NUMPY - DATA SCIENCE ( PART 2 )
Numpy provides a quality performance array processing package with a multidimensional array object and tools for working with these arrays. It also contributes to scientific uses, by enabling efficient multi-dimensional container for generic data and scientific computing with python.
GENERAL - METHODS IN NUMPY
ARRAY ( NUMPY WAY )
In Numpy the arrays are of all same type usually indexed by a tuple of positive integers. The dimension of the array are called the rank of the array. An array in Numpy class is usually called as ndarray.
Creation of array in Numpy ( Python )
import numpy as np arr = np.array([10, 20, 30]) # Creation of array with rank 1 Array print("Array with Rank 1 = \n",arr) # Creation of array with rank 2 Array arr = np.array([[10, 20, 30],[40, 50, 60]]) print("Array with Rank 2 = \n", arr) # Creation an array from tuple arr = np.array((10, 30, 20)) print("\nArray created using passed tuple = \n", arr)
Operations - Basic Operation is Array using Numpy
import numpy as np # Defining Array 1 a = np.array([[10, 20],[30, 40]]) # Defining Array 2 b = np.array([[40, 30],[20, 10]]) # Adding 1 to every element print ("Adding 1 to every element:", a + 1) # Subtracting 2 from each element print ("\nSubtracting 2 from each element:", b - 2) # sum of array elements # Performing Unary operations print ("\nSum of all array elements: ", a.sum()) # Adding two arrays # Performing Binary operations print ("\nArray sum:\n", a + b)
The above was an abstraction to shoe the use of Numpy in Data Science, but it needs to be emphasized more understand it in a more clear way, to know more the below-attached section is advised to refer.