Numpy.dot() method **“****returns the dot product of vectors a and b.”** It can handle 2D arrays but considers them as matrices and will perform matrix multiplication.

**Syntax**

`numpy.dot(vector_a, vector_b, out = None)`

**Parameters**

The dot() function takes mainly three parameters:

**vector_a**: This is the first vector.**vector_b**: This is the second vector.**out**: Argument Production. This must have the same sort that would be returned unless used. Specifically, it must have the appropriate form, must be C-contiguous, and its dtype must be the form returned for dot(a, b). That is a feature of the performance. Therefore, if those conditions are not met, instead of attempting to be flexible, an exception is made.

**Return Value**

The numpy.dot() method returns the dot product of two given vectors. If any of the vectors or both vectors are complex, then its complex conjugate calculates the dot product.

**Example 1: How to Use numpy.dot() method**

```
# Program to show working of numpy.dot
# When both the vectors are 1D
# Importing numpy
import numpy as np
# We will create an 1D array
arr1 = np.array([1, 2, 3, 4])
arr2 = np.array([2, 3, 4, 5])
# Printing the array
print("The first array is: ", arr1)
print("The second array is: ", arr2)
# Shape of the array
print("Shape of the first array is : ", np.shape(arr1))
print("Shape of the second array is : ", np.shape(arr2))
# Printing dot product of the arr1.arr2
out = np.dot(arr1, arr2)
print("Dot product of arr1 and arr2")
print(out)
# When both are complex
a = 6+1j
b = 4+3j
out1 = np.dot(a, b)
print("Dot product of a and b")
print(out1)
```

**Output**

```
The first array is: [1 2 3 4]
The second array is: [2 3 4 5]
Shape of the first array is : (4,)
Shape of the second array is : (4,)
Dot product of arr1 and arr2
40
Dot product of a and b
(21+22j)
```

We can see that we got the answer 40. According to the rule of the dot product, it did like this way.

*(1*2+2*3+3*4+4*5) = 40.*

**Example 2**

```
# Program to show the working of numpy.dot
# When both the vectors are 2D
# Importing numpy
import numpy as np
# We will create an 1D array
arr1 = np.array([[1, 2], [5, 6]])
arr2 = np.array([[2, 3], [2, 4]])
# Printing the array
print("The first array is:\n ", arr1)
print("\nThe second array is: \n ", arr2)
# Shape of the array
print("Shape of the first array is : ", np.shape(arr1))
print("Shape of the second array is : ", np.shape(arr2))
# Printing dot product of the arr1.arr2
out = np.dot(arr1, arr2)
print("Dot product of arr1 and arr2")
print(out)
```

**Output**

```
The first array is:
[[1 2]
[5 6]]
The second array is:
[[2 3]
[2 4]]
Shape of the first array is : (2, 2)
Shape of the second array is : (2, 2)
Dot product of arr1 and arr2
[[ 6 11]
[22 39]]
```

**Example 3: Get the Dot Product of Two Scalars**

```
import numpy as np
arr = 11
arr1 = 19
arr2 = np.dot(arr, arr1)
print(arr2)
```

**Output**

`209`

## Example 4: Get the Dot Product of Two Complex Numbers

```
import numpy as np
arr = 21 + 19j
arr1 = 19 + 21j
arr2 = np.dot(arr, arr1)
print(arr2)
```

**Output**

`(-31+92j)`

**Example 5: Get Dot Product of 2D Arrays**

```
import numpy as np
arr1 = np.array([[11, 21], [19, 46]])
arr2 = np.array([[1, 2], [3, 4]])
arr = np.dot(arr1, arr2)
print(arr)
```

**Output**

```
[[ 74 106]
[157 222]]
```

That’s it.

Ankit Lathiya is a Master of Computer Application by education and Android and Laravel Developer by profession and one of the authors of this blog. He is also expert in JavaScript and Python development.