Question bank

Pick an answer to see if you're right — with an explanation.

PandasMedium

Which is the fastest way to create a column that is `amount * 0.1` for members and 0 otherwise?

adf.apply(lambda r: ..., axis=1)

bA for loop with iterrows()

cdf.itertuples() with a list

dnp.where(df['member'], df['amount'] * 0.1, 0)

PandasHard

What is printed?

a = pd.Series([1, 2], index=['x', 'y'])
b = pd.Series([10, 20], index=['y', 'z'])
print((a + b).tolist())

a[nan, 12.0, nan]

b[11, 22]

c[1, 12, 20]

dA ValueError about mismatched lengths

PandasHard

What does this produce?

g = df.groupby(["branch", "category"])["amount"].sum()
t = g.unstack("category", fill_value=0)

aA long Series with a 3-level index

bA DataFrame with branches as rows and categories as columns

cA DataFrame with categories as rows and branches as columns

dAn error because unstack needs a DataFrame

PandasHard

After `pd.concat([df1, df2])`, `df.loc[0]` returns two rows. Why?

aconcat always duplicates the first row

bBoth frames kept their original index labels; pass ignore_index=True to renumber

c`loc` always returns a DataFrame

dThe two frames have different columns

PandasHard

Which statement about `df[df['city'] == 'Pune']['amount'] = 0` is correct?

aIt reliably sets Pune amounts to 0

bIt raises a SyntaxError

cIt deletes Pune rows

dIt may modify a temporary copy, leaving df unchanged; use df.loc[mask, 'amount'] = 0