CSC111 Lab 8 2014
--D. Thiebaut (talk) 14:01, 24 March 2014 (EDT)
This lab deals with strings and list operations, and transforming strings into lists and lists into strings.
Splitting Strings
Work in the console, and try these different commands. Observe what the different operations do.
>>> line = "The quick, red fox jumped. It jumped over the lazy, sleepy, brown dog." >>> line >>> line.split() >>> words = line.split() >>> words >>> words[0] >>> words[1] >>> words[-1] >>> words[-2] >>> chunks = line.split( ',' ) # split on commas >>> chunks >>> chunks = line.split( '.' ) # split on periods >>> chunks >>> words >>> separator = "+" >>> newLine = separator.join( words ) # join the words into a new string and use separator as the glue >>> newLine >>> separator = "$$$" >>> newLine = separator.join( words ) # same but use $$$ as the glue >>> newLine >>> words # verify that you still have individual words in this list >>> newWords = [ words[0], words[3], words[4], words[7], words[8], words[12] ] # create a new list >>> newWords >>> " ".join( newWords ) # join strings in newWords list with a space
Mini Assignments
The solution program for the Exercises we saw in class on Monday and Wednesday contains good models of code that can be used to answer most of the challenges in this lab.
Challenge 1 |
- Use a judicious mix() of split() and join operations to convert the string
"1 China 1,339,190,000 9,596,960.00 139.54 3,705,405.45 361.42"
- into a new string:
"China 1339190000"
- Note 1: the lack of commas in the number! (Hints: string objects have replace methods that could prove useful here!)
- Note 2: that this line is taken from a table from this URL where the numbers after the country indicate a) the population, the area and population density expressed with square-kilometers, and the area and population density expressed with square-miles.
Challenge 2 |
- Given the following list, store it into a multi-line variable called text, split it into individual lines, and apply your transformation to each line so that your program outputs only the country names and their populations.
Bangladesh 164,425,000 144,000.00 1,141.84 55,598.69 2,957.35 Brazil 193,364,000 8,511,965.00 22.72 3,286,486.71 58.84 China 1,339,190,000 9,596,960.00 139.54 3,705,405.45 361.42 Egypt 78,848,000 1,001,450.00 78.73 386,661.85 203.92 Ethiopia 79,221,000 1,127,127.00 70.29 435,185.99 182.04 Germany 81,757,600 357,021.00 229.00 137,846.52 593.11 India 1,184,639,000 3,287,590.00 360.34 1,269,345.07 933.27 Indonesia 234,181,400 1,919,440.00 122.01 741,099.62 315.99 Iran 75,078,000 1,648,000.00 45.56 636,296.10 117.99 Japan 127,380,000 377,835.00 337.13 145,882.85 873.17 Mexico 108,396,211 1,972,550.00 54.95 761,605.50 142.33 Nigeria 170,123,000 923,768.00 171.32 356,668.67 443.71 Pakistan 170,260,000 803,940.00 211.78 310,402.84 548.51 Phillipines 94,013,200 300,000.00 313.38 115,830.60 811.64 Russia 141,927,297 17,075,200.00 8.31 6,592,768.87 21.53 United-States 309,975,000 9,629,091.00 32.19 3,717,811.29 83.38 Vietnam 85,789,573 329,560.00 260.32 127,243.78 674.21
- Your first variable should be text, defined as follows:
text = """ Bangladesh 164,425,000 144,000.00 1,141.84 55,598.69 2,957.35 Brazil 193,364,000 8,511,965.00 22.72 3,286,486.71 58.84 China 1,339,190,000 9,596,960.00 139.54 3,705,405.45 361.42 Egypt 78,848,000 1,001,450.00 78.73 386,661.85 203.92 Ethiopia 79,221,000 1,127,127.00 70.29 435,185.99 182.04 Germany 81,757,600 357,021.00 229.00 137,846.52 593.11 India 1,184,639,000 3,287,590.00 360.34 1,269,345.07 933.27 Indonesia 234,181,400 1,919,440.00 122.01 741,099.62 315.99 Iran 75,078,000 1,648,000.00 45.56 636,296.10 117.99 Japan 127,380,000 377,835.00 337.13 145,882.85 873.17 Mexico 108,396,211 1,972,550.00 54.95 761,605.50 142.33 Nigeria 170,123,000 923,768.00 171.32 356,668.67 443.71 Pakistan 170,260,000 803,940.00 211.78 310,402.84 548.51 Phillipines 94,013,200 300,000.00 313.38 115,830.60 811.64 Russia 141,927,297 17,075,200.00 8.31 6,592,768.87 21.53 United-States 309,975,000 9,629,091.00 32.19 3,717,811.29 83.38 Vietnam 85,789,573 329,560.00 260.32 127,243.78 674.21"""
Challenge 3 |
- Take your solution for Challenge 2 and make it output the country with the largest population.
Challenge 4 |
- Same as Challenge 3, but this time make your program output the country with the largest population density.
Sorting Lists, Reversing List, finding the Min or Max of a List
Enter the different commands below in the console, and observe how Python executes each line.
>>> seven = [ "Sleepy", "Sneezy", "Bashful", "Happy", "Grumpy", "Dopey", "Doc" ] >>> seven.sort() >>> seven >>> seven.reverse() >>> seven >>> nums = [0, 10, -200, 3, 4, 100] >>> nums.sort() >>> nums >>> nums.reverse() >>> nums >>> dwarvesHeight = [('Doc', 2), ('Dopey', 6), ('Grumpy', 4.5), ('Happy', 7),('Bashful', 3)] >>> dwarvesHeight.sort() >>> dwarvesHeight >>> heightDwarves = [] >>> for pair in dwarvesHeight: name = pair[0] height = pair[1] heightDwarves.append( (height, name ) ) >>> heightDwarves >>> heightDwarves.sort() >>> heightDwarves >>> heightDwarves.reverse() >>> heightDwarves >>> min( heightDwarves ) >>> max( heightDwarves ) >>>
Challenge 5 |
- Make your program store the pairs (population, country name) into a list
- Make your program output the country with the smallest population, nicely formatted (i.e. no parentheses or commas printed)
- Make your program output the country with the largest population.
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- Figure out a way to take a string of the form "Pakistan 108 166 226" where the first word is a country name, and the following three numbers are estimated populations of this country in 1900, 2008, and 2025, into a new string with only the first and last words, i.e. "Pakistan 226".
China 1,458
India 1,398
United-States 352
Indonesia 273
Brazil 223
Pakistan 226
Bangladesh 198
Nigeria 208
Russia 137
Japan 126