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#Another plotting library, a little fancier than matplotlib
import seaborn as sns

#Another distribution of carrot lengths
#(The 'scale' is the standard deviation parameter)
virginias_vegetables = np.random.normal(loc = mu, scale = 3, size = n)

#A third distribution of carrot lengths
raouls_roots = np.random.normal(loc = mu, scale = 5, size = n)

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#Function to calculate variance
def find_var(X):
    
    mu = X.mean()
    n = len(X)
    
    variance = np.sum(
                        (X - mu)**2 
                    ) / n         
                    

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#Function to calculate standard deviation
def find_std(X):
    
    mu = X.mean()
    n = len(X)
    
    sigma = np.sqrt(
                    np.sum(
                                (X - mu)**2 
                           ) / n         

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#Mean: carrot length in cm
mu = 20

#Size of population: number of carrots in market
n = 1000

# Random variable, normally-distributed: carrot length
import numpy as np
carrot_lengths = np.random.normal(loc = mu, size = n)

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import pandas as pd

#Toy data: brightness of color of each carrot from Fancy Farms.
#(Ideal brightness is at the mean.)
brightness = (carrot_lengths / (5 + np.random.random(1000)))

#Extra toy data from the farm: dampness of the soil from which the 
#carrot was pulled. 50 is normal dampness; 100 is sodden; 0 is dry.
dampness = np.random.normal(loc = 50, scale = 25, size = n)

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