Problem 1: Tracing Clues

A = [
     'Fox',
     'Giraffe', 
     'Hippo'
    ]
A.append('Iguana')
A[:].reverse()
B = A
for anim in B:
    if anim[1] == 'i':
        B.remove(anim)
print(A)

Problem 2a: Some Cleaning

• This is a coding problem. Work in pairs or trios on a single computer
  • Discuss your algorithm BEFORE YOU WRITE ANY CODE. Make sure everyone understands.
• The file raw_data.py contains the constant DATA, which is a large list of strings
• Many of those strings are numbers, but some are not
• Your first task is to use a loop and append to create a list of only the entries that are numbers
• Your result should be a list of numbers, so you’ll need to convert them from strings

Problem 2b: Cleaning Comprehension

• Rewrite your above code to generate the exact same list, but using a list comprehension
• Should be able to compare the lists you got from each method, and they should have identical contents

Problem 2c: Purging

• Both the previous approaches created a new list in memory with the desired traits
• What if we wanted to prune the “bad” values out of the original list?
• Write an approach that would accomplish this, ensuring that the list id before your code and after you code is still the same!

A Tabulation Aside

• Lists can also be useful when you have a collection of values and you need to count how many values fall into different “bins”
  • This is commonly called tabulation
• You could accomplish this with multiple counting variables, but it is unreasonable to define and track 50+ variables
  • With a list, the number at each index represents a count of something
  • Instead of creating 50+ variables, you just have a list of 50+ counts
• Useful if you can equate bins to indexes directly, but sometimes you just need to define what index corresponds to what category

Problem 2d: Tabulation

• The cleaned data list contains number values ranging from 0 to 25
• Use tabulation to count how many times each number appears in the array
• What are the top three more frequently occurring numbers?

Letter Frequencies

• Code breakers can often use information about how likely a letter is to appear in different languages to determine what a symbol is likely to correspond to
• We have the English dictionary at our disposal, so let’s determine the 5 least frequently occurring letters in the English dictionary