In this Python tutorial, we will discuss **Python NumPy square** and also we will cover the below examples:

Python numpy square root

Python numpy square sum

Python numpy squared norm

Python numpy square array

Python numpy square wave

Python numpy square difference

Python numpy square vector

Python numpy square vs **

## Python numpy square

In this section we will learn about **Python numpy square**.

It is a mathematical function that helps the user to calculate square value of each element in the array.

It always return an array with square value of each array.

The source array remain unchanged.

In this method we can easily use the function np.square().

The numpy square() function calculates the square of a numeric input.

**Syntax:**

Here is the Syntax of numpy square()

numpy.square

(

x,

out=None,

*,

Where=True,

casting=’same_kind’

dtype=None

)

It consists of few parameters

**X**: The x parameter enables you to specify the input to the function (i.e., the argument).

**OUT:** A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned.

**Where:** This condition is broadcast over the input. At locations where the condition is True, the *out* array will be set to the ufunc result.

**Returns:** Element-wise *x*x*, of the same shape and dtype as *x*.

**Example:**

Let’s take an example to check **how to use NumPy square**.

**Example1:** Calculate the square of values in 1d array

First, we have to import a numpy library and then create a numpy array using the numpy array function.

Now that we have an array, we can run numpy square by using the numpy square function.

import numpy as np

arr1 = np.array([4, 5, 6, 7])

res = np.square(arr1)

print(res)

Here is the Screenshot of the following given code

**Example2:** Calculate the square of values in complex number arrays

import numpy as np

arr1 = np.array([1+2j, 2+3j, 6+5j, 7+8j])

res = np.square(arr1)

print(res)

Here is the Screenshot of the following given code

Read: Python NumPy to list

## Python numpy square root

In this section we will learn about **Python numpy square root**.

In this method we can easily use the function **numpy sqrt()**.

Numpy sqrt() function is used to determine the positive square root of an array element wise.

The numpy square root function calculates the square root of input values.

So if you give an input x,the numpy square function will calculate and give the result in the form of under root of x.

**Syntax:**

Here is the syntax of the numpy square root

numpy.sqrt

(

x,

out=None,

*,

Where=True,

casting=’same_kind’

dtype=None

)

It consists of few parameters.

**X:** The x parameter enables you to specify the input to the np.sqrt function.

**OUT:** The out parameter enables you to specify an array where the output will be stored.

**Returns:** An array of the same shape as *x*, containing the positive square-root of each element in *x*.

**Examples:**

Let’s take an example to check how to use numpy square root.

**Example1:** Calculate the square root of values in 1d array

First, we have to import a numpy library and then create a numpy array using the numpy array function.

Now that we have an array, we can run numpy square root by using the numpy sqrt() function.

import numpy as np

arr1 = np.array([36,25,49,4,9,121])

res = np.sqrt(arr1)

print(res)

Here is the Screenshot of the following given code

**Example2:** Calculate the square root of values in 2d array

First, we create a simple 2d array using function np.array and then use this array as the input to sqrt function.

When we use a 2D NumPy array as the input, the np.sqrt function simply calculates the square root of every element of the array.

import numpy as np

arr1 = np.array([[36,25,49],[16,64,81]])

res = np.sqrt(arr1)

print(res)

Here is the Screenshot of the following given code

Read: Python NumPy read CSV

## Python numpy square sum

In this section we will learn about **Python numpy square sum**.

In this method first we create an array by using a function np.array.Now that we can have an array we have to square the values of each array by using np.square function.

After that the numpy sum() function calculates the given square values of an array.

**Example:**

Let’s take an example to check how to use NumPy square sum.

import numpy as np

arr1 = np.array([[36,25,49],[16,64,81]])

res= np.square(arr1)

print(res)

new_res = np.sum(res)

print(new_res)

Here is the Screenshot of the following given code

## Python numpy squared norm

In this section, we will learn about **numpy squared norm**.

In simple words the norm is a quantity that describe the size of a vector.

The vector means set of numbers.

In this method we can easily use the function numpy.linalg.norm.

This function is able to return one of eight different matrix norms, or one of an infinite number of vector norms depending on the value of the ord parameter.

**Syntax:**

Here is the syntax of the numpy norm

numpy.linalg.norm

(

x,

ord=None,

axis=None

)

It consists of few parameters.

**X:** It is an input array. If *axis* is None, *x* must be 1-D or 2-D, unless *ord* is None. If both *axis* and *ord* are None, the 2nd norm will be returned.

**ord:** It is an optional parameter. The default value is none.

**Axis: **If *axis* is an integer, it specifies the axis of *x* along which to compute the vector norms. If *axis* is a 2-tuple, it specifies the axes that hold 2-D matrices, and the matrix norms of these matrices are computed.

**Returns:** Norm of the vector.

**Example:**

Let’s take an example to check how to use NumPy square norm

import numpy as np

arr = np.array([[ 2, 4, 6],

[ 2, 2, 3]])

e = np.square(arr)

print(e)

d = np.linalg.norm(e, ord=1, axis=1)

print(d)

In the above-given example, we have created a NumPy array using the function np. array and square the values of the given array after that use lin. alg. norm function to calculate the size of the vector.

Here is the Screenshot of the following given code

Read: Python NumPy log + Examples

## Python numpy square array

In this section we will learn about **Python numpy square array**.

In this method we will compute the square of the values in a numpy array.

First we have to create a numpy array using the function np.array and pass the values in an arguments.

Take a variable in which you have to store a result in the form of array and also take a numpy square() function to calculates the square of a numeric input.

**Syntax:**

Here is the Syntax of the numpy square

numpy.square

(

x,

out=None,

*,

Where=True,

casting=’same_kind’

dtype=None

)

**Examples:**

Let’s take an example to check how to use a numpy square array.

**Example1**: Calculate the square of values in a 2d numpy array

import numpy as np

arr = np.array([[ 4, 5, 6],

[ 2, 2, 3]])

res = np.square(arr)

print(res)

Here is the Screenshot of the following given code

**Example2:** Calculate the square of values in a 3d NumPy array

First we will create a 3d numpy array with the values from 1 to 9 and arrange in 3 by 3 reshape.

We will use the numpy arange function with the combination of reshape function.

Now the np.sqrt function simply calculates the square root of every element of the array.

import numpy as np

arr = np.arange(1,10).reshape((3,3))

res = np.square(arr)

print(res)

Here is the Screenshot of the following given code

Read: Python NumPy where with examples

## Python numpy square wave

In this section we will learn about **Python numpy square wave**.

Square waves are period waveforms.

In this method we can easily use the function scipy.signal.square.

It will return the result a periodic square wave waveform.

A square wave is a periodic waveform in which the amplitude alternates at a steady frequency between the fixed minimum and maximum values, with the same duration at minimum and maximum.

**Syntax:**

Here is the Syntax of numpy square wave

scipy.signal.square

(

t,

duty=0.5

)

It consists of few parameters.

**T:** The input time array.

**duty:** Default value is 0.5.

**Returns:** Output array containing the square waveform.

**Example:**

import numpy as np

from scipy import signal

import matplotlib.pyplot as plt

t = np.linspace(0, 1, 500, endpoint=False)

plt.plot(t, signal.square(2 * np.pi * 5 * t),’b’)

plt.ylim(-2, 2)

plt.grid()

plt.show()

Here is the Screenshot of the following given code

## Python numpy square difference

In this section we will learn about **Python numpy square difference**.

In this method first we create an array by using a function np.array.Now that we can have an array we have to square the values of each array by using np.square function.

After that the numpy diff() function calculates the given square values of an array.

**Example:**

Let’s take an example to check how to use NumPy square diff

import numpy as np

arr1 = np.array([[4,5,6],[2,3,4]])

res= np.square(arr1)

print(res)

new_res = np.diff(res)

print(new_res)

Here is the Screenshot of the following given code

Read: Python NumPy linspace

## Python numpy square vector

In this section we will learn about Python numpy square vector.

A **vector** is an array with a single dimension (there’s no difference between row and column **vectors**), while a matrix refers to an array with two dimensions.

We can think of a **vector** as a list of numbers, and **vector** algebra as operations performed on the numbers in the list.

In this method we can easily use the function numpy square function to display the vector ndarray shape.

**Syntax:**

numpy.square

(

x,

out=None,

*,

Where=True,

casting=’same_kind’

dtype=None

)

**Example:**

import numpy as np

vect = np.array([[4, 5, 6, 7],

[3,4,5,6 ],

[5,6,7,8]])

res_vect = np.square(vect)

print(res_vect)

Here is the Screenshot of the following given code

## Python numpy square vs **

In this section we will learn about **Python numpy square vs ****.

The standard pythonic ** is faster than the numpy.square().

The numpy functions are often more flexible and precise.

**Example:**

import numpy as np

Arr1 = np.array([[3, 3],[3, 3]])

res=np.square(Arr1)

b = Arr1 ** 2

print(res)

print(b)

Here is the Screenshot of the following given code

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In this Python tutorial, we will discuss **Python NumPy square** and also we will cover the below examples:

Python numpy square root

Python numpy square sum

Python numpy squared norm

Python numpy square array

Python numpy square wave

Python numpy square difference

Python numpy square vector

Python numpy square vs **