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Array basics

NumPy: Fundamentals Of Python Data Science Creating 1-dimensional arrays Array basics

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Question 1 of 3

1. Question
What is the primary benefit of using NumPy arrays for numerical computation instead of Python lists or tuples?
- 1. NumPy arrays can store elements of different types.
- 2. NumPy arrays are implemented in C, bypassing Python's speed limitations.
- 3. NumPy arrays can store elements in an unordered manner.
- 4. NumPy arrays are more memory-intensive than Python lists or tuples.
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Question 2 of 3

2. Question
What is the purpose of the dtype argument when creating a NumPy array using the np.array() function?
- 1. To set the dimensions of the resulting NumPy array.
- 2. To explicitly specify the data type of the elements in the resulting NumPy array.
- 3. To control the memory usage of the resulting NumPy array.
- 4. To determine the shape of the resulting NumPy array.
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Question 3 of 3

3. Question
Fill in the blank space
- When creating a NumPy array from a list of elements, use the following function:
  
  a = np. ([1, 2, 3])
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