Scipy Intro Tutorial with answers

 Tutorial on scipy intro..

# Tutorial on Scipy intro
# SciPy is a scientific computation library above NumPy.
# SciPy stands for Scientific Python.
# Ref w3schools.com


from scipy import constants
print(constants.liter) # 0.001
print(constants.pi) # 3.141592653589793


import scipy
print(scipy.__version__) # 1.8.1


from scipy import constants
print(constants.yotta)    #1e+24
print(constants.zetta)    #1e+21
print(constants.exa)      #1e+18
print(constants.peta)     #1000000000000000.0
print(constants.tera)     #1000000000000.0
print(constants.giga)     #1000000000.0
print(constants.mega)     #1000000.0
print(constants.kilo)     #1000.0
print(constants.hecto)    #100.0
print(constants.deka)     #10.0
print(constants.deci)     #0.1
print(constants.centi)    #0.01
print(constants.milli)    #0.001
print(constants.micro)    #1e-06
print(constants.nano)     #1e-09
print(constants.pico)     #1e-12
print(constants.femto)    #1e-15
print(constants.atto)     #1e-18
print(constants.zepto)    #1e-21
#
# # Root of an equation ..optimizer
from scipy.optimize import root
from math import cos

def eqn(x):
  return x + cos(x)
myroot = root(eqn, 0)
print(myroot.x)         #[-0.73908513]

#Sparse Matrix to avoid unused elements
import numpy as np
from scipy.sparse import csr_matrix
arr = np.array([0, 0, 0, 0, 0, 1, 1, 0, 2])
print(csr_matrix(arr))
'''
  (0, 5)   1
  (0, 6)   1
  (0, 8)   2
'''

import numpy as np
from scipy.sparse import csr_matrix

arr = np.array([[0, 0, 0], [0, 0, 1], [1, 0, 2]])

mat = csr_matrix(arr)
mat.eliminate_zeros()

print(mat)
'''
 (0, 5)    1
  (0, 6)   1
  (0, 8)   2
  (1, 2)   1
  (2, 0)   1
  (2, 2)   2
'''


#SCIPY sIGNIFICANt aspects

import numpy as np
from scipy.stats import ttest_ind

x1 = np.random.normal(size=50)
x2 = np.random.normal(size=50)
#T-Test
print( ttest_ind(x1, x2))
 #Ttest_indResult(statistic=-0.4602568443314745, pvalue=0.6463514741682714)

# pvalue printing
print(ttest_ind(x1, x2).pvalue) #0.3861625359272315


import numpy as np
from scipy.stats import kstest
x = np.random.normal(size=50)
print(kstest(x, 'norm'))
#KstestResult(statistic=0.07440265161334647, pvalue=0.9254263997475219)


#Statistical Distribuition

import numpy as np
from scipy.stats import describe
x = np.random.normal(size=50)
print(describe(x))
# DescribeResult(nobs=50, minmax=(-2.252010033738992, 1.694245094326367),
# mean=-0.34129189546033056, variance=0.7245922182755077, skewness=0.4445284516357619,
# kurtosis=0.11748436830854025)

# Normality Tests (Skewness and Kurtosis)
'''
Skewness:
A measure of symmetry in data, For normal distributions it is 0.
If it is negative, it means the data is skewed left.
If it is positive it means the data is skewed right.
'''
import numpy as np
from scipy.stats import skew, kurtosis

x = np.random.normal(size=50)

print(skew(x))            #0.25667677411004536   skewed right
print(skew(x))            #-0.49188030505407804  skewed left

'''
Kurtosis:
A measure of whether the data is heavy or lightly tailed with 
respect  to a normal distribution.
Positive kurtosis : heavily tailed.
Negative kurtosis : lightly tailed.
'''
print(kurtosis(x))        #-0.37504350194077585 lightly tailed
print(kurtosis(x))        #0.010624259343263276  heavily tailed

from scipy.stats import normaltest
# To check normal distribution of data
print(normaltest(x)) #NormaltestResult(statistic=7.187162674258679,
# pvalue=0.027499668274458256)