Given numpy array, the task is to replace negative value with zero in numpy array. Let’s see a few examples of this problem.
Method #1: Naive Method
Python3
# Python code to demonstrate# to replace negative value with 0import numpy as npini_array1 = np.array([1, 2, -3, 4, -5, -6])# printing initial arraysprint("initial array", ini_array1)# code to replace all negative value with 0ini_array1[ini_array1<0] = 0# printing resultprint("New resulting array: ", ini_array1) |
initial array [ 1 2 -3 4 -5 -6] New resulting array: [1 2 0 4 0 0]
The time complexity of this code is O(n), where n is the size of the ini_array1.
The auxiliary space complexity of this code is O(1), which means it uses a constant amount of extra space, regardless of the input size.
Method #2: Using np.where
Python3
# Python code to demonstrate# to replace negative values with 0import numpy as npini_array1 = np.array([1, 2, -3, 4, -5, -6])# printing initial arraysprint("initial array", ini_array1)# code to replace all negative value with 0result = np.where(ini_array1<0, 0, ini_array1)# printing resultprint("New resulting array: ", result) |
initial array [ 1 2 -3 4 -5 -6] New resulting array: [1 2 0 4 0 0]
Method #3: Using np.clip
Python3
# Python code to demonstrate# to replace negative values with 0import numpy as np# supposing maxx value array can holdmaxx = 1000ini_array1 = np.array([1, 2, -3, 4, -5, -6])# printing initial arraysprint("initial array", ini_array1)# code to replace all negative value with 0result = np.clip(ini_array1, 0, 1000)# printing resultprint("New resulting array: ", result) |
initial array [ 1 2 -3 4 -5 -6] New resulting array: [1 2 0 4 0 0]
Method #4: Comparing the given array with an array of zeros and write in the maximum value from the two arrays as the output.
Python3
# Python code to demonstrate# to replace negative values with 0import numpy as np ini_array1 = np.array([1, 2, -3, 4, -5, -6]) # printing initial arraysprint("initial array", ini_array1) # Creating a array of 0zero_array = np.zeros(ini_array1.shape, dtype=ini_array1.dtype)print("Zero array", zero_array)# code to replace all negative value with 0ini_array2 = np.maximum(ini_array1, zero_array)# printing resultprint("New resulting array: ", ini_array2) |
initial array [ 1 2 -3 4 -5 -6] Zero array [0 0 0 0 0 0] New resulting array: [1 2 0 4 0 0]
The time complexity of the given Python code is O(n), where n is the size of the input array ini_array1
The auxiliary space complexity of the code is O(n), as it creates a new array of the same size as the input array to store the 0 values.
Method #5: Using np.vectorize
You could use a lambda function to transform the elements of the array and replace negative values with zeros. This can be done using the NumPy vectorize function.
Python3
import numpy as np# Initialize the arrayarr = np.array([1, 2, -3, 4, -5, -6])# Print the initial arrayprint("Initial array:", arr)# Replace negative values with zeros using a lambda functionreplace_negatives = np.vectorize(lambda x: 0 if x < 0 else x)result = replace_negatives(arr)# Print the resulting arrayprint("Resulting array:", result)#This code is contributed by Edula Vinay Kumar Reddy |
Output:
Initial array: [ 1 2 -3 4 -5 -6]
Resulting array: [1 2 0 4 0 0]
Time complexity: O(n) where n is the number of elements in the array
Auxiliary Space: O(n) as a new array with the transformed elements is created
