From 64cb2ae954f7f9ff4cc93f6e1008d45d201dc028 Mon Sep 17 00:00:00 2001
From: Andy Kuemmel <kuemmel@wisc.edu>
Date: Wed, 2 Nov 2022 11:23:48 -0500
Subject: [PATCH] Upload New File

---
 .../lec24_comprehensions_completed.ipynb      | 778 ++++++++++++++++++
 1 file changed, 778 insertions(+)
 create mode 100644 f22/andy_lec_notes/lec24_Nov02_Comprehensions/lec24_comprehensions_completed.ipynb

diff --git a/f22/andy_lec_notes/lec24_Nov02_Comprehensions/lec24_comprehensions_completed.ipynb b/f22/andy_lec_notes/lec24_Nov02_Comprehensions/lec24_comprehensions_completed.ipynb
new file mode 100644
index 0000000..358661b
--- /dev/null
+++ b/f22/andy_lec_notes/lec24_Nov02_Comprehensions/lec24_comprehensions_completed.ipynb
@@ -0,0 +1,778 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Illinois': 52331,\n",
+       " 'Iowa': 30448,\n",
+       " 'Michigan': 47907,\n",
+       " 'Minnesota': 52017,\n",
+       " 'Northwestern': 22316,\n",
+       " 'Ohio State': 61369,\n",
+       " 'Wisconsin': 45540}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warmup 1: Sort this dictionary by keys\n",
+    "\n",
+    "# source: https://en.wikipedia.org/wiki/Big_Ten_Conference#Members (2021)\n",
+    "\n",
+    "enrollments = { \"Wisconsin\": 45540, \"Michigan\": 47907, \"Illinois\": 52331,\n",
+    "        \"Iowa\": 30448, \"Minnesota\": 52017, \"Ohio State\": 61369, \"Northwestern\": 22316}\n",
+    "\n",
+    "# your answer here:\n",
+    "dict(sorted(enrollments.items()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Illinois', 52331),\n",
+       " ('Iowa', 30448),\n",
+       " ('Michigan', 47907),\n",
+       " ('Minnesota', 52017),\n",
+       " ('Northwestern', 22316),\n",
+       " ('Ohio State', 61369),\n",
+       " ('Wisconsin', 45540)]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# wrong way? \n",
+    "sorted(enrollments.items())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Wisconsin': 45540, 'Michigan': 47907, 'Illinois': 52331, 'Iowa': 30448, 'Minnesota': 52017, 'Ohio State': 61369, 'Northwestern': 22316}\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'Northwestern': 22316,\n",
+       " 'Iowa': 30448,\n",
+       " 'Wisconsin': 45540,\n",
+       " 'Michigan': 47907,\n",
+       " 'Minnesota': 52017,\n",
+       " 'Illinois': 52331,\n",
+       " 'Ohio State': 61369}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warmup 2: Sort enrollments by values ....use `extract`\n",
+    "print(enrollments)\n",
+    "\n",
+    "def extract(s):\n",
+    "    return s[-1]\n",
+    "\n",
+    "dict(sorted(enrollments.items(), key=extract))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Northwestern': 22316,\n",
+       " 'Iowa': 30448,\n",
+       " 'Wisconsin': 45540,\n",
+       " 'Michigan': 47907,\n",
+       " 'Minnesota': 52017,\n",
+       " 'Illinois': 52331,\n",
+       " 'Ohio State': 61369}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# alternate way to do the same thing with lambdas:\n",
+    "\n",
+    "dict(sorted(enrollments.items(), key = lambda t : t[-1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Gabriella', 'hours': 45},\n",
+       " {'name': 'Jayden', 'hours': 62},\n",
+       " {'name': 'Patrice', 'hours': 72},\n",
+       " {'name': 'Skylar', 'hours': 53}]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warmup 3: Sort this list of dictionaries by 'name'\n",
+    "volunteers = [  {\"name\": \"Skylar\", \"hours\": 53}, \n",
+    "             {\"name\":\"Patrice\", \"hours\": 72},\n",
+    "              {\"name\":\"Gabriella\", \"hours\": 45},\n",
+    "              {\"name\": \"Jayden\", \"hours\": 62} ]\n",
+    "\n",
+    "def extract_name(d):\n",
+    "    return d['name']\n",
+    "\n",
+    "sorted(volunteers, key=extract_name)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Gabriella', 'hours': 45},\n",
+       " {'name': 'Jayden', 'hours': 62},\n",
+       " {'name': 'Patrice', 'hours': 72},\n",
+       " {'name': 'Skylar', 'hours': 53}]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# alternate way with lambda:\n",
+    "sorted(volunteers, key= lambda d:d['name'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# What is a lambda function? \n",
+    "# an abstraction (shortcut) for a function\n",
+    "# remove the def, name, and return statement\n",
+    "# just describe the parameter and the return value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Patrice', 'hours': 72},\n",
+       " {'name': 'Jayden', 'hours': 62},\n",
+       " {'name': 'Skylar', 'hours': 53},\n",
+       " {'name': 'Gabriella', 'hours': 45}]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warmup 4: Sort the above list of dictionaries by 'hours' \n",
+    "sorted(volunteers, key= lambda d:d['hours'], reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['fig', 'date', 'apple', 'cherry', 'blueberry', 'grapefruit']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warmup 5: Sort this lists of strings by length of string\n",
+    "fruits = [\"apple\", \"blueberry\", \"cherry\", \"date\", \"fig\", \"grapefruit\"]\n",
+    "\n",
+    "sorted(fruits, key = len)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Waukesha': ['Brookfield'],\n",
+       " 'Rock': ['Janesville', 'Beloit'],\n",
+       " 'Milwaukee': ['Milwaukee', 'West Allis', 'Wauwatosa'],\n",
+       " 'Dane': ['Madison', 'Sun Prairie', 'Middleton', 'Waunakee']}"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Warmup 6: Sort this dictionary of lists by length of list\n",
+    "cities_by_county = {\"Dane\": [\"Madison\", \"Sun Prairie\", \"Middleton\", \"Waunakee\"],\n",
+    "                     \"Rock\": [\"Janesville\", \"Beloit\"],\n",
+    "                   \"Milwaukee\": [\"Milwaukee\", \"West Allis\", \"Wauwatosa\"],\n",
+    "\n",
+    "                   \"Waukesha\": [\"Brookfield\"]}\n",
+    "\n",
+    "dict(sorted(cities_by_county.items(), key = lambda t: len(t[-1])))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Lecture 24:  Comprehensions\n",
+    "\n",
+    "Learning Objectives:\n",
+    "\n",
+    "- Create list and dictionary comprehensions\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 25.1 Create list and dictionary comprehensions\n",
+    "\n",
+    "\n",
+    "Python frequently gives us ways to shorten code\n",
+    "\n",
+    "A comprehension is a shortcut to using a for loop\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Given this list what kind of filtered list could we make from it? \n",
+    "# By filtered, think about keeping certain words\n",
+    "fruits = [\"apple\", \"blueberry\", \"cherry\", \"DATE\", \"FIG\", \"grapefruit\"]\n",
+    "# Talk to your partner and come up with different ideas\n",
+    "\n",
+    "# just words that all uppercase\n",
+    "\n",
+    "\n",
+    "# just the lengths of each word\n",
+    "\n",
+    "\n",
+    "# keep the short words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['blueberry', 'cherry', 'grapefruit']"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# long way: using a for loop\n",
+    "new_list = []\n",
+    "for w in fruits:\n",
+    "    if len(w) > 5:\n",
+    "        new_list.append(w)\n",
+    "new_list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['blueberry', 'cherry', 'grapefruit']"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Do the same thing using a comprehension\n",
+    "# Tip:  Write them in reverse, naming the variable last\n",
+    "[ w for w in fruits if len(w) > 5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['DATE', 'FIG']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list of words in fruits that are all uppercase\n",
+    "fruits = [\"apple\", \"blueberry\", \"cherry\", \"DATE\", \"FIG\", \"grapefruit\"]\n",
+    "\n",
+    "upper_words = [ w for w in fruits if w == w.upper() ]\n",
+    "upper_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5, 9, 6, 4, 3, 10]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list of just the lengths of each word\n",
+    "[ len(w) for w in fruits ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['APPLE', 'DATE', 'FIG']"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list of all the short words,  length < 6 but make them uppercase\n",
+    "[ w.upper() for w in fruits if len(w) < 6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['apple', 'DATE', 'grapefruit']"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# list of words that contain the letter \"A\" or \"a\"\n",
+    "[ w for w in fruits if \"A\" in w.upper()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Given this list of dictionaries, what kind of lists could we make from it?\n",
+    "# Talk to your partner and come up with different ideas\n",
+    "volunteers = [  {\"name\": \"Skylar\", \"hours\": 53}, \n",
+    "             {\"name\":\"Patrice\", \"hours\": 72},\n",
+    "              {\"name\":\"Gabriella\", \"hours\": 45},\n",
+    "              {\"name\": \"Jayden\", \"hours\": 62}\n",
+    "             ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Skylar', 'Patrice', 'Gabriella', 'Jayden']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[ d['name'] for d in volunteers]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[72, 45, 62]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[ d['hours'] for d in volunteers if \"e\" in d['name'] ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Skylar', 53), ('Patrice', 72), ('Gabriella', 45), ('Jayden', 62)]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[    (d['name'], d['hours'] )  for d in volunteers   ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Given this dictionary, what list could we make?\n",
+    "cities_by_county = {\"Dane\": [\"Madison\", \"Sun Prairie\", \"Middleton\", \"Waunakee\"],\n",
+    "                   \"Milwaukee\": [\"Milwaukee\", \"West Allis\", \"Wauwatosa\"],\n",
+    "                   \"Rock\": [\"Janesville\", \"Beloit\"],\n",
+    "                   \"Waukesha\": [\"Brookfield\"]}\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The syntax of Comprehensions"
+   ]
+  },
+  {
+   "attachments": {
+    "comprehensions.png": {
+     "image/png": "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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![comprehensions.png](attachment:comprehensions.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### if/else in a comprehension has a different ordering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0, 7.222222222222222, 32.22222222222222, 100.0]"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# If/else...\n",
+    "# convert these Fahrenheit temps to an int in Celsius using C = 5/9 * (F-32)\n",
+    "temps = [\"32\", \"45\", \"90\", \"212\"]\n",
+    "\n",
+    "[5/9 * (int(f)-32) for f in temps]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0.0, 7.222222222222222, 32.22222222222222, None, 100.0]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# do it again, but change empty strings to None\n",
+    "# need to use an if else clause which must come before the 'for'\n",
+    "# new_list = [expression if conditional_expression else alternate_expression for val in iterable ]\n",
+    "\n",
+    "temps = [\"32\", \"45\", \"90\", \"\", \"212\"]\n",
+    "[5/9 * (int(f)-32) if f != \"\" else None for f in temps ]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Dictionary Comprehensions\n",
+    "\n",
+    "We use a for loop that iterates over dict.items() \n",
+    "\n",
+    "We use the concept of 'tuple unpacking' by writing a `for (k,v) in dictname.items()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Bob': 83, 'Cindy': 87, 'Alice': 90, 'Meena': 93}"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scores_dict = {\"Bob\": [18, 72, 83], \n",
+    "               \"Cindy\" : [27, 11, 55, 73, 87], \n",
+    "               \"Alice\": [16, 33, 42, 89, 90], \n",
+    "               \"Meena\": [39, 93, 9, 3, 55, 72, 19]}\n",
+    "\n",
+    "# Create a dictionary where the key is the same, but the value is the max score in each list\n",
+    "{ k : max(v) for (k,v) in scores_dict.items() }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Create a dictionary where the key is the same, but the value is the average score\n",
+    "{ k : sum(v)/len(v) for (k,v) in scores_dict.items() }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Skylar': 53, 'Patrice': 72, 'Gabriella': 45, 'Jayden': 62}"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create a single dictionary where the key is the name and the value is the hours.\n",
+    "volunteers = [  {\"name\": \"Skylar\", \"hours\": 53}, \n",
+    "             {\"name\":\"Patrice\", \"hours\": 72},\n",
+    "              {\"name\":\"Gabriella\", \"hours\": 45},\n",
+    "              {\"name\": \"Jayden\", \"hours\": 62} ]\n",
+    "\n",
+    "{ d['name'] : d['hours'] for d in volunteers}\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# For more reference\n",
+    "# https://www.w3schools.com/python/python_lists_comprehension.asp\n",
+    "# https://www.datacamp.com/community/tutorials/python-dictionary-comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
GitLab