In this article, we will cover how to replace NaN with zero and fill negative infinity values in Python using NumPy.
Example
Input: [ nan -inf 5.]
Output: [0.00000e+00 9.99999e+05 5.00000e+00]
Explanation: Replacing NaN with 0 and negative inf with any value.
numpy.nan_to_num method
The numpy.nan_to_num method is used to replace Nan values with zero and it fills 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, negative infinity, 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 100 using the nan parameter, and negative infinity is replaced with 999999 using the neginf parameter.
Python3
# import packageimport numpy as np# Creating an array of imaginary numbersarray = np.array([np.nan,-np.inf,5])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 0 and# negative infinity is replaced with 999999print("After replacement the array is : ", np.nan_to_num(array,nan= 0, neginf=999999)) |
Output:
[ nan -inf 5.]
Shape of the array is : (3,)
The dimension of the array is : 1
Datatype of our Array is : float64
After replacement the array is : [0.00000e+00 9.99999e+05 5.00000e+00]
Example 2:
In this example, we are creating an array with the help of complex numbers and integers. Here, np.nan is replaced with 100 using the nan parameter, np.inf is replaced with 100000 using the posinf parameter and negative infinity is replaced with 999999 using the neginf parameter.
Python3
# import packageimport numpy as np# Creating an array of imaginary numbersarray = np.array([complex(np.nan, -np.inf),1,2, 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 100 and np.inf is# replaced with 100000 negative infinity is replaced with 999999print("After replacement the array is: ", np.nan_to_num(array,nan= 100, posinf = 100000, neginf=999999)) |
Output:
[nan-infj 1. +0.j 2. +0.j inf +0.j]
Shape of the array is : (4,)
The dimension of the array is : 1
Datatype of our Array is : complex128
After replacement the array is : [1.e+02+999999.j 1.e+00 +0.j 2.e+00 +0.j 1.e+05 +0.j]
