Why NumPy over Python lists - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

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

NumPy

import [1] as np

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

Ascipy

Bpandas

Cmatplotlib

Dnumpy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong library like pandas or matplotlib.

Not using the alias 'np' which is a common convention.

NumPy is imported using the alias np for easy access to its functions.

2fill in blank

medium

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

NumPy

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

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Alist

Barray

Cmatrix

Dseries

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list() which does not exist.

Confusing arrays with matrices or series.

The np.array() function converts a Python list into a NumPy array.

3fill in blank

hard

Fix the error in the code to calculate the mean of a NumPy array.

NumPy

mean_value = arr.[1]()

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

Amean

Baverage

Cmedian

Dmode

Attempts:

3 left

💡 Hint

Common Mistakes

Using average() which is not a NumPy array method.

Confusing mean with median or mode.

The correct method to calculate the average value of a NumPy array is mean().

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if the length is greater than 3.

NumPy

{word: [1] for word in words if [2] > 3}

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Alen(word)

Bword

Dword.length

Attempts:

3 left

💡 Hint

Common Mistakes

Using word.length which is not valid in Python.

Using the word itself instead of its length.

The dictionary comprehension uses len(word) to get the length and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase words to their counts only if the count is greater than 0.

NumPy

result = [1]: [2] for [3], count in data.items() if count > 0}

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Aword.upper()

Bcount

Cword

Ddata

Attempts:

3 left

💡 Hint

Common Mistakes

Using the original word as key without converting to uppercase.

Mixing up keys and values in the comprehension.

The comprehension maps the uppercase version of each word to its count, filtering counts greater than zero.

Practice

1. Why is NumPy preferred over Python lists for handling large numeric data?

easy

A. NumPy arrays use less memory and are faster for numeric operations

B. Python lists can only store strings

C. NumPy arrays are slower but more flexible

D. Python lists support multi-dimensional arrays natively

Solution

1. Step 1: Understand memory usage difference

   NumPy arrays store data in a compact way, using less memory than Python lists which store pointers.

2. Step 2: Consider speed for numeric operations

   NumPy uses optimized C code for math, making operations faster than Python lists.

3. Final Answer:

   NumPy arrays use less memory and are faster for numeric operations -> Option A

4. Quick Check:

   NumPy is faster and memory efficient [OK]

Hint: NumPy = faster math + less memory [OK]

Common Mistakes:

• Thinking Python lists are faster for numbers
• Believing NumPy arrays only store strings
• Confusing flexibility with speed

2. Which of the following is the correct way to create a NumPy array from a Python list [1, 2, 3]?

easy

A. np.array([1, 2, 3])

B. np.list([1, 2, 3])

C. array([1, 2, 3])

D. np.create_array(1, 2, 3)

Solution

1. Step 1: Recall NumPy array creation syntax

   The function to create arrays is np.array() which takes a list as input.

2. Step 2: Check each option

   Only np.array([1, 2, 3]) uses correct function and syntax; others are invalid or undefined.

3. Final Answer:

   np.array([1, 2, 3]) -> Option A

4. Quick Check:

   Use np.array() to create arrays [OK]

Hint: Use np.array() to convert lists [OK]

Common Mistakes:

• Using undefined functions like np.list()
• Omitting np prefix
• Passing multiple arguments instead of a list

3. What will be the output of this code?

import numpy as np
list1 = [1, 2, 3]
arr1 = np.array(list1)
print(arr1 * 2)

medium

A. Error: unsupported operand

B. [1, 2, 3, 1, 2, 3]

C. [[1, 2, 3], [1, 2, 3]]

D. [2 4 6]

Solution

1. Step 1: Convert list to NumPy array

   The list [1, 2, 3] becomes a NumPy array arr1.

2. Step 2: Multiply array by 2

   NumPy multiplies each element by 2, resulting in [2 4 6].

3. Final Answer:

   [2 4 6] -> Option D

4. Quick Check:

   NumPy multiplies element-wise [OK]

Hint: NumPy * number multiplies each element [OK]

Common Mistakes:

• Thinking it concatenates the list
• Expecting nested lists
• Assuming error on multiplication

4. Find the error in this code snippet:

import numpy as np
list1 = [1, 2, 3]
arr1 = np.array(list1)
result = arr1 + '2'
print(result)

medium

A. print statement syntax error

B. np.array() is used incorrectly

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

D. list1 should be a tuple

Solution

1. Step 1: Identify data types involved

   arr1 is an integer array; '2' is a string.

2. Step 2: Understand operation error

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

3. Final Answer:

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

4. Quick Check:

   Type mismatch causes error [OK]

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

Common Mistakes:

• Thinking np.array() usage is wrong
• Assuming list must be tuple
• Believing print syntax is incorrect

5. You have a large dataset of 1 million numbers. Which is the best reason to use NumPy arrays instead of Python lists to store and process this data?

hard

A. Python lists automatically parallelize operations

B. NumPy arrays allow faster element-wise math and use less memory

C. NumPy arrays can store mixed data types efficiently

D. Python lists are optimized for numeric calculations

Solution

1. Step 1: Consider memory usage for large data

   NumPy arrays store data compactly, saving memory compared to Python lists.

2. Step 2: Consider speed of numeric operations

   NumPy uses optimized code for fast element-wise math on large arrays.

3. Step 3: Evaluate other options

   Python lists do not parallelize automatically and are slower; NumPy arrays do not store mixed types efficiently.

4. Final Answer:

   NumPy arrays allow faster element-wise math and use less memory -> Option B

5. Quick Check:

   NumPy = speed + memory efficiency for big data [OK]

Hint: Big numeric data? Use NumPy for speed and memory [OK]

Common Mistakes:

• Believing Python lists are faster for big data
• Thinking NumPy supports mixed types efficiently
• Assuming Python lists auto-parallelize