Random choice from array in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does the numpy function np.random.choice() do?

It selects one or more random elements from a given array or list. You can choose with or without replacement.

beginner

How do you select 3 random elements from an array arr without repeating any element?

Use np.random.choice(arr, size=3, replace=False). The replace=False means no repeats.

beginner

What happens if you set replace=True in np.random.choice()?

Elements can be picked more than once. This means the same element can appear multiple times in the output.

intermediate

How can you assign different probabilities to elements when using np.random.choice()?

Use the p parameter with a list of probabilities that sum to 1. For example, p=[0.1, 0.9].

beginner

What is the default behavior of np.random.choice() if you do not specify size?

It returns a single random element from the array, not an array of elements.

What does np.random.choice(arr, size=5, replace=False) do?

A Returns the original array arr

B Selects 5 random elements from arr allowing repeats

C Selects 5 unique random elements from arr

D Selects 1 random element from arr

With replace=False, elements cannot repeat, so 5 unique elements are chosen.

How do you make sure elements can repeat when randomly choosing from an array?

A Set <code>replace=True</code>

B Set <code>replace=False</code>

C Set <code>size=1</code>

D Set <code>p=None</code>

Setting replace=True allows elements to be picked multiple times.

What parameter lets you give different chances to elements in np.random.choice()?

A <code>replace</code>

B <code>size</code>

C <code>arr</code>

D <code>p</code>

The p parameter is a list of probabilities for each element.

If you want only one random element from an array, what is the simplest call?

A <code>np.random.choice(arr)</code>

B <code>np.random.choice(arr, size=5)</code>

C <code>np.random.choice(arr, replace=False)</code>

D <code>np.random.choice(arr, p=[0.5, 0.5])</code>

By default, size is 1, so it returns one element.

What will happen if you try np.random.choice(arr, size=10, replace=False) but arr has only 5 elements?

A It will repeat elements to fill 10

B It will cause an error

C It will return 5 elements only

D It will return an empty array

Without replacement, you cannot pick more elements than exist, so it raises an error.

Explain how to use np.random.choice() to pick 4 random elements from an array with no repeats.

Think about the parameters controlling number and repetition.

Describe how to assign different probabilities to elements when randomly choosing from an array using numpy.

Look for the parameter that controls chances for each element.

Practice

1.

What does the numpy.random.choice function do?

easy

A. It picks one or more random elements from an array.

B. It sorts the array in ascending order.

C. It calculates the mean of the array elements.

D. It removes duplicate elements from the array.

Solution

1. Step 1: Understand the function purpose

   numpy.random.choice is used to select random elements from an array.

2. Step 2: Compare with other options

   Sorting, calculating mean, or removing duplicates are different functions and unrelated to random choice.

3. Final Answer:

   It picks one or more random elements from an array. -> Option A

4. Quick Check:

   Random choice = pick random elements [OK]

Hint: Random choice picks elements randomly from an array [OK]

Common Mistakes:

• Confusing random choice with sorting
• Thinking it calculates statistics like mean
• Assuming it removes duplicates

2.

Which of the following is the correct syntax to pick 3 random elements from a numpy array arr with replacement?

import numpy as np
arr = np.array([10, 20, 30, 40, 50])

easy

A. np.random.choice(arr, size=3, replace=False)

B. np.random.choice(arr, 3, replace=False)

C. np.random.choice(arr, size=3, replace=True)

D. np.random.choice(arr, 3, replace=True, axis=1)

Solution

1. Step 1: Check correct parameter names

   The function uses size to specify number of elements and replace to allow repeats.

2. Step 2: Validate each option

   np.random.choice(arr, size=3, replace=True) uses correct parameters: size=3 and replace=True. Others have wrong parameter names or extra invalid ones.

3. Final Answer:

   np.random.choice(arr, size=3, replace=True) -> Option C

4. Quick Check:

   Correct syntax = size=3, replace=True [OK]

Hint: Use size= and replace= parameters correctly [OK]

Common Mistakes:

• Using positional argument without size=
• Setting replace=False when repeats are needed
• Adding invalid parameters like axis

3.

What is the output of this code?

import numpy as np
np.random.seed(0)
arr = np.array([1, 2, 3, 4, 5])
result = np.random.choice(arr, size=4, replace=False)
print(result)

medium

A. [5 1 4 3]

B. [4 5 1 3]

C. [1 2 3 4]

D. [2 5 1 4]

Solution

1. Step 1: Understand seed and choice without replacement

   Setting seed ensures reproducible random results. Choosing 4 unique elements without replacement picks 4 different values from arr.

2. Step 2: Run code or recall output

   With seed 0, the output is [2 5 1 4].

3. Final Answer:

   [2 5 1 4] -> Option D

4. Quick Check:

   Seed 0 + replace=False = [2 5 1 4] [OK]

Hint: Use np.random.seed for consistent random output [OK]

Common Mistakes:

• Ignoring seed and expecting different output
• Confusing replace=True and replace=False results
• Assuming output is sorted

4.

Identify the error in this code snippet:

import numpy as np
arr = np.array([1, 2, 3])
result = np.random.choice(arr, size=5, replace=False)
print(result)

medium

A. Size is larger than array length without replacement, causing an error.

B. Array is not defined properly.

C. Missing import statement for numpy.

D. replace parameter should be True to avoid error.

Solution

1. Step 1: Check size vs array length with replace=False

   Choosing 5 elements without replacement from an array of length 3 is invalid and raises an error.

2. Step 2: Confirm other parts are correct

   Array is defined, numpy is imported, and replace parameter is valid. The main issue is size too large without replacement.

3. Final Answer:

   Size is larger than array length without replacement, causing an error. -> Option A

4. Quick Check:

   Size > array length + replace=False = error [OK]

Hint: Size must not exceed array length if replace=False [OK]

Common Mistakes:

• Ignoring size vs array length mismatch
• Assuming replace=True by default
• Not importing numpy before use

5.

You have a numpy array data = np.array([10, 20, 30, 40, 50]). You want to randomly select 3 unique elements but ensure the number 20 is always included in the result. Which approach is correct?

hard

A. Pick 3 elements with replacement and filter for 20 after.

B. Remove 20 from array, pick 2 without replacement, then add 20 back.

C. Use np.random.choice(data, size=3, replace=False) directly.

D. Pick 3 elements with replacement and add 20 manually.

Solution

1. Step 1: Understand the requirement

   We want 3 unique elements including 20 always.

2. Step 2: Choose method to guarantee 20

   Remove 20, pick 2 unique elements from remaining, then add 20 to result ensures 20 is included and no duplicates.

3. Step 3: Evaluate other options

   Direct random choice may exclude 20. Picking with replacement can cause duplicates or exclude 20. Adding 20 manually after picking with replacement may cause duplicates.

4. Final Answer:

   Remove 20 from array, pick 2 without replacement, then add 20 back. -> Option B

5. Quick Check:

   Guarantee element by picking rest then adding it [OK]

Hint: Pick others without 20, then add 20 to ensure inclusion [OK]

Common Mistakes:

• Assuming random choice always includes 20
• Using replacement causing duplicates
• Adding 20 after picking with replacement causing repeats