Add arguments element-wise and display the result in a different type in Numpy



To add arguments element-wise, use the numpy.add() method in Python Numpy. The output is set "float" using the "dtype" parameter.

The out is a location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.

The condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default out=None, locations within it where the condition is False will remain uninitialized.

Steps

At first, import the required library −

import numpy as np

Create two 2D arrays with int elements −

arr1 = np.array([[5, 10, 15], [25, 30, 35]])
arr2 = np.array([[7, 14, 21], [28, 35, 56]])

Display the arrays −

print("Array 1...
", arr1) print("
Array 2...
", arr2)

Get the type of the arrays −

print("
Our Array 1 type...
", arr1.dtype) print("
Our Array 2 type...
", arr2.dtype)

Get the dimensions of the Arrays −

print("
Our Array 1 Dimensions...
",arr1.ndim) print("
Our Array 2 Dimensions...
",arr2.ndim)

Get the shape of the Arrays −

print("
Our Array 1 Shape...
",arr1.shape) print("
Our Array 2 Shape...
",arr2.shape)

To add arguments element-wise, use the numpy.add() method in Python numpy. The output is set "float" using the "dtype" parameter −

print("
Result (adding element-wise)...
",np.add(arr1, arr2, dtype = 'float'))

Example

import numpy as np

# Create two 2D arrays with int elements
arr1 = np.array([[5, 10, 15], [25, 30, 35]])
arr2 = np.array([[7, 14, 21], [28, 35, 56]])

# Display the arrays
print("Array 1...
", arr1) print("
Array 2...
", arr2) # Get the type of the arrays print("
Our Array 1 type...
", arr1.dtype) print("
Our Array 2 type...
", arr2.dtype) # Get the dimensions of the Arrays print("
Our Array 1 Dimensions...
",arr1.ndim) print("
Our Array 2 Dimensions...
",arr2.ndim) # Get the shape of the Arrays print("
Our Array 1 Shape...
",arr1.shape) print("
Our Array 2 Shape...
",arr2.shape) # To add arguments element-wise, use the numpy.add() method in Python Numpy # The output is set "float" using the "dtype" parameter print("
Result (adding element-wise)...
",np.add(arr1, arr2, dtype = 'float'))

Output

Array 1...
[[ 5 10 15]
[25 30 35]]

Array 2...
[[ 7 14 21]
[28 35 56]]

Our Array 1 type...
int64

Our Array 2 type...
int64

Our Array 1 Dimensions...
2

Our Array 2 Dimensions...
2

Our Array 1 Shape...
(2, 3)

Our Array 2 Shape...
(2, 3)

Result (adding element-wise)...
[[12. 24. 36.]
[53. 65. 91.]]

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