How to start ML & DL? NumPy Module Part - 2.5

Hello Friends!!! Welcome again,

Today we are going to discuss more methods of NumPy module. I hope you all do well with NumPy. So, let's start with NumPy and learn some more methods.

• numpy.delete - this method is used to remove elements from an array. it returns a new array with sub-array along with axis that is deleted.
• Syntax: numpy.delete(array,index,axis=None)
• Here, array - represents an array from which you want to remove elements.
• index - represents indexes of sub-arrays to remove along the specified axis. it may be any object.
• axis - is an optional parameter. represents an axis along which to delete the sub-array defined in an index argument. 
• It returns a ndarray. A copy of an array with the elements specified by the index removed.
• Example,

	#numpy delete method
	import numpy as np
	ar=np.array([1,2,3,4,5,6])
	arr=np.delete(ar,[1,3]) # removes element at first index and element ar third index
	print(ar)
	#Output [1 2 3 4 5 6]
	print(arr)
	#Output [1 3 5 6]
	ar=np.array([[1,2,3],[4,5,6]])
	arr=np.delete(ar,[1,3])
	print(ar)
	#Output
	#[[1 2 3]
	# [4 5 6]]
	print(arr)
	#Output [1 3 5 6]
	ar=np.array([[1,2,3],[4,5,6],[7,8,9]])
	arr=np.delete(ar,[2,3,5],0)
	print(ar)
	#Output
	#[[1 2 3]
	# [4 5 6]
	# [7 8 9]]
	print(arr)
	#Output
	#[[1 2 3]
	# [4 5 6]]
	ar=np.array([[1,2,3],[4,5,6],[7,8,9]])
	arr=np.delete(ar,[2,3,5],1)
	print(ar)
	#Output
	#[[1 2 3]
	# [4 5 6]
	# [7 8 9]]
	print(arr)
	#Output
	#[[1 2]
	# [4 5]
	# [7 8]]
	ar=np.array([[1,2,3],[4,5,6],[7,8,9]])
	arr=np.delete(ar,[2,3,5],None) # if axis is None, returned array becomes flattern
	print(ar)
	#Output
	#[[1 2 3]
	# [4 5 6]
	# [7 8 9]]
	print(arr)
	#Output [1 2 5 7 8 9]

view raw numpy_delete.py hosted with ❤ by GitHub

• numpy.concatenate - this method is used to join different arrays along with an existing axis.
• Syntax: numpy.concatenate((ar1, ar2, ...),axis=0,out=None)
• Here, ar1, ar2 - represents a sequence of arrays, all the array must have the same shape.
• axis - is an optional argument, represents axis along which arrays will be joined. here default value of this parameter is 0.
• out - is an optional parameter, it is ndarray object. represents the destination to place the result.
• It returns ndarray object after concatenation of arrays. 
• Example,  

	#NumPy concatenate method
	import numpy as np
	ar1=np.array([1,2,3])
	ar2=np.array([4,5,6])
	ar=np.concatenate([ar1,ar2])
	print(ar)
	#Output [1 2 3 4 5 6]
	ar1=np.array([[1,2,3],[7,8,9]])
	ar2=np.array([[4,5,6]])
	ar=np.concatenate([ar1,ar2])
	print(ar)
	#Output
	#[[1 2 3]
	# [7 8 9]
	# [4 5 6]]
	ar1=np.array([[1,2,3],[7,8,9]])
	ar2=np.array([[4,5,6],[11,12,13]])
	ar=np.concatenate([ar1,ar2],axis=0)#default axis
	print(ar)
	#Output
	#[[ 1 2 3]
	# [ 7 8 9]
	# [ 4 5 6]
	# [11 12 13]]
	ar1=np.array([[1,2,3],[7,8,9]])
	ar2=np.array([[4,5,6],[11,12,13]])
	ar=np.concatenate([ar1,ar2],axis=1)
	print(ar)
	#Output
	#[[ 1 2 3 4 5 6]
	# [ 7 8 9 11 12 13]]

view raw numpy_concate.py hosted with ❤ by GitHub

These are methods of NumPy for this article that's it, in next article we will learn more methods.
Happy Learning & Coding...☺😊