Get started on your learning journey towards data science using Python. Equip yourself with practical skills in Python programming for the purpose of basic data manipulation and analysis.

More often than not, data analysis will involve some form of mathematical calculations. Having a large data set also means that you would not want to have to manually count each data point. What you can do is to perform mathematical calculations using arrays.

Basic mathematical functions on arrays are element wise operators. Let's take a look at what this means.

Let's start by creating some arrays to work with:

There are 2 ways to perform element-wise addition with arrays.

**Method 1: **Simply just add them together using `+`

operator

print(x + y)

**Method 2:** Use `np.add`

print(np.add(x,y))

Similarly, to subtract use `np.subtract`

or `-`

, to multiply use `np.multiply`

or `*`

, and to divide use `np.divide`

or `/`

.

To perform element-wise square root we can use `np.sqrt`

**Syntax**

print(np.sqrt(x+y))

To look for the inner product, or matrix multiplication of arrays, use `np.dot`

.

We have to first make sure that the rows and columns match, just like we would for any other matrix multiplication.

To sum up elements in an array, use `np.sum`

**Syntax**

(np.sum(array))

To find the sum of each column (adding up all row values for each column) we just need to specify the `axis = 0 `

**Syntax**

print(np.sum(x, axis=0))

To find the sum of each row we use `axis = 1`

**Syntax**

print(np.sum(x, axis=1))

Use this array for the following practice:

myArray = np.arange(10)

1. Find the square of every number in array

2. Find the square root of every number in array

3. Multiply the square of each number in array with its respective square root