np.ix_() for open mesh indexing in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create open mesh indices using np.ix_.

NumPy

import numpy as np
rows = np.array([0, 2, 4])
cols = np.array([1, 3])
mesh = np.[1](rows, cols)
print(mesh)

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Aix_

Bmeshgrid

Copen_mesh

Dindices

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.meshgrid instead of np.ix_

Trying to use np.indices which creates grid indices for shape, not from arrays

np.ix_() creates open mesh indexing arrays from 1D arrays, perfect for selecting rows and columns.

2fill in blank

medium

Complete the code to select elements from a 2D array using np.ix_.

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
rows = [0, 2]
cols = [1, 2]
selected = arr[[1](rows, cols)]
print(selected)

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Ameshgrid

Bopen_mesh

Cindices

Dix_

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.meshgrid which returns coordinate matrices, not suitable for fancy indexing here

Trying to index directly with lists without np.ix_

np.ix_() creates index arrays that allow selecting the cross product of rows and columns.

3fill in blank

hard

Fix the error in the code to correctly use np.ix_ for indexing.

NumPy

import numpy as np
arr = np.arange(16).reshape(4,4)
rows = [1, 3]
cols = [0, 2]
subset = arr[[1](rows, cols)]
print(subset)

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Ameshgrid

Bix_

Cindices

Dopen_mesh

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.meshgrid which returns coordinate matrices, not suitable for fancy indexing

Using np.indices which expects shape, not arrays

np.ix_ is the correct function to create open mesh indices for fancy indexing.

4fill in blank

hard

Fill both blanks to create a dictionary of squares for numbers greater than 3 using dictionary comprehension.

NumPy

numbers = [1, 2, 3, 4, 5]
squares = [1]: [2] for n in numbers if n > 3

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An

Bn**2

Dn*2

Attempts:

3 left

💡 Hint

Common Mistakes

Using n*2 instead of n**2 for squares

Swapping key and value positions

The dictionary comprehension uses n as key and n squared as value for numbers greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary of uppercase keys and values greater than 10.

NumPy

data = {'a': 5, 'b': 15, 'c': 20}
result = [1]: [2] for k, v in data.items() if v [3] 10}}

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Ak.upper()

Bv

C>

Dk

Attempts:

3 left

💡 Hint

Common Mistakes

Using k instead of k.upper() for keys

Using < instead of > in the condition

The dictionary comprehension creates keys as uppercase letters and includes only values greater than 10.