BADM 211 EXAM STUDY GUIDE 2026
QUESTIONS AND ANSWERS
COMPREHENSIVE PREP RESOURCE
●● 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:
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]
●● 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])
●● 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])
c. np.array([1, 1, 2]) + np.array([3, 4, -1])
**d. np.array([4, 3, 0]) + np.array([0, 2, 2])
●● Assuming matplotlib.pyplot is imported as plt. Which of the
following commands plots a histogram of the variable named X? (Intro
to matplotlib)
Answer: a. plt.plot.hist(X)
QUESTIONS AND ANSWERS
COMPREHENSIVE PREP RESOURCE
●● 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:
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]
●● 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])
●● 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])
c. np.array([1, 1, 2]) + np.array([3, 4, -1])
**d. np.array([4, 3, 0]) + np.array([0, 2, 2])
●● Assuming matplotlib.pyplot is imported as plt. Which of the
following commands plots a histogram of the variable named X? (Intro
to matplotlib)
Answer: a. plt.plot.hist(X)
