What is NumPy - Interactive Quiz & Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the NumPy library with the common alias.

NumPy

import [1] as np

Drag options to blanks, or click blank then click option'

Anumpy

Bmatplotlib

Cpandas

Dscipy

Attempts:

3 left

💡 Hint

Common Mistakes

Using the wrong library name like 'pandas' or 'matplotlib'.

Forgetting to use the alias 'np' after import.

NumPy is usually imported with the alias np to make code shorter and easier to read.

2fill in blank

medium

Complete the code to create a NumPy array from a Python list.

NumPy

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

Drag options to blanks, or click blank then click option'

Alist

Bdataframe

Cmatrix

Darray

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'list' which is a Python type, not a NumPy function.

Using 'dataframe' which belongs to pandas library.

The array function creates a NumPy array from a list or other sequence.

3fill in blank

hard

Fix the error in the code to get the shape of a NumPy array.

NumPy

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

Drag options to blanks, or click blank then click option'

Ashape()

Bshape

Csize()

Dlength

Attempts:

3 left

💡 Hint

Common Mistakes

Using parentheses like a function call (e.g., shape()).

Using 'size()' which returns total elements, not shape.

The shape attribute (without parentheses) gives the dimensions of the array.

4fill in blank

hard

Fill both blanks to create a 2x2 NumPy array filled with zeros.

NumPy

import numpy as np
zeros_array = np.[1]((2, [2]))

Drag options to blanks, or click blank then click option'

Azeros

Bones

C3

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'ones' instead of 'zeros'.

Using wrong shape dimensions like (2, 3).

The zeros function creates an array filled with zeros. The shape is given as a tuple (2, 2).

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps each word to its length if length is greater than 3.

NumPy

words = ['apple', 'bat', 'car', 'door']
lengths = { [1]: [2] for [3] in words if len([3]) > 3 }

Drag options to blanks, or click blank then click option'

Aword

Blen(word)

Ditem

Attempts:

3 left

💡 Hint

Common Mistakes

Using different variable names inconsistently.

Not using len() to get the length.

This dictionary comprehension uses word as the variable, maps it to its length with len(word), and iterates over words.

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