From 7e2f67e1e3f34e569c888e83980c4c3c5b68d9ee Mon Sep 17 00:00:00 2001
From: Anna Meyer <annapmeyer95@gmail.com>
Date: Sun, 16 Jul 2023 16:54:30 -0500
Subject: [PATCH] copying lecture
---
.../lec11_dictionaries_notes.ipynb | 22057 +++++++++++++++-
.../13_Copying/13_Copying.pdf | Bin 0 -> 840938 bytes
.../13_Copying/lec_13_Copying_template.ipynb | 545 +
.../lec_13_recursion_template.ipynb | 639 +
4 files changed, 23129 insertions(+), 112 deletions(-)
create mode 100644 sum23/lecture_materials/13_Copying/13_Copying.pdf
create mode 100644 sum23/lecture_materials/13_Copying/lec_13_Copying_template.ipynb
create mode 100644 sum23/lecture_materials/13_Recursion/lec_13_recursion_template.ipynb
diff --git a/sum23/lecture_materials/11_Dictionaries/lec11_dictionaries_notes.ipynb b/sum23/lecture_materials/11_Dictionaries/lec11_dictionaries_notes.ipynb
index 3671511..58e5d0b 100644
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+ " 'No',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Data Science',\n",
+ " '53715',\n",
+ " '43.073051',\n",
+ " '-89.40123',\n",
+ " 'none (just cheese)',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '47.606209',\n",
+ " '-122.332069',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '20.924325',\n",
+ " '-156.690102',\n",
+ " 'sausage',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '21',\n",
+ " 'Business: Actuarial',\n",
+ " '53715',\n",
+ " '43.073051',\n",
+ " '-89.40123',\n",
+ " 'pineapple',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Business: Actuarial',\n",
+ " '53715',\n",
+ " '60.391262',\n",
+ " '5.322054',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Data Science',\n",
+ " '53715',\n",
+ " '23.697809',\n",
+ " '120.960518',\n",
+ " 'pepperoni',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '40.712776',\n",
+ " '74.005974',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '21',\n",
+ " 'Statistics',\n",
+ " '53703',\n",
+ " '31.230391',\n",
+ " '121.473701',\n",
+ " 'pineapple',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '65.68204',\n",
+ " '-18.090534',\n",
+ " 'sausage',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53715',\n",
+ " '41.73849',\n",
+ " '-71.30418',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Computer Science',\n",
+ " '53706',\n",
+ " '40.744678',\n",
+ " '-73.758072',\n",
+ " 'mushroom',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Undecided',\n",
+ " '53706',\n",
+ " '43.2967',\n",
+ " '87.9876',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '47.48',\n",
+ " '-122.28',\n",
+ " 'basil/spinach',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '20',\n",
+ " 'Science: Physics',\n",
+ " '53715',\n",
+ " '64.963051',\n",
+ " '-19.020836',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '21',\n",
+ " 'Languages',\n",
+ " '53511',\n",
+ " '39.952583',\n",
+ " '-75.165222',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53703',\n",
+ " '11.89',\n",
+ " '-85',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '33.873417',\n",
+ " '-115.900993',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '45.40857',\n",
+ " '-91.73542',\n",
+ " 'sausage',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Engineering: Industrial',\n",
+ " '53706',\n",
+ " '20.798363',\n",
+ " '-156.331924',\n",
+ " 'none (just cheese)',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Undecided',\n",
+ " '53715',\n",
+ " '43.041069',\n",
+ " '-87.909416',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '43',\n",
+ " '-88.27',\n",
+ " 'Other',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '20',\n",
+ " 'Business: Other|Accounting',\n",
+ " '53726',\n",
+ " '43',\n",
+ " '-89',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53562',\n",
+ " '42.66544',\n",
+ " '21.165319',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '33.748997',\n",
+ " '-84.387985',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Business: Actuarial',\n",
+ " '53706',\n",
+ " '39.299236',\n",
+ " '-76.609383',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '45.87128',\n",
+ " '-89.711632',\n",
+ " 'pepperoni',\n",
+ " 'neither',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '',\n",
+ " 'Computer Science',\n",
+ " '53703',\n",
+ " '43.07',\n",
+ " '-89.4',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53703',\n",
+ " '40.712776',\n",
+ " '-74.005974',\n",
+ " 'basil/spinach',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '40.712776',\n",
+ " '-74.005974',\n",
+ " 'Other',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '20',\n",
+ " 'Economics',\n",
+ " '53703',\n",
+ " '22.54',\n",
+ " '114.05',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '36.974117',\n",
+ " '-122.030792',\n",
+ " 'pepperoni',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Mathematics/AMEP',\n",
+ " '53706',\n",
+ " '42.99571',\n",
+ " '-90',\n",
+ " 'sausage',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53715',\n",
+ " '24.713552',\n",
+ " '46.675297',\n",
+ " 'basil/spinach',\n",
+ " 'neither',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53705',\n",
+ " '46.589146',\n",
+ " '-112.039108',\n",
+ " 'none (just cheese)',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes']]}"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Let's bucketize the data\n",
"buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
@@ -1623,11 +12496,10 @@
" if not lecture in buckets:\n",
" buckets[lecture] = []\n",
" buckets[lecture].append(row)\n",
- "# buckets"
+ "buckets"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1636,7 +12508,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1645,16 +12516,10946 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'LEC001': [['LEC001',\n",
+ " '22',\n",
+ " 'Engineering: Biomedical',\n",
+ " '53703',\n",
+ " '43.073051',\n",
+ " '-89.40123',\n",
+ " 'none (just cheese)',\n",
+ " 'neither',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '',\n",
+ " 'Mathematics/AMEP',\n",
+ " '53706',\n",
+ " '31.230391',\n",
+ " '121.473701',\n",
+ " 'basil/spinach',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Economics (Mathematical Emphasis)',\n",
+ " '53703',\n",
+ " '48.86',\n",
+ " '2.3522',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53703',\n",
+ " '24.7',\n",
+ " '46.7',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '23',\n",
+ " 'Computer Science',\n",
+ " '53711',\n",
+ " '43.073929',\n",
+ " '-89.385239',\n",
+ " 'sausage',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '22',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53719',\n",
+ " '43.073051',\n",
+ " '-89.40123',\n",
+ " 'none (just cheese)',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '18',\n",
+ " 'Computer Science',\n",
+ " '53706',\n",
+ " '26.2992',\n",
+ " '87.2625',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '24',\n",
+ " 'Business: Information Systems',\n",
+ " '53703',\n",
+ " '43.073051',\n",
+ " '-89.40123',\n",
+ " 'macaroni/pasta',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '',\n",
+ " 'Computer Science',\n",
+ " '53715',\n",
+ " '34.052235',\n",
+ " '-118.243683',\n",
+ " 'green pepper',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '26',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53703',\n",
+ " '41.902782',\n",
+ " '12.496365',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '25',\n",
+ " 'Economics',\n",
+ " '53703',\n",
+ " '40.712776',\n",
+ " '-74.005974',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '19',\n",
+ " 'Computer Science',\n",
+ " '53706',\n",
+ " '48.855709',\n",
+ " '2.29889',\n",
+ " 'pepperoni',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '21',\n",
+ " 'Computer Science',\n",
+ " '53715',\n",
+ " '16.306652',\n",
+ " '80.436539',\n",
+ " 'Other',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '18',\n",
+ " 'Engineering: Industrial',\n",
+ " '53715',\n",
+ " '30.733315',\n",
+ " '76.779419',\n",
+ " 'green pepper',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Economics (Mathematical Emphasis)',\n",
+ " '53703',\n",
+ " '43.073929',\n",
+ " '-89.385239',\n",
+ " 'macaroni/pasta',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '21',\n",
+ " 'Business: Finance',\n",
+ " '53711',\n",
+ " '43.073929',\n",
+ " '-89.385239',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '19',\n",
+ " 'Computer Science',\n",
+ " '53703',\n",
+ " '51.507351',\n",
+ " '-0.127758',\n",
+ " 'sausage',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '21',\n",
+ " 'Science: Biology/Life',\n",
+ " '53703',\n",
+ " '36',\n",
+ " '117',\n",
+ " 'macaroni/pasta',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '18',\n",
+ " 'Engineering: Other',\n",
+ " '53706',\n",
+ " '41.902782',\n",
+ " '12.496365',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '18',\n",
+ " 'Engineering: Industrial',\n",
+ " '53706',\n",
+ " '19.655041',\n",
+ " '-101.169891',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Business: Actuarial',\n",
+ " '53703',\n",
+ " '42.28',\n",
+ " '-83.74',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '19',\n",
+ " 'Business: Information Systems',\n",
+ " '53703',\n",
+ " '39.481655',\n",
+ " '-106.038353',\n",
+ " 'macaroni/pasta',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Business: Other|Business: Accounting',\n",
+ " '53703',\n",
+ " '31.230391',\n",
+ " '121.473701',\n",
+ " 'pepperoni',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '35.142441',\n",
+ " '-223.154297',\n",
+ " 'green pepper',\n",
+ " 'neither',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '19.8968',\n",
+ " '155.5828',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '21',\n",
+ " 'Computer Science',\n",
+ " '53703',\n",
+ " '43.21518',\n",
+ " '-87.94241',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '22',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53703',\n",
+ " '47.497913',\n",
+ " '19.040236',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Business andministration',\n",
+ " '53703',\n",
+ " '37.389091',\n",
+ " '-5.984459',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Computer Science',\n",
+ " '53703',\n",
+ " '40.7128',\n",
+ " '74.006',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC001',\n",
+ " '22',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53726',\n",
+ " '36.97447',\n",
+ " '122.02899',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '19',\n",
+ " 'Data Science',\n",
+ " '53703',\n",
+ " '41',\n",
+ " '87',\n",
+ " 'sausage',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '20',\n",
+ " 'Science: Other|Environmental Science',\n",
+ " '53715',\n",
+ " '41.878113',\n",
+ " '-87.629799',\n",
+ " 'green pepper',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC001',\n",
+ " '24',\n",
+ " 'Economics',\n",
+ " '53703',\n",
+ " '40',\n",
+ " '-90',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC001',\n",
+ " '',\n",
+ " 'Data Science',\n",
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+ " 'Business: Actuarial',\n",
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+ " '-89.40123',\n",
+ " 'pineapple',\n",
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+ " '120.960518',\n",
+ " 'pepperoni',\n",
+ " 'cat',\n",
+ " 'No',\n",
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+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '40.712776',\n",
+ " '74.005974',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '21',\n",
+ " 'Statistics',\n",
+ " '53703',\n",
+ " '31.230391',\n",
+ " '121.473701',\n",
+ " 'pineapple',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '65.68204',\n",
+ " '-18.090534',\n",
+ " 'sausage',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53715',\n",
+ " '41.73849',\n",
+ " '-71.30418',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Computer Science',\n",
+ " '53706',\n",
+ " '40.744678',\n",
+ " '-73.758072',\n",
+ " 'mushroom',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Undecided',\n",
+ " '53706',\n",
+ " '43.2967',\n",
+ " '87.9876',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '47.48',\n",
+ " '-122.28',\n",
+ " 'basil/spinach',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '20',\n",
+ " 'Science: Physics',\n",
+ " '53715',\n",
+ " '64.963051',\n",
+ " '-19.020836',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '21',\n",
+ " 'Languages',\n",
+ " '53511',\n",
+ " '39.952583',\n",
+ " '-75.165222',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53703',\n",
+ " '11.89',\n",
+ " '-85',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '33.873417',\n",
+ " '-115.900993',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53715',\n",
+ " '45.40857',\n",
+ " '-91.73542',\n",
+ " 'sausage',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
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+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Engineering: Industrial',\n",
+ " '53706',\n",
+ " '20.798363',\n",
+ " '-156.331924',\n",
+ " 'none (just cheese)',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Undecided',\n",
+ " '53715',\n",
+ " '43.041069',\n",
+ " '-87.909416',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
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+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '43',\n",
+ " '-88.27',\n",
+ " 'Other',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '20',\n",
+ " 'Business: Other|Accounting',\n",
+ " '53726',\n",
+ " '43',\n",
+ " '-89',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53562',\n",
+ " '42.66544',\n",
+ " '21.165319',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '33.748997',\n",
+ " '-84.387985',\n",
+ " 'mushroom',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Business: Actuarial',\n",
+ " '53706',\n",
+ " '39.299236',\n",
+ " '-76.609383',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53706',\n",
+ " '45.87128',\n",
+ " '-89.711632',\n",
+ " 'pepperoni',\n",
+ " 'neither',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '',\n",
+ " 'Computer Science',\n",
+ " '53703',\n",
+ " '43.07',\n",
+ " '-89.4',\n",
+ " 'pepperoni',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'no preference',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53703',\n",
+ " '40.712776',\n",
+ " '-74.005974',\n",
+ " 'basil/spinach',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '40.712776',\n",
+ " '-74.005974',\n",
+ " 'Other',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'No'],\n",
+ " ['LEC003',\n",
+ " '20',\n",
+ " 'Economics',\n",
+ " '53703',\n",
+ " '22.54',\n",
+ " '114.05',\n",
+ " 'pineapple',\n",
+ " 'dog',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Data Science',\n",
+ " '53706',\n",
+ " '36.974117',\n",
+ " '-122.030792',\n",
+ " 'pepperoni',\n",
+ " 'cat',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '18',\n",
+ " 'Mathematics/AMEP',\n",
+ " '53706',\n",
+ " '42.99571',\n",
+ " '-90',\n",
+ " 'sausage',\n",
+ " 'dog',\n",
+ " 'Yes',\n",
+ " 'night owl',\n",
+ " 'Yes'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Industrial',\n",
+ " '53715',\n",
+ " '24.713552',\n",
+ " '46.675297',\n",
+ " 'basil/spinach',\n",
+ " 'neither',\n",
+ " 'Yes',\n",
+ " 'early bird',\n",
+ " 'Maybe'],\n",
+ " ['LEC003',\n",
+ " '19',\n",
+ " 'Engineering: Mechanical',\n",
+ " '53705',\n",
+ " '46.589146',\n",
+ " '-112.039108',\n",
+ " 'none (just cheese)',\n",
+ " 'cat',\n",
+ " 'No',\n",
+ " 'night owl',\n",
+ " 'Yes']]}"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
- "def bucketize():\n",
- " pass"
+ "def bucketize(data, header, col_name):\n",
+ " buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
+ " for row in data:\n",
+ " lecture = row[header.index(col_name)]\n",
+ " if not lecture in buckets:\n",
+ " buckets[lecture] = []\n",
+ " buckets[lecture].append(row)\n",
+ " return buckets\n",
+ "\n",
+ "buckets = bucketize(cs220_data, cs220_header, 'Lecture')\n",
+ "buckets"
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1663,7 +23464,39 @@
]
},
{
- "attachments": {},
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def cell(row_idx, col_name):\n",
+ " \"\"\"\n",
+ " Returns the data value (cell) corresponding to the row index and \n",
+ " the column name of a CSV file.\n",
+ " \"\"\"\n",
+ " # TODO: get the index of col_name\n",
+ " col_index = cs220_header.index(col_name)\n",
+ "# print(\"col index is\", col_index)\n",
+ " \n",
+ " # TODO: get the value of cs220_data at the specified cell\n",
+ " data_value = cs220_data[row_idx][col_index]\n",
+ "# print(\"data_value is\", data_value)\n",
+ " \n",
+ " # TODO: handle missing values, by returning None\n",
+ " if data_value == '':\n",
+ " return None\n",
+ " \n",
+ " # TODO: handle type conversions\n",
+ " if col_name in ['Age', 'Zip Code']:\n",
+ " data_value = int(data_value)\n",
+ " \n",
+ " if col_name in ['Latitude', 'Longitude']:\n",
+ " data_value = float(data_value)\n",
+ " \n",
+ " return data_value"
+ ]
+ },
+ {
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1673,9 +23506,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "19.608180839612487"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"def col_avg(data, header, col_name, min_bound, max_bound):\n",
" \"\"\"\n",
@@ -1686,17 +23530,20 @@
" \n",
" Assumes the data in col_name is numerical (float or int)\n",
" \"\"\"\n",
- " buckets = dict() # Key: unique bucketize column value; Value: list of lists (rows having that unique column value)\n",
- " for row in data:\n",
- " lecture = row[header.index(col_name)]\n",
- " if lecture < min_bound or lecture > max_bound:\n",
- " continue\n",
- " if not lecture in buckets:\n",
- " buckets[lecture] = []\n",
- " buckets[lecture].append(row)\n",
+ " total = 0\n",
+ " count = 0\n",
+ " for row_idx in range(len(data)):\n",
+ " data_value = cell(row_idx, col_name)\n",
+ " \n",
+ " if data_value != None:\n",
+ " if data_value > min_bound and data_value < max_bound:\n",
+ " \n",
+ " total += data_value\n",
+ " count += 1\n",
" \n",
+ " return total / count\n",
+ " \n",
" \n",
- " return buckets\n",
"\n",
"min_age = 0\n",
"max_age = 118\n",
@@ -1704,7 +23551,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1712,7 +23558,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1722,21 +23567,21 @@
},
{
"cell_type": "code",
- "execution_count": 80,
+ "execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "{'LEC001': 20.049180327868854,\n",
- " 'LEC006': 18.63157894736842,\n",
- " 'LEC004': 19.98941798941799,\n",
- " 'LEC005': 19.42222222222222,\n",
- " 'LEC002': 255.91573033707866,\n",
- " 'LEC003': 19.137096774193548}"
+ "{'LEC001': 19.43502824858757,\n",
+ " 'LEC006': 19.72222222222222,\n",
+ " 'LEC004': 19.43820224719101,\n",
+ " 'LEC005': 19.459302325581394,\n",
+ " 'LEC002': 19.435754189944134,\n",
+ " 'LEC003': 19.56}"
]
},
- "execution_count": 80,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -1746,23 +23591,19 @@
" \"\"\"\n",
" Computes and returns column average per bucket\n",
" \"\"\"\n",
- " bucket_avg = {}\n",
- " for bucket in buckets:\n",
- " # TODO find average age\n",
- " num_students = 0\n",
- " total_age = 0\n",
- " for row in buckets[bucket]:\n",
- " age = row[header.index(avg_col_name)]\n",
- " if age != '':\n",
- " total_age += int(age)\n",
- " num_students += 1\n",
- " bucket_avg[bucket] = total_age/num_students\n",
- " return bucket_avg\n",
+ " avg_of_buckets = dict()\n",
+ " \n",
+ " for key in buckets:\n",
+ " avg = col_avg(buckets[key], header, avg_col_name, 0, 118)\n",
+ " \n",
+ " avg_of_buckets[key] = avg\n",
+ " \n",
+ " return avg_of_buckets\n",
+ "\n",
"avg_per_bucket(buckets, \"Age\", cs220_header) "
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1777,7 +23618,6 @@
"source": []
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -1792,7 +23632,6 @@
"source": []
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12802,7 +34641,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12829,7 +34667,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12854,7 +34691,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12862,7 +34698,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12943,7 +34778,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
@@ -12971,7 +34805,6 @@
]
},
{
- "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
diff --git a/sum23/lecture_materials/13_Copying/13_Copying.pdf b/sum23/lecture_materials/13_Copying/13_Copying.pdf
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diff --git a/sum23/lecture_materials/13_Copying/lec_13_Copying_template.ipynb b/sum23/lecture_materials/13_Copying/lec_13_Copying_template.ipynb
new file mode 100644
index 0000000..c1a5154
--- /dev/null
+++ b/sum23/lecture_materials/13_Copying/lec_13_Copying_template.ipynb
@@ -0,0 +1,545 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Copying"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# import statements\n",
+ "import copy"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Objects and reference review"
+ ]
+ },
+ {
+ "attachments": {
+ "image-2.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Answer questions A, B, and C based on above picture. Explain why.\n",
+ "# A: 2 - Variables refer to (also known as 'point to') objects.\n",
+ "# B: 1 - Person and Hurricane could be subtypes of namedtuple that we create\n",
+ "# to ddhold that objects, such as name, age, duration, etc.\n",
+ "# An unnamed collection of objects is a tuple (or a list, or a set).\n",
+ "# C: 2 - Variables can be inside stack frames, but objects can only be on the heap.\n",
+ "# Variables can also live on the heap, inside of an object.\n",
+ "# (Such as a list of lists, or a tuple of dictionaries)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this program in PythonTutor\n",
+ "# How many objects are created?\n",
+ "\n",
+ "x = [\"A\",\"B\",\"C\"]\n",
+ "y = x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this program in PythonTutor\n",
+ "# How many objects are created?\n",
+ "\n",
+ "def f(y):\n",
+ " pass\n",
+ "\n",
+ "x = [\"A\", \"B\", \"C\"]\n",
+ "f(x)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this program in PythonTutor\n",
+ "# Predict the program output.\n",
+ "\n",
+ "x = {}\n",
+ "y = x\n",
+ "y[\"WI\"] = \"Madison\"\n",
+ "print(x[\"WI\"])\n",
+ "\n",
+ "# Question: Why does the program output Madison?\n",
+ "# Answer: x and y refer to the same dictionary. Because we change y, we change\n",
+ "# the dictionary that they share, and thus both refer to {\"WI\": \"Madison\"}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this program in PythonTutor\n",
+ "# How many objects are created? What is the output of lines 12 and 13?\n",
+ "\n",
+ "def foo(nums):\n",
+ " nums.append(3)\n",
+ " print(nums)\n",
+ " \n",
+ "items = [1, 2]\n",
+ "numbers = items\n",
+ "foo(numbers)\n",
+ "\n",
+ "print(items)\n",
+ "print(numbers)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this program in PythonTutor\n",
+ "# How many objects are created? What is the output of line 7?\n",
+ "\n",
+ "x = [\"aaa\", \"bbb\"]\n",
+ "y = x[:]\n",
+ "x.pop(0)\n",
+ "print(len(y))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# TODO: Run the following code to create two dictionaries, a fun dictionary\n",
+ "# and a not-so-fun dictionary. \n",
+ "fun_dict = {\n",
+ " \"shenanigans\": {\n",
+ " \"definition\": \"silly or high-spirited behavior; mischief.\",\n",
+ " \"usage\": \"widespread financial shenanigans had ruined the fortunes of many\",\n",
+ " \"fun_to_say\": 7 # on a scale of 1-10\n",
+ " },\n",
+ " \"bamboozle\": {\n",
+ " \"definition\": \"fool or cheat (someone).\",\n",
+ " \"usage\": \"Tom Sawyer bamboozled the neighborhood boys into painting for him\",\n",
+ " \"fun_to_say\": 8 # on a scale of 1-10\n",
+ " },\n",
+ " \"gnarly\": {\n",
+ " \"definition\": \"gnarled.\",\n",
+ " \"usage\": \"twisted trees and gnarly roots\",\n",
+ " \"fun_to_say\": 2 # on a scale of 1-10\n",
+ " }\n",
+ "}\n",
+ "\n",
+ "boring_dict = {\n",
+ " \"sailboat\": {\n",
+ " \"definition\": \"a boat propelled by sails.\",\n",
+ " \"usage\": \"she sailed the sailboat across the Mississippi\",\n",
+ " \"fun_to_say\": 3 # on a scale of 1-10\n",
+ " },\n",
+ " \"canvas\": {\n",
+ " \"definition\": \"a strong, coarse unbleached cloth.\",\n",
+ " \"usage\": \"he painted on the canvas\",\n",
+ " \"fun_to_say\": 1 # on a scale of 1-10\n",
+ " },\n",
+ " \"shoe\": {\n",
+ " \"definition\": \"a covering for the foot.\",\n",
+ " \"usage\": \"the shoe was worn and tattered\",\n",
+ " \"fun_to_say\": 2 # on a scale of 1-10\n",
+ " }\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def change_usage(dict_to_change, word, new_example_sentence):\n",
+ " \"\"\"\n",
+ " Changes the usage of the specified word to the new example sentence.\n",
+ " \"\"\"\n",
+ " dict_to_change[word]['usage'] = new_example_sentence"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Test your function! Call it with different inputs. Does it work?\n",
+ "\n",
+ "print('sailboat usage before:', boring_dict['sailboat']['usage'])\n",
+ "print('bamboozle usage before:', fun_dict['bamboozle']['usage'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "change_usage(boring_dict, 'sailboat', 'my sailboat crashed into the dock!')\n",
+ "change_usage(fun_dict, 'bamboozle', 'the Stormtroopers were bamboozled by Jedi mindtricks')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print('sailboat usage after:', boring_dict['sailboat']['usage'])\n",
+ "print('bamboozle usage after:', fun_dict['bamboozle']['usage'])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Explain why it works. In particular, explain how we are changing the dictionary\n",
+ "# *inside of a function*. How is that possible? What are the implications?\n",
+ "\n",
+ "# A dictionary, like a list, is a mutable object. When we pass the fun or boring dictionary\n",
+ "# to the function, the function's dict_to_change will reference the same dictionary.\n",
+ "# Therefore, if we change any of the key-value mappings of dict_to_change, we will also\n",
+ "# be changing them for the caller as they refer to the same dictionary.\n",
+ "# Use PythonTutor to explore further!\n",
+ "# \n",
+ "# This means that change_usage will mutate the dictionary. If we wanted to go back\n",
+ "# to a previous usage, we should have made a copy first."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Summary notes on objects and references\n",
+ "\n",
+ "- Assignment operation creates a reference copy. \n",
+ " - That is `y = x` indicates that `y` should reference whatever is being referenced by `x`. \n",
+ " - This is true immaterial of whether `x` stores a reference to a mutable or immutable object instance.\n",
+ "- Parameter variables store reference copies:\n",
+ " - Parameter variables should reference whatever is being referenced by the argument variables.\n",
+ "- Any operations on immutable types create a brand new object.\n",
+ " - Reasoning: immutable objects cannot be modified. Hence immutable.\n",
+ "- `+` concatenation operator creates a brand new object.\n",
+ " - This is true immaterial of whether the operator is used on mutable (ex:list) or immutable types (ex: string)\n",
+ "- mutable type methods have an impact on the original object instance.\n",
+ " - For example: `list` methods: `append`, `sort`, `pop`\n",
+ " - For example: `dict` method of `pop`\n",
+ "- `sorted` function always returns a brand new object instance.\n",
+ " - The original object reference passed as argument does not get modified.\n",
+ "- Slicing creates a new object instance.\n",
+ " - This is true immaterial of whether the operator is used on mutable (ex:list) or immutable types (ex: string)\n",
+ "- Extremely corner case:\n",
+ " - `+=` changes original object instance unlike `some_list = some_list + new_list`.\n",
+ " - That is syntactic sugar shortcut of `+=` behaves slightly differently when compared to the expanded expression."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Learning Objectives\n",
+ "After this lecture you will be able to...\n",
+ " - Correctly predict the output of code that uses reference variable mutation and reassignment.\n",
+ " - Compare and contrast the 3 levels of copying: \n",
+ " - reference copy \n",
+ " - shallow copy(copy.copy)\n",
+ " - deep copy (copy.deepcopy)\n",
+ " - Write code that copies a data structure at the appropriate level for a given use case. "
+ ]
+ },
+ {
+ "attachments": {
+ "three%20levels%20copy.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run this in Python Tutor, and uncomment one option at a time. \n",
+ "\n",
+ "import copy\n",
+ "\n",
+ "w =[\n",
+ " {\"name\":\"A\", \"score\":88},\n",
+ " {\"name\":\"B\", \"score\":111},\n",
+ " {\"name\":\"C\", \"score\":100}]\n",
+ "\n",
+ "#x = w # reference copy\n",
+ "#y = copy.copy(w) # shallow copy...copies the first level\n",
+ "#z = copy.deepcopy(w) # deep copy...copies down to every level\n",
+ "\n",
+ "# takeway: Write a note to yourself\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Write code that copies a data structure at the appropriate level for a given use case. "
+ ]
+ },
+ {
+ "attachments": {
+ "player_score.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ ""
+ ]
+ },
+ {
+ "attachments": {
+ "use%20case%201.png": {
+ "image/png": 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"
+ }
+ },
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use Case 1: Get Max Score\n",
+ "Loop through each player's score and find the maximum score.\n",
+ "\n",
+ "We only need to use a *reference copy* -- we shouldn't change the data in the function."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run this code in PythonTutor\n",
+ "\n",
+ "player_scores = [\n",
+ " {\"name\":\"Anna\", \"score\":88},\n",
+ " {\"name\":\"Bianca\", \"score\":111},\n",
+ " {\"name\":\"Carl\", \"score\":100}\n",
+ "]\n",
+ "\n",
+ "def max_score(score_list):\n",
+ " best_person = None\n",
+ " high_score = None\n",
+ " for person in score_list:\n",
+ " # TODO: complete the conditional for determining max score\n",
+ " \n",
+ " return best_person\n",
+ "\n",
+ "print(player_scores)\n",
+ "print(max_score(player_scores))\n",
+ "print(player_scores)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use Case 2: Get Median Score\n",
+ "Sort the dictionary by score and find the median player's score.\n",
+ "\n",
+ "We should use a *shallow copy* to ensure that we don't change the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run this code in PythonTutor\n",
+ "\n",
+ "player_scores = [\n",
+ " {\"name\":\"Anna\", \"score\":88},\n",
+ " {\"name\":\"Bianca\", \"score\":111},\n",
+ " {\"name\":\"Carl\", \"score\":100}\n",
+ "]\n",
+ "\n",
+ "def median_score(score_list):\n",
+ " \n",
+ " # Make a shallow copy of score_list\n",
+ " copied = copy.copy(score_list)\n",
+ " \n",
+ " # We have not learned this yet.\n",
+ " # Do NOT use lambdas in your programs. You will lose points!\n",
+ " # This is for demonstration purposes only.\n",
+ " copied.sort(key = lambda person: person['score'])\n",
+ "\n",
+ " # Find the median\n",
+ " len_copied = len(copied)\n",
+ " med_score = None\n",
+ " if len_copied % 2 == 0:\n",
+ " left_mid = copied[len_copied // 2 - 1]['score']\n",
+ " right_mid = copied[len_copied // 2]['score']\n",
+ " med_score = (left_mid + right_mid) / 2\n",
+ " else:\n",
+ " med_score = copied[len_copied // 2]['score']\n",
+ " \n",
+ " # Return the median\n",
+ " return med_score\n",
+ " \n",
+ "\n",
+ "print(player_scores)\n",
+ "print(median_score(player_scores))\n",
+ "print(player_scores) # should be unchanged"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Use Case 3: Backup Scores\n",
+ "Create a backup of the original player_scores in case the user wants to revert their changes.\n",
+ "\n",
+ "We should use a *deep copy* to ensure that we save all of the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run this code in PythonTutor\n",
+ "\n",
+ "player_scores = [\n",
+ " {\"name\":\"Anna\", \"score\":88},\n",
+ " {\"name\":\"Bianca\", \"score\":111},\n",
+ " {\"name\":\"Carl\", \"score\":100}\n",
+ "]\n",
+ "\n",
+ "# Create a deepcopy of player_scores\n",
+ "players_before = copy.deepcopy(player_scores) \n",
+ "\n",
+ "# Change Anna's score by 10\n",
+ "player_scores[0][\"score\"] += 10\n",
+ "\n",
+ "# Print them both\n",
+ "print('players_before:', players_before)\n",
+ "print('player_scores:', player_scores)\n",
+ "\n",
+ "answer = input(\"enter y to keep changes: \")\n",
+ "# TODO: Only keep the changes if the user types in 'y'\n",
+ "???\n",
+ "\n",
+ "# Print them both\n",
+ "print('players_before:', players_before)\n",
+ "print('player_scores:', player_scores)\n",
+ "\n",
+ "# after you write this code, run it in Python Tutor"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Note:** Deepcopy is also known as a Recursive copy. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Specific Copying\n",
+ "Suppose that we have a big data object: a list of basketball players, where each player is a dictionary of their name, age, total_points, demographic information (which is a dictionary of ...), game history (which is a list of ...), and academic records (which is a dictionary of ...).\n",
+ "\n",
+ "We want to create a copy of just the high-scoring players. How could we accomplish this? Why might we only want to create a copy of some of the data?\n",
+ "\n",
+ "Write your answer in the cell below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For good exam practice, complete this week's worksheet and interactive exercises. Try tracing reference copy, shallow copy, and deep copy by hand."
+ ]
+ }
+ ],
+ "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": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/sum23/lecture_materials/13_Recursion/lec_13_recursion_template.ipynb b/sum23/lecture_materials/13_Recursion/lec_13_recursion_template.ipynb
new file mode 100644
index 0000000..eeab645
--- /dev/null
+++ b/sum23/lecture_materials/13_Recursion/lec_13_recursion_template.ipynb
@@ -0,0 +1,639 @@
+{
+ "cells": [
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Recursion"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Review 1\n",
+ "\n",
+ "- Why does Python have the complexity of separate references and objects?\n",
+ "- Why not follow the original organization we saw for everything (i.e., boxes of data with labels)?\n",
+ " - Reason 1: Performance\n",
+ " - Reason 2: Centralized Updates"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Reason 1: Performance\n",
+ "# Try this example in PythonTutor\n",
+ "\n",
+ "x = \"this string is millions of characters\" + \"!\" * (10 ** 6)\n",
+ "y = x # this is fast! Why? \n",
+ " # Answer: Recall that assignment just creates a reference copy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Reason 2: Centralized Updates\n",
+ "\n",
+ "# Try this example in PythonTutor\n",
+ "alice = {\"name\":\"Alice\", \"score\":10, \"age\":30}\n",
+ "bob = {\"name\":\"Bob\", \"score\":8, \"age\":25}\n",
+ "winner = alice\n",
+ "\n",
+ "alice[\"age\"] += 1\n",
+ "print(\"Winner age:\", winner[\"age\"]) \n",
+ "# what line 9 will output?\n",
+ "# Answer: 31"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Review 2\n",
+ "\n",
+ "Assignment creates reference copy immaterial of whether it is into a variable or into another data structure. Recall that references can be stored either in a variable or inside object instances for other data structures."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this example in PythonTutor\n",
+ "\n",
+ "alice = {\"name\":\"Alice\", \"score\":10, \"age\":30}\n",
+ "bob = {\"name\":\"Bob\", \"score\":8, \"age\":25}\n",
+ "team = [alice, bob] \n",
+ "# TODO: discuss does this create new inner dictionaries?\n",
+ "# Answer: this line only create a new object for outer \n",
+ "# list, which directly stores references to existing\n",
+ "# dictionary object instances\n",
+ "\n",
+ "players = {\"A\": alice, \"B\": bob} \n",
+ "# TODO: discuss does this create new inner dictionaries?\n",
+ "# Answer: this line only create a new object for outer \n",
+ "# dictionary, which directly stores references to \n",
+ "# existing dictionary object instances"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Review 3"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Use 'in' to determine if the given thing is in my_list\n",
+ "my_list = [\"meet\", \"me\", \"after\", \"2:00pm\"]\n",
+ "print(\"me\" in my_list) # TODO: predict the output\n",
+ "print(\"Meena\" in my_list) # TODO: predict the output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Let's try a nested list\n",
+ "my_list = [11, \"meet\", [\"me\", \"them\", \"us\"], [84, 19, 22], \"school\", 2.54]\n",
+ "print(\"me\" in my_list) # TODO: predict the output\n",
+ "print(84 in my_list) # TODO: predict the output\n",
+ "print(11 in my_list) # TODO: predict the output"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def search_list_depth2(target, some_list):\n",
+ " ''' returns True if thing in some_list, False otherwise'''\n",
+ " for thing in some_list:\n",
+ " #print(thing, type(thing))\n",
+ " if type(thing) != list:\n",
+ " if target == thing:\n",
+ " return True\n",
+ " else:\n",
+ " continue # do we need this?\n",
+ " else: # its a list\n",
+ " if target in thing:\n",
+ " return True\n",
+ " else:\n",
+ " continue # do we need this?\n",
+ " \n",
+ " return False # after all possible searching....not found\n",
+ "\n",
+ "print(my_list)\n",
+ "print(search_list_depth2(\"school\", my_list)) # in list\n",
+ "print(search_list_depth2(22, my_list)) # in nested list\n",
+ "print(search_list_depth2(\"house\", my_list)) # not anywhere"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Warmup 2"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "list_3_deep = [22, [33, 44, [55, 66], 77], 88]\n",
+ "\n",
+ "# let's try it our previous function\n",
+ "print(search_list_depth2(22, list_3_deep)) # in list\n",
+ "print(search_list_depth2(99, list_3_deep)) # not in list\n",
+ "\n",
+ "# write other tests to be sure that it works\n",
+ "print(search_list_depth2(33, list_3_deep)) # in nested list\n",
+ "print(search_list_depth2(55, list_3_deep)) # in nested nested list"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# what about ANY depth list? \n",
+ "# that is the goal of today's lecture"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Learning Objectives\n",
+ "\n",
+ "After today's Lecture you will be able to: \n",
+ "\n",
+ "Define recursion and be able to identify\n",
+ "- base case\n",
+ "- recursive case\n",
+ "- infinite recursion\n",
+ "\n",
+ "Explain why the following can be recursively defined\n",
+ "- lists\n",
+ "- dictionaries\n",
+ "\n",
+ "Trace a recursive function\n",
+ "- involving numeric computation\n",
+ "- involving nested data structures"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=\"attachment:Droste_effect.png\" width=\"450\"/>\n",
+ "</div>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Recursion is defined as the process of defining something in terms of itself.\n",
+ "\n",
+ "**Good example:**\n",
+ "\n",
+ "*Hofstadter's Law*: “It always takes longer than you expect, even when you take into account *Hofstadter's Law*.” (From Gödel, Escher, Bach)\n",
+ "\n",
+ "**Unhelpful self-reference example:**\n",
+ "\n",
+ "*mountain*: “a landmass that projects conspicuously above its surroundings and is higher than a *hill*”\n",
+ "\n",
+ "*hill*: “a usually rounded natural elevation of land lower than a *mountain*”\n",
+ "(From Merriam-Webster dictionary)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Mathematical example:**\n",
+ "\n",
+ "A number x is a *positive even number* if:\n",
+ "- x is 2 or\n",
+ "- x equals another *positive even number* plus two\n",
+ "\n",
+ "<div>\n",
+ "<img src=\"attachment:Recursive%20structure.png\" width=\"450\"/>\n",
+ "</div>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Term**: branch\n",
+ "\n",
+ "**Definition**: wooden stick, with an end splitting into other *branches*, OR terminating with a leaf\n",
+ "\n",
+ "\n",
+ "<div>\n",
+ "<img src=\"attachment:Trees.png\" width=\"400\"/>\n",
+ "</div>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=\"attachment:Base_case_recursive_case.png\" width=\"250\"/>\n",
+ "</div>"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "**Term**: directory\n",
+ "\n",
+ "**Definition**: a collection of files and *directories*\n",
+ " \n",
+ "<div>\n",
+ "<img src=\"attachment:Directory_structure-2.png\" width=\"450\"/>\n",
+ "</div> "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Define recursion and be able to identify \n",
+ "- base case\n",
+ "- recursive case\n",
+ "- infinite recursion"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Factorial of a number"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Goal: work from examples to get to recursive code\n",
+ "\n",
+ "#### Step 1: Come up with examples\n",
+ "\n",
+ "```python\n",
+ "1! = 1\n",
+ "2! = 1*2 = 2\n",
+ "3! = 1*2*3 = 6\n",
+ "4! = 1*2*3*4 = 24\n",
+ "5! = 1*2*3*4*5 = 120\n",
+ "```\n",
+ "\n",
+ "#### Step 2: Identify self-reference\n",
+ "```python\n",
+ "1! = # don't need a pattern at the start\n",
+ "2! = \n",
+ "3! = \n",
+ "4! = \n",
+ "5! = \n",
+ "```\n",
+ "\n",
+ "#### Step 3: Recursive definition\n",
+ "```python\n",
+ "1! is ???\n",
+ "N! is ??? for N > 1\n",
+ "```\n",
+ "\n",
+ "#### Step 4: Convert to Python code\n",
+ "- **Rule 1**: Base case should always be defined and be terminal\n",
+ "- **Rule 2**: Recursive case should make progress towards base case"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def fact(n):\n",
+ " if n == 1:\n",
+ " return 1\n",
+ " p = fact(n-1) \n",
+ " return n * p"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How does Python keep all the variables separate?\n",
+ "- Frames\n",
+ "\n",
+ "<div>\n",
+ "<img src=\"attachment:Frames.png\" width=\"450\"/>\n",
+ "</div> "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Try this in PythonTutor\n",
+ "fact(3)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "<div>\n",
+ "<img src=\"attachment:Factorial.png\" width=\"500\"/>\n",
+ "</div> "
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### RecursionError\n",
+ "\n",
+ "#### If there is no base case what happens in the above example? \n",
+ "- recursion never ends......infinite recursion\n",
+ "\n",
+ "#### infinite recursion can also happen if the recursive case does not move towards the base"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def fact(n):\n",
+ " #if n == 1:\n",
+ " # return 1\n",
+ " p = fact(n-1) \n",
+ " return n * p\n",
+ "fact(3)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Self-check: Tracing example"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Tracing a recursive function in the Python Tutor\n",
+ "# do this on your own\n",
+ "# Example 1\n",
+ "\n",
+ "def foo(n):\n",
+ " # I always start recursive functions by printing the parameters\n",
+ " print(\"Starting foo with n= \" , n)\n",
+ " if n < 0:\n",
+ " print(\"base case, returning 100\")\n",
+ " return 100\n",
+ " else:\n",
+ " temp = foo(n-2)\n",
+ " print(\"Ending foo returning \" , (n + temp))\n",
+ " return n + temp\n",
+ "\n",
+ "foo(13)\n",
+ "\n",
+ "# What happens if we replace < with == ? "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Example 2\n",
+ "# The Collatz Conjecture problem \n",
+ "# https://en.wikipedia.org/wiki/Collatz_conjecture\n",
+ "# run this in Python Tutor on your ownn\n",
+ "\n",
+ "def collatz(n):\n",
+ " # I always start recursive functions by printing the parameters\n",
+ " print(\"in collatz with n = \" , n)\n",
+ " if n == 1:\n",
+ " return 1 # base case\n",
+ " elif n % 2 == 0:\n",
+ " return collatz(n//2)\n",
+ " else:\n",
+ " return collatz (3*n+1)\n",
+ "\n",
+ "collatz(13) # try other numbers\n"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Trace a recursive function involving nested data structures"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Write a recursive function to search *ANY* list of lists/tuples \n",
+ "# for a given word\n",
+ "def search_list_recursive(target, some_list):\n",
+ " ''' returns True if target in some_list, False otherwise'''\n",
+ " \n",
+ " # base case 1: success!\n",
+ " if target in some_list:\n",
+ " return True\n",
+ " \n",
+ " # Strategy: iterate over all elements in some_list\n",
+ " success = False\n",
+ " for thing in some_list:\n",
+ " # Because of base case 1, we know that thing is not target\n",
+ " # TODO also want to include tuples\n",
+ " if type(thing) == type([]):\n",
+ " pass\n",
+ "\n",
+ " return success"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fav_stuff = [\"apples\", \"peaches\", \"oranges\", \n",
+ " [\"A\", \"B\", \"C\", \"D\"],\n",
+ " [[\"sedan\", \"SUV car\", \"minivan\"], \n",
+ " [\"bicycle\", \"road bike\", \"scooter\"]]\n",
+ " ]\n",
+ "\n",
+ "print(search_list_recursive(\"apples\", fav_stuff)) # outer list\n",
+ "print(search_list_recursive(\"D\", fav_stuff)) # list @ depth 1\n",
+ "print(search_list_recursive(\"road bike\", fav_stuff))# list @ depth 2\n",
+ "print(search_list_recursive(\"bicycle\", fav_stuff)) # list @ depth 2\n",
+ "print(search_list_recursive(\"pizza\", fav_stuff)) # doesn't exist"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def pretty_print(items, indent = 0):\n",
+ " \"\"\"\n",
+ " prints the nested data structure with proper indentation\n",
+ " \"\"\"\n",
+ " pass\n",
+ "\n",
+ "#data = [\"A\", \"B\", \"C\"]\n",
+ "data = [\"A\", [\"1\", \"2\", \"3\",], \"B\", [\"4\", [\"i\", \"ii\"]]]\n",
+ "pretty_print(data)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Explain why the following can be recursively defined\n",
+ "\n",
+ "- lists\n",
+ "- dictionaries\n",
+ "- JSON objects"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### dictionaries can have a recursive structure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "person_info = { \"name\": \"Meena\", \n",
+ " \"age\": 250, \n",
+ " \"family\" : {\"spouse\": \"Rogers\", \n",
+ " \"child1\": {\"name\": \"Viyan\", \n",
+ " \"age\": 2}, \n",
+ " }\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# let's try to search through a deep dictionary. \n",
+ "def search_dict_recursive(target_key, some_dict):\n",
+ " ''' returns the Value associated with target_key if tarket_key \n",
+ " in any level of some_dict, None otherwise'''\n",
+ " if target_key in some_dict: # base case\n",
+ " return some_dict[target_key]\n",
+ " else:\n",
+ " for key in some_dict:\n",
+ " if type(some_dict[key]) == dict: # recursive case\n",
+ " return search_dict_recursive(target_key, \\\n",
+ " some_dict[key]) \n",
+ " return None\n",
+ "\n",
+ "print(search_dict_recursive(\"child1\", person_info))\n",
+ "print(search_dict_recursive(\"father\", person_info))"
+ ]
+ }
+ ],
+ "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": "3.9.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
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