In this article we will discuss how to create a Numpy Array of different shapes and initialized with same identical values using numpy.full().
numpy.full()
Python’s Numpy module provides a function to create a numpy array of given shape and all elements initialized with a given value,
numpy.full(shape, fill_value, dtype=None, order='C')
Arguments:
shape: Shape of the new array
fill_value : Intialization value
dtype : Data type of elements  Optional
It returns a Numpy array of given shape and type, all elements in it will be initialized with fill_value.
To use Numpy in our code we need to include following module i.e.
import numpy as np
Checkout some examples,
Example 1:
Create a 1D Numpy Array of length 10 & all elements initialized with value 5
# Create a 1D Numpy Array of length 10 & all elements initialized with value 5 arr = np.full(10, 5)
Contents of the Create Numpy array:
[5 5 5 5 5 5 5 5 5 5]
Data Type of Contents of the Numpy Array : int32
Shape of the Numpy Array : (10,)
Example 2:
Create a 2D Numpy Array of 4 rows  5 columns & all elements initialized with value 7
#Create a 2D Numpy Array of 4 rows & 5 columns. All intialized with value 7 arr = np.full((4,5), 7)
Contents of the Create Numpy array:
[[7 7 7 7 7] [7 7 7 7 7] [7 7 7 7 7] [7 7 7 7 7]]
Data Type of Contents of the Numpy Array : int32
Shape of the Numpy Array : (4,5)
Example 3:
Create a 3D Numpy Array of shape (2,4,5) & all elements initialized with value 8
# Create a 3D Numpy array & all elements initialized with value 8 arr = np.full((2,4,5), 8)
Contents of the Create Numpy array:
[[[8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8]] [[8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8]]]
Data Type of Contents of the Numpy Array : int32
Shape of the Numpy Array : (2, 4, 5)
Example 4:
Create initialized Numpy array of specified data type
Along with initialization value, we can specify the data type too i.e.
# Create a 1D Numpy array & all float elements initialized with value 9 arr = np.full(10, 9, dtype=float)
Contents of the Create Numpy array:
[9. 9. 9. 9. 9. 9. 9. 9. 9. 9.]
Data Type of Contents of the Numpy Array : float64
Complete example is as follows,
import numpy as np def main(): print('*** Create 1D Numpy Array filled with identical values ***') # Create a 1D Numpy Array of length 10 & all elements intialized with value 5 arr = np.full(10, 5) print('Contents of the Numpy Array : ' , arr) print('Data Type of Contents of the Numpy Array : ', arr.dtype) print('Shape of the Numpy Array : ', arr.shape) print('*** Create 2D Numpy Array filled with identical values ***') #Create a 2D Numpy Array of 4 rows & 5 columns. All intialized with value 7 arr = np.full((4,5), 7) print('Contents of the Numpy Array : ', arr, sep='\n') print('Data Type of Contents of the Numpy Array : ', arr.dtype) print('Shape of the Numpy Array : ', arr.shape) print('*** Create 3D Numpy Array filled with identical values ***') # Create a 3D Numpy array & all elements initialized with value 8 arr = np.full((2,4,5), 8) print('Contents of the Numpy Array : ', arr, sep='\n') print('Data Type of Contents of the Numpy Array : ', arr.dtype) print('Shape of the Numpy Array : ', arr.shape) print('*** Create 1D Numpy Array of specified Data Type filled with identical values ***') # Create a 1D Numpy array & all float elements initialized with value 9 arr = np.full(10, 9, dtype=float) print('Contents of the Numpy Array : ', arr) print('Data Type of Contents of the Numpy Array : ', arr.dtype) print('Shape of the Numpy Array : ', arr.shape) if __name__ == '__main__': main()
Output:
*** Create 1D Numpy Array filled with identical values *** Contents of the Numpy Array : [5 5 5 5 5 5 5 5 5 5] Data Type of Contents of the Numpy Array : int32 Shape of the Numpy Array : (10,) *** Create 2D Numpy Array filled with identical values *** Contents of the Numpy Array : [[7 7 7 7 7] [7 7 7 7 7] [7 7 7 7 7] [7 7 7 7 7]] Data Type of Contents of the Numpy Array : int32 Shape of the Numpy Array : (4, 5) *** Create 3D Numpy Array filled with identical values *** Contents of the Numpy Array : [[[8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8]] [[8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8] [8 8 8 8 8]]] Data Type of Contents of the Numpy Array : int32 Shape of the Numpy Array : (2, 4, 5) *** Create 1D Numpy Array of specified Data Type filled with identical values *** Contents of the Numpy Array : [9. 9. 9. 9. 9. 9. 9. 9. 9. 9.] Data Type of Contents of the Numpy Array : float64 Shape of the Numpy Array : (10,)
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Pandas Tutorial Part #9  Filter DataFrame Rows

Pandas Tutorial Part #10  Add/Remove DataFrame Rows & Columns

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