np.zeros() for zero-filled arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.zeros() do in NumPy?

np.zeros() creates a new array filled with zeros. You specify the shape (size) of the array, and it returns an array of that shape with all elements set to zero.

beginner

How do you create a 3x3 zero-filled array using np.zeros()?

Use np.zeros((3, 3)). The argument is a tuple representing the shape: 3 rows and 3 columns.

beginner

What data type does np.zeros() use by default?

By default, np.zeros() creates an array of floats (floating-point numbers).

intermediate

How can you create a zero-filled array of integers using np.zeros()?

Use the dtype parameter: np.zeros((3, 3), dtype=int) creates a 3x3 array of zeros as integers.

beginner

Why might you use np.zeros() in data science?

It helps to initialize arrays before filling them with data or results. For example, creating empty containers for calculations or placeholders for missing data.

What is the output of np.zeros(5)?

A [0 0 0 0 0]

B [1 1 1 1 1]

C [5 5 5 5 5]

D An error

np.zeros(5) creates a 1D array of length 5 filled with zeros.

How do you specify the shape of a 2D zero array with 4 rows and 2 columns?

A np.zeros((4, 2))

B np.zeros([4, 2])

C np.zeros(4, 2)

D np.zeros{4, 2}

The shape must be a tuple, so np.zeros((4, 2)) is correct.

What is the default data type of elements in np.zeros()?

A int

B str

C float

D bool

By default, np.zeros() creates arrays of floats.

Which parameter changes the data type of zeros in np.zeros()?

A type

B shape

C size

D dtype

The dtype parameter sets the data type of the array elements.

Why use np.zeros() instead of a Python list of zeros?

A NumPy arrays are slower but prettier

B NumPy arrays are faster and use less memory

C Python lists cannot hold zeros

D Python lists do not support zeros

NumPy arrays are optimized for numerical operations, making them faster and more memory efficient than Python lists.

Explain how to create a zero-filled 2D array with 5 rows and 4 columns using np.zeros(). Include how to specify the data type as integers.

Remember the shape is a tuple and use dtype=int.

Describe three common reasons why data scientists use np.zeros() when working with data.

Think about starting points for data or results.