Storing Data (Need) Exercises

Exercise 1 (Total recall?) ☻

Purely from memory, jot down:

  1. Two similarities between lists and arrays.
  2. Two differences between lists and arrays.
  3. What is a dictionary?

If you cannot recall the answers, please refer to the course notes and put this information you could not recall in italics.

Exercise 2 (Indexing) ☻

Modify the following code to print out all elements with an odd number. I have done the one corresponding to i9 for you.

py_list = ["a1", "b2", "c3", "d4", "e5", "f6", "g7", "h8", "i9", "j10"]
                    # Prints 'a1'
                    # Prints 'c3'
                    # Prints 'e5'
                    # Prints 'g7'
print(py_list[8])   # Prints 'i9'
i9

Exercise 3 (Index again) ☻

Given the following list in Python:

elements = ['Hydrogen',
            'Helium', 'Lithium',
            'Beryllium', 'Boron', 'Carbon',
            'Nitrogen', 'Oxygen',
            'Fluorine',
            'Neon']
  1. Access and print the element at index 4 using forward indexing.
  2. Access and print the element at index 4 from the end of the list using reverse indexing.

Exercise 4 (How many ones) ☻

Use the concepts you learned in this chapter to determine the number of 1’s in the following list of numbers.

numbers=[45, 60, 1, 30, 96, 1, 96, 57, 16, 1,
        99, 62, 86, 43, 42, 60, 59, 1, 1, 35,
        83, 47, 34, 28, 68, 23, 22, 92, 1, 79,
        1, 29, 94, 72, 46, 47, 1, 74, 32, 20,
        8, 37, 35, 1, 89, 29, 86, 19, 43, 61]

Here are some hints:

  1. Use a NumPy array.
  2. Ask a question.
  3. False is considered 0, and True is considered 1 by sum().

Exercise 5 (A Matter of Statistics) The following shows the data related to \(x_1\) and \(x_2\).

x_1 = [1, 4, 9, 16, 25, 36, 49, 64, 81, 100]
x_2 = [1, 8, 27, 64, 125, 216, 343, 512, 729, 1000]

Using NumPy:

  1. Determine the means of \(x_1\) and \(x_2\).
  2. Determine the standard deviations of \(x_1\) and \(x_2\).
  3. Determine the Pearson correlation coefficient \(r\) for the two datasets. Don’t use np.corrcoef()!
  4. Compare your results to np.corrcoef().

HINTS: np.mean(), np.std(), np.sum(), np.sqrt()

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