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:

• Python 
• PIP or Conda (depending upon user preference)

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]]