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These problems are taken from past quizzes and exams. Work on them
on paper, since the quizzes and exams you take in this
course will also be on paper.
We encourage you to complete these
problems during discussion section. Solutions will be made available
after all discussion sections have concluded. You don’t need to submit
your answers anywhere.
Note: We do not plan to cover all of
these problems during the discussion section; the problems we don’t
cover can be used for extra practice.
For the problems that follow, we will work with a dataset consisting
of various skyscrapers in the US, which we’ve loaded into a DataFrame
called sky. The first few rows of sky are
shown below (though the full DataFrame has more rows):
Each row of sky corresponds to a single skyscraper. For
each skyscraper, we have:
its name, which is stored in the index of sky
(string)
the 'material' it is made up of (string)
the 'city' in the US where it is located
(string)
the number of 'floors' (levels) it contains
(int)
its 'height' in meters (float), and
the 'year' in which it was opened (int)
Below, identify the data type of the result of each of the following expressions, or select “error” if you believe the expression results in an error.
sky.sort_values('height')int or float
Boolean
string
array
Series
DataFrame
error
Answer: DataFrame
sky is a DataFrame. All the sort_values
method does is change the order of the rows in the Series/DataFrame it
is called on, it does not change the data structure. As such,
sky.sort_values('height') is also a DataFrame.
The average score on this problem was 87%.
sky.sort_values('height').get('material').loc[0]int or float
Boolean
string
array
Series
DataFrame
error
Answer: error
sky.sort_values('height') is a DataFrame, and
sky.sort_values('height').get('material') is a Series
corresponding to the 'material' column, sorted by
'height' in increasing order. So far, there are no
errors.
Remember, the .loc accessor is used to access
elements in a Series based on their index.
sky.sort_values('height').get('material').loc[0] is asking
for the element in the
sky.sort_values('height').get('material') Series with index
0. However, the index of sky is made up of building names.
Since there is no building named 0, .loc[0]
causes an error.
The average score on this problem was 79%.
sky.sort_values('height').get('material').iloc[0]int or float
Boolean
string
array
Series
DataFrame
error
Answer: string
As we mentioned above,
sky.sort_values('height').get('material') is a Series
containing values from the 'material' column (but sorted).
Remember, there is no element in this Series with an index of 0, so
sky.sort_values('height').get('material').loc[0] errors.
However, .iloc[0] works differently than
.loc[0]; .iloc[0] will give us the first
element in a Series (independent of what’s in the index). So,
sky.sort_values('height').get('material').iloc[0] gives us
back a value from the 'material' column, which is made up
of strings, so it gives us a string. (Specifically, it gives us the
'material' type of the skyscraper with the smallest
'height'.)
The average score on this problem was 89%.
sky.get('floors').max()int or float
Boolean
string
array
Series
DataFrame
error
Answer: int or float
The Series sky.get('floors') is made up of integers, and
sky.get('floors').max() evaluates to the largest number in
the Series, which is also an integer.
The average score on this problem was 91%.
sky.index[0]int or float
Boolean
string
array
Series
DataFrame
error
Answer: string
sky.index contains the values
'Bayard-Condict Building',
'The Yacht Club at Portofino',
'City Investing Building', etc. sky.index[0]
is then 'Bayard-Condict Building', which is a string.
The average score on this problem was 91%.
Write a single line of code that evaluates to the name of the tallest
skyscraper in the sky DataFrame.
Answer:
sky.sort_values(by='height', ascending=False).index[0]
In order to answer this question, we must first sort the values of
the column we are interested in. As such, we sort the entire DataFrame
by the height column, and because we are interested in the
name of the tallest building, we should set the ascending
parameter to False because we would like the heights to be
ordered in descending order, thus leading to the line
sky.sort_values(by='height', ascending=False). After
sorting in descending order, we know that the tallest building is going
to be the first row of the new sky DataFrame, and thus we
now only need to get the name of the skyscraper, which happens to be in
the index. In order to access the index of the DataFrame we can use
sky.index, and in our case because we know that we want the
first index, we would need to write sky.index[0]. Finally,
putting it all together, in order to get the name of the tallest
skyscraper in the sky DataFrame, we would need to write
sky.sort_values(by='Height', ascending=False).index[0].
Write a single line of code that evaluates to the average number of floors across all skyscrapers in the DataFrame.
Answer: sky.get('floors').mean()
In order to answer the question, we must first figure out how to get
the number of floors each skyscraper has. We can do this with a line of
code like sky.get('floors') which will get the number of
floors each skyscraper has. After doing this, we now need to find out
the average number of floors each skyscraper has. We can do this by
using the .mean() method, which in our case will get the
average number of floors each skyscraper has. Putting this all togther,
we get a line of code that looks like
sky.get('floors').mean().
Suppose students is a DataFrame of all students who took
DSC 10 last quarter. students has one row per student,
where:
The index contains students’ PIDs as strings starting with
"A".
The "Overall" column contains students’ overall
percentage grades as floats.
The "Animal" column contains students’ favorite
animals as strings.
What type is students.get("Overall")? If this expression
errors, select “this errors."
float
string
array
Series
this errors
Answer: Series
The average score on this problem was 73%.
What type is students.get("PID")? If this expression
errors, select “this errors."
float
string
array
Series
this errors
Answer: this errors
The average score on this problem was 67%.
Vanessa is one student who took DSC 10 last quarter. Her PID is A12345678, she earned the sixth-highest overall percentage grade in the class, and her favorite animal is the giraffe.
Supposing that students is already sorted by
"Overall" in descending order, fill in the
blanks so that animal_one and animal_two
both evaluate to "giraffe".
animal_one = students.get(__(x)__).loc[__(y)__]
animal_two = students.get(__(x)__).iloc[__(z)__]Answer:
x: "Animal"y: "A12345678"z: 5
The average score on this problem was 69%.
If students wasn’t already sorted by
"Overall" in descending order, which of your answers would
need to change?
Neither y nor z would need to change
Both y and z would need to change
y only
z only
Answer: z only
The average score on this problem was 82%.
You are given a DataFrame called sports, indexed by
'Sport' containing one column,
'PlayersPerTeam'. The first few rows of the DataFrame are
shown below:
| Sport | PlayersPerTeam |
|---|---|
| baseball | 9 |
| basketball | 5 |
| field hockey | 11 |
Which of the following evaluates to
'basketball'?
sports.loc[1]
sports.iloc[1]
sports.index[1]
sports.get('Sport').iloc[1]
Answer: sports.index[1]
We are told that the DataFrame is indexed by 'Sport' and
'basketball' is one of the elements of the index. To access
an element of the index, we use .index to extract the index
and square brackets to extract an element at a certain position.
Therefore, sports.index[1] will evaluate to
'basketball'.
The first two answer choices attempt to use .loc or
.iloc directly on a DataFrame. We typically use
.loc or .iloc on a Series that results from
using .get on some column. Although we don’t typically do
it this way, it is possible to use .loc or
.iloc directly on a DataFrame, but doing so would produce
an entire row of the DataFrame. Since we want just one word,
'basketball', the first two answer choices must be
incorrect.
The last answer choice is incorrect because we can’t use
.get with the index, only with a column. The index is never
considered a column.
The average score on this problem was 88%.
Suppose you are given a DataFrame of employees for a given company.
The DataFrame, called employees, is indexed by
'employee_id' (string) with a column called
'years' (int) that contains the number of years each
employee has worked for the company.
Suppose that the code
employees.sort_values(by='years', ascending=False).index[0]outputs '2476'.
True or False: The number of years that employee 2476 has worked for the company is greater than the number of years that any other employee has worked for the company.
True
False
Answer: False
This is false because there could be other employees who worked at the company equally long as employee 2476.
The code says that when the employees DataFrame is
sorted in descending order of 'years', employee 2476 is in
the first row. There might, however, be a tie among several employees
for their value of 'years'. In that case, employee 2476 may
wind up in the first row of the sorted DataFrame, but we cannot say that
the number of years employee 2476 has worked for the company is greater
than the number of years that any other employee has worked for the
company.
If the statement had said greater than or equal to instead of greater than, the statement would have been true.
The average score on this problem was 29%.
What will be the output of the following code?
employees.assign(start=2021-employees.get('years'))
employees.sort_values(by='start').index.iloc[-1]the employee id of an employee who has worked there for the most years
the employee id of an employee who has worked there for the fewest years
an error message complaining about iloc[-1]
an error message complaining about something else
Answer: an error message complaining about something else
The problem is that the first line of code does not actually add a
new column to the employees DataFrame because the
expression is not saved. So the second line tries to sort by a column,
'start', that doesn’t exist in the employees
DataFrame and runs into an error when it can’t find a column by that
name.
This code also has a problem with iloc[-1], since
iloc cannot be used on the index, but since the problem
with the missing 'start' column is encountered first, that
will be the error message displayed.
The average score on this problem was 27%.
Suppose flower_data is a DataFrame with information on
different species of flowers, where:
The "species" column contains the name of the
species of flower, as a string. Each value in this column is
unique.
The "petals" column contains the average number of
petals of flowers of this species, as an int.
The "length" column contains the average stem length
of flowers of this species in inches, as a float.
One of these three columns is a good choice to use as the index of
this DataFrame. Write a line of code that sets this column as the index
of flower_data, and assigns the resulting DataFrame to the
variable flowers.
Answer:
flowers = flower_data.set_index("species")
The average score on this problem was 79%.
Important: The following questions will use
flowers instead of flower_data.
Which of the following expressions evaluates to a DataFrame that is
sorted by "petals" in descending order?
flowers.sort_values(by = "petals", ascending = True)
flowers.sort_values(by = "petals", ascending = False)
flowers.get("petals").sort_values(ascending = True)
flowers.get("petals").sort_values(ascending = False)
Answer: Option B
The average score on this problem was 83%.
Suppose that the 4th row of flowers corresponds to a
rare species of flower named "fire lily". Fill in the
blanks below so that both of these expressions evaluate to the stem
length in inches of "fire lily".
i. flowers.get("length").loc[__(x)__]
ii. flowers.get("length").iloc[__(y)__]Answer: (x): "fire lily", (y):
3
The average score on this problem was 83%.
Suppose that the 3rd row of flowers corresponds to the
species "stinking corpse lily". Using the
flowers DataFrame and the string method
.split(), write an expression that evaluates to
"corpse".
Answer:
flowers.index[2].split(" ")[1]
The average score on this problem was 46%.
An art museum records information about its collection in a DataFrame
called art. The columns of art are as
follows:
"title" (str): the name of the art piece."artist" (str): the name of the artist."year" (int): the year the art piece was produced."price" (float): the selling price of the art piece in
dollars Write an expression that evaluates to the number of art pieces made in 1950 that cost less than $10,000.
Answer:
art[(art.get("year") == 1950) & (art.get("price") < 10000)].shape[0]
The average score on this problem was 72%.
The laptops DataFrame contains information on various
factors that influence the pricing of laptops. Each row represents a
laptop, and the columns are:
"Mfr" (str): the company that manufactures the laptop,
like “Apple” or “Dell”."Model" (str): the model name of the laptop, such as
“MacBook Pro”."OS" (str): the operating system, such as “macOS” or
“Windows 11”."Screen Size" (float): the diagonal length of the
screen, in inches."Price" (float): the price of the laptop, in dollars.
Fill in the blanks so that rotten_apple evaluates to the
number of laptops manufactured by "Apple" that are priced
below the median price of all laptops.
x = __(a)__
y = __(b)__
rotten_apple = laptops[x __(c)__ y].__(d)__Note: (a) and (b) are interchangeable
Answer (a):
laptops.get("Mfr") == "Apple"
The average score on this problem was 71%.
Answer (b):
laptops.get("Price") < laptops.get("Price").median()
The average score on this problem was 71%.
Answer (c): &
The average score on this problem was 43%.
Answer (d): shape[0]
The average score on this problem was 43%.
The DataFrame items describes various items available to
collect or purchase using bells, the currency used in the game
Animal Crossing: New Horizons.
For each item, we have:
"Item" (str): The name of the item."Cost" (int): The cost of the item in bells. Items that
cost 0 bells cannot be purchased and must be collected through other
means (such as crafting)."Location" (str): The store or action through which the
item can be obtained.The first 6 rows of items are below, though
items has more rows than are shown here.
Fill in the blanks so that count_1 and
count_2 both evaluate to the number of items in
items with a "Cost" of 0.
count_1 = items.groupby(__(a)__).__(b)__().get("Item").loc[__(c)__]
count_2 = items[__(d)__].shape[0]Answer:
a: "Cost"b: countc: 0d: items.get("Cost") == 0
The average score on this problem was 81%.