Week 3 was Presidents' Day, no class

import math
from matplotlib import pyplot
import random

def Interest_normal(principal):
    rate=random.normalvariate(0.03,0.015)
    return principal*rate

#mean is 0.03, standard deviation is 0.015. Imagine a variable return on stocks, or inflation, for instance.

#simulation of 10 years of variable interest
years=10
sims=10000
end_balances=[]
for i in range(sims):
    time=0
    balance=1200
    while(time<years):
        balance+=Interest_normal(balance)
        time+=1
    end_balances.append(balance)

pyplot.hist(end_balances, bins=20)
pyplot.show()

pyplot.boxplot(end_balances)
pyplot.show()

import pandas as pd
import numpy as np
import seaborn as sns

employees = pd.read_excel('employee_data.xlsx')
employees.head()

	Employee	Gender	Age	Prior Experience	Beta Experience	Education	Annual Salary
0	1	1	39	5	12	4	57700
1	2	0	44	12	8	6	76400
2	3	0	24	0	2	4	44000
3	4	1	25	2	1	4	41600
4	5	0	56	5	25	8	163900

#last time we used groupby. This week we'll use crosstabs to create a summary table
table = pd.crosstab(employees.Education, employees.Gender)
table

Gender	0	1
Education
0	2	8
2	9	10
4	46	69
6	24	27
8	4	5

table2 = pd.crosstab(employees.Gender, employees.Education)
table2

Education	0	2	4	6	8
Gender
0	2	9	46	24	4
1	8	10	69	27	5

sns.heatmap(table2)

<AxesSubplot:xlabel='Education', ylabel='Gender'>

sns.heatmap(table2,annot=True,linewidths=.5,cmap="YlGnBu")

<AxesSubplot:xlabel='Education', ylabel='Gender'>

dict = {
    "FirstName": "Jonathan",
    "LastName": "Freeman",
    "LoginCount": 4,
    "isWriter": True,
    "WorksWith": ['Spantree Technology Group', 'InfoWorld'],
    "Pets": [
        {
            "name": "Lilly",
            "type": "Raccoon"
        }
    ]
    
}
dict

{'FirstName': 'Jonathan',
 'LastName': 'Freeman',
 'LoginCount': 4,
 'isWriter': True,
 'WorksWith': ['Spantree Technology Group', 'InfoWorld'],
 'Pets': [{'name': 'Lilly', 'type': 'Raccoon'}]}

my_dict = {'Computer':1500,'Monitor':300,'Printer':150,'Desk':250}
my_dict

{'Computer': 1500, 'Monitor': 300, 'Printer': 150, 'Desk': 250}

df = pd.DataFrame(list(my_dict.items()),columns = ['Products','Prices'])
df

	Products	Prices
0	Computer	1500
1	Monitor	300
2	Printer	150
3	Desk	250