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SCIPY - DATASCIENCE ( PART 3 )

In this article, we will learn about Scipy and it's implementation

Scipy is a Python-based library mainly used for technical computing and scientific computing. It contains varieties of sub-packages and scientific libraries only to GNU Scientific Library for C and C++ or Matlab. I also support to operate on an array of Numpy Library and easy to use and has the fast computational power to increase efficiency.

Advantages of using Scipy

  • Most new DataScience features are available on Scipy than Numpy
  • It is a fully-featured version of Linear Algebra
  • Built on the cream top of Numpy

Installation

 - On WINDOWS -

Python3 -m pip install --user numpy scipy

- On MAC -

sudo port install py35-scipy py35-numpy

- On LINUX -

sudo apt-get install  python-scipy python-numpy

Example

import numpy as np
from scipy import linalg

# We are trying to solve a linear algebra system which can be given as:
#               11x + 2y =50
#               23x + 14y =65

# Create input array
A= np.array([[11,2],[23,14]])

# Solution Array
B= np.array([[50],[65]])

# Solve the linear algebra
X= linalg.solve(A,B)

# Print results
print(X)

# Checking Results
print("\n Checking results, following vector should be all zeros")
print(A.dot(X)-B)

The above example clearly illustrates how a linear algebra equation can be solved in a scipy environment. Well to master scipy very versed practice is needed to make sure that each and every function is known to you. 

References

https://www.scipy.org/

https://scipy-lectures.org/

https://github.com/scipy/scipy