# Python Arrays

Array’s are the foundation for all data science in Python. Arrays can be multidimensional, & all elements in an array need to be of the same type, all integers or all floats.

# Advantages of Using an Array

- Arrays can handle very large datasets efficiently
- Computationally-memory efficient
- Faster calculations & analysis than lists
- Diverse functionality (many functions in Python packages). With several Python packages that make trend modeling, statistics, & visualization easier

# Basics of an Array

In Python, we can create new datatypes, called arrays using the NumPy package. NumPy arrays are optimized for numerical analyses & contain only a single data type.

We first import NumPy & then use the `array()`

function to create an array. The `array()`

function takes a list as an input.

The type of `my_array`

is a `numpy.ndarray`

.

# Array Examples

In the below example, we will convert a list to an array using the `array()`

function from NumPy. We will create a list `a_list`

comprising of integers. Then, using the `array()`

function, convert it an array.

**Example of Creating an Array**

**Example of an Array Operation**

In the below example, we add two numpy arrays. The result is an element-wise sum of both the arrays.

**Example of Array Indexing**

We can select a specific index element of an array using indexing notation.

We can also slice a range of elements using the slicing notation specifying a range of indices.

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