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.