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data <- read.table("C:/Users/DASA0/Desktop/Stat 524/wichern data/T11-4.dat", sep="")
data <- as.data.frame(data)
names(data) <- c("X1", "X2", "X3","X4", "X5")
summary(data)
xbar <- colMeans(data) 
S <- cov(data)
Distance <- mahalanobis(data, xbar, S)
pairs(~X1+X2+X3+X4,data=data)

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list = []
for i in range(1, 101):
    list.append(i**2)
sumofsq = sum(list)
sumlist = sum(range(1,101))
sqofsum = sumlist**2
diff = sumofsq - sqofsum
print(diff)

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def pythagoreantriplet(sum):
    for a in range(1, sum):
        for b in range(a, sum):
            c = sum - a - b
            if a**2 + b**2 == c**2:
                return a, b, c, a*b*c

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def sum_prime(n):
   
    primes = []
    for num in range(2, n):
        if all(num%i!=0 for i in range(2,num)):
            #print(i, "is prime")
            primes.append(num)
    return primes, sum(primes)

accessnash

superherodata <- read.csv("C:/Users/DASA0/Desktop/books & research/superheroes.csv", sep=",")
superherodata <- as.data.frame(superherodata)
colSums(x, na.rm = FALSE, dims = 1)
superherochars <- read.csv("C:/Users/DASA0/Desktop/books & research/supercharstc.csv", sep=",")
slices <- c(sum(superherochars$Gender=="Female", na.rm=TRUE), sum(superherochars$Gender=="Male", na.rm=TRUE), sum(superherochars$Gender=="-", na.rm=TRUE))
lbls <- c("Female", "Male", "No data")

pie(slices, labels = lbls, main="Superheroes by Gender", col = colors)

slices2 <- c(sum(superherochars$Alignment=="good", na.rm=TRUE), sum(superherochars$Alignment=="bad", na.rm=TRUE),sum(superherochars$Alignment=="neutral", na.rm=TRUE), sum(superherochars$Alignment=="-", na.rm=TRUE))

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import math
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import scipy.stats as sc

# Intrinsic Value of an European Call

K = 800
S = np.linspace(600, 1000, 50)

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import math
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import scipy.stats as sc

def BS_Put(St, K, t, T, r, sigma):
    
    d1 = (math.log(St/K) + (r + 0.5*sigma**2)*(T-t))/(sigma*math.sqrt(T-t))
    d2 = d1 - sigma*math.sqrt(T-t)

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import math
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import scipy.stats as sc

def BS_Calltheta(St, K, t, T, r, sigma):
    
    d1 = (math.log(St/K) + (r + 0.5*sigma**2)*(T-t))/(sigma*math.sqrt(T-t))
    d2 = d1 - sigma*math.sqrt(T-t)

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import math
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import scipy.stats as sc

def BS_Puttheta(St, K, t, T, r, sigma):
    
    d1 = (math.log(St/K) + (r + 0.5*sigma**2)*(T-t))/(sigma*math.sqrt(T-t))
    d2 = d1 - sigma*math.sqrt(T-t)

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import math
import numpy as np
import matplotlib as mp
import matplotlib.pyplot as plt
import scipy.stats as sc
#import mp_toolkits.mplot3d.axes3d as p3

def BS_Calldelta(St, K, t, T, r, sigma):
    
    d1 = (math.log(St/K) + (r + 0.5*sigma**2)*(T-t))/(sigma*math.sqrt(T-t))