Python NumPy Repeat

In this Python tutorial, we will learn how to use NumPy repeat in Python. Let us see various examples on Python NumPy repeat. Also, we will cover these topics.

Python NumPy repeat array
Python NumPy repeat data
Python NumPy repeat 2d
Python NumPy repeat example
Python NumPy repeat each row
Python NumPy delete repeat
Python NumPy repeat array n times
Python NumPy duplicate elements
Python NumPy loop over rows
Python numpy loop through array
Python numpy tile function
Python numpy repeat vector

Table of Contents

In this section, we will discuss how to use NumPy repeat in Python.
In Python, if you want to repeat the elements multiple times in the NumPy array then you can use the numpy.repeat() function.
In Python, this method is available in the NumPy module and this function is used to return the numpy array of the repeated items along with axis such as 0 and 1.
Suppose if you want to apply this function in the 2-dimensional array then by default the values are set in a one-dimensional array.

Syntax:

Here is the Syntax of numpy.repeat() function

numpy.repeat
            (
             a,
             repeats,
             axis=None
            )

It consists of few parameters
The arguments are important so let’s take a look at them.
- a: This parameter specifies the array that we are going to use as input to this function.
- repeats: This parameter indicates how many times does an item of the numpy array needs to be repeated.
- axis: By default its value is None and it specifies the axis along which you will repeat the items.

Examples:

Now let us see how to use the np.repeat() function in the Python example

import numpy as np

new_arr=[56,156,83,20]
result=np.repeat(new_arr,3)
print(result)

In the above, we have imported a numpy library and then define an array ‘new_arr’. After that, we have used the np.repeat() function for repeating elements.

In this example, we want the elements repeated thrice. Once you will print ‘result’ then the output will display the repeated elements.

Here is the Screenshot of the following given code

Another example to repeat the elements of a 2-dimensional array

In this program, we are going to create a 2-dimensional array by using the np.array() function and assigning four integers value to it. Now use the np.repeat() function for repeating the items of the numpy array.

Here is the Source Code:

import numpy as np

new_values = np.array([[53,167],[97,83]])
new_output=np.repeat(new_values,2)
print(new_output)

Once you will print ‘new_output’ then the output will display the repeat element 2 times and put it into a one-dimensional array.

Here is the execution of the following given code

Read Python NumPy 2d array

Python NumPy repeat array

In this program, we will discuss how to repeat elements in Numpy array.
To perform this particular task we are going to use the np.tile() method. In Python, this function is used to repeat the number of items which is available in a NumPy array.
In this method, the ‘A’ parameter indicates the number of times as signified by the ‘repetition parameter.

Syntax:

Here is the Syntax of np.tile() method

numpy.tile
          (
           A,
           reps
          )

Example:

import numpy as np

new_val = np.array([76,23,17])
re_elements = 2
new_output = np.tile(new_val, (re_elements, 1))
print(new_output)

In the above code, we have created an array by using np.array() and the array contains three numbers. Now we have applied the np.tile() method and set the values as 2.

You can refer to the below Screenshot

As you can see in the Screenshot the output array stores the given input arrays 76,23,17 two times.

Read Python NumPy Data types

Python NumPy repeat data

In this section, we will discuss how to repeat the data in NumPy Python.
By using the np.repeat() function we are going to repeat the elements which are available in the array. In this example, we have mentioned the axis along which to repeat the values.
In this example, we are going to use the axis argument that specifies the direction in which we will repeat the elements. For e.g suppose we have a 2-dimensional array and now we want to repeat the elements horizontally across the rows so, we are going to set the axis=1.

Example:

import numpy as np

new_arr = np.array([[12,34], [21,87]])
z = np.repeat(new_arr, 5)
result=np.repeat(new_arr, 5, axis=1)
print(result)

Here is the Output of the following given code

As you can see in the Screenshot the output will display a new 2-dimensional array.

Read Python NumPy 3d array

Python NumPy repeat 2d

Let us see how to repeat a 2-dimensional array in NumPy Python.
To do this task first we will create a 2-d array by using the np.array() method and then use the numpy repeat() function and if you don’t mention the axis parameter in the example then it will flatten the numpy array.
In this example, we are going to set the axis=0 that indicates the item of the array will be repeated along rows.

Syntax:

Here is the Syntax of np.repeat() function

numpy.repeat
            (
             a,
             repeats,
             axis=0
            )

Example:

Let’s take an example and understand how to repeat a 2-dimensional array in Python

Here is the Source Code:

import numpy as np

elements = np.array([[98,76], [178,145]])
result=np.repeat(elements, 4, axis=0)
print(result)

You can refer to the below Screenshot

Read Python NumPy Split

Python NumPy repeat example

In this example, we are going to use a 1-d,2-d numpy array along with an axis in Python.
To perform this particular task we can easily use the numpy.repeat() function for returning the numpy array of the repeated items along with axis such as 0 and 1.

Example:

import numpy as np

new_arr = np.repeat(18, 6)
print("One-d array:",new_arr)

new_arr2 = np.array([[76, 34], [45, 68], [13, 99]])

output = np.repeat(new_arr2, 3)

print("2-d array:",output)
val_ax = np.repeat(new_arr2, 3, axis=0)
print("Along with axis:",val_ax)

val_ax2 = np.repeat(new_arr2, 3, axis=1)
print("Along with axis1:",val_ax2)

Here is the Screenshot of the following given code

As you can see in the Screenshot the output will display the repeated items

Check out, Python NumPy Normalize

Python NumPy repeat each row

In this section, we will discuss repeating the elements with each row in NumPy Python.
By using the np.tile() method we can solve this particular task. In Python, the numpy.tile() is used to create a new array by repeating a number of times as per the condition.

Syntax:

Here is the Syntax of np.tile() method

numpy.tile
          (
           A,
           reps
          )

Source Code:

import numpy as np

arr_elements = np.array([85,34,88])
final_res = np.tile(arr_elements, (4, 1))
print(final_res)

In the above code first we have created a one-dimensional array and then use the np.tile() function in which we assign two arguments(4,1) that indicates how many time we have repeated the elements in each row.

Here is the execution of the following given code

Read Python NumPy Random [30 Examples]

Python NumPy delete repeat

In this section we will discuss how to delete repeat elements in NumPy array Python.
To do this task we are going to use the np.unique method. In Python, the np.unique() method is used to identify the unique items of a numpy array and always return unique elements as a sorted array.

Syntax:

Here is the Syntax of np.unique() function

numpy.unique
            (
             ar,
             return_index=False,
             return_inverse=False,
             return_count=False,
             axis=None
            )

Source Code:

import numpy as np

new_arr = np.array([[67,55,43,21,89],
                 [67,55,9,30,89],
                 [67,55,2,21,89]])

b= np.unique(new_arr)
print(b)

Here is the Screenshot of the following given code

Read Python NumPy max

Python NumPy repeat array n times

Here we can see how to repeat numpy array with n times in Python. In this example ‘n’ indicates that how many times we have to repeat the array in Python.
In this example, we are going to use the np.vstack() method. In Python, the np.vstack() function is used to stack the sequence of numpy arrays in the form of tuple along with vertical.
This method is available in the NumPy module package with the name of np.vstack().

Syntax:

Here is the Syntax of np.vstack() method

numpy.vstack
            (
             tup
            )

Note: It consists of only parameters and in this example, we need to provide the names of the array that we want to combine and the array must be contained in tuple form.

Source Code:

import numpy as np

new_arr = np.array([76,23,189,98])
N = 11
result=np.vstack([new_arr]*N)
print(result)

In the above code, we have created an array by using the np.array() function and then declare a variable ‘N’ that represents the number of repetitions.

Now use the np.vstack() method and combine the array with ‘N’. Once you will print ‘result’ then the output will display the array 11 times.

You can refer to the below Screenshot

Read Python NumPy nan

Python NumPy duplicate elements

In this program, we will discuss how to find duplicate elements in NumPy array by using Python.
In Python the counter is like a container that stores the count of each of the items available in the container.

Source Code:

import numpy as np
from collections import Counter

new_arr = np.array([56, 83, 56, 83, 90, 56, 19])
b= [item for item, count in Counter(new_arr).items() if count > 1]
print(b)

Here is the implementation of the following given code

As you can see in the Screenshot the output will display duplicate elements which are present in the array

Read Valueerror: Setting an array element with a sequence

Python NumPy loop over rows

In this section, we will discuss how to iterate over rows by using loop method in Python.
To perform this particular task we are going to use the np.arange() with np.reshape() method. In Python, the arange() method is used to create an array with a given interval and it is based on the numerical ranges.
In Python the numpy.reshape() function is used to reshape the data and it takes only one parameter that signifies the new shape of the array.

Example:

import numpy as np

new_arr = np.arange(8).reshape(4,2)
for m in new_arr:
    print(m)

By using for loop we have iterate over rows in given array

Here is the Screenshot of the following given code

Read Check if NumPy Array is Empty in Python

Python numpy loop through array

In this Program, we will discuss how to iterate loop through an array in Python NumPy.
By using the np.nditer() method we can easily perform this particular task. In Python, the nditer() is used to iterate a numpy array of objects, and also we can easily get a transpose of the array that represents converting the column into a row.

Syntax:

Here is the Syntax of np.nditer() method

numpy.nditer
            (
             op,
             flags=None,
             op_flags=None,
             op_dtypes=None,
             order='K',
             casting='safe',
             op_axes=None,
             itershape=None,
             buffersize=0
            )

Source Code:

import numpy as np

new_arr = np.arange(8).reshape(2,4)
for new_arr in np.nditer(new_arr):
    print(new_arr, end=' ')

You can refer to the below Screenshot

Python numpy loop through array — Python numpy loop through an array

Read Python NumPy zeros

Python numpy tile function

In this section, we will discuss how to use the tile() function in Python.
In Python the numpy.tile() function is used for repeating the number of items available in an array.
In this method, the ‘A’ parameter indicates the number of times as signified by the ‘repetition parameter.

Syntax:

Here is the Syntax of np.tile() method

np.tile
       (
        A,
        reps
       )

Note: This function takes two arguments that enable to control how the method works.

A: This parameter takes the input as an array.
Reps: This parameter indicates how to repeat the input array and if you set reps=2, it will produce an output with two instance of the numpy array.

Source Code:

import numpy as np

new_values = np.array([34, 44, 56])
result = np.tile(new_values, (4,4))
print(result)

In the above code we have created an array by using np.array() method and then use the np.tile() method and assign reps=4. Once you will print ‘result’ then the output will display four times reptition.

You can refer to the below Screenshot

Read Python NumPy Average

Python numpy repeat vector

In this section, we will discuss how to repeat vector in NumPy Python.
In this example we are going to use the np.tile() method and assign numpy.transpose() method in it.

Example:

import numpy as np

arr=np.array(([[67,83,92]]))
new_output = np.tile(arr.transpose(), (2, 3))
print(new_output)

Here is the Screenshot of the following given code

You may like the following Python NumPy tutorials:

In this Python tutorial, we will learn how to use NumPy repeat in Python. Also, we will cover these topics.

Python NumPy repeat array
Python NumPy repeat data
Python NumPy repeat 2d
Python NumPy repeat example
Python NumPy repeat each row
Python NumPy delete repeat
Python NumPy repeat array n times
Python NumPy duplicate elements
Python NumPy loop over rows
Python numpy loop through array
Python numpy tile function
Python numpy repeat vector

Bijay Kumar

I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.

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