In this article, we are going to see how to map a function over a NumPy array in Python.
numpy.vectorize() method
The numpy.vectorize() function maps functions on data structures that contain a sequence of objects like NumPy arrays. The nested sequence of objects or NumPy arrays as inputs and returns a single NumPy array or a tuple of NumPy arrays.
Python3
import numpy as nparr = np.array([1, 2, 3, 4, 5])def addTwo(i): return i+2 applyall = np.vectorize(addTwo)res = applyall(arr)print(res) |
Output:
[3 4 5 6 7]
Explanation: The function is passed to the vectorized method and again the array is passed to it and the function will return the array on which the array is applied.
Using lambda function
The lambda is an anonymous function, it takes any number of arguments but evaluates one expression.
Python3
import numpy as nparr = np.array([1, 2, 3, 4, 5])def applyall(i): return i + 2res = applyall(arr)print(res) |
Output:
[3 4 5 6 7]
Using lambda function to Map a Function Over N-D NumPy Array
Here we are using lambda function and mapping it with the a function over a multi dimension numpy array.
Python3
import numpy as np#create NumPy arraydata = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])#view NumPy arrayprint(data)#define functionmy_function = lambda x: x*2+5#apply function to NumPy arraymy_function(data) |
Output:
[[1 2 3 4] [5 6 7 8]]
Using an array as the parameter of a function to map over a NumPy array
We can map a function over a NumPy array just by passing the array to the function.
Python3
import numpy as nparr = np.array([1, 2, 3, 4, 5])def applyall(a): return a+2res = applyall(arr)print(res) |
Output:
[3 4 5 6 7]
Explanation: The array is passed to the applyall() method and it will map the function to the entire array and the resultant array is returned.
