Combining conditions in NumPy - Mini Project: Build & Apply

Filtering Data with Combined Conditions using NumPy

📖 Scenario: You work in a store and have a list of product prices. You want to find which products are both affordable and on sale.

🎯 Goal: Learn how to use NumPy to filter data by combining two conditions.

📋 What You'll Learn

Create a NumPy array with given prices

Create a price limit variable

Use combined conditions with NumPy to find affordable and on-sale products

Print the filtered prices

💡 Why This Matters

🌍 Real World

Filtering data with multiple conditions is common in sales, finance, and many other fields to find items that meet several criteria.

💼 Career

Data scientists and analysts often combine conditions to clean and analyze data efficiently using tools like NumPy.

1

Create the prices array

Create a NumPy array called prices with these exact values: 10, 25, 30, 45, 50, 60.

NumPy

import numpy as np
# Create the prices array with the exact values
# Your code here

Hint

Use np.array([...]) to create the array with the given numbers.

2

Set the price limit

Create a variable called price_limit and set it to 40.

NumPy

import numpy as np
prices = np.array([10, 25, 30, 45, 50, 60])
# Create price_limit variable and set it to 40
# Your code here

Hint

Just assign the number 40 to the variable price_limit.

3

Filter prices with combined conditions

Create a variable called affordable_and_on_sale that selects prices from prices which are less than price_limit and also less than 50. Use combined conditions with & and parentheses.

NumPy

import numpy as np
prices = np.array([10, 25, 30, 45, 50, 60])
price_limit = 40
# Use combined conditions to filter prices less than price_limit and less than 50
# Your code here

Hint

Use parentheses around each condition and combine them with & inside the brackets.

4

Print the filtered prices

Print the variable affordable_and_on_sale to see the filtered prices.

NumPy

import numpy as np
prices = np.array([10, 25, 30, 45, 50, 60])
price_limit = 40
affordable_and_on_sale = prices[(prices < price_limit) & (prices < 50)]
# Print the filtered prices
# Your code here

Hint

Use print(affordable_and_on_sale) to show the result.

Practice

1. Which of the following is the correct way to combine two conditions a > 5 and b < 10 in NumPy to select elements where both are true?

easy

A. Use (a > 5) & (b < 10)

B. Use a > 5 & b < 10 without parentheses

C. Use (a > 5) | (b < 10)

D. Use a > 5 or b < 10

Solution

1. Step 1: Understand combining conditions in NumPy

   NumPy requires each condition to be in parentheses when using & (AND) or | (OR) operators.

2. Step 2: Identify correct syntax for AND condition

   The correct way to combine a > 5 and b < 10 with AND is (a > 5) & (b < 10).

3. Final Answer:

   Use (a > 5) & (b < 10) -> Option A

4. Quick Check:

   Parentheses + & = correct AND condition [OK]

Hint: Always put each condition in parentheses when combining [OK]

Common Mistakes:

• Omitting parentheses around conditions
• Using Python 'and' instead of '&' for arrays
• Using 'or' instead of '|' for arrays

2. Which of the following is the correct syntax to select elements from a NumPy array arr where values are NOT equal to 0 and less than 10?

easy

A. arr[arr != 0 & arr < 10]

B. arr[(arr != 0) & (arr < 10)]

C. arr[(arr != 0) | (arr < 10)]

D. arr[~arr != 0 & arr < 10]

Solution

1. Step 1: Use parentheses for each condition

   Each condition must be enclosed in parentheses: (arr != 0) and (arr < 10).

2. Step 2: Combine with AND operator

   Use & to combine conditions for selecting elements satisfying both.

3. Final Answer:

   arr[(arr != 0) & (arr < 10)] -> Option B

4. Quick Check:

   Parentheses + & + correct conditions = syntax correct [OK]

Hint: Use parentheses around each condition and '&' for AND [OK]

Common Mistakes:

• Missing parentheses causing syntax errors
• Using bitwise NOT (~) incorrectly
• Using Python 'and' instead of '&'

3. Given the code:

import numpy as np
arr = np.array([1, 5, 8, 12, 3, 7])
result = arr[(arr > 3) | (arr == 1)]
print(result)

What is the output?

medium

A. [5 8 12 7]

B. [1 5 8 12 3 7]

C. [5 8 12]

D. [1 5 8 12 7]

Solution

1. Step 1: Evaluate each condition on the array

   arr > 3 is True for 5, 8, 12, 7; arr == 1 is True for 1.

2. Step 2: Combine conditions with OR operator

   Elements where either condition is True are selected: 1, 5, 8, 12, 7.

3. Final Answer:

   [1 5 8 12 7] -> Option D

4. Quick Check:

   OR condition selects 1 and all >3 values [OK]

Hint: OR (|) selects elements matching either condition [OK]

Common Mistakes:

• Forgetting to include elements equal to 1
• Using AND (&) instead of OR (|)
• Misreading the array values

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([2, 4, 6, 8])
filtered = arr[arr > 3 && arr < 8]
print(filtered)

medium

A. Missing parentheses around conditions

B. Using Python 'and' instead of '&'

C. Using '&&' instead of '&' for combining conditions

D. No error, code runs fine

Solution

1. Step 1: Identify operator error

   NumPy uses bitwise operators '&' and '|' for element-wise logical operations, not '&&'.

2. Step 2: Correct operator usage

   Replace '&&' with '&' and add parentheses around each condition.

3. Final Answer:

   Using '&&' instead of '&' for combining conditions -> Option C

4. Quick Check:

   '&&' is invalid in NumPy, use '&' with parentheses [OK]

Hint: Use '&' not '&&' for NumPy condition combining [OK]

Common Mistakes:

• Using '&&' from other languages
• Not adding parentheses around conditions
• Using Python 'and' instead of '&'

5. You have a NumPy array data = np.array([10, 15, 20, 25, 30, 35]). You want to select elements that are either less than 20 or greater than or equal to 30, but NOT equal to 15. Which code correctly filters data?

hard

A. data[((data < 20) | (data >= 30)) & (data != 15)]

B. data[(data < 20) | (data >= 30) & (data != 15)]

C. data[(data < 20) & (data >= 30) & (data != 15)]

D. data[~((data < 20) | (data >= 30) & (data == 15))]

Solution

1. Step 1: Combine OR conditions inside parentheses

   Use (data < 20) | (data >= 30) to select elements less than 20 or greater or equal to 30.

2. Step 2: Exclude elements equal to 15 with AND

   Combine with & (data != 15) to exclude 15.

3. Final Answer:

   data[((data < 20) | (data >= 30)) & (data != 15)] -> Option A

4. Quick Check:

   Parentheses + OR + AND + NOT = correct filter [OK]

Hint: Group OR conditions, then AND with NOT condition [OK]

Common Mistakes:

• Missing parentheses causing wrong precedence
• Using AND instead of OR for first condition
• Incorrect negation of 15