Numpy – Set All Values to Nan in Array

The Numpy library in Python comes with a number of built-in functions to manipulate the data in arrays. In this tutorial, we will look at a function that helps us set all the values in a Numpy array to nan.

How to set all values to nan in Numpy?

You can use the numpy.ndarray.fill() function to set all the values in a Numpy array to nan. Pass numpy.nan as the argument (this is the value used to fill all the values in the array).

The following is the syntax –

# set all values in numpy array ar to nan
ar.fill(np.nan)

It modifies the array in-place, filling each value with nan (the passed value).

Let’s now look at a step-by-step example of using the above function.

Step 1 – Create a Numpy array

First, we will create a Numpy array that we will use throughout this tutorial.

import numpy as np

# create a numpy array
ar = np.array([-2, -1, 0, 1, 2, 3, -4.1])
# display the array
print(ar)

Output:

[-2.  -1.   0.   1.   2.   3.  -4.1]

Here, we used the numpy.array() function to create a Numpy array containing some numbers.

Step 2 – Set each value to nan using numpy.ndarray.fill()

Apply the numpy.ndarray.fill() function on the array and pass numpy.nan as the parameter to set each value to nan in the array.

Let’s apply this function to the array created above.

# set all values to nan
ar.fill(np.nan)
# display the array
ar

Output:

array([nan, nan, nan, nan, nan, nan, nan])

You can see that each value in the array ar is now nan.

Note that if all the values in the array are of integer type, using the above method will fill the array with zeros instead of nans.

# create a numpy array
ar = np.array([-2, -1, 0, 1, 2, 3, -4])
# set all values to nan
ar.fill(np.nan)
# display the array
ar

Output:

array([0, 0, 0, 0, 0, 0, 0])

This happens because np.nan is of float type which is being used to fill an integer array.

In order to prevent this behavior and fill the array with nans, first convert the array to float type and then apply the numpy.ndarray.fill() function.

# create a numpy array
ar = np.array([-2, -1, 0, 1, 2, 3, -4])
# convert to float
ar = ar.astype('float')
# set all values to nan
ar.fill(np.nan)
# display the array
ar

Output:

array([nan, nan, nan, nan, nan, nan, nan])

Now all the values in the array are nan.

You might also be interested in –

• Numpy – Get the Sign of Each Element in Array
• Get the Median of Numpy Array – (With Examples)
• Numpy – Get Standard Deviation of Array Values
• Numpy – Get Min Value in Array