# How to Install Numpy on Windows?

**Python NumPy** is a general-purpose array processing package that provides tools for handling n-dimensional arrays. It provides various computing tools such as comprehensive mathematical functions, linear algebra routines. NumPy provides both the flexibility of Python and the speed of well-optimized compiled C code. Its easy-to-use syntax makes it highly accessible and productive for programmers from any background.

## Pre-requisites:

The only thing that you need for installing Numpy on Windows are:

## Installing Numpy on Windows:

### For Conda Users:

If you want the installation to be done through *conda, *you can use the below command:

conda install -c anaconda numpy

You will get a similar message once the installation is complete

Make sure you follow the best practices for installation using *conda *as:

- Use an environment for installation rather than in the base environment using the below command:

conda create -n my-env conda activate my-env

**Note:** If your preferred method of installation is* conda-forge,* use the below command:

conda config --env --add channels conda-forge

### For PIP Users:

Users who prefer to use pip can use the below command to install NumPy:

pip install numpy

You will get a similar message once the installation is complete:

Now that we have installed Numpy successfully in our system, let’s take a look at few simple examples.

**Example 1: **Basic Numpy Array characters

## Python3

`# Python program to demonstrate` `# basic array characteristics` `import` `numpy as np` ` ` `# Creating array object` `arr ` `=` `np.array( [[ ` `1` `, ` `2` `, ` `3` `],` ` ` `[ ` `4` `, ` `2` `, ` `5` `]] )` ` ` `# Printing type of arr object` `print` `(` `"Array is of type: "` `, ` `type` `(arr))` ` ` `# Printing array dimensions (axes)` `print` `(` `"No. of dimensions: "` `, arr.ndim)` ` ` `# Printing shape of array` `print` `(` `"Shape of array: "` `, arr.shape)` ` ` `# Printing size (total number of elements) of array` `print` `(` `"Size of array: "` `, arr.size)` ` ` `# Printing type of elements in array` `print` `(` `"Array stores elements of type: "` `, arr.dtype)` |

**Output:**

Array is of type: No. of dimensions: 2 Shape of array: (2, 3) Size of array: 6 Array stores elements of type: int64

**Example 2: **Basic Numpy operations

## Python3

`# Python program to demonstrate` `# basic operations on single array` `import` `numpy as np` ` ` `a ` `=` `np.array([` `1` `, ` `2` `, ` `5` `, ` `3` `])` ` ` `# add 1 to every element` `print` `(` `"Adding 1 to every element:"` `, a` `+` `1` `)` ` ` `# subtract 3 from each element` `print` `(` `"Subtracting 3 from each element:"` `, a` `-` `3` `)` ` ` `# multiply each element by 10` `print` `(` `"Multiplying each element by 10:"` `, a` `*` `10` `)` ` ` `# square each element` `print` `(` `"Squaring each element:"` `, a` `*` `*` `2` `)` ` ` `# modify existing array` `a ` `*` `=` `2` `print` `(` `"Doubled each element of original array:"` `, a)` ` ` `# transpose of array` `a ` `=` `np.array([[` `1` `, ` `2` `, ` `3` `], [` `3` `, ` `4` `, ` `5` `], [` `9` `, ` `6` `, ` `0` `]])` ` ` `print` `(` `"\nOriginal array:\n"` `, a)` `print` `(` `"Transpose of array:\n"` `, a.T)` |

**Output:**

Adding 1 to every element: [2 3 6 4] Subtracting 3 from each element: [-2 -1 2 0] Multiplying each element by 10: [10 20 50 30] Squaring each element: [ 1 4 25 9] Doubled each element of original array: [ 2 4 10 6] Original array: [[1 2 3] [3 4 5] [9 6 0]] Transpose of array: [[1 3 9] [2 4 6] [3 5 0]]