diff --git a/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/amfam_survey_data.csv b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/amfam_survey_data.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c223c886d305b1d9d536851bfe389576d22f9212
--- /dev/null
+++ b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/amfam_survey_data.csv
@@ -0,0 +1,33 @@
+pizza topping,state,years,sleep preference,month,pets,lat-long
+mushroom,Florida,7,early bird,March,,"30.263214888389417, -81.54792098150529"
+pineapple,Wisconsin,4,night owl,April,other,"43.1581437, -89.2921125"
+sausage,Wisconsin,10,early bird,July,other,"43.15645, -89.28814"
+pepperoni,WI,7,no preference,September,"dog,cat","43.073051, -89.401230"
+mushroom,madison,7,early bird,November,,
+pepperoni,FL,1,no preference,December,dog,"42.35623761108948, -71.05691488946681"
+pepperoni,Wisconsin,2,night owl,February,,"43.159045128642774, -89.29146323507756"
+mushroom,Florida,0.5,night owl,May,other,"43.160601, -89.287671"
+mushroom,Wisconsin,10,no preference,January,"dog,fish","43.1562216,-89.2880086"
+pineapple,Wisconsin,8,night owl,July,dog,"43.158655, -89.289895"
+sausage,Minnesota,15,no preference,August,"dog,cat","45.13881645889933, -93.47636590830673"
+pepperoni,New Jersey,1,night owl,May,other,"43.07148896663423, -89.40567798752735"
+basil,Rhode Island,1,night owl,March,dog,"43.156490793353775, -89.28796434617352"
+mushroom,TX,1,no preference,January,dog,
+pineapple,Florida,3,early bird,July,other,"27.979191147972834, -82.33356380365498"
+sausage,Wisconsin,0,early bird,December,"dog,cat","43.15631441766965, -89.28785659081201"
+pineapple,Wisconsin,6,no preference,June,dog,"43.157716440341964, -89.28939262164963"
+mushroom,Florida,7,no preference,July,other,"30.053546, -81.514610"
+sausage,Florida,3,early bird,January,"dog,fish","30.263357, -81.547884"
+mac&cheese,Wisconsin,5,night owl,July,dog,"43.158328032172754, -89.28946714938327"
+pepperoni,Wisconsin,10,early bird,April,other,"43.1884213,-89.2762121"
+other,Wisconsin,10,early bird,August,other,"43.15833, -89.28988"
+sausage,WI,14,night owl,September,"dog,cat","43.15733597381252, -89.29013010509833"
+sausage,Wisconsin,6,no preference,August,"dog,cat","43.159061371631616, -89.29141118826759"
+pepperoni,Wisconsin,8,early bird,September,"dog,cat,fish",43.158359 -89.289972
+pineapple,Florida,8,night owl,October,,"30.263432655702932, -81.54807118535949"
+pineapple,TX,4,night owl,October,dog,"42.3558293029345, -71.05683171712127"
+other,WI,2,early bird,June,,
+mushroom,Wisconsin,20,early bird,September,dog,"43.15826500058843, -89.28945716165009"
+sausage,Wisconsin,8,night owl,June,dog,"43.15839022178169, -89.28998287477457"
+sausage,Wisconsin,20,night owl,April,bird,"43.15648555750267, -89.28783647996661"
+pineapple,Texas,0.5,early bird,August,other,"43, 89"
\ No newline at end of file
diff --git a/f22/andy_lec_notes/lec_17/lec17_dictionaries_amfam.ipynb b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/lec17_dictionaries1_completed.ipynb
similarity index 54%
rename from f22/andy_lec_notes/lec_17/lec17_dictionaries_amfam.ipynb
rename to f22/andy_lec_notes/lec17_Oct17_Dictionaries1/lec17_dictionaries1_completed.ipynb
index 3757fd09ecb838d9d7960e19400c40d4e0f343c6..addf396c03397d658423aec9fb78176e3bc9ad21 100644
--- a/f22/andy_lec_notes/lec_17/lec17_dictionaries_amfam.ipynb
+++ b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/lec17_dictionaries1_completed.ipynb
@@ -20,20 +20,9 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "721"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists\n",
+ "# Warmup 1: Read in the file 'affam_survey_data.csv' into a lists of lists\n",
"\n",
"\n",
"# source: Automate the Boring Stuff with Python Ch 12\n",
@@ -44,40 +33,19 @@
" exampleFile.close() \n",
" return exampleData\n",
"\n",
- "survey_data = process_csv('cs220_survey_data.csv') # change this\n",
+ "survey_data = None # change this\n",
"\n",
- "# show the length of this list of lists \n",
- "len(survey_data)"
+ "# show the length of this list of lists \n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Lecture',\n",
- " 'Age',\n",
- " 'Primary major',\n",
- " 'Other majors',\n",
- " 'Zip Code',\n",
- " 'Pizza topping',\n",
- " 'Pet owner',\n",
- " 'Runner',\n",
- " 'Sleep habit',\n",
- " 'Procrastinator']"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Warmup 2: store the first row in a variable called header\n",
- "header = survey_data[0]\n",
+ "header = None\n",
"header"
]
},
@@ -87,7275 +55,38 @@
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "[['LEC002',\n",
- " '19',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53711',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Science: Physics',\n",
- " 'Astronomy-Physics, History',\n",
- " '53726',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Science: Chemistry',\n",
- " '',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '19',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '20',\n",
- " 'Other',\n",
- " 'Economics ',\n",
- " '53715',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Statistics',\n",
- " '',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Mathematics/AMEP',\n",
- " '',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Data Science',\n",
- " 'Stats',\n",
- " '53715',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Business: Finance',\n",
- " '',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'No',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Other',\n",
- " 'I am undecided – thinking about Data Science Major',\n",
- " '53706',\n",
- " 'basil/spinach',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '19',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Statistics',\n",
- " 'psychology',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC004',\n",
- " '20',\n",
- " 'Statistics',\n",
- " 'Mathematics ',\n",
- " '53726',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '20',\n",
- " 'Mathematics/AMEP',\n",
- " '',\n",
- " '53711',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Science: Physics',\n",
- " 'Data Science',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Data Science',\n",
- " 'Economics',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Engineering: Mechanical',\n",
- " 'nuclear engineering',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '21',\n",
- " 'Science: Chemistry',\n",
- " '',\n",
- " '',\n",
- " 'green pepper',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Computer Science',\n",
- " 'Data Science',\n",
- " '53706',\n",
- " 'basil/spinach',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '21',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Computer Science',\n",
- " 'Data Science',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Languages',\n",
- " 'Linguistics',\n",
- " '53706',\n",
- " 'macaroni/pasta',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Other',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Science: Physics',\n",
- " 'Mathematics',\n",
- " '53703',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC002',\n",
- " '28',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53703',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Science: Physics',\n",
- " 'Life Science Communication',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " 'pre-medicine',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53711',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'macaroni/pasta',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC004',\n",
- " '21',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Business: Information Systems',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Business: Actuarial',\n",
- " 'Data Science and Analytics',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '22',\n",
- " 'Engineering: Industrial',\n",
- " '',\n",
- " '',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '20',\n",
- " 'Other',\n",
- " 'data science, business',\n",
- " '53703',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'No',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Data Science',\n",
- " 'Sports Journalism certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Data Science',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Statistics',\n",
- " 'Data Science, Math',\n",
- " '53715',\n",
- " 'mushroom',\n",
- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '20',\n",
- " 'Other',\n",
- " 'Data science certificate',\n",
- " '',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '20',\n",
- " 'Engineering: Industrial',\n",
- " 'Computer science',\n",
- " '53719',\n",
- " 'sausage',\n",
- " 'No',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '',\n",
- " 'Computer Science',\n",
- " 'Minors in Data Science and Chicano and Latino Studies',\n",
- " '53715',\n",
- " 'macaroni/pasta',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " '',\n",
- " '',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Business: Finance',\n",
- " 'Economics',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '19',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
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- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Engineering: Industrial',\n",
- " '',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '20',\n",
- " 'Science: Biology/Life',\n",
- " 'Life Science Communication',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '19',\n",
- " 'Science: Biology/Life',\n",
- " 'Data Science',\n",
- " '',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '22',\n",
- " 'Computer Science',\n",
- " '',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Business: Information Systems',\n",
- " '',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Business: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Other',\n",
- " 'Economics/Philosophy, Data Science Certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']]"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "TypeError",
+ "evalue": "'NoneType' object is not subscriptable",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Warmup 3: store the rest of the data in a variable called rows\u001b[39;00m\n\u001b[1;32m 2\u001b[0m rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mrows\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
+ "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
+ ]
}
],
"source": [
"# Warmup 3: store the rest of the data in a variable called rows\n",
- "rows = survey_data[1:]\n",
- "rows\n",
+ "rows = None\n",
+ "\n",
+ "# show the first 3 rows\n",
+ "\n",
"\n"
]
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[['LEC001',\n",
- " '19',\n",
- " 'Business: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Other',\n",
- " 'Economics/Philosophy, Data Science Certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Warmup 4: show the last 3 rows of data\n",
- "rows[-3:]"
+ "# Warmup 4: show the last 3 rows of data\n"
]
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
@@ -7363,16 +94,15 @@
"\n",
"def count_col_frequency(value, col_name):\n",
" ''' returns the frequency of value in col_name '''\n",
- " count = 0\n",
- " for row in rows:\n",
- " if row[header.index(col_name)] == value:\n",
- " count += 1\n",
- " return count\n"
+ " \n",
+ " #Hint: use if row[header.index(col_name)] == value:\n",
+ " return None\n",
+ "\n"
]
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"outputs": [
{
@@ -7381,7 +111,7 @@
"80"
]
},
- "execution_count": 8,
+ "execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@@ -7393,63 +123,17 @@
},
{
"cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "581"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "count_col_frequency(\"Yes\", \"Pet owner\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "89"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "count_col_frequency(\"21\", \"Age\")"
- ]
+ "outputs": [],
+ "source": []
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "142"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "count_col_frequency(\"early bird\", \"Sleep habit\")"
- ]
+ "outputs": [],
+ "source": []
},
{
"cell_type": "code",
@@ -7465,7 +149,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## March 7: Dictionaries\n",
+ "## Lecture 17: Dictionaries\n",
"\n",
"Learning Objectives:\n",
"- Use correct dictionary syntax \n",
@@ -7507,7 +191,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"outputs": [
{
@@ -7524,7 +208,7 @@
" 'hot dog': 4.99}"
]
},
- "execution_count": 15,
+ "execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@@ -7534,17 +218,18 @@
"# create a dictionary of key/value pairs\n",
"# notice the curly brackets\n",
"# notice it can span over more than one line, indenting doesn't matter\n",
- "price_dict = { 'pie': 3.95, 'ala mode':1.50, \n",
- " 'donut': 1.25, 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79, 'milk':1.65, 'loaf': 5.99,\n",
- " 'hot dog': 4.99, } # feel free to add some of your own here\n",
+ "price_dict = { 'pie': 3.95, \n",
+ " 'ala mode':1.50, \n",
+ " 'donut': 1.25, 'muffin': 2.25,\n",
+ " 'brownie': 3.15,\n",
+ " 'cookie': 0.79, 'milk':1.65, 'loaf': 5.99,\n",
+ " 'hot dog': 4.99} # feel free to add some of your own here\n",
"price_dict"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -7552,35 +237,23 @@
"output_type": "stream",
"text": [
"9\n",
- "1.25\n",
- "5.99\n"
- ]
- },
- {
- "ename": "KeyError",
- "evalue": "1.25",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-19-731003293220>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# what's wrong with this line?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprice_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.25\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;31mKeyError\u001b[0m: 1.25"
+ "5.99\n",
+ "1.25\n"
]
}
],
"source": [
"# print the length of the dictionary\n",
- "print(len(price_dict)) # number of key/value pairs\n",
+ "print(price_dict) # number of key/value pairs\n",
"\n",
"#get the price for a certain item\n",
- "print(price_dict['donut']) # name of dict [ key]\n",
+ "print(...) # name of dict [ key]\n",
"\n",
"#get the price for donut\n",
- "print(price_dict['loaf'])\n",
+ "print(...)\n",
"\n",
"# what's wrong with this line? \n",
- "print(price_dict[1.25])\n",
- "# can't lookup the key associated with a given value\n"
+ "# print(price_dict[1.25])\n"
]
},
{
@@ -7592,19 +265,39 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 3,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'pie': 3.95,\n",
+ " 'ala mode': 1.5,\n",
+ " 'donut': 1.25,\n",
+ " 'muffin': 2.25,\n",
+ " 'brownie': 3.15,\n",
+ " 'cookie': 0.79,\n",
+ " 'milk': 1.65,\n",
+ " 'loaf': 5.99,\n",
+ " 'hot dog': 4.99,\n",
+ " 'drink': 2.49}"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# add a new key/value pair using [ ] notation\n",
- "price_dict['scone'] = 2.99\n",
+ "\n",
"\n",
"# add one of your own\n"
]
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -7619,10 +312,10 @@
" 'milk': 1.65,\n",
" 'loaf': 5.99,\n",
" 'hot dog': 4.99,\n",
- " 'scone': 2.49}"
+ " 'drink': 2.99}"
]
},
- "execution_count": 21,
+ "execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
@@ -7630,85 +323,89 @@
"source": [
"# change the value associated with a key....syntax is like add\n",
"\n",
- "price_dict['scone'] = 2.49\n",
+ "\n",
"\n",
"# now you add one\n",
"price_dict"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The .pop() method removes a key/value pair"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 21,
"metadata": {},
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'hot dog'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-23-f8655347dcaf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# use pop to delete a key/value pair\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprice_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hot dog'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# or del(price_dict['hot dog'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# delete another key/value pair\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m# price_dict.pop()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: 'hot dog'"
- ]
- }
- ],
+ "outputs": [],
"source": [
"# use pop to delete a key/value pair\n",
"price_dict.pop('hot dog') # or del(price_dict['hot dog'])\n",
"\n",
"# delete another key/value pair\n",
- "# price_dict.pop()\n",
+ "price_dict.pop()\n",
"\n",
"# try deleting someting that is not there\n",
- "price_dict.pop('pizza')\n",
+ "#price_dict.pop('pizza')\n",
"\n",
"# fix this with an if statement\n",
"if 'pizza' in price_dict:\n",
" price_dict.pop('pizza')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The .keys() and .values() methods return a list-like object"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk', 'loaf', 'scone']\n"
+ "dict_keys(['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk'])\n",
+ "['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk']\n"
]
}
],
"source": [
"# get all keys and convert to a list\n",
- "print(list(price_dict.keys()))\n",
+ "print(price_dict.keys())\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "[3.95, 1.5, 1.25, 2.25, 3.15, 0.79, 1.65, 5.99, 2.49]\n"
+ "dict_values([3.95, 1.5, 1.25, 2.25, 3.15, 0.79, 1.65])\n",
+ "[3.95, 1.5, 1.25, 2.25, 3.15, 0.79, 1.65]\n"
]
}
],
"source": [
"# get all values and convert to a list\n",
- "print(list(price_dict.values()))\n"
+ "print(price_dict.values())\n"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 27,
"metadata": {},
"outputs": [
{
@@ -7725,10 +422,10 @@
"source": [
"# use 'in' price_dict, price_dict.keys(), price_dict.values()\n",
"\n",
- "print('donut' in price_dict) # default is to check the keys\n",
- "print(3.95 in price_dict) # default is NOT values\n",
- "print('apple' in price_dict.keys()) # can call out the keys\n",
- "print(3.95 in price_dict.values()) # can check the values"
+ "#print('donut' in price_dict) # default is to check the keys\n",
+ "#print(9.95 in price_dict) # default is NOT values\n",
+ "#print('apple' in price_dict.keys()) # can call out the keys\n",
+ "#print(3.95 in price_dict.values()) # can check the values"
]
},
{
@@ -7742,7 +439,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 29,
"metadata": {},
"outputs": [
{
@@ -7750,6 +447,7 @@
"output_type": "stream",
"text": [
"['pie', 'donut', 'milk', 'cookie', 'tofu']\n",
+ "tofu is not in the dictionary\n",
"Your total is $7.64\n"
]
}
@@ -7761,8 +459,7 @@
"\n",
"total_cost = 0\n",
"for item in order:\n",
- " if item in price_dict:\n",
- " total_cost += price_dict[item]\n",
+ " pass\n",
" \n",
"# find the total of the items in the order\n",
"print (\"Your total is ${:.2f}\".format(total_cost))"
@@ -7770,7 +467,7 @@
},
{
"cell_type": "code",
- "execution_count": 33,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -7791,18 +488,14 @@
"sentence = \"Meet me at the bike racks after school at 3:30 today.\"\n",
"\n",
"for letter in sentence:\n",
- " letter = letter.lower()\n",
- " if letter not in letter_freq:\n",
- " letter_freq [letter] = 1\n",
- " else:\n",
- " letter_freq [letter] += 1\n",
+ " pass\n",
"\n",
"print(letter_freq)\n"
]
},
{
"cell_type": "code",
- "execution_count": 34,
+ "execution_count": 32,
"metadata": {},
"outputs": [
{
@@ -7817,11 +510,8 @@
"# Example 2b: find the letter that occurred the most\n",
"most_used_key = None \n",
"max_value = 0\n",
- "\n",
"for key in letter_freq:\n",
- " if letter_freq[key] > max_value:\n",
- " most_used_key = key\n",
- " max_value = letter_freq[key]\n",
+ " pass\n",
"\n",
"print(\"the character {} appeared {} times\".format(str(most_used_key), max_value))"
]
@@ -7902,7 +592,7 @@
}
],
"source": [
- "# Example 3a. Same as 2a, but use the survey_data\n",
+ "# Example 3a. Same as 2a, but use the survey_data for pizza topping\n",
"major_freq = dict() # another way to make a dictionary\n",
"\n",
"major_freq"
@@ -7917,24 +607,28 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "the major Engineering appeared 328 times\n"
+ "ename": "NameError",
+ "evalue": "name 'most_used_key' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Example 3b: use the algorithm from 2b to find the pizza topping with the highest frequency\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# find the frequency of each major\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe pizza topping \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m appeared \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m times\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mstr\u001b[39m(\u001b[43mmost_used_key\u001b[49m), max_value))\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'most_used_key' is not defined"
]
}
],
"source": [
- "# Example 3b: use the algorithm from 2b to find the major with the highest frequency\n",
+ "# Example 3b: use the algorithm from 2b to find the pizza topping with the highest frequency\n",
"\n",
"# find the frequency of each major\n",
"\n",
"\n",
- "print(\"the major {} appeared {} times\".format(str(most_used_key), max_value))\n"
+ "print(\"the pizza topping {} appeared {} times\".format(str(most_used_key), max_value))\n"
]
},
{
@@ -8001,7 +695,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -8015,7 +709,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.1"
+ "version": "3.9.12"
}
},
"nbformat": 4,
diff --git a/f22/andy_lec_notes/lec_17/lec17_dictionaries_template.ipynb b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/lec17_dictionaries1_template.ipynb
similarity index 98%
rename from f22/andy_lec_notes/lec_17/lec17_dictionaries_template.ipynb
rename to f22/andy_lec_notes/lec17_Oct17_Dictionaries1/lec17_dictionaries1_template.ipynb
index 4f155350c33f8d7424cc8491684cb1c8d351aa86..676a6398f32b5b60b191a6327b9889ce305dcbbc 100644
--- a/f22/andy_lec_notes/lec_17/lec17_dictionaries_template.ipynb
+++ b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/lec17_dictionaries1_template.ipynb
@@ -18,22 +18,11 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "721"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
- "# Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists\n",
+ "# Warmup 1: Read in the file 'afmfam_survey_data.csv' into a lists of lists\n",
"\n",
"\n",
"# source: Automate the Boring Stuff with Python Ch 12\n",
@@ -51,29 +40,9 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Lecture',\n",
- " 'Age',\n",
- " 'Primary major',\n",
- " 'Other majors',\n",
- " 'Zip Code',\n",
- " 'Pizza topping',\n",
- " 'Pet owner',\n",
- " 'Runner',\n",
- " 'Sleep habit',\n",
- " 'Procrastinator']"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"# Warmup 2: store the first row in a variable called header\n",
"header = None\n",
@@ -82,53 +51,27 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 5,
"metadata": {},
"outputs": [
{
- "data": {
- "text/plain": [
- "[['LEC001',\n",
- " '19',\n",
- " 'Business: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Other',\n",
- " 'Economics/Philosophy, Data Science Certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
+ "ename": "TypeError",
+ "evalue": "'NoneType' object is not subscriptable",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [5]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Warmup 3: store the rest of the data in a variable called rows\u001b[39;00m\n\u001b[1;32m 2\u001b[0m rows \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mrows\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
+ "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not subscriptable"
+ ]
}
],
"source": [
"# Warmup 3: store the rest of the data in a variable called rows\n",
"rows = None\n",
- "rows\n",
+ "\n",
+ "# show the first 3 rows\n",
+ "\n",
"\n"
]
},
@@ -151,11 +94,10 @@
"\n",
"def count_col_frequency(value, col_name):\n",
" ''' returns the frequency of value in col_name '''\n",
- " count = 0\n",
- " for row in rows:\n",
- " if row[header.index(col_name)] == value:\n",
- " count += 1\n",
- " return count\n"
+ " \n",
+ " #Hint: use if row[header.index(col_name)] == value:\n",
+ " return None\n",
+ "\n"
]
},
{
@@ -207,7 +149,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "## March 7: Dictionaries\n",
+ "## Lecture 17: Dictionaries\n",
"\n",
"Learning Objectives:\n",
"- Use correct dictionary syntax \n",
@@ -305,10 +247,10 @@
"print(price_dict) # number of key/value pairs\n",
"\n",
"#get the price for a certain item\n",
- "print(price_dict) # name of dict [ key]\n",
+ "print(...) # name of dict [ key]\n",
"\n",
"#get the price for donut\n",
- "print(price_dict)\n",
+ "print(...)\n",
"\n",
"# what's wrong with this line? \n",
"# print(price_dict[1.25])\n"
@@ -387,6 +329,13 @@
"price_dict"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The .pop() method removes a key/value pair"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 21,
@@ -407,6 +356,13 @@
" price_dict.pop('pizza')"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The .keys() and .values() methods return a list-like object"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 23,
@@ -636,7 +592,7 @@
}
],
"source": [
- "# Example 3a. Same as 2a, but use the survey_data\n",
+ "# Example 3a. Same as 2a, but use the survey_data for pizza topping\n",
"major_freq = dict() # another way to make a dictionary\n",
"\n",
"major_freq"
@@ -651,24 +607,28 @@
},
{
"cell_type": "code",
- "execution_count": 37,
+ "execution_count": 6,
"metadata": {},
"outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "the major Engineering appeared 328 times\n"
+ "ename": "NameError",
+ "evalue": "name 'most_used_key' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Example 3b: use the algorithm from 2b to find the pizza topping with the highest frequency\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# find the frequency of each major\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mthe pizza topping \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m appeared \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m times\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mstr\u001b[39m(\u001b[43mmost_used_key\u001b[49m), max_value))\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'most_used_key' is not defined"
]
}
],
"source": [
- "# Example 3b: use the algorithm from 2b to find the major with the highest frequency\n",
+ "# Example 3b: use the algorithm from 2b to find the pizza topping with the highest frequency\n",
"\n",
"# find the frequency of each major\n",
"\n",
"\n",
- "print(\"the major {} appeared {} times\".format(str(most_used_key), max_value))\n"
+ "print(\"the pizza topping {} appeared {} times\".format(str(most_used_key), max_value))\n"
]
},
{
@@ -735,7 +695,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
@@ -749,7 +709,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.1"
+ "version": "3.9.12"
}
},
"nbformat": 4,
diff --git a/f22/andy_lec_notes/lec_17/readme.md b/f22/andy_lec_notes/lec17_Oct17_Dictionaries1/readme.md
similarity index 100%
rename from f22/andy_lec_notes/lec_17/readme.md
rename to f22/andy_lec_notes/lec17_Oct17_Dictionaries1/readme.md
diff --git a/f22/andy_lec_notes/lec_17/.ipynb_checkpoints/lec17_dictionaries_template-checkpoint.ipynb b/f22/andy_lec_notes/lec_17/.ipynb_checkpoints/lec17_dictionaries_template-checkpoint.ipynb
deleted file mode 100644
index 1b3a47697296732bad24e0176c04315c29e1d4f2..0000000000000000000000000000000000000000
--- a/f22/andy_lec_notes/lec_17/.ipynb_checkpoints/lec17_dictionaries_template-checkpoint.ipynb
+++ /dev/null
@@ -1,745 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "import csv"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "721"
- ]
- },
- "execution_count": 2,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists\n",
- "\n",
- "\n",
- "# source: Automate the Boring Stuff with Python Ch 12\n",
- "def process_csv(filename):\n",
- " exampleFile = open(filename, encoding=\"utf-8\") \n",
- " exampleReader = csv.reader(exampleFile) \n",
- " exampleData = list(exampleReader) \n",
- " exampleFile.close() \n",
- " return exampleData\n",
- "\n",
- "survey_data = None # change this\n",
- "\n",
- "# show the length of this list of lists \n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Lecture',\n",
- " 'Age',\n",
- " 'Primary major',\n",
- " 'Other majors',\n",
- " 'Zip Code',\n",
- " 'Pizza topping',\n",
- " 'Pet owner',\n",
- " 'Runner',\n",
- " 'Sleep habit',\n",
- " 'Procrastinator']"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 2: store the first row in a variable called header\n",
- "header = None\n",
- "header"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[['LEC001',\n",
- " '19',\n",
- " 'Business: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Other',\n",
- " 'Economics/Philosophy, Data Science Certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']]"
- ]
- },
- "execution_count": 9,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 3: store the rest of the data in a variable called rows\n",
- "rows = None\n",
- "rows\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 4: show the last 3 rows of data\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 5: Write a function that counts the frequency of a value in a column\n",
- "\n",
- "def count_col_frequency(value, col_name):\n",
- " ''' returns the frequency of value in col_name '''\n",
- " count = 0\n",
- " for row in rows:\n",
- " if row[header.index(col_name)] == value:\n",
- " count += 1\n",
- " return count\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "80"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#test your function\n",
- "count_col_frequency(\"pineapple\", \"Pizza topping\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 6: Think about it: Is there an easy way to count *every* topping frequency? \n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## March 7: Dictionaries\n",
- "\n",
- "Learning Objectives:\n",
- "- Use correct dictionary syntax \n",
- " - to create a dictionary using either {} or dict()\n",
- " - to lookup, insert, update, and pop key/value pairs\n",
- "- Use a for loop, the in operator, and common methods when working with dictionaries.\n",
- "- Write code that uses a dictionary\n",
- " - to store frequencies\n",
- " - to iterate through all key/value pairs"
- ]
- },
- {
- "attachments": {
- "collections.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As we are getting more sophisticated in this course, its time to define...\n",
- "### Data Structure <br>\n",
- "a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data (Wikipedia)\n",
- "\n",
- "Python contains built-in Data Structures called Collections\n",
- "\n",
- "\n",
- "\n",
- "Today you will learn how store data in Dictionaries. \n",
- "\n",
- "#### Dictionary <br>\n",
- "A dictionary is like a list, but more general. In a list, the indices have to be integers; but a dictionary they can be any **immutable** type. \n",
- "\n",
- "You can think of a dictionary as a mapping between a set of indices (which are called keys) and a set of values. Each key maps to a value. The association of a key and a value is called a key-value pair or sometimes an item. \n",
- "\n",
- "(from Think Python, Chapter 11)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'pie': 3.95,\n",
- " 'ala mode': 1.5,\n",
- " 'donut': 1.25,\n",
- " 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79,\n",
- " 'milk': 1.65,\n",
- " 'loaf': 5.99,\n",
- " 'hot dog': 4.99}"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# a dictionary that stores prices of bakery items\n",
- "# create a dictionary of key/value pairs\n",
- "# notice the curly brackets\n",
- "# notice it can span over more than one line, indenting doesn't matter\n",
- "price_dict = { 'pie': 3.95, \n",
- " 'ala mode':1.50, \n",
- " 'donut': 1.25, 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79, 'milk':1.65, 'loaf': 5.99,\n",
- " 'hot dog': 4.99} # feel free to add some of your own here\n",
- "price_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "9\n",
- "5.99\n",
- "1.25\n"
- ]
- }
- ],
- "source": [
- "# print the length of the dictionary\n",
- "print(price_dict) # number of key/value pairs\n",
- "\n",
- "#get the price for a certain item\n",
- "print(price_dict) # name of dict [ key]\n",
- "\n",
- "#get the price for donut\n",
- "print(price_dict)\n",
- "\n",
- "# what's wrong with this line? \n",
- "# print(price_dict[1.25])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dictionaries are Mutable\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'pie': 3.95,\n",
- " 'ala mode': 1.5,\n",
- " 'donut': 1.25,\n",
- " 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79,\n",
- " 'milk': 1.65,\n",
- " 'loaf': 5.99,\n",
- " 'hot dog': 4.99,\n",
- " 'drink': 2.49}"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# add a new key/value pair using [ ] notation\n",
- "\n",
- "\n",
- "# add one of your own\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'pie': 3.95,\n",
- " 'ala mode': 1.5,\n",
- " 'donut': 1.25,\n",
- " 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79,\n",
- " 'milk': 1.65,\n",
- " 'loaf': 5.99,\n",
- " 'hot dog': 4.99,\n",
- " 'drink': 2.99}"
- ]
- },
- "execution_count": 4,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# change the value associated with a key....syntax is like add\n",
- "\n",
- "\n",
- "\n",
- "# now you add one\n",
- "price_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [],
- "source": [
- "# use pop to delete a key/value pair\n",
- "price_dict.pop('hot dog') # or del(price_dict['hot dog'])\n",
- "\n",
- "# delete another key/value pair\n",
- "price_dict.pop()\n",
- "\n",
- "# try deleting someting that is not there\n",
- "#price_dict.pop('pizza')\n",
- "\n",
- "# fix this with an if statement\n",
- "if 'pizza' in price_dict:\n",
- " price_dict.pop('pizza')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_keys(['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk'])\n",
- "['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk']\n"
- ]
- }
- ],
- "source": [
- "# get all keys and convert to a list\n",
- "print(price_dict.keys())\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_values([3.95, 1.5, 1.25, 2.25, 3.15, 0.79, 1.65])\n",
- "[3.95, 1.5, 1.25, 2.25, 3.15, 0.79, 1.65]\n"
- ]
- }
- ],
- "source": [
- "# get all values and convert to a list\n",
- "print(price_dict.values())\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 27,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "True\n",
- "False\n",
- "False\n",
- "True\n"
- ]
- }
- ],
- "source": [
- "# use 'in' price_dict, price_dict.keys(), price_dict.values()\n",
- "\n",
- "#print('donut' in price_dict) # default is to check the keys\n",
- "#print(9.95 in price_dict) # default is NOT values\n",
- "#print('apple' in price_dict.keys()) # can call out the keys\n",
- "#print(3.95 in price_dict.values()) # can check the values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Applications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['pie', 'donut', 'milk', 'cookie', 'tofu']\n",
- "tofu is not in the dictionary\n",
- "Your total is $7.64\n"
- ]
- }
- ],
- "source": [
- "# Example 1: given a list of items, find the total cost of the order\n",
- "order = ['pie', 'donut', 'milk', 'cookie', 'tofu'] # add more items to the order\n",
- "print(order)\n",
- "\n",
- "total_cost = 0\n",
- "for item in order:\n",
- " pass\n",
- " \n",
- "# find the total of the items in the order\n",
- "print (\"Your total is ${:.2f}\".format(total_cost))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 30,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'m': 2, 'e': 6, 't': 6, ' ': 10, 'a': 5, 'h': 2, 'b': 1, 'i': 1, 'k': 2, 'r': 2, 'c': 2, 's': 2, 'f': 1, 'o': 3, 'l': 1, '3': 2, ':': 1, '0': 1, 'd': 1, 'y': 1, '.': 1}\n"
- ]
- }
- ],
- "source": [
- "# Example 2a: find the frequency of characters in a sentence\n",
- "\n",
- "# start with an empty dictionary\n",
- "letter_freq = {} \n",
- "# letter_freq = dict() # other way\n",
- "\n",
- "sentence = \"Meet me at the bike racks after school at 3:30 today.\"\n",
- "\n",
- "for letter in sentence:\n",
- " pass\n",
- "\n",
- "print(letter_freq)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "the character appeared 10 times\n"
- ]
- }
- ],
- "source": [
- "# Example 2b: find the letter that occurred the most\n",
- "most_used_key = None \n",
- "max_value = 0\n",
- "for key in letter_freq:\n",
- " pass\n",
- "\n",
- "print(\"the character {} appeared {} times\".format(str(most_used_key), max_value))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'letter_freq' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-18-51a323b4acb5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# why not use for i in range?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mletter_freq\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[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\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 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNameError\u001b[0m: name 'letter_freq' is not defined"
- ]
- }
- ],
- "source": [
- "# think about it...why did we use None ? \n",
- "# why use 0 ? \n",
- "# why not use for i in range?\n",
- "\n",
- "for i in range (len(letter_freq)):\n",
- " print (i)\n",
- "\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Recall: survey data\n",
- "# rows\n",
- "rows[-1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{}"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Example 3a. Same as 2a, but use the survey_data\n",
- "major_freq = dict() # another way to make a dictionary\n",
- "\n",
- "major_freq"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "the major Engineering appeared 328 times\n"
- ]
- }
- ],
- "source": [
- "# Example 3b: use the algorithm from 2b to find the major with the highest frequency\n",
- "\n",
- "# find the frequency of each major\n",
- "\n",
- "\n",
- "print(\"the major {} appeared {} times\".format(str(most_used_key), max_value))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### After Lecture Practice"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 1. Read the slides to learn how dictionaries relate to lists and sets."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 2. Review this summary of common dictionary methods...you should know these\n",
- "# https://www.w3schools.com/python/python_ref_dictionary.asp"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n"
- ]
- }
- ],
- "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/f22/andy_lec_notes/lec_17/cs220_survey_data.csv b/f22/andy_lec_notes/lec_17/cs220_survey_data.csv
deleted file mode 100644
index efb10cc377003b4daf25c8d582b5acd5fbca9a39..0000000000000000000000000000000000000000
--- a/f22/andy_lec_notes/lec_17/cs220_survey_data.csv
+++ /dev/null
@@ -1,721 +0,0 @@
-Lecture,Age,Primary major,Other majors,Zip Code,Pizza topping,Pet owner,Runner,Sleep habit,Procrastinator
-LEC002,19,Engineering: Mechanical,,53711,pepperoni,Yes,No,night owl,Maybe
-LEC002,20,Science: Physics,"Astronomy-Physics, History",53726,pineapple,Yes,Yes,night owl,Yes
-LEC001,20,Science: Chemistry,,53703,pepperoni,Yes,No,early bird,No
-LEC004,19,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,night owl,No
-LEC004,20,Other,Economics ,53715,mushroom,Yes,Yes,no preference,Maybe
-LEC003,18,Statistics,,53706,Other,Yes,No,night owl,Yes
-LEC003,18,Mathematics/AMEP,,53706,sausage,No,No,night owl,No
-LEC004,18,Engineering: Biomedical,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC003,19,Data Science,Stats,53715,pineapple,Yes,No,no preference,No
-LEC003,19,Business: Finance,,53703,sausage,Yes,Yes,early bird,Yes
-LEC003,18,Engineering: Mechanical,,53706,Other,No,No,no preference,No
-LEC004,18,Other,I am undecided – thinking about Data Science Major,53706,basil/spinach,Yes,No,night owl,Maybe
-LEC004,19,Engineering: Other,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC003,18,Statistics,psychology,53706,mushroom,No,No,night owl,Yes
-LEC004,20,Statistics,Mathematics ,53726,pepperoni,Yes,Yes,early bird,Maybe
-LEC004,20,Mathematics/AMEP,,53711,sausage,Yes,No,night owl,Yes
-LEC003,18,Science: Physics,Data Science,53706,pepperoni,No,Yes,early bird,No
-LEC003,19,Data Science,Economics,53715,pepperoni,No,Yes,no preference,Maybe
-LEC003,19,Engineering: Mechanical,nuclear engineering,53706,sausage,Yes,No,night owl,Yes
-LEC003,21,Science: Chemistry,,,green pepper,Yes,No,early bird,Maybe
-LEC003,18,Engineering: Other,,53706,pepperoni,Yes,Yes,no preference,Yes
-LEC003,,Engineering: Other,,,pineapple,Yes,No,early bird,Maybe
-LEC002,20,Computer Science,Data Science,53706,basil/spinach,Yes,No,night owl,Maybe
-LEC002,21,Science: Other,,53703,sausage,Yes,No,early bird,Maybe
-LEC001,21,Computer Science,Data Science,53715,pepperoni,Yes,No,night owl,Maybe
-LEC004,18,Engineering: Mechanical,,53706,pepperoni,Yes,No,early bird,Maybe
-LEC002,18,Languages,Linguistics,53706,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC002,18,Engineering: Mechanical,,53706,Other,No,Yes,night owl,Maybe
-LEC002,18,Other,,53706,none (just cheese),Yes,Yes,night owl,Yes
-LEC001,19,Science: Other,,53706,mushroom,Yes,No,night owl,Yes
-LEC001,18,Engineering: Biomedical,,,pepperoni,Yes,No,no preference,Maybe
-LEC003,19,Engineering: Biomedical,,53706,none (just cheese),Yes,No,night owl,Maybe
-LEC001,20,Science: Physics,Mathematics,53703,pineapple,Yes,No,early bird,No
-LEC002,28,Science: Other,,53703,pineapple,Yes,Yes,night owl,Maybe
-LEC001,18,Other,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,20,Engineering: Other,,53715,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Science: Physics,Life Science Communication,53706,pineapple,Yes,No,night owl,Yes
-LEC003,18,Engineering: Biomedical,pre-medicine,53706,sausage,Yes,Yes,early bird,No
-LEC003,,Engineering: Biomedical,,53706,none (just cheese),No,Yes,early bird,Yes
-LEC001,21,Science: Other,,53711,pepperoni,Yes,No,night owl,No
-LEC002,18,Engineering: Biomedical,,53706,sausage,Yes,No,no preference,No
-LEC001,18,Engineering: Biomedical,,53706,macaroni/pasta,Yes,No,early bird,Yes
-LEC004,21,Engineering: Biomedical,,53703,pepperoni,Yes,No,no preference,Yes
-LEC004,18,Business: Information Systems,,53706,pepperoni,Yes,Yes,night owl,No
-LEC001,19,Business: Actuarial,Data Science and Analytics,53706,pepperoni,Yes,Yes,night owl,No
-LEC001,22,Engineering: Industrial,,,sausage,Yes,No,night owl,Yes
-LEC003,20,Other,"data science, business",53703,mushroom,Yes,Yes,no preference,Maybe
-LEC004,18,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,18,Engineering: Other,,53706,mushroom,No,No,early bird,No
-LEC001,19,Data Science,Sports Journalism certificate,53703,pepperoni,Yes,Yes,no preference,No
-LEC004,18,Data Science,,53706,none (just cheese),Yes,No,night owl,Yes
-LEC002,20,Statistics,"Data Science, Math",53715,mushroom,No,No,night owl,No
-LEC001,19,Engineering: Biomedical,,53706,mushroom,Yes,Yes,early bird,No
-LEC003,20,Other,Data science certificate,,sausage,Yes,Yes,no preference,Yes
-LEC003,20,Engineering: Industrial,Computer science,53719,sausage,No,No,early bird,Maybe
-LEC003,,Computer Science,Minors in Data Science and Chicano and Latino Studies,53715,macaroni/pasta,No,Yes,night owl,Yes
-LEC002,19,Computer Science,,,mushroom,Yes,No,no preference,No
-LEC002,18,Engineering: Biomedical,,,pepperoni,Yes,No,night owl,Yes
-LEC002,20,Business: Finance,Economics,53715,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Engineering: Biomedical,,53706,sausage,Yes,Yes,no preference,Maybe
-LEC002,19,Engineering: Biomedical,,52706,pepperoni,Yes,Yes,early bird,No
-LEC001,19,Science: Biology/Life,,53703,basil/spinach,Yes,No,night owl,Maybe
-LEC002,19,Engineering: Mechanical,History,53706,none (just cheese),No,Yes,no preference,Yes
-LEC002,21,Computer Science,Math,53715,sausage,Yes,No,night owl,Yes
-LEC004,19,Data Science,Economics,53706,pepperoni,No,No,night owl,Yes
-LEC001,18,Engineering: Mechanical,,53715,none (just cheese),Yes,Yes,no preference,Maybe
-LEC004,18,Engineering: Biomedical,,53706,pineapple,Yes,No,night owl,Yes
-LEC003,18,Other,Sociology,53706,pineapple,Yes,No,night owl,Yes
-LEC004,18,Engineering: Biomedical,,53706,pepperoni,Yes,Yes,early bird,No
-LEC001,23,Business: Other,,53705,pineapple,No,No,no preference,No
-LEC004,18,Engineering: Biomedical,,53706,mushroom,Yes,Yes,no preference,Maybe
-LEC001,18,Data Science,,53703,pepperoni,Yes,No,night owl,No
-LEC001,19,Business: Finance,,53706,pineapple,No,No,night owl,Maybe
-LEC004,19,Science: Biology/Life,"Environmental Sciences, Conservation Biology",53715,basil/spinach,Yes,No,no preference,No
-LEC001,20,Computer Science,,53715,pepperoni,Yes,Yes,night owl,Yes
-LEC004,18,Computer Science,Data Science,53706,none (just cheese),Yes,No,early bird,Yes
-LEC003,18,Science: Other,,53706,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Engineering: Biomedical,,53706,sausage,Yes,Yes,no preference,Yes
-LEC001,19,Computer Science,Economics,53715,sausage,Yes,No,no preference,Yes
-LEC001,21,Other,,,mushroom,No,No,night owl,Maybe
-LEC004,21,Data Science,,53703,none (just cheese),Yes,No,night owl,Yes
-LEC002,20,Data Science,,53703,pineapple,Yes,Yes,early bird,Maybe
-LEC002,18,Data Science,,53715,Other,Yes,No,early bird,No
-LEC003,19,Mathematics/AMEP,Double major math and economics,,pepperoni,Yes,Yes,night owl,No
-LEC003,18,Science: Biology/Life,,53706,none (just cheese),No,Yes,night owl,Yes
-LEC003,20,Computer Science,Computer Engineering,,pepperoni,Yes,No,night owl,Maybe
-LEC002,20,Engineering: Industrial,Maybe Data Science,53703,none (just cheese),Yes,No,night owl,Yes
-LEC003,18,Data Science,Biochemistry,53706,basil/spinach,No,Yes,no preference,Yes
-LEC003,19,Science: Other,,53706,Other,No,Yes,early bird,No
-LEC003,20,Engineering: Mechanical,,53706,pepperoni,No,No,night owl,Maybe
-LEC001,36,Other,,53705,sausage,No,No,no preference,Maybe
-LEC003,18,Data Science,,53706,pineapple,Yes,No,early bird,No
-LEC003,19,Engineering: Mechanical,,,pepperoni,Yes,No,no preference,No
-LEC004,20,Science: Biology/Life,,53703,pepperoni,Yes,No,night owl,Yes
-LEC001,22,Engineering: Biomedical,,53703,sausage,Yes,No,night owl,Yes
-LEC002,18,Business: Information Systems,,53706,macaroni/pasta,Yes,Yes,no preference,Maybe
-LEC001,18,Engineering: Other,,53703,basil/spinach,Yes,Yes,no preference,Yes
-LEC002,19,Statistics,mathematics,53703,Other,No,Yes,night owl,Yes
-LEC001,20,Engineering: Biomedical,,53715,pepperoni,Yes,No,early bird,Yes
-LEC002,24,Science: Other,,53703,mushroom,Yes,No,night owl,Yes
-LEC001,20,Computer Science,Data science,53715,pepperoni,Yes,Yes,night owl,No
-LEC001,19,Mathematics/AMEP,Spanish,53715,pepperoni,Yes,Yes,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC003,20,Data Science,,53726,sausage,Yes,No,night owl,Maybe
-LEC004,20,Other,,53713,pineapple,Yes,No,early bird,Maybe
-LEC002,23,Engineering: Other,,53705,pineapple,Yes,No,night owl,Maybe
-LEC001,21,Engineering: Mechanical,,53706,pepperoni,No,Yes,night owl,Yes
-LEC003,21,Science: Biology/Life,,53726,basil/spinach,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,No
-LEC004,19,Engineering: Other,,53706,sausage,Yes,No,night owl,Yes
-LEC001,19,Science: Physics,,53706,Other,Yes,No,night owl,Maybe
-LEC002,20,Engineering: Industrial,,53715,basil/spinach,Yes,No,night owl,Yes
-LEC003,19,Science: Biology/Life,Data sciences ,53706,pineapple,Yes,Yes,early bird,No
-LEC003,19,Other,undecided/exploring majors in science/math/tech,53706,macaroni/pasta,Yes,Yes,night owl,Maybe
-LEC001,19,Engineering: Industrial,,53703,sausage,Yes,No,night owl,Yes
-LEC003,20,Engineering: Industrial,,53703,sausage,Yes,Yes,night owl,Yes
-LEC002,18,Other,,53706,pepperoni,Yes,No,no preference,Yes
-LEC003,19,Business: Information Systems,Management and Human Resources ,53706,none (just cheese),No,No,night owl,No
-LEC001,19,Computer Science,Computer engineering,53726,pepperoni,Yes,Yes,night owl,Yes
-LEC001,18,Business: Finance,Minor: Data Science,53703,pepperoni,Yes,No,night owl,Maybe
-LEC002,18,Engineering: Mechanical,,53706,Other,Yes,No,night owl,Yes
-LEC004,18,Engineering: Mechanical,,53715,sausage,Yes,No,no preference,Maybe
-LEC002,19,Engineering: Biomedical,,53715,pepperoni,Yes,No,night owl,Maybe
-LEC002,22,Science: Other,,53715,sausage,Yes,Yes,night owl,Yes
-LEC001,19,Other,Education Studies,53715,mushroom,No,No,night owl,Yes
-LEC001,24,Business: Actuarial,,53713,sausage,Yes,No,night owl,Maybe
-LEC001,18,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,25,Engineering: Industrial,,53705,pineapple,No,No,night owl,Maybe
-LEC003,20,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC003,18,Engineering: Mechanical,business,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC002,21,Engineering: Biomedical,,53703,basil/spinach,Yes,No,night owl,Maybe
-LEC003,19,Computer Science,,53703,pepperoni,Yes,No,no preference,No
-LEC003,18,Data Science,,53703,pepperoni,Yes,Yes,early bird,No
-LEC001,19,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC003,18,Mathematics/AMEP,,52706,mushroom,Yes,Yes,night owl,No
-LEC001,19,Data Science,,53706,macaroni/pasta,Yes,No,night owl,Maybe
-LEC003,18,Business: Other,I wasn't sure what to answer in the question above because I'm a Freshman and I'm Pre-Business.,53703,none (just cheese),Yes,No,night owl,Yes
-LEC001,21,Data Science,,53715,pepperoni,Yes,Yes,early bird,No
-LEC003,18,Computer Science,,53706-1203,Other,Yes,No,night owl,Yes
-LEC001,20,Computer Science,,53706,pepperoni,No,No,night owl,Yes
-LEC003,19,Business: Information Systems,,53706,sausage,Yes,Yes,no preference,No
-LEC001,21,Business: Actuarial,Risk Management and Insurance,53715,pineapple,Yes,No,night owl,Maybe
-LEC003,19,Science: Biology/Life,Data Science,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC004,20,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,early bird,No
-LEC002,21,Other,Economics with Math Emphasis,53703,pepperoni,Yes,No,no preference,Yes
-LEC001,20,Business: Other,Certificates in Data Science and Digital Studies,53715,sausage,Yes,Yes,early bird,Maybe
-LEC001,18,Engineering: Mechanical,,,pineapple,No,No,no preference,Yes
-LEC003,19,Computer Science,,53706,pepperoni,No,Yes,no preference,Maybe
-LEC003,18,Statistics,Data Science ,53706,pepperoni,Yes,No,night owl,No
-LEC004,18,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC002,26,Engineering: Other,,53705,Other,Yes,Yes,early bird,Yes
-LEC001,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,18,Engineering: Mechanical,,53706,basil/spinach,Yes,No,night owl,Yes
-LEC001,27,Computer Science,,53703,sausage,No,No,early bird,Maybe
-LEC003,20,Engineering: Biomedical,,,mushroom,Yes,No,early bird,Yes
-LEC001,18,Statistics,,,sausage,Yes,No,night owl,Maybe
-LEC004,19,Statistics,Considering data science as my secondary field of study.,53726,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Engineering: Industrial,,,basil/spinach,Yes,Yes,night owl,Yes
-LEC001,20,Other,,53706,macaroni/pasta,Yes,Yes,night owl,Maybe
-LEC004,20,Other,,53703,sausage,Yes,Yes,night owl,Yes
-LEC001,21,Engineering: Other,,53715,pepperoni,No,No,night owl,Yes
-LEC004,20,Engineering: Mechanical,,53711,mushroom,Yes,No,night owl,Yes
-LEC003,20,Business: Information Systems,,53715,pepperoni,Yes,No,night owl,Yes
-LEC003,21,Engineering: Other,,,mushroom,Yes,No,night owl,Yes
-LEC001,24,Statistics,data science,53703,basil/spinach,Yes,No,early bird,No
-LEC003,19,Computer Science,math,53706,basil/spinach,Yes,No,early bird,Maybe
-LEC004,21,Computer Science,,53715,pepperoni,Yes,Yes,early bird,No
-LEC002,21,Mathematics/AMEP,,53715,pepperoni,Yes,No,early bird,Maybe
-LEC001,,Science: Biology/Life,,,Other,Yes,Yes,early bird,No
-LEC003,18,Engineering: Mechanical,Computer Science Certificate,53706,basil/spinach,No,Yes,early bird,No
-LEC002,18,Other,Data Science,53706,basil/spinach,Yes,No,early bird,No
-LEC003,18,Business: Information Systems,Data Science Certificate,53706,basil/spinach,Yes,Yes,early bird,No
-LEC002,19,Engineering: Industrial,,53706,sausage,Yes,No,early bird,Maybe
-LEC004,18,Engineering: Mechanical,,53706,sausage,Yes,No,night owl,Maybe
-LEC001,22,Science: Other,Mathematics,53726,pepperoni,Yes,Yes,no preference,Yes
-LEC001,18,Engineering: Industrial,,53706,mushroom,No,Yes,early bird,Yes
-LEC002,19,Engineering: Mechanical,,53706,green pepper,No,Yes,night owl,No
-LEC003,18,Statistics,mathematics,53706,mushroom,Yes,No,night owl,No
-LEC003,19,Other,,53706,pepperoni,Yes,Yes,no preference,Yes
-LEC003,20,Other,"Education, Psychology, Data Science",53715,pineapple,Yes,Yes,no preference,Yes
-LEC003,19,Statistics,,53703,pepperoni,Yes,No,early bird,Maybe
-LEC003,20,Data Science,,53703,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC004,20,Business: Actuarial,,53706,pepperoni,Yes,No,early bird,No
-LEC003,20,Data Science,,53703,mushroom,Yes,No,night owl,Yes
-LEC003,19,Mathematics/AMEP,finance,53706,sausage,No,Yes,early bird,Maybe
-LEC003,21,Other,Political Science,53703,pepperoni,Yes,No,night owl,Maybe
-LEC002,19,Engineering: Mechanical,,53706,basil/spinach,Yes,Yes,night owl,Maybe
-LEC001,19,Mathematics/AMEP,Data Science,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC001,18,Computer Science,Information Systems (Maybe),53706,sausage,Yes,No,early bird,Yes
-LEC001,20,Business: Actuarial,Business: Risk Management,53703,pepperoni,Yes,No,early bird,Yes
-LEC002,26,Engineering: Other,,53705,mushroom,No,No,night owl,Maybe
-LEC001,18,Business: Information Systems,,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,23,Engineering: Other,Environmental Science,53703,mushroom,Yes,Yes,early bird,Maybe
-LEC003,18,Science: Biology/Life,,53706,pineapple,No,No,early bird,Yes
-LEC002,18,Engineering: Biomedical,,53706,pepperoni,Yes,No,no preference,No
-LEC001,18,Other,,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,Data Science,53726,sausage,Yes,No,no preference,Yes
-LEC003,20,Data Science,,53715,pepperoni,Yes,No,night owl,Yes
-LEC003,19,Engineering: Biomedical,,53706,pepperoni,No,Yes,early bird,No
-LEC004,19,Business: Information Systems,,53715,none (just cheese),Yes,No,night owl,Yes
-LEC001,20,Computer Science,,53703,mushroom,Yes,Yes,early bird,Maybe
-LEC002,18,Data Science,,53703,none (just cheese),Yes,No,night owl,Yes
-LEC004,19,Engineering: Mechanical,,53575,sausage,Yes,No,night owl,Maybe
-LEC004,20,Business: Other,Information Systems,53703,sausage,Yes,Yes,no preference,Maybe
-LEC003,18,Engineering: Biomedical,,53715,pineapple,Yes,No,no preference,Yes
-LEC004,19,Engineering: Mechanical,,53706,mushroom,Yes,No,early bird,Maybe
-LEC003,,Engineering: Biomedical,Certificate in French,,macaroni/pasta,Yes,Yes,night owl,No
-LEC003,21,Business: Information Systems,,53703,pepperoni,Yes,Yes,night owl,Maybe
-LEC001,,Data Science,,5 3706,mushroom,Yes,No,night owl,No
-LEC004,19,Engineering: Biomedical,,53715,none (just cheese),Yes,Yes,no preference,Yes
-LEC002,19,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,night owl,No
-LEC003,20,Computer Science,,53711,sausage,No,No,night owl,Maybe
-LEC004,21,Science: Biology/Life,,53711,sausage,Yes,Yes,night owl,No
-LEC003,21,Other,"Psychology, Chinese",53703,Other,Yes,Yes,night owl,Maybe
-LEC003,20,Data Science,Minor - Comp Sci,53703,basil/spinach,Yes,Yes,no preference,Yes
-LEC004,21,Science: Other,"Global Health is main major, possibly on the premed track, Data Science Certificate",53715,pineapple,Yes,Yes,early bird,No
-LEC003,20,Engineering: Mechanical,,53726,pepperoni,Yes,Yes,night owl,Yes
-LEC001,22,Science: Biology/Life,,53703,green pepper,Yes,No,night owl,Yes
-LEC002,19,Science: Biology/Life,,53703,pepperoni,Yes,No,night owl,Maybe
-LEC004,21,Engineering: Biomedical,,53715,green pepper,Yes,Yes,night owl,Maybe
-LEC002,20,Business: Finance,Real Estate,53703,pepperoni,Yes,Yes,night owl,No
-LEC004,21,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC002,19,Engineering: Industrial,"not positive on IE, maybe ME",53703,pepperoni,Yes,No,night owl,Maybe
-LEC004,18,Engineering: Biomedical,,53706,mushroom,Yes,No,early bird,No
-LEC003,19,Business: Actuarial,Data Science,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,24,Other,Life Science Communications,53703,pineapple,Yes,No,night owl,No
-LEC004,22,Engineering: Other,,53715,pepperoni,No,Yes,early bird,No
-LEC002,18,Engineering: Mechanical,,53715,pepperoni,Yes,Yes,night owl,Maybe
-LEC004,19,Data Science,business: finance,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC003,19,Business: Other,"Economics, Data Science",53703,pepperoni,Yes,Yes,early bird,No
-LEC004,18,Engineering: Other,,53706,pineapple,Yes,Yes,night owl,Maybe
-LEC003,19,Engineering: Mechanical,,53706,none (just cheese),Yes,No,early bird,No
-LEC002,18,Engineering: Mechanical,,53706,Other,Yes,Yes,early bird,No
-LEC001,19,Other,,53706,green pepper,Yes,Yes,night owl,Yes
-LEC004,18,Engineering: Biomedical,,53706,basil/spinach,Yes,Yes,no preference,No
-LEC001,19,Business: Information Systems,,53726,green pepper,No,Yes,night owl,Maybe
-LEC001,18,Engineering: Biomedical,,53706,sausage,Yes,No,night owl,Yes
-LEC003,19,Engineering: Industrial,,53715,pepperoni,No,Yes,early bird,Yes
-LEC002,27,Business: Information Systems,,53703,mushroom,No,Yes,night owl,No
-LEC001,30,Business: Other,,57305,pineapple,Yes,No,night owl,Yes
-LEC004,18,Engineering: Biomedical,Neuroscience/pre-med,53706,none (just cheese),Yes,No,night owl,Yes
-LEC002,20,Data Science,,53703,mushroom,No,No,early bird,Yes
-LEC001,19,Data Science,,53706,Other,Yes,Yes,no preference,Maybe
-LEC001,22,Engineering: Biomedical,,53706,sausage,Yes,No,night owl,Yes
-LEC003,20,Data Science,,,mushroom,Yes,No,no preference,Maybe
-LEC003,20,Other,Economics with Math emphasis,53703,pineapple,No,No,early bird,Maybe
-LEC002,20,Computer Science,Data Science,53706,basil/spinach,Yes,No,no preference,Yes
-LEC001,24,Science: Biology/Life,,53706,mushroom,Yes,Yes,early bird,No
-LEC004,20,Business: Information Systems,Real Estate,53703,pepperoni,Yes,No,night owl,Maybe
-LEC001,20,Data Science,Economics,53703,sausage,Yes,No,no preference,Maybe
-LEC002,20,Engineering: Mechanical,,53703,pepperoni,Yes,Yes,night owl,Maybe
-LEC004,20,Engineering: Mechanical,,53715,pineapple,Yes,Yes,night owl,No
-LEC004,20,Science: Biology/Life,Data Science Certificate (maybe) ,53703,sausage,Yes,Yes,night owl,Maybe
-LEC004,18,Engineering: Mechanical,,19002,pepperoni,Yes,No,no preference,Yes
-LEC001,19,Engineering: Other,,53706,pepperoni,Yes,No,no preference,Maybe
-LEC002,18,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,early bird,Maybe
-LEC004,19,Computer Science,Mathematics,53706,pineapple,Yes,No,no preference,Maybe
-LEC003,18,Business: Information Systems,,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,19,Science: Physics,,53706,pineapple,Yes,Yes,night owl,Maybe
-LEC004,18,Other,,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC001,25,Engineering: Other,"Architect, Landscape Planner",,mushroom,Yes,Yes,early bird,No
-LEC001,21,Engineering: Mechanical,Physics,53706,mushroom,No,Yes,no preference,Maybe
-LEC004,20,Other,"I major in economics, hoping to obtain a data science certificate.",53703,pepperoni,Yes,No,night owl,Yes
-LEC001,20,Data Science,Economics,53703,none (just cheese),No,Yes,night owl,Maybe
-LEC001,21,Science: Other,,53703,mushroom,Yes,No,night owl,Yes
-LEC002,18,Data Science,,53706,pepperoni,Yes,No,night owl,No
-LEC002,24,Business: Other,,53711,sausage,Yes,No,night owl,Yes
-LEC001,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC003,20,Business: Actuarial,,53703,pepperoni,No,Yes,night owl,No
-LEC001,21,Data Science,Economics,53715,pineapple,Yes,No,night owl,Maybe
-LEC001,23,Other,"Marketing, Data science ",,none (just cheese),No,No,early bird,Maybe
-LEC002,22,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,night owl,No
-LEC003,18,Computer Science,,53703,sausage,Yes,No,night owl,Yes
-LEC003,19,Science: Physics,Astronomy-Physics ,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53715,pepperoni,Yes,No,early bird,No
-LEC001,18,Data Science,,53706,pepperoni,Yes,Yes,early bird,Yes
-LEC001,18,Business: Information Systems,,53706,pepperoni,No,No,night owl,Yes
-LEC002,20,Mathematics/AMEP,data and risk analysis (data science),53726,pineapple,Yes,No,night owl,Yes
-LEC001,18,Other,,53706,mushroom,Yes,No,no preference,Yes
-LEC002,20,Science: Biology/Life,Economics with Math Emphasis,53703,pepperoni,Yes,No,early bird,Yes
-LEC001,18,Data Science,,53706,none (just cheese),Yes,No,night owl,Yes
-LEC001,,Statistics,Econ,,pineapple,No,No,night owl,Maybe
-LEC003,19,Engineering: Biomedical,,53706,pineapple,Yes,No,night owl,Yes
-LEC003,18,Engineering: Mechanical,,53706,Other,Yes,Yes,night owl,Yes
-LEC003,18,Engineering: Biomedical,,53089,pepperoni,Yes,No,night owl,Yes
-LEC003,18,Mathematics/AMEP,,53703,sausage,No,No,no preference,Maybe
-LEC001,18,Data Science,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC003,19,Data Science,,53706,pepperoni,Yes,No,early bird,Yes
-LEC003,21,Engineering: Biomedical,,53726,sausage,Yes,No,early bird,Maybe
-LEC004,22,Business: Other,,53703,green pepper,Yes,Yes,night owl,Yes
-LEC002,19,Engineering: Mechanical,computer science,53706,pineapple,Yes,Yes,night owl,Maybe
-LEC004,21,Science: Biology/Life,,53703,sausage,Yes,No,early bird,No
-LEC002,18,Engineering: Other,,53706,sausage,Yes,Yes,night owl,Maybe
-LEC001,20,Data Science,Economics,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC003,19,Engineering: Industrial,,53703,pepperoni,Yes,Yes,early bird,Maybe
-LEC003,21,Computer Science,no,53703,pineapple,Yes,No,night owl,No
-LEC002,20,Engineering: Mechanical,,53706,mushroom,Yes,No,night owl,Yes
-LEC003,21,Business: Finance,,53715,pepperoni,Yes,No,night owl,Yes
-LEC001,20,Science: Other,,53703,Other,Yes,Yes,night owl,Maybe
-LEC001,20,Engineering: Other,,53715,pepperoni,Yes,Yes,night owl,Yes
-LEC003,19,Engineering: Biomedical,,53706,green pepper,Yes,Yes,early bird,No
-LEC002,19,Engineering: Mechanical,"German Certificate, Theatre Certificate",53706,pepperoni,Yes,No,night owl,Yes
-LEC001,20,Engineering: Biomedical,,53703,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Statistics,,53715,sausage,Yes,No,night owl,Yes
-LEC001,18,Engineering: Industrial,,53706,none (just cheese),Yes,No,night owl,Maybe
-LEC004,22,Data Science,Economics,53703,pepperoni,Yes,No,night owl,Maybe
-LEC001,18,Other,,53703,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC001,23,Other,Biological Anthropology,53705,none (just cheese),Yes,No,early bird,Yes
-LEC001,19,Engineering: Biomedical,,53706,pineapple,Yes,Yes,no preference,Maybe
-LEC004,19,Business: Actuarial,econ,53715,sausage,Yes,No,night owl,Yes
-LEC001,18,Engineering: Mechanical,,53703,macaroni/pasta,Yes,No,night owl,Yes
-LEC002,18,Business: Other,,53706,mushroom,No,No,night owl,Maybe
-LEC002,20,Other,,53703,mushroom,Yes,Yes,no preference,Yes
-LEC002,19,Business: Actuarial,,53703,Other,Yes,No,no preference,Maybe
-LEC001,21,Business: Other,,53715,pepperoni,Yes,No,night owl,Yes
-LEC001,21,Business: Other,econ with math emphasis,53715,mushroom,Yes,Yes,night owl,Maybe
-LEC004,21,Science: Biology/Life,,53703,none (just cheese),Yes,No,night owl,Maybe
-LEC004,22,Other,"Psychology, communications",53715,basil/spinach,Yes,No,night owl,Yes
-LEC003,18,Statistics,,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC001,20,Statistics,,53703,pepperoni,Yes,Yes,night owl,Maybe
-LEC002,21,Data Science,,,pepperoni,Yes,Yes,no preference,Maybe
-LEC001,18,Engineering: Biomedical,,,sausage,Yes,Yes,early bird,No
-LEC003,20,Statistics,,53706,sausage,Yes,No,night owl,No
-LEC002,21,Business: Other,,53703,pineapple,Yes,Yes,night owl,Maybe
-LEC001,22,Data Science,,53715,pineapple,Yes,Yes,night owl,Maybe
-LEC003,25,Computer Science,,53705,mushroom,Yes,No,night owl,Yes
-LEC004,20,Other,,53715,pepperoni,Yes,Yes,early bird,Yes
-LEC002,19,Computer Science,"ds,econ",53711,Other,Yes,No,night owl,No
-LEC002,18,Other,,53706,pepperoni,No,No,night owl,Yes
-LEC002,21,Business: Actuarial,Management,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Business: Finance,Data science,53703,pepperoni,No,No,no preference,Maybe
-LEC003,18,Engineering: Mechanical,,53703,pineapple,Yes,Yes,no preference,No
-LEC001,21,Business: Other,"Consumer Behavior & Marketplace Studies, Data Science",53703,pepperoni,Yes,No,night owl,No
-LEC002,20,Business: Finance,,53715,sausage,Yes,No,night owl,Yes
-LEC001,19,Other,Psychology,53703,pepperoni,No,Yes,night owl,Yes
-LEC003,18,Engineering: Biomedical,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Business: Information Systems,,53711,sausage,Yes,No,night owl,No
-LEC003,21,Computer Science,,53715,sausage,No,Yes,early bird,Yes
-LEC004,20,Business: Other,,53703,pineapple,Yes,Yes,early bird,Yes
-LEC001,,Other,,53706,pineapple,Yes,No,no preference,Maybe
-LEC001,18,Statistics,economics,53703,pineapple,Yes,No,no preference,Yes
-LEC003,19,Business: Finance,,53706,mushroom,Yes,No,night owl,Maybe
-LEC001,18,Computer Science,Data Science,53706,mushroom,No,No,night owl,Maybe
-LEC003,20,Statistics,,53703,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Engineering: Biomedical,,,macaroni/pasta,Yes,No,night owl,Yes
-LEC003,19,Data Science,,53715,green pepper,Yes,No,early bird,Maybe
-LEC001,19,Other,Psychology,53703,pepperoni,Yes,Yes,night owl,Maybe
-LEC003,21,Business: Finance,Economics,53703,pepperoni,Yes,No,night owl,Maybe
-LEC002,24,Engineering: Other,,53703,sausage,Yes,No,night owl,Yes
-LEC003,19,Engineering: Industrial,,53703,pepperoni,Yes,Yes,no preference,Maybe
-LEC002,20,Engineering: Other,"urban & regional planning, environmental engineering, data science",53706-1406,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC003,23,Engineering: Other,,53705,pepperoni,No,Yes,night owl,Yes
-LEC001,19,Science: Biology/Life,,53703,pepperoni,Yes,No,early bird,No
-LEC001,18,Data Science,,53706,basil/spinach,Yes,No,night owl,Maybe
-LEC003,19,Business: Information Systems,,53703,macaroni/pasta,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC004,18,Science: Biology/Life,,53706,pepperoni,Yes,Yes,early bird,No
-LEC003,27,Science: Biology/Life,,53705,mushroom,Yes,No,early bird,Maybe
-LEC004,,Computer Science,,53715,pepperoni,Yes,No,night owl,Yes
-LEC004,20,Engineering: Biomedical,,53715,pepperoni,Yes,Yes,early bird,No
-LEC001,18,Computer Science,,53706,none (just cheese),No,Yes,night owl,Yes
-LEC004,17,Science: Biology/Life,Data science certificate,53706,pepperoni,Yes,No,no preference,Maybe
-LEC002,19,Data Science,"Econ, data science",53715,Other,Yes,No,night owl,Maybe
-LEC001,18,Computer Science,Data Science,53706,mushroom,No,No,night owl,Yes
-LEC002,18,Data Science,Economics,,pineapple,No,Yes,no preference,Yes
-LEC002,18,Engineering: Industrial,,53703-1104,sausage,Yes,No,night owl,Maybe
-LEC001,20,Business: Actuarial,Risk Management & Insurance,53703,pepperoni,Yes,No,early bird,No
-LEC001,19,Data Science,,53715,mushroom,Yes,Yes,no preference,Maybe
-LEC001,18,Engineering: Mechanical,,53706-1127,pepperoni,Yes,Yes,night owl,Yes
-LEC003,18,Engineering: Other,,53703,Other,No,Yes,early bird,No
-LEC001,24,Science: Other,data science,53715,pepperoni,Yes,Yes,early bird,Yes
-LEC004,19,Engineering: Biomedical,,53715,green pepper,Yes,No,early bird,Yes
-LEC003,20,Engineering: Biomedical,,53703,pepperoni,Yes,Yes,early bird,Maybe
-LEC003,21,Mathematics/AMEP,Biochemistry,53715,none (just cheese),Yes,Yes,early bird,No
-LEC003,20,Business: Other,,53706,sausage,Yes,No,night owl,Maybe
-LEC003,19,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,no preference,Yes
-LEC003,21,Engineering: Industrial,,53711,basil/spinach,No,Yes,night owl,Yes
-LEC001,20,Engineering: Industrial,,53703,pepperoni,Yes,Yes,no preference,No
-LEC003,18,Engineering: Industrial,,53706,sausage,Yes,Yes,night owl,No
-LEC003,20,Science: Biology/Life,Global Health,55416,pineapple,Yes,Yes,night owl,Yes
-LEC003,18,Engineering: Biomedical,,53706,basil/spinach,No,No,no preference,Maybe
-LEC003,19,Business: Other,,53706,sausage,Yes,No,night owl,Yes
-LEC003,18,Engineering: Industrial,,53706,green pepper,Yes,Yes,night owl,Yes
-LEC004,20,Data Science,Economics - math emphasis,53703,pepperoni,Yes,No,no preference,Yes
-LEC003,19,Business: Information Systems," Operations, Technology, Management",53715,pepperoni,Yes,No,night owl,Maybe
-LEC004,21,Engineering: Other,,53706,pineapple,No,Yes,early bird,No
-LEC003,19,Engineering: Mechanical,,53715,Other,Yes,No,early bird,No
-LEC003,20,Computer Science,Data Science,53703,none (just cheese),No,Yes,night owl,Maybe
-LEC003,18,Science: Biology/Life,Data science,53706,sausage,Yes,No,night owl,No
-LEC004,20,Engineering: Mechanical,,53715,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC002,,Business: Other,Double Business Major (Supply Chain and Business Management),53703,basil/spinach,Yes,No,night owl,Maybe
-LEC003,18,Business: Finance,,53715,none (just cheese),No,Yes,night owl,Yes
-LEC002,20,Engineering: Industrial,,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC001,22,Data Science,Stat or CS,53705,basil/spinach,Yes,Yes,early bird,No
-LEC003,20,Business: Information Systems,"International Business, French",53703,basil/spinach,Yes,Yes,early bird,No
-LEC004,19,Engineering: Other,,53706,none (just cheese),Yes,Yes,night owl,Maybe
-LEC002,20,Other,ECONOMICS,53715,none (just cheese),Yes,Yes,night owl,Maybe
-LEC004,19,Engineering: Mechanical, ,53715,pepperoni,Yes,No,night owl,Maybe
-LEC004,19,Engineering: Mechanical,,53715,pepperoni,Yes,Yes,no preference,Yes
-LEC004,20,Statistics,,53703,pepperoni,Yes,No,early bird,Yes
-LEC001,19,Business: Actuarial,RMI,53706,basil/spinach,Yes,Yes,night owl,Yes
-LEC001,20,Engineering: Biomedical,,53703,sausage,No,Yes,night owl,Yes
-LEC004,19,Engineering: Biomedical,,53706,basil/spinach,Yes,No,early bird,Yes
-LEC003,18,Data Science,,53706,none (just cheese),Yes,No,night owl,Maybe
-LEC001,21,Computer Science,,53703,Other,Yes,Yes,night owl,Maybe
-LEC001,19,Engineering: Industrial,,53706,pepperoni,Yes,No,night owl,No
-LEC004,20,Science: Other,,53713,pineapple,Yes,Yes,night owl,Maybe
-LEC004,18,Data Science,,53706,macaroni/pasta,Yes,No,night owl,Yes
-LEC004,20,Engineering: Industrial,NA,54636,macaroni/pasta,Yes,Yes,early bird,Maybe
-LEC001,19,Computer Science,,53711,mushroom,Yes,No,night owl,Yes
-LEC004,19,Computer Science,,53711,sausage,No,No,night owl,Maybe
-LEC004,19,Engineering: Biomedical,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC004,19,Engineering: Mechanical,,53711,macaroni/pasta,Yes,No,night owl,Yes
-LEC004,19,Engineering: Mechanical,,53597,pepperoni,No,Yes,night owl,No
-LEC004,18,Engineering: Biomedical,,53706,sausage,Yes,Yes,night owl,Maybe
-LEC004,18,Computer Science,Data science,53706,basil/spinach,No,Yes,no preference,Maybe
-LEC004,21,Engineering: Biomedical,,53703,sausage,Yes,Yes,night owl,Yes
-LEC004,19,Business: Information Systems,Accounting,53706,mushroom,Yes,No,night owl,No
-LEC004,18,Engineering: Other,,53706,sausage,Yes,Yes,night owl,Yes
-LEC004,20,Data Science,,53715,Other,Yes,No,night owl,Yes
-LEC004,18,Engineering: Mechanical,,53706,mushroom,Yes,Yes,night owl,Yes
-LEC004,18,Engineering: Mechanical,,53706,macaroni/pasta,Yes,Yes,no preference,Maybe
-LEC001,18,Engineering: Biomedical,,53706,Other,No,No,night owl,Maybe
-LEC004,19,Business: Finance,Industrial Engineering,53706,sausage,Yes,No,night owl,Maybe
-LEC001,18,Business: Other,Main one is economics and data science,53706,pepperoni,No,No,no preference,Maybe
-LEC004,18,Engineering: Industrial,,83001,sausage,Yes,Yes,night owl,Yes
-LEC004,20,Engineering: Biomedical,,53715,pepperoni,Yes,Yes,night owl,Maybe
-LEC004,18,Engineering: Mechanical,,53706,none (just cheese),No,No,night owl,Yes
-LEC004,20,Other,,53715,sausage,No,No,night owl,Maybe
-LEC004,19,Business: Information Systems,Business: Supply Chain Management,53703,pepperoni,Yes,No,no preference,Maybe
-LEC004,20,Other,,53703,basil/spinach,Yes,No,night owl,Yes
-LEC004,18,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC004,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,27,Computer Science,,53711,mushroom,Yes,Yes,no preference,Yes
-LEC004,19,Business: Actuarial,,53706-1188,sausage,Yes,No,no preference,Yes
-LEC004,21,Other,,53703,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Business: Other,,53703,pepperoni,Yes,No,no preference,Maybe
-LEC004,18,Business: Finance,Data Science,53706,basil/spinach,Yes,No,night owl,Yes
-LEC004,18,Science: Physics,,53706,mushroom,Yes,No,night owl,Yes
-LEC002,19,Mathematics/AMEP,Actuarial Science,53706,sausage,Yes,Yes,night owl,Yes
-LEC004,19,Business: Finance,data science,53706,pepperoni,Yes,Yes,no preference,Maybe
-LEC002,19,Data Science,"Electrical engineering, physics",53703,none (just cheese),Yes,No,night owl,Yes
-LEC004,21,Engineering: Biomedical,History Certificate ,53715,green pepper,Yes,No,no preference,Maybe
-LEC001,19,Business: Finance,,53703,sausage,Yes,Yes,night owl,Maybe
-LEC001,18,Business: Finance,,53703,pepperoni,Yes,No,night owl,Yes
-LEC004,21,Engineering: Mechanical,Mechanical Engineering ,53703,green pepper,No,No,no preference,No
-LEC004,19,Business: Information Systems,,53706,pepperoni,Yes,Yes,no preference,Maybe
-LEC002,18,Computer Science,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC001,21,Computer Science,,43706,mushroom,Yes,Yes,no preference,Yes
-LEC002,20,Business: Finance,Data Science,53703,pepperoni,No,Yes,no preference,No
-LEC002,20,Engineering: Biomedical,,53703,pineapple,Yes,Yes,early bird,Maybe
-LEC001,19,Business: Actuarial,risk management and insurance,53711,pepperoni,No,No,night owl,Yes
-LEC002,21,Other,"Linguistics, Communication Sciences and Disorders",53715,green pepper,Yes,Yes,night owl,No
-LEC001,19,Engineering: Mechanical,,53706,none (just cheese),Yes,Yes,night owl,Yes
-LEC002,18,Engineering: Mechanical,,53706,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC001,19,Data Science,,53703,pineapple,Yes,No,night owl,Yes
-LEC001,18,Science: Biology/Life,"Either stats, data science, or math (undecided)",53706,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC001,19,Data Science,Mathematics,53703,green pepper,Yes,Yes,night owl,Maybe
-LEC001,23,Business: Other,,53711,pineapple,Yes,No,night owl,Maybe
-LEC001,20,Data Science,economics,53703,none (just cheese),Yes,No,early bird,Yes
-LEC001,18,Computer Science,Planning on Data Sci but unsure,53708,macaroni/pasta,Yes,No,night owl,Yes
-LEC002,18,Science: Other,,53706,pepperoni,Yes,No,early bird,Maybe
-LEC001,18,Computer Science,,53706,green pepper,Yes,Yes,night owl,Yes
-LEC001,19,Statistics,,53703,pineapple,Yes,No,night owl,No
-LEC001,20,Computer Science,Data Science,53703,pepperoni,Yes,Yes,no preference,Yes
-LEC001,19,Business: Information Systems,,53706,basil/spinach,Yes,No,night owl,Yes
-LEC001,19,Data Science,,53703,pineapple,No,Yes,night owl,Maybe
-LEC001,18,Engineering: Mechanical,,53706,macaroni/pasta,Yes,Yes,night owl,No
-LEC001,18,Data Science,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Engineering: Industrial,Data Science,53706,green pepper,Yes,No,night owl,Yes
-LEC004,21,Other,,53726,sausage,Yes,No,night owl,Yes
-LEC001,19,Engineering: Mechanical,,53704,sausage,Yes,No,no preference,Yes
-LEC001,18,Computer Science,Data Science,53706,pepperoni,No,Yes,no preference,No
-LEC001,19,Other,,53705,pepperoni,No,No,night owl,Yes
-LEC001,21,Computer Science,data science,53706,pineapple,No,No,night owl,Yes
-LEC001,19,Statistics,,53703,sausage,Yes,No,night owl,Maybe
-LEC001,19,Science: Chemistry,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,20,Other,legal study,53705,sausage,Yes,No,no preference,Maybe
-LEC001,19,Statistics,biochemistry,53703,pineapple,Yes,No,no preference,Yes
-LEC001,22,Engineering: Biomedical,,,basil/spinach,Yes,Yes,night owl,Maybe
-LEC001,19,Engineering: Industrial,,53706,sausage,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53711,sausage,Yes,No,no preference,Yes
-LEC001,19,Engineering: Mechanical,,53703,none (just cheese),Yes,Yes,night owl,Yes
-LEC001,21,Computer Science,Computer Engineering,53703,sausage,No,No,night owl,Yes
-LEC003,20,Engineering: Mechanical,,53703,sausage,No,No,night owl,No
-LEC001,21,Computer Science,Electrical Engineering,53715,pepperoni,Yes,Yes,night owl,Maybe
-LEC001,19,Engineering: Industrial,Business,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC001,18,Other,,53706,none (just cheese),Yes,No,night owl,Yes
-LEC001,18,Science: Biology/Life,"Data Science Minor, French",53706,pineapple,Yes,No,night owl,Yes
-LEC001,21,Other,,53703,mushroom,Yes,Yes,early bird,Yes
-LEC001,22,Computer Science,DS,53711,Other,Yes,No,no preference,Maybe
-LEC003,19,Other,,53703,pepperoni,No,No,no preference,Yes
-LEC001,19,Data Science,,53706,Other,No,Yes,early bird,Yes
-LEC002,18,Engineering: Mechanical,Minor in Business ,53706,sausage,Yes,No,no preference,Yes
-LEC001,21,Engineering: Other,Civil Engineering,53715,Other,No,Yes,no preference,Yes
-LEC002,19,Statistics,Economics,53703,pepperoni,Yes,No,night owl,Yes
-LEC002,20,Business: Actuarial,,53703,sausage,Yes,No,night owl,No
-LEC001,24,Business: Other,,53703,green pepper,No,No,early bird,Maybe
-LEC004,18,Engineering: Biomedical,,53706,sausage,Yes,No,no preference,No
-LEC001,23,Other,,53703,pineapple,Yes,No,night owl,Yes
-LEC003,19,Statistics,,53706,none (just cheese),Yes,Yes,early bird,Yes
-LEC002,18,Other,,53706,basil/spinach,Yes,No,night owl,Yes
-LEC001,20,Statistics,,53703,mushroom,Yes,Yes,night owl,Yes
-LEC004,18,Computer Science,Data Science,,none (just cheese),Yes,Yes,night owl,Yes
-LEC002,19,Engineering: Mechanical,,53706,Other,Yes,No,no preference,Yes
-LEC002,22,Science: Biology/Life,,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC003,,Computer Science,Possibly Data Science (Definitely a Certificate),53706,Other,No,No,night owl,Yes
-LEC002,19,Engineering: Mechanical,,53562,pepperoni,Yes,Yes,night owl,Yes
-LEC002,19,Other,Data Science,53715,green pepper,Yes,Yes,no preference,Yes
-LEC003,18,Statistics,,53706,Other,No,No,night owl,Yes
-LEC004,19,Engineering: Mechanical,,53715,sausage,Yes,No,night owl,Yes
-LEC003,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC002,21,Business: Other,,53703,mushroom,No,No,no preference,Yes
-LEC003,19,Business: Information Systems,,53711,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Business: Actuarial,,53706,sausage,No,No,night owl,Yes
-LEC002,21,Data Science,,53703,sausage,Yes,Yes,night owl,Yes
-LEC001,23,Data Science,,53705,mushroom,Yes,No,night owl,Yes
-LEC002,20,Computer Science,Data Science,53726,pineapple,Yes,Yes,night owl,Maybe
-LEC003,18,Engineering: Industrial,,53706,pepperoni,Yes,No,night owl,Yes
-LEC002,27,Data Science,,53705,basil/spinach,Yes,No,night owl,Yes
-LEC002,18,Computer Science,Data Science,53706,mushroom,Yes,No,early bird,Yes
-LEC001,22,Data Science,,53706,sausage,Yes,No,night owl,Yes
-LEC002,20,Computer Science,,53715,pepperoni,No,Yes,night owl,Yes
-LEC002,21,Data Science,,53703,macaroni/pasta,No,No,night owl,No
-LEC002,20,Computer Science,,,mushroom,Yes,No,early bird,Maybe
-LEC001,19,Computer Science,prolly data science,92376,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC002,19,Engineering: Mechanical,,,none (just cheese),Yes,No,night owl,Yes
-LEC002,19,Data Science,,53717,none (just cheese),Yes,No,night owl,Yes
-LEC002,22,Science: Other,,53715,green pepper,Yes,Yes,early bird,Yes
-LEC002,19,Engineering: Biomedical,,53706,sausage,Yes,No,early bird,No
-LEC002,20,Business: Finance,,53703,pepperoni,Yes,No,early bird,No
-LEC002,18,Business: Actuarial,,53706,pepperoni,Yes,No,early bird,No
-LEC002,19,Engineering: Mechanical,,53706,Other,Yes,No,night owl,Yes
-LEC002,20,Data Science,economics,internation student,mushroom,Yes,Yes,early bird,Maybe
-LEC003,19,Engineering: Mechanical,,,Other,No,No,night owl,Yes
-LEC002,19,Engineering: Industrial,,53703,sausage,No,Yes,night owl,Yes
-LEC002,19,Engineering: Mechanical,,53701,pepperoni,Yes,Yes,no preference,Yes
-LEC002,22,Computer Science,,53703,sausage,Yes,No,night owl,Yes
-LEC001,19,Engineering: Industrial,,53715,pepperoni,Yes,No,no preference,Maybe
-LEC002,18,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC002,23,Mathematics/AMEP,,53719,sausage,No,Yes,early bird,Yes
-LEC002,18,Engineering: Industrial,,53706,pineapple,No,Yes,no preference,Maybe
-LEC002,20,Other,Communication Arts,53711,sausage,Yes,Yes,no preference,Maybe
-LEC002,21,Business: Information Systems,Finance ,53703,pepperoni,Yes,Yes,night owl,Maybe
-LEC002,21,Science: Physics,Astrophysics,,macaroni/pasta,Yes,No,no preference,Maybe
-LEC002,21,Science: Biology/Life,,53703,green pepper,Yes,No,early bird,Maybe
-LEC003,21,Engineering: Other,,53706,none (just cheese),Yes,No,early bird,Maybe
-LEC002,19,Data Science,Economics,53715,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Data Science,,53706,none (just cheese),No,Yes,night owl,Yes
-LEC001,20,Statistics,"economics, social science",53715,pepperoni,No,No,no preference,Yes
-LEC004,19,Business: Other,"Information systems, Data science",53706,sausage,Yes,Yes,night owl,Yes
-LEC004,18,Computer Science,,53706,basil/spinach,Yes,No,no preference,Yes
-LEC003,18,Computer Science,,53703,pepperoni,Yes,No,night owl,Yes
-LEC003,,Mathematics/AMEP,,,mushroom,No,Yes,night owl,Yes
-LEC004,19,Data Science,,53706,mushroom,Yes,No,night owl,Yes
-LEC001,19,Science: Chemistry,Data Science,53706,sausage,Yes,No,night owl,No
-LEC002,19,Mathematics/AMEP,,53703,Other,No,No,night owl,Yes
-LEC003,18,Other,,53703,pineapple,No,No,early bird,No
-LEC004,19,Engineering: Mechanical,,53703,pepperoni,Yes,No,early bird,Yes
-LEC003,19,Statistics,thinking about a data science certificate or switching major to data science,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Other,,53706,pepperoni,Yes,Yes,night owl,Maybe
-LEC003,18,Statistics,data science,53706,pineapple,No,No,night owl,Maybe
-LEC003,21,Computer Science,,53705,mushroom,Yes,No,night owl,Maybe
-LEC002,21,Other,Data Science,53705,sausage,Yes,No,night owl,Yes
-LEC003,20,Science: Biology/Life,,53703,pineapple,No,No,early bird,Maybe
-LEC003,18,Other,,53715,pepperoni,Yes,No,early bird,No
-LEC004,18,Engineering: Biomedical,,53706,pepperoni,Yes,Yes,early bird,Yes
-LEC001,21,Computer Science,,53715,macaroni/pasta,Yes,No,night owl,Yes
-LEC003,21,Science: Other,Data Science,53711,mushroom,Yes,Yes,night owl,Yes
-LEC004,19,Engineering: Mechanical,,,sausage,No,No,early bird,No
-LEC002,20,Engineering: Industrial,,53715,mushroom,No,No,night owl,Yes
-LEC002,19,Engineering: Mechanical,,53706,pepperoni,Yes,No,no preference,No
-LEC002,22,Science: Physics,,53703,sausage,Yes,No,night owl,Yes
-LEC004,19,Engineering: Other,,53706,sausage,Yes,Yes,no preference,Maybe
-LEC001,19,Engineering: Biomedical,,53711,macaroni/pasta,Yes,No,night owl,Yes
-LEC001,23,Data Science,,53703,mushroom,Yes,Yes,night owl,Maybe
-LEC001,20,Engineering: Industrial,,53703,pepperoni,Yes,No,night owl,Yes
-LEC003,18,Science: Other,,53706,pineapple,Yes,Yes,night owl,Yes
-LEC003,25,Computer Science,,53713,sausage,Yes,No,night owl,Yes
-LEC001,31,Data Science,,53575,sausage,Yes,Yes,early bird,Maybe
-LEC001,19,Data Science,,53715,pepperoni,Yes,Yes,night owl,Yes
-LEC002,21,Computer Science,,53703,pepperoni,Yes,No,night owl,Yes
-LEC003,20,Business: Actuarial,Risk Management and Insurance,53715,pepperoni,Yes,No,night owl,No
-LEC004,19,Data Science,,53715,pepperoni,Yes,Yes,night owl,Yes
-LEC001,19,Computer Science,,53706,mushroom,Yes,No,early bird,Maybe
-LEC001,19,Mathematics/AMEP,,,pepperoni,Yes,No,night owl,Maybe
-LEC001,19,Engineering: Mechanical,,53705,sausage,Yes,No,night owl,Yes
-LEC004,19,Engineering: Mechanical,,53706,pineapple,Yes,No,night owl,Yes
-LEC002,19,Science: Physics,,53706,Other,Yes,No,no preference,Yes
-LEC001,21,Computer Science,Data science,53703,basil/spinach,No,Yes,night owl,No
-LEC003,19,Mathematics/AMEP,data science,53706,sausage,Yes,No,night owl,Yes
-LEC002,18,Science: Biology/Life,data science certificate,53706,pineapple,Yes,Yes,night owl,Yes
-LEC004,18,Statistics,,53706,sausage,No,Yes,night owl,Yes
-LEC003,21,Engineering: Industrial,,53562,pepperoni,Yes,No,night owl,Maybe
-LEC001,20,Engineering: Mechanical,,53715,green pepper,Yes,No,early bird,Yes
-LEC003,19,Engineering: Mechanical,,,pineapple,No,No,early bird,No
-LEC003,20,Statistics,,53703,mushroom,Yes,Yes,no preference,No
-LEC002,18,Engineering: Mechanical,"Industrial, Buisness",53701,pepperoni,No,No,night owl,Maybe
-LEC001,18,Other,Legal Studies,53706,mushroom,No,No,night owl,Yes
-LEC001,20,Data Science,,53703,none (just cheese),Yes,Yes,night owl,Yes
-LEC001,21,Other,,53703,Other,Yes,Yes,no preference,Maybe
-LEC001,22,Engineering: Biomedical,pre-med,53715,none (just cheese),Yes,Yes,no preference,Yes
-LEC003,20,Other,"Philosophy, Data Science Certificate, Pre-Med",53703,basil/spinach,No,Yes,early bird,Yes
-LEC001,21,Business: Finance,Economics,53703,basil/spinach,Yes,No,night owl,Yes
-LEC003,19,Statistics,,53705,none (just cheese),Yes,Yes,no preference,Yes
-LEC001,18,Engineering: Industrial,,53703,sausage,Yes,Yes,night owl,Yes
-LEC003,21,Science: Biology/Life,My majors are Environmental Science and Spanish,53703,macaroni/pasta,Yes,No,night owl,Maybe
-LEC001,18,Other,,,pepperoni,Yes,No,no preference,Yes
-LEC004,23,Science: Physics,Astronomy,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC002,21,Computer Science,,53711,sausage,Yes,No,night owl,Maybe
-LEC002,18,Engineering: Mechanical,,53706,sausage,Yes,Yes,early bird,Yes
-LEC003,19,Engineering: Other,Environmental Science,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,19,Science: Other,Life science communications,53706,Other,Yes,No,night owl,Maybe
-LEC004,21,Engineering: Mechanical,,53703,sausage,Yes,Yes,no preference,No
-LEC001,20,Computer Science,,53703,pineapple,Yes,No,night owl,Yes
-LEC001,20,Other,,53703,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC001,22,Other,"psychology, legal studies, certificate in criminal justice ",53711,sausage,Yes,No,night owl,Maybe
-LEC002,21,Data Science,,53711,none (just cheese),Yes,No,night owl,Yes
-LEC003,21,Other,,53703,mushroom,Yes,No,early bird,Yes
-LEC002,20,Engineering: Industrial,,53703,pineapple,Yes,Yes,early bird,Yes
-LEC001,19,Computer Science,data science,53706,pineapple,No,No,night owl,No
-LEC003,19,Statistics,Data Science,53703,pineapple,No,No,night owl,Maybe
-LEC001,20,Computer Science,,53726,none (just cheese),Yes,No,night owl,Yes
-LEC002,,Computer Science,,,pepperoni,Yes,No,night owl,Maybe
-LEC001,18,Computer Science,,53706,pineapple,No,No,no preference,Maybe
-LEC001,19,Computer Science,data science,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC003,19,Other,Undecided in STEM,53706,pepperoni,No,No,night owl,No
-LEC001,18,Computer Science,data science,53590,Other,No,No,night owl,Yes
-LEC004,18,Other,,53706,Other,Yes,No,night owl,Maybe
-LEC003,19,Data Science,,53706,basil/spinach,Yes,No,no preference,Maybe
-LEC001,19,Business: Finance,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC001,19,Engineering: Industrial,,53704,basil/spinach,No,No,no preference,Yes
-LEC004,18,Engineering: Other,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC002,18,Computer Science,,,macaroni/pasta,Yes,Yes,night owl,Yes
-LEC003,20,Engineering: Biomedical,,53715,none (just cheese),Yes,Yes,no preference,Maybe
-LEC001,18,Other,,52816,none (just cheese),Yes,No,night owl,Yes
-LEC002,18,Engineering: Mechanical,Computes Science Certificate Potentially,53706,sausage,Yes,Yes,night owl,Yes
-LEC002,18,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,no preference,No
-LEC003,20,Business: Finance,Business: Risk Management ,53703,sausage,Yes,Yes,night owl,Yes
-LEC001,19,Science: Chemistry,,53706,pineapple,Yes,No,night owl,Yes
-LEC001,20,Engineering: Mechanical,,59301,pepperoni,Yes,Yes,no preference,Maybe
-LEC001,22,Mathematics/AMEP,Economics ,53715,basil/spinach,Yes,Yes,early bird,No
-LEC001,22,Other,,53703,green pepper,Yes,Yes,night owl,Yes
-LEC001,19,Engineering: Other,,53715,none (just cheese),Yes,Yes,night owl,No
-LEC002,18,Engineering: Mechanical,,53706,sausage,Yes,Yes,night owl,Yes
-LEC001,23,Engineering: Other,,53711,green pepper,Yes,Yes,no preference,Maybe
-LEC001,18,Science: Chemistry,,53706,sausage,Yes,Yes,night owl,No
-LEC001,23,Engineering: Mechanical,,53715,pepperoni,Yes,Yes,night owl,Maybe
-LEC002,19,Data Science,Economics,53706,macaroni/pasta,No,No,early bird,Yes
-LEC001,20,Engineering: Industrial,"Science: Other, Economics",53703,sausage,Yes,Yes,early bird,No
-LEC003,21,Data Science,"Economics, Social Science",53703,sausage,Yes,Yes,no preference,Maybe
-LEC002,18,Data Science,,10306,none (just cheese),Yes,No,night owl,Maybe
-LEC002,20,Mathematics/AMEP,Environmental Sciences,53715,pepperoni,No,No,night owl,Maybe
-LEC002,18,Statistics,,53706,pepperoni,Yes,No,night owl,Maybe
-LEC003,21,Engineering: Mechanical,,53715,pepperoni,Yes,Yes,night owl,Yes
-LEC002,20,Engineering: Biomedical,,53703,pepperoni,Yes,No,night owl,Yes
-LEC002,19,Data Science,,53703,pineapple,Yes,No,no preference,Yes
-LEC001,21,Engineering: Other,,53715,mushroom,No,No,early bird,Maybe
-LEC003,18,Data Science,possibly Statistics / Math,53706,mushroom,Yes,No,night owl,Yes
-LEC002,,Business: Other,,,pepperoni,Yes,No,early bird,No
-LEC002,19,Other,,53706,pepperoni,Yes,No,night owl,Yes
-LEC001,19,Engineering: Other,,53706,pineapple,Yes,No,night owl,Maybe
-LEC003,19,Computer Science,data science I havent decided on a major yet but it might be either one of these,53726,none (just cheese),No,No,night owl,Maybe
-LEC003,20,Business: Finance,,53703,Other,Yes,No,night owl,Yes
-LEC001,21,Science: Other,,53703,sausage,No,No,night owl,Yes
-LEC001,20,Other,,53703,pepperoni,No,No,night owl,Yes
-LEC004,20,Engineering: Other,,53703,none (just cheese),Yes,No,night owl,Yes
-LEC001,21,Business: Information Systems,,53703,Other,Yes,Yes,no preference,No
-LEC003,21,Mathematics/AMEP,,,mushroom,No,No,night owl,Yes
-LEC001,18,Other,,53703,mushroom,Yes,No,night owl,Yes
-LEC003,19,Business: Actuarial,,53175,sausage,Yes,Yes,early bird,Yes
-LEC003,20,Engineering: Mechanical,Naval Architecture & Marnie Engineering (self-tutored),53711,green pepper,Yes,No,night owl,Maybe
-LEC002,20,Business: Other,,53703,pineapple,Yes,No,night owl,Maybe
-LEC003,20,Data Science,"computer science, stats ",53711,pineapple,Yes,No,early bird,Yes
-LEC004,19,Statistics,,53706,pepperoni,Yes,No,night owl,Yes
-LEC003,18,Engineering: Industrial,Data science ,53715,pepperoni,No,Yes,early bird,Maybe
-LEC004,20,Other,"Economics, Data Science",53715,mushroom,Yes,No,no preference,Maybe
-LEC001,19,Engineering: Mechanical,,53706,sausage,Yes,Yes,night owl,Yes
-LEC002,21,Engineering: Mechanical,Spanish,53719,none (just cheese),Yes,Yes,night owl,Maybe
-LEC001,24,Engineering: Industrial,Business,53726,mushroom,Yes,No,night owl,Maybe
-LEC002,20,Other,NA,53703,basil/spinach,Yes,Yes,night owl,Yes
-LEC004,18,Engineering: Mechanical,,53706,sausage,Yes,Yes,early bird,Yes
-LEC001,19,Other,"Data Science Certificate, Economics",53703,sausage,No,Yes,night owl,Yes
-LEC001,18,Engineering: Mechanical,,53706,pepperoni,No,No,night owl,Yes
-LEC003,18,Engineering: Mechanical,,53706,pepperoni,Yes,Yes,night owl,Yes
-LEC004,19,Engineering: Biomedical,,53706,none (just cheese),Yes,No,no preference,Yes
-LEC001,20,Computer Science,,53715,sausage,Yes,No,night owl,Yes
-LEC001,17,Engineering: Mechanical,,53706,pineapple,Yes,No,night owl,Yes
-LEC002,20,Data Science,,53703,pepperoni,Yes,Yes,night owl,Yes
-LEC003,18,Engineering: Mechanical,,53715,pineapple,No,No,night owl,Maybe
-LEC003,19,Engineering: Biomedical,,53703,none (just cheese),Yes,Yes,night owl,Yes
-LEC003,20,Other,Data Science,53715,mushroom,Yes,Yes,early bird,Maybe
-LEC003,19,Mathematics/AMEP,,53705,pineapple,No,No,night owl,Yes
-LEC002,19,Engineering: Mechanical,chemical engineering,53711,green pepper,Yes,No,night owl,Maybe
-LEC003,21,Computer Science,Data Science,53715,mushroom,No,No,night owl,Maybe
-LEC003,19,Data Science,,53590,pepperoni,No,No,no preference,Yes
-LEC001,20,Computer Science,,,pepperoni,Yes,No,early bird,Yes
-LEC001,20,Data Science,"Biology, Bioinformatics",53703,sausage,Yes,No,no preference,Yes
-LEC002,21,Engineering: Mechanical,,53705,none (just cheese),Yes,No,no preference,Maybe
-LEC001,19,Computer Science,Data Science,53706,Other,No,Yes,night owl,No
-LEC001,20,Business: Finance,Data Science,53715,sausage,Yes,Yes,night owl,Yes
-LEC001,19,Data Science,Computer science,53706,pineapple,No,Yes,no preference,Yes
-LEC002,23,Science: Other,Computer Science,53711,pineapple,Yes,Yes,early bird,No
-LEC003,18,Engineering: Mechanical,,53706,sausage,No,No,night owl,No
-LEC001,19,Computer Science,Data Science,53703,Other,No,No,no preference,Maybe
-LEC001,19,Science: Other,,53706,macaroni/pasta,Yes,No,night owl,Yes
-LEC003,19,Other,I do not have a secondary major but my major is International Studies. ,53076,pepperoni,Yes,Yes,early bird,Yes
-LEC001,21,Science: Biology/Life,,53715,pepperoni,Yes,No,night owl,Yes
-LEC001,20,Engineering: Mechanical,,53726,pepperoni,Yes,No,night owl,Yes
-LEC002,20,Engineering: Industrial,,53715,pepperoni,Yes,No,no preference,Yes
-LEC003,20,Science: Biology/Life,Life Science Communication,53703,pepperoni,Yes,No,early bird,Maybe
-LEC002,19,Science: Biology/Life,Data Science,,pepperoni,No,No,no preference,Maybe
-LEC002,22,Computer Science,,53703,sausage,Yes,No,night owl,Yes
-LEC001,20,Business: Information Systems,,53706,mushroom,Yes,No,night owl,Yes
-LEC001,19,Business: Other,,53706,pepperoni,Yes,No,early bird,Yes
-LEC001,21,Other,"Economics/Philosophy, Data Science Certificate",53703,pepperoni,Yes,No,no preference,Yes
-LEC003,19,Computer Science,Data science,53706,pineapple,Yes,Yes,night owl,Yes
\ No newline at end of file
diff --git a/f22/andy_lec_notes/lec_17/lec17_dictionaries_850.ipynb b/f22/andy_lec_notes/lec_17/lec17_dictionaries_850.ipynb
deleted file mode 100644
index 1d6ee785cc9a656fcad94742b90f1a2e74d3b174..0000000000000000000000000000000000000000
--- a/f22/andy_lec_notes/lec_17/lec17_dictionaries_850.ipynb
+++ /dev/null
@@ -1,8034 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "import csv"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "721"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists\n",
- "\n",
- "\n",
- "# source: Automate the Boring Stuff with Python Ch 12\n",
- "def process_csv(filename):\n",
- " exampleFile = open(filename, encoding=\"utf-8\") \n",
- " exampleReader = csv.reader(exampleFile) \n",
- " exampleData = list(exampleReader) \n",
- " exampleFile.close() \n",
- " return exampleData\n",
- "\n",
- "survey_data = process_csv('cs220_survey_data.csv')\n",
- "\n",
- "# show the length of this list of lists \n",
- "len(survey_data)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['Lecture',\n",
- " 'Age',\n",
- " 'Primary major',\n",
- " 'Other majors',\n",
- " 'Zip Code',\n",
- " 'Pizza topping',\n",
- " 'Pet owner',\n",
- " 'Runner',\n",
- " 'Sleep habit',\n",
- " 'Procrastinator']"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 2: store the first row in a variable called header\n",
- "header = survey_data[0]\n",
- "header"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[['LEC002',\n",
- " '19',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53711',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Science: Physics',\n",
- " 'Astronomy-Physics, History',\n",
- " '53726',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Science: Chemistry',\n",
- " '',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '19',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '20',\n",
- " 'Other',\n",
- " 'Economics ',\n",
- " '53715',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Statistics',\n",
- " '',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Mathematics/AMEP',\n",
- " '',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Data Science',\n",
- " 'Stats',\n",
- " '53715',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Business: Finance',\n",
- " '',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'No',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Other',\n",
- " 'I am undecided – thinking about Data Science Major',\n",
- " '53706',\n",
- " 'basil/spinach',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '19',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Statistics',\n",
- " 'psychology',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC004',\n",
- " '20',\n",
- " 'Statistics',\n",
- " 'Mathematics ',\n",
- " '53726',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '20',\n",
- " 'Mathematics/AMEP',\n",
- " '',\n",
- " '53711',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Science: Physics',\n",
- " 'Data Science',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Data Science',\n",
- " 'Economics',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Engineering: Mechanical',\n",
- " 'nuclear engineering',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '21',\n",
- " 'Science: Chemistry',\n",
- " '',\n",
- " '',\n",
- " 'green pepper',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Computer Science',\n",
- " 'Data Science',\n",
- " '53706',\n",
- " 'basil/spinach',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '21',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Computer Science',\n",
- " 'Data Science',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Languages',\n",
- " 'Linguistics',\n",
- " '53706',\n",
- " 'macaroni/pasta',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Other',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Science: Physics',\n",
- " 'Mathematics',\n",
- " '53703',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC002',\n",
- " '28',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53703',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Engineering: Other',\n",
- " '',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Science: Physics',\n",
- " 'Life Science Communication',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " 'pre-medicine',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'none (just cheese)',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53711',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC002',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '18',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53706',\n",
- " 'macaroni/pasta',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC004',\n",
- " '21',\n",
- " 'Engineering: Biomedical',\n",
- " '',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC004',\n",
- " '18',\n",
- " 'Business: Information Systems',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Business: Actuarial',\n",
- " 'Data Science and Analytics',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '22',\n",
- " 'Engineering: Industrial',\n",
- " '',\n",
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- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Maybe'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Data Science',\n",
- " '',\n",
- " '53590',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Computer Science',\n",
- " '',\n",
- " '',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Data Science',\n",
- " 'Biology, Bioinformatics',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '21',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53705',\n",
- " 'none (just cheese)',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data Science',\n",
- " '53706',\n",
- " 'Other',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Business: Finance',\n",
- " 'Data Science',\n",
- " '53715',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Data Science',\n",
- " 'Computer science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'No',\n",
- " 'Yes',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '23',\n",
- " 'Science: Other',\n",
- " 'Computer Science',\n",
- " '53711',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'No'],\n",
- " ['LEC003',\n",
- " '18',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53706',\n",
- " 'sausage',\n",
- " 'No',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'No'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data Science',\n",
- " '53703',\n",
- " 'Other',\n",
- " 'No',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Science: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'macaroni/pasta',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Other',\n",
- " 'I do not have a secondary major but my major is International Studies. ',\n",
- " '53076',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Science: Biology/Life',\n",
- " '',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Engineering: Mechanical',\n",
- " '',\n",
- " '53726',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC002',\n",
- " '20',\n",
- " 'Engineering: Industrial',\n",
- " '',\n",
- " '53715',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '20',\n",
- " 'Science: Biology/Life',\n",
- " 'Life Science Communication',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '19',\n",
- " 'Science: Biology/Life',\n",
- " 'Data Science',\n",
- " '',\n",
- " 'pepperoni',\n",
- " 'No',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Maybe'],\n",
- " ['LEC002',\n",
- " '22',\n",
- " 'Computer Science',\n",
- " '',\n",
- " '53703',\n",
- " 'sausage',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '20',\n",
- " 'Business: Information Systems',\n",
- " '',\n",
- " '53706',\n",
- " 'mushroom',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'night owl',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '19',\n",
- " 'Business: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Other',\n",
- " 'Economics/Philosophy, Data Science Certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']]"
- ]
- },
- "execution_count": 7,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 3: store the rest of the data in a variable called rows\n",
- "rows = survey_data[1:]\n",
- "rows\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "[['LEC001',\n",
- " '19',\n",
- " 'Business: Other',\n",
- " '',\n",
- " '53706',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'early bird',\n",
- " 'Yes'],\n",
- " ['LEC001',\n",
- " '21',\n",
- " 'Other',\n",
- " 'Economics/Philosophy, Data Science Certificate',\n",
- " '53703',\n",
- " 'pepperoni',\n",
- " 'Yes',\n",
- " 'No',\n",
- " 'no preference',\n",
- " 'Yes'],\n",
- " ['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']]"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Warmup 4: show the last 3 rows of data\n",
- "rows[-3:]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 5: Write a function that counts the frequency of a value in a column\n",
- "\n",
- "def count_col_frequency(value, col_name):\n",
- " ''' returns the frequency of value in col_name '''\n",
- " count = 0\n",
- " for row in rows:\n",
- " if row[header.index(col_name)] == value:\n",
- " count += 1\n",
- " return count\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "80"
- ]
- },
- "execution_count": 10,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "#test your function\n",
- "count_col_frequency(\"pineapple\", \"Pizza topping\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 12,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "0"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# decide with your neighbor what you want to count next\n",
- "count_col_frequency(\"21\", \"Age\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "446"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# decide with your neighbor what you want to count next\n",
- "count_col_frequency(\"night owl\", \"Sleep habit\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "81"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# decide with your neighbor what you want to count next\n",
- "count_col_frequency(\"Data Science\", \"Primary major\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Warmup 6: Think about it: Is there an easy way to count *every* topping frequency? \n",
- "\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## March 7: Dictionaries\n",
- "\n",
- "Learning Objectives:\n",
- "- Use correct dictionary syntax \n",
- " - to create a dictionary using either {} or dict()\n",
- " - to lookup, insert, update, and pop key/value pairs\n",
- "- Use a for loop, the in operator, and common methods when working with dictionaries.\n",
- "- Write code that uses a dictionary\n",
- " - to store frequencies\n",
- " - to iterate through all key/value pairs"
- ]
- },
- {
- "attachments": {
- "collections.png": {
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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "As we are getting more sophisticated in this course, its time to define...\n",
- "### Data Structure <br>\n",
- "a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data (Wikipedia)\n",
- "\n",
- "Python contains built-in Data Structures called Collections\n",
- "\n",
- "\n",
- "\n",
- "Today you will learn how store data in Dictionaries. \n",
- "\n",
- "#### Dictionary <br>\n",
- "A dictionary is like a list, but more general. In a list, the indices have to be integers; but a dictionary they can be any **immutable** type. \n",
- "\n",
- "You can think of a dictionary as a mapping between a set of indices (which are called keys) and a set of values. Each key maps to a value. The association of a key and a value is called a key-value pair or sometimes an item. \n",
- "\n",
- "(from Think Python, Chapter 11)\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'pie': 3.95,\n",
- " 'ala mode': 1.5,\n",
- " 'donut': 1.25,\n",
- " 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79,\n",
- " 'milk': 1.65,\n",
- " 'loaf': 5.99,\n",
- " 'hot dog': 4.99}"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# a dictionary that stores prices of bakery items\n",
- "# create a dictionary of key/value pairs\n",
- "# notice the curly brackets\n",
- "# notice it can span over more than one line, indenting doesn't matter\n",
- "price_dict = { 'pie': 3.95, \n",
- " 'ala mode':1.50, \n",
- " 'donut': 1.25, 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 0.79, 'milk':1.65, 'loaf': 5.99,\n",
- " 'hot dog': 4.99} # feel free to add some of your own here\n",
- "price_dict"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "9\n",
- "3.15\n",
- "1.25\n"
- ]
- },
- {
- "ename": "KeyError",
- "evalue": "1.25",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-22-6598b7f1b8cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# what's wrong with this line?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprice_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.25\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;31mKeyError\u001b[0m: 1.25"
- ]
- }
- ],
- "source": [
- "# print the length of the dictionary\n",
- "print(len(price_dict) ) # number of key/value pairs\n",
- "\n",
- "#get the price for a certain item\n",
- "print(price_dict['brownie']) # name of dict [ key]\n",
- "\n",
- "#get the price for donut\n",
- "print(price_dict['donut'])\n",
- "\n",
- "# what's wrong with this line? \n",
- "print(price_dict[1.25])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Dictionaries are Mutable\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 23,
- "metadata": {},
- "outputs": [],
- "source": [
- "# add a new key/value pair using [ ] notation\n",
- "price_dict['bagel'] = 1.19\n",
- "\n",
- "# add one of your own\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{'pie': 3.95,\n",
- " 'ala mode': 1.5,\n",
- " 'donut': 1.25,\n",
- " 'muffin': 2.25,\n",
- " 'brownie': 3.15,\n",
- " 'cookie': 1.79,\n",
- " 'milk': 1.65,\n",
- " 'loaf': 5.99,\n",
- " 'hot dog': 4.99,\n",
- " 'bagel': 1.19}"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# change the value associated with a key....syntax is like add\n",
- "\n",
- "price_dict['cookie'] = 1.79\n",
- "\n",
- "# now you add one\n",
- "price_dict "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 26,
- "metadata": {},
- "outputs": [
- {
- "ename": "KeyError",
- "evalue": "'hot dog'",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-26-f7b7dee58630>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# use pop to delete a key/value pair\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprice_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hot dog'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# or del(price_dict['hot dog'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# delete another key/value pair\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprice_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'bagel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mKeyError\u001b[0m: 'hot dog'"
- ]
- }
- ],
- "source": [
- "# use pop to delete a key/value pair\n",
- "price_dict.pop('hot dog') # or del(price_dict['hot dog'])\n",
- "\n",
- "# delete another key/value pair\n",
- "price_dict.pop('bagel')\n",
- "\n",
- "# try deleting someting that is not there\n",
- "#price_dict.pop('pizza')\n",
- "\n",
- "# fix this with an if statement\n",
- "if 'pizza' in price_dict: # checks if it is IN the KEYS\n",
- " price_dict.pop('pizza')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 29,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_keys(['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk', 'loaf', 'bagel'])\n",
- "['pie', 'ala mode', 'donut', 'muffin', 'brownie', 'cookie', 'milk', 'loaf', 'bagel']\n"
- ]
- }
- ],
- "source": [
- "# get all keys and convert to a list\n",
- "print(price_dict.keys())\n",
- "\n",
- "\n",
- "print(list(price_dict.keys()))\n",
- "\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 31,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "dict_values([3.95, 1.5, 1.25, 2.25, 3.15, 1.79, 1.65, 5.99, 1.19])\n",
- "[3.95, 1.5, 1.25, 2.25, 3.15, 1.79, 1.65, 5.99, 1.19]\n"
- ]
- }
- ],
- "source": [
- "# get all values and convert to a list\n",
- "print(price_dict.values())\n",
- "\n",
- "print(list(price_dict.values()))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 33,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "True\n",
- "False\n",
- "False\n",
- "True\n"
- ]
- }
- ],
- "source": [
- "# use 'in' price_dict, price_dict.keys(), price_dict.values()\n",
- "\n",
- "print('donut' in price_dict) # default is to check the keys\n",
- "print(3.95 in price_dict) # default is NOT values\n",
- "print('apple' in price_dict.keys()) # can call out the keys\n",
- "print(3.95 in price_dict.values()) # can check the values"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [],
- "source": [
- "# Applications"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 36,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['pie', 'donut', 'milk', 'cookie', 'tofu']\n",
- "tofu is not in the dictionary\n",
- "Your total is $8.64\n"
- ]
- }
- ],
- "source": [
- "# Example 1: given a list of items, find the total cost of the order\n",
- "order = ['pie', 'donut', 'milk', 'cookie', 'tofu'] # add more items to the order\n",
- "print(order)\n",
- "\n",
- "total_cost = 0\n",
- "for item in order:\n",
- " if item in price_dict:\n",
- " total_cost += price_dict[item]\n",
- " else:\n",
- " print(item, \"is not in the dictionary\")\n",
- "\n",
- " \n",
- "# find the total of the items in the order\n",
- "print (\"Your total is ${:.2f}\".format(total_cost))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 38,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "{'m': 2, 'e': 6, 't': 6, ' ': 10, 'a': 5, 'h': 2, 'b': 1, 'i': 1, 'k': 2, 'r': 2, 'c': 2, 's': 2, 'f': 1, 'o': 3, 'l': 1, '3': 2, ':': 1, '0': 1, 'd': 1, 'y': 1, '.': 1}\n"
- ]
- }
- ],
- "source": [
- "# Example 2a: find the frequency of characters in a sentence\n",
- "\n",
- "# start with an empty dictionary\n",
- "letter_freq = {} \n",
- "# letter_freq = dict() # other way\n",
- "\n",
- "sentence = \"Meet me at the bike racks after school at 3:30 today.\"\n",
- "sentence = sentence.lower()\n",
- "for letter in sentence:\n",
- " if letter in letter_freq:\n",
- " letter_freq [letter] += 1\n",
- " else:\n",
- " letter_freq [letter ] = 1\n",
- "\n",
- "print(letter_freq)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 39,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "the character appeared 10 times\n"
- ]
- }
- ],
- "source": [
- "# Example 2b: find the letter that occurred the most\n",
- "most_used_key = None \n",
- "max_value = 0\n",
- "for key in letter_freq:\n",
- " if letter_freq[key] > max_value:\n",
- " max_value = letter_freq[key]\n",
- " most_used_key = key \n",
- "\n",
- "print(\"the character {} appeared {} times\".format(str(most_used_key), max_value))"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'letter_freq' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "\u001b[0;32m<ipython-input-18-51a323b4acb5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m# why not use for i in range?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mletter_freq\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[0m\u001b[1;32m 6\u001b[0m \u001b[0mprint\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 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
- "\u001b[0;31mNameError\u001b[0m: name 'letter_freq' is not defined"
- ]
- }
- ],
- "source": [
- "# think about it...why did we use None ? \n",
- "# why use 0 ? \n",
- "# why not use for i in range?\n",
- "\n",
- "for i in range (len(letter_freq)):\n",
- " print (i)\n",
- "\n",
- "# Answer: dictionaries cannot be indexed or sliced\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "['LEC003',\n",
- " '19',\n",
- " 'Computer Science',\n",
- " 'Data science',\n",
- " '53706',\n",
- " 'pineapple',\n",
- " 'Yes',\n",
- " 'Yes',\n",
- " 'night owl',\n",
- " 'Yes']"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Recall: survey data\n",
- "# rows\n",
- "rows[-1]"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "{}"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Example 3a. Same as 2a, but use the survey_data\n",
- "major_freq = dict() # another way to make a dictionary\n",
- "\n",
- "major_freq"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "the major Engineering appeared 328 times\n"
- ]
- }
- ],
- "source": [
- "# Example 3b: use the algorithm from 2b to find the major with the highest frequency\n",
- "\n",
- "# find the frequency of each major\n",
- "\n",
- "\n",
- "print(\"the major {} appeared {} times\".format(str(most_used_key), max_value))\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "attachments": {
- "Screen%20Shot%202022-03-07%20at%208.32.53%20AM.png": {
- "image/png": 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"
- }
- },
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- ""
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### After Lecture"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 1. Read the slides to learn how dictionaries relate to lists and sets."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# 2. Review this summary of common dictionary methods...do not need to memorize\n",
- "# https://www.w3schools.com/python/python_ref_dictionary.asp"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "\n"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.9.1"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}