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# To work with SciPy, do I need to import the NumPy functions explicitly?

When SciPy is imported, you do not need to explicitly import the NumPy functions because by default all the NumPy functions are available through SciPy namespace. But as SciPy is built upon the NumPy arrays, we must need to know the basics of NumPy.

As most parts of linear algebra deals with vectors and matrices only, let us understand the basic functionalities of NumPy vectors and matrices.

**Creating NumPy vectors by converting Python array-like objects**

Let us understand this with the help of following example−

## Example

import numpy as np list_objects = [10,20,30,40,50,60,70,80,90] array_new = np.array(list_objects) print (array_new)

## Output

[10 20 30 40 50 60 70 80 90]

### NumPy array creation

NumPy has various functions to create arrays from scratch. Let us understand them along with examples −

**Using zeros()**

This function will create an array filled with 0 values with the specified shape.

## Example

import numpy as np print (np.zeros((3, 4)))

## Output

[[0. 0. 0. 0.] [0. 0. 0. 0.] [0. 0. 0. 0.]]

**Using ones()**

This function will create an array filled with 1 values with the specified shape.

## Example

import numpy as np print (np.ones((3, 4)))

## Output

[[1. 1. 1. 1.] [1. 1. 1. 1.] [1. 1. 1. 1.]]

**Using arange()**

This function will create an array with regularly incrementing values.

## Example

import numpy as np print (np.arange(15))

**Output**

[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14]

**Using linspace()**

This function will create arrays spaced equally between the specified start and end values.

## Example

import numpy as np print (np.linspace(1.0, 3.0, num=5))

## Output

[1. 1.5 2. 2.5 3. ]

**Creating matrices and applying some functions over matrices using NumPy**

A matrix is a specialized two-dimensional (2D) array. Matrices retains its nature through all the operations applied over it.

**Creating a matrix**

Let us understand this with the help of following example −

**Example**

import numpy as np matrix = np.matrix('10 20; 30 40') print(matrix)

## Output

[[10 20] [30 40]]

**Transpose a Matrix**

This function will return the transpose of a matrix. The below example will transpose of the above created matrix −

## Example

matrix.T

## Output

matrix([[10, 30], [20, 40]])

**Multiplication of two Matrices**

We can use the dot() function of NumPy for doing multiplications of two matrices. Let us understand this with the help of following example −

## Example

import numpy as np matrix1 = np.array([[1,6,9],[3,4,10]]) matrix2 = np.array([[2,5],[4,7],[7,8]]) matrix3 = np.dot(matrix1,matrix2) print(matrix3)

## Output

[[ 89 119] [ 92 123]]

You can learn in detail about NumPy at https://www.tutorialspoint.com/numpy/index.htm .

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