Week 1 QUIZ

What does df.groupby('Department').mean() do?

• Groups rows by Department and shows row count

• Filters only numeric columns

• Groups data by Department and calculates mean of each group

• None of the above

​ How do you filter rows where column Age > 30 in a DataFrame df?

• df[df['Age'] > 30]

• df['Age'] > 30

• df.select('Age' > 30)

• df.filter(Age > 30)

command returns the shape of an array arr

What is list comprehension equivalent of:

• [i*i for i in range(5)]

• [i*i if i in range(5)]

• (i*i for i in range(5))

• map(lambda i: i*i, range(5))

function returns evenly spaced numbers in given range inclusive of both.

What is the output of the following code?

def extendList(val, lst=[]):

lst.append(val)

return lst

list1 = extendList(10)

list2 = extendList(123, [])

list3 = extendList('a')

print(list1)

print(list2)

print(list3)

• [10], [123], ['a']

• [10, 'a'], [123], [10, 'a']

• [10, 'a'], [123], ['a']

• [10], [123], [10, 'a']

What is the output?

a = [1, 2, 3]

b = a

a += [4, 5]

print(a)

print(b)

• [1,2,3,4,5], [1,2,3]

• [1,2,3,4,5], [1,2,3,4,5]

• [1,2,3], [1,2,3]

• Error

Which of the following best describes what df.iloc[:, ::-1] does?

• Reverses rows

• Selects every second column

• Reverses columns

• Errors out

What does this return?

df = pd.DataFrame({'A': [1, 2, 3, 4, 5]})

print(df.iloc[::2])

• Rows with even indices (0,2,4)

• Rows with odd indices (1,3)

• Reversed DataFrame

• Error

Consider the following:

df = pd.DataFrame({

    'A': ['foo', 'bar', 'foo', 'bar'],

    'B': [1, 2, 3, 4]

})

print(df.groupby('A').transform('sum'))

• Group-wise sum applied to original rows

• Collapsed grouped result

• Same as groupby().sum()

• Error