BADM 211 - Final Exam (UIUC)
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. - answera. [[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:
a = [1,2,3,4,5,6,7,8,9]
Which of the following options would you choose? (Python List) - answera. 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) -
answera. 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. - answera. 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). - answera. 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() - answera. 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) - answera.
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. - answerp=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) - answera. 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) - answera. 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? - answera.
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) - answera. plt.plot.hist(X)
**b. plt.hist(X)
c. plt.histogram(X)
, d. plt.plot(X)
How would you create a Figure with 6 Axes objects organized in 3 rows and 2 columns?
(matplotlib grammar) - answera. fig, ax = plt.axes((2, 3))
b. fig, ax = plt.subplots[3, 2]
c. fig, ax = plt.subplots((2, 3))
**d. fig, ax = plt.subplots(3, 2)
What is the correct way to calculate 2 to the power of 10? (python basics) - answera.
2^10
b. 2pow10
c. 2%*%10
**d. 2**10
Which one is NOT like the others? (python basics) - answer**a. dataframe
b. boolean
c. int
d. str
e. float
Consider the below code and fill in the blanks
# Create the areas list (python lists): areas = ["hallway", 11.25, "kitchen", 18.0, "living
room", 20.0, "bedroom", 10.75, "bathroom", 9.50]
# Print out second element from areas
print(areas[___])
# Print out last element from areas
print(areas[___]) - answera .2, 10
b. 2, -1
c. 1, -1
**d. 1, 9
Select correct option to print below output (python basics)
height_in_cms = [101, 101.2,108, 100,103]
height_in_cms = np.array(height_in_cms)
Output: (5,) - answer**a. height_in_cms.shape
b. height_in_cms.len()
c. height_in_cms.shape()
d. len(height_in_cms)
Fill in the following blank to inspect a dataframe "df". (loading data in pandas)
print (df.______) - answera. info
**b. info()
c. inspect
d. inspect()
You have a pandas dataframe denoted as 'df' with the following data:
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. - answera. [[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:
a = [1,2,3,4,5,6,7,8,9]
Which of the following options would you choose? (Python List) - answera. 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) -
answera. 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. - answera. 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). - answera. 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() - answera. 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) - answera.
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. - answerp=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) - answera. 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) - answera. 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? - answera.
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) - answera. plt.plot.hist(X)
**b. plt.hist(X)
c. plt.histogram(X)
, d. plt.plot(X)
How would you create a Figure with 6 Axes objects organized in 3 rows and 2 columns?
(matplotlib grammar) - answera. fig, ax = plt.axes((2, 3))
b. fig, ax = plt.subplots[3, 2]
c. fig, ax = plt.subplots((2, 3))
**d. fig, ax = plt.subplots(3, 2)
What is the correct way to calculate 2 to the power of 10? (python basics) - answera.
2^10
b. 2pow10
c. 2%*%10
**d. 2**10
Which one is NOT like the others? (python basics) - answer**a. dataframe
b. boolean
c. int
d. str
e. float
Consider the below code and fill in the blanks
# Create the areas list (python lists): areas = ["hallway", 11.25, "kitchen", 18.0, "living
room", 20.0, "bedroom", 10.75, "bathroom", 9.50]
# Print out second element from areas
print(areas[___])
# Print out last element from areas
print(areas[___]) - answera .2, 10
b. 2, -1
c. 1, -1
**d. 1, 9
Select correct option to print below output (python basics)
height_in_cms = [101, 101.2,108, 100,103]
height_in_cms = np.array(height_in_cms)
Output: (5,) - answer**a. height_in_cms.shape
b. height_in_cms.len()
c. height_in_cms.shape()
d. len(height_in_cms)
Fill in the following blank to inspect a dataframe "df". (loading data in pandas)
print (df.______) - answera. info
**b. info()
c. inspect
d. inspect()
You have a pandas dataframe denoted as 'df' with the following data:
