NumPy ndarray.fill() Method

NumPy ndarray.fill()

The numpy.ndarray.fill() method fills an entire NumPy array with a specified scalar value. This is an in-place operation, meaning it modifies the existing array rather than creating a new one.

Syntax

ndarray.fill(value)

Parameters

Parameter	Type	Description
value	scalar	The scalar value used to fill the array.

Return Value

The method does not return a new array but modifies the existing array in place by filling it with the given value.

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Examples

1. Filling an Array with a Single Value

In this example, we create an empty NumPy array and use fill() to populate it with a constant value.

import numpy as np

# Create an array of shape (3, 3) with uninitialized values
arr = np.empty((3, 3))

# Fill the entire array with the value 7
arr.fill(7)

# Print the modified array
print(arr)

Output:

[[7. 7. 7.]
 [7. 7. 7.]
 [7. 7. 7.]]

The fill() method replaces all elements in the array with the value 7, modifying it in place.

2. Using fill() with Floating-Point Values

We can also use fill() with floating-point numbers.

import numpy as np

# Create an array of shape (2, 4) with uninitialized values
arr = np.empty((2, 4))

# Fill the array with the value 3.14 (Pi approximation)
arr.fill(3.14)

# Print the modified array
print(arr)

Output:

[[3.14 3.14 3.14 3.14]
 [3.14 3.14 3.14 3.14]]

The method works the same way regardless of whether the value is an integer or a floating-point number.

3. Filling an Integer Array

If the array has an integer data type, fill() will round floating-point values before storing them.

import numpy as np

# Create an integer array of shape (2, 2)
arr = np.empty((2, 2), dtype=int)

# Fill the array with the value 5.9 (which will be converted to 5)
arr.fill(5.9)

# Print the modified array
print(arr)

Output:

[[5 5]
 [5 5]]

Since the array has an integer data type, the floating-point value 5.9 is truncated to 5.

4. Filling a Boolean Array

The fill() method can also be used with boolean arrays.

import numpy as np

# Create a boolean array of shape (3, 2)
arr = np.empty((3, 2), dtype=bool)

# Fill the array with True
arr.fill(True)

# Print the modified array
print(arr)

Output:

[[ True  True]
 [ True  True]
 [ True  True]]

For boolean arrays, fill() will assign True or False based on the given value.