ndarray as the core data structure in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is an ndarray in NumPy?

An ndarray is a multi-dimensional, fixed-size container of items of the same data type in NumPy. It is the core data structure used for numerical computations.

beginner

How do you create a 2D ndarray with NumPy?

You can create a 2D ndarray by passing a list of lists to np.array(). For example: np.array([[1, 2], [3, 4]]) creates a 2x2 array.

beginner

What does the shape attribute of an ndarray 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.

intermediate

Why must all elements in an ndarray have the same data type?

Having the same data type allows NumPy to store data efficiently in memory and perform fast mathematical operations.

intermediate

How does an ndarray differ from a Python list?

An ndarray is fixed-size, multi-dimensional, and stores elements of the same type for fast computation. A Python list is flexible in size and can hold mixed data types but is slower for numerical tasks.

What is the main advantage of using an ndarray over a Python list for numerical data?

A Automatically resizes when adding elements

B Can store mixed data types

C Supports variable length elements

D Faster mathematical operations due to fixed data type and memory layout

NumPy's ndarray stores data in a fixed type and contiguous memory, enabling fast math operations.

Which NumPy function is used to create an ndarray from a Python list?

A np.array()

B np.create()

C np.ndarray()

D np.list_to_array()

The np.array() function converts Python lists into NumPy ndarray objects.

If an ndarray has shape (5, 3), what does this mean?

A 5 rows and 3 columns

B 5 elements total

C 5 columns and 3 rows

D 3 elements total

Shape (5, 3) means 5 rows and 3 columns in the array.

Can an ndarray contain elements of different data types?

A Yes, it can mix types freely

B No, all elements must have the same data type

C Only if you use special functions

D Only for 1D arrays

All elements in an ndarray must be of the same data type for efficiency.

What attribute of an ndarray tells you the number of dimensions it has?

A shape

B size

C ndim

D dtype

The ndim attribute shows how many dimensions the array has.

Explain what an ndarray is and why it is important in NumPy.

Think about how NumPy handles numbers compared to Python lists.

Describe how you would create a 3x3 ndarray filled with zeros and check its shape.

NumPy has special functions to create arrays filled with zeros.

Practice

1.

What is the main purpose of a numpy ndarray?

easy

A. To display images on screen

B. To create text documents

C. To store numbers in one or more dimensions for fast math operations

D. To connect to databases

Solution

1. Step 1: Understand what ndarray stores

   ndarray is designed to hold numbers arranged in rows and columns or more dimensions.

2. Step 2: Recognize its use for math

   It allows fast math operations on many numbers at once, unlike regular Python lists.

3. Final Answer:

   To store numbers in one or more dimensions for fast math operations -> Option C

4. Quick Check:

   ndarray = fast number storage [OK]

Hint: ndarray = fast multi-dimensional number storage [OK]

Common Mistakes:

• Thinking ndarray stores text or images
• Confusing ndarray with database tools
• Assuming ndarray is for display only

2.

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

import numpy as np
# Which line is correct?

easy

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

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

C. arr = np.array(1, 2, 3)

D. arr = np.array{1, 2, 3}

Solution

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

   np.array() takes a list or nested lists inside parentheses.

2. Step 2: Check each option

   arr = np.array([1, 2, 3]) uses correct syntax with square brackets inside parentheses. Others have wrong function calls or braces.

3. Final Answer:

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

4. Quick Check:

   np.array(list) syntax = correct [OK]

Hint: Use np.array() with list inside parentheses [OK]

Common Mistakes:

• Using np.ndarray() directly without parameters
• Passing multiple arguments instead of one list
• Using curly braces {} instead of []

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. (4,)

B. (2, 2)

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: Check print output

   arr.shape returns a tuple with dimensions, so output is (2, 2).

3. Final Answer:

   (2, 2) -> Option B

4. Quick Check:

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

Hint: Shape shows rows and columns as tuple [OK]

Common Mistakes:

• Confusing shape with total elements
• Expecting a list instead of tuple
• Mixing up rows and columns count

4.

Find the error in this code snippet:

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

medium

A. SyntaxError due to wrong print statement

B. No error, prints 3

C. TypeError because arr is not a list

D. IndexError because index 3 is out of bounds

Solution

1. Step 1: Check array length and indexing

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

2. Step 2: Understand error type

   Accessing arr[3] causes IndexError because index is invalid.

3. Final Answer:

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

4. Quick Check:

   Index out of range = IndexError [OK]

Hint: Check array length before indexing [OK]

Common Mistakes:

• Thinking arr[3] returns last element
• Confusing IndexError with SyntaxError
• Assuming numpy arrays allow out-of-range access

5.

You have a nested list [[1, 2], [3, 4], [5, 6]]. How do you create a numpy ndarray and then get the sum of all elements?

import numpy as np
nested_list = [[1, 2], [3, 4], [5, 6]]
# Fill in the blanks
arr = np.array(nested_list)
total = _____
print(total)

hard

A. arr.sum()

B. sum(arr)

C. arr.add()

D. np.sum(arr, axis=1)

Solution

1. Step 1: Create ndarray from nested list

   np.array(nested_list) creates a 2D array with shape (3, 2).

2. Step 2: Use ndarray method to sum all elements

   arr.sum() returns sum of all elements in the array.

3. Final Answer:

   arr.sum() -> Option A

4. Quick Check:

   Use arr.sum() for total sum [OK]

Hint: Use arr.sum() to sum all elements [OK]

Common Mistakes:

• Using Python sum() which sums rows, not elements
• Using arr.add() which does not exist
• Using np.sum with axis=1 sums rows, not total