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Session 7 Part 3 quiz

Question 1

What is NumPy mainly used for?

  • Fast numeric work with arrays.
  • Creating virtual environments.
  • Styling markdown files.
  • Running Git commands.

Answer: 1 Type: single Time: 45 Explanation: NumPy is designed for efficient numeric arrays and calculations.

Question 2

What is the common import alias for NumPy?

  • import numpy as np
  • import numpy as pd
  • import np as numpy
  • import array as np

Answer: 1 Type: single Time: 45 Explanation: NumPy is almost always imported with the alias np.

Question 3

What does array.shape show?

  • The size of the array in each dimension.
  • The first value in the array.
  • The average value in the array.
  • The file path of the array.

Answer: 1 Type: single Time: 45 Explanation: shape describes the dimensions of the array.

Question 4

What is a vectorised operation?

  • An operation applied to many values at once.
  • A command that deletes missing values.
  • A way to rename DataFrame columns.
  • A special kind of markdown heading.

Answer: 1 Type: single Time: 60 Explanation: Vectorised operations let NumPy calculate across arrays without an explicit Python loop.

Question 5

What does this code print?

scores = np.array([70, 85, 90])
print(scores[scores >= 80])
  • [85 90]
  • [70 85 90]
  • [True True False]
  • [70]

Answer: 1 Type: single Time: 60 Explanation: The boolean condition keeps only values greater than or equal to 80.

Question 6

How can you convert a pandas column to a NumPy array?

  • df["column"].to_numpy()
  • df["column"].to_list_array()
  • np.read_column(df, "column")
  • df.numpy["column"]

Answer: 1 Type: single Time: 45 Explanation: pandas Series objects have a to_numpy() method.