# NumPy dot: How to calculate the inner product of vectors in Python

Here is an example to calculate an inner product of two vectors in Python.

```
import numpy as np
v = np.array([1, 2])
w = np.array([3, 4])
i = np.dot(v, w)
print(i) # 11
print(type(i)) # <class 'numpy.int64'>
```

The `dot()`

returns the inner product of v and w.

\[ 1 \times 3 + 2 \times 4 = 11 \]

Another example.

```
import numpy as np
v = np.array([1, 2.9])
w = np.array([3, 4.5])
i = np.dot(v, w)
print(i) # 16.049999999999997
print(type(i)) # <class 'numpy.float64'>
```

## Inner

```
import numpy as np
a = np.array([1, 2])
b = np.array([-5, 4])
dot = np.dot(a, b)
inner = np.inner(a, b)
print(dot) # 3
print(inner) # 3
```

The `inner()`

returns the the same value as the `dot()`

in 1D arrays (that is "vectors") product.

## The dot and inner product of 2D arrays

```
import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6], [7, 8]])
dot = np.dot(a, b)
inner = np.inner(a, b)
print(dot)
# [[19 22]
# [43 50]]
print(inner)
# [[17 23]
# [39 53]]
```

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