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!!
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References
- https://numpy.org/doc/stable/reference/generated/numpy.ones.html#numpy.ones
- https://numpy.org/doc/stable/reference/generated/numpy.ones_like.html#numpy.ones_like
