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)

Conclusion :

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.

References:

https://numpy.org/

https://www.python-course.eu/numpy.php

https://docs.scipy.org/doc/numpy/reference/