NumPy full() Function with Examples

The NumPy full() function in Python is used to create an array with a specified shape and fill it with a given value. In this article, I will explain syntax and how to use the numpy.full() function which returns an array of fill_value with the given shape, order, and datatype.

1. Quick Examples of full() Function

If you are in a hurry, below are some quick examples of how to use NumPy full() function in Python.


# Quick examples of full() function

# Example 1: Use numpy.full() function 
# On 1-D array
arr2 = np.full(6, 4)

# Example 2: Use numpy.full() function 
# With two-dimensional arrays
arr2 = np.full((4, 3),8)

# Example 3: Return array with dtype=str
arr2 = np.full(shape=(4,3),fill_value='2',dtype=str)

# Example 4: Return array with dtype=str
arr2 = np.full(shape=(3,4),fill_value='3',dtype=float)

# Example 5: Return array with dtype=int
arr2 = np.full((2,4),'7',dtype=int)

2. Syntax of NumPy full()

Following is the syntax to create numpy.full() function.


# Syntax of numpy.full() 
numpy.full(shape, fill_value, dtype=None, order='C', *, like=None)

2.1 Parameters of full()

Following are the parameters of full().

shape - It defines the shape of the array which is an int or sequence of ints. The shape of the new array, e.g., (4, 3) or 2.
fill_value - Value to fill the array with.
dtype - It is an optional parameter that specifies the data type of the returned array.
order - {‘C’, ‘F’}, optional: To store multi-dimensional data in row-major (C) or column-major (F) order/pattern in the memory location.
like - value should be array_like, optional

2.2 Return value of full()

It returns ndarray of fill_value with the given shape, order, and datatype.

3. Use NumPy full() Function on 1-D Array

To create a one-dimensional Numpy array of size 6, with the value 4 use NumPy full() function. Here shape=6 is used to specify the length of the array, you’re indicating that you want the output to have six elements and fill_value=4 specifies the array to be filled with value 4.

In the below example, np.full(shape=6, fill_value=4) creates a 1-D array with a shape of 6, and all elements are filled with the value 4. Adjust the shape and fill_value parameters as needed for your specific use case.


# Import numpy module
import numpy as np

# Use numpy full() function on 1-D array
arr2 = np.full(shape=6, fill_value=4)
print("Full 1-D array:\n", arr2)

Yields below output.

4. Use NumPy full() Function with Two-Dimensional Arrays

You can use the numpy.full() function to create a two-dimensional array. For instance, np.full(shape=(4, 3),fill_value=8) creates a 2-D array with a shape of (4, 3), and all elements are filled with the value 8. Adjust the shape and fill_value parameters as needed for your specific use case.


# Use numpy full() function 
# With two-dimensional arrays
arr2 = np.full(shape=(4, 3),fill_value=8)
print("Full 2-D array:\n", arr2)

# Output:
# Full 2-D array:
#  [[8 8 8]
#  [8 8 8]
#  [8 8 8]
#  [8 8 8]]

5. Return Array with dtype=str

To create a NumPy array with a specified data type (dtype) of string, you can use the dtype parameter in the numpy.full() function.

In the below example, np.full(shape=(4,3),fill_value='2',dtype=str) creates a 2-D array with a shape of (4, 3), and all elements are filled with the string value '2', and the data type is set explicitly to str. Adjust the shape, fill_value, and dtype parameters as needed for your specific use case.


# Return array with dtype=str
arr2 = np.full(shape=(4,3),fill_value='2',dtype=str)
print("2-D Array with dtype=str::\n", arr2)

# Output:
# 2-D Array with dtype=str::
#  [['2' '2' '2']
#  ['2' '2' '2']
#  ['2' '2' '2']
#  ['2' '2' '2']]

6. Return Array with dtype=float

If you want to create a NumPy array with a specified data type (dtype) of float, you can use the dtype parameter in the numpy.full() function.

In the below example, np.full(shape=(3,4),fill_value='3',dtype=float) creates a 2-D array with a shape of (3, 4), and all elements are filled with the float value 3, and the data type is set explicitly to float. Adjust the shape, fill_value, and dtype parameters as needed for your specific use case.


# Return array with dtype=str
arr2 = np.full(shape=(3,4),fill_value='3',dtype=float)
print("2-D Array with dtype=float:\n", arr2)

# Output:
# 2-D Array with dtype=float:
#  [[3. 3. 3. 3.]
#  [3. 3. 3. 3.]
#  [3. 3. 3. 3.]]

Frequently Asked Questions

The numpy.full() function in NumPy is used to create an array with a specified shape and fill it with a constant value. Its primary purpose is to initialize an array where all elements have the same predetermined value.

You can create a 1-D array using the numpy.full() function by specifying the shape parameter as a single integer.

o create a 2-D array using numpy.full(), you need to specify the shape parameter as a tuple of two integers representing the number of rows and columns in the array.

To create an array with a specific data type using numpy.full(), you can use the dtype parameter. The dtype parameter allows you to explicitly specify the data type of the array.

To create a 2-D array with a specific order (either row-major or column-major), you can use the order parameter in the numpy.full() function. The order parameter takes a string argument, where 'C' stands for row-major (default), and 'F' stands for column-major.

The numpy.full() function fills the entire array with a constant value. If you need different values for each element, consider using other functions like numpy.array() or specifying a list of values manually.

Conclusion

In this article, I have explained how to use numpy.full() function which returns an array of fill_value with the given shape, order, and datatype. By using this function, I have also explained how to fill values on 1-D and 2-D arrays and fill values with string & float types.

Happy Learning!!