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Lec17 update

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s23/Gurmail_lecture_notes/17_Dictionaries-1/cs220_survey_data.csv
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# this returns the text of the book, in file pg420.txt.
# just use it!
# (you don't need to understand how it works at this point in the semester)
def read_book():
with open('pg420.txt') as f:
return f.read()
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s23/Gurmail_lecture_notes/17_Dictionaries-1/lec17_dictionaries.ipynb
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%% Cell type:markdown id: tags:
 
# Dictionaries
 
* Download ALL files for today's lecture
* Read
* Downey Ch 11 ("A Dictionary is a Mapping" through "Looping and Dictionaries")
* [Python for Everybody, 10.1 - 10.7](https://runestone.academy/ns/books/published/py4e-int/dictionaries/toctree.html)
* [Exam 2 Conflict Form](https://docs.google.com/forms/d/e/1FAIpQLSegJSzTsDHEnygijU3-HQvZDUbTCkHFPKccDkqMt1dGzC67_w/viewform)
* [Regrade Request](https://piazza.com/class/ld8bqui1lgeas/post/105)
* Exam 1: Post questions on Piazza or visit office hour
%% Cell type:markdown id: tags:
# Data Structure Methods
* [String Methods](https://www.w3schools.com/python/python_strings_methods.asp)
* [List Methods](https://www.w3schools.com/python/python_ref_list.asp)
* [Set Methods](https://www.w3schools.com/python/python_ref_set.asp)
* [Dict Methods](https://www.w3schools.com/python/python_ref_dictionary.asp)
%% Cell type:code id: tags:
 
``` python
import csv
```
 
%% Cell type:markdown id: tags:
 
### Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists `csv_data`
 
%% Cell type:code id: tags:
 
``` python
# inspired by https://automatetheboringstuff.com/2e/chapter16/
def process_csv(filename):
example_file = open(filename, encoding="utf-8")
example_reader = csv.reader(example_file)
example_data = list(example_reader)
example_file.close()
 
return example_data
```
 
%% Cell type:code id: tags:
 
``` python
csv_data = process_csv("cs220_survey_data.csv") # TODO: change this
 
# TODO: compute the length of this list of lists
len(csv_data)
```
 
%% Output
 
993
 
%% Cell type:markdown id: tags:
 
### Warmup 2: store the first row in a variable called `cs220_header`
 
%% Cell type:code id: tags:
 
``` python
cs220_header = csv_data[0] # TODO: change this
cs220_header
```
 
%% Output
 
['Lecture',
'Age',
'Major',
'Zip Code',
'Latitude',
'Longitude',
'Pizza topping',
'Pet preference',
'Runner',
'Sleep habit',
'Procrastinator']
 
%% Cell type:markdown id: tags:
 
### Warmup 3: store the rest of the data in a variable called `cs220_data`
 
%% Cell type:code id: tags:
 
``` python
cs220_data = csv_data[1:] # TODO: change this
cs220_data[0]
```
 
%% Output
 
['LEC001',
'22',
'Engineering: Biomedical',
'53703',
'43.073051',
'-89.40123',
'none (just cheese)',
'neither',
'No',
'no preference',
'Maybe']
 
%% Cell type:markdown id: tags:
 
### Warmup 4: show the last 3 rows of data
 
%% Cell type:code id: tags:
 
``` python
cs220_data[-3:]
```
 
%% Output
 
[['LEC001',
'18',
'Undecided',
'53706',
'44.8341',
'87.377',
'basil/spinach',
'dog',
'No',
'no preference',
'Yes'],
['LEC003',
'19',
'Engineering: Mechanical',
'53705',
'46.589146',
'-112.039108',
'none (just cheese)',
'cat',
'No',
'night owl',
'Yes'],
['LEC001',
'20',
'Economics',
'53703',
'39.631506',
'118.143239',
'pineapple',
'dog',
'No',
'night owl',
'Maybe']]
 
%% Cell type:markdown id: tags:
 
### Warmup 5: what is the output of `cs220_data[-1:]`
 
- Be careful with slicing.
- Slicing a string gives a new string
- Slicing a list gives a new list
- Slicing a list of list will always give a new list of list (even if your slice only contains one of the inner lists)
 
%% Cell type:code id: tags:
 
``` python
cs220_data[-1:]
```
 
%% Output
 
[['LEC001',
'20',
'Economics',
'53703',
'39.631506',
'118.143239',
'pineapple',
'dog',
'No',
'night owl',
'Maybe']]
 
%% Cell type:markdown id: tags:
 
### Warmup 6: Write a function that counts the frequency of a value in a column
 
%% Cell type:code id: tags:
 
``` python
def column_frequency(value, col_name):
''' Returns the frequency of value in col_name. '''
count = 0
for row in cs220_data:
if row[cs220_header.index(col_name)].lower() == value.lower():
count += 1
return count
```
 
%% Cell type:code id: tags:
 
``` python
# Test your function
column_frequency("pineapple", "Pizza topping")
```
 
%% Output
 
112
 
%% Cell type:code id: tags:
 
``` python
# Try other test cases
column_frequency("macaroni/pasta", "Pizza topping")
```
 
%% Output
 
34
 
%% Cell type:markdown id: tags:
 
#### TODO: Discuss: Is there an easy way to count *every* topping's frequency?
 
%% Cell type:markdown id: tags:
 
## Learning Objectives:
 
- Use correct dictionary syntax
- to create a dictionary using either {} or dict()
- to lookup, insert, update, and pop key-value pairs
- Use a for loop, the in operator, and common methods when working with dictionaries.
- Write code that uses a dictionary
- to store frequencies
- to iterate through all key-value pairs
 
%% Cell type:markdown id: tags:
 
## Data Structure
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 (source: Wikipedia).
 
<div>
<img src="attachment:Lists.png" width="500"/>
</div>
 
%% Cell type:markdown id: tags:
 
Python contains built-in Data Structures called Collections
 
<div>
<img src="attachment:Collections.png" width="500"/>
</div>
 
Today we'll learn how store data and perform various operations in Dictionaries.
 
%% Cell type:markdown id: tags:
 
## Mappings
 
Common data structure approach:
- store many values
- give each value a label
- use labels to lookup values
 
`list` is an example of a mapping-based data structure
 
%% Cell type:code id: tags:
 
``` python
# index 0 1 2 3
nums_list = [300, 200, 400, 100]
nums_list[2] # lookup using index label
```
 
%% Output
 
400
 
%% Cell type:markdown id: tags:
 
Labels in a list are inflexible. They can only be consecutive `int`s starting at label 0.
 
%% Cell type:markdown id: tags:
 
### Dictionary
 
A dictionary (`dict`) 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. Just like lists, values can be anything.
A dictionary (`dict`) is like a `list`, but more general. In a list, the indices have to be integers; but in a dictionary they can be any **immutable** type. Just like lists, values can be anything.
 
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.
 
(from Think Python, Chapter 11)
 
%% Cell type:code id: tags:
 
``` python
# empty dictionary
some_dict = {} # we use curly braces to create a dictionary
# We'll shortly discuss about dict versus set
 
# empty dictionary
some_other_dict = dict()
```
 
%% Cell type:markdown id: tags:
 
Just like a `list`, `dict` key-value pairs are separated by a `,`.
 
The `key` and the `value` are separated by a `:`. That is `key:value`.
 
%% Cell type:code id: tags:
 
``` python
# TODO: let's define nums_dict
 
nums_list = [300, 200, 400, 100]
nums_dict = {
"first": 300,
"second": 200,
"third": 400,
"fourth": 100,
}
 
nums_dict
```
 
%% Output
 
{'first': 300, 'second': 200, 'third': 400, 'fourth': 100}
 
%% Cell type:code id: tags:
 
``` python
# a dictionary that stores prices of bakery items
# Notice that a dict can span over more than one line, indentation doesn't matter
 
price_dict = { 'broccoli': 3.95,
'spinach': 1.50,
'donut': 1.25, 'muffin': 2.25, "ice cream": 3.99,
'brownie': 3.15,
'cookie': 0.79, 'milk': 1.65, 'loaf': 5.99,
'cauliflower': 3.99} # feel free to add some of your own here
price_dict
```
 
%% Output
 
{'broccoli': 3.95,
'spinach': 1.5,
'donut': 1.25,
'muffin': 2.25,
'ice cream': 3.99,
'brownie': 3.15,
'cookie': 0.79,
'milk': 1.65,
'loaf': 5.99,
'cauliflower': 3.99}
 
%% Cell type:markdown id: tags:
 
#### Dictionaries maintain insertion based ordering in recent versions of Python (3.7 and above versions).
 
- Go back to the previous cell and add 'ice cream': 3.99 key-value pair before 'brownie': 3.15. Re-run the cell to see how the `dict` definition changes with respect to insertion order.
 
%% Cell type:markdown id: tags:
 
### Dictionary lookups
 
- same syntax as `list` indexing
- `some_dict[key]`
 
%% Cell type:markdown id: tags:
 
### Lookup price of 'brownie', 'cookie', and 'ice cream'.
 
%% Cell type:code id: tags:
 
``` python
print(price_dict["brownie"])
print(price_dict["cookie"])
print(price_dict["ice cream"])
```
 
%% Output
 
3.15
0.79
3.99
 
%% Cell type:markdown id: tags:
 
### Lookup price of 'sugar'.
 
%% Cell type:code id: tags:
 
``` python
# print(price_dict['sugar']) # KeyError
# doesn't work
```
 
%% Cell type:markdown id: tags:
 
### Can you perform lookup using values? Answer is no, the mapping is one-way, that is key to value and not vice versa.
 
%% Cell type:code id: tags:
 
``` python
# print(price_dict[0.79]) # KeyError
# doesn't work
```
 
%% Cell type:markdown id: tags:
 
<div>
<img src="attachment:Parenthetical_characters_1.png" width="700"/>
</div>
 
%% Cell type:markdown id: tags:
 
<div>
<img src="attachment:Parenthetical_characters_2.png" width="500"/>
</div>
 
%% Cell type:markdown id: tags:
 
### Dictionaries are Mutable
 
- update existing key's value
- insert a new key-value pair
- pop method to delete a key-value pair
 
%% Cell type:code id: tags:
 
``` python
# TODO: change price of 'cauliflower' to 2.99
price_dict['cauliflower'] = 2.99
price_dict
```
 
%% Output
 
{'broccoli': 3.95,
'spinach': 1.5,
'donut': 1.25,
'muffin': 2.25,
'ice cream': 3.99,
'brownie': 3.15,
'cookie': 0.79,
'milk': 1.65,
'loaf': 5.99,
'cauliflower': 2.99}
 
%% Cell type:code id: tags:
 
``` python
# TODO: insert new key-value pair 'carrot' mapping to 1.99
price_dict["carrot"] = 1.99
price_dict
```
 
%% Output
 
{'broccoli': 3.95,
'spinach': 1.5,
'donut': 1.25,
'muffin': 2.25,
'ice cream': 3.99,
'brownie': 3.15,
'cookie': 0.79,
'milk': 1.65,
'loaf': 5.99,
'cauliflower': 2.99,
'carrot': 1.99}
 
%% Cell type:code id: tags:
 
``` python
nums_list = [10, 20, 30]
# nums_list[3] = 40 # Recall that this doesn't work on a list due to IndexError
 
# TODO: comment out line 2 and use proper syntax to add item 40 to nums_list
nums_list.append(40)
nums_list
```
 
%% Output
 
[10, 20, 30, 40]
 
%% Cell type:code id: tags:
 
``` python
# use pop to delete the 'spinach' key-value pair
price_dict.pop("spinach")
 
# Alternate
del(price_dict['donut'])
 
# try deleting someting that is not there
price_dict.pop('pizza') # KeyError
```
 
%% Output
 
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
Input In [21], in <cell line: 8>()
5 del(price_dict['donut'])
7 # try deleting someting that is not there
----> 8 price_dict.pop('pizza')
KeyError: 'pizza'
 
%% Cell type:markdown id: tags:
 
### `in` operator enables us to check whether a key exists in the dictionary
 
%% Cell type:code id: tags:
 
``` python
# TODO: fix the above example with a conditional
if "pizza" in price_dict:
price_dict.pop('pizza')
else:
print("Oops couldn't find it!")
```
 
%% Output
 
Oops couldn't find it!
 
%% Cell type:markdown id: tags:
 
### `len` built-in function returns the number of key-value pairs in a dictionary
 
%% Cell type:code id: tags:
 
``` python
# TODO: print length of price_dict
len(price_dict)
```
 
%% Output
 
9
 
%% Cell type:markdown id: tags:
 
### `for` loop enables us to iterate over keys in a dictionary
 
%% Cell type:code id: tags:
 
``` python
# TODO: iterate over price_dict and print each key-value pair in its own line
 
for key in price_dict:
print(key, price_dict[key])
```
 
%% Output
 
broccoli 3.95
muffin 2.25
ice cream 3.99
brownie 3.15
cookie 0.79
milk 1.65
loaf 5.99
cauliflower 2.99
carrot 1.99
 
%% Cell type:markdown id: tags:
 
### `keys` method
 
- retrieves keys of a dictionary
- can be converted into a list
 
%% Cell type:code id: tags:
 
``` python
# get all keys and convert to a list
print(price_dict.keys())
```
 
%% Output
 
dict_keys(['broccoli', 'muffin', 'ice cream', 'brownie', 'cookie', 'milk', 'loaf', 'cauliflower', 'carrot'])
 
%% Cell type:markdown id: tags:
 
### `values` method
 
- retrieves values of a dictionary
- can be converted into a list
 
%% Cell type:code id: tags:
 
``` python
# get all values and convert to a list
print(price_dict.values())
```
 
%% Output
 
dict_values([3.95, 2.25, 3.99, 3.15, 0.79, 1.65, 5.99, 2.99, 1.99])
 
%% Cell type:code id: tags:
 
``` python
# use 'in' price_dict, price_dict.keys(), price_dict.values()
 
print('donut' in price_dict) # default is to check the keys
print(9.95 in price_dict) # default is NOT values
print('apple' in price_dict.keys()) # can call out the keys
print(3.95 in price_dict.values()) # can check the values
```
 
%% Output
 
False
False
False
True
 
%% Cell type:markdown id: tags:
 
### Example 1: find total cost of shopping order
 
%% Cell type:code id: tags:
 
``` python
order = ['pie', 'donut', 'milk', 'cookie', 'tofu'] # add more items to the order
print(order)
 
total_cost = 0
for item in order:
# TODO: check if item is a key in price_dict
# if yes, retrieve the value and add it to total_cost
# if not, display "Couldn't find <item> in price list!"
if item in price_dict:
total_cost += price_dict[item]
else:
print("Couldn't find {} in price list!".format(item))
 
# find the total of the items in the order
print ("Your total cost is ${:.2f}".format(total_cost))
```
 
%% Output
 
['pie', 'donut', 'milk', 'cookie', 'tofu']
Couldn't find pie in price list!
Couldn't find donut in price list!
Couldn't find tofu in price list!
Your total cost is $2.44
 
%% Cell type:markdown id: tags:
 
### Example 2a: find the letter that occurred the most in a sentence
 
%% Cell type:code id: tags:
 
``` python
# start with an empty dictionary
letter_freq = {} # KEY: unique letter; VALUE: count of unique letter
 
sentence = "Meet me at the bike racks after school at 3:30 today."
 
for letter in sentence:
# TODO: check if letter is a key in letter_freq
# if yes, increment letter frequency by 1
# if no, insert a new key-value pair
if letter in letter_freq:
letter_freq[letter] += 1
else:
letter_freq[letter] = 1
 
print(letter_freq)
```
 
%% Output
 
{'M': 1, 'e': 6, 't': 6, ' ': 10, 'm': 1, '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}
 
%% Cell type:markdown id: tags:
 
### Example 2b: find the letter that occurred the most
 
%% Cell type:code id: tags:
 
``` python
most_used_key = None
max_value = None
 
for letter in letter_freq:
# TODO: you already know how to use a for loop to compute max
if max_value == None or letter_freq[letter] > max_value:
max_value = letter_freq[letter]
most_used_key = letter
 
print("The character \"{}\" appeared {} times.".format(str(most_used_key), max_value))
```
 
%% Output
 
The character " " appeared 10 times.
 
%% Cell type:code id: tags:
 
``` python
# TODO: discuss: why not use range-based for loop?
 
for i in range(len(letter_freq)):
print(i) # can you do anything with i in this letter_freq dictionary?
```
 
%% Output
 
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
 
%% Cell type:markdown id: tags:
 
### Example 3a: Survey dataset: count every primary major's frequency
 
%% Cell type:code id: tags:
 
``` python
major_freq = {} # KEY: unique major; VALUE: count of unique major
 
# TODO: iterate over each student's data from cs220_data
# TODO: extract "Primary major" column's value
# TODO: check if current student's major already a key in major_freq
# - if yes, increase the corresponding value by 1
# - if no, insert a new key-value pair
 
for row in cs220_data:
major = row[cs220_header.index("Major")]
if major in major_freq:
major_freq[major] += 1
else:
major_freq[major] = 1
 
major_freq
```
 
%% Output
 
{'Engineering: Biomedical': 45,
'Undecided': 23,
'Engineering: Industrial': 58,
'Engineering: Other|Engineering: Computer': 1,
'Data Science': 164,
'Mathematics/AMEP': 34,
'Engineering: Other': 13,
'Economics': 53,
'Psychology': 7,
'Science: Biology/Life': 37,
'Engineering: Mechanical': 198,
'Economics (Mathematical Emphasis)': 7,
'Computer Science': 115,
'Science: Other|Political Science': 1,
'Business: Other': 11,
'Business: Other|Real Estate': 2,
'Engineering: Other|Engineering Physics: Scientific Computing': 1,
'Business: Finance': 30,
'Business: Information Systems': 24,
'Statistics': 26,
'Business: Actuarial': 22,
'Science: Physics': 8,
'Science: Other': 9,
'Business: Other|Accounting': 2,
'Business: Other|business analytics': 1,
'Science: Other|animal sciences': 1,
'Mathematics': 2,
'Health Promotion and Health Equity': 2,
'Art': 1,
'Mathematics, Data Science': 1,
'Science: Other|Science: Genetics and Genomics': 1,
'Statistics (actuarial route)': 1,
'Business: Other|Business: Accounting': 1,
'Engineering: Other|Computer Engineering': 1,
'Engineering: Other|Computer engineering': 1,
'Engineering: Other|Material Science Engineering': 1,
'Civil engineering - hydropower engineering': 1,
'Science: Chemistry': 6,
'Communication arts': 1,
'Business andministration': 1,
'Education': 2,
'Pre-business': 1,
'Science: Other|Environmental Science': 4,
'History': 2,
'Information science': 2,
'consumer behavior and marketplace studies': 1,
'Conservation Biology': 1,
'Engineering: Other|Chemical Engineering': 1,
'Science: Other|Biophysics PhD': 1,
'Business: Other|Technology Strategy/ Product Management': 1,
'Political Science': 6,
'Graphic Design': 1,
'Business: Other|Marketing': 3,
'Cartography and GIS': 1,
'Sociology': 2,
'Business: Other|Consumer Behavior and Marketplace Studies': 1,
'Atmospheric Sciences': 1,
'Languages': 4,
'Engineering Mechanics (Aerospace Engineering)': 1,
'Science: Other|Psychology': 2,
'Engineering: Other|Civil and Environmental Engineering': 1,
'International Studies': 2,
'Agricultural and Applied Economics': 1,
'Business: Other|MHR': 1,
'Medicine': 1,
'Science: Other|Personal Finance': 1,
'Environmental science': 1,
'Geoscience': 1,
'Business: Other|accounting': 1,
'Design Studies': 1,
'Science: Other|Environmetal Science': 1,
'Science: Other|Atmospheric and Oceanic Sciences (AOS)': 1,
'Business: Other|Business Analytics': 1,
'Journalism': 2,
'Science: Other|Politcal Science': 1,
'Communication Sciences and Disorder': 1,
'Science: Other|Geoscience': 1,
'Science: Other|Atmospheric and oceanic science': 1,
'Engineering: Other|Engineering Mechanics': 1,
'Pre-Business': 1,
'Industrial Engineering': 1,
'Mechanical Engineering': 1,
'Science: Other|Environmental science': 1,
'Life Sciences Communication': 1,
'Science: Other|Atmospheric and Oceanic Sciences': 1,
'Rehabilitation Psychology': 1,
'Accounting': 1,
'Engineering: Other|Civil- Intelligent Transportation System': 1,
'Science: Other|Animal and Dairy Science': 1,
'Interior Architecture': 1,
'Science: Other|Atmospheric & Oceanic Sciences': 1,
'Computer Science and Statistics': 1,
'Business analytics': 1,
'Legal Studies': 1,
'Journalism: Strategic Comm./Advertising': 1,
'Master of Public Affairs': 1,
'Environment & Resources': 1,
'Environmental Studies': 1}
 
%% Cell type:markdown id: tags:
 
### Example 3b: find primary major with highest frequency
 
%% Cell type:code id: tags:
 
``` python
# Example 3b: use the algorithm from 2b to find the major with the highest frequency
 
most_used_key = None
max_value = None
 
for major in major_freq:
# TODO: you already know how to use a for loop to compute max
if max_value == None or major_freq[major] > max_value:
max_value = major_freq[major]
most_used_key = major
 
print("The major \"{}\" appeared {} times.".format(str(most_used_key), max_value))
```
 
%% Output
 
The major "Engineering: Mechanical" appeared 198 times.
 
%% Cell type:markdown id: tags:
 
### After Lecture Practice
 
%% Cell type:markdown id: tags:
 
Organize your data structure notes ...
 
<div>
<img src="attachment:DataStructure_notes.png" width="700"/>
</div>
 
%% Cell type:markdown id: tags:
 
#### Review slide deck
 
%% Cell type:markdown id: tags:
 
#### Review this summary of common dictionary methods:
https://www.w3schools.com/python/python_ref_dictionary.asp
......
s23/Gurmail_lecture_notes/17_Dictionaries-1/lec17_dictionaries_template.ipynb → s23/Gurmail_lecture_notes/17_Dictionaries-1/lec17_dictionaries_template_Gurmail_lec1.ipynb
+ 22
− 3
View file @ 0e84a6d8
%% Cell type:markdown id: tags:
 
# Dictionaries
 
* Download ALL files for today's lecture
* Read
* Downey Ch 11 ("A Dictionary is a Mapping" through "Looping and Dictionaries")
* [Python for Everybody, 10.1 - 10.7](https://runestone.academy/ns/books/published/py4e-int/dictionaries/toctree.html)
* [Exam 2 Conflict Form](https://docs.google.com/forms/d/e/1FAIpQLSegJSzTsDHEnygijU3-HQvZDUbTCkHFPKccDkqMt1dGzC67_w/viewform)
* [Regrade Request](https://piazza.com/class/ld8bqui1lgeas/post/105)
* Exam 1: Post questions on Piazza or visit office hour
%% Cell type:markdown id: tags:
# Data Structure Methods
* [String Methods](https://www.w3schools.com/python/python_strings_methods.asp)
* [List Methods](https://www.w3schools.com/python/python_ref_list.asp)
* [Set Methods](https://www.w3schools.com/python/python_ref_set.asp)
* [Dict Methods](https://www.w3schools.com/python/python_ref_dictionary.asp)
%% Cell type:code id: tags:
 
``` python
import csv
```
 
%% Cell type:markdown id: tags:
 
### Warmup 1: Read in the file 'cs220_survey_data.csv' into a lists of lists `csv_data`
 
%% Cell type:code id: tags:
 
``` python
# inspired by https://automatetheboringstuff.com/2e/chapter16/
def process_csv(filename):
example_file = open(filename, encoding="utf-8")
example_reader = csv.reader(example_file)
example_data = list(example_reader)
example_file.close()
 
return example_data
```
 
%% Cell type:code id: tags:
 
``` python
csv_data = None # TODO: change this
 
# TODO: compute the length of this list of lists
```
 
%% Cell type:markdown id: tags:
 
### Warmup 2: store the first row in a variable called `cs220_header`
 
%% Cell type:code id: tags:
 
``` python
cs220_header = None # TODO: change this
cs220_header
```
 
%% Cell type:markdown id: tags:
 
### Warmup 3: store the rest of the data in a variable called `cs220_data`
 
%% Cell type:code id: tags:
 
``` python
cs220_data = None # TODO: change this
cs220_data[0]
```
 
%% Cell type:markdown id: tags:
 
### Warmup 4: show the last 3 rows of data
 
%% Cell type:code id: tags:
 
``` python
```
 
%% Cell type:markdown id: tags:
 
### Warmup 5: what is the output of `cs220_data[-1:]`
 
- Be careful with slicing.
- Slicing a string gives a new string
- Slicing a list gives a new list
- Slicing a list of list will always give a new list of list (even if your slice only contains one of the inner lists)
 
%% Cell type:code id: tags:
 
``` python
```
 
%% Cell type:markdown id: tags:
 
### Warmup 6: Write a function that counts the frequency of a value in a column
 
%% Cell type:code id: tags:
 
``` python
def column_frequency(value, col_name):
''' Returns the frequency of value in col_name. '''
count = 0
for row in rows:
if row[header.index(col_name)].lower() == value.lower():
count += 1
return count
```
 
%% Cell type:code id: tags:
 
``` python
# Test your function
column_frequency("pineapple", "Pizza topping")
```
 
%% Cell type:code id: tags:
 
``` python
# Try other test cases
```
 
%% Cell type:markdown id: tags:
 
#### TODO: Discuss: Is there an easy way to count *every* topping's frequency?
 
%% Cell type:markdown id: tags:
 
## Learning Objectives:
 
- Use correct dictionary syntax
- to create a dictionary using either {} or dict()
- to lookup, insert, update, and pop key-value pairs
- Use a for loop, the in operator, and common methods when working with dictionaries.
- Write code that uses a dictionary
- to store frequencies
- to iterate through all key-value pairs
 
%% Cell type:markdown id: tags:
 
## Data Structure
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 (source: Wikipedia).
 
<div>
<img src="attachment:Lists.png" width="500"/>
</div>
 
%% Cell type:markdown id: tags:
 
Python contains built-in Data Structures called Collections
 
<div>
<img src="attachment:Collections.png" width="500"/>
</div>
 
Today we'll learn how store data and perform various operations in Dictionaries.
 
%% Cell type:markdown id: tags:
 
## Mappings
 
Common data structure approach:
- store many values
- give each value a label
- use labels to lookup values
 
`list` is an example of a mapping-based data structure
 
%% Cell type:code id: tags:
 
``` python
# index 0 1 2 3
nums_list = [300, 200, 400, 100]
nums_list[2] # lookup using index label
```
 
%% Cell type:markdown id: tags:
 
Labels in a list are inflexible. They can only be consecutive `int`s starting at label 0.
 
%% Cell type:markdown id: tags:
 
### Dictionary
 
A dictionary (`dict`) 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. Just like lists, values can be anything.
A dictionary (`dict`) is like a `list`, but more general. In a list, the indices have to be integers; but in a dictionary they can be any **immutable** type. Just like lists, values can be anything.
 
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.
 
(from Think Python, Chapter 11)
 
%% Cell type:code id: tags:
 
``` python
# empty dictionary
some_dict = {} # we use curly braces to create a dictionary
# We'll shortly discuss about dict versus set
 
# empty dictionary
some_other_dict = dict()
```
 
%% Cell type:markdown id: tags:
 
Just like a `list`, `dict` key-value pairs are separated by a `,`.
 
The `key` and the `value` are separated by a `:`. That is `key:value`.
 
%% Cell type:code id: tags:
 
``` python
# TODO: let's define nums_dict
 
nums_list = [300, 200, 400, 100]
 
 
nums_dict
```
 
%% Cell type:code id: tags:
 
``` python
# a dictionary that stores prices of bakery items
# Notice that a dict can span over more than one line, indentation doesn't matter
 
price_dict = { 'broccoli': 3.95,
'spinach': 1.50,
'donut': 1.25, 'muffin': 2.25,
'brownie': 3.15,
'cookie': 0.79, 'milk': 1.65, 'loaf': 5.99,
'cauliflower': 3.99} # feel free to add some of your own here
price_dict
```
 
%% Cell type:markdown id: tags:
 
#### Dictionaries maintain insertion based ordering in recent versions of Python (3.7 and above versions).
 
- Go back to the previous cell and add 'ice cream': 3.99 key-value pair before 'brownie': 3.15. Re-run the cell to see how the `dict` definition changes with respect to insertion order.
 
%% Cell type:markdown id: tags:
 
### Dictionary lookups
 
- same syntax as `list` indexing
- `some_dict[key]`
 
%% Cell type:markdown id: tags:
 
### Lookup price of 'brownie', 'cookie', and 'ice cream'.
 
%% Cell type:code id: tags:
 
``` python
print()
print()
print()
```
 
%% Cell type:markdown id: tags:
 
### Lookup price of 'sugar'.
 
%% Cell type:code id: tags:
 
``` python
print()
```
 
%% Cell type:markdown id: tags:
 
### Can you perform lookup using values? Answer is no, the mapping is one-way, that is key to value and not vice versa.
 
%% Cell type:code id: tags:
 
``` python
print(price_dict[0.79]) # KeyError
```
 
%% Cell type:markdown id: tags:
 
<div>
<img src="attachment:Parenthetical_characters_1.png" width="700"/>
</div>
 
%% Cell type:markdown id: tags:
 
<div>
<img src="attachment:Parenthetical_characters_2.png" width="500"/>
</div>
 
%% Cell type:markdown id: tags:
 
### Dictionaries are Mutable
 
- update existing key's value
- insert a new key-value pair
- pop method to delete a key-value pair
 
%% Cell type:code id: tags:
 
``` python
# TODO: change price of 'cauliflower' to 2.99
 
price_dict
```
 
%% Cell type:code id: tags:
 
``` python
# TODO: insert new key-value pair 'carrot' mapping to 1.99
 
price_dict
```
 
%% Cell type:code id: tags:
 
``` python
nums_list = [10, 20, 30]
nums_list[3] = 40 # Recall that this doesn't work on a list due to IndexError
 
# TODO: comment out line 2 and use proper syntax to add item 40 to nums_list
 
 
nums_list
```
 
%% Cell type:code id: tags:
 
``` python
# use pop to delete the 'spinach' key-value pair
 
 
# Alternate
del(price_dict['donut'])
 
# try deleting someting that is not there
price_dict.pop('pizza') # KeyError
```
 
%% Cell type:markdown id: tags:
 
### `in` operator enables us to check whether a key exists in the dictionary
 
%% Cell type:code id: tags:
 
``` python
# TODO: fix the above example with a conditional
if ???:
price_dict.pop('pizza')
else:
print("Oops couldn't find it!")
```
 
%% Cell type:markdown id: tags:
 
### `len` built-in function returns the number of key-value pairs in a dictionary
 
%% Cell type:code id: tags:
 
``` python
# TODO: print length of price_dict
```
 
%% Cell type:markdown id: tags:
 
### `for` loop enables us to iterate over keys in a dictionary
 
%% Cell type:code id: tags:
 
``` python
# TODO: iterate over price_dict and print each key-value pair in its own line
```
 
%% Cell type:markdown id: tags:
 
### `keys` method
 
- retrieves keys of a dictionary
- can be converted into a list
 
%% Cell type:code id: tags:
 
``` python
# get all keys and convert to a list
print(price_dict.keys())
```
 
%% Cell type:markdown id: tags:
 
### `values` method
 
- retrieves values of a dictionary
- can be converted into a list
 
%% Cell type:code id: tags:
 
``` python
# get all values and convert to a list
print(price_dict.values())
```
 
%% Cell type:code id: tags:
 
``` python
# use 'in' price_dict, price_dict.keys(), price_dict.values()
 
print('donut' in price_dict) # default is to check the keys
print(9.95 in price_dict) # default is NOT values
print('apple' in price_dict.keys()) # can call out the keys
print(3.95 in price_dict.values()) # can check the values
```
 
%% Cell type:markdown id: tags:
 
### Example 1: find total cost of shopping order
 
%% Cell type:code id: tags:
 
``` python
order = ['pie', 'donut', 'milk', 'cookie', 'tofu'] # add more items to the order
print(order)
 
total_cost = 0
for item in order:
# TODO: check if item is a key in price_dict
# if yes, retrieve the value and add it to total_cost
# if not, display "Couldn't find <item> in price list!"
pass
 
# find the total of the items in the order
print ("Your total cost is ${:.2f}".format(total_cost))
```
 
%% Cell type:markdown id: tags:
 
### Example 2a: find the letter that occurred the most in a sentence
 
%% Cell type:code id: tags:
 
``` python
# start with an empty dictionary
letter_freq = {} # KEY: unique letter; VALUE: count of unique letter
 
sentence = "Meet me at the bike racks after school at 3:30 today."
 
for letter in sentence:
# TODO: check if letter is a key in letter_freq
# if yes, increment letter frequency by 1
# if no, insert a new key-value pair
pass
 
print(letter_freq)
```
 
%% Cell type:markdown id: tags:
 
### Example 2b: find the letter that occurred the most
 
%% Cell type:code id: tags:
 
``` python
most_used_key = None
max_value = None
 
for letter in letter_freq:
# TODO: you already know how to use a for loop to compute max
pass
 
print("The character \"{}\" appeared {} times.".format(str(most_used_key), max_value))
```
 
%% Cell type:code id: tags:
 
``` python
# TODO: discuss: why not use range-based for loop?
 
for i in range(len(letter_freq)):
print(i) # can you do anything with i in this letter_freq dictionary?
```
 
%% Cell type:markdown id: tags:
 
### Example 3a: Survey dataset: count every primary major's frequency
 
%% Cell type:code id: tags:
 
``` python
major_freq = {} # KEY: ???; VALUE: ???
 
# TODO: iterate over each student's data from cs220_data
# TODO: extract "Primary major" column's value
# TODO: check if current student's major already a key in major_freq
# - if yes, increase the corresponding value by 1
# - if no, insert a new key-value pair
 
major_freq
```
 
%% Cell type:markdown id: tags:
 
### Example 3b: find primary major with highest frequency
 
%% Cell type:code id: tags:
 
``` python
# Example 3b: use the algorithm from 2b to find the major with the highest frequency
 
 
print("The major \"{}\" appeared {} times.".format(str(most_used_key), max_value))
```
 
%% Cell type:markdown id: tags:
 
### After Lecture Practice
 
%% Cell type:markdown id: tags:
 
Organize your data structure notes ...
 
<div>
<img src="attachment:DataStructure_notes.png" width="700"/>
</div>
 
%% Cell type:markdown id: tags:
 
#### Review slide deck
 
%% Cell type:markdown id: tags:
 
#### Review this summary of common dictionary methods:
https://www.w3schools.com/python/python_ref_dictionary.asp
......
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