Practice Numpy (numerical Python) (4.1) - Python for Data Science - Data Science Basic

Practice: NumPy (Numerical Python)

Lesson 4.1Python for Data Science

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10 questions

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4 cards from this section. Quick, good the night before a test.
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6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Create a 1D NumPy array containing numbers 1 to 5.
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Use `np.array` to create an array.
Question 2.
What is the output of np.array([1, 2, 3]).mean()?
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Consider what the mean of {1, 2, 3} is.
3Question 3.
What does the command np.array([5, 10, 15]) do?
- Creates a list with values 5
- 10
- and 15
- Creates a NumPy array with values 5
- 10
- and 15
- Creates a dictionary with values 5
- 10
- and 15
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Consider what the prefix 'np' indicates.
4Question 4.
True or False: An ndarray can contain elements of different data types.
- True
- False
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Think about the structure of data in NumPy.
Question 5.
Given two NumPy arrays, a = np.array([1, 2, 3]) and b = np.array([4, 5, 6]), write a function that returns their element-wise product.
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Think of how NumPy allows operations on arrays directly.
Question 6.
Create a 2D NumPy array representing a dataset of 3 different plants with 4 features each, and compute the standard deviation of each feature.
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Take advantage of NumPy’s abilities to handle multidimensional data.

6 more questions in this section. Enroll free to practise them all.