What is NumPy - Practice Questions & Exercises

Challenge - 5 Problems

🧠 Conceptual

intermediate

What is the primary purpose of NumPy?

NumPy is a popular Python library. What is its main use?

A To perform fast numerical computations with arrays and matrices.

B To create interactive web pages.

C To manage databases and SQL queries.

D To build machine learning models without data preprocessing.

💡 Hint

Think about what kind of data structure NumPy mainly works with.

NumPy provides efficient tools to work with arrays and matrices, enabling fast numerical calculations.

❓ Predict Output

intermediate

What is the output of this NumPy code?

Look at the code below. What will it print?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)

A [1 2 3 1 2 3]

B TypeError: unsupported operand type(s) for *: 'list' and 'int'

C [3 4 5]

D [2 4 6]

💡 Hint

Multiplying a NumPy array by a number multiplies each element.

NumPy arrays support element-wise operations. Multiplying by 2 doubles each element.

❓ data_output

advanced

What is the shape of the resulting NumPy array?

Consider this code creating a 2D array. What is the shape of arr?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)

A (1, 6)

B (6,)

C (2, 3)

D (3, 2)

💡 Hint

Count rows and columns in the nested list.

The array has 2 rows and 3 columns, so its shape is (2, 3).

🔧 Debug

advanced

What error does this NumPy code raise?

What error will this code produce?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
print(arr[3])

A IndexError: index 3 is out of bounds for axis 0 with size 3

B TypeError: 'int' object is not subscriptable

C ValueError: could not broadcast input array

D SyntaxError: invalid syntax

💡 Hint

Remember Python indexing starts at 0 and the last index is length-1.

Index 3 is out of range for an array of size 3 (indices 0,1,2).

🚀 Application

expert

Which option produces the correct mean of a NumPy array?

Given arr = np.array([1, 2, 3, 4, 5]), which code correctly calculates the mean?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4, 5])

A arr.sum() / arr.size

B all of the above

C np.mean(arr)

D arr.mean()

💡 Hint

Think about different ways to calculate average in NumPy.

All options A, B, and D correctly compute the mean of the array.

Practice

1. What is NumPy mainly used for in data science?

easy

A. Handling and performing math on large sets of numbers efficiently

B. Creating web pages and styling them

C. Managing databases and SQL queries

D. Writing scripts to automate emails

Solution

1. Step 1: Understand NumPy's purpose

   NumPy is designed to handle large arrays of numbers and perform math operations quickly.

2. Step 2: Compare options with NumPy's role

   Creating web pages and styling them, managing databases and SQL queries, and writing scripts to automate emails are not NumPy's focus.

3. Final Answer:

   Handling and performing math on large sets of numbers efficiently -> Option A

4. Quick Check:

   NumPy = Efficient number handling [OK]

Hint: NumPy is about numbers and math, not web or databases [OK]

Common Mistakes:

• Confusing NumPy with web or database tools
• Thinking NumPy is for text processing
• Assuming NumPy handles emails or automation

2. Which of the following is the correct way to import NumPy in Python?

easy

A. import numpy as numpy

B. import numpy as np

C. import np as numpy

D. import numpy from np

Solution

1. Step 1: Recall standard NumPy import syntax

   The common and correct way is 'import numpy as np' to use 'np' as shorthand.

2. Step 2: Check other options for syntax errors

   'import numpy as numpy' repeats 'numpy' without proper aliasing; 'import np as numpy' and 'import numpy from np' have wrong order or keywords.

3. Final Answer:

   import numpy as np -> Option B

4. Quick Check:

   Standard import = import numpy as np [OK]

Hint: Remember: 'import numpy as np' is the standard [OK]

Common Mistakes:

• Using wrong keywords like 'from' in import
• Swapping alias and module names
• Not using 'as' for aliasing

3. What will be the output of this code?

import numpy as np
arr = np.array([1, 2, 3])
print(arr * 2)

medium

A. [1 2 3 1 2 3]

B. Error: unsupported operand

C. [3 4 5]

D. [2 4 6]

Solution

1. Step 1: Understand NumPy array multiplication

   Multiplying a NumPy array by 2 multiplies each element by 2.

2. Step 2: Calculate element-wise multiplication

   Elements 1, 2, 3 become 2, 4, 6 respectively.

3. Final Answer:

   [2 4 6] -> Option D

4. Quick Check:

   Array * 2 doubles each element [OK]

Hint: NumPy arrays multiply element-wise, not concatenate [OK]

Common Mistakes:

• Thinking * duplicates the array
• Expecting addition instead of multiplication
• Confusing with Python lists behavior

4. Find the error in this code:

import numpy as np
arr = np.array([1, 2, 3])
print(arr + '2')

medium

A. Array syntax is incorrect

B. Missing import statement for NumPy

C. Cannot add string '2' to a NumPy array of integers

D. No error, output will be [3 4 5]

Solution

1. Step 1: Analyze the operation arr + '2'

   Adding a string to an integer array causes a type error in NumPy.

2. Step 2: Confirm error type

   NumPy does not support adding strings to numeric arrays directly.

3. Final Answer:

   Cannot add string '2' to a NumPy array of integers -> Option C

4. Quick Check:

   Type mismatch causes error [OK]

Hint: NumPy arrays need matching data types for operations [OK]

Common Mistakes:

• Assuming automatic type conversion
• Ignoring data type mismatch errors
• Thinking string addition works like number addition

5. You have a list of daily temperatures: [20, 22, 19, 24, 21]. Using NumPy, how can you quickly find the average temperature?

hard

A. np.mean([20, 22, 19, 24, 21])

B. sum([20, 22, 19, 24, 21]) / 5

C. np.sum([20, 22, 19, 24, 21])

D. len([20, 22, 19, 24, 21])

Solution

1. Step 1: Identify NumPy function for average

   NumPy provides np.mean() to calculate the average of array elements.

2. Step 2: Compare with other options

   len() counts elements; sum() / 5 calculates average manually without NumPy's dedicated function; np.sum() sums values; np.mean() finds the average quickly.

3. Final Answer:

   np.mean([20, 22, 19, 24, 21]) -> Option A

4. Quick Check:

   Use np.mean() for average [OK]

Hint: Use np.mean() to get average quickly [OK]

Common Mistakes:

• Using sum without dividing for average
• Confusing sum and mean functions
• Not using NumPy functions for array operations