The **numpy.full() **function in the NumPy library is designed to create a new array with a specified shape, filling all its elements with a constant value. It provides a convenient and efficient way to generate arrays with uniform values, such as creating matrices initialized with a specific constant. This function is particularly useful in scenarios where a predefined constant needs to be assigned to an entire array of a given shape.

In this article, we will understand Python **numpy.full()** function, and its syntax, and demonstrate it with various examples. Let’s get started.

*Also Read: NumPy Array Addition with numpy.add() and Addition Operator*

## Syntax of numpy.full() Function

The **numpy.full()** function takes the **shape **of the desired array, the **constant **value to be assigned to each element, an optional **data type** parameter, and an optional **order **parameter specifying the memory layout.

**Syntax:**

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

**Parameters:**

**shape:**The shape of the new array. It can be an integer or a tuple of integers.**fill_value:**The constant value to fill the array with.**dtype (optional):**The data type of the array. If not specified, the data type is inferred from the**fill_value**.**order (optional):**Specifies the memory layout of the array (‘C’ for C-style, ‘F’ for Fortran-style). The default is ‘C’.

**Return:**

It returns a new array of the defined shape filled with the specified constant value.

Let us now look at some examples to demonstrate the use of **numpy.full()** function in Python.

## Creating a 1-D Array Filled with Constant Value Using numpy.full()

Let’s pass the **shape** of the output array and the **fill_value** to get a one-dimensional array filled with a constant value **fill_value**.

**Example:**

```
import numpy as np
f1=np.full(3,79)
print(f1)
```

Here we have first imported the **NumPy** library as **np **and then we have passed the first parameter which is the **shape** of our output array as **3 **means there will be **3** elements in our 1-D array and passed the second parameter **fill_value** which means the constant value in the output array as **79**. We then saved the result in the variable **f1** and printed it.

**Output:**

We can see in the output that we got a one-dimensional array filled with constant value **79** of data type integer.

## Changing the Data Type of the Output Array in numpy.full()

By passing parameter **dtype** into the **numpy.full()** function we can change the data type of the output array to some other data type as by default the data type of the output array is the same as the data type of **fill_value** which we have passed.

**Example:**

```
import numpy as np
f1 = np.full(3,79,dtype=float)
print(f1)
```

The only difference with the above example is that here we have passed the** dtype** parameter as **float** which means our output array will be of float data type.

**Output:**

We can see in the output that we got **floating-point** numbers in our output array.

## Creating a 2-D Array Filled with Constant Value Using numpy.full()

We can also create a two-dimensional full array using **numpy.full() **function, for this we have to pass the number of rows and columns of the **2-D** array in the form of a tuple for the **shape** parameter.

**Example:**

```
import numpy as np
f2=np.full((2,3),50.9)
print(f2)
```

Here we have passed **(2,3) **which means in the output array there will be **2 **rows and **3 **columns and there will be a constant value of** 50.9** at each position in the full array as we have passed** 50.9** for the second parameter which is **fill_value**.

**Output:**

In the output above, we got the two-dimensional array of **2 **rows and **3** columns and at each position, there is a constant value of **50.9 **in the array.

## Creating a 3-D Array Filled with String Using numpy.full()

In this example, we will create a three-dimensional full array by passing the **shape** in the **numpy.full() **function which contains all three dimensions of an array in the form of a list, with this we also pass a string as a constant value.

**Example:**

```
import numpy as np
f3=np.full([2,3,4],'python')
print(f3)
```

**Output:**

We got a three-dimensional full array where at each position there is a constant value of string data type.

## Conclusion

Now that we have reached the end of this article, we hope it has elaborated on the different ways to create new arrays using** numpy.full()** function exclusively from the NumPy library. Here’s another article that explains how to use the numpy.where() in Python. CodeForGeek has many other entertaining and equally informative articles that can be of great help to those who want to advance in Python, so be sure to check them out as well.

## Reference

https://numpy.org/doc/stable/reference/generated/numpy.full.html