np.where() for conditional selection in NumPy - Mini Project: Build & Apply

Using np.where() for Conditional Selection

📖 Scenario: You work in a store that tracks daily sales numbers. You want to quickly find which days had sales above a certain target.

🎯 Goal: Build a small program that uses np.where() to find the days with sales above a target number.

📋 What You'll Learn

Create a numpy array with exact daily sales numbers

Create a variable for the sales target

Use np.where() to find indices where sales exceed the target

Print the indices of days with sales above the target

💡 Why This Matters

🌍 Real World

Stores and businesses often need to quickly find days or items that meet certain conditions, like sales targets, to make decisions.

💼 Career

Data analysts and scientists use conditional selection with numpy to filter and analyze data efficiently.

1

Create the sales data array

Create a numpy array called sales with these exact values: [150, 200, 90, 300, 250]

NumPy

import numpy as np
# Create the numpy array called sales with values [150, 200, 90, 300, 250]
# Your code here

Hint

Use np.array() to create the array with the exact values.

2

Set the sales target

Create a variable called target and set it to 180

NumPy

import numpy as np
sales = np.array([150, 200, 90, 300, 250])
# Create a variable called target and set it to 180
# Your code here

Hint

Just assign the number 180 to the variable target.

3

Use np.where() to find days exceeding the target

Use np.where() with the condition sales > target and assign the result to a variable called days_above_target

NumPy

import numpy as np
sales = np.array([150, 200, 90, 300, 250])
target = 180
# Use np.where() with condition sales > target and assign to days_above_target
# Your code here

Hint

Use np.where(condition) to get indices where the condition is true.

4

Print the days with sales above the target

Print the variable days_above_target to show the indices of days with sales above the target

NumPy

import numpy as np
sales = np.array([150, 200, 90, 300, 250])
target = 180
days_above_target = np.where(sales > target)
# Print days_above_target
# Your code here

Hint

Use print(days_above_target) to display the result.

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