Class 14 — Friday September 25

Reading is fundamental

You're so random

Look out my window — And see random acts of grace — By everyone

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Look both ways

• Back or ahead

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Agenda

• Dataset access and analysis

• Introduce randomness using Python module random

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Special download

• Module url.py

• Defines function url.get_contents( link )

• Returns the contents of the web resource indicated by parameter link as a (normal) string.

• In the future more capabilities will be added.

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Downloads for today

• Program lotta_books.py

• Program pick_a_number.py

• Program chosen.py

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Downloads for interested students (and I hope you are all interested).

• Program bit_by_bit.py

• Program octance.py

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To do list

• Review class artifacts.

• Complete homeworks

• Prepare for Test 1 (10 / 2 / 2020)

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For the fun of it

• Reveal one of your super power(s).

• Share a selfie.

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Review

• A dataset is a collection of informations arranged as list of rows, where each row is a list of cells (dataset value).

• There are two basic ways of accessing the rows in the dataset

dataset = ... # get the dataset

... # set up dataset processing (if needed)

for row in dataset : # consider rows one by one

  # process current row of the dataset

  ... # process the row

... # finish off dataset (if needed)

dataset = ... # get the dataset

nbr_rows = len( dataset ) # determine number of rows

... # set up dataset processing (if needed)

for r in range( 0, nbr_rows) : # consider row indices one by one

  # process row r of the dataset

  row = dataset[ r ] # pick off the row of the dataset

  ... # process the row

... # finish off dataset (if needed)

• There are two basic ways of accessing the individual cells in a dataset

dataset = ... # get the dataset

... # set up dataset processing (if needed)

for row in dataset : # consider rows one by one

  # process current row of the dataset

  ... # prepare to process the cells (if needed)

  for cell in row : # consider cells of the row one by one

  # process the current cell for the row

  ... # process the cell

  ... # finish off row (if needed )

... # finish off dataset (if needed)

dataset = ... # get the dataset

nbr_rows = len( dataset ) # determine number of rows

for r in range( 0, nbr_rows) : # consider row indices one by one

  # process row r of the dataset

  row = dataset[ r ] # pick off the row of the dataset

  nbr_columns = len( row ) # get its number of columns

  ... # prepare to process the cells (if needed)

  # process the cells of the current row

  for c in range( 0, nbr_columns ) : # consider row's column indices one by one

  # process cell at dataset[ r ][ c ]; that

  # is column c of row (i.e., row[ c ])

  cell = row[ c ] # pick off the cell of the row

  ... # process the cell

  ... # finish off row (if needed )

... # finish off dataset if neeed

• Create a copy of a column of a dataset

dataset = ... # get the dataset

c = ... # get the column

column_copy = [] # need a cell accumulator

for row in dataset : # consider rows one by one

  # get cell from row' column c

  cell = row [ c ] # get the cell in column c

  column_copy.append( c ) # copy the cell into accumulator

... # analyze the column

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Program lotta_books.py

• Examines a literal dataset based on the web dataset best_sellers.csv

Program run

header: ['Name', 'Author', 'Language', 'Date', 'Sales']

sales column: 4

name column: 0

date column: 3

books: [["Alice's Adventures in Wonderland", 'Carroll', 'English', 1865, 100000000], ['And Then There Were None', 'Christie', 'English', 1939, 100000000], ['Dream of the Red Chamber', 'Xueqin', 'Chinese', 1754, 100000000], ['Don Quixote', 'de Cervantes', 'Spanish', 1605, 500000000], ['Harry Potter', 'Rowling', 'English', 1997, 447000000], ['The Hobbit', 'Tolkien', 'English', 1937, 150000000], ['The Little Prince', 'de Saint-Exupery', 'French', 1943, 150000000], ['The Lord of the Rings', 'Tolkien', 'English', 1954, 150000000], ['A Tale of Two Cities', 'Dickens', 'English', 1859, 200000000]]

row: ["Alice's Adventures in Wonderland", 'Carroll', 'English', 1865, 100000000]

row: ['And Then There Were None', 'Christie', 'English', 1939, 100000000]

row: ['Dream of the Red Chamber', 'Xueqin', 'Chinese', 1754, 100000000]

row: ['Don Quixote', 'de Cervantes', 'Spanish', 1605, 500000000]

row: ['Harry Potter', 'Rowling', 'English', 1997, 447000000]

row: ['The Hobbit', 'Tolkien', 'English', 1937, 150000000]

row: ['The Little Prince', 'de Saint-Exupery', 'French', 1943, 150000000]

row: ['The Lord of the Rings', 'Tolkien', 'English', 1954, 150000000]

row: ['A Tale of Two Cities', 'Dickens', 'English', 1859, 200000000]

total sold: 1897000000

total sold: 1.897 billion

dates: [1865, 1939, 1754, 1605, 1997, 1937, 1943, 1954, 1859]

earliest: 1605

latest : 1997

average date: 1872

row with earliest book: 3

row with latest book : 4

info on earliest: ['Don Quixote', 'de Cervantes', 'Spanish', 1605, 500000000]

info on latest: ['Harry Potter', 'Rowling', 'English', 1997, 447000000]

name of earliest: Don Quixote

name of latest: Harry Potter

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Being random

• Python has a standard module random with extensive capabilities for generating pseudo-random values.

• Why say pseudo-random? Since we are using algorithm there is some determinism involved.

• There are algorithms that can generate values that appear to be so random that can fool everyone (at least for now).

• The module section of our website explains a subset of that module.

• There are three random module functions of particular interest to us now.

• Function choice(): choose value from a string, list, set, or tuple

• Function seed(): specifically prime the random number generator

• Function randrange(): choose value from a range interval

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Program chosen.py

• Demonstrates some simple usage of random module function choice()

Some program runs

Enter a string: abcdefghij

Four random characters from string abcdefghij : e a j d

Enter some words: th fo ju ov th la lo

Four random words from list ['th', 'fo', 'ju', 'ov', 'th', 'la', 'ol'] : ov lo la ov

Enter a string: abcdefghij

Four random characters from string abcdefghij : a i b h

Enter some words: th fo ju ov th la lo

Four random words from list ['th', 'fo', 'ju', 'ov', 'th', 'la', 'ol'] : th fo th ju

Enter a string: simultaneously

Four random characters from string simultaneously : i y m t

Enter some words: 31 41 597 2 71

Four random words from list ['31', '41', '597', '2', '71'] : 71 597 31 71

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Program pick_a_number.py

• Demonstrates some simple usage of random module function randrange()

Some program runs

Enter two integers: 10 100

Four random values from interval [ 0, 10 ): 7 4 3 5

Four random values from interval [ 0, 100 ): 15 28 64 87

Enter four integers: 10 20 75 100

Four random values from interval [ 0, 10 ): 4 4 8 8

Four random values from interval [ 0, 100 ): 22 64 2 80

Enter two integers: 10 100

Four random values from interval [ 0, 10 ): 1 2 8 6

Four random values from interval [ 0, 100 ): 57 63 81 18

Enter four integers: 10 20 75 100

Four random values from interval [ 10 , 20 ): 11 11 19 15

Four random values from interval [ 75 , 100 ): 89 85 76 99

Enter two integers: 10 100

Four random values from interval [ 0, 10 ): 3 5 3 6

Four random values from interval [ 0, 100 ): 29 12 32 5

Enter four integers: 10 20 75 100

Four random values from interval [ 10 , 20 ): 13 14 16 19

Four random values from interval [ 75 , 100 ): 92 98 75 75

Enter two integers: 2 10

Four random values from interval [ 0, 2 ): 1 0 0 1

Four random values from interval [ 0, 10 ): 6 6 2 8

Enter four integers: 0 8 31 39

Four random values from interval [ 0 , 8 ): 7 2 1 3

Four random values from interval [ 31 , 39 ): 37 36 32 37

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Program bit_by_bit.py — generating random lists of values

• Generates a list of n bits (binary digits) for a user-supplied n.

Some possible program runs

How many bits: 8

[1, 0, 0, 0, 0, 0, 1, 1]

How many bits: 8

[1, 0, 0, 1, 1, 0, 0, 1]

How many bits: 28

[0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1]

How many bits: 28

[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1]

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Program octane.py

• Prompts and gets three user-supplied integers s, n, and d. Uses integer s as a seed to the Python random number generator.

• Accumulates a list of n octal digits (base 8 digits; that is digits from the interval 0 to 7) one by one. Afterwards, prints the list of octal digits.

• Determines and prints the number of occurrences of d in the list of octals (check out the list module for a helpful function.

Some possible program runs

Enter three numbers: 1112 12 1

octal digit list: [4, 1, 4, 0, 6, 1, 5, 2, 5, 1, 1, 2]

number of times value 1 in list: 4

Enter three numbers: 1112 12 5

octal digit list: [4, 1, 4, 0, 6, 1, 5, 2, 5, 1, 1, 2]

number of times value 5 in list: 2

Enter three numbers: 8 9 6

octal digit list: [3, 5, 6, 2, 3, 0, 1, 2, 3]

number of times value 6 in list: 1

Enter three numbers: 8 9 6

octal digit list: [3, 5, 6, 2, 3, 0, 1, 2, 3]

number of times value 3 in list: 3

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© 2020 Jim Cohoon

Resources from previous semesters are available.