In [260]:
#Import libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('display.max_columns', None)
In [152]:
#Import data files 

data= pd.read_excel(r'C:\Users\user1\Documents\muriyo\gmu\hap725\week5\q2\data\DataonFallsinNursingHome.xlsx')
data
Out[152]:
Case Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 7 Time 8 Time 9
0 1 0.25 0.55 0.40 0.15 0.55 0.75 0.20 0.35 0.40
1 2 0.40 0.25 0.70 0.45 0.60 0.45 0.15 0.80 0.50
2 3 0.70 0.40 0.60 0.70 0.45 0.05 0.10 0.50 0.25
3 4 0.40 0.45 0.55 0.80 0.50 0.90 0.25 0.55 0.70
4 5 0.15 0.20 0.70 0.45 0.65 0.50 0.60 0.75 0.40
5 6 0.20 0.65 0.60 0.60 0.65 0.60 0.70 0.35 0.55
6 7 0.50 0.10 0.55 0.25 0.25 0.70 0.40 0.60 0.30
7 8 0.50 0.50 0.30 0.10 0.35 0.35 0.35 0.45 0.75
8 9 0.30 0.75 0.65 0.80 0.60 0.65 0.50 0.30 0.20
9 10 0.20 0.35 0.60 0.40 0.40 0.40 0.75 0.65 0.60
10 11 0.40 0.65 0.05 0.25 0.35 0.60 0.65 0.75 0.55
11 12 0.30 0.20 0.25 0.65 0.10 0.25 0.70 0.40 0.60
12 13 0.45 0.65 0.45 0.80 0.40 0.75 0.55 0.45 0.65
13 14 0.25 0.30 0.65 0.25 0.50 0.30 0.65 0.55 0.75
14 15 0.25 0.25 0.70 0.60 0.25 0.25 0.70 0.35 0.60
15 16 0.40 0.45 0.60 0.80 0.65 0.40 0.35 0.75 0.75
16 17 0.45 0.30 0.25 0.85 0.25 0.75 0.65 0.60 0.45
17 18 0.35 0.50 0.75 0.45 0.45 0.75 0.40 0.25 0.45
18 19 0.25 0.75 NaN 0.50 0.70 0.55 0.70 0.50 NaN
19 20 0.10 0.60 NaN 0.20 0.60 0.70 NaN 0.65 NaN
20 21 NaN NaN NaN 0.45 NaN NaN NaN NaN NaN
21 Reported Falls 8.00 6.00 7.00 8.00 5.00 6.00 4.00 5.00 4.00
In [153]:
#drop the Case column
data=data.drop(columns = '\xa0Case', axis = 1)
data
Out[153]:
Time 1 Time 2 Time 3 Time 4 Time 5 Time 6 Time 7 Time 8 Time 9
0 0.25 0.55 0.40 0.15 0.55 0.75 0.20 0.35 0.40
1 0.40 0.25 0.70 0.45 0.60 0.45 0.15 0.80 0.50
2 0.70 0.40 0.60 0.70 0.45 0.05 0.10 0.50 0.25
3 0.40 0.45 0.55 0.80 0.50 0.90 0.25 0.55 0.70
4 0.15 0.20 0.70 0.45 0.65 0.50 0.60 0.75 0.40
5 0.20 0.65 0.60 0.60 0.65 0.60 0.70 0.35 0.55
6 0.50 0.10 0.55 0.25 0.25 0.70 0.40 0.60 0.30
7 0.50 0.50 0.30 0.10 0.35 0.35 0.35 0.45 0.75
8 0.30 0.75 0.65 0.80 0.60 0.65 0.50 0.30 0.20
9 0.20 0.35 0.60 0.40 0.40 0.40 0.75 0.65 0.60
10 0.40 0.65 0.05 0.25 0.35 0.60 0.65 0.75 0.55
11 0.30 0.20 0.25 0.65 0.10 0.25 0.70 0.40 0.60
12 0.45 0.65 0.45 0.80 0.40 0.75 0.55 0.45 0.65
13 0.25 0.30 0.65 0.25 0.50 0.30 0.65 0.55 0.75
14 0.25 0.25 0.70 0.60 0.25 0.25 0.70 0.35 0.60
15 0.40 0.45 0.60 0.80 0.65 0.40 0.35 0.75 0.75
16 0.45 0.30 0.25 0.85 0.25 0.75 0.65 0.60 0.45
17 0.35 0.50 0.75 0.45 0.45 0.75 0.40 0.25 0.45
18 0.25 0.75 NaN 0.50 0.70 0.55 0.70 0.50 NaN
19 0.10 0.60 NaN 0.20 0.60 0.70 NaN 0.65 NaN
20 NaN NaN NaN 0.45 NaN NaN NaN NaN NaN
21 8.00 6.00 7.00 8.00 5.00 6.00 4.00 5.00 4.00
In [154]:
#Transpose the dataset columns

df_t = data.T  
df_t
Out[154]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0
In [155]:
#Adding Time Period Column

df_t['TimePeriod']= ['Time 1','Time 2','Time 3','Time 4','Time 5','Time 6','Time 7','Time 8','Time 9']
df_t
Out[155]:
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 TimePeriod
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9
In [156]:
#Renaming columns names
df_t.rename(columns ={0: 'C0', 1: 'C1', 2: 'C2', 3: 'C3', 4: 'C4', 5: 'C5', 6: 'C6', 7: 'C7', 8: 'C8', 9: 'C9', 10: 'C10', 
                      11: 'C11', 12: 'C12', 13: 'C13', 14: 'C14', 15: 'C15', 16: 'C16', 17: 'C17', 18: 'C18', 19: 'C19', 
                      20: 'C20', 21:'ReportedFalls'}, inplace = True)
df_t
Out[156]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9
In [157]:
#Count the number of cases by time period
# Resulting count values took into effect the Reported Falls and TimePeriod column, which need to be excluded

df_t['Count'] = df_t.loc[:, 'C0':].count(axis=1)
df_t
Out[157]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod Count
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 22
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 22
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 20
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 23
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 22
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 22
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 21
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 22
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 20
In [158]:
#Excluding Reported Falls and TimePeriod column

df_t['FinalCount'] = (df_t['Count']-2)
df_t
Out[158]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod Count FinalCount
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 22 20
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 22 20
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 20 18
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 23 21
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 22 20
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 22 20
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 21 19
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 22 20
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 20 18
In [159]:
#drop the Count column
df_t=df_t.drop(columns = 'Count', axis = 1)
df_t
Out[159]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18
In [160]:
#Calculate the Observed Rate = Number of Falls / Total Count

df_t['ObservedRate'] = (df_t['ReportedFalls'])/(df_t['FinalCount'])
df_t
Out[160]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount ObservedRate
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20 0.400000
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20 0.300000
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18 0.388889
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21 0.380952
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20 0.250000
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20 0.300000
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19 0.210526
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20 0.250000
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18 0.222222
In [247]:
#Expected Rate = average probablity of all cases for each timeperiod

df_t['ExpectedRate'] = df_t[['C0','C1','C2','C3','C4','C5','C6','C7','C8','C9','C10','C11','C12','C13','C14','C15','C16',
                             'C17','C18','C19','C20']].mean(axis=1)
df_t
Out[247]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount ObservedRate ExpectedRate
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20 0.400000 0.340000
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20 0.300000 0.442500
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18 0.388889 0.519444
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21 0.380952 0.500000
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20 0.250000 0.462500
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20 0.300000 0.532500
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19 0.210526 0.492105
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20 0.250000 0.527500
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18 0.222222 0.525000
In [248]:
            
#select specific columns from df_t dataframe

df2=pd.DataFrame(df_t.iloc[:,0:21])# first 20 columns of data frame (C0 to C20)
 
df2
Out[248]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN
In [257]:
#Expected Deviation (Part 1)
#Create new dataframe using data from df2 and calculate risk

df3=pd.DataFrame()
for (columnName, columnData) in df2.iteritems(): 
    df3[columnName+'Risk']=(df2[columnName])*(1-df2[columnName])
In [256]:
#Print df3 dataframe
df3
Out[256]:
C0Risk C1Risk C2Risk C3Risk C4Risk C5Risk C6Risk C7Risk C8Risk C9Risk C10Risk C11Risk C12Risk C13Risk C14Risk C15Risk C16Risk C17Risk C18Risk C19Risk C20Risk
Time 1 0.1875 0.2400 0.2100 0.2400 0.1275 0.1600 0.2500 0.2500 0.2100 0.1600 0.2400 0.2100 0.2475 0.1875 0.1875 0.2400 0.2475 0.2275 0.1875 0.0900 NaN
Time 2 0.2475 0.1875 0.2400 0.2475 0.1600 0.2275 0.0900 0.2500 0.1875 0.2275 0.2275 0.1600 0.2275 0.2100 0.1875 0.2475 0.2100 0.2500 0.1875 0.2400 NaN
Time 3 0.2400 0.2100 0.2400 0.2475 0.2100 0.2400 0.2475 0.2100 0.2275 0.2400 0.0475 0.1875 0.2475 0.2275 0.2100 0.2400 0.1875 0.1875 NaN NaN NaN
Time 4 0.1275 0.2475 0.2100 0.1600 0.2475 0.2400 0.1875 0.0900 0.1600 0.2400 0.1875 0.2275 0.1600 0.1875 0.2400 0.1600 0.1275 0.2475 0.2500 0.1600 0.2475
Time 5 0.2475 0.2400 0.2475 0.2500 0.2275 0.2275 0.1875 0.2275 0.2400 0.2400 0.2275 0.0900 0.2400 0.2500 0.1875 0.2275 0.1875 0.2475 0.2100 0.2400 NaN
Time 6 0.1875 0.2475 0.0475 0.0900 0.2500 0.2400 0.2100 0.2275 0.2275 0.2400 0.2400 0.1875 0.1875 0.2100 0.1875 0.2400 0.1875 0.1875 0.2475 0.2100 NaN
Time 7 0.1600 0.1275 0.0900 0.1875 0.2400 0.2100 0.2400 0.2275 0.2500 0.1875 0.2275 0.2100 0.2475 0.2275 0.2100 0.2275 0.2275 0.2400 0.2100 NaN NaN
Time 8 0.2275 0.1600 0.2500 0.2475 0.1875 0.2275 0.2400 0.2475 0.2100 0.2275 0.1875 0.2400 0.2475 0.2475 0.2275 0.1875 0.2400 0.1875 0.2500 0.2275 NaN
Time 9 0.2400 0.2500 0.1875 0.2100 0.2400 0.2475 0.2100 0.1875 0.1600 0.2400 0.2475 0.2400 0.2275 0.1875 0.2400 0.1875 0.2475 0.2475 NaN NaN NaN
In [258]:
#Expected Deviation (Part 2)
#Determine Sum of Risk using df3 dataframe

df3['ExpectedRate'] = df3[['C0Risk','C1Risk','C2Risk','C3Risk','C4Risk','C5Risk','C6Risk','C7Risk','C8Risk','C9Risk',
                             'C10Risk','C11Risk','C12Risk','C13Risk','C14Risk','C15Risk','C16Risk',
                             'C17Risk','C18Risk','C19Risk','C20Risk']].sum(axis=1)
df3
Out[258]:
C0Risk C1Risk C2Risk C3Risk C4Risk C5Risk C6Risk C7Risk C8Risk C9Risk C10Risk C11Risk C12Risk C13Risk C14Risk C15Risk C16Risk C17Risk C18Risk C19Risk C20Risk ExpectedRate
Time 1 0.1875 0.2400 0.2100 0.2400 0.1275 0.1600 0.2500 0.2500 0.2100 0.1600 0.2400 0.2100 0.2475 0.1875 0.1875 0.2400 0.2475 0.2275 0.1875 0.0900 NaN 4.1000
Time 2 0.2475 0.1875 0.2400 0.2475 0.1600 0.2275 0.0900 0.2500 0.1875 0.2275 0.2275 0.1600 0.2275 0.2100 0.1875 0.2475 0.2100 0.2500 0.1875 0.2400 NaN 4.2125
Time 3 0.2400 0.2100 0.2400 0.2475 0.2100 0.2400 0.2475 0.2100 0.2275 0.2400 0.0475 0.1875 0.2475 0.2275 0.2100 0.2400 0.1875 0.1875 NaN NaN NaN 3.8475
Time 4 0.1275 0.2475 0.2100 0.1600 0.2475 0.2400 0.1875 0.0900 0.1600 0.2400 0.1875 0.2275 0.1600 0.1875 0.2400 0.1600 0.1275 0.2475 0.2500 0.1600 0.2475 4.1050
Time 5 0.2475 0.2400 0.2475 0.2500 0.2275 0.2275 0.1875 0.2275 0.2400 0.2400 0.2275 0.0900 0.2400 0.2500 0.1875 0.2275 0.1875 0.2475 0.2100 0.2400 NaN 4.4425
Time 6 0.1875 0.2475 0.0475 0.0900 0.2500 0.2400 0.2100 0.2275 0.2275 0.2400 0.2400 0.1875 0.1875 0.2100 0.1875 0.2400 0.1875 0.1875 0.2475 0.2100 NaN 4.0525
Time 7 0.1600 0.1275 0.0900 0.1875 0.2400 0.2100 0.2400 0.2275 0.2500 0.1875 0.2275 0.2100 0.2475 0.2275 0.2100 0.2275 0.2275 0.2400 0.2100 NaN NaN 3.9475
Time 8 0.2275 0.1600 0.2500 0.2475 0.1875 0.2275 0.2400 0.2475 0.2100 0.2275 0.1875 0.2400 0.2475 0.2475 0.2275 0.1875 0.2400 0.1875 0.2500 0.2275 NaN 4.4675
Time 9 0.2400 0.2500 0.1875 0.2100 0.2400 0.2475 0.2100 0.1875 0.1600 0.2400 0.2475 0.2400 0.2275 0.1875 0.2400 0.1875 0.2475 0.2475 NaN NaN NaN 3.9975
In [267]:
#Expected Deviation (Part 3)
#Determine Square Root of Risk using df3 dataframe

df3['SqRootRisk'] = df3['ExpectedRate']**(1/2)
df3
Out[267]:
C0Risk C1Risk C2Risk C3Risk C4Risk C5Risk C6Risk C7Risk C8Risk C9Risk C10Risk C11Risk C12Risk C13Risk C14Risk C15Risk C16Risk C17Risk C18Risk C19Risk C20Risk ExpectedRate SqRootRisk
Time 1 0.1875 0.2400 0.2100 0.2400 0.1275 0.1600 0.2500 0.2500 0.2100 0.1600 0.2400 0.2100 0.2475 0.1875 0.1875 0.2400 0.2475 0.2275 0.1875 0.0900 NaN 4.1000 2.024846
Time 2 0.2475 0.1875 0.2400 0.2475 0.1600 0.2275 0.0900 0.2500 0.1875 0.2275 0.2275 0.1600 0.2275 0.2100 0.1875 0.2475 0.2100 0.2500 0.1875 0.2400 NaN 4.2125 2.052438
Time 3 0.2400 0.2100 0.2400 0.2475 0.2100 0.2400 0.2475 0.2100 0.2275 0.2400 0.0475 0.1875 0.2475 0.2275 0.2100 0.2400 0.1875 0.1875 NaN NaN NaN 3.8475 1.961505
Time 4 0.1275 0.2475 0.2100 0.1600 0.2475 0.2400 0.1875 0.0900 0.1600 0.2400 0.1875 0.2275 0.1600 0.1875 0.2400 0.1600 0.1275 0.2475 0.2500 0.1600 0.2475 4.1050 2.026080
Time 5 0.2475 0.2400 0.2475 0.2500 0.2275 0.2275 0.1875 0.2275 0.2400 0.2400 0.2275 0.0900 0.2400 0.2500 0.1875 0.2275 0.1875 0.2475 0.2100 0.2400 NaN 4.4425 2.107724
Time 6 0.1875 0.2475 0.0475 0.0900 0.2500 0.2400 0.2100 0.2275 0.2275 0.2400 0.2400 0.1875 0.1875 0.2100 0.1875 0.2400 0.1875 0.1875 0.2475 0.2100 NaN 4.0525 2.013082
Time 7 0.1600 0.1275 0.0900 0.1875 0.2400 0.2100 0.2400 0.2275 0.2500 0.1875 0.2275 0.2100 0.2475 0.2275 0.2100 0.2275 0.2275 0.2400 0.2100 NaN NaN 3.9475 1.986832
Time 8 0.2275 0.1600 0.2500 0.2475 0.1875 0.2275 0.2400 0.2475 0.2100 0.2275 0.1875 0.2400 0.2475 0.2475 0.2275 0.1875 0.2400 0.1875 0.2500 0.2275 NaN 4.4675 2.113646
Time 9 0.2400 0.2500 0.1875 0.2100 0.2400 0.2475 0.2100 0.1875 0.1600 0.2400 0.2475 0.2400 0.2275 0.1875 0.2400 0.1875 0.2475 0.2475 NaN NaN NaN 3.9975 1.999375
In [268]:
#Add the SqRootRisk column from df3 into the df_t dataframe

df_t['SqRootRisk']= df3['SqRootRisk']
df_t
Out[268]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount ObservedRate ExpectedRate SqRootRisk
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20 0.400000 0.340000 2.024846
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20 0.300000 0.442500 2.052438
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18 0.388889 0.519444 1.961505
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21 0.380952 0.500000 2.026080
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20 0.250000 0.462500 2.107724
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20 0.300000 0.532500 2.013082
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19 0.210526 0.492105 1.986832
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20 0.250000 0.527500 2.113646
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18 0.222222 0.525000 1.999375
In [270]:
#Calculate Expected Deviation

df_t['ExpectedDeviation']=df_t['SqRootRisk']/df_t['FinalCount']
df_t
Out[270]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount ObservedRate ExpectedRate SqRootRisk ExpectedDeviation
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20 0.400000 0.340000 2.024846 0.101242
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20 0.300000 0.442500 2.052438 0.102622
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18 0.388889 0.519444 1.961505 0.108972
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21 0.380952 0.500000 2.026080 0.096480
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20 0.250000 0.462500 2.107724 0.105386
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20 0.300000 0.532500 2.013082 0.100654
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19 0.210526 0.492105 1.986832 0.104570
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20 0.250000 0.527500 2.113646 0.105682
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18 0.222222 0.525000 1.999375 0.111076
In [271]:
#Add T-Value based on degrees of freedom for FinalCount Column in df_t dataframe
#T-Table source --  https://www.sjsu.edu/faculty/gerstman/StatPrimer/t-table.pdf

# dof 17, t = 2.110
# dof 18, t = 2.101
# dof 19, t = 2.093
# dof 20, t = 2.086

df_t['TValue']=[2.093, 2.093, 2.110, 2.086, 2.093, 2.093, 2.101, 2.093, 2.110]
df_t
Out[271]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount ObservedRate ExpectedRate SqRootRisk ExpectedDeviation TValue
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20 0.400000 0.340000 2.024846 0.101242 2.093
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20 0.300000 0.442500 2.052438 0.102622 2.093
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18 0.388889 0.519444 1.961505 0.108972 2.110
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21 0.380952 0.500000 2.026080 0.096480 2.086
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20 0.250000 0.462500 2.107724 0.105386 2.093
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20 0.300000 0.532500 2.013082 0.100654 2.093
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19 0.210526 0.492105 1.986832 0.104570 2.101
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20 0.250000 0.527500 2.113646 0.105682 2.093
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18 0.222222 0.525000 1.999375 0.111076 2.110
In [275]:
#Calculate UCL & LCL
#UCL = Expected Rate + (TValue * Expected Deviation)
#LCL = Expected Rate - (TValue * Expected Deviation)

df_t['UCL'] = round(df_t['ExpectedRate'] + (df_t['TValue']*df_t['ExpectedDeviation']), 2)
df_t['LCL'] = round(df_t['ExpectedRate'] - (df_t['TValue']*df_t['ExpectedDeviation']), 2)
df_t
Out[275]:
C0 C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C20 ReportedFalls TimePeriod FinalCount ObservedRate ExpectedRate SqRootRisk ExpectedDeviation TValue UCL LCL
Time 1 0.25 0.40 0.70 0.40 0.15 0.20 0.50 0.50 0.30 0.20 0.40 0.30 0.45 0.25 0.25 0.40 0.45 0.35 0.25 0.10 NaN 8.0 Time 1 20 0.400000 0.340000 2.024846 0.101242 2.093 0.55 0.13
Time 2 0.55 0.25 0.40 0.45 0.20 0.65 0.10 0.50 0.75 0.35 0.65 0.20 0.65 0.30 0.25 0.45 0.30 0.50 0.75 0.60 NaN 6.0 Time 2 20 0.300000 0.442500 2.052438 0.102622 2.093 0.66 0.23
Time 3 0.40 0.70 0.60 0.55 0.70 0.60 0.55 0.30 0.65 0.60 0.05 0.25 0.45 0.65 0.70 0.60 0.25 0.75 NaN NaN NaN 7.0 Time 3 18 0.388889 0.519444 1.961505 0.108972 2.110 0.75 0.29
Time 4 0.15 0.45 0.70 0.80 0.45 0.60 0.25 0.10 0.80 0.40 0.25 0.65 0.80 0.25 0.60 0.80 0.85 0.45 0.50 0.20 0.45 8.0 Time 4 21 0.380952 0.500000 2.026080 0.096480 2.086 0.70 0.30
Time 5 0.55 0.60 0.45 0.50 0.65 0.65 0.25 0.35 0.60 0.40 0.35 0.10 0.40 0.50 0.25 0.65 0.25 0.45 0.70 0.60 NaN 5.0 Time 5 20 0.250000 0.462500 2.107724 0.105386 2.093 0.68 0.24
Time 6 0.75 0.45 0.05 0.90 0.50 0.60 0.70 0.35 0.65 0.40 0.60 0.25 0.75 0.30 0.25 0.40 0.75 0.75 0.55 0.70 NaN 6.0 Time 6 20 0.300000 0.532500 2.013082 0.100654 2.093 0.74 0.32
Time 7 0.20 0.15 0.10 0.25 0.60 0.70 0.40 0.35 0.50 0.75 0.65 0.70 0.55 0.65 0.70 0.35 0.65 0.40 0.70 NaN NaN 4.0 Time 7 19 0.210526 0.492105 1.986832 0.104570 2.101 0.71 0.27
Time 8 0.35 0.80 0.50 0.55 0.75 0.35 0.60 0.45 0.30 0.65 0.75 0.40 0.45 0.55 0.35 0.75 0.60 0.25 0.50 0.65 NaN 5.0 Time 8 20 0.250000 0.527500 2.113646 0.105682 2.093 0.75 0.31
Time 9 0.40 0.50 0.25 0.70 0.40 0.55 0.30 0.75 0.20 0.60 0.55 0.60 0.65 0.75 0.60 0.75 0.45 0.45 NaN NaN NaN 4.0 Time 9 18 0.222222 0.525000 1.999375 0.111076 2.110 0.76 0.29
In [277]:
#Plot the chart

plt.plot('TimePeriod','ObservedRate',data=df_t, marker = 's',color = 'blue')
plt.plot('TimePeriod','UCL',data=df_t, markersize=0, color='red')
plt.plot('TimePeriod','LCL',data=df_t, markersize=0, color='red')
plt.legend()
plt.title('Comparison of Rates')
plt.xlabel('Time Period')
plt.ylabel('Observed Rate')
Out[277]:
Text(0,0.5,'Observed Rate')