Labeled Data Structures: 10 practice questions

Question 1 of 10

Which pandas structure represents a one-dimensional labeled collection that can hold any data type?

1. AA Series with an index
2. BA label-free NumPy matrix
3. CA dictionary without values
4. DA DataFrame with two axes

Question 2 of 10

Which object has labeled rows and columns whose columns may use different dtypes?

1. AA pandas DataFrame
2. BAn unlabeled Python tuple
3. CA single NumPy dtype
4. DA pandas scalar

Question 3 of 10

Which attribute reports the axis dimensions of a pandas object?

1. A`shape`
2. B`array`
3. C`dtype`
4. D`columns`

Question 4 of 10

What happens when a scalar is passed as Series data with an explicit five-label index?

1. AOnly the first label receives the scalar
2. BFive unlabeled DataFrame columns are created
3. CThe scalar is repeated across the five labels
4. DThe labels are discarded and one row remains

Question 5 of 10

How does a DataFrame constructor combine a dictionary of Series with different indexes?

1. AIt combines values strictly by their current positions
2. BIt discards every label absent from the first Series
3. CIt aligns by labels and uses the union of indexes
4. DIt requires all Series labels to be identical first

Question 6 of 10

What structural information is omitted by `DataFrame.to_numpy()`?

1. AThe common NumPy dtype
2. BThe row and column labels
3. CThe number of array dimensions
4. DEvery numeric cell value

Question 7 of 10

What index does an operation between differently indexed Series produce?

1. AThe union of both Series indexes
2. BA fresh range index of the shorter Series
3. CThe left Series positions without labels
4. DThe intersection with missing labels removed

Question 8 of 10

An analyst has two named variable lists, but one has four values and the other has five. The result must be a five-row DataFrame with explicit row labels and no invented observation. What should happen before construction?

1. AConstruct immediately and let pandas silently repeat the shorter list
2. BDrop both variable names and combine the lists by position
3. CResolve the unequal column lengths, then supply five row labels
4. DSupply four row labels and let pandas truncate the longer list

Question 9 of 10

A mixed-type DataFrame has meaningful row and column labels. A NumPy-only function needs an ndarray, but the analyst must first document the table schema and avoid assuming the conversion is free. Which workflow fits?

1. ARead only `shape`, then assume the ndarray will preserve both labeled axes
2. BCall `to_numpy()` first and treat its common dtype as proof that every source column matched
3. CInspect labels, shape, and dtypes, then call `to_numpy()` and allow for coercion or copying
4. DUse `columns` as the numeric array because it retains heterogeneous cell values

Question 10 of 10

Two Series contain measurements for labels `a,b,c` and `b,c,d`. The calculation must preserve every observed label and must not pair values merely because they occupy the same position. Which result behavior is required?

1. APair first with first, retain three positions, and discard all labels
2. BRepeat endpoint values so all four labels have two numeric operands
3. CKeep only `b,c` because every result must have two present operands
4. DAlign by label, retain `a,b,c,d`, and mark unmatched operands missing