Here we will be focusing on the comparison done using NumPy on arrays. Comparing two NumPy arrays determines whether they are equivalent by checking if every element at each corresponding index is the same.
Method 1: We generally use the == operator to compare two NumPy arrays to generate a new array object. Call ndarray.all() with the new array object as ndarray to return True if the two NumPy arrays are equivalent.
Python3
import numpy as npan_array = np.array([[1, 2], [3, 4]])another_array = np.array([[1, 2], [3, 4]])comparison = an_array == another_arrayequal_arrays = comparison.all()print(equal_arrays) |
Output:
True
Method 2: We can also use greater than, less than and equal to operators to compare. To understand, have a look at the code below.
Syntax : numpy.greater(x1, x2[, out]) Syntax : numpy.greater_equal(x1, x2[, out]) Syntax : numpy.less(x1, x2[, out]) Syntax : numpy.less_equal(x1, x2[, out])
Python3
import numpy as npa = np.array([101, 99, 87])b = np.array([897, 97, 111])print("Array a: ", a)print("Array b: ", b)print("a > b")print(np.greater(a, b))print("a >= b")print(np.greater_equal(a, b))print("a < b")print(np.less(a, b))print("a <= b")print(np.less_equal(a, b)) |
Output:
Method 3: Using array_equal()
This array_equal() function checks if two arrays have the same elements and same shape.
Syntax:
numpy.array_equal(arr1, arr2)
Parameters:
- arr1 : [array_like]Input array or object whose elements, we need to test.
- arr2 : [array_like]Input array or object whose elements, we need to test.
Return Type: True, two arrays have the same elements and same shape.; otherwise False
Example
Python3
import numpy as nparr1 = np.array([[1, 2], [3, 4]])arr2 = np.array([[1, 2], [3, 4]])# Comparing the arraysif np.array_equal(arr1, arr2): print("Equal")else: print("Not Equal") |
Output:
Equal
