NumPy is a popular Python library for numerical computations. One of the most important data types supported by NumPy is arrays. Arrays are efficient and provide vectorized operations required for numerical computations. In this article, we will discuss how to create an np nan array with code examples.
What is an np nan array?
An np nan array an array that contains Not a Number (nan) values. The nan values represent undefined or unrepresentable numerical and mathematical results, such as 0/0 or ∞/∞, that cannot be computed numerically.
Creating an np nan array with np.nan
NumPy provides a method to create an np nan array with the np.nan() method. The np.nan() method returns a floatingpoint value representing nan. We can create an np nan array with the np.nan() method using the following code:
import numpy as np
nan_array = np.array([np.nan, np.nan, np.nan])
This code creates an np nan array with three elements. The np.nan() method is called three times to create three nan values, and these values are stored in an np array.
Creating an np nan array with np.empty
Another way to create an np nan array is to use the np.empty() method. The np.empty() method creates an empty array of a specified shape and data type. We can create an np nan array with the np.empty() method using the following code:
import numpy as np
nan_array = np.empty([3], dtype=float)
nan_array[:] = np.nan
This code creates an np empty array with three elements and the data type float
. We use the [:]
slice notation to assign nan values to all the elements of the array.
Creating an np nan array with np.full
The np.full() method can also be used to create an np nan array. The np.full() method creates a new array with a specified shape and fills it with a specified value. We can use np.nan as the value argument to fill the array with nan values. We can create an np nan array with the np.full() method using the following code:
import numpy as np
nan_array = np.full([3], np.nan, dtype=float)
This code creates an np nan array with three elements and the data type float
. We pass np.nan as the value argument to fill the array with nan values.
Conclusion
In this article, we discussed how to create an np nan array with code examples. We saw three methods to create an np nan array: using the np.nan() method, the np.empty() method, and the np.full() method. These methods allow us to create an np array filled with nan values, which is useful for numerical computations that involve undefined or unrepresentable results. With these methods, we can efficiently create an np nan array and perform numerical computations that require nan values.
Sure! Let's dive deeper into the topics discussed in the previous article.
NumPy Arrays
Arrays are an essential data structure in NumPy, and they provide a way to hold and process large amounts of numerical data efficiently. NumPy arrays can be onedimensional, twodimensional, or multidimensional, and they can hold integers, floatingpoint numbers, and other numeric data types.
NumPy arrays support a wide range of operations, including elementwise arithmetic operations, array broadcasting, slicing and indexing, and broadcasting. NumPy arrays are also compatible with many other Python libraries, such as Matplotlib and SciPy.
Creating a NumPy Array
To create a NumPy array, we can use the np.array()
method, which takes a list or tuple of values as input and returns a new NumPy array:
import numpy as np
my_array = np.array([1, 2, 3])
print(my_array)
Output: [1 2 3]
We can also create a NumPy array with zeros or ones using the np.zeros()
or np.ones()
methods, respectively:
import numpy as np
zeros_array = np.zeros([3, 3])
print(zeros_array)
ones_array = np.ones([4, 4])
print(ones_array)
Output:
[[0. 0. 0.]
[0. 0. 0.]
[0. 0. 0.]]
[[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]
[1. 1. 1. 1.]]
NumPy NaN Array
Sometimes, we may need to represent undefined or unrepresentable numerical and mathematical results, such as 0/0 or ∞/∞, that cannot be computed numerically. For such cases, NumPy provides a special NaN (Not a Number) value that we can use to represent these undefined or uncomputable results.
To create a NumPy NaN array, we can use one of the three methods outlined in the previous article: np.nan()
, np.empty()
, or np.full()
.
import numpy as np
nan_array_1 = np.array([np.nan, np.nan, np.nan])
nan_array_2 = np.empty([3], dtype=float)
nan_array_2[:] = np.nan
nan_array_3 = np.full([3], np.nan, dtype=float)
print(nan_array_1)
print(nan_array_2)
print(nan_array_3)
Output:
[nan nan nan]
[nan nan nan]
[nan nan nan]
Concluding Thoughts
NumPy is a powerful Python library for numerical computations and provides a wide range of functionality for dealing with arrays, matrices, and other numerical data structures. Creating a NumPy NaN array is essential for handling undefined or unrepresentable numerical and mathematical results. We can create a NumPy NaN array using different methods, including np.nan()
, np.empty()
, or np.full()
.
Popular questions

What is a NumPy NaN array?
A NumPy NaN array is an array that contains NaN (Not a Number) values. NaN values represent undefined or unrepresentable numerical and mathematical results that cannot be computed numerically. 
How can we create a NumPy NaN array using the np.nan() method?
We can create a NumPy NaN array using the np.nan() method by calling it with the desired number of NaN values and storing the return value in a NumPy array, as follows:
import numpy as np
nan_array = np.array([np.nan, np.nan, np.nan])
 How can we create a NumPy NaN array using the np.empty() method?
We can create an array of the desired size with the dtypefloat
using the np.empty() method, then fill the array with NaN values using slicing, as follows:
import numpy as np
nan_array = np.empty([3], dtype=float)
nan_array[:] = np.nan
 How can we create a NumPy NaN array using the np.full() method?
We can create an array of the desired size and data type using the np.full() method, then fill the array with NaN values using the NaN constant, as follows:
import numpy as np
nan_array = np.full([3], np.nan, dtype=float)
 Can we perform mathematical operations on NumPy NaN arrays?
Yes, we can perform operations on a NumPy NaN array, but the result may be NaN. For example, adding or subtracting NaN with a number will always result in NaN. It's important to handle NaN values appropriately when working with numerical data.
Tag
"Numpyarray"