Why is NumPy Array good compared to Python Lists?

CS628 – Data Science Monroe College

Week 5 assignment – Jupyter and NumPy

Do online research and answer the following questions on Python NumPy Package! Read

chapter 2 of the following e-book. https://jakevdp.github.io/PythonDataScienceHandbook

30 points (3 points for each question)

Name:

1

Why is NumPy Array good compared to Python Lists?

2

How many dimensions can a NumPy array have?

3.

Consider the two-dimensional array, arr2d.

a) Write a code to slice this array to display the last column,

This is the solution (Submit the code) = [[3] [6] [9]]

b) Write a code to slice this array to display the last 2 elements of middle array,

This is the solution (submit the code) = [5 6]

arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])

https://jakevdp.github.io/PythonDataScienceHandbook

4.

What is the difference between indexing and slicing in NumPy?

5.

Generate 10 random numbers of 0 and 1 digits using NumPy package.

6.

Generate a 2 x 4 array of ints between 0 and 10, inclusive:

7.

Create a 5 x 3 matrix of random numbers. Create a vector of three random number. What happens when you run matrix * vector (the product of the matrix and vector)?

8.

Compute the transpose of the matrix you created in question 7.

9.

What is the use of “ndim” attribute in NumPy?

10.

Create a vector with 10 random generated values from 1 to 50, then sort the values in the vector.

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