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)

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

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)

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

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)

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

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)

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

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.

Practice

1. What does the np.choose() function do in NumPy?

easy

A. Sorts an array in ascending order

B. Selects elements from multiple arrays based on an index array

C. Calculates the mean of an array

D. Generates random numbers

Solution

1. Step 1: Understand the purpose of np.choose()

   np.choose() uses an index array to pick elements from multiple arrays at corresponding positions.

2. Step 2: Compare with other options

   Sorting, mean calculation, and random number generation are unrelated to np.choose().

3. Final Answer:

   Selects elements from multiple arrays based on an index array -> Option B

4. Quick Check:

   np.choose() = Select elements [OK]

Hint: Remember: np.choose picks from arrays using index array [OK]

Common Mistakes:

• Confusing np.choose() with sorting functions
• Thinking np.choose() calculates statistics
• Assuming np.choose() generates random values

2. Which of the following is the correct syntax to use np.choose() with an index array idx and option arrays a, b, c?

easy

A. np.choose(idx, [a, b, c])

B. np.choose([a, b, c], idx)

C. np.choose(a, b, c, idx)

D. np.choose(idx, a, b, c)

Solution

1. Step 1: Recall np.choose() parameter order

   The first argument is the index array, the second is a list of arrays to choose from.

2. Step 2: Match syntax with options

   Only np.choose(idx, [a, b, c]) matches the correct order and structure.

3. Final Answer:

   np.choose(idx, [a, b, c]) -> Option A

4. Quick Check:

   Index first, then list of arrays [OK]

Hint: Index array first, then list of arrays in np.choose() [OK]

Common Mistakes:

• Swapping index array and options
• Passing options as separate arguments instead of a list
• Using incorrect argument order

3. What is the output of the following code?

import numpy as np
idx = np.array([0, 2, 1, 0])
a = np.array([10, 20, 30, 40])
b = np.array([100, 200, 300, 400])
c = np.array([1000, 2000, 3000, 4000])
result = np.choose(idx, [a, b, c])
print(result)

medium

A. [10 2000 300 40]

B. [10 300 200 40]

C. [100 3000 2000 400]

D. [1000 300 200 40]

Solution

1. Step 1: Understand index array and options

   Index array is [0, 2, 1, 0]. Arrays are a=[10,20,30,40], b=[100,200,300,400], c=[1000,2000,3000,4000].

2. Step 2: Select elements using np.choose()

   For each position, pick from arrays based on index:
   pos0: idx=0 -> a[0]=10
   pos1: idx=2 -> c[1]=2000
   pos2: idx=1 -> b[2]=300
   pos3: idx=0 -> a[3]=40
   So result = [a[0], c[1], b[2], a[3]] = [10, 2000, 300, 40]

3. Step 3: Check options

   [10 300 200 40] incorrectly uses 300 and 200 instead of 2000 and 300. [10 2000 300 40] matches our result.

4. Final Answer:

   [10 2000 300 40] -> Option A

5. Quick Check:

   np.choose picks arrays by index, then elements by position [OK]

Hint: Index picks array, position picks element [OK]

Common Mistakes:

• Mixing up index meaning (array vs element)
• Swapping elements from wrong arrays
• Confusing np.choose with fancy indexing

4. The following code raises an error. What is the most likely cause?

import numpy as np
idx = np.array([0, 1, 2])
a = np.array([1, 2])
b = np.array([3, 4])
c = np.array([5, 6])
result = np.choose(idx, [a, b, c])
print(result)

medium

A. np.choose() requires index array to be boolean

B. Index array contains values outside the range of options

C. Index array and option arrays have incompatible shapes causing mismatch

D. np.choose() cannot handle more than two option arrays

Solution

1. Step 1: Check index array and options

   Index array is [0,1,2], which is valid for 3 options (a,b,c).

2. Step 2: Check shapes of option arrays

   a, b, c each have shape (2,), but index array has length 3. np.choose expects index and options to have compatible shapes.

3. Step 3: Identify cause of error

   Index array length 3 vs option arrays length 2 causes shape mismatch error.

4. Final Answer:

   Index array and option arrays have incompatible shapes causing mismatch -> Option C

5. Quick Check:

   Shape mismatch causes np.choose error [OK]

Hint: Ensure index and option arrays have compatible shapes [OK]

Common Mistakes:

• Assuming index values cause error
• Thinking np.choose needs boolean index
• Believing np.choose limits number of options

5. You have three arrays representing scores from three different tests:

test1 = np.array([80, 90, 70])
test2 = np.array([85, 88, 75])
test3 = np.array([78, 92, 72])

And an index array indicating the best test for each student:

best = np.array([1, 2, 0])

Using np.choose(), how do you create an array of the best scores for each student?

hard

A. np.choose(best, [test1, test3, test2])

B. np.choose(best, [test3, test1, test2])

C. np.choose(best, [test2, test3, test1])

D. np.choose(best, [test1, test2, test3])

Solution

1. Step 1: Understand index array meaning

   Index array best = [1, 2, 0] means:
   For student 0, best test is index 1 -> test2
   For student 1, best test is index 2 -> test3
   For student 2, best test is index 0 -> test1

2. Step 2: Match option arrays order to index

   To pick correctly, option arrays must be in order [test1, test2, test3] so index 0 picks test1, index 1 picks test2, index 2 picks test3.

3. Step 3: Verify np.choose usage

   Using np.choose(best, [test1, test2, test3]) picks correct scores per student.

4. Final Answer:

   np.choose(best, [test1, test2, test3]) -> Option D

5. Quick Check:

   Order options to match index meaning [OK]

Hint: Order option arrays to match index values [OK]

Common Mistakes:

• Assuming option arrays order matches test numbers
• Not matching index values to option array order
• Using wrong order causing incorrect selection