# NumPy - Numerical Python

NumPy (stands for **Numerical Python** ) is a fast and versatile library for the Python programming language, adding support for large, **N-dimensional arrays** and matrices, along with a large collection of comprehensive mathematical functions to operate on these **multidimensional arrays** . NumPy was created in 2005 by Travis Oliphant. It is one of the fundamental package for **scientific computing** in Python. NumPy arrays are stored at one continuous place in memory unlike Python lists (which can **grow dynamically** ), so processes can access and manipulate them very fast and efficiently.

## How To Install NumPy

### Prerequisites

- Python installed on your system

The easiest way to install NumPy is by using **Pip Installs Packages** (Pip). If you don't have Pip installed on your system, you need to set up the package manager that corresponds to the version of **Python** you have.

With Pip set up, you can use its command line for **installing NumPy** .

## Importing the NumPy module

If you have large amounts of calls to **NumPy functions** , it can become tedious to write numpy.x() over and over again. Instead, it is common to import under the briefer name np.

## Create a NumPy array

Creating a **1-D NumPy array** with continuous 9 values.

### Check the type of NumPy array

### Create a 2-D NumPy array

Creating a **2-D NumPy array** with 2 rows and 4 columns.

### Create a 3-D NumPy array

Creating a **3-D NumPy array** with 2 rows, 3 columns and 4 depth.

## NumPy Array Shape

The shape property of a **NumPy array** returns a tuple with the size of each array dimension.

### 1-D NumPy Array shape

Here npArr is a **1-D NumPy array** so the shape of the array is (9,), this means that the array has continuous 9 values only.

### 2-D NumPy Array shape

Here npArr is a 2-D NumPy array so the shape of the array is (2, 4), this means that the array has **2 rows and 4 columns** dimensions.

### 3-D NumPy Array shape

Here npArr is a 3-D NumPy array so the shape of the array is (2, 3, 4), this means that the array has **2 rows, 3 columns and 4 depth** dimensions.