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This quiz was administered in-person. It was closed-book and
closed-note; students were not allowed to use the DSC
10 Reference Sheet. Students had 20 minutes to work on
the quiz.
This quiz covered Lectures 1-4 of the Winter 2024 offering
of DSC 10.
⚠️ PDF version available here .
Select all the true statements below.
Mixing ints and floats in an arithmetic
expression will always result in a float.
Dividing two ints will sometimes result in an
int.
Any float can be converted to a string using the
function str().
Any string can be converted to a float using the
function float().
Answer: Option 1 and Option 3
Option 1: True. When an arithmetic expression mixes
an int and a float, Python converts the
int to a float so that no information after
the decimal point is lost. The result is therefore always a
float.
Option 2: False. Dividing with / always
produces a float, even when there is no remainder. For
example, 4 / 2 evaluates to 2.0, not
2.
Option 3: True. Every value can be written out as
text, so str() works on any float.
Option 4: False. float() only works on
strings that look like a number. For example,
float('hello') raises a ValueError because
there’s no number to convert.
The average score on this problem was 87%.
Consider the assignment statement below.
pear = [6, 10.1, "pear", 13]What does the expression np.array(pear) evaluate to?
array([6, 10.1, "pear", 13])
array([6, 10.1, pear, 13])
array(["6", "10.1", "pear", "13"])
array(["pear"])
array([pear])
This expression errors
Answer:
array(["6", "10.1", "pear", "13"])
Every element of an array must have the same type. When
np.array() receives a list with mixed types, (if possible)
it converts all of the elements to a single type that can represent
every one of them. A number can always be written as a string, but not
every string can be written as a number, so every element in this array
is converted to a string.
Option 1: array([6, 10.1, "pear", 13]).
Incorrect. This keeps the original types of the elements, so the array
would hold numbers and strings together
Option 2: array([6, 10.1, pear, 13]).
Incorrect. Without quotes, pear refers to the variable (the
whole list), not the string "pear", which still mixes
types
Option 3:
array(["6", "10.1", "pear", "13"]). Correct. All
four elements become strings, so the array has a single type
Option 4: array(["pear"]). Incorrect.
np.array() doesn’t throw away elements whose type doesn’t
match. All four elements are kept
Option 5: array([pear]). Incorrect.
This would be an array containing the variable pear itself,
rather than the elements of the list. This nests the list
pear within another list, ultimately resulting in
array([['6', '10.1', 'pear', '13']]).
Option 6: This expression errors. Incorrect. NumPy converts every element to a common type, which in this problem is a string.
The average score on this problem was 50%.
Suppose x and y are both ints
that have been previously defined, with x < y. Now,
define:
peach = np.arange(x, y, 2)Say that the spread of peach is the difference
between the largest and smallest values in peach. The
spread should be a non-negative integer.
Using array methods, write an expression that
evaluates to the spread of peach.
Answer: peach.max() - peach.min()
The spread is the largest value minus the smallest value. Arrays have
the methods .max() and .min(), which return
the largest and smallest elements of the array.
max(peach) and
np.max(peach) also find the largest value, but they are
functions, note the array goes inside the parentheses. A
method is called on the array with a dot, like
peach.max()peach.max without () refers to the method
itself and doesn’t call it, so peach.max - peach.min
errors.
The average score on this problem was 62%.
Without using any methods or functions, write an
expression that evaluates to the spread of peach.
Hint: Use [ ].
Answer:
peach[len(peach) - 1] - peach[0] or
peach[-1] - peach[0]
The key is that np.arange(x, y, 2) produces values in
increasing order. That means:
peach[0].peach[-1]. A negative index counts from the end of the
array, so -1 is the last element.Common mistakes:
peach[len(peach)] - peach[0] errors.
Indexing starts at 0, so the index of the last element is
len(peach) - 1, not len(peach).y - x or
peach[y] - peach[x]. y is
never in peach because np.arange stops
before y. Also, x and y
are values in the array, not positions in it.
The average score on this problem was 36%.
Choose the correct way to fill in the blank in this sentence:
The spread of peach is ______ the
value of y - x.
always less than
sometimes less than and sometimes equal to
always greater than
sometimes greater than and sometimes equal to
always equal to
Answer: always less than
The smallest value in peach is x. Because
np.arange excludes the stop value, the
largest value in peach is always strictly less than
y. It’s either y - 1 or y - 2,
depending on whether y - x is odd or even. So the spread is
(largest value) − x, which is strictly less than
y - x.
Why the other options are wrong:
y - x if
y were in the array. np.arange never includes
the stop value, so equality never happens.peach lies
between x and y, and x is the
smallest, so the spread can’t exceed y - x.
The average score on this problem was 48%.
Suppose fruits is a DataFrame of the fruits Ashley
bought at the grocery store, where:
The "fruit" column contains the name of the fruit,
as a string. All values in this column are distinct.
The "price" column contains the amount in dollars
spent on the fruit, as a float.
The "pounds" column contains the number of
pounds purchased, as an int.
Fill in the blanks below to add a new column to fruits
called "price_per_ounce" that contains the price per ounce
of each of the fruits in fruits. There are 16
ounces in a pound.
fruits = fruits.__(x)__(price_per_ounce = __(y)__ / __(z)__)Answer (x): assign
We know that we must create a new column called
price_per_ounce, and .assign() is the
DataFrame method that adds a new column. The name before the
= (price_per_ounce) becomes the column’s name,
and the value after the = becomes its values. Notice that
.assign() doesn’t change the original DataFrame. It returns
a new one, which is why the result is saved back into
fruits with fruits = ....
The average score on this problem was 71%.
Answer (y): fruits.get("price")
To compute the price per ounce, we start with the total price paid
for each fruit. fruits.get("price") gives the
"price" column as a Series. Because we do arithmetic on the
whole Series at once, the calculation happens for every fruit (every
row) in one step.
A common mistake is to write just "price". That’s a
string, not the column, so dividing it by a number errors.
The average score on this problem was 67%.
Answer (z):
(fruits.get("pounds") * 16)
Price per ounce = (total price) / (number of ounces). The
"pounds" column is in pounds, so we convert to ounces by
multiplying by 16, since each pound is 16 ounces.
Common mistakes:
fruits.get("price") / fruits.get("pounds") * 16 computes
(price / pounds) × 16. That’s 16 times the price per pound, the
opposite of what we want. The parentheses make Python find the number of
ounces first.fruits.get("pounds") / 16 converts in the wrong
direction.
The average score on this problem was 43%.
Write a line of code that evaluates to the amount of money, in dollars, that Ashley spent on fruit at the grocery store.
Answer: fruits.get("price").sum() or
sum(fruits.get("price"))
Each row’s "price" is the amount Ashley spent on that
fruit, so her total spending is the sum of the "price"
column. First get the column with fruits.get("price"), then
add up its values, either with the .sum() method or the
sum() function.
Common mistakes:
"price_per_ounce". Summing
prices per ounce doesn’t give a total amount of money, because each
fruit was bought in a different quantity. "price" is
already the total spent on each fruit..count() instead of
.sum(). .count() gives the number of
fruits, not the total price.fruits.sum(). This sums every
column of the DataFrame, not just "price", so it doesn’t
evaluate to a single number.
The average score on this problem was 62%.
Fill in the blanks so that the expression below evaluates to the name of the fruit with the highest price per ounce.
(fruits.sort_values(by = "price_per_ounce", ascending = __(x)__)
.get(__(y)__).iloc[0])Answer (x): False
The expression ends with .iloc[0], which takes the
first element. For the fruit with the highest
price per ounce to come first, we need to sort from largest to smallest,
which is ascending = False.
Using .iloc[0] gives the fruit with the lowest
price per ounce.
The average score on this problem was 87%.
Answer (y): "fruit"
The question asks for the fruit’s name, which is in
the "fruit" column. After sorting,
.get("fruit") takes that column, and .iloc[0]
takes the name in the first row.
Common mistakes:
.get("price_per_ounce") gives the
highest price per ounce value, not the name of the fruit.
The average score on this problem was 65%.
Assuming that "mango" is one of the fruits Ashley
bought, fill in the blanks so that the expression below evaluates to the
price per ounce of "mango".
fruits.__(x)__("fruit").get("price_per_ounce").__(y)__["mango"]Answer (x): set_index
To look up a row by the fruit’s name, the fruit names need to be the
index (the row labels).
.set_index("fruit") moves the "fruit" column
into the index, so each row is labeled by its fruit name instead of a
number. This works because the problem says all values in
"fruit" are distinct, so each label refers
to exactly one row.
A common mistake is to try sort_values or
get here. Neither of these changes the row labels, so you
still can’t look rows up by name afterward.
The average score on this problem was 41%.
Answer (y): loc
After .get("price_per_ounce"), we have a Series of
prices per ounce whose index is the fruit names.
.loc["mango"] looks up the value whose
label is "mango".
The common mistake is using .iloc instead.
.iloc looks up by integer position (0, 1,
2, …), so .iloc["mango"] errors.
The average score on this problem was 83%.