import numpy as np

import pandas as panda

import matplotlib.pyplot as plt

from scipy.stats import norm

import statistics

db = panda.read_csv('cardio_train.csv')

Age = db.loc[db.cardio == 1].age

Age /= 356

Age = np.floor(Age)

ageMean = Age.mean()

ageMedian = Age.median()

ageMode = Age.mode()

ageSD = Age.std()

ageQ25,ageQ75 = np.percentile(Age,[25,75])

plt.boxplot(Age)

plt.title("BoxPlot Of The Age")

plt.ylabel("Age")

plt.show()

# Data Is Clean Already

unique,count = np.unique(Age, return_counts=True)

r = statistics.correlation(unique,count)

print("Correlation Between Age And Heart Disease = " +  str(r))

x,y = np.polyfit(unique,count,1)

plt.scatter(unique,count)

plt.plot(unique,x*unique + y)

plt.title("Best Linear Fit Of The Data")

plt.ylabel("Frequency")

plt.xlabel("Age")

plt.show()

plt.hist(Age,10)

plt.title("Histogram Of The Age Column Of Patient")

plt.ylabel("Frequency")

plt.xlabel("Age")

plt.show()

AgeRightSide = Age.loc[Age >= ageMean]

AgeLeftSide = Age.loc[Age <= ageMean]

rightSideMean = AgeRightSide.mean()

leftSideMean = AgeLeftSide.mean()

print("Data Mean = " + str(ageMean))

print("Right Side Mean = " + str(rightSideMean))

print("Left Side Mean = " + str(leftSideMean))

rightSideCount = AgeRightSide.count()

leftSideCount = AgeLeftSide.count()

PhighAge = (rightSideCount) * 100 / (Age.count())

PLowAge = (leftSideCount) * 100 / (Age.count())

print("Propability Of High Age And Heart Disease = " + str(PhighAge))

print("Propability Of Low Age And Heart Disease = " + str(PLowAge))