In this article, we will try to understand the full() function of the NumPy package in Python.

NumPy is a popular Python library for scientific computing that provides tools for working with large, multi-dimensional arrays and matrices of numerical data. One of the functions provided by NumPy is `full()`

, which returns a new array of given shape and type, filled with a fill value.

The function allows you to create an array of any size and shape, and fill it with a specified value. This can be useful when you need to create an array of a certain size and type as a placeholder for other data, or when you want to initialize an array with a default value.

The `full()`

function is a simple and efficient way to create and populate arrays with a single function call.

## What is the full() function in NumPy?

This function is used to create a new array of given shapes and types. The values of the array are initialized with the `fill_value`

passed in parameters. One can also provide the data type of the new array, and its order in the parameter.

## Syntax of NumPy full()

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

### Parameters

**shape: int or sequence of ints**- Required
- Dimensions of a new array; can be single integers or tuples of integers.

**fill_value: scalar or array_like**- Required
- Value to be added to the new array

**dtype: data-type**- Optional
- Data-type of the elements in the new array; default = None, means
`np.array(fill_value).dtype`

.

**order: {â€˜Câ€™, â€˜Fâ€™}**- Optional
- Which order should be used to store multidimensional data – C or Fortran-contiguous (row- or column-wise)

**like: array_like**- Optional
- To enable the generation of arrays that aren’t NumPy arrays, reference objects are provided. The outcome will be determined by an array-like provided in as like if it complies with the
**array function**protocol. In this instance, it makes sure that an array object is created that is compatible with the one that was provided as an argument.

## Implementation of NumPy full()

Make sure to import the NumPy package in your IDE before implementing the function. To do so, run the following line of code.

```
import numpy as np
```

### Example 1. Passing only required parameters

Let’s create an array of a specific size and shape filled with a constant value

```
#one dimentional array
np.full(2,4)
#two dimentional array with singular element
np.full((2,3),4)
#two dimentional array with multiple element
np.full((2,3),[1,2,3])
```

### Example 2. Passing other parameters

Let’s now create an array of a specific size and data type filled with a constant value.

```
np.full((3,3), [2,4,6], dtype=np.float16)
np.full((3,2), [2.8,1.9], dtype=int, order='F')
np.full((3,3), [1+2j], dtype=complex)
```

## Conclusion

NumPy packages make working with arrays much easy. the full() function is a simple way to return a new array of a given shape and type, filled with *fill_value*.

## Reference

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