np.where() for conditional selection in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.where() do in numpy?

np.where() helps you pick values from arrays based on a condition. It returns elements chosen from two options depending on whether the condition is true or false.

beginner

How do you use np.where() to replace negative numbers in an array with zero?

You write a condition to check for negative numbers, then tell np.where() to put zero if true, else keep the original number.

np.where(arr < 0, 0, arr)

beginner

What are the three arguments of np.where(condition, x, y)?

• condition: A test that returns True or False for each element.
• x: Values to pick when condition is True.
• y: Values to pick when condition is False.

intermediate

What happens if you use np.where() with only a condition argument?

It returns the indices (positions) where the condition is True, instead of picking values.

beginner

Why is np.where() useful in data science?

It lets you quickly select or change data based on conditions without writing loops. This makes your code faster and easier to read.

What does np.where(arr > 5, 10, 0) do?

A Returns the original array unchanged

B Returns indices where elements are greater than 5

C Replaces elements less than 5 with 10, others with 0

D Replaces elements greater than 5 with 10, others with 0

np.where() picks 10 where condition is true (arr > 5), else 0.

What does np.where(condition) return if only one argument is given?

A Indices where condition is True

B Values where condition is True

C Boolean array of condition

D Error because arguments are missing

With one argument, np.where() returns indices where the condition is True.

Which of these is a correct use of np.where() to replace negative values with zero?

A np.where(arr, 0, arr)

B np.where(arr > 0, arr, 0)

C np.where(arr < 0, 0, arr)

D np.where(arr == 0, 0, arr)

Condition checks for negative values, replaces them with zero, else keeps original.

If arr = np.array([1, 2, 3]), what does np.where(arr > 2) return?

A array([False, False, True])

B (array([2]),)

C array([3])

D array([1, 2])

Returns a tuple with indices where condition is True, here index 2.

Why might np.where() be preferred over a for-loop for conditional selection?

A It is faster and uses less code

B It always uses more memory

C It only works with small arrays

D It requires writing complex loops

np.where() is vectorized, so it runs faster and is simpler than loops.

Explain how np.where() can be used to change values in an array based on a condition.

Think about how you pick between two options depending on a test.

Describe the difference between using np.where() with one argument versus three arguments.

Focus on what output you get in each case.

Practice

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

easy

A. Selects elements from two arrays based on a condition

B. Sorts an array in ascending order

C. Calculates the mean of an array

D. Creates a new array filled with zeros

Solution

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

   np.where() is used to choose elements from two arrays depending on a condition.

2. Step 2: Compare with other options

   Sorting, calculating mean, or creating zeros are different functions like np.sort(), np.mean(), and np.zeros().

3. Final Answer:

   Selects elements from two arrays based on a condition -> Option A

4. Quick Check:

   np.where() = conditional selection [OK]

Hint: np.where picks from two arrays based on a condition [OK]

Common Mistakes:

• Confusing np.where() with sorting or aggregation functions
• Thinking np.where() creates new arrays without conditions
• Assuming np.where() only finds indices, not selects values

2. Which of the following is the correct syntax to use np.where() to select elements from arrays a and b based on condition cond?

easy

A. np.where(a, cond, b)

B. np.where(cond, a, b)

C. np.where(a, b, cond)

D. np.where(cond, b, a)

Solution

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

   The correct order is np.where(condition, x, y) where elements from x are chosen if condition is True, else from y.

2. Step 2: Match parameters with options

   np.where(cond, a, b) matches this order: np.where(cond, a, b). Others mix the order incorrectly.

3. Final Answer:

   np.where(cond, a, b) -> Option B

4. Quick Check:

   np.where(condition, x, y) correct syntax [OK]

Hint: Remember: condition first, then true array, then false array [OK]

Common Mistakes:

• Swapping the order of arrays and condition
• Putting arrays before the condition
• Using np.where() without parentheses

3. What is the output of the following code?

import numpy as np
arr = np.array([10, 20, 30, 40])
result = np.where(arr > 25, arr, 0)
print(result)

medium

A. [ 0 0 30 40]

B. [10 20 30 40]

C. [10 20 0 0]

D. [30 40 0 0]

Solution

1. Step 1: Evaluate the condition arr > 25

   Elements greater than 25 are 30 and 40 at indices 2 and 3.

2. Step 2: Apply np.where() selection

   For elements > 25, keep original value; else replace with 0. So result is [0, 0, 30, 40].

3. Final Answer:

   [ 0 0 30 40] -> Option A

4. Quick Check:

   Condition true keeps value, false replaces with 0 [OK]

Hint: Check condition element-wise, replace false with second array [OK]

Common Mistakes:

• Confusing which elements satisfy the condition
• Mixing up true and false array values
• Forgetting np.where returns a new array

4. Identify the error in this code snippet:

import numpy as np
x = np.array([1, 2, 3, 4])
y = np.array([10, 20, 30, 40])
result = np.where(x > 2, y)
print(result)

medium

A. Condition x > 2 is invalid syntax

B. Arrays x and y have different lengths

C. Missing third argument for false condition in np.where()

D. np.where() cannot be used with arrays

Solution

1. Step 1: Check np.where() arguments

   np.where() requires three arguments for conditional selection: condition, array if True, array if False.

2. Step 2: Identify missing argument

   The code only provides condition and one array, missing the third argument for false case.

3. Final Answer:

   Missing third argument for false condition in np.where() -> Option C

4. Quick Check:

   np.where needs condition, true array, false array [OK]

Hint: np.where needs three arguments for selection [OK]

Common Mistakes:

• Using np.where with only two arguments for selection
• Assuming np.where can infer missing arrays
• Confusing np.where with np.nonzero

5. Given arrays a = np.array([5, 15, 25, 35]) and b = np.array([100, 200, 300, 400]), how can you create a new array that takes elements from b when a is greater than 20, otherwise from a, using np.where()?

hard

A. np.where(a > 20, a, b)

B. np.where(b > 20, a, b)

C. np.where(a < 20, b, a)

D. np.where(a > 20, b, a)

Solution

1. Step 1: Understand the condition and selection

   We want elements from b where a > 20, else from a.

2. Step 2: Apply np.where() with correct order

   Use np.where(condition, x, y) where x is chosen if condition True, else y. So np.where(a > 20, b, a).

3. Final Answer:

   np.where(a > 20, b, a) -> Option D

4. Quick Check:

   Condition true picks b, false picks a [OK]

Hint: Condition first, then true array, then false array [OK]

Common Mistakes:

• Swapping arrays in np.where() parameters
• Using wrong comparison operator
• Confusing which array to pick on true condition