Combining conditions in NumPy

Introduction

We combine conditions to select or filter data that meets multiple rules at the same time. This helps us find exactly what we want in big data sets.

Finding people who are both older than 30 and live in a certain city.

Selecting products that cost less than $20 but have high ratings.

Filtering sensor data where temperature is above 50 and humidity is below 30.

Choosing students who passed math and science exams.

Extracting rows from a table where multiple conditions are true.

Syntax

NumPy

combined_condition = (condition1) & (condition2)
combined_condition = (condition1) | (condition2)
combined_condition = ~(condition1)

Use & for AND, | for OR, and ~ for NOT.

Always put each condition inside parentheses to avoid errors.

Examples

Select numbers greater than 20 AND less than 50.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
condition = (arr > 20) & (arr < 50)
print(arr[condition])

Select numbers less than 20 OR greater than 40.

NumPy

import numpy as np
arr = np.array([10, 20, 30, 40, 50])
condition = (arr < 20) | (arr > 40)
print(arr[condition])

Select numbers NOT equal to 30.

NumPy

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

Sample Program

This program finds ages between 30 and 45 by combining two conditions with AND.

NumPy

import numpy as np

# Create an array of ages
ages = np.array([18, 25, 30, 35, 40, 45, 50])

# Condition 1: Age greater than or equal to 30
cond1 = ages >= 30

# Condition 2: Age less than or equal to 45
cond2 = ages <= 45

# Combine conditions with AND to find ages between 30 and 45 inclusive
combined = cond1 & cond2

# Print the filtered ages
print(ages[combined])

Important Notes

Use parentheses around each condition to avoid mistakes.

Use & for AND, | for OR, and ~ for NOT when combining numpy conditions.

Combined conditions return a boolean array you can use to filter data.

Summary

Combine conditions with & (AND), | (OR), and ~ (NOT).

Always put each condition inside parentheses.

Use combined conditions to filter numpy arrays easily.

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