NumPy array foundation review in SciPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a NumPy array?

A NumPy array is a grid of values, all of the same type, indexed by a tuple of nonnegative integers. It is like a list but faster and can handle large data efficiently.

beginner

How do you create a NumPy array from a Python list?

Use numpy.array() function. For example, np.array([1, 2, 3]) creates a 1D array with elements 1, 2, and 3.

beginner

What is the difference between a Python list and a NumPy array?

Python lists can hold different data types and are slower. NumPy arrays hold elements of the same type and are optimized for numerical operations, making them faster and more memory efficient.

beginner

What does the shape attribute of a NumPy array tell you?

The shape attribute shows the size of the array in each dimension. For example, a shape of (3, 4) means 3 rows and 4 columns.

beginner

How can you access the element in the second row and third column of a 2D NumPy array?

Use zero-based indexing: array[1, 2] accesses the element in the second row and third column.

Which function creates a NumPy array from a list?

A np.create()

B np.list()

C np.array()

D np.make_array()

The correct function to create a NumPy array from a list is np.array().

What is the data type requirement for elements in a NumPy array?

A Elements can be of any type

B Only strings are allowed

C Only integers are allowed

D All elements must be of the same type

NumPy arrays require all elements to be of the same data type for efficient computation.

If a NumPy array has shape (4, 5), how many elements does it contain?

A 20

B 9

C 10

D 5

The total elements are rows × columns = 4 × 5 = 20.

How do you access the first element of a 1D NumPy array named arr?

A arr[0]

B arr[1]

C arr.first()

D arr.get(0)

NumPy arrays use zero-based indexing, so the first element is accessed with arr[0].

Which of these is NOT a benefit of using NumPy arrays over Python lists?

A Faster numerical operations

B Supports mixed data types

C Uses less memory

D Supports multi-dimensional arrays

NumPy arrays do NOT support mixed data types; all elements must be the same type.

Explain what a NumPy array is and why it is useful compared to a Python list.

Think about how computers handle numbers and speed.

Describe how to create a 2D NumPy array and how to find its shape.

Use nested lists and check the size with a property.

Practice

1. What is a key advantage of using a NumPy array over a regular Python list for storing numbers?

easy

A. NumPy arrays automatically sort the data

B. NumPy arrays can store different data types in the same array

C. NumPy arrays use less memory and allow faster math operations

D. NumPy arrays are slower but more flexible than lists

Solution

1. Step 1: Understand memory and speed benefits

   NumPy arrays store data in a compact way and use optimized code for math.

2. Step 2: Compare with Python lists

   Python lists store references and are slower for math operations.

3. Final Answer:

   NumPy arrays use less memory and allow faster math operations -> Option C

4. Quick Check:

   NumPy arrays = efficient storage + fast math [OK]

Hint: NumPy arrays are built for fast math and memory efficiency [OK]

Common Mistakes:

• Thinking NumPy arrays can store mixed data types like lists
• Assuming NumPy arrays sort data automatically
• Believing NumPy arrays are slower than lists

2. Which of the following is the correct way to create a 1-dimensional NumPy array with elements 1, 2, and 3?

easy

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

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

C. np.array{1, 2, 3}

D. np.array([1; 2; 3])

Solution

1. Step 1: Recall correct syntax for np.array

   NumPy arrays are created by passing a list inside parentheses: np.array([elements]).

2. Step 2: Check each option

   np.array([1, 2, 3]) uses correct syntax with square brackets inside parentheses. Others have syntax errors.

3. Final Answer:

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

4. Quick Check:

   np.array needs a list inside parentheses [OK]

Hint: Use square brackets inside parentheses to create arrays [OK]

Common Mistakes:

• Omitting square brackets around elements
• Using curly braces instead of square brackets
• Separating elements with semicolons inside list

3. What will be the output of the following code?

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

medium

A. (2, 2)

B. (4,)

C. (1, 4)

D. (2,)

Solution

1. Step 1: Understand the array shape

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

2. Step 2: Confirm shape output

   Printing arr.shape returns a tuple with dimensions (2, 2).

3. Final Answer:

   (2, 2) -> Option A

4. Quick Check:

   2 rows and 2 columns = (2, 2) [OK]

Hint: Shape shows (rows, columns) for 2D arrays [OK]

Common Mistakes:

• Confusing total elements with shape
• Expecting a flat tuple like (4,)
• Misreading rows and columns order

4. Identify the error in this code snippet:

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

medium

A. IndexError because index 3 is out of bounds

B. SyntaxError due to wrong array creation

C. TypeError because arr is not subscriptable

D. No error, prints 3

Solution

1. Step 1: Check array length and indexing

   Array has elements at indices 0, 1, 2. Index 3 is out of range.

2. Step 2: Understand error type

   Accessing arr[3] causes IndexError for out-of-bounds access.

3. Final Answer:

   IndexError because index 3 is out of bounds -> Option A

4. Quick Check:

   Index 3 invalid for length 3 array [OK]

Hint: Array indices start at 0; max index is length-1 [OK]

Common Mistakes:

• Thinking index 3 accesses the last element
• Confusing syntax error with runtime error
• Assuming arr[3] prints 3

5. Given a 2D NumPy array arr = np.array([[1, 2, 3], [4, 5, 6]]), which code correctly extracts the second column as a 1D array?

hard

A. arr[:, 2]

B. arr[1, :]

C. arr[1]

D. arr[:, 1]

Solution

1. Step 1: Understand array slicing for columns

   To get a column, select all rows (:) and the column index.

2. Step 2: Identify correct column index

   Second column has index 1 (zero-based indexing).

3. Step 3: Check options

   arr[:, 1] uses arr[:, 1] which selects all rows, column 1 correctly.

4. Final Answer:

   arr[:, 1] -> Option D

5. Quick Check:

   All rows, column 1 = arr[:, 1] [OK]

Hint: Use arr[:, col_index] to get a column [OK]

Common Mistakes:

• Using arr[1, :] which selects a row, not a column
• Using arr[1] which selects second row
• Using wrong column index