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for n in range (1,100):
    if (n % 3 == 0 and n % 5 == 0):
        print ("FizzBuzz")
    elif n % 5 == 0:
        print("Buzz")
    elif n % 3 == 0:
        print("Fizz")
    else:
        print(n)

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#bartender app

import random

questions = {
    "stiff": "Do you like stiff drinks?",
    "citrusy": "Do you like your drinks scurvy-free?",
    "sweet": "Are you a sissy who likes fruity drinks?",
    "bitter": "Do you like your drinks bitter?",
    "classy": "Do you like your drinks with a twist of class?",

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#constructing classes and objects 

class musician(object):
    #defines the musician object and initiates a solo method for all musicians
    def __init__(self, sounds):
        self.sounds = sounds
        
    def solo(self, length):
        for i in range(length):
            print(self.sounds[i % len(self.sounds)], end=" ")

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#modeling the bicycle industry

#create the customer class
class customer(object):
    def __init__(self, name, fund):
        self.name = name
        self.fund = fund

#create the bicycle class so that each bike created has a model name, weight, and cost to produce
class bicycle(object):

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#modeling the bicycle industry

#classes
class bicycle(object):
    
#create the bicycle class so that each bike created has a model name, weight, and cost to produce
    def __init__(self, model, weight, cost):
        self.model = model
        self.weight = weight
        self.cost = cost

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from sqlalchemy import create_engine
from sqlalchemy import Column, Integer, String, ForeignKey, DateTime
from sqlalchemy.orm import sessionmaker
from sqlalchemy.orm import relationship
from sqlalchemy.ext.declarative import declarative_base

engine = create_engine('postgresql://ubuntu:thinkful@localhost:5432/tbay')
Session = sessionmaker(bind=engine)
session = Session()
Base = declarative_base()

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import numpy as np 
import scipy.stats as stats
import collections
import matplotlib.pyplot as plt


x = [1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4, 4, 4, 4, 5, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 9, 9]

c = collections.Counter(x)
count_sum = sum(c.values())

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import matplotlib.pyplot as plt
import pandas as pd
import scipy.stats as stats

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

loansData.dropna(inplace=True)

loansData.boxplot(column='Amount.Requested')
plt.savefig('Amount Requested Boxplot.png')

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from scipy import stats
import collections
import pandas as pd
import matplotlib.pyplot as plt

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

loansData.dropna(inplace=True)

freq = collections.Counter(loansData['Open.CREDIT.Lines'])

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import statsmodels.api as sm

loansData = pd.read_csv('https://github.com/Thinkful-Ed/curric-data-001-data-sets/raw/master/loans/loansData.csv')

#Remove '%' from 'Interest.Rate' column and contert to number
loansData['Interest.Rate']=loansData['Interest.Rate'].map(lambda x: round(float(x.rstrip('%')) / 100, 4))
#Remove 'months' from the 'Loan.Length' column