Data Acquisition & Wrangling with pandas — Quiz | Data Science using Python

Question 1

What is a pandas Series?

A A whole table with many columns

B A single labelled column of data

C A NumPy function

D A database connection

Question 2

A column shows dtype object. What does that usually mean?

A It contains dates

B It contains text (strings)

C It is empty

D It is the index

Question 3

Which function reads a CSV file into a DataFrame?

A pd.open_csv()

B pd.read_csv()

C pd.load()

D pd.csv()

Question 4

What is the difference between loc and iloc?

A They are identical

B loc selects by label, iloc by integer position

C loc is faster

D iloc only works on columns

Question 5

How do you combine two filter conditions on a DataFrame?

A with and / or

B with & / |, each condition in parentheses

C with commas

D you cannot combine them

Question 6

Which method counts missing values per column?

A df.missing()

B df.isna().sum()

C df.count_nulls()

D df.empty()

Question 7

Why might you fill missing numbers with the median rather than the mean?

A The median is always larger

B The median is more robust to outliers in skewed data

C The mean cannot be computed

D It is required by pandas

Question 8

What does pd.to_numeric(col, errors='coerce') do to values it cannot convert?

A Raises an error and stops

B Turns them into NaN

C Leaves them as text

D Deletes the whole row

Question 9

Which operation joins two tables on a shared key column?

A concat

B merge

C pivot

D melt

Question 10

groupby('region')['amount'].sum() follows which pattern?

A map-reduce-filter

B split-apply-combine

C sort-merge-join

D load-clean-save

Question 11

What does pd.get_dummies(df, columns=['region']) produce?

A A pivot table

B One-hot 0/1 columns, one per category

C A correlation matrix

D A merged table

Question 12

Why prefer Parquet over CSV for saving cleaned intermediate data?

A It is human-readable

B It is compressed, columnar and preserves each column's dtype

C It opens in Excel

D It is the only format pandas supports