Checkpoint: Cleaning Real Data

Question 1 of 5

• Some quantities are fractions; round them with round().
• Some cells are blank, classic NaN is a float, and astype("Int64") keeps integers with <NA> for the gaps.
• read_csv always loads numbers as floats; use astype(int).
• The file is corrupted; reload it with keep_default_na=False.

Question 2 of 5

• Use fillna(0) so totals are complete.
• Drop the 12 rows.
• Switch to astype(float), which is stricter.
• Print the original strings for those rows and extend the cleaning rule for each format you find.

Question 3 of 5

• 3 April 2025 and 15 April 2025
• 4 March 2025 and an error
• 4 March 2025 and 15 April 2025
• Two NaT values

Question 4 of 5

• Nothing; the order of steps does not matter.
• strip() will remove letters from the names.
• Copies that differ only by spaces ("Ali Evans" and "Ali Evans ") are not recognised as duplicates and both survive.
• drop_duplicates will raise an error on text columns.

Question 5 of 5

• title() does not work on capitals such as EGYPT.
• Some values have leading or trailing spaces, so " Egypt" and "Egypt" are still different.
• nunique() counts missing values as a country.
• pandas caches the old values until the file is reloaded.