Class 16 — Wednesday September 29

Web and Dataset Chrestomathics

CVS Drugstore — Is not to be examined — CSV files yes

May the light of peace — For you and your families — Always shine brightly

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Look both ways

• Back and ahead

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Agenda

• Dataset processing

• Chrestomathics

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Readability

• Recommended practices

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Downloads

• Program csv_is_not_a_pharmacy.py

• Program lotta_books.py

• Program where_is_it.py

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To do

• Homework.

• Review artifacts

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Program csv_is_not_a_pharmacy.py

• Some program runs

Enter name of dataset: oceania.csv

dataset:

Country, Females, Males

Australia, 11175724, 11092660

Fiji, 421365, 439258

French Polynesia, 132082, 138682

New Caledonia, 125322, 125548

New Zealand, 2223281, 2144855

Papua New Guinea, 3359979, 3498287

Solomon Islands, 259909, 278239

Vanuatu, 117573, 122078

header:

['Country', 'Females', 'Males']

data:

['Country', 'Females', 'Males']

['Australia', 11175724, 11092660]

['Fiji', 421365, 439258]

['French Polynesia', 132082, 138682]

['New Caledonia', 125322, 125548]

['New Zealand', 2223281, 2144855]

['Papua New Guinea', 3359979, 3498287]

['Solomon Islands', 259909, 278239]

['Vanuatu', 117573, 122078]

Enter name of dataset: elevations.csv

dataset:

Location, Author, Max Height, Min Height

Narnia, Lewis, 4810, -10

Neverland, Milne, 426, -2

Oz, Baum, 1231, 679

Sleepy Hollow, Irving, 1629, 304

Stars Hollow, Sherman-Palladino, 725, 152

Toyland, MacDonough, 6187, 0

Wonderland, Carroll, 5895, -5

header:

['Location', 'Author', 'Max Height', 'Min Height']

data:

['Narnia', 'Lewis', 4810, -10]

['Neverland', 'Milne', 426, -2]

['Oz', 'Baum', 1231, 679]

['Sleepy Hollow', 'Irving', 1629, 304]

['Stars Hollow', 'Sherman-Palladino', 725, 152]

['Toyland', 'MacDonough', 6187, 0]

['Wonderland', 'Carroll', 5895, -5]

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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

total sold: 1897000000

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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Program where_is_it.py

• Determines a rough estimate of the center of continental USA by making use of CSV dataset usa-continental.csv

• usa-continental.csv

King City, CA, 36.21106, -121.05986

Fischer, TX, 29.960139, -98.21846

Alma, AR, 35.48891, -94.20897

Mount Moriah, MO, 40.30922, -93.794818

Sparkman, AR, 33.91855, -92.80484

Logansport, LA, 31.991863, -93.98356

...

Steeleville, IL, 38.002188, -89.66723

Underwood, IN, 38.603451, -85.76711

Caledonia, ND, 47.472415, -96.8887

Wales, WI, 43.002534, -88.37771

Greenwich, KS, 37.78335, -97.205419

Lansing, WV, 38.079509, -81.06238

• Question

• How should we determine the estimate?

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Lebanon, KS, 66952, 39°48′38″N 98°33′22″W

 

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