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CSE 163

Groupby and Apply�

Suh Young Choi�

🎶 Listening to: Minecraft soundtrack

💬 Before Class: If you were a kitchen appliance, what would you be?

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This Time

  • Keyword Arguments
  • Groupby
  • Apply

Last Time

  • Imports
  • Pandas
    • How to read a file
    • Accessing columns
    • Element-wise operators
    • Filtering
    • .loc

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Announcements

  • THA 1 due tomorrow!

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  • THA 0 feedback out tomorrow

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  • Resubmission Cycles start this Friday (7/11)
    • You can resubmit or late-submit 1 homework per cycle
    • Resubmission cycle closes on Tuesday (7/15)
    • Be on the lookout for an Ed post with a Google form
    • MUST complete Google form so that we can track your resubmission!

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Keyword Arguments

  • Can specify parameters “by position” or “by name”

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def div(a, b):

return a / b

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# Same behavior

div(1, 2)

div(a=1, b=2)

div(b=2, a=1)

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# Different behavior

div(b=1, a=2)

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DataFrame

  • One of the basic data types from pandas is a DataFrame
    • It’s essentially a table with column and rows!

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id

year

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month

day

latitude

longitude

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name

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magnitude

0

nc72666881

2016

7

27

37.672333

-121.619000

California

1.43

1

us20006i0y

2016

7

27

21.514600

94.572100

Burma

4.90

2

nc72666891

2016

7

27

37.576500

-118.859167

California

0.06

Columns

Index (row)

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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result = data.groupby(‘col1’)

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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result = data.groupby(‘col1’)

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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result = data.groupby(‘col1’)

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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result = data.groupby(‘col1’)

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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result = data.groupby(‘col1’)

A Groupby DataFrame

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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result = data.groupby(‘col1’)[‘col2’]

col2

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

A

1

B

2

C

3

A

4

C

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

col2

.sum()

.sum()

.sum()

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

B

2

C

8

A

5

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

col2

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Groupby Demo

 

col1 

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

B

2

C

8

A

5

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

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Group By

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

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col1

col2

0

A

1

1

B

2

2

C

3

3

A

4

4

C

5

​

col2

C

3

5

​

col2

B

2

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col2

A

1

4

A

5

B

2

C

8

A

5

B

2

C

8

Data�DataFrame

Split

Apply

Combine�Series

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Apply

  • We have shown how to filter and group your data, but sometimes you want to transform your data
  • Pretty easy to change numerical data using the operators we learned last time (+, -, /, *, abs(), min(), max(), etc.)
  • With Strings, it’s not so easy

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  • The last two pass a function as a parameter!

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data['name'].str.len()

data['name'].str.upper()

data['name'].apply(len)

data['name'].apply(my_function)

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Group Work:

Best Practices

When you first working with this group:

  • Introduce yourself!
  • If possible, angle one of your screens so that everyone can discuss together

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Tips:

  • Starts with making sure everyone agrees to work on the same problem
  • Make sure everyone gets a chance to contribute!
  • Ask if everyone agrees and periodically ask each other questions!
  • Call TAs over for help if you need any!

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Before Next Time

  • Complete Lesson 8
    • Remember not for points, but do go towards Checkpoint Tokens
  • Keep working on THA 1
  • Go to section tomorrow!

Next Time

  • Data visualization

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