# numpy dot product

In this post, let's learn about the **dot** product of the **Python** programming language. **Python** provides the **numpy.dot()** function to return the dot product of two arrays.

**NumPy** is the fundamental package for scientific computing with Python. It is a highly optimised library for numerical operations. The support of NumPy makes the task easier. Several libraries, like OpenCV, SciPy, and Matplotlib, when combined with NumPy, increase the number of weapons in your arsenal.

## Syntax of numpy.dot()

Here is the syntax of **numpy.dot()** method-

**numpy.dot(x, y, out=None)**

Here, **x** and **y** are two input arrays. Both arrays should be 1-D or 2-D. The **out** is the output argument for 1-D array scalar to be returned. It returns a dot product of two arrays, x and y. If both the arrays 'x' and 'y' are 1-D arrays, the **dot()** function performs the inner product of vectors and returns a scalar. If both the arrays 'x' and 'y' are 2-D arrays, the **dot()** function performs the matrix multiplication. If 'x' is an N-dimensional array and 'y' is a 1-dimensional array, then the **dot()** function performs the sum-product over the last axis of x and y. For N-dimensional arrays, it is a sum product over the last axis of x and the second-last axis of y.

## Example1: Numpy Dot product of scalars

In the given example, we have taken two scalars and calculated their dot product using **numpy.dot()** function. It returns the multiplication of the two scalars.

```
import numpy as np
a = 30
b = 24
output = np.dot(a,b)
print(output)
```

**Output of the above code-**

`720`

## Example2: numpy.dot() product of 1D array

In the given example, we have taken two numpy one-dimensional arrays and calculated their dot product using the **numpy.dot()** function. The output would be an inner product of these two vectors.

```
import numpy as np
#initialize arrays
A = np.array([5, 2, 9, 3])
B = np.array([6, 9, 1, 7])
#dot product
output = np.dot(A, B)
print(output)
```

**Output of the above code-**

`78`

## Example3: numpy.dot() product of 1D array

Here is another example of numpy dot product of two one-dimensional arrays.

```
import numpy.matlib
import numpy as np
x = np.array([11,23])
y = np.array([31,25])
print(np.dot(x,y))
```

**Output of the above code -**

`916`

## Example4: numpy.dot() product of 2D array

Here is an example of **numpy.dot()** of 2-D array. In this, we have taken two two-dimensional arrays and calculated their dot product using **numpy.dot()** function. The output would be a matrix multiplication of the two input arrays.

```
import numpy.matlib
import numpy as np
x = np.array([[11,23],[2,3]])
y = np.array([[31,25],[4,1]])
print(np.dot(x,y))
```

**Output of the above code -**

```
[[433 298]
[ 74 53]]
```

## Example5: numpy.dot() product of 3D array

Here is an example of **numpy.dot()** of two 3-D arrays.

```
import numpy.matlib
import numpy as np
x = np.array([[2,4,6],[3,1,4],[8,5,3]])
y = np.array([[3,7,2],[2,4,7],[3,5,1]])
print(np.dot(x,y))
```

**Output of the above code-**

```
[[32 60 38]
[23 45 17]
[43 91 54]]
```

## Example6: numpy.dot() product of 1D and 2D arrays

In this, we have taken one 1D and one 2D array and calculated their dot product using the **numpy.dot()** function.

```
import numpy.matlib
import numpy as np
x = np.array([5, 2])
y = np.array([[6, 2, -3],
[1, 1, 3]])
res = np.dot(x, y)
print(res)
```

**Output of the above code-**

`[32 12 -9]`

## Example7: numpy.dot() product of complex vectors

In this, we have taken two complex vectors and calculated their dot product using the **numpy.dot()** function.

```
import numpy as np
vector_x = 5 + 2j
vector_y = 2 + 3j
product = np.dot(vector_x, vector_y)
print("Dot Product : ", product)
```

**Output of the above code-**

`Dot Product : (4+19j)`

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