Masked arrays concept in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a masked array from a numpy array.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])
masked_arr = np.ma.[1](arr)

Drag options to blanks, or click blank then click option'

Amask

Barray

Cma_array

Dmasked_array

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.array instead of np.ma.masked_array

Trying to use np.mask which is not a constructor

Using an incorrect function name like ma_array

The np.ma.masked_array() function creates a masked array from a regular numpy array.

2fill in blank

medium

Complete the code to mask all elements equal to 3 in the array.

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 3])
masked_arr = np.ma.masked_array(arr, mask=arr [1] 3)

Drag options to blanks, or click blank then click option'

A==

B!=

C>

D<

Attempts:

3 left

💡 Hint

Common Mistakes

Using != instead of ==

Using > or < which mask wrong elements

The mask should be True where elements equal 3, so use arr == 3.

3fill in blank

hard

Fix the error in the code to correctly mask elements less than 0.

NumPy

import numpy as np
arr = np.array([-1, 0, 1, 2])
masked_arr = np.ma.masked_array(arr, mask=arr [1] 0)

Drag options to blanks, or click blank then click option'

A<=

B<

C>

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using > or == which mask wrong elements

Using <= which masks zero as well

To mask elements less than 0, use the less than operator <.

4fill in blank

hard

Fill both blanks to create a masked array masking values greater than 10 and then fill masked values with -1.

NumPy

import numpy as np
arr = np.array([5, 12, 7, 15, 3])
masked_arr = np.ma.masked_array(arr, mask=arr [1] 10)
filled_arr = masked_arr.[2](-1)

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A>

Bfill

Cfilled

Dmask

Attempts:

3 left

💡 Hint

Common Mistakes

Using fill() instead of filled()

Using mask instead of filled to replace masked values

Mask elements greater than 10 using arr > 10. Use filled(-1) to replace masked values with -1.

5fill in blank

hard

Fill all three blanks to create a masked array from a list, mask negative values, and get the compressed (unmasked) data.

NumPy

import numpy as np
lst = [4, -2, 7, -5, 9]
masked_arr = np.ma.[1](lst, mask=[x [2] 0 for x in lst])
compressed_data = masked_arr.[3]()

Drag options to blanks, or click blank then click option'

Amasked_array

B<

Ccompressed

Dmask

Attempts:

3 left

💡 Hint

Common Mistakes

Using mask instead of masked_array to create the array

Using > instead of < for masking negative values

Using mask() instead of compressed() to get unmasked data

Create a masked array with np.ma.masked_array(). Mask negative values using x < 0. Use compressed() to get unmasked data.