CSE 163

Section AX

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TA1 & TA2

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QOTD (sample): If you were a data structure, which one would you be and why?

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Housekeeping 🏡

Important Dates and Reminders

• NO CLASS JULY 3
• Programming Practice 1 is due TODAY @ 11:59PM
• #2 is due next Thursday (already released)
• Section Check-In 2 is due tomorrow (Friday) @ 11:59PM
• HW 1: Processing is due Friday (July 03) @ 11:59 PM

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All submissions are done through Gradescope

Recap

What we’ve Learned so far:

• Week 1
• Basic Python Syntax
• Control Flow
• Built-in Data Structures
• Week 2
• File Processing
• Pandas Fundamentals

Game Plan

What We’ll Cover Today

• Data structures review
• Dictionary practice problem
• Dataframes and pandas review
• Practice problems
• Groupby (if time)�

List

• General purpose multi value storage
• Multi-type
• Mutable
• append()
• extend()
• remove(), pop()
• Indexable

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Use Cases:

• Need to store multiple values
• Unknown number of values

new_list = list()

new_list = []

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new_list.append("a") # ["a"]

new_list.append(2) # ["a", 2]

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new_list[0] # "a"

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new_list.extend(["b"])

# ["a", 2, "b"]

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new_list.remove("b")

# ["a", 2]

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new_list.pop(0) # returns “a”

# new_list = [2]

Tuple

• General purpose multi value storage
• Multi-type
• Immutable
• Memory benefits
• Indexable
• Can be defined arbitrarily with parentheses or commas
• Can hold more than two values

Use Cases:

• Fixed size multi-value situations
• Optimization

tup = ("hello", "world")

tup[0] # "hello"

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# takes in params a and b

def pack(a, b):

# returns a tuple of (a, b)

return a, b

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Sets

• Same-type unordered storage
• Can only store immutable values
• Does not store duplicates
• Lightning fast membership queries
• Mutable
• add()
• remove()
• update()
• | → union, & → intersection,

- → difference (in THA2!)

• Note: Cannot be initialized with {}

Use Cases:

• Need fast membership queries
• Need to store only unique values

my_set = set()

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my_set.add(10) # {10}

my_set.add(30) # {10, 30}

my_set.add(30) # {10, 30}

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my_set.add([]) # Error!

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if 10 in my_set: # Membership Query

print("Found 10!")

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Dictionaries

• Stores unordered Key-Value pairs
• Keys must be immutable (no duplicates)
• Values can be mutable and duplicated
• Lots of tooling
• .keys()
• .values()
• .items()
• Use key-checking pattern

Use Cases:

• Storing corresponding mappings
• Student Name -> ID Number
• Region -> Area Code
• Very powerful.

d = dict()

d = {} # same symbol as sets!

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d['Triangle'] = 3

d['Square'] = 4

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d['Square'] # 4

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things = ["cat", "hat", "bat", "cat"]

my_dict = dict()

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for i in things: # also really fast!

if i not in my_dict:

my_dict[i] = 0

my_dict[i] += 1

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my_dict

# {"cat": 2, "hat": 1, "bat": 1}

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File Processing

• “with open” syntax
• Read with .readlines() function
• Two approaches:
• Line based
• Token based

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file_path = "home/doggies.txt"

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with open(file_path) as f:

lines = f.readlines()

for l in lines:

print(l)

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file_path = "home/doggies.txt"

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with open(file_path) as f:

lines = f.readlines()

for l in lines:

words = l.split()

for w in words:

print(w)

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Practice Problem

Solution

DataFrames Review

Data

Goal: Represent real-world phenomena in a tabular format

CSV

• Ubiquitous representation of data
• Comma separated values.
• First row is columns
• Every other row is a “record” or instance of data

# cats.csv

import pandas as pd

data = pd.read_csv("cats.csv")

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print(data)

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===============================

name color age outdoor

0 fluffy ginger 2 True

1 sparky grey 8 False

2 storm black 1 True

===============================

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# cats.csv in a list of dicts

cats = [

{"name": "fluffy", "age": 2},

{"name": "sparky", "age": 8},

{"name": "storm", "age": 1}

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Processing:

List of dicts

• Loop through the list of dicts
• Every iteration will set your iteration variable to a new row
• Extract variables with dictionary indexing
• Notice- Row-by-row processing

# cats.csv in a list of dicts

cats = [

{"name": "fluffy", "age": 2},

{"name": "sparky", "age": 8},

{"name": "storm", "age": 1}

]

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total_age = 0

for c in cats:

# c stores a cat dictionary

# ex: {"name": __, "age": __}

age = c['age']

total_age += age

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total_age # 11

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DataFrames

• Tabular Data
• Similar to list of dictionaries, but more powerful
• Columns are Series
• Single rows are Series
• Able to get entire columns
• data[‘column_name’]
• data[ [‘col1’, ‘col2’] ]
• Aggregate data
• data[‘column_name’].sum()
• .mean(), .max(), .unique()...
• Series-based operations
• data[‘column’] + …

import pandas as pd

data = pd.read_csv("cats.csv")

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print(data)

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===============================

name color age outdoor

0 fluffy ginger 2 True

1 sparky grey 8 False

2 storm black 1 True

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data['age'] # Series([2, 8, 1])

data['age'].sum() # 11

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data['age'] * 4 # Series([8, 32, 4])

DataFrame Filtering

• Masks
• Boolean Series from a mask
• data[‘age’] > 3
• Filter by using mask in dataframe
• data[data[‘age’] > 3]
• Combine masks with &, |, ~
• data[mask1 & mask2]

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data['age'] > 1

# Series([True, True, False])

data[data['age'] > 1]

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===============================

name color age outdoor

0 fluffy ginger 2 True

1 sparky grey 8 False

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data['outdoor'] == True

# Series([True, False, True])

data[data['outdoor'] == True]

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name color age outdoor

0 fluffy ginger 2 True

2 storm black 1 True

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DataFrame Filtering

cont.

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name color age outdoor

0 fluffy ginger 2 True

1 sparky grey 8 False

2 storm black 1 True

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old_age = data['age'] > 1

# Series([True, True, False])

outdoors = data['outdoor'] == True

# Series([True, False, True])

old_age & outdoors

# Series([True, False, False])

data[old_age & outdoors]

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===============================

name color age outdoor

0 fluffy ginger 2 True

===============================

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DataFrame Indexing with .loc

• Use a row_indexer and column_indexer to specify where to extract data.
• Data.loc[row_indexer, col_indexer]
• Can also get you single values
• data.loc[0, ‘age’]
• Can use slices too!
• Data.loc[0:2] gets rows 0, 1, and 2
• Use “:” for “all”
• Can use lists
• data.loc[0, [‘name’, ‘age’]

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df.loc[0] # same as df.loc[0, :]

# Series(["fluffy", "ginger", 2, True])

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df.loc[0:1] # same as df.loc[0:1, :]

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# DataFrame

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name color age outdoor

0 fluffy ginger 2 True

1 sparky grey 8 False

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df.loc[0:1, "color"]

# Series(["ginger", "grey"])

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Practice problems!

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Solutions

DF manipulation:

Warm-up

Groupby Demo

Group By

result = data.groupby('col1')['col2'].sum()

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Data�DataFrame

Split

Apply

Combine�Series

Groupby problems solutions

1. # first, groupby the city of origin, then access the flavor score column and find the index of the max value

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grouped_cities = gsb.groupby("CityOfOrigin")["FlavorScore"].idxmax()

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# then, use the grouping from above as our row indexer in .loc (since we used idxmax()) and specify the other columns we want to display

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gsb_grouped = gsb.loc[grouped_cities, ["Name", "CityOfOrigin", "FlavorScore"]]

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1. # groupby both columns (order matters), then call .size() to show the count

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city_year_count = gsb.groupby(["CityOfOrigin", "StartedBaking"]).size()

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Section Code:

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Logistics & Reminders:

• HW 2: Pokemon released tomorrow, due Friday (July 10) @ 11:59 PM
• Section Check-In 2 due tomorrow (Friday, July 03) @ 11:59PM

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Remember you can come into office hours or post on Ed if you have any questions. Good luck and thanks for coming to section :)

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