CS 1112: Spring 2020

Name, Author, Language, Date, Sales
Don Quixote, de Cervantes, Spanish, 1605, 500000000
A Tale of Two Cities, Dickens, English, 1859, 200000000
The Lord of the Rings, Tolkien, English, 1954, 150000000
The Little Prince, de Saint-Exupery, French, 1943, 140000000
Harry Potter and the Philosopher's Stone, Rowling, English, 1997, 120000000
The Hobbit, Tolkien, English, 2017, 100000000
And Then There Were None, Christie, English, 2019, 100000000
Dream of the Red Chamber, Xueqin, Chinese, 1754, 100000000
Alice's Adventures in Wonderland, Carroll, English, 1865, 100000000

elevations.csv

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

oceania.csv

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

rows_of_stuff.csv

Asta,Hachiko,Laika,Lassie
59.0,TruE
faLse,3.14,271
01,10,10.0,ABC

continental-usa.csv

Zipcode, Town, State, Latitude, Longitude
00210, Portsmouth, NH, 43.005895, -71.013202
00211, Portsmouth, NH, 43.005895, -71.013202
00212, Portsmouth, NH, 43.005895, -71.013202
00213, Portsmouth, NH, 43.005895, -71.013202
00214, Portsmouth, NH, 43.005895, -71.013202
00215, Portsmouth, NH, 43.005895, -71.013202
00501, Holtsville, NY, 40.922326, -72.637078
00544, Holtsville, NY, 40.922326, -72.637078
...
99403, Clarkston, WA, 46.400365, -117.08313
99536, Kennewick, WA, 46.216706, -119.160173

Program weathering_heights.py — you don't need a weather man to know which way the wind blows

Makes use of module url.py to access a weather forecast for a user-specified zipcode by querying the forecast.weather.gov website

Insight by examples

https://forecast.weather.gov/zipcity.php?inputstring=22903

<!DOCTYPE html> <html class="no-js"> ...
<div class="row row-odd row-forecast"><div class="col-sm-2 forecast-label"><b>Today</b></div> <div class="col-sm-10 forecast-text"> Partly sunny, with a high near 55. East wind around 6 mph. </div> ...
</body> </html>

https://forecast.weather.gov/zipcity.php?inputstring=83278

<!DOCTYPE html> <html class="no-js"> ...
<div class="row row-odd row-forecast"><div class="col-sm-2 forecast-label"><b>Today</b></div> <div class="col-sm-10 forecast-text"> Snow likely. Mostly cloudy, with a high near 23. Calm wind becoming northwest around 5 mph in the afternoon. Chance of precipitation is 70%. Total daytime snow accumulation of around an inch possible. </div> ...
</body> </html>

https://forecast.weather.gov/zipcity.php?inputstring=96737

<!DOCTYPE html> <html class="no-js"> ...
<div class="row row-odd row-forecast"><div class="col-sm-2 forecast-label"><b>Today</b></div> <div class="col-sm-10 forecast-text"> Mostly sunny, with a high near 78. Breezy, with an east wind 9 to 15 mph, with gusts as high as 21 mph. </div> ...
</body> </html>

Some program runs

Enter zipcode: 22903

Partly sunny, with a high near 55. East wind around 6 mph.

Enter zipcode: 83278

Snow likely. Mostly cloudy, with a high near 23. Calm wind becoming northwest around 5 mph in the afternoon. Chance of precipitation is 70%. Total daytime snow accumulation of around an inch possible.

Enter zipcode: 96737

Mostly sunny, with a high near 78. Breezy, with an east wind 9 to 15 mph, with gusts as high as 21 mph

Algorithm

Get access to url get web content capabilities

Define helpful constants — base query, forecast delimiters, lengths of forecast delimiteres

Get user zip code of interest

Create the query link to get forecast from national weather service

Get the contents of the page found at the forecast link

Find indexes of query front and rear delimiters

Find indexes of forecast front and rear

Get the forecast substring from the page

Print the forecast

Program where_is_it.py — where is it

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

Question

How should we determine the estimate?

Last class program get_a_dataset.py — getting a dataset step by step

Get a user-specified web csv resource as a Python dataset

Some program runs

Enter name of dataset: best-sellers.csv

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

data:
[['Don Quixote', 'de Cervantes', 'Spanish', '1605', '500000000'], ['A Tale of Two Cities', 'Dickens', 'English', '1859', '200000000'], ['The Lord of the Rings', 'Tolkien', 'English', '1954', '150000000'], ['The Little Prince', 'de Saint-Exupery', 'French', '1943', '140000000'], ["Harry Potter and the Philosopher's Stone", 'Rowling', 'English', '1997', '120000000'], ['The Hobbit', 'Tolkien', 'English', '2017', '100000000'], ['And Then There Were None', 'Christie', 'English', '2019', '100000000'], ['Dream of the Red Chamber', 'Xueqin', 'Chinese', '1754', '100000000'], ["Alice's Adventures in Wonderland", 'Carroll', 'English', '1865', '100000000']]

Enter name of dataset: elevations.csv

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]]

Program csv_is_not_a_pharmacy.py — streamlining getting a dataset

Makes use of module url.py

Some program runs

Enter name of dataset: oceania.csv

['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']

['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: rows_of_stuff.csv

['Asta', 'Hachiko', 'Laika', 'Lassie']
['59.0', 'TruE']
['faLse', '3.14', '271']
['01', '10', '10.0', 'ABC']

['Asta', 'Hachiko', 'Laika', 'Lassie']
[59.0, True]
[False, 3.14, 271]
[1, 10, 10.0, 'ABC']

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

Thanks for the rose

33041

Resources from previous semesters are available.