y = np.array([1,5,6,8,1,7,3,6,9])# Where y is greater than 5, returns index positionnp.where(y>5)array([2, 3, 5, 7, 8], dtype=int64),)# First will replace the values that match the condition,# second will replace the values t...
==1condarray([False, True, False, True, False, False, False, True, False, True, False, True])# Use extract to get the valuesnp.extract(cond, array)array([ 1, 19, 11, 13, 3])# Apply condition on extract directlynp.extract(((array < 3) | (array > 15)), array)array...
Array converted to a float type: [ 1. 2. 3. 4.] Click me to see the sample solution8. 2D Array (Border 1, Inside 0)Write a NumPy program to create a 2D array with 1 on the border and 0 inside. Expected Output:Original array: [[ 1. 1. 1. 1. 1.] ... [ 1. 1....
numpy.where(condition to check, x, y) Return x or y as elements based on condition check. condition array_like, bool x,y x is returned if condition is True, y otherwise Examples : Updating data We will create an array by using arange(). We will multiply each element by 3 if they...
2)==1condarray([False,True,False,True,False,False,False,True,False,True,False,True])# Use extract to get the valuesnp.extract(cond,array)array([1,19,11,13,3])# Apply condition on extract directly
import numpy as np # Create three 1D NumPy arrays array_1 = np.array([1, 2, 3, 4, 5]) array_2 = np.array([10, 20, 30, 40, 50]) condition_array = np.array([True, False, True, False, True]) # Use np.where ufunc to create a new array based on the condition result...
condition = np.array([[ True, False], [ False, True], [ True, False]]) true_value = np.array([[-0.32313401, -1.18761309, 0.4641033 , -0.05341635], [-0.34072785, 0.45333183, 0.06974008, -1.4338561 ]]) false_value = np.array([[-0.0962484 , 0.5257979 , 1.22036481, 1.41949077], ...
If there are no elements, the if condition will become true and it will print the empty message. If our array is equal to: a = numpy.array([]) The output of the above code will be as below: Find the index of a value To find the index of value, we can use the where() method...
Boolean indexing allows us to filter elements from an array based on a specific condition. In NumPy, boolean indexing allows us to filter elements from an array based on a specific condition. We use boolean masks to specify the condition. Before we learn
This could be a single value, in which case, that value will be the output wheneverconditionisTrue. But this can also be an array or array-like object, such as a list. If it’s an array-like object, the output of np.where will be the item in theoutput-if-truearray that correspond...