CSE 163

Section AX

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TA 1 & TA 2

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Question of the Day: What food describes your mood?

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QoD:

What food describes your current mood?

Housekeeping 🏡

Important Dates and Reminders

• Take-Home Assessment 1: Processing is due tonight (7/10) @ 11:59 PM.
• Checkpoint 1: Pandas released tomorrow, due Monday (7/14) @ 11:59 PM.
• Take-Home Assessment 2: Pokemon released tomorrow, due Thursday (7/17) @ 11:59PM

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• Resub Cycle 1 (HW0) closes Tuesday (7/15) @ 11:59 PM.

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Game Plan

What We’ll Cover Today

• Quick Review
• Groupby!
• [problems of your choice] (2 & 6 & …)

Groupby Demo

Group By

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

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6

Data�DataFrame

Split

Apply

Combine�Series

data

result = data.groupby(‘col1’)

result = data.groupby(‘col1’)

result = data.groupby(‘col1’)

result = data.groupby(‘col1’)

result = data.groupby(‘col1’)[‘col2’]

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

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

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

data

result

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

Recap!

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

Recap!

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

Recap!

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

Recap!

What is the value of result?

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result = data.groupby(‘col2’)[‘col1’].max()

Your Turn!

data

What is the value of result?

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result = data.groupby(‘col2’)[‘col1’].max()

Your Turn!

data

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])

out_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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30

Ed Lessons