1|Page
BADM 211 - Final Exam 2026 (UIUC) Questions
With Correct Answers
A list can contain any Python type. But a list itself is also a Python type.
That means that a list can also contain a list! Python is getting funkier by
the minute, but fear not, just remember the list syntax: (Python List
Assignment)
my_list = [el1, el2, el3]
Can you tell which ones of the following lines of Python code are valid
ways to build a list? Please choose all correct answers. - ANSWER -a.
[[1, 2, 3], [4, 5, 7]]
b. [1 + 2, "a" * 5, 3]
c. [1, 3, 4, 2]
**d. All of the above
Assume, you are given two lists:
a = [1,2,3,4,5]
b = [6,7,8,9]
The task is to create a list which has all the elements of a and b in one
dimension.
Output:
pg. 1
,2|Page
a = [1,2,3,4,5,6,7,8,9]
Which of the following options would you choose? (Python List) -
ANSWER -a. a.join(b)
b. a "+" b
c. a.append(b)
**d. a.extend(b)
You want to print the top 5 ids with highest income from this dataset
(dataframe shows a partial view):
What should be the correct sequence of commands? (Data
Manipulation with Pandas) - ANSWER -a. df.sort_values(), df.head()
b. df.head(), df.sort_values()
**c. df.sort_values("Income"), df.head()
d. df.sort_values("Age"), df.head()
Filter all the rows where age is less than 40 in this dataset (Data
Manipulation with Pandas).
Choose all correct answers. - ANSWER -a. df.filter('Age')<40
**b. df[df.age<40]
c. df.sort_values("Age")<40
**d. df [df['Age']<40]
pg. 2
,3|Page
Get the proportion of male and female entries in this dataset (Data
Manipulation with Pandas). - ANSWER -a. df["Gender"].numbers()
**b. df ["Gender"].value_counts(normalize=True)
c. df["Gender"].counts()
d. df["Gender"].value_counts()
Complete the following command to get the mean income of each
gender from this dataset. (Data Manipulation with Pandas)
Complete this command:
df.______ ("_____")["______"].mean() - ANSWER -a. groupby, age,
income
b. groupby, age, gender
c. groupby, income, gender
**d. groupby, gender, income
You have the following dataframe df: (Data Manipulation with Pandas) -
ANSWER -a. print(df.iloc([2:3])
b. print(df.iloc[3:]
c. print(df.iloc([0:3])
**d. print(df.iloc[1:3])
pg. 3
, 4|Page
Match the variable on the left with its datatype on the right. - ANSWER
-p=3 -- int
q="False" -- str
r=True -- bool
Suppose you have the following data in a csv file named as sales.csv :
(Data Manipulation with Pandas) - ANSWER -a. pd.readcsv("sales.csv")
b.pd. read_excel("sales.csv)
**c. pd.read_csv("sales.csv")
d. pd.read_excel("sales.xlsx")
Use the table and choose the correct code to generate the output
shown. (Data Manipulation with Pandas) - ANSWER -a.
df['Sold_Qty'].mean()
**b. df['Sold_Qty'].max()
c. df['Sold_Qty'].min()
d. df['Sold_Qty'].sum()
Have a look at this line of code: (python basics)
np.array([True, 1, 2]) + np.array([3, 4, False])
Can you tell which code chunk builds the exact same Python object? -
ANSWER -a. np.array([True, 1, 2, 3, 4, False])
b. np.array([0, 1, 2, 3, 4, 5])
pg. 4
BADM 211 - Final Exam 2026 (UIUC) Questions
With Correct Answers
A list can contain any Python type. But a list itself is also a Python type.
That means that a list can also contain a list! Python is getting funkier by
the minute, but fear not, just remember the list syntax: (Python List
Assignment)
my_list = [el1, el2, el3]
Can you tell which ones of the following lines of Python code are valid
ways to build a list? Please choose all correct answers. - ANSWER -a.
[[1, 2, 3], [4, 5, 7]]
b. [1 + 2, "a" * 5, 3]
c. [1, 3, 4, 2]
**d. All of the above
Assume, you are given two lists:
a = [1,2,3,4,5]
b = [6,7,8,9]
The task is to create a list which has all the elements of a and b in one
dimension.
Output:
pg. 1
,2|Page
a = [1,2,3,4,5,6,7,8,9]
Which of the following options would you choose? (Python List) -
ANSWER -a. a.join(b)
b. a "+" b
c. a.append(b)
**d. a.extend(b)
You want to print the top 5 ids with highest income from this dataset
(dataframe shows a partial view):
What should be the correct sequence of commands? (Data
Manipulation with Pandas) - ANSWER -a. df.sort_values(), df.head()
b. df.head(), df.sort_values()
**c. df.sort_values("Income"), df.head()
d. df.sort_values("Age"), df.head()
Filter all the rows where age is less than 40 in this dataset (Data
Manipulation with Pandas).
Choose all correct answers. - ANSWER -a. df.filter('Age')<40
**b. df[df.age<40]
c. df.sort_values("Age")<40
**d. df [df['Age']<40]
pg. 2
,3|Page
Get the proportion of male and female entries in this dataset (Data
Manipulation with Pandas). - ANSWER -a. df["Gender"].numbers()
**b. df ["Gender"].value_counts(normalize=True)
c. df["Gender"].counts()
d. df["Gender"].value_counts()
Complete the following command to get the mean income of each
gender from this dataset. (Data Manipulation with Pandas)
Complete this command:
df.______ ("_____")["______"].mean() - ANSWER -a. groupby, age,
income
b. groupby, age, gender
c. groupby, income, gender
**d. groupby, gender, income
You have the following dataframe df: (Data Manipulation with Pandas) -
ANSWER -a. print(df.iloc([2:3])
b. print(df.iloc[3:]
c. print(df.iloc([0:3])
**d. print(df.iloc[1:3])
pg. 3
, 4|Page
Match the variable on the left with its datatype on the right. - ANSWER
-p=3 -- int
q="False" -- str
r=True -- bool
Suppose you have the following data in a csv file named as sales.csv :
(Data Manipulation with Pandas) - ANSWER -a. pd.readcsv("sales.csv")
b.pd. read_excel("sales.csv)
**c. pd.read_csv("sales.csv")
d. pd.read_excel("sales.xlsx")
Use the table and choose the correct code to generate the output
shown. (Data Manipulation with Pandas) - ANSWER -a.
df['Sold_Qty'].mean()
**b. df['Sold_Qty'].max()
c. df['Sold_Qty'].min()
d. df['Sold_Qty'].sum()
Have a look at this line of code: (python basics)
np.array([True, 1, 2]) + np.array([3, 4, False])
Can you tell which code chunk builds the exact same Python object? -
ANSWER -a. np.array([True, 1, 2, 3, 4, False])
b. np.array([0, 1, 2, 3, 4, 5])
pg. 4
