# Create Numpy Array of different shapes & initialize with identical values using numpy.full() in Python

In this article we will discuss how to create a Numpy Array of different shapes and initialized with same identical values using numpy.full().

## numpy.full()

Python’s Numpy module provides a function to create a numpy array of given shape and all elements initialized with a given value,

`numpy.full(shape, fill_value, dtype=None, order='C')`

Arguments:
shape: Shape of the new array
fill_value : Intialization value
dtype : Data type of elements | Optional

It returns a Numpy array of given shape and type, all elements in it will be initialized with fill_value.

To use Numpy in our code we need to include following module i.e.

`import numpy as np`

Checkout some examples,

Example 1:

### Create a 1D Numpy Array of length 10 & all elements initialized with value 5

```# Create a 1D Numpy Array of length 10 & all elements initialized with value 5
arr = np.full(10, 5)
```

Contents of the Create Numpy array:

`[5 5 5 5 5 5 5 5 5 5]`

Data Type of Contents of the Numpy Array : int32
Shape of the Numpy Array : (10,)

Example 2:

### Create a 2D Numpy Array of 4 rows | 5 columns & all elements initialized with value 7

```#Create a 2D Numpy Array of 4 rows & 5 columns. All intialized with value 7
arr = np.full((4,5), 7)
```

Contents of the Create Numpy array:

```[[7 7 7 7 7]
[7 7 7 7 7]
[7 7 7 7 7]
[7 7 7 7 7]]```

Data Type of Contents of the Numpy Array : int32
Shape of the Numpy Array : (4,5)

Example 3:

### Create a 3D Numpy Array of shape (2,4,5) & all elements initialized with value 8

```# Create a 3D Numpy array & all elements initialized with value 8
arr = np.full((2,4,5), 8)
```

Contents of the Create Numpy array:

```[[[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]]

[[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]]]```

Data Type of Contents of the Numpy Array : int32
Shape of the Numpy Array : (2, 4, 5)

Example 4:

### Create initialized Numpy array of specified data type

Along with initialization value, we can specify the data type too i.e.

```# Create a 1D Numpy array & all float elements initialized with value 9
arr = np.full(10, 9, dtype=float)
```

Contents of the Create Numpy array:

`[9. 9. 9. 9. 9. 9. 9. 9. 9. 9.]`

Data Type of Contents of the Numpy Array : float64

Complete example is as follows,

```import numpy as np

def main():

print('*** Create 1D Numpy Array filled with identical values ***')
# Create a 1D Numpy Array of length 10 & all elements intialized with value 5
arr = np.full(10, 5)

print('Contents of the Numpy Array : ' , arr)
print('Data Type of Contents of the Numpy Array : ', arr.dtype)
print('Shape of the Numpy Array : ', arr.shape)

print('*** Create 2D Numpy Array filled with identical values ***')
#Create a 2D Numpy Array of 4 rows & 5 columns. All intialized with value 7
arr = np.full((4,5), 7)

print('Contents of the Numpy Array : ', arr, sep='\n')
print('Data Type of Contents of the Numpy Array : ', arr.dtype)
print('Shape of the Numpy Array : ', arr.shape)

print('*** Create 3D Numpy Array filled with identical values ***')
# Create a 3D Numpy array & all elements initialized with value 8
arr = np.full((2,4,5), 8)

print('Contents of the Numpy Array : ', arr, sep='\n')
print('Data Type of Contents of the Numpy Array : ', arr.dtype)
print('Shape of the Numpy Array : ', arr.shape)

print('*** Create 1D Numpy Array of specified Data Type filled with identical values ***')

# Create a 1D Numpy array & all float elements initialized with value 9
arr = np.full(10, 9, dtype=float)

print('Contents of the Numpy Array : ', arr)
print('Data Type of Contents of the Numpy Array : ',  arr.dtype)
print('Shape of the Numpy Array : ', arr.shape)

if __name__ == '__main__':
main()

```

Output:

```*** Create 1D Numpy Array filled with identical values ***
Contents of the Numpy Array :  [5 5 5 5 5 5 5 5 5 5]
Data Type of Contents of the Numpy Array :  int32
Shape of the Numpy Array :  (10,)
*** Create 2D Numpy Array filled with identical values ***
Contents of the Numpy Array :
[[7 7 7 7 7]
[7 7 7 7 7]
[7 7 7 7 7]
[7 7 7 7 7]]
Data Type of Contents of the Numpy Array :  int32
Shape of the Numpy Array :  (4, 5)
*** Create 3D Numpy Array filled with identical values ***
Contents of the Numpy Array :
[[[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]]

[[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]
[8 8 8 8 8]]]
Data Type of Contents of the Numpy Array :  int32
Shape of the Numpy Array :  (2, 4, 5)
*** Create 1D Numpy Array of specified Data Type filled with identical values ***
Contents of the Numpy Array :  [9. 9. 9. 9. 9. 9. 9. 9. 9. 9.]
Data Type of Contents of the Numpy Array :  float64
Shape of the Numpy Array :  (10,)
```

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