": "execute_result" } @@ -4253,12 +4142,12 @@ } }, "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`." + "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](http://www.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&curInstr=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": 148, + "execution_count": 142, "metadata": { "slideshow": { "slide_type": "slide" @@ -4271,10 +4160,10 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 143, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], @@ -4282,12 +4171,12 @@ "def add_xyz(arg):\n", " \"\"\"Append letters to a list.\"\"\"\n", " arg.extend([\"x\", \"y\", \"z\"])\n", - " return # = None" + " return # None" ] }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 144, "metadata": { "slideshow": { "slide_type": "fragment" @@ -4300,10 +4189,10 @@ }, { "cell_type": "code", - "execution_count": 151, + "execution_count": 145, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4313,7 +4202,7 @@ "['a', 'b', 'c', 'x', 'y', 'z']" ] }, - "execution_count": 151, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -4335,7 +4224,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 146, "metadata": { "slideshow": { "slide_type": "skip" @@ -4348,7 +4237,7 @@ }, { "cell_type": "code", - "execution_count": 153, + "execution_count": 147, "metadata": { "slideshow": { "slide_type": "skip" @@ -4361,7 +4250,7 @@ "['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']" ] }, - "execution_count": 153, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -4405,7 +4294,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 148, "metadata": { "slideshow": { "slide_type": "slide" @@ -4418,10 +4307,10 @@ }, { "cell_type": "code", - "execution_count": 155, + "execution_count": 149, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4431,7 +4320,7 @@ "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)" ] }, - "execution_count": 155, + "execution_count": 149, "metadata": {}, "output_type": "execute_result" } @@ -4453,10 +4342,10 @@ }, { "cell_type": "code", - "execution_count": 156, + "execution_count": 150, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [], @@ -4466,10 +4355,10 @@ }, { "cell_type": "code", - "execution_count": 157, + "execution_count": 151, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -4479,7 +4368,7 @@ "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)" ] }, - "execution_count": 157, + "execution_count": 151, "metadata": {}, "output_type": "execute_result" } @@ -4499,6 +4388,150 @@ "As before, `numbers` is an object on its own." ] }, + { + "cell_type": "code", + "execution_count": 152, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "139941463332656" + ] + }, + "execution_count": 152, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id(numbers)" + ] + }, + { + "cell_type": "code", + "execution_count": 153, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tuple" + ] + }, + "execution_count": 153, + "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": 154, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "empty_tuple = ()" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "()" + ] + }, + "execution_count": 155, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "empty_tuple" + ] + }, + { + "cell_type": "code", + "execution_count": 156, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "tuple" + ] + }, + "execution_count": 156, + "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": 157, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [], + "source": [ + "one_tuple = (1,) # we could ommit the parentheses but not the comma" + ] + }, { "cell_type": "code", "execution_count": 158, @@ -4511,7 +4544,7 @@ { "data": { "text/plain": [ - "140417315626832" + "(1,)" ] }, "execution_count": 158, @@ -4520,7 +4553,7 @@ } ], "source": [ - "id(numbers)" + "one_tuple" ] }, { @@ -4544,18 +4577,7 @@ } ], "source": [ - "type(numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "While we could use empty parentheses `()` to create an empty `tuple` object ..." + "type(one_tuple)" ] }, { @@ -4568,7 +4590,7 @@ }, "outputs": [], "source": [ - "empty_tuple = ()" + "no_tuple = (1)" ] }, { @@ -4583,7 +4605,7 @@ { "data": { "text/plain": [ - "()" + "1" ] }, "execution_count": 161, @@ -4592,7 +4614,7 @@ } ], "source": [ - "empty_tuple" + "no_tuple" ] }, { @@ -4603,139 +4625,6 @@ "slide_type": "skip" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "tuple" - ] - }, - "execution_count": 162, - "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": 163, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "one_tuple = (1,) # we could ommit the parentheses but not the comma" - ] - }, - { - "cell_type": "code", - "execution_count": 164, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1,)" - ] - }, - "execution_count": 164, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "one_tuple" - ] - }, - { - "cell_type": "code", - "execution_count": 165, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "tuple" - ] - }, - "execution_count": 165, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(one_tuple)" - ] - }, - { - "cell_type": "code", - "execution_count": 166, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "no_tuple = (1)" - ] - }, - { - "cell_type": "code", - "execution_count": 167, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "1" - ] - }, - "execution_count": 167, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "no_tuple" - ] - }, - { - "cell_type": "code", - "execution_count": 168, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, "outputs": [ { "data": { @@ -4743,7 +4632,7 @@ "int" ] }, - "execution_count": 168, + "execution_count": 162, "metadata": {}, "output_type": "execute_result" } @@ -4765,10 +4654,10 @@ }, { "cell_type": "code", - "execution_count": 169, + "execution_count": 163, "metadata": { "slideshow": { - "slide_type": "skip" + "slide_type": "slide" } }, "outputs": [ @@ -4778,7 +4667,7 @@ "(1,)" ] }, - "execution_count": 169, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -4789,7 +4678,7 @@ }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 164, "metadata": { "slideshow": { "slide_type": "fragment" @@ -4802,7 +4691,7 @@ "('i', 't', 'e', 'r', 'a', 'b', 'l', 'e')" ] }, - "execution_count": 170, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -4832,47 +4721,12 @@ "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." + "`tuple` objects are *sequences* exhibiting the familiar *four* behaviors. So, `numbers` holds a *finite* number of elements ..." ] }, { "cell_type": "code", - "execution_count": 171, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 171, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(numbers, abc.Sequence)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - " So, `numbers` holds a *finite* number of elements ..." - ] - }, - { - "cell_type": "code", - "execution_count": 172, + "execution_count": 165, "metadata": { "slideshow": { "slide_type": "slide" @@ -4885,7 +4739,7 @@ "12" ] }, - "execution_count": 172, + "execution_count": 165, "metadata": {}, "output_type": "execute_result" } @@ -4907,7 +4761,7 @@ }, { "cell_type": "code", - "execution_count": 173, + "execution_count": 166, "metadata": { "slideshow": { "slide_type": "fragment" @@ -4929,10 +4783,10 @@ }, { "cell_type": "code", - "execution_count": 174, + "execution_count": 167, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -4962,7 +4816,7 @@ }, { "cell_type": "code", - "execution_count": 175, + "execution_count": 168, "metadata": { "slideshow": { "slide_type": "fragment" @@ -4975,7 +4829,7 @@ "False" ] }, - "execution_count": 175, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -4986,31 +4840,7 @@ }, { "cell_type": "code", - "execution_count": 176, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 176, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1 in numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 177, + "execution_count": 169, "metadata": { "slideshow": { "slide_type": "skip" @@ -5023,7 +4853,31 @@ "True" ] }, - "execution_count": 177, + "execution_count": 169, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "1 in numbers" + ] + }, + { + "cell_type": "code", + "execution_count": 170, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -5045,7 +4899,7 @@ }, { "cell_type": "code", - "execution_count": 178, + "execution_count": 171, "metadata": { "slideshow": { "slide_type": "slide" @@ -5058,7 +4912,7 @@ "7" ] }, - "execution_count": 178, + "execution_count": 171, "metadata": {}, "output_type": "execute_result" } @@ -5069,10 +4923,10 @@ }, { "cell_type": "code", - "execution_count": 179, + "execution_count": 172, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5082,7 +4936,7 @@ "4" ] }, - "execution_count": 179, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -5093,10 +4947,10 @@ }, { "cell_type": "code", - "execution_count": 180, + "execution_count": 173, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5106,7 +4960,7 @@ "(2, 6, 9, 10, 1, 4)" ] }, - "execution_count": 180, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -5128,10 +4982,10 @@ }, { "cell_type": "code", - "execution_count": 181, + "execution_count": 174, "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "fragment" } }, "outputs": [ @@ -5142,7 +4996,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\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[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\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;36m99\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\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;36m99\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" ] } @@ -5159,49 +5013,12 @@ } }, "source": [ - "We can verify the immutability with the `MutableSequence` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module: [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) returns `False`. So, a data type that is a `Sequence` may be mutable or not. If it is a `MutableSequence`, it is mutable. If it is *not* a `MutableSequence`, it is *immutable*. There is *no* `ImmutableSequence` ABC." + "The `+` and `*` operators work with `tuple` objects as well: They always create *new* `tuple` objects." ] }, { "cell_type": "code", - "execution_count": 182, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 182, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(numbers, abc.MutableSequence)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The `+` and `*` operators work with `tuple` objects as well. However, we should *not* do that as the whole point of immutability is to *not* mutate an object.\n", - "\n", - "So, instead of writing something like below, we should use a `list` object and call its `append()` method." - ] - }, - { - "cell_type": "code", - "execution_count": 183, + "execution_count": 175, "metadata": { "slideshow": { "slide_type": "skip" @@ -5214,7 +5031,7 @@ "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 99)" ] }, - "execution_count": 183, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -5225,7 +5042,7 @@ }, { "cell_type": "code", - "execution_count": 184, + "execution_count": 176, "metadata": { "slideshow": { "slide_type": "skip" @@ -5238,7 +5055,7 @@ "(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": 184, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -5260,7 +5077,7 @@ }, { "cell_type": "code", - "execution_count": 185, + "execution_count": 177, "metadata": { "slideshow": { "slide_type": "skip" @@ -5273,7 +5090,7 @@ "0" ] }, - "execution_count": 185, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -5284,7 +5101,7 @@ }, { "cell_type": "code", - "execution_count": 186, + "execution_count": 178, "metadata": { "slideshow": { "slide_type": "skip" @@ -5297,7 +5114,7 @@ "10" ] }, - "execution_count": 186, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -5319,7 +5136,7 @@ }, { "cell_type": "code", - "execution_count": 187, + "execution_count": 179, "metadata": { "slideshow": { "slide_type": "skip" @@ -5332,7 +5149,7 @@ "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)" ] }, - "execution_count": 187, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -5343,7 +5160,7 @@ }, { "cell_type": "code", - "execution_count": 188, + "execution_count": 180, "metadata": { "slideshow": { "slide_type": "skip" @@ -5356,7 +5173,7 @@ "True" ] }, - "execution_count": 188, + "execution_count": 180, "metadata": {}, "output_type": "execute_result" } @@ -5367,7 +5184,7 @@ }, { "cell_type": "code", - "execution_count": 189, + "execution_count": 181, "metadata": { "slideshow": { "slide_type": "skip" @@ -5380,7 +5197,7 @@ "True" ] }, - "execution_count": 189, + "execution_count": 181, "metadata": {}, "output_type": "execute_result" } @@ -5391,7 +5208,7 @@ }, { "cell_type": "code", - "execution_count": 190, + "execution_count": 182, "metadata": { "slideshow": { "slide_type": "skip" @@ -5404,7 +5221,7 @@ "True" ] }, - "execution_count": 190, + "execution_count": 182, "metadata": {}, "output_type": "execute_result" } @@ -5413,30 +5230,6 @@ "numbers < (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)" ] }, - { - "cell_type": "code", - "execution_count": 191, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 191, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "(0, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4) < numbers" - ] - }, { "cell_type": "markdown", "metadata": { @@ -5445,17 +5238,17 @@ } }, "source": [ - "While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains mutable objects, the entire nested structure is *not* immutable as a whole.\n", + "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": 192, + "execution_count": 183, "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "skip" } }, "outputs": [], @@ -5465,10 +5258,10 @@ }, { "cell_type": "code", - "execution_count": 193, + "execution_count": 184, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5478,7 +5271,7 @@ "(1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)" ] }, - "execution_count": 193, + "execution_count": 184, "metadata": {}, "output_type": "execute_result" } @@ -5489,10 +5282,10 @@ }, { "cell_type": "code", - "execution_count": 194, + "execution_count": 185, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [], @@ -5502,10 +5295,10 @@ }, { "cell_type": "code", - "execution_count": 195, + "execution_count": 186, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5515,7 +5308,7 @@ "(1, [99, 99, 99], 12)" ] }, - "execution_count": 195, + "execution_count": 186, "metadata": {}, "output_type": "execute_result" } @@ -5550,7 +5343,7 @@ }, { "cell_type": "code", - "execution_count": 196, + "execution_count": 187, "metadata": { "slideshow": { "slide_type": "slide" @@ -5563,7 +5356,7 @@ }, { "cell_type": "code", - "execution_count": 197, + "execution_count": 188, "metadata": { "slideshow": { "slide_type": "fragment" @@ -5576,7 +5369,7 @@ "7" ] }, - "execution_count": 197, + "execution_count": 188, "metadata": {}, "output_type": "execute_result" } @@ -5587,10 +5380,10 @@ }, { "cell_type": "code", - "execution_count": 198, + "execution_count": 189, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5600,7 +5393,7 @@ "11" ] }, - "execution_count": 198, + "execution_count": 189, "metadata": {}, "output_type": "execute_result" } @@ -5611,10 +5404,10 @@ }, { "cell_type": "code", - "execution_count": 199, + "execution_count": 190, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5624,7 +5417,7 @@ "8" ] }, - "execution_count": 199, + "execution_count": 190, "metadata": {}, "output_type": "execute_result" } @@ -5646,7 +5439,7 @@ }, { "cell_type": "code", - "execution_count": 200, + "execution_count": 191, "metadata": { "slideshow": { "slide_type": "skip" @@ -5660,7 +5453,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)" ] } @@ -5671,7 +5464,7 @@ }, { "cell_type": "code", - "execution_count": 201, + "execution_count": 192, "metadata": { "slideshow": { "slide_type": "skip" @@ -5685,7 +5478,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\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;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)" ] } @@ -5709,7 +5502,7 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 193, "metadata": { "slideshow": { "slide_type": "slide" @@ -5722,10 +5515,10 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 194, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5735,7 +5528,7 @@ "7" ] }, - "execution_count": 203, + "execution_count": 194, "metadata": {}, "output_type": "execute_result" } @@ -5746,10 +5539,10 @@ }, { "cell_type": "code", - "execution_count": 204, + "execution_count": 195, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5759,21 +5552,21 @@ "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]" ] }, - "execution_count": 204, + "execution_count": 195, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "middle" + "middle # always a list!" ] }, { "cell_type": "code", - "execution_count": 205, + "execution_count": 196, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -5783,79 +5576,7 @@ "4" ] }, - "execution_count": 205, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "last" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "If we do not need the `middle` elements, we go with the underscore `_` convention and \"throw\" them away." - ] - }, - { - "cell_type": "code", - "execution_count": 206, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "first, *_, last = numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 207, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 207, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "first" - ] - }, - { - "cell_type": "code", - "execution_count": 208, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 208, + "execution_count": 196, "metadata": {}, "output_type": "execute_result" } @@ -5879,10 +5600,10 @@ }, { "cell_type": "code", - "execution_count": 209, + "execution_count": 197, "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "skip" } }, "outputs": [], @@ -5892,10 +5613,10 @@ }, { "cell_type": "code", - "execution_count": 210, + "execution_count": 198, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -5927,10 +5648,10 @@ }, { "cell_type": "code", - "execution_count": 211, + "execution_count": 199, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "skip" } }, "outputs": [ @@ -5957,12 +5678,12 @@ } }, "source": [ - "Unpacking also works for nested objects. Below, we wrap [zip()](https://docs.python.org/3/library/functions.html#zip) with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `i` inside the `for`-loop. In each iteration, a `tuple` object consisting of `i` and another `tuple` object is created. The inner one then holds the `name` and `position`." + "Unpacking also works for nested objects. Below, we wrap [zip()](https://docs.python.org/3/library/functions.html#zip) with the [enumerate()](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": 212, + "execution_count": 200, "metadata": { "slideshow": { "slide_type": "skip" @@ -5973,15 +5694,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "0 -> Berthold is a goalkeeper\n", - "1 -> Oliver is a defender\n", - "2 -> Carl is a midfielder\n" + "Berthold (jersey #1) is a goalkeeper\n", + "Oliver (jersey #2) is a defender\n", + "Carl (jersey #3) is a midfielder\n" ] } ], "source": [ - "for i, (name, position) in enumerate(zip(names, positions)):\n", - " print(i, \"->\", name, \"is a\", position)" + "for number, (name, position) in enumerate(zip(names, positions), start=1):\n", + " print(f\"{name} (jersey #{number}) is a {position}\")" ] }, { @@ -6010,7 +5731,7 @@ }, { "cell_type": "code", - "execution_count": 213, + "execution_count": 201, "metadata": { "slideshow": { "slide_type": "slide" @@ -6035,7 +5756,7 @@ }, { "cell_type": "code", - "execution_count": 214, + "execution_count": 202, "metadata": { "slideshow": { "slide_type": "fragment" @@ -6045,31 +5766,57 @@ "source": [ "temp = a\n", "a = b\n", - "b = temp" + "b = temp\n", + "\n", + "del temp" ] }, { "cell_type": "code", - "execution_count": 215, + "execution_count": 203, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ { "data": { "text/plain": [ - "(1, 0)" + "1" ] }, - "execution_count": 215, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "a, b" + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 204, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 204, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b" ] }, { @@ -6085,7 +5832,7 @@ }, { "cell_type": "code", - "execution_count": 216, + "execution_count": 205, "metadata": { "slideshow": { "slide_type": "slide" @@ -6093,16 +5840,15 @@ }, "outputs": [], "source": [ - "a = 0\n", - "b = 1" + "a, b = 0, 1" ] }, { "cell_type": "code", - "execution_count": 217, + "execution_count": 206, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], @@ -6112,10 +5858,10 @@ }, { "cell_type": "code", - "execution_count": 218, + "execution_count": 207, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6125,7 +5871,7 @@ "(1, 0)" ] }, - "execution_count": 218, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } @@ -6138,11 +5884,11 @@ "cell_type": "markdown", "metadata": { "slideshow": { - "slide_type": "skip" + "slide_type": "slide" } }, "source": [ - "#### Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)" + "##### Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)" ] }, { @@ -6153,28 +5899,15 @@ } }, "source": [ - "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)." + "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way." ] }, { "cell_type": "code", - "execution_count": 219, + "execution_count": 208, "metadata": { "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 220, - "metadata": { - "slideshow": { - "slide_type": "skip" + "slide_type": "slide" } }, "outputs": [], @@ -6187,21 +5920,7 @@ "\n", " Returns:\n", " ith_fibonacci (int)\n", - "\n", - " Raises:\n", - " TypeError: if i is not an integer or not integer-like\n", - " ValueError: if i is not positive\n", " \"\"\"\n", - " if not isinstance(i, numbers.Integral):\n", - " if isinstance(i, numbers.Real):\n", - " if i != int(i):\n", - " raise TypeError(\"i is not an integer-like value; it has decimals\")\n", - " i = int(i)\n", - " else:\n", - " raise TypeError(\"i must be an integer\")\n", - " if i < 0:\n", - " raise ValueError(\"i must be non-negative\")\n", - "\n", " a, b = 0, 1\n", "\n", " for _ in range(i - 1):\n", @@ -6212,10 +5931,10 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 209, "metadata": { "slideshow": { - "slide_type": "skip" + "slide_type": "fragment" } }, "outputs": [ @@ -6225,7 +5944,7 @@ "144" ] }, - "execution_count": 221, + "execution_count": 209, "metadata": {}, "output_type": "execute_result" } @@ -6234,67 +5953,6 @@ "fibonacci(12)" ] }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Because of the *goose typing*, we may pass `float` objects to `fibonacci()` as long as they contain no decimals." - ] - }, - { - "cell_type": "code", - "execution_count": 222, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "144" - ] - }, - "execution_count": 222, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "fibonacci(12.0)" - ] - }, - { - "cell_type": "code", - "execution_count": 223, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "i is not an integer-like value; it has decimals", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\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[0;32m----> 1\u001b[0;31m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12.3\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;36mfibonacci\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\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 16\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\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---> 17\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 18\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: i is not an integer-like value; it has decimals" - ] - } - ], - "source": [ - "fibonacci(12.3)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -6323,7 +5981,7 @@ }, { "cell_type": "code", - "execution_count": 224, + "execution_count": 210, "metadata": { "slideshow": { "slide_type": "slide" @@ -6356,10 +6014,10 @@ }, { "cell_type": "code", - "execution_count": 225, + "execution_count": 211, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6369,7 +6027,7 @@ "42" ] }, - "execution_count": 225, + "execution_count": 211, "metadata": {}, "output_type": "execute_result" } @@ -6391,10 +6049,10 @@ }, { "cell_type": "code", - "execution_count": 226, + "execution_count": 212, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6404,7 +6062,7 @@ "100" ] }, - "execution_count": 226, + "execution_count": 212, "metadata": {}, "output_type": "execute_result" } @@ -6426,10 +6084,10 @@ }, { "cell_type": "code", - "execution_count": 227, + "execution_count": 213, "metadata": { "slideshow": { - "slide_type": "slide" + "slide_type": "skip" } }, "outputs": [ @@ -6440,8 +6098,8 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\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[0;32m----> 1\u001b[0;31m \u001b[0mproduct\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[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\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[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\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[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\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[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mIndexError\u001b[0m: tuple index out of range" ] } @@ -6463,7 +6121,7 @@ }, { "cell_type": "code", - "execution_count": 228, + "execution_count": 214, "metadata": { "slideshow": { "slide_type": "slide" @@ -6476,10 +6134,10 @@ }, { "cell_type": "code", - "execution_count": 229, + "execution_count": 215, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6489,13 +6147,13 @@ "[2, 5, 10]" ] }, - "execution_count": 229, + "execution_count": 215, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "product(one_hundred)" + "product(one_hundred) # a semantic error!" ] }, { @@ -6511,7 +6169,7 @@ }, { "cell_type": "code", - "execution_count": 230, + "execution_count": 216, "metadata": { "slideshow": { "slide_type": "fragment" @@ -6524,7 +6182,7 @@ "100" ] }, - "execution_count": 230, + "execution_count": 216, "metadata": {}, "output_type": "execute_result" } @@ -6546,7 +6204,7 @@ }, { "cell_type": "code", - "execution_count": 231, + "execution_count": 217, "metadata": { "slideshow": { "slide_type": "fragment" @@ -6559,7 +6217,7 @@ "100" ] }, - "execution_count": 231, + "execution_count": 217, "metadata": {}, "output_type": "execute_result" } @@ -6578,14 +6236,12 @@ "source": [ "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, 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.\n", - "\n", - "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." + "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": 232, + "execution_count": 218, "metadata": { "slideshow": { "slide_type": "skip" @@ -6606,7 +6262,7 @@ }, { "cell_type": "code", - "execution_count": 233, + "execution_count": 219, "metadata": { "slideshow": { "slide_type": "skip" @@ -6628,7 +6284,7 @@ }, { "cell_type": "code", - "execution_count": 234, + "execution_count": 220, "metadata": { "slideshow": { "slide_type": "skip" @@ -6677,7 +6333,7 @@ }, { "cell_type": "code", - "execution_count": 235, + "execution_count": 221, "metadata": { "slideshow": { "slide_type": "slide" @@ -6703,10 +6359,10 @@ }, { "cell_type": "code", - "execution_count": 236, + "execution_count": 222, "metadata": { "slideshow": { - "slide_type": "fragment" + "slide_type": "slide" } }, "outputs": [], @@ -6727,10 +6383,10 @@ }, { "cell_type": "code", - "execution_count": 237, + "execution_count": 223, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [], @@ -6746,12 +6402,12 @@ } }, "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`." + "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**." ] }, { "cell_type": "code", - "execution_count": 238, + "execution_count": 224, "metadata": { "slideshow": { "slide_type": "fragment" @@ -6761,24 +6417,24 @@ { "data": { "text/plain": [ - "140416708056240" + "94715539542800" ] }, - "execution_count": 238, + "execution_count": 224, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "id(Point) # classes are objects as well" + "id(Point)" ] }, { "cell_type": "code", - "execution_count": 239, + "execution_count": 225, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6788,7 +6444,7 @@ "type" ] }, - "execution_count": 239, + "execution_count": 225, "metadata": {}, "output_type": "execute_result" } @@ -6810,10 +6466,10 @@ }, { "cell_type": "code", - "execution_count": 240, + "execution_count": 226, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6823,7 +6479,7 @@ "__main__.Point" ] }, - "execution_count": 240, + "execution_count": 226, "metadata": {}, "output_type": "execute_result" } @@ -6845,7 +6501,7 @@ }, { "cell_type": "code", - "execution_count": 241, + "execution_count": 227, "metadata": { "slideshow": { "slide_type": "slide" @@ -6869,10 +6525,10 @@ }, { "cell_type": "code", - "execution_count": 242, + "execution_count": 228, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6882,7 +6538,7 @@ "Point(x=4, y=2)" ] }, - "execution_count": 242, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } @@ -6904,7 +6560,7 @@ }, { "cell_type": "code", - "execution_count": 243, + "execution_count": 229, "metadata": { "slideshow": { "slide_type": "fragment" @@ -6914,10 +6570,10 @@ { "data": { "text/plain": [ - "140417315422528" + "139941443365056" ] }, - "execution_count": 243, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } @@ -6928,10 +6584,10 @@ }, { "cell_type": "code", - "execution_count": 244, + "execution_count": 230, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -6941,7 +6597,7 @@ "__main__.Point" ] }, - "execution_count": 244, + "execution_count": 230, "metadata": {}, "output_type": "execute_result" } @@ -6963,7 +6619,7 @@ }, { "cell_type": "code", - "execution_count": 245, + "execution_count": 231, "metadata": { "slideshow": { "slide_type": "slide" @@ -6976,7 +6632,7 @@ "4" ] }, - "execution_count": 245, + "execution_count": 231, "metadata": {}, "output_type": "execute_result" } @@ -6987,10 +6643,10 @@ }, { "cell_type": "code", - "execution_count": 246, + "execution_count": 232, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "fragment" } }, "outputs": [ @@ -7000,7 +6656,7 @@ "2" ] }, - "execution_count": 246, + "execution_count": 232, "metadata": {}, "output_type": "execute_result" } @@ -7022,7 +6678,7 @@ }, { "cell_type": "code", - "execution_count": 247, + "execution_count": 233, "metadata": { "slideshow": { "slide_type": "skip" @@ -7036,7 +6692,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\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;32m\u001b[0m in \u001b[0;36m\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'" ] } @@ -7055,41 +6711,17 @@ "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." + "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": 248, + "execution_count": 234, "metadata": { "slideshow": { "slide_type": "slide" } }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 248, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(current_position, abc.Sequence)" - ] - }, - { - "cell_type": "code", - "execution_count": 249, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, "outputs": [ { "data": { @@ -7097,7 +6729,7 @@ "4" ] }, - "execution_count": 249, + "execution_count": 234, "metadata": {}, "output_type": "execute_result" } @@ -7108,10 +6740,10 @@ }, { "cell_type": "code", - "execution_count": 250, + "execution_count": 235, "metadata": { "slideshow": { - "slide_type": "-" + "slide_type": "skip" } }, "outputs": [ @@ -7121,7 +6753,7 @@ "2" ] }, - "execution_count": 250, + "execution_count": 235, "metadata": {}, "output_type": "execute_result" } @@ -7132,7 +6764,7 @@ }, { "cell_type": "code", - "execution_count": 251, + "execution_count": 236, "metadata": { "slideshow": { "slide_type": "fragment" @@ -7155,10 +6787,10 @@ }, { "cell_type": "code", - "execution_count": 252, + "execution_count": 237, "metadata": { "slideshow": { - "slide_type": "skip" + "slide_type": "fragment" } }, "outputs": [ @@ -7176,17 +6808,6 @@ " print(number)" ] }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## The Map-Filter-Reduce Paradigm" - ] - }, { "cell_type": "markdown", "metadata": { @@ -7195,216 +6816,12 @@ } }, "source": [ - "Whenever we process sequential data, most tasks can be classified into one of the three categories **map**, **filter**, or **reduce**. This paradigm has caught attention in recent years as it enables **[parallel computing](https://en.wikipedia.org/wiki/Parallel_computing)**, and this gets important when dealing with big amounts of data.\n", - "\n", - "Let's look at a simple example." + "Because of that it has \"length.\"" ] }, { "cell_type": "code", - "execution_count": 253, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Mapping" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "**Mapping** refers to the idea of applying a transformation to every element in a sequence.\n", - "\n", - "For example, let's square each element in `numbers` and add `1` to it. In essence, we apply the transformation $y := x^2 + 1$ expressed as the `transform()` function below." - ] - }, - { - "cell_type": "code", - "execution_count": 254, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "def transform(element):\n", - " \"\"\"Map elements to their squares plus 1.\"\"\"\n", - " return (element ** 2) + 1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "With the syntax we know so far, we revert to a `for`-loop that iteratively appends the transformed elements to the initially empty `transformed_numbers` list." - ] - }, - { - "cell_type": "code", - "execution_count": 255, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "transformed_numbers = []\n", - "\n", - "for old in numbers:\n", - " new = transform(old)\n", - " transformed_numbers.append(new)" - ] - }, - { - "cell_type": "code", - "execution_count": 256, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]" - ] - }, - "execution_count": 256, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "As this kind of data processing is so common, Python provides the [map()](https://docs.python.org/3/library/functions.html#map) built-in. In its simplest usage form, it takes two arguments: A transformation function that takes one positional argument and an iterable.\n", - "\n", - "We call [map()](https://docs.python.org/3/library/functions.html#map) with the `transform()` function and the `numbers` list as the arguments and store the result in the variable `transformer` to inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": 257, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "transformer = map(transform, numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We might expect to get back a materialized sequence (i.e., all elements exist in memory), and a `list` object would feel the most natural because of the type of the `numbers` argument. However, `transformer` is an object of type `map`." - ] - }, - { - "cell_type": "code", - "execution_count": 258, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 258, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformer" - ] - }, - { - "cell_type": "code", - "execution_count": 259, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "map" - ] - }, - "execution_count": 259, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(transformer)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Like `range` objects, `map` objects generate a series of objects \"on the fly\" (i.e., one by one), and we use the built-in [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the next object in line. So, we should think of a `map` object as a \"rule\" stored in memory that only knows how to calculate the next object of possibly *infinitely* many.\n", - "\n", - "It is essential to understand that by creating a `map` object with the [map()](https://docs.python.org/3/library/functions.html#map) built-in, nothing happens in memory except the creation of the `map` object. In particular, no second `list` object derived from `numbers` is created. Also, we may view `range` objects as a special case of `map` objects: They are constrained to generating `int` objects only, and the *iterable* argument is replaced with *start*, *stop*, and *step* arguments." - ] - }, - { - "cell_type": "code", - "execution_count": 260, + "execution_count": 238, "metadata": { "slideshow": { "slide_type": "fragment" @@ -7414,4746 +6831,16 @@ { "data": { "text/plain": [ - "50" + "2" ] }, - "execution_count": 260, + "execution_count": 238, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "next(transformer)" - ] - }, - { - "cell_type": "code", - "execution_count": 261, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "122" - ] - }, - "execution_count": 261, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(transformer)" - ] - }, - { - "cell_type": "code", - "execution_count": 262, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "65" - ] - }, - "execution_count": 262, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(transformer)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "If we are sure that a `map` object generates a *finite* number of elements, we may materialize them into a `list` object with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in. In the example, this is the case as `transformer` is derived from a *finite* `list` object.\n", - "\n", - "In summary, instead of creating an empty list first and appending it in a `for`-loop as above, we write the following one-liner and obtain an equal `transformed_numbers` list." - ] - }, - { - "cell_type": "code", - "execution_count": 263, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "transformed_numbers = list(map(transform, numbers))" - ] - }, - { - "cell_type": "code", - "execution_count": 264, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]" - ] - }, - "execution_count": 264, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Filtering" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "**Filtering** refers to the idea of creating a subset of a sequence with a **boolean filter** function that indicates if an element should be kept or not.\n", - "\n", - "In the example, let's only keep the even elements in `numbers`. The `is_even()` function implements that as a filter." - ] - }, - { - "cell_type": "code", - "execution_count": 265, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "def is_even(element):\n", - " \"\"\"Filter out odd numbers.\"\"\"\n", - " if element % 2 == 0:\n", - " return True\n", - " return False" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "As before, we must revert to a `for`-loop that appends the elements to be kept iteratively to an initially empty `even_numbers` list." - ] - }, - { - "cell_type": "code", - "execution_count": 266, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "even_numbers = []\n", - "\n", - "for number in transformed_numbers:\n", - " if is_even(number):\n", - " even_numbers.append(number)" - ] - }, - { - "cell_type": "code", - "execution_count": 267, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[50, 122, 26, 10, 82, 2]" - ] - }, - "execution_count": 267, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "even_numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "As filtering is also a common task, we use the [filter()](https://docs.python.org/3/library/functions.html#filter) built-in that returns an object of type `filter` stored in the `evens` variable." - ] - }, - { - "cell_type": "code", - "execution_count": 268, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "evens = filter(is_even, transformed_numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 269, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 269, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evens" - ] - }, - { - "cell_type": "code", - "execution_count": 270, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "filter" - ] - }, - "execution_count": 270, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(evens)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "`evens` works like `transformer` above, and we use the built-in [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the even numbers one by one. So, the \"next\" element in line is simply the next even `int` object the `filter` object encounters." - ] - }, - { - "cell_type": "code", - "execution_count": 271, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]" - ] - }, - "execution_count": 271, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "transformed_numbers # for quick reference" - ] - }, - { - "cell_type": "code", - "execution_count": 272, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "50" - ] - }, - "execution_count": 272, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(evens)" - ] - }, - { - "cell_type": "code", - "execution_count": 273, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "122" - ] - }, - "execution_count": 273, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(evens)" - ] - }, - { - "cell_type": "code", - "execution_count": 274, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "26" - ] - }, - "execution_count": 274, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(evens)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "As above, we must explicitly create a materialized `list` object with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in." - ] - }, - { - "cell_type": "code", - "execution_count": 275, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[50, 122, 26, 10, 82, 2]" - ] - }, - "execution_count": 275, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(filter(is_even, transformed_numbers))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We may also chain mappings and filters based on the original `numbers` list." - ] - }, - { - "cell_type": "code", - "execution_count": 276, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[50, 122, 26, 10, 82, 2]" - ] - }, - "execution_count": 276, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(filter(is_even, map(transform, numbers)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well." - ] - }, - { - "cell_type": "code", - "execution_count": 277, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[65, 145, 5, 37, 101, 17]" - ] - }, - "execution_count": 277, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(map(transform, filter(is_even, numbers)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Reducing" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Lastly, **reducing** sequential data means to summarize the elements into a single statistic.\n", - "\n", - "A simple example is the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function." - ] - }, - { - "cell_type": "code", - "execution_count": 278, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 278, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(map(transform, filter(is_even, numbers)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Other straightforward examples are the built-in [min()](https://docs.python.org/3/library/functions.html#min) or [max()](https://docs.python.org/3/library/functions.html#max) functions." - ] - }, - { - "cell_type": "code", - "execution_count": 279, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 279, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "min(map(transform, filter(is_even, numbers)))" - ] - }, - { - "cell_type": "code", - "execution_count": 280, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "145" - ] - }, - "execution_count": 280, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "max(map(transform, filter(is_even, numbers)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "[sum()](https://docs.python.org/3/library/functions.html#sum), [min()](https://docs.python.org/3/library/functions.html#min), and [max()](https://docs.python.org/3/library/functions.html#max) can be regarded as special cases.\n", - "\n", - "The generic way of reducing a sequence is to apply a function of *two* arguments on a rolling horizon: Its first argument is the reduction of the elements processed so far, and the second the next element to be reduced.\n", - "\n", - "For illustration, let's replicate [sum()](https://docs.python.org/3/library/functions.html#sum) as such a function, called `add()`. Its implementation only adds two numbers." - ] - }, - { - "cell_type": "code", - "execution_count": 281, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "def add(sum_so_far, next_number):\n", - " \"\"\"Reduce a sequence by addition.\"\"\"\n", - " return sum_so_far + next_number" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Further, we create a *new* `map` object derived from `numbers` ..." - ] - }, - { - "cell_type": "code", - "execution_count": 282, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "evens_transformed = map(transform, filter(is_even, numbers))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "... and loop over all *but* the first element it generates. That we capture separately as the initial `result` with the [next()](https://docs.python.org/3/library/functions.html#next) function. So, `map` objects must be *iterable* as we may loop over them.\n", - "\n", - "We know that `evens_transformed` generates *six* elements. That is why we see *five* growing `result` values resembling a [cumulative sum](http://mathworld.wolfram.com/CumulativeSum.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 283, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "65 210 215 252 353 " - ] - } - ], - "source": [ - "result = next(evens_transformed) # first element is the initial value\n", - "\n", - "for number in evens_transformed: # iterate over the remaining elements\n", - " print(result, end=\" \") # line added for didactical purposes\n", - " result = add(result, number)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The final `result` is the same `370` as above." - ] - }, - { - "cell_type": "code", - "execution_count": 284, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 284, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "result" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function in the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides more convenience replacing the `for`-loop. It takes two arguments in the same way as the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins.\n", - "\n", - "[reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) is **[eager](https://en.wikipedia.org/wiki/Eager_evaluation)** meaning that all computations implied by the contained `map` and `filter` \"rules\" are executed right away, and the code cell returns `370`. On the contrary, [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) create **[lazy](https://en.wikipedia.org/wiki/Lazy_evaluation)** `map` and `filter` objects, and we have to use the [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the elements." - ] - }, - { - "cell_type": "code", - "execution_count": 285, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "from functools import reduce" - ] - }, - { - "cell_type": "code", - "execution_count": 286, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 286, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reduce(add, map(transform, filter(is_even, numbers)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Lambda Expressions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "[map()](https://docs.python.org/3/library/functions.html#map), [filter()](https://docs.python.org/3/library/functions.html#filter), and [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) take a `function` object as their first argument, and we defined `transform()`, `is_even()`, and `add()` to be used precisely for that.\n", - "\n", - "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n", - "\n", - "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n", - "\n", - "So, the above `add()` function could be rewritten as a `lambda` expression like so ..." - ] - }, - { - "cell_type": "code", - "execution_count": 287, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(sum_so_far, next_number)>" - ] - }, - "execution_count": 287, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lambda sum_so_far, next_number: sum_so_far + next_number" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "... or even shorter." - ] - }, - { - "cell_type": "code", - "execution_count": 288, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(x, y)>" - ] - }, - "execution_count": 288, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lambda x, y: x + y" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "With the new concepts in this section, we can rewrite the entire example in just a few lines of code *without* any `for`, `if`, and `def` statements. The resulting code is concise, easy to read, quick to modify, and even faster in execution. Most importantly, it is optimized to handle big amounts of data as *no* temporary `list` objects are materialized in memory." - ] - }, - { - "cell_type": "code", - "execution_count": 289, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 289, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n", - "evens = filter(lambda x: x % 2 == 0, numbers)\n", - "transformed = map(lambda x: (x ** 2) + 1, evens)\n", - "sum(transformed)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "If `numbers` comes as a sorted sequence of whole numbers, we may use the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in and get away *without any* `list` object in memory at all!" - ] - }, - { - "cell_type": "code", - "execution_count": 290, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 290, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers = range(1, 13)\n", - "evens = filter(lambda x: x % 2 == 0, numbers)\n", - "transformed = map(lambda x: (x ** 2) + 1, evens)\n", - "sum(transformed)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To additionally save the temporary variables, `numbers`, `evens`, and `transformed`, we write the entire computation as *one* expression." - ] - }, - { - "cell_type": "code", - "execution_count": 291, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 291, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(map(lambda x: (x ** 2) + 1, filter(lambda x: x % 2 == 0, range(1, 13))))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "PythonTutor visualizes the differences in the number of computational steps and memory usage:\n", - "- [Version 1](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps\n", - "- [Version 2](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aevens%20%3D%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20numbers%29%0Atransformed%20%3D%20map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20evens%29%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With named `map` and `filter` objects -> **58** steps\n", - "- [Version 3](http://pythontutor.com/visualize.html#code=result%20%3D%20sum%28map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20range%281,%2013%29%29%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): Everything in *one* expression -> **55** steps\n", - "\n", - "Versions 2 and 3 are the same, except for the three additional steps required to create the temporary variables. The *major* downside of Version 1 is that, in the worst case, it may need *three times* the memory as compared to the other two versions!\n", - "\n", - "An experienced Pythonista would probably go with Version 2 in a production system to keep the code readable and maintainable." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### List Comprehensions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "For [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter), Python provides a nice syntax appealing to people who like mathematics.\n", - "\n", - "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously." - ] - }, - { - "cell_type": "code", - "execution_count": 292, - "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": 293, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "evens_transformed = []\n", - "\n", - "for number in numbers:\n", - " if number % 2 == 0:\n", - " evens_transformed.append((number ** 2) + 1)" - ] - }, - { - "cell_type": "code", - "execution_count": 294, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[65, 145, 5, 37, 101, 17]" - ] - }, - "execution_count": 294, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evens_transformed" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "**List comprehensions**, or **listcomps** for short, are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). A single *expression* like below can replace the compound `for` *statement* from above." - ] - }, - { - "cell_type": "code", - "execution_count": 295, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[65, 145, 5, 37, 101, 17]" - ] - }, - "execution_count": 295, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(n ** 2) + 1 for n in numbers if n % 2 == 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "A list comprehension may be used in place of any `list` object.\n", - "\n", - "For example, let's add up all the elements with [sum()](https://docs.python.org/3/library/functions.html#sum). The code below *materializes* all elements in memory *before* summing them up. So, this code might cause a `MemoryError` when executed with a bigger `numbers` list. [PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `list` object exists in memory at step 17 and then \"gets lost\" right after. " - ] - }, - { - "cell_type": "code", - "execution_count": 296, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 296, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum([(n ** 2) + 1 for n in numbers if n % 2 == 0])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "#### Example: Nested Lists" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "List comprehensions may come with several `for`'s and `if`'s.\n", - "\n", - "The cell below creates a `list` object that contains other `list` objects with numbers in them. The starting number in each inner `list` object is offset by `1`." - ] - }, - { - "cell_type": "code", - "execution_count": 297, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]" - ] - }, - { - "cell_type": "code", - "execution_count": 298, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]" - ] - }, - "execution_count": 298, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nested_numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To do something meaningful with the numbers, we have to get rid of the inner layer of `list` objects and flatten the data.\n", - "\n", - "Without list comprehensions, we would probably write two nested `for`-loops." - ] - }, - { - "cell_type": "code", - "execution_count": 299, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "flat_numbers = []\n", - "\n", - "for inner_numbers in nested_numbers:\n", - " for number in inner_numbers:\n", - " flat_numbers.append(number)" - ] - }, - { - "cell_type": "code", - "execution_count": 300, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]" - ] - }, - "execution_count": 300, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "flat_numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "That translates into a list comprehension like below. The order of the `for`'s may be confusing at first but is the *same* as writing out the nested `for`-loops." - ] - }, - { - "cell_type": "code", - "execution_count": 301, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]" - ] - }, - "execution_count": 301, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[number for inner_numbers in nested_numbers for number in inner_numbers]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Now, we may use the `list` object resulting from the list comprehension in any context we want.\n", - "\n", - "As an example, we add up the flattened numbers with [sum()](https://docs.python.org/3/library/functions.html#sum). The same caveat holds as before: The `list` object passed into [sum()](https://docs.python.org/3/library/functions.html#sum) is *materialized* before the sum is calculated!" - ] - }, - { - "cell_type": "code", - "execution_count": 302, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "105" - ] - }, - "execution_count": 302, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum([number for inner_numbers in nested_numbers for number in inner_numbers])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "In this particular example, however, we can exploit the fact that any sum of numbers can be expressed as the sum of sums of mutually exclusive and collectively exhaustive subsets of these numbers and get away with just *one* `for` in the list comprehension." - ] - }, - { - "cell_type": "code", - "execution_count": 303, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "105" - ] - }, - "execution_count": 303, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum([sum(inner_numbers) for inner_numbers in nested_numbers])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "#### Example: Cartesian Products" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "A popular use case of nested list comprehensions is applying a transformation to each $2$-tuple of the [Cartesian product](https://en.wikipedia.org/wiki/Cartesian_product) of two iterables.\n", - "\n", - "For example, let's add `1` to each quotient obtained by taking the numerator from `[10, 20, 30]` and the denominator from `[40, 50, 60]`, and then find the product of all quotients. The table below visualizes the calculations: The result is the product of *nine* entries.\n", - "\n", - "||**10**|**20**|**30**|\n", - "|-|-|-|-|\n", - "|**40**|1.25|1.50|1.75|\n", - "|**50**|1.20|1.40|1.60|\n", - "|**60**|1.17|1.33|1.50|\n", - "\n", - "To express that in Python, we start by creating two `list` objects, `first` and `second`." - ] - }, - { - "cell_type": "code", - "execution_count": 304, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "first = [10, 20, 30]\n", - "second = [40, 50, 60]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "For a Cartesian product, we loop over *all* possible $2$-tuples where one element is drawn from `first` and the other from `second`. That is equivalent to two nested `for`-loops." - ] - }, - { - "cell_type": "code", - "execution_count": 305, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]" - ] - }, - "execution_count": 305, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cartesian_product = []\n", - "\n", - "for numerator in first:\n", - " for denominator in second:\n", - " quotient = numerator / denominator\n", - " cartesian_product.append(quotient + 1)\n", - "\n", - "cartesian_product" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We translate the two `for`-loops into one list comprehensions with two `for`'s in it and use `x` and `y` as shorter variable names." - ] - }, - { - "cell_type": "code", - "execution_count": 306, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]" - ] - }, - "execution_count": 306, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(x / y) + 1 for x in first for y in second]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The order of the `for`'s is *important*: The list comprehension above divides numbers from `first` by numbers from `second`, whereas the list comprehension below does the opposite." - ] - }, - { - "cell_type": "code", - "execution_count": 307, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[5.0, 3.0, 2.333333333333333, 6.0, 3.5, 2.666666666666667, 7.0, 4.0, 3.0]" - ] - }, - "execution_count": 307, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(x / y) + 1 for x in second for y in first]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To find the overall product, we *unpack* the first list comprehension right into the `product()` function from the \"*Packing & Unpacking*\" section above." - ] - }, - { - "cell_type": "code", - "execution_count": 308, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20.58" - ] - }, - "execution_count": 308, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "product(*[(x / y) + 1 for x in first for y in second])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Alternatively, we use a `lambda` expression with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module." - ] - }, - { - "cell_type": "code", - "execution_count": 309, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20.58" - ] - }, - "execution_count": 309, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reduce(lambda x, y: x * y, [(x / y) + 1 for x in first for y in second])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "While this example is stylized, Cartesian products are hidden in many applications, and it shows how the various language features introduced in this chapter can be seamlessly combined to process sequential data." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Generator Expressions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Pythonistas would forgo materialized `list` objects, and, thus, also list comprehensions, all together, and use a more memory-efficient approach with **[generator expressions](https://docs.python.org/3/reference/expressions.html#generator-expressions)**, or **genexps** for short. Syntactically, they work like list comprehensions except that parentheses replace the brackets.\n", - "\n", - "Let's go back to the original example in this section and find the transformation $y := x^2 + 1$ of all even elements in `numbers`." - ] - }, - { - "cell_type": "code", - "execution_count": 310, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To filter and transform `numbers`, we wrote this list comprehension above ..." - ] - }, - { - "cell_type": "code", - "execution_count": 311, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[65, 145, 5, 37, 101, 17]" - ] - }, - "execution_count": 311, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "[(n ** 2) + 1 for n in numbers if n % 2 == 0]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "... that now becomes a generator expression." - ] - }, - { - "cell_type": "code", - "execution_count": 312, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - " at 0x7fb57436f3d0>" - ] - }, - "execution_count": 312, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "((n ** 2) + 1 for n in numbers if n % 2 == 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We can think of it as yet another \"rule\" in memory that knows how to generate the individual objects in a series one by one. Whereas a list comprehension materializes its elements in memory *when* it is evaluated, the opposite holds for generator expressions, and *no* object is created in memory except the \"rule\" itself. Because of this behavior, we describe generator expressions as *lazy* and list comprehensions as *eager*.\n", - "\n", - "So, to materialize all elements specified by a generator expression, we revert to the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in." - ] - }, - { - "cell_type": "code", - "execution_count": 313, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[65, 145, 5, 37, 101, 17]" - ] - }, - "execution_count": 313, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(((n ** 2) + 1 for n in numbers if n % 2 == 0))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Whenever a generator expression is the only argument to a function, we may leave out the parentheses." - ] - }, - { - "cell_type": "code", - "execution_count": 314, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[65, 145, 5, 37, 101, 17]" - ] - }, - "execution_count": 314, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list((n ** 2) + 1 for n in numbers if n % 2 == 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum()](https://docs.python.org/3/library/functions.html#sum). [PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory, whereas a list comprehension would (cf., step 17)." - ] - }, - { - "cell_type": "code", - "execution_count": 315, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "370" - ] - }, - "execution_count": 315, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum((n ** 2) + 1 for n in numbers if n % 2 == 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Let's assign the object returned from a generator expression to a variable and inspect it." - ] - }, - { - "cell_type": "code", - "execution_count": 316, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "gen = ((n ** 2) + 1 for n in numbers if n % 2 == 0)" - ] - }, - { - "cell_type": "code", - "execution_count": 317, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - " at 0x7fb57436f650>" - ] - }, - "execution_count": 317, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gen" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Unsurprisingly, generator expressions create objects of type `generator`." - ] - }, - { - "cell_type": "code", - "execution_count": 318, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "generator" - ] - }, - "execution_count": 318, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(gen)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "With the [next()](https://docs.python.org/3/library/functions.html#next) function, we can retrieve the generated elements one by one." - ] - }, - { - "cell_type": "code", - "execution_count": 319, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "65" - ] - }, - "execution_count": 319, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 320, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "145" - ] - }, - "execution_count": 320, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 321, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "5" - ] - }, - "execution_count": 321, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 322, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "37" - ] - }, - "execution_count": 322, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 323, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "101" - ] - }, - "execution_count": 323, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 324, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "17" - ] - }, - "execution_count": 324, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Once a `generator` object runs out of elements, it raises a `StopIteration` exception. We say that the `generator` object is **exhausted**, and to loop over its elements again, we must create a *new* one." - ] - }, - { - "cell_type": "code", - "execution_count": 325, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "ename": "StopIteration", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mStopIteration\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mStopIteration\u001b[0m: " - ] - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "code", - "execution_count": 326, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "ename": "StopIteration", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mStopIteration\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mStopIteration\u001b[0m: " - ] - } - ], - "source": [ - "next(gen)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Calling the [next()](https://docs.python.org/3/library/functions.html#next) function repeatedly with the *same* `generator` object as the argument is essentially what a `for`-loop automates for us. So, `generator` objects are *iterable*." - ] - }, - { - "cell_type": "code", - "execution_count": 327, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "65 145 5 37 101 17 " - ] - } - ], - "source": [ - "for number in ((n ** 2) + 1 for n in numbers if n % 2 == 0):\n", - " print(number, end=\" \")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "#### Example: Nested Lists (revisited)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "If we are only interested in a *reduction* of `nested_numbers` into a single statistic, as the overall sum in the \"*Nested Lists*\" example, we should replace lists or list comprehensions with generator expressions wherever possible.\n", - "\n", - "The result is the *same*, but no intermediate lists are materialized! That makes our code scale to larger amounts of data and uses the available hardware more efficiently.\n", - "\n", - "Let's adapt the example but keep `nested_numbers` unchanged for now." - ] - }, - { - "cell_type": "code", - "execution_count": 328, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]" - ] - }, - { - "cell_type": "code", - "execution_count": 329, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]" - ] - }, - "execution_count": 329, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nested_numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We leave out the brackets and keep everything else as-is: The argument to [sum()](https://docs.python.org/3/library/functions.html#sum), a list comprehension in the initial implementation above, becomes a generator expression." - ] - }, - { - "cell_type": "code", - "execution_count": 330, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "105" - ] - }, - "execution_count": 330, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(number for inner_numbers in nested_numbers for number in inner_numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "That also holds for the alternative formulation as a sum of sums." - ] - }, - { - "cell_type": "code", - "execution_count": 331, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "105" - ] - }, - "execution_count": 331, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(sum(inner_numbers) for inner_numbers in nested_numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Because `nested_numbers` has an internal structure, we can make it **memoryless** by expressing it as a generator expression derived from `range` objects. [PythonTutor](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` object materializes at any point in time." - ] - }, - { - "cell_type": "code", - "execution_count": 332, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "nested_numbers = ((range(x, y + 1)) for x, y in zip(range(1, 4), range(7, 10)))" - ] - }, - { - "cell_type": "code", - "execution_count": 333, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - " at 0x7fb57436fad0>" - ] - }, - "execution_count": 333, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "nested_numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 334, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "105" - ] - }, - "execution_count": 334, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(number for inner_numbers in nested_numbers for number in inner_numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum()](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that." - ] - }, - { - "cell_type": "code", - "execution_count": 335, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 335, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sum(sum(inner_numbers) for inner_numbers in nested_numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "#### Example: Cartesian Products (revisited)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Let's also rewrite the \"*Cartesian Products*\" example from above with generator expressions.\n", - "\n", - "As a first optimization, we replace the materialized `list` objects, `first` and `second`, with memoryless `range` objects." - ] - }, - { - "cell_type": "code", - "execution_count": 336, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "first = range(10, 31, 10) # = [10, 20, 30]\n", - "second = range(40, 61, 10) # = [40, 50, 60]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Now, the first of the two alternatives may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n", - "\n", - "The code cell below *unpacks* the elements produced by `((x / y) + 1 for x in first for y in second)` into the `product()` function from the \"*Packing & Unpacking*\" section above. However, inside `product()`, the elements are *packed* into `args`, a materialized `tuple` object. So, all the memory efficiency gained with the generator expression is voided! [PythonTutor](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%28%28x%20/%20y%29%20%2B%201%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58." - ] - }, - { - "cell_type": "code", - "execution_count": 337, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20.58" - ] - }, - "execution_count": 337, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "product(*((x / y) + 1 for x in first for y in second))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "On the contrary, the solution with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* all elements materialized at the same time, and [PythonTutor](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%28%28x%20/%20y%29%20%2B%201%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient." - ] - }, - { - "cell_type": "code", - "execution_count": 338, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20.58" - ] - }, - "execution_count": 338, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reduce(lambda x, y: x * y, ((x / y) + 1 for x in first for y in second))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "In summary, we learn from this example that unpacking generator expressions *may* be a *bad* idea." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "### Tuple Comprehensions" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "There is no syntax to derive *new* `tuple` objects out of existing ones. However, we can mimic such a construct by combining the [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-in with a generator expression.\n", - "\n", - "So, to convert the list comprehension `[(n ** 2) + 1 for n in numbers if n % 2 == 0]` from above into a \"tuple comprehension,\" we write the following." - ] - }, - { - "cell_type": "code", - "execution_count": 339, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(65, 145, 5, 37, 101, 17)" - ] - }, - "execution_count": 339, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tuple((n ** 2) + 1 for n in numbers if n % 2 == 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Boolean Reducers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Besides [min()](https://docs.python.org/3/library/functions.html#min), [max()](https://docs.python.org/3/library/functions.html#max), and [sum()](https://docs.python.org/3/library/functions.html#sum), Python provides two boolean reduce functions: [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any).\n", - "\n", - "Let's look at straightforward examples involving `numbers` again." - ] - }, - { - "cell_type": "code", - "execution_count": 340, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "[all()](https://docs.python.org/3/library/functions.html#all) takes an *iterable* argument and returns `True` if *all* elements are *truthy*.\n", - "\n", - "For example, let's check if the square of each element in `numbers` is below `100` or `150`, respectively. We express the computation with a generator expression passed as the only argument to [all()](https://docs.python.org/3/library/functions.html#all)." - ] - }, - { - "cell_type": "code", - "execution_count": 341, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 341, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all(x ** 2 < 100 for x in numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 342, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 342, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all(x ** 2 < 150 for x in numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise." - ] - }, - { - "cell_type": "code", - "execution_count": 343, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 343, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "threshold = 100\n", - "\n", - "for number in numbers:\n", - " if number ** 2 >= threshold: # = the opposite of what we are checking for\n", - " all_below_threshold = False\n", - " break\n", - "else:\n", - " all_below_threshold = True\n", - "\n", - "all_below_threshold" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The documentation of [all()](https://docs.python.org/3/library/functions.html#all) shows in another way what it does with code: By placing a `return` statement inside the `for`-loop of a function's body, iteration is stopped prematurely once an element does *not* meet the condition. That is the familiar *early exit* pattern at work." - ] - }, - { - "cell_type": "code", - "execution_count": 344, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "def all_alt(iterable):\n", - " \"\"\"Alternative implementation of the built-in all() function.\"\"\"\n", - " for element in iterable:\n", - " if not element: # = the opposite of what we are checking for\n", - " return False\n", - " return True" - ] - }, - { - "cell_type": "code", - "execution_count": 345, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 345, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_alt(x ** 2 < 100 for x in numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 346, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 346, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "all_alt(x ** 2 < 150 for x in numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Similarly, [any()](https://docs.python.org/3/library/functions.html#any) checks if *at least* one element in the *iterable* argument is *truthy*.\n", - "\n", - "To continue the example, let's check if the square of *any* element in `numbers` is above `100` or `150`, respectively." - ] - }, - { - "cell_type": "code", - "execution_count": 347, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 347, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "any(x ** 2 > 100 for x in numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 348, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 348, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "any(x ** 2 > 150 for x in numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Expressed with a `for`-loop, the implementation below reveals that [any()](https://docs.python.org/3/library/functions.html#any) follows the *short-circuiting* strategy as well. Here, we do *not* need to check for the opposite condition." - ] - }, - { - "cell_type": "code", - "execution_count": 349, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 349, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "threshold = 100\n", - "\n", - "for number in numbers:\n", - " if number ** 2 > threshold:\n", - " any_above_threshold = True\n", - " break\n", - "else:\n", - " any_above_threshold = False\n", - "\n", - "any_above_threshold" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The alternative formulation in the documentation of [any()](https://docs.python.org/3/library/functions.html#any) is straightforward." - ] - }, - { - "cell_type": "code", - "execution_count": 350, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "def any_alt(iterable):\n", - " \"\"\"Alternative implementation of the built-in any() function.\"\"\"\n", - " for element in iterable:\n", - " if element:\n", - " return True\n", - " return False" - ] - }, - { - "cell_type": "code", - "execution_count": 351, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 351, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "any_alt(x ** 2 > 100 for x in numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 352, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "False" - ] - }, - "execution_count": 352, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "any_alt(x ** 2 > 150 for x in numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "### Example: Averaging Even Numbers (revisited)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n", - "\n", - "We assume the `average_evens()` function below is called with a *finite* and *iterable* object, which generates a \"stream\" of numeric objects that can be cast as `int` objects because the idea of even and odd numbers only makes sense in the context of whole numbers.\n", - "\n", - "The generator expression `(int(n) for n in numbers)` implements the type casting, and when it is evaluated, *nothing* happens except that a `generator` object is stored in `integers`. Then, with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers produced by the inner generator expression `((n, 1) for n in integers if n % 2 == 0)`. That results in a `generator` object producing `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `lambda` expression as the `x` and `y` arguments. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next even number to be added to the running total. The result of the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average.\n", - "\n", - "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis." - ] - }, - { - "cell_type": "code", - "execution_count": 353, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "def average_evens(numbers):\n", - " \"\"\"Calculate the average of all even integers.\n", - "\n", - " Args:\n", - " numbers (iterable): a finite stream of numbers;\n", - " may be integers or floats; floats are truncated\n", - "\n", - " Returns:\n", - " float: average\n", - " \"\"\"\n", - " integers = (int(n) for n in numbers)\n", - " total, count = reduce(\n", - " lambda x, y: (x[0] + y[0], x[1] + y[1]),\n", - " ((n, 1) for n in integers if n % 2 == 0)\n", - " )\n", - " return total / count" - ] - }, - { - "cell_type": "code", - "execution_count": 354, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "7.0" - ] - }, - "execution_count": 354, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "An argument generating `float` objects works as well." - ] - }, - { - "cell_type": "code", - "execution_count": 355, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "7.0" - ] - }, - "execution_count": 355, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "average_evens([7., 11., 8., 5., 3., 12., 2., 6., 9., 10., 1., 4.])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To show that `average_evens()` can process a **stream** of data, we simulate `10_000_000` randomly drawn integers between `0` and `100` with the [randint()](https://docs.python.org/3/library/random.html#random.randint) function from the [random](https://docs.python.org/3/library/random.html) module. We use a generator expression derived from a `range` object as the `numbers` argument. So, at *no* point in time is there a materialized `list` or `tuple` object in memory. The result approaching `50` tells us that [randint()](https://docs.python.org/3/library/random.html#random.randint) must be based on a uniform distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 356, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "import random" - ] - }, - { - "cell_type": "code", - "execution_count": 357, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "random.seed(42)" - ] - }, - { - "cell_type": "code", - "execution_count": 358, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "49.994081434519636" - ] - }, - "execution_count": 358, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "average_evens(random.randint(0, 100) for _ in range(10_000_000))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To show that `average_evens()` filters out odd numbers, we simulate another stream of `10_000_000` randomly drawn odd integers between `1` and `99`. As no function in the [random](https://docs.python.org/3/library/random.html) module does that \"out of the box,\" we must be creative: Doubling a number drawn from `random.randint(0, 49)` results in an even number between `0` and `98`, and adding `1` makes it odd. Then, `average_evens()` raises a `TypeError`, essentially because `(int(n) for n in numbers)` does not generate any element." - ] - }, - { - "cell_type": "code", - "execution_count": 359, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "reduce() of empty sequence with no initial value", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\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[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\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[0m", - "\u001b[0;32m\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m 12\u001b[0m total, count = reduce(\n\u001b[1;32m 13\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\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[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m )\n\u001b[1;32m 16\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value" - ] - } - ], - "source": [ - "average_evens(2 * random.randint(0, 49) + 1 for _ in range(10_000_000))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Iterators vs. Iterables" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "In the \"*Collections vs. Sequences*\" section above, we studied the three and four *behaviors* of collections and sequences. The latter two are *abstract* ideas, and we mainly use them to classify *concrete* data types.\n", - "\n", - "Similarly, we have introduced data types in this chapter that all share the \"behavior\" of modeling some \"rule\" in memory to generate objects \"on the fly:\" They are the `map`, `filter`, and `generator` types. Their main commonality is supporting the built-in [next()](https://docs.python.org/3/library/functions.html#next) function. In computer science terminology, such data types are called **[iterators](https://en.wikipedia.org/wiki/Iterator)**, and the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module formalizes them with the `Iterator` ABC.\n", - "\n", - "So, an example of an iterator is `evens_transformed` below, an object of type `generator`." - ] - }, - { - "cell_type": "code", - "execution_count": 360, - "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": 361, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "evens_transformed = ((x ** 2) + 1 for x in numbers if x % 2 == 0)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Let's first confirm that `evens_transformed` is indeed an `Iterator`, \"abstractly speaking.\"" - ] - }, - { - "cell_type": "code", - "execution_count": 362, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 362, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(evens_transformed, abc.Iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "In Python, iterators are *always* also iterables. The reverse does *not* hold! To be precise, iterators are *specializations* of iterables. That is what the \"Inherits from\" columns means in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documentation." - ] - }, - { - "cell_type": "code", - "execution_count": 363, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 363, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(evens_transformed, abc.Iterable)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Furthermore, we revise our definition of *iterables* from above: Just as we defined an *iterator* to be an object that supports the [next()](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be an object that supports the built-in [iter()](https://docs.python.org/3/library/functions.html#iter) function.\n", - "\n", - "The confused reader may now be wondering how the two concepts relate to each other.\n", - "\n", - "In short, the [iter()](https://docs.python.org/3/library/functions.html#iter) function is the general way to create an *iterator* object out of a given *iterable* object. In real-world code, we hardly ever see [iter()](https://docs.python.org/3/library/functions.html#iter) as Python calls it for us in the background. Then, the *iterator* object manages the iteration over the *iterable* object.\n", - "\n", - "For illustration, let's do that ourselves and create *two* iterators out of the iterable `numbers` and see what we can do with them." - ] - }, - { - "cell_type": "code", - "execution_count": 364, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "iterator1 = iter(numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 365, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "iterator2 = iter(numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "`iterator1` and `iterator2` are of type `list_iterator`." - ] - }, - { - "cell_type": "code", - "execution_count": 366, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "list_iterator" - ] - }, - "execution_count": 366, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(iterator1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "*Iterators* are useful for only *one* operation: Get the next object from the associated *iterable*.\n", - "\n", - "By calling [next()](https://docs.python.org/3/library/functions.html#next) three times with `iterator1` as the argument, we obtain the first three elements of `numbers`." - ] - }, - { - "cell_type": "code", - "execution_count": 367, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(7, 11, 8)" - ] - }, - "execution_count": 367, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(iterator1), next(iterator1), next(iterator1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "`iterator1` and `iterator2` keep their *states* separate. So, we could \"manually\" loop over an *iterable* in parallel." - ] - }, - { - "cell_type": "code", - "execution_count": 368, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(5, 7)" - ] - }, - "execution_count": 368, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(iterator1), next(iterator2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We can also play a \"trick\" and exchange some elements in `numbers`. `iterator1` and `iterator2` do *not* see these changes and present us with the new elements. So, *iterators* not only have state on their own but also keep this separate from the underlying *iterable*." - ] - }, - { - "cell_type": "code", - "execution_count": 369, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "numbers[1], numbers[4] = 99, 99" - ] - }, - { - "cell_type": "code", - "execution_count": 370, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(99, 99)" - ] - }, - "execution_count": 370, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(iterator1), next(iterator2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Let's re-assign the elements in `numbers` so that they are in order. Now, the numbers returned from [next()](https://docs.python.org/3/library/functions.html#next) also tell us how often [next()](https://docs.python.org/3/library/functions.html#next) was called with `iterator1` or `iterator2`. We conclude that `list_iterator` objects must be keeping track of the *last* index obtained from the underlying *iterable*." - ] - }, - { - "cell_type": "code", - "execution_count": 371, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "numbers[:] = list(range(1, 13))" - ] - }, - { - "cell_type": "code", - "execution_count": 372, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]" - ] - }, - "execution_count": 372, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 373, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(6, 3)" - ] - }, - "execution_count": 373, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(iterator1), next(iterator2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "With the concepts introduced in this section, we can now understand the first sentence in the documentation on the [zip()](https://docs.python.org/3/library/functions.html#zip) built-in better: \"Make an *iterator* that aggregates elements from each of the *iterables*.\"\n", - "\n", - "Because *iterators* are always also *iterables*, we pass `iterator1` and `iterator2` as arguments to [zip()](https://docs.python.org/3/library/functions.html#zip).\n", - "\n", - "The returned `zipper` object is of type `zip` and, \"abstractly speaking,\" an `Iterator` as well." - ] - }, - { - "cell_type": "code", - "execution_count": 374, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "zipper = zip(iterator1, iterator2)" - ] - }, - { - "cell_type": "code", - "execution_count": 375, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 375, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zipper" - ] - }, - { - "cell_type": "code", - "execution_count": 376, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "zip" - ] - }, - "execution_count": 376, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "type(zipper)" - ] - }, - { - "cell_type": "code", - "execution_count": 377, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 377, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isinstance(zipper, abc.Iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "So far, we have always used [zip()](https://docs.python.org/3/library/functions.html#zip) with a `for` statement and looped over it. That was our earlier definition of an *iterable*. Our revised definition in this section states that an *iterable* is an object that supports the [iter()](https://docs.python.org/3/library/functions.html#iter) function. So, let's see what happens if we pass `zipper` to [iter()](https://docs.python.org/3/library/functions.html#iter)." - ] - }, - { - "cell_type": "code", - "execution_count": 378, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "zipper_iterator = iter(zipper)" - ] - }, - { - "cell_type": "code", - "execution_count": 379, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 379, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zipper_iterator" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "`zipper_iterator` references the *same* object as `zipper`! That holds for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter()](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself." - ] - }, - { - "cell_type": "code", - "execution_count": 380, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 380, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "zipper is zipper_iterator" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The Python core developers made that design decision so that *iterators* may also be looped over.\n", - "\n", - "The `for`-loop below prints out *six* more `tuple` objects derived from the now ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned the first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already provided *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`." - ] - }, - { - "cell_type": "code", - "execution_count": 381, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "7 > 4 8 > 5 9 > 6 10 > 7 11 > 8 12 > 9 " - ] - } - ], - "source": [ - "for x, y in zipper:\n", - " print(x, \">\", y, end=\" \")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "`zipper` is now *exhausted*. So, the `for`-loop below does *not* make any iteration at all." - ] - }, - { - "cell_type": "code", - "execution_count": 382, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "for x, y in zipper:\n", - " print(x, \">\", y, end=\" \")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "We verify that `iterator1` is exhausted by passing it to [next()](https://docs.python.org/3/library/functions.html#next) again, which raises a `StopIteration` exception." - ] - }, - { - "cell_type": "code", - "execution_count": 383, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "ename": "StopIteration", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mStopIteration\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;31mStopIteration\u001b[0m: " - ] - } - ], - "source": [ - "next(iterator1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "On the contrary, `iterator2` is *not* yet exhausted." - ] - }, - { - "cell_type": "code", - "execution_count": 384, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10" - ] - }, - "execution_count": 384, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "next(iterator2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### The `for` Statement (revisited)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n", - "\n", - "Let's print out the elements of a `list` object as the *iterable* to be looped over." - ] - }, - { - "cell_type": "code", - "execution_count": 385, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [], - "source": [ - "iterable = [0, 1, 2, 3, 4]" - ] - }, - { - "cell_type": "code", - "execution_count": 386, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1 2 3 4 " - ] - } - ], - "source": [ - "for element in iterable:\n", - " print(element, end=\" \")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Our previous and equivalent formulation with a `while` statement is like so." - ] - }, - { - "cell_type": "code", - "execution_count": 387, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1 2 3 4 " - ] - } - ], - "source": [ - "index = 0\n", - "while index < len(iterable):\n", - " element = iterable[index]\n", - " print(element, end=\" \")\n", - " index += 1\n", - "del index" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "What happens behind the scenes in the Python interpreter is shown below.\n", - "\n", - "First, Python calls [iter()](https://docs.python.org/3/library/functions.html#iter) with the `iterable` to be looped over, and obtains an `iterator`. That contains the entire logic of how the `iterable` is looped over. In particular, the `iterator` may or may not pick the `iterable`'s elements in a predictable order. That is up to the \"rule\" it models.\n", - "\n", - "Second, Python enters an *indefinite* `while`-loop. It tries to obtain the next element with [next()](https://docs.python.org/3/library/functions.html#next). If that succeeds, the `for`-loop's code block is executed. Below, that code is placed within the `else`-clause that runs only if *no* exception is raised in the `try`-clause. Then, Python jumps into the next iteration and tries to obtain the next element from the `iterator`, and so on. Once the `iterator` is exhausted, it raises a `StopIteration` exception, and Python leaves the `while`-loop with the `break` statement." - ] - }, - { - "cell_type": "code", - "execution_count": 388, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "0 1 2 3 4 " - ] - } - ], - "source": [ - "iterator = iter(iterable)\n", - "\n", - "while True:\n", - " try:\n", - " element = next(iterator)\n", - " except StopIteration:\n", - " break\n", - " else:\n", - " print(element, end=\" \")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### sorted() vs. reversed()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in this chapter in detail and make sure we understand what is going on in memory. This sub-section is thus a great summary of this chapter as well.\n", - "\n", - "We use two simple examples, `numbers` and `memoryless`, to guide us through the discussion. `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))." - ] - }, - { - "cell_type": "code", - "execution_count": 389, - "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": 390, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "memoryless = range(1, 13)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The [sorted()](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* and *iterable* object as its argument and *materializes* its elements into a *new* `list` object that is returned.\n", - "\n", - "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))." - ] - }, - { - "cell_type": "code", - "execution_count": 391, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]" - ] - }, - "execution_count": 391, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 392, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]" - ] - }, - "execution_count": 392, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(memoryless)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))." - ] - }, - { - "cell_type": "code", - "execution_count": 393, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]" - ] - }, - "execution_count": 393, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(numbers, reverse=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 394, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]" - ] - }, - "execution_count": 394, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(memoryless, reverse=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The order in `numbers` remains *unchanged*, and `memoryless` is still *not* materialized." - ] - }, - { - "cell_type": "code", - "execution_count": 395, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - "execution_count": 395, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 396, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "range(1, 13)" - ] - }, - "execution_count": 396, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "memoryless" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in takes a *sequence* object as its argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed()](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed()](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n", - "\n", - "**Side Note**: Even though `range` objects, like `memoryless` here, do *not* \"contain\" references to other objects, they count as *sequence* types, and as such, they are also *container* types. The `in` operator works with `range` objects because we can always cast the object to be checked as an `int` and check if that lies within the `range` object's *start* and *stop* values, taking a potential *step* value into account (cf., this [blog post](https://treyhunner.com/2018/02/python-range-is-not-an-iterator/) for more details on the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in)." - ] - }, - { - "cell_type": "code", - "execution_count": 397, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 397, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reversed(numbers)" - ] - }, - { - "cell_type": "code", - "execution_count": 398, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 398, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reversed(memoryless)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To materialize the elements, we can pass the returned *iterators* to, for example, the [list()](https://docs.python.org/3/library/functions.html#func-list) or [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-ins. That creates *new* `list` and `tuple` objects (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n", - "\n", - "To reiterate some more new terminology from this chapter, we describe [reversed()](https://docs.python.org/3/library/functions.html#reversed) as *lazy*, whereas [list()](https://docs.python.org/3/library/functions.html#func-list) and [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) are *eager*. The former has no significant side effect in memory, while the latter may require a lot of memory." - ] - }, - { - "cell_type": "code", - "execution_count": 399, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]" - ] - }, - "execution_count": 399, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(reversed(numbers))" - ] - }, - { - "cell_type": "code", - "execution_count": 400, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)" - ] - }, - "execution_count": 400, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tuple(reversed(memoryless))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Of course, we can also loop over the returned *iterators* instead.\n", - "\n", - "That works because *iterators* are always *iterables*; in particular, as the previous \"*The for Statement (revisited)*\" sub-section explains, the `for`-loops below call `iter(reversed(numbers))` and `iter(reversed(memoryless))` behind the scenes. However, the *iterators* returned by [iter()](https://docs.python.org/3/library/functions.html#iter) are the *same* as the `reversed(numbers)` and `reversed(memoryless)` iterators passed in! In summary, the `for`-loops below involve many subtleties that together make Python the expressive language it is." - ] - }, - { - "cell_type": "code", - "execution_count": 401, - "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": "code", - "execution_count": 402, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "12 11 10 9 8 7 6 5 4 3 2 1 " - ] - } - ], - "source": [ - "for element in reversed(memoryless):\n", - " print(element, end=\" \")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "As with [sorted()](https://docs.python.org/3/library/functions.html#sorted), the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in does *not* mutate its argument." - ] - }, - { - "cell_type": "code", - "execution_count": 403, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - "execution_count": 403, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 404, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "range(1, 13)" - ] - }, - "execution_count": 404, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "memoryless" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "To point out the potentially obvious, we compare the results of *sorting* `numbers` in *reverse* order with *reversing* it: These are *different* concepts!" - ] - }, - { - "cell_type": "code", - "execution_count": 405, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - "execution_count": 405, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 406, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]" - ] - }, - "execution_count": 406, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sorted(numbers, reverse=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 407, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]" - ] - }, - "execution_count": 407, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "list(reversed(numbers))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "Whereas both [sorted()](https://docs.python.org/3/library/functions.html#sorted) and [reversed()](https://docs.python.org/3/library/functions.html#reversed) do *not* mutate their arguments, the *mutable* `list` type comes with two methods, [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()`, that implement the same logic but mutate an object, like `numbers` below, *in place*. To indicate that all changes occur *in place*, the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()` methods always return `None`, which is not shown." - ] - }, - { - "cell_type": "code", - "execution_count": 408, - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]" - ] - }, - "execution_count": 408, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "The `reverse()` method is *eager*, as opposed to the *lazy* [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in. That means the mutations causes by the `reverse()` method are written into memory right away." - ] - }, - { - "cell_type": "code", - "execution_count": 409, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [], - "source": [ - "numbers.reverse()" - ] - }, - { - "cell_type": "code", - "execution_count": 410, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]" - ] - }, - "execution_count": 410, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "*Sorting* `numbers` in *reverse* order below is of course still *different* from simply *reversing* it above." - ] - }, - { - "cell_type": "code", - "execution_count": 411, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], - "source": [ - "numbers.sort(reverse=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 412, - "metadata": { - "slideshow": { - "slide_type": "-" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]" - ] - }, - "execution_count": 412, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" - ] - }, - { - "cell_type": "code", - "execution_count": 413, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [], - "source": [ - "numbers.sort()" - ] - }, - { - "cell_type": "code", - "execution_count": 414, - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]" - ] - }, - "execution_count": 414, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "numbers" + "len(current_position)" ] }, { @@ -12189,13 +6876,7 @@ "\n", "`list` objects are **mutable**. That means we can change the references to the other objects it contains, and, in particular, re-assign them.\n", "\n", - "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**.\n", - "\n", - "The tasks we do with sequential data follow the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n", - "\n", - "An essential idea in this chapter is that, in many situations, we need *not* have all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis.\n", - "\n", - "Examples for iterators are the `map`, `filter`, and `generator` types." + "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**." ] } ], diff --git a/07_sequences_01_review.ipynb b/07_sequences_01_review.ipynb index d374b3c..de51edc 100644 --- a/07_sequences_01_review.ipynb +++ b/07_sequences_01_review.ipynb @@ -19,7 +19,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) of the book. Then, work through the questions below." + "The questions below assume that you have read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) in the book.\n", + "\n", + "Be concise in your answers! Most questions can be answered in *one* sentence." ] }, { @@ -47,7 +49,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -61,7 +63,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -75,7 +77,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -89,7 +91,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -103,7 +105,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -117,7 +119,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -133,7 +135,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -147,7 +149,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -161,104 +163,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q9**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q10**: Looking at the `lambda` expression inside [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) below, what \"simple\" [built-in function](https://docs.python.org/3/library/functions.html) is mimicked here?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```python\n", - "from functools import reduce\n", - "\n", - "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n", - "\n", - "reduce(lambda x, y: x if x > y else y, numbers)\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q11**: What is the primary use case of **list comprehensions**? Why do we describe them as **eager**?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q12**: **Generator expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q13**: What does it mean for the **boolean reducers**, the built-in [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q14**: What is an **iterator**? How does it relate to an **iterable**?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " + " < your answer >" ] }, { @@ -279,84 +184,42 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Q15**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)." + "**Q9**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Q16**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*." + "**Q10**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Q17**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope." + "**Q11**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q18**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q19**: Using **generator expressions** in place of **list comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q20**: Just as **list comprehensions** create `list` objects, **tuple comprehensions** create `tuple` objects." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " + " < your answer >" ] } ], diff --git a/07_sequences_02_exercises.ipynb b/07_sequences_02_exercises.ipynb index daad5e0..575c9c9 100644 --- a/07_sequences_02_exercises.ipynb +++ b/07_sequences_02_exercises.ipynb @@ -19,7 +19,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) of the book. Then, work through the exercises below." + "The exercises below assume that you have read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) in the book.\n", + "\n", + "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas." ] }, { @@ -54,7 +56,19 @@ "outputs": [], "source": [ "def nested_sum(list_of_lists):\n", - " ..." + " \"\"\"Add up numbers in nested lists.\n", + " \n", + " Args:\n", + " list_of_lists (list): A list containing the lists with the numbers\n", + " \n", + " Returns:\n", + " sum (int or float)\n", + " \"\"\"\n", + " ...\n", + " ...\n", + " ...\n", + "\n", + " return ..." ] }, { @@ -70,27 +84,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Q1.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n", + "**Q1.2**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n", "\n", - "Hints: Use a *list comprehension*, or maybe even a *generator expression*. You may want to use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function several times." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q1.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n", - "\n", - "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing)." + "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing): Use some appropriate abstract base class (ABC) from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)." ] }, { @@ -117,8 +113,24 @@ "metadata": {}, "outputs": [], "source": [ - "def mixed_sum(list_of_lists):\n", - " ..." + "def mixed_sum(list_of_lists_or_numbers):\n", + " \"\"\"Add up numbers in nested lists.\n", + " \n", + " Args:\n", + " list_of_lists_or_numbers (list): A list containing both numbers and\n", + " lists with numbers\n", + " \n", + " Returns:\n", + " sum (int or float)\n", + " \"\"\"\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + "\n", + " return ..." ] }, { @@ -134,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Q1.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n", + "**Q1.3.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n", "\n", "Hint: The idea behind is similar to the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics." ] @@ -155,7 +167,23 @@ "outputs": [], "source": [ "def cum_sum(numbers):\n", - " ..." + " \"\"\"Create the cumulative sums for some numbers.\n", + "\n", + " Args:\n", + " numbers (list): A list with numbers for that the cumulative sums\n", + " are calculated\n", + " \n", + " Returns:\n", + " cum_sums (list): A list with all the cumulative sums\n", + " \"\"\"\n", + " ...\n", + " ...\n", + "\n", + " ...\n", + " ...\n", + " ...\n", + "\n", + " return ..." ] }, { @@ -171,7 +199,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Q1.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n", + "**Q1.3.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case?\n", "\n", "Hint: It is possible to write this without any extra input validation." ] @@ -185,6 +213,13 @@ "cum_sum([])" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " < your answer >" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -228,7 +263,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -283,7 +318,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -322,7 +357,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -386,7 +421,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -425,7 +460,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -504,7 +539,10 @@ "source": [ "def product(*args, ...):\n", " \"\"\"Multiply all arguments.\"\"\"\n", - " ..." + " ...\n", + " ...\n", + "\n", + " return ..." ] }, { @@ -609,7 +647,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - " " + " < your answer >" ] }, { @@ -638,7 +676,13 @@ "source": [ "def product(*args, ...):\n", " \"\"\"Multiply all arguments.\"\"\"\n", - " ..." + " ...\n", + " ...\n", + "\n", + " ...\n", + " ...\n", + "\n", + " return ..." ] }, { @@ -706,7 +750,7 @@ "metadata": {}, "outputs": [], "source": [ - "product([2, 5, 10]) # the argument is a special collection type, namely a sequence" + "product([2, 5, 10]) # the argument is a collection that is also a sequence" ] }, { @@ -758,7 +802,15 @@ "source": [ "def product(*args, ...):\n", " \"\"\"Multiply all arguments.\"\"\"\n", - " ..." + " ...\n", + " ...\n", + " ...\n", + " ...\n", + "\n", + " ...\n", + " ...\n", + "\n", + " return ..." ] }, { @@ -769,1011 +821,6 @@ "source": [ "product()" ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we have an implementation of `product()` that is convenient to use for the caller of our function. In particular, we can pass it a *collection* with or without *unpacking* it.\n", - "\n", - "However, this version of `product()` suffers from one more flaw: We cannot pass it a *stream* of data, as modeled, for example, with an *iterator* object that produces elements on a one-by-one basis.\n", - "\n", - "Let's look at an example. The [*stream.py*](https://github.com/webartifex/intro-to-python/blob/master/stream.py) module in the repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from stream import make_finite_stream" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stream = make_finite_stream()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stream" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(stream)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Being a `generator`, `stream` is also an `Iterator` in the abstract sense." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "isinstance(stream, abc.Iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "*Iterators* are good for only *one* thing: Giving us the \"next\" element in a line of many." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "next(stream)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "They themselves have *no* idea of how many elements they produce eventually: The built-in [len()](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(stream)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to *materialize* the elements. However, in a real-world scenario, these may *not* fit into our machine's memory!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(stream)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To be more realistic, `make_finite_stream()` creates `generator` objects producing a varying number of elements." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(make_finite_stream())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(make_finite_stream())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "list(make_finite_stream())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's see what happens if we pass an *iterator*, as created by `make_finite_stream()`, instead of a materialized *collection*, like `one_hundred`, to `product()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product(make_finite_stream())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q2.11**: What line causes the `TypeError`? What line is really the problem in the latest implementation of `product()`? Describe what happens on each line in the function's body until the exception is raised!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q2.12**: Adapt `product()` one last time to make it work with *iterators* as well!\n", - "\n", - "Hints: This task is as easy as replacing `Collection` with something else. Which of the three behaviors of *collections* do *iterators* also exhibit? You may want to look at the documentations on the built-in [max()](https://docs.python.org/3/library/functions.html#max), [min()](https://docs.python.org/3/library/functions.html#min), and [sum()](https://docs.python.org/3/library/functions.html#sum) functions: What kind of argument do they take?" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def product(*args, ...):\n", - " \"\"\"Multiply all arguments.\"\"\"\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The final version of `product()` behaves like built-ins in edge cases, ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... works with the arguments passed either as independent *positional* arguments, *packed* into a single *collection* argument, or *unpacked*, ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product(42)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product(2, 5, 10)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product([2, 5, 10])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product(*[2, 5, 10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... and can handle *streaming* data with *indefinite* \"length.\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "product(make_finite_stream())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In real-world projects, the data science practitioner must decide if it is worthwhile to make a function usable in various different forms as we did in this exercise, or if that is over-engineered.\n", - "\n", - "Yet, two lessons are important to take away:\n", - "- It is always a good idea to *mimic* the behavior of *built-ins* when in doubt.\n", - "- Make functions capable of working with *streaming* data." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Removing Outliers in Streaming Data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we are given a `list` object with random integers like `sample` below, and we want to calculate some basic statistics on them." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample = [\n", - " 45, 46, 40, 49, 36, 53, 49, 42, 25, 40, 39, 36, 38, 40, 40, 52, 36, 52, 40, 41,\n", - " 35, 29, 48, 43, 42, 30, 29, 33, 55, 33, 38, 50, 39, 56, 52, 28, 37, 56, 45, 37,\n", - " 41, 41, 37, 30, 51, 32, 23, 40, 53, 40, 45, 39, 99, 42, 34, 42, 34, 39, 39, 53,\n", - " 43, 37, 46, 36, 45, 42, 32, 38, 57, 34, 36, 44, 47, 51, 46, 39, 28, 40, 35, 46,\n", - " 41, 51, 41, 23, 46, 40, 40, 51, 50, 32, 47, 36, 38, 29, 32, 53, 34, 43, 39, 41,\n", - " 40, 34, 44, 40, 41, 43, 47, 57, 50, 42, 38, 25, 45, 41, 58, 37, 45, 55, 44, 53,\n", - " 82, 31, 45, 33, 32, 39, 46, 48, 42, 47, 40, 45, 51, 35, 31, 46, 40, 44, 61, 57,\n", - " 40, 36, 35, 55, 40, 56, 36, 35, 86, 36, 51, 40, 54, 50, 49, 36, 41, 37, 48, 41,\n", - " 42, 44, 40, 43, 51, 47, 46, 50, 40, 23, 40, 39, 28, 38, 42, 46, 46, 42, 46, 31,\n", - " 32, 40, 48, 27, 40, 40, 30, 32, 25, 31, 30, 43, 44, 29, 45, 41, 63, 32, 33, 58,\n", - "]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(sample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.1**: `list` objects are **sequences**. What *four* behaviors do they always come with?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n", - "\n", - "Hints: You can solve this task with [built-in functions](https://docs.python.org/3/library/functions.html) only. A `for`-loop is *not* needed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def mean(sequence):\n", - " ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_mean = mean(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_mean" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.3**: Write a function `std()` that calculates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n", - "\n", - "Hints: Parts of the code are probably too long to fit within the suggested 79 characters per line. So, use *temporary variables* inside your function. Instead of a `for`-loop, you may want to use a *list comprehension* or, even better, a memoryless *generator expression*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from math import sqrt" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def std(sequence):\n", - " ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_std = std(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_std" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n", - "\n", - "Hint: You may want to use a *list comprehension* instead of a `for`-loop." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def standardize(sequence, *, digits=3):\n", - " ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_scores = standardize(sample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The [pprint()](https://docs.python.org/3/library/pprint.html#pprint.pprint) function from the [pprint](https://docs.python.org/3/library/pprint.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to \"pretty print\" long `list` objects compactly." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from pprint import pprint" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pprint(z_scores, compact=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We know that `standardize()` works correctly if the resulting z-scores' mean and standard deviation approach `0` and `1` for a long enough `sequence`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean(z_scores), std(z_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Even though `standardize()` calls `mean()` and `std()` only once each, `mean()` is called *twice*! That is so because `std()` internally also re-uses `mean()`!\n", - "\n", - "**Q3.5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n", - "\n", - "Hint: You must check if `seq_mean` is still the default value." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def std(sequence, *, seq_mean=None):\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`std()` continues to work as before." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_std = std(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample_std" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def standardize(sequence, *, digits=3):\n", - " ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_scores = standardize(sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean(z_scores), std(z_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n", - "\n", - "Hint: Use the [abs()](https://docs.python.org/3/library/functions.html#abs) and [zip()](https://docs.python.org/3/library/functions.html#zip) built-ins." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py*](https://github.com/webartifex/intro-to-python/blob/master/stream.py) file in the repository)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from stream import data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`data` is of type `generator` and has *no* length." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Being a `generator`, it is an `Iterator` in the abstract sense ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import collections.abc as abc" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "isinstance(data, abc.Iterator)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "... and so the only thing we can do with it is to pass it to the built-in [next()](https://docs.python.org/3/library/functions.html#next) function and go over the numbers it streams one by one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "next(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n", - "\n", - "Hints: If you try it out, you may have to press the \"Stop\" button in the toolbar at the top. Your computer should *not* crash, but you will *have to* restart this Jupyter notebook with \"Kernel\" > \"Restart\" and import `data` again." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean(data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.8**: Write a function `take_sample()` that takes an `iterator` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n", - "\n", - "Hints: [next()](https://docs.python.org/3/library/functions.html#next) and the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in may be helpful. You may want to use a *list comprehension* instead of a `for`-loop and write a one-liner. Audacious students may want to look at [isclice()](https://docs.python.org/3/library/itertools.html#itertools.islice) in the [itertools](https://docs.python.org/3/library/itertools.html) module in the [standard library](https://docs.python.org/3/library/index.html)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def take_sample(iterator, *, n=1_000):\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We take a `new_sample` from the stream of `data`, and its statistics are similar to the initial `sample`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "new_sample = take_sample(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "len(new_sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mean(new_sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "std(new_sample)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n", - "\n", - "To calculate a z-score, we need the stream's overall mean and standard deviation, and that is *impossible* to calculate if we do not know how long the stream is, and, in particular, if it is *infinite*. So, `standardized()` first takes a sample from the `iterable` internally, and uses the sample's mean and standard deviation to calculate the z-scores.\n", - "\n", - "Hint: `standardized()` *must* return a `generator` object. So, use a *generator expression* as the return value; unless you know about the `yield` statement already (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-yield-statement))." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def standardized(iterable, *, digits=3):\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`standardized()` works almost like `standardize()` except that we use it with [next()](https://docs.python.org/3/library/functions.html#next) to obtain the z-scores one by one." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_scores = standardized(data)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "z_scores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(z_scores)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "next(z_scores)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n", - "\n", - "The first steps in `skip_outliers()` are the same as in `standardized()`: We take a `sample` from the stream of `data` and calculate its statistics." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sample = ...\n", - "seq_mean = ...\n", - "seq_std = ..." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n", - "\n", - "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "standardizer = (... for ... in data)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`standardizer` should produce `tuple` objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "next(standardizer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.10.3**: Write another generator expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use *tuple unpacking*." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "threshold_z = 1.96" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "no_outliers = (... for ... in standardizer if ...)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`no_outliers` should produce `int` objects." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "next(no_outliers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def skip_outliers(iterable, *, threshold_z=1.96):\n", - " sample = ...\n", - " seq_mean = ...\n", - " seq_std = ...\n", - " standardizer = ...\n", - " no_outliers = ...\n", - " return no_outliers" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can create a `generator` object and loop over the `data` in the stream with outliers skipped. Instead of the default `1.96`, we use a `threshold_z` of only `0.05`: That filters out all numbers except `42`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "skipper = skip_outliers(data, threshold_z=0.05)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "skipper" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "type(skipper)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "next(skipper)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Q3.11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " " - ] } ], "metadata": {