How to Check if a Numpy Array is Empty?

In this tutorial, we will look at how to check if a given Numpy array is empty or not with the help of some examples.

Steps to check whether an array is empty

An array is said to be empty if it does not contain any value. There are a number of ways to check for an empty array in Numpy –

• Check whether the array’s length is 0.
• Use the array’s .size attribute and check if it’s equal to 0 (an array’s size is zero if it has no elements).
• Use the array’s .shape attribute which returns the shape of a numpy array. If the array is empty, the first value in the returned tuple would be 0.

The following is the syntax –

## check if numpy array is empty

# using the len() function
len(ar) == 0
# using the .shape attribute
ar.size == 0
# using the .shape attribute
ar.shape[0] == 0

Let’s now look at some examples of using the above syntax –

Let’s create some Numpy arrays that we will be using throughout this tutorial.

import numpy as np

# an empty array
ar1 = np.array([])
# a non-empty array
ar2 = np.array([1, 2, 3])
# array with None values
ar3 = np.array([None, None])

Example 1 – Check if a Numpy array is empty using the len() function

The idea here is that, for an empty array, the length will be 0. So we can check whether an array is empty or not by comparing its length with 0.

Let’s now check whether the three arrays created above are empty or not.

# check if array is empty
print(len(ar1)==0)
print(len(ar2)==0)
print(len(ar3)==0)

Output:

True
False
False

We get True for ar1 which is an empty array and False for ar2 and ar3 which are non-empty arrays. Note that an array with None values is considered non-empty.

Example 2 – Check if a Numpy array is empty using the .size property

The .size property of a Numpy array returns the number of elements in an array. For an empty array, its size should be zero.

# check if array is empty
print(ar1.size == 0)
print(ar2.size == 0)
print(ar3.size == 0)

Output:

True
False
False

We get the same results as above. True for ar1 which is empty and False for the other arrays (which are not empty).

Example 3 – Check if a Numpy array is empty using the .shape property

The .shape property of an array gives the shape of the array. For example, for a 2D array, it gives the number of rows and the number of columns. To check if an array is empty, we may use the shape to determine whether the first dimension is 0 or not.

# check if array is empty
print(ar1.shape[0] == 0)
print(ar2.shape[0] == 0)
print(ar3.shape[0] == 0)

Output:

True
False
False

We get the same result as above.

Methods to avoid

There can be other methods as well but be careful about what these methods are actually checking for. For example, you can use the numpy.any() function on an empty array and it will return False, on the other hand, it’ll also return False for a numpy array with False values (which is not considered an empty array).

Let’s see this in action.

# create numpy arrays
ar1 = np.array([])
ar2 = np.array([False, False, False])

# check if the above arrays are empty
print(np.any(ar1))
print(np.any(ar2))

Output:

False
False

We get False for both, thus, this method cannot reliably be used to check whether a numpy array is empty or not.

You might also be interested in –

• Numpy – Check If an Array contains a NaN value
• Check If Two Numpy Arrays are Equal
• Numpy – Check If an Element is NaN