python-06-maths

correlation_coefficient_01.py

################################################################################################
#	name:	correlation_coefficient_01.py
#	desc:	correlation coefficient
#	date:	2018-12-22
#	Author:	conquistadorjd
################################################################################################
import numpy as np
from scipy import stats

#Calculate mean by python

a = [10,20,30,40,50,60]
b = [9,9,10,8,9,10]

#Using scipy to calculate person's coefficient
pearsonr_val = stats.pearsonr(a,b)
print('pearsonr_val : ', pearsonr_val)
#pearsonr_val :  (0.2130214807490179, 0.6853010393640564)

#Using numpy
corrcoef_val = np.corrcoef(a,b)
print('corrcoef_val : ', corrcoef_val)
#corrcoef_val : [[1.         0.21302148]
#[0.21302148 1.        ]]

#Using scipy to calculate Spearmanr
spearmanr_val = stats.spearmanr(a,b)
print('spearmanr_val : ', spearmanr_val)
#spearmanr_val :  SpearmanrResult(correlation=0.24688535993934707, pvalue=0.6371960853462737)

#Using scipy to calculate kendalltau
kendalltau_val = stats.kendalltau(a,b)
print('kendalltau_val : ', kendalltau_val)

#kendalltau_val :  KendalltauResult(correlation=0.2335496832484569, pvalue=0.5374525191136282)

print('*** Program ended ***')

discriptive_statistics_01.py

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#	name:	discriptive_statistics_01.py
#	desc:	identify type of progression
#	date:	2018-12-22
#	Author:	conquistadorjd
################################################################################################
import numpy as np
from scipy import stats

#Calculate mean by python

input_data = input('Input elements separated by comma :') 

# Convert input into List
input_list = list(map(int, input_data.split(',')))

print ("input_list", input_list , type(input_list))

# Mean calculation using simple python
mean = sum(input_list)/ len(input_list)
print('mean', mean)

# Mean calculation using numpy
mean = np.mean(input_list)
print('mean', mean)

# Median calculation using numpy
median = np.median(input_list)
print('median', median)

# Mode calculation using scipy
mode = stats.c(input_list)
print('mode', mode)