In this article, we will cover how to fill Nan for complex input values and infinity values with large finite numbers in Python using NumPy.
Example:
Input: [complex(np.nan,np.inf)]
Output: [1000.+1.79769313e+308j]
Explanation: Replace Nan with complex values and infinity values with large finite.
numpy.nan_to_num method
The numpy.nan_to_num method is used to replace Nan values with zero, fills positive infinity and negative infinity values with a user-defined value or a big positive number. neginf is the keyword used for this purpose.
Syntax: numpy.nan_to_num(arr, copy=True)
Parameter:
- arr: [array_like] Input data.
- copy: [bool, optional] Default is True.
Return: New Array with the same shape as arr and dtype of the element in arr with the greatest precision.
Example 1:
In this example, an array is created using numpy.array() method which consists of np.nan and positive infinity. The shape, datatype, and dimensions of the array can be found by .shape, .dtype, and .ndim attributes. Here, np.nan is replaced with 1000 using the nan parameter, and n infinity is replaced with a large finite number.
Python3
# import packageimport numpy as np# Creating an array of imaginary numbersarray = np.array([complex(np.nan,np.inf)])print(array)# shape of the array isprint("Shape of the array is : ",array.shape)# dimension of the arrayprint("The dimension of the array is : ",array.ndim)# Datatype of the arrayprint("Datatype of our Array is : ",array.dtype)# np.nan is replaced with 1000 and# infinity is replaced with a large positive numberprint("After replacement the array is : ", np.nan_to_num(array,nan= 1000)) |
Output:
[nan+infj] Shape of the array is : (1,) The dimension of the array is : 1 Datatype of our Array is : complex128 After replacement the array is : [1000.+1.79769313e+308j]
Example 2:
In this example, positive infinity is replaced with a user-defined value.
Python3
# import packageimport numpy as np# Creating an array of imaginary numbersarray = np.array([complex(np.nan,np.inf)])print(array)# shape of the array isprint("Shape of the array is : ",array.shape)# dimension of the arrayprint("The dimension of the array is : ",array.ndim)# Datatype of the arrayprint("Datatype of our Array is : ",array.dtype)# np.nan is replaced with 1000 and# infinity is replaced with a large positive numberprint("After replacement the array is : ", np.nan_to_num(array,nan= 1000)) |
Output:
[nan+infj] Shape of the array is : (1,) The dimension of the array is : 1 Datatype of our Array is : complex128 After replacement the array is : [1000.+99999.j]
