DATA MUNGING
DATA CLEANING PYTHON

In [1]:

```
## How to calculate dot product of two vectors
def Kickstarter_Example_14():
print()
print(format('How to calculate dot product of two vectors','*^72'))
# Load library
import numpy as np
# Create two vectors
vectorA = np.array([1,2,3])
vectorB = np.array([4,5,6])
# Calculate Dot Product (Method 1)
print(); print(np.dot(vectorA, vectorB))
# Calculate Dot Product (Method 2)
print(); print(vectorA @ vectorB)
Kickstarter_Example_14()
```

In [2]:

```
## How to calculate dot product of two matrices
def Kickstarter_Example_14_1():
print()
print(format('How to calculate dot product of two matrices','*^72'))
# Load library
import numpy as np
# Create two vectors
matrixA = np.array([[2, 3, 23],
[5, 6, 25],
[8, 9, 28]])
matrixB = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
# Calculate Dot Product (Method 1)
print(); print(np.dot(matrixA, matrixB))
# Calculate Dot Product (Method 2)
print(); print(matrixA @ matrixB)
Kickstarter_Example_14_1()
```

In [3]:

```
## How to describe a matrix
def Kickstarter_Example_14_2():
print()
print(format('How to describe a matrix','*^72'))
# Load library
import numpy as np
# Create a matrix
matrixA = np.array([[2, 3, 23],
[5, 6, 25],
[8, 9, 28]])
# View number of rows and columns
print(); print("Shape: ", matrixA.shape)
# View number of elements (rows * columns)
print(); print("Size: ", matrixA.size)
# View number of dimensions
print(); print("Dimention: ", matrixA.ndim)
Kickstarter_Example_14_2()
```

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