NumPy Exercises¶
Now that we’ve learned about NumPy let’s test your knowledge. We’ll start off with a few simple tasks, and then you’ll be asked some more complicated questions.
Import NumPy as np¶
import numpy as np
Create an array of 10 zeros¶
np.zeros(10)
Create an array of 10 ones¶
arr = np.ones(10)
arr
Create an array of 10 fives¶
arr * 5
Create an array of the integers from 10 to 50¶
SOLUTIONS includes 50 like this:
np.arange(10,51)
np.arange(10,50)
Create an array of all the even integers from 10 to 50¶
SOLUTIONS includes 50 like this:
np.arange(10,51,2)
same here - 51 to be used so 50 is included - noted for others like this as well
np.arange(10,50,2)
Create a 3x3 matrix with values ranging from 0 to 8¶
np.arange(0,9).reshape(3,3)
Create a 3x3 identity matrix¶
np.eye(3)
Use NumPy to generate a random number between 0 and 1¶
np.random.rand(1)
Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution¶
np.random.randn(25)
Create the following matrix:¶
SOLUTIONS a little different but same result
np.linspace(0.01,1,100).reshape(10,10)
Create an array of 20 linearly spaced points between 0 and 1:¶
np.linspace(0,1,20)
Numpy Indexing and Selection¶
Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:
mat = np.arange(1,26).reshape(5,5)
mat
mat[2:,1:]
mat[3,4]
SOLUTIONS includes 3rd element to return them like below:
mat[:3,1:2]
mat[:3,1]
mat[4]
mat[3:]
Now do the following¶
Get the sum of all the values in mat¶
np.sum(mat)
Get the standard deviation of the values in mat¶
np.std(mat)
Get the sum of all the columns in mat¶
np.sum(mat, axis=0)