## Python reverse NumPy array

In this Python tutorial, we will discuss Python reverse NumPy array with a few examples like below:

Python reverse array sort reverse
Python numpy inverse array
Python numpy invert array
Python numpy flip array

## Python reverse numpy array

In this section, we will discuss Python reverse numpy array. We can easily use the list slicing() method to reverse an array in Python.
We actually create a new list in the reverse order as that of the original one.
Let’s take an example to check how to implement a reverse numpy array
Basically there are many ways to check reverse numpy array.
Using list slicing method
Using flip() function
Using reverse() function
Using flipud() method
Using fliplr() function
Using length() function

## Using List slicing

In this method first, we will create a NumPy array and then use the slicing method.

Example:

import numpy as np

arr= np.array([1, 2, 3, 6, 4, 5])
result = arr[::-1]

print(“Reverse array”,(result))

Here is the Screenshot of the following given code

Python reverse numpy array

## Using flip() function

In this method, we can easily use the Python function flip() to reverse an original array.
The flip() function is used to reverse the order of elements in an array along the given axis.
The flip() function is defined under numpy, which can be imported as import numpy as np, and we can create multidimensional arrays and derive other mathematical statistics with the help of numpy, which is a library in Python.
The shape of the array is preserved, but the elements are reordered.

Syntax:

Here is the Syntax of the flip() function

numpy.flip
(
arr,
axis=None
)

It consists of few parameters

arr: input array

axis: The default, axis=None, will flip over all of the axes of the input array. If axis is negative it counts from the last to the first axis.

Example:

Let’s take an example to check how to implement a reverse NumPy array by using the flip() function.

import numpy as np

arr= np.array([9, 8, 3, 6, 2, 1])
result = np.flip(arr)

print(“Reverse array”,(result))

In the above example, we will first import a NumPy library and create a NumPy array using the function np. array. After that create a variable and assign the function np. fill in which passes an argument as an array and print the result. The output will display in the form of reverse order.

Here is the Screenshot of the following given code

Python reverse numpy array by the flip method

## Using reverse() function

In this method, we can easily use the function reverse() to reverse an original array.
The reversed() function returns the reversed iterator of the given sequence.
It reverses an array at its original location, hence doesn’t require extra space for storing the results.
It is an inbuilt method in Python programming language that reverses objects of list in place.

Example:

Let’s take an example to check how to implement a reverse NumPy array by using the reverse() function.

import array

arr=array.array(‘i’,[4,5,9,1,9,3])
print(arr)

#reversing using reverse()
arr.reverse()
print(“Reversed Array:”,arr)

In the above example first, we will import an array library and then create an original array and pass string and array as an argument.

Here is the Screenshot of the following given code

Python reverse numpy array reverse method

## Using flipud() method

In this method, we can easily use the flipud() method to reverse an original array.
The flipud() function is used to flip an given array in the up/down direction.
Flip the entries in each column in the up/down direction.
The flipud() function is used to shift the function from up to down.
The ud means Up / Down. The np.flipud() returns a view. Because a view shares memory with the original array, changing one value changes the other.

Syntax:

Here is the Syntax of flipud() method

numpy.flipud
(
array
)

It consists of few parameters

array: input array

Returns: Flipped array in up-down direction

Example:

Let’s take an example to check how to implement a reverse numpy array by using the flipud() method.

import numpy as np

arr= np.array([9, 8, 3, 6, 2, 1])
result = np.flipud(arr)

print(“Reverse array”,(result))

In the above example, we will first import a NumPy library and create a NumPy array using the function np. array. After that create a variable and assign the function np.flipud in which passes an argument as an array and prints the result. The output will display in the form of reverse order.

Here is the Screenshot of the following given code

Python reverse numpy array by flipud method

## Using fliplr() function

In this method, we can easily use the fliplr() function to reverse an original array.
The np.fliplr() function flips the array(entries in each column) in left-right direction. The numpy flipr() function accepts an array as an argument and returns the array the same array as flipped in the left-right direction.
It reverse the order of elements along axis 1 (left/right).

Syntax:

Here is the Syntax of fliplr() function

numpy.fliplr
(
arr
)

It consists of few parameters

arr: input array

Returns: It returns an output array with the columns reversed. Since the operation is returned the operation

Example:

Let’s take an example to check how to implement a reverse NumPy array by using the fliplr() function.

import numpy as np

arr= np.array([[3, 5, 6, 7, 2, 1],
[2,5,6,7,8,9]])
result = np.fliplr(arr)

print(“Reverse array”,(result))

Here is the Screenshot of the following given code

Python reverse numpy array fliplr method

## Using length() function

In this method, we can easily use the length() function.
We are going to begin by pointing to the first element within our given list. Take a start index variable which is equal to zero. After that we are going to the last element of our list which is end_index and that’s going to equal the length of our list.
So the length function itself is going to return as an integer value

Example:

Let’s take an example to check how to implement a reverse NumPy array by using the length() function.

def reverse(nums):

start_index = 0
end_index = len(nums)-1

while end_index > start_index:
nums[start_index],nums[end_index] = nums[end_index],nums[start_index]
start_index = start_index + 1
end_index = end_index -1

if __name__ == ‘__main__’:
n = [1,2,3,4,5]
reverse(n)
print(n)

Here is the Screenshot of the following given code

Python reverse numpy array using the length function

## Python reverse array sort reverse

In this section, we will discuss Python reverse array sort reverse.
In Numpy, the sort() function does not allow us to sort an array in descending order. Instead, we can reverse an array utilizing list slicing in Python, after it has been sorted in ascending order.
The slice notation [::1] with default start and stop indices and negative step size -1 reverses a given list.
Use slicing notation s[start:stop:step] to access every step-th element starting from index start (included) and ending in index stop (excluded).

Example:

import numpy as np
arr = np.array([2, 5, 1, 6, 7, 2, 4])

sort_arr = np.sort(arr)
# Reverse the sorted array
reve_arr = sort_arr[::-1]
print(sort_arr)
print(reve_arr)

In the above example first we will import a numpy library and create an array using the np.array function. After that create a variable and arrange the elements using np.sort() function.
Reverse the sorted array using slicing method and print the result.

Here is the Screenshot of the following given code

Python reverse numpy array reverse method

## Python numpy inverse array

In this section, we will discuss Python numpy inverse array.
For matrix inverse, we need to use numpy.linalg.inv() function.
This function will inverse the given matrix. The inverse of a matrix is such that if it is multiplied by the original matrix, it results in identity matrix.
It consists of one parameter that is A and A can be a matrix.
Python provides an easy method to calculate the inverse of the matrix. The function numpy.linalg.inv() is available in the Python numpy library.

Syntax:

numpy.linalg.inv(a)

Example:

Let’s take an example to check how to inverse an array in python

import numpy as np

a = np.array([[4,3],[2,7]])
inverse_matrix = (np.linalg.inv(a))
print(inverse_matrix)

In the above example first, we will import a numpy library and create an array using the np. array function. After that create a variable and assign the function np.linalg and display the result.

Here is the Screenshot of the following given code

Python numpy inverse array

## Another method to check Python numpy inverse array

In this method, we can easily use the function np.matrix to inverse the elements of an array.
It Returns a matrix from an array-like object, or from a string of data.
A matrix is a specialized 2-D array that retains its 2-D nature through operations.
In this method we use the I attribute to inverse the elements of a given matrices.

Syntax:

Here is the Syntax of np.matrix()

numpy.matrix
(
data,
dtype=None,
copy=True
)

It consists of few parameters

data: it is interpreted as a matrix with commas or spaces separating columns, and semicolons separating rows.

dtype: Data type of the matrix

Example:

Let’s take an example to check how to inverse an array in python

import numpy as np

m = np.matrix([[4,6],[7,8]])
print (m.I)

Here is the Screenshot of the following given code

Python numpy inverse array matrix method

## Python numpy invert array

In this section, we will discuss Python numpy invert array. Here we can easily use the function numpy.invert().
This function is used to compute the bit-wise inversion of an array element wise.
It Computes the bit-wise NOT of the underlying binary representation of the integers in the input arrays.

Syntax:

Here is the Syntax of numpy.invert()

numpy.invert
(
x,
out=None,
Where=True,
casting=’same_kind’,
order=’K’,
dtype=None
)

It consists of few parameters

X: input array (Only integer and boolean types are handled).

Out: Its an optional parameter. A location into which the result is stored. If provided, it must have a shape that the inputs broadcast.

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.

Example:

Let’s take an example to check how to implement a numpy invert array

import numpy as np

arr= np.array([4, 5, 5, 6, 2, 1])
result = np.invert(arr)

print(“Invert array”,(result))

Here is the Screenshot of the following given code

Python numpy invert array

## Python numpy flip array

In this section, we will discuss Python numpy flip array. For this we can easily use the function numpy.flip().
This function reverses the order of array elements along the specified axis, preserving the shape of the array.
The shape of the array is preserved, but the elements are reordered.

Syntax:

Here is the Syntax of the flip() function

numpy.flip
(
arr,
axis=None
)

Example:

Let’s take an example to check how to implement a reverse NumPy array by using the flip() function.

import numpy as np

arr2= np.array([4, 2, 3, 2, 1, 8])
res = np.flip(arr2)

print(res)

Python numpy flip array

You may like the following Python NumPy tutorials:

In this Python tutorial, we will discuss Python reverse NumPy array with a few examples like below:

Python reverse array sort reverse
Python numpy inverse array
Python numpy invert array
Python numpy flip array

## Python NumPy empty array with examples

In this Python tutorial, we will discuss Python NumPy empty array with a few examples like below:

Python numpy empty array append
Python numpy array empty check
Python empty numpy array without shape
Python numpy empty 2d array
Python numpy concatenate empty array
Python numpy declare empty array
Python numpy empty string array
Python numpy empty 3d array
Python fill empty numpy array
Python Create Empty Numpy array and append rows
Python Create Empty Numpy array and append Columns

## Python numpy empty array

In this section, we will discuss Python numpy empty array, specially how to create an empty array using Python NumPy.
In this method we can easily use the function np.empty().
The Python NumPy empty() function is used to create a new array of given shape and type, without initializing entries.
To work with arrays, the python library provides a numpy empty array function. It is used to create a new empty array as per user instruction means given data type and shape of array without initializing elements.
It accepts shape and data type as arguments. If data type argument is not provided then the default data type of all Values in the returned numpy array will be float.

Syntax:

Here is the Syntax of numpy.empty()

numpy.empty
(
shape,
dtype=float,
order=’C’
)

It Consists of few parameters.

Shape: Shape of the empty array, e.g:(4,3)

dtype: its an optional parameter by default value is float.

order: Whether to store multi-dimensional data in row and column-wise.

Example:

Let’s take an example to check how to implement a numpy empty array
Basically there are two ways to check numpy empty array.
Using numpy empty array function.
Using numpy zero’s array function

## Using numpy empty array function

In this method, we implement a NumPy array empty function. But empty does not mean that the array values will be zeros.

import numpy as np

arr = np.empty(5)
print(arr)

In the above example, we implement a simple NumPy empty array and it returns uninitialized values with shape and data type.

Here is the Screenshot of the following given code

Python numpy empty array

To create a NumPy empty array, we can pass the empty list to the np.array() function and it will make the empty array.

Example:

import numpy as np

list = []
arr = np.array(list)
print(arr)

Here is the Screenshot of the following given code

Create numpy empty array

This is how to create an empty array using Python NumPy.

## Using numpy zero’s array function

In this method, we implement a numpy zero’s array function.
This function returns a new array of given shape and type, with zeros.

Syntax:

Here is the Syntax of numpy zeros() function

numpy.zeros
(
shape,
dtype = None,
order = ‘C’
)

Example:

import numpy as np

arr = np.zeros([2,2],dtype=’int’)
print(arr)

Here is the Screenshot of the following given code

Python numpy empty array by using zeros

This is how to create a NumPy array by using zeros.

## Python numpy empty array append

In this section, we will discuss Python numpy empty array append.
In this method we can easily use the function numpy.append().
The Numpy append function allows us to add new values to the end of an existing NumPy array.
This function returns a copy of the existing array with the values appended to the specified axis.
If we have an empty array and want to append new rows to it inside a loop, we can use the numpy.empty() function.
After that we can then append new rows to the empty array with numpy.append() function.

Example:

Let’s take an example to check how to implement a numpy empty array with append.

import numpy as np

arr = np.empty((0,3), int)

arr2 = np.append(arr, np.array([[2,4,5]]), axis=0)
array = np.append(arr2, np.array([[7,8,9]]), axis=0)

print(array)

In the above example first, we will create an empty array by using the function np. empty() and define its datatype. After that, we appended two rows along the 0 axes by using the function numpy.append()

Here is the Screenshot of the following given code

Python numpy empty array append

## Python numpy empty array append example

In this section, we will discuss Python numpy empty array append.
In this method we can easily use the list method to append with numpy empty array.
The list.append() function appends an element to the end of the list.
We can then convert this list to a numpy array with np.array() function.

Example:

import numpy as np

list = []
list.append([2,4,6])
list.append([9,5,7])
arr2 = np.array(list)
print(arr2)

Here is the Screenshot of the following given code

Python numpy empty array append list method

## Python numpy array empty check

If you want to check if a NumPy array is empty or not, follow an article on Check if NumPy Array is Empty in Python.

## Python empty numpy array without shape

In this section, we will discuss Python empty numpy array without shape.
In this method we can easily use the function append() to get the empty numpy array without shape.
First we will create a python list and convert that to a numpy array and take a variable y which is iterable.
It doesn’t accepts shape and datatype as an argument.

Example:

import numpy as np
y=[]
a = np.array([2,3,4,5])
for x in y:
a = np.append(a, x)
print(y)

Here is the Screenshot of the following given code

Python empty numpy array without shape

The above code we can use to create empty NumPy array without shape in Python.

## Python numpy empty 2d array

In this section, we will discuss Python numpy empty 2d array.
To create an empty 2D Numpy array we can pass the shape of the 2D array ( i.e. row & column count) as a tuple to the empty() function.
In this method we can easily use the function numpy.empty().
It accepts shape and data type as arguments.
It returns an empty 2d numpy array of rows and columns but all values in this 2d numpy array were not initialized.
In this method we did not provide datatype as an argument by default it takes value as float.

Syntax:

numpy.empty
(
shape,
dtype=float,
order=’C’
)

Example:

import numpy as np
arr = np.empty((4, 3), int)
print(‘Empty 2D Numpy array:’)
print(arr)

In the above example first, we will import the numpy library and create an empty 2d numpy array with 4 rows and 3 columns and print the result.

Here is the Screenshot of the following given code

Python numpy empty 2d array method

This is how to create an empty 2D Numpy array in Python.

## Python Create a numpy empty 2d array

To create an empty array in Numpy 2d array we use column and row in shape argument.
Let’s use these two functions to create an empty 2D Numpy array and append items to it as rows or columns.
In this method create an empty array with 3 columns or 0 rows.

Example:

import numpy as np

arr = np.empty((0, 3), int)
print(‘Empty 2D Numpy array:’)
print(arr)

Here is the Screenshot of the following given code

Python creates a numpy empty 2d array

## Python numpy concatenate empty array

In this section, we will discuss Python numpy concatenate empty array.
We need to create an array of a specific size m*n where m is the number of rows and n is number of columns filled with empty values so that when we concatenate to that array the initial values.
In this method we can easily use the function numpy.concatenate().
This function is used to join two or more given NumPy arrays along the existing axis.
The np.concatenate() function appends an element to the end of the list.
In this method we can easily use the both function np.concatenate and np.empty() to get the empty array.

Example:

import numpy as np
a = np.empty([2,2])
b = np.array([[4,5],[6,7]])
arr = np.concatenate((a,b))
print(arr)

Here is the Screenshot of the following given code

Python numpy concatenate empty array

The above code we can use to concatenate empty array in Python NumPy.

## Python numpy declare empty array

In this section, we will discuss Python numpy declare empty array.
In this method we can easily use the function numpy.empty().
The empty() function is used to create a new array of given shape and type, without initializing entries.

Syntax:

Here is the Syntax of numpy.empty()

numpy.empty
(
shape,
dtype=float,
order=’C’
)

Example:

To Declare an empty NumPy array of some specific data type, we can pass that data type as a dtype argument in the empty() function.

import numpy as np
a = np.empty([2,2], dtype= complex)
print(a)

In the above example, we will create an empty numpy array of complex numbers, we need to pass complex as dtype argument in the numpy.empty() function.

Here is the Screenshot of the following given code

Python numpy declare empty array

This is how to declare empty array using Python NumPy.

## Declare numpy empty array in Python

To create an empty NumPy array of integers, we need to pass int as dtype argument in the numpy.empty() function.

Example:

import numpy as np
a = np.empty([3,3], dtype= ‘int’)
print(a)

In the above example, we will create an empty NumPy array of integers numbers, we need to pass int as dtype argument in the NumPy.empty() function.

Here is the Screenshot of the following given code

Python numpy declare empty array integer method

This is how to create an empty NumPy array of integers.

## Python numpy empty string array

In this section, we will discuss Python numpy empty string array.
To create an empty numpy array of strings we can easily use the function numpy.empty().
To create an empty numpy array of 4 strings (with size 3), we need to pass ‘S3’ as dtype argument in the numpy.empty() function.
It accepts shape and data type as arguments.

Example:

Let’s take an example to check how to create a numpy empty string array

import numpy as np

array = np.empty(4, dtype=’S3′)
print(array)

Here ‘S3’ translates to “Unicode string of length 3

Here is the Screenshot of the following given code

Python numpy empty string array

The above code we can use to create an empty string array in Python using NumPy.

## Python numpy empty 3d array

In this section, we will discuss Python numpy empty 3d array.
To create an empty 3D Numpy array we can pass the shape of the 3D array as a tuple to the empty() function.
In this method we can easily use the function numpy.empty() to get the empty 3d array.
Let’s create a empty 3D Numpy array with matrix of 3 rows and 3 columns.
In this method we didnot provide any datatype argument. But all values in this 3D numpy array were not initialized.

Example:

import numpy as np

array = np.empty((1, 3, 3)) # where 1 is the length of matrix
print(array)

In the above example first, we will import a numpy library and create an empty array in which we assign an argument as the length of the matrix which is 1 and no of rows and columns.

Here is the Screenshot of the following given code

Python numpy empty 3d array

This is an example of a Python NumPy empty 3d array.

## Python fill empty numpy array

In this section, we will discuss Python fill empty numpy array.
In this method we can easily use the function numpy.empty().
When you create an array with np.empty, you need to specify the exact shape of the output by using the shape parameter.
Let’s create an empty array and use the function a.fill().

fill() method is used to fill the numpy array with a scalar value.
In this method we didnot need to use loops to initialize an array if we are using this fill() function.

Example:

import numpy as np

a = np.empty(4)
a.fill(3)
print(a)

Here is the Screenshot of the following given code

Python fill empty numpy array

The above code we can use to fill empty NumPy array in Python.

## Python Create an Empty Numpy array and append rows

In this section, we will discuss Empty Numpy array and append rows.
In this method, first we will create an empty numpy array with 4 columns and 0 rows.
Now to append a new row to this empty 2D Numpy array, we can use the numpy.append().
But we need to pass the row as a numpy array of same shape only, and pass axis=0, so that it can be appended along the column.

Example:

import numpy as np

arr = np.empty((0, 4), int)
print(‘Empty 2D Numpy array:’)
print(arr)
# Append a row to the 2D numpy array
emp_arr = np.append(arr, np.array([[4, 3, 6, 7]]), axis=0)
# Append 2nd rows to the 2D Numpy array
emp_arr = np.append(emp_arr, np.array([[2, 6, 9, 10]]), axis=0)
print(‘2D Numpy array:’)
print(emp_arr)

In the above example our 2D Numpy array had 4 columns, therefore to add a new row we need to pass this row as a separate 2D NumPy array with dimension (2,4).

Here is the Screenshot of the following given code

Python Create an Empty Numpy array and append rows

The above code we can use to Create an Empty Numpy array and append rows in Python.

## Python Create Empty Numpy array and append Columns

In this section, we will discuss Empty Numpy array and append columns.
In this method, first we will create an empty numpy array with 0 column and 4 rows.
Now to append a new column to this empty 2D Numpy array, we can use the numpy.append().
But we need to pass the column as a numpy array of same shape only and argument axis=1, so that it can be appended along the column.

Example:

import numpy as np

arr = np.empty((4, 0), int)
print(‘Empty 2D Numpy array:’)
print(arr)
# Column list1
emp_arr = np.append(arr, np.array([[4, 3, 6, 7]]).transpose(), axis=1)

print(‘2D Numpy array:’)
print(emp_arr)

In the above example our empty numpy array has 4 rows & 0 columns, so to add a new column we need to pass this column as a separate 2D numpy array with dimension (4,1).

Here is the Screenshot of the following given code

Python Create Empty Numpy array and append Columns

The above code we can use to create an empty Numpy array and append Columns in Python.

You may like the following Python tutorials:

In this Python tutorial, we will discuss Python NumPy empty array with a few examples like below:

Python numpy empty array append
Python numpy array empty check
Python empty numpy array without shape
Python numpy empty 2d array
Python numpy concatenate empty array
Python numpy declare empty array
Python numpy empty string array
Python numpy empty 3d array
Python fill empty numpy array
Python Create Empty Numpy array and append rows
Python Create Empty Numpy array and append Columns