NumPy: numpy.full() function

Last update on March 21 2023 12:08:53 (UTC/GMT +8 hours)

numpy.full() function

The numpy.full() function is used to create a new array of the specified shape and type, filled with a specified value.

Syntax:

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

Parameters:

Name	Description	Required / Optional
shape	Shape of the new array, e.g., (2, 3) or 2.	Required
fill_value	Fill value.	Required
dtype	The desired data-type for the array The default, None, means np.array(fill_value).dtype.	optional
order	Whether to store multidimensional data in C- or Fortran-contiguous (row- or column-wise) order in memory	optional

Return value:

[ndarray] Array of fill_value with the given shape, dtype, and order.

Example: Create arrays filled with a constant value using numpy.full()

>>> import numpy as np
>>> np.full((3, 3), np.inf)
array([[ inf,  inf,  inf],
       [ inf,  inf,  inf],
       [ inf,  inf,  inf]])
>>> np.full((3, 3), 10.1)
array([[ 10.1,  10.1,  10.1],
       [ 10.1,  10.1,  10.1],
       [ 10.1,  10.1,  10.1]])

The above code creates arrays filled with a constant value using the numpy.full() function. In the first example, np.full((3, 3), np.inf) creates a 3x3 numpy array filled with np.inf (infinity). np.inf is a special floating-point value that represents infinity, and is often used in calculations involving limits and asymptotes.
In the second example, np.full((3, 3), 10.1) creates a 3x3 numpy array filled with the value 10.1. Here, the dtype parameter is omitted, so numpy infers the data type of the array from the given value.

Pictorial Presentation:

Example: Create an array filled with a single value using np.full()

>>> import numpy as np
>>> np.full((3,3), 55, dtype=int)
array([[55, 55, 55],
       [55, 55, 55],
       [55, 55, 55]])

In the above code, np.full((3,3), 55, dtype=int) creates a 3x3 numpy array filled with the integer value 55. The dtype parameter is explicitly set to int, so the resulting array has integer data type.

Pictorial Presentation:

Python - NumPy Code Editor:

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