# Numpy Ndarray Operation

2019-08-19 Omsingh Bais

### Numpy Ndarray Operation

In last article we learnd about the basic of Numpy array and creation of ndarray object. In this article we are going to learn about the various operation on ndarray.

If you are new to numpy array then check previous article

Follwing is the operation that can be performed on ndarray :

• Creating empty array :
 import numpy as np  x = np.array([ ]) # array creation using list y = np.array(( )) # array creation using tuples z = np.array({ }) # array creation using dictionary print (x) print (y) print (z)

Output :

 [] [] {}

• Creating one dimensions array
 import numpy as np  x = np.array([1,2,3]) # array creation using list y = np.array((4,5,6 )) # array creation using tuples z = np.array({'Name': 'solutionstouch', 'Founded': 2019}) #array creation using dictionary print (x) print (y) print (z)

Output:

 [1 2 3] [4 5 6] {'Founded': 2019, 'Name': 'solutionstouch'}

• Creating 2-dimensions array

 import numpy as np  x = np.array([[1,2],[3,4]]) # array creation using list y = np.array(((5,6),(7,8))) # array creation using tuples z = np.array({'Name': 'solutionstouch', 'Founded': 2019}) #array creation using dictionary print (x) print (y) print (z)

Output :

 [[1 2]  [3 4]] [[5 6]  [7 8]] {'Founded': 2019, 'Name': 'solutionstouch'}

• Creating ndarray with dtype parameter :
 import numpy as np  x = np.array([11, 25, 68], dtype = float) # Float datatype y = np.array([11, 25, 68], dtype = complex) # Complex datatype z = np.array([11.0, 25.0, 68.0], dtype = int) # integer datatype  print (x) print (y) print (z)

Output :

 [ 11. 25. 68.] [ 11.+0.j 25.+0.j 68.+0.j] [11 25 68]

•  Creating ndarray with the use of copy parameter

Copy parameter Return an array copy of the given object.

 import numpy as np x = np.array([11, 22, 33]) #create an array. y = x #y is a reference variable z = np.copy(x) #copy of a x save in z  x = 10 print(x == y) # output will be True print(x == z) # Output will be False because z having the initial value of x

Output:

 True False

• Creating ndarray with the use of ndmin parameter

Ndmin means minimum dimensions of the resulting array.

 import numpy as np  x = np.array([11,22,33,44], ndmin = 2) #ndmin is 2 y = np.array([11,22,33,44], ndmin = 4) #ndmin is 4 z = np.array([11,22,33,44], ndmin = 6) #ndmin is 6 print(x) print(y) print(z)

Output:

 [[11 22 33 44]] [[[[11 22 33 44]]]] [[[[[[11 22 33 44]]]]]]

Happy coding..... 