np.choose() for conditional selection in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select elements from arrays using np.choose.

NumPy

import numpy as np
choices = [np.array([1, 2, 3]), np.array([4, 5, 6])]
index = np.array([0, 1, 0])
result = np.choose([1], choices)
print(result)

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

Anp.array

Bchoices

Cresult

Dindex

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the choices list as the first argument instead of the index array.

Confusing the order of arguments in np.choose.

The np.choose function uses the index array to select elements from the choices list of arrays.

2fill in blank

medium

Complete the code to create an index array for np.choose based on a condition.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40])
index = (arr > 25).astype([1])
print(index)

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Afloat

Bint

Cbool

Dstr

Attempts:

3 left

💡 Hint

Common Mistakes

Using boolean type directly without conversion.

Using float or string types which cause errors.

We convert the boolean condition to integers (0 or 1) to use as indices for np.choose.

3fill in blank

hard

Fix the error in the np.choose call to select elements correctly.

NumPy

import numpy as np
choices = [np.array([100, 200, 300]), np.array([400, 500, 600])]
index = np.array([1, 0, 1])
result = np.choose([1], choices)
print(result)

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Aindex

Bchoices

Cnp.array

Dresult

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping the order of arguments in np.choose.

Passing the choices list as the first argument.

The first argument to np.choose must be the index array, not the choices list.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that uses np.choose to select values based on a condition.

NumPy

import numpy as np
values = {'a': 5, 'b': 15, 'c': 25}
index = np.array([0 if v < 10 else 1 for v in values.values()])
choices = [np.array([10, 20, 30]), np.array([40, 50, 60])]
result = {k: np.choose([1], choices)[i] for i, k in enumerate(values.keys()) if values[k] [2] 20}
print(result)

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Aindex

Bvalues

C<

D>

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong variable for the index array.

Using the wrong comparison operator causing wrong filtering.

The index array is used to select from choices. The condition < 20 filters keys with values less than 20.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that uses np.choose to select values and filters keys based on a condition.

NumPy

import numpy as np
scores = {'x': 8, 'y': 15, 'z': 22}
index = np.array([0 if s [1] 10 else 1 for s in scores.values()])
choices = [np.array([100, 200, 300]), np.array([400, 500, 600])]
result = [2]: np.choose(index, choices)[i] for i, [2] in enumerate(scores.keys()) if scores[[2]] [3] 20}
print(result)

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A<

Bv

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators.

Using incorrect variable names in the dictionary comprehension.

The first blank uses < to check scores less than 10. The second blank is the key variable v for dictionary keys. The third blank filters scores less than 20.