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

## Python NumPy repeat

- 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

## 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.

## 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.

## 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

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:

- Python NumPy Sum
- Python NumPy arange
- Python NumPy absolute value with examples
- Python NumPy square
- Python NumPy to list with examples

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

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, etcâ€¦ for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.