What you imagine is what you see on board/screen.
Information is beautiful. Knowledge is beautiful.
= information & function & visual form & story (IFVS)
Refer for Data Visualization Good Practices(5)
Play with Pandas [Data manipulation with Data]
Case Studies
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)
