How to start ML & DL? NumPy Module Part - 2.7

Hello Friends!!!

In this article, we are going to learn more methods of NumPy module. At the end of this article, you will be able to work on,

• ones
• zeros

So, let's start with these methods, these two methods are powerful as it is generally used to build feature vector with some default values while developing deep learning models.

• numpy.ones() - method returns an array with default value 1.0 with given shape or we can say newly returned array filled with 1.0 or 1 based on data type and shape.
• Syntax: numpy.ones(shape, dtype=None, order='C')
• Here, shape - is the shape of an array that you want to retrieve.
• dtype - is an optional argument, represents the data type of an array that you want to retrieve, the default data type is float.
• order{'C','F'} - is an optional argument, represents whether to store multi - dim array in C-style or Fortran style. C-style represents row-major and Fortran-style represents column-major in memory. 
• It returns ndarray object in the form of an array with the given shape, order and data type.

Let's move to another method.

• numpy.zeros() - method returns an array with default value 0.0, with given shape or we can say newly returned array filled with 0.0 or 0 based on data type and shape.
• Syntax: numpy.zeros(shape,dtype=float,order='C') 
• Here, shape - is the shape of an array that you want to retrieve.
• dtype - is an optional argument, represents the data type of an array that you want to retrieve, the default data type is float.
• order{'C','F'} - is an optional argument, represents whether to store multi - dim array in C-style or Fortran style. C-style represents row-major and Fortran-style represents column-major in memory. 
• It returns ndarray object in the form of an array with the given shape, order and data type.

Let's take an example of the above methods:

	#Ones Demo
	import numpy as np
	ar=np.ones((2,3))
	print(ar)
	#Output
	#[[1. 1. 1.]
	# [1. 1. 1.]]
	ar=np.ones((2,3),dtype='int8')
	print(ar)
	#Output
	#[[1 1 1]
	# [1 1 1]]
	ar=np.ones((2),dtype='int16')
	print(ar) #Output: [1 1]
	#Zeros Demo
	ar=np.zeros(5)
	print(ar) #Output: [0. 0. 0. 0. 0.]
	ar=np.zeros([2,4])
	print(ar)
	#Output
	#[[0. 0. 0. 0.]
	# [0. 0. 0. 0.]]
	ar=np.zeros([2,4],dtype='int8')
	print(ar)
	#Output
	#[[0 0 0 0]
	# [0 0 0 0]]

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