From 1e8f07b842e7f3062a89654e2c542591a496ef51 Mon Sep 17 00:00:00 2001
From: melanieshimano <melanieshimano@yahoo.com>
Date: Wed, 1 Nov 2017 13:37:52 -0400
Subject: [PATCH 1/2] Work Orders closed on time calculations

Uses the KPI metrics for work orders closed on time and the provided data to determine the percentage of work orders closed on time for a given fiscal year
---
 ...ders+Closed+on+Time+KPI+Measure-Cleaned.py | 156 ++++++++++++++++++
 1 file changed, 156 insertions(+)
 create mode 100644 Percent+of+Work+Orders+Closed+on+Time+KPI+Measure-Cleaned.py

diff --git a/Percent+of+Work+Orders+Closed+on+Time+KPI+Measure-Cleaned.py b/Percent+of+Work+Orders+Closed+on+Time+KPI+Measure-Cleaned.py
new file mode 100644
index 00000000..58d2e5f8
--- /dev/null
+++ b/Percent+of+Work+Orders+Closed+on+Time+KPI+Measure-Cleaned.py
@@ -0,0 +1,156 @@
+
+# coding: utf-8
+
+# In[1]:
+
+get_ipython().system(' pip install ipywidgets')
+
+
+# In[2]:
+
+import os
+import pandas as pd
+from pandas import ExcelWriter
+from pandas import ExcelFile
+import numpy as np
+import matplotlib.pyplot as plt
+from ipywidgets import *
+get_ipython().magic('matplotlib inline')
+
+
+# In[3]:
+
+#import excel sheet with work order data
+df =pd.read_excel('C:\\Users\\melanie.shimano\\Documents\\Copy15 of FY18 Outcome Budgeting--Data Validation.xlsx', sheetname ='FMD.FY16')
+
+
+# In[4]:
+
+#index the dataframe
+df.set_index("WO_ID",  inplace=True)
+
+
+# In[5]:
+
+#***SET THE FISCAL YEAR for analysis
+FYyear = 2016
+
+
+# In[6]:
+
+df.head()
+
+
+# In[7]:
+
+#remove rows where work order duration = open because won't be able to calculate anything from these
+#keep only work orders in FY for financial analysis indicated above in FYyear
+df =df[df.WO_duration != 'open']
+df = df[df.FY == FYyear]
+
+
+# In[8]:
+
+#add count of each prob_type to the data index
+df['Counts']=df.groupby(['prob_type'])['WO_duration'].transform('count')
+
+
+# In[9]:
+
+#drop unneccesary columns in dataframe for ease of view
+col_list = ['BLDG#','FY','Month','prob_type', 'WO_duration', 'Counts']
+df = df[col_list]
+
+
+# In[10]:
+
+df['total_duration']= df.groupby(['prob_type'])['WO_duration'].transform('sum')
+
+
+# In[11]:
+
+#calculate average work order duration per problem type (=counts/total_duration) and add value in new column
+df['avg_total_duration']=df['total_duration']/df['Counts']
+
+
+# In[12]:
+
+#check to see if individual work order closed at or equal to the average work order duration for that problem type
+#add new column "KPI_met" fill with "yes" if WO_duration <= avg_total_duration, otherwise fill 'no'
+df['closed_on_time']= np.where(df['WO_duration']<=df['avg_total_duration'], 'yes','no')
+
+
+# In[13]:
+
+#create new df of only KPIs that are met
+df_yes = df[df.closed_on_time == 'yes']
+
+
+# In[14]:
+
+#count number of KPI met per problem type
+df_count = df_yes.groupby('prob_type')['closed_on_time'].count()
+
+
+# In[15]:
+
+#turn series into a df to later merge with original dataframe and keep WO_ID
+df_KPI = pd.DataFrame({'prob_type': df_count.index, 'on_time_count':df_count.values})
+
+
+# In[16]:
+
+#new df (by merge) with number of KPI met in new column
+df_wo = df.assign(on_time_count =df['prob_type'].map(df_KPI.set_index('prob_type')['on_time_count']))
+
+
+# In[17]:
+
+#new df (by merge) with number of KPI met in new column
+df_wo = df.assign(on_time_count =df['prob_type'].map(df_KPI.set_index('prob_type')['on_time_count']))
+
+
+# In[18]:
+
+#calculate percentage of Work Orders that meet KPI and store in new column 'percent_KPI_met'
+df_wo['percent_KPI_met']=(df_wo['on_time_count']/df_wo['Counts'])*100
+
+
+# In[19]:
+
+#create dataframe of only unique problem types for high level analysis of KPIs met
+df_wo_final = df_wo.drop_duplicates(subset = 'prob_type', keep ='last')
+df_wo_final
+
+
+# In[20]:
+
+#get total values to calculate total number of KPI met
+WO_total_count = df_wo_final['Counts'].sum()
+
+WO_KPI_met_count = df_wo_final['on_time_count'].sum()
+
+percent_KPI_met = (WO_KPI_met_count/WO_total_count)*100
+
+
+# In[21]:
+
+#print total percentage of KPI met and total number of KPI met for the year
+print('Percent of Work Orders closed on time in FY '+ str(FYyear) +' = ' + str(round (percent_KPI_met, 2)) + "%, which means " +
+      str(round(WO_KPI_met_count,0)) + ' out of ' + str(round (WO_total_count,2)) + ' work orders were closed on time')
+
+
+# In[22]:
+
+#sort work order problem types by the percentage that did not meet the KPI (i.e. the percent that weren't closed on time)
+df_wo_final['not_on_time']=df_wo_final['Counts'] -df_wo_final['on_time_count']
+df_wo_final['percent_KPI_not_met']= (df_wo_final['not_on_time']/df_wo_final['Counts'])*100
+df_wo_final = df_wo_final.sort_values('percent_KPI_not_met')
+#graph the top 20 percent work orders by problem type that were not closed on time
+df_wo_final[50:].plot(x='prob_type', y='percent_KPI_not_met', kind='barh', title= 'Percent of Work Orders Not Closed on Time by Problem Type', figsize=(20,20))
+
+
+# In[ ]:
+
+
+

From 824193de1a592c3433966511f37f39872271ccc1 Mon Sep 17 00:00:00 2001
From: melanieshimano <melanieshimano@yahoo.com>
Date: Wed, 1 Nov 2017 16:19:58 -0400
Subject: [PATCH 2/2] Percent of PM:CM calculations

Calculations for the KPI (key performance indicator) that measures the percentage of preventative maintenance (PM) to corrective maintenance (CM) performed on buildings.
---
 ...e+Maintenance+out+of+Total+Work+Orders+.py | 116 ++++++++++++++++++
 1 file changed, 116 insertions(+)
 create mode 100644 Percent+of+Preventative+Maintenance+out+of+Total+Work+Orders+.py

diff --git a/Percent+of+Preventative+Maintenance+out+of+Total+Work+Orders+.py b/Percent+of+Preventative+Maintenance+out+of+Total+Work+Orders+.py
new file mode 100644
index 00000000..4294aaae
--- /dev/null
+++ b/Percent+of+Preventative+Maintenance+out+of+Total+Work+Orders+.py
@@ -0,0 +1,116 @@
+
+# coding: utf-8
+
+# In[1]:
+
+get_ipython().system(' pip install ipywidgets')
+
+
+# In[2]:
+
+import os
+import pandas as pd
+from pandas import ExcelWriter
+from pandas import ExcelFile
+import numpy as np
+
+
+# In[3]:
+
+#import excel sheet with work order data
+df =pd.read_excel('C:\\Users\\melanie.shimano\\Documents\\Copy15 of FY18 Outcome Budgeting--Data Validation.xlsx', sheetname ='FMD.FY16')
+
+
+# In[4]:
+
+#index the dataframe
+df.set_index("WO_ID",  inplace=True)
+
+
+# In[5]:
+
+#***SET THE FISCAL YEAR for analysis
+FYyear = 2015
+
+
+# In[6]:
+
+df.head()
+
+
+# In[7]:
+
+#remove rows where work order duration = open because won't be able to calculate anything from these
+#keep only work orders in FY for financial analysis indicated above in FYyear
+#keep only PM:CM relevant work orders: HVAC PM, boiler, chiller, HVAC, HVAC Infrastructure, HVAC repair
+df =df[df.WO_duration != 'open']
+df = df[df.FY == FYyear]
+
+
+# In[8]:
+
+#drop unneccesary columns in dataframe for ease of view
+col_list = ['BLDG#','FY','Month','prob_type']
+df = df[col_list]
+
+
+# In[9]:
+
+#count number of work orders by problem type
+df['count'] = df.groupby(['prob_type'])['prob_type'].transform('count')
+
+
+# In[10]:
+
+#keep only one of each problem type for analysis 
+df_prob_type = df.drop_duplicates(subset ='prob_type', keep='last')
+
+
+# In[11]:
+
+#drop values that are NaN
+df_prob_type.dropna()
+
+
+# In[12]:
+
+#keep only preventative and corrective HVAC related work order problem types
+df_pmcm = df_prob_type[df_prob_type['prob_type'].str.contains('HVAC|BOILER|CHILLERS|PREVENTIVE MAINT')==True]
+
+
+# In[13]:
+
+#create new dataframe of only preventative maintenance to then sum count
+df_pm = df_pmcm[df_pmcm['prob_type'].str.contains('PM|PREVENTIVE MAINT')==True]
+
+
+# In[14]:
+
+#total number of preventative maintenance work orders
+PM = df_pm['count'].sum()
+
+
+# In[15]:
+
+#sum the corrective maintenance tasks
+#create new dataframe of only corrective maintenance to then sum count
+#need to delete HVAC|PM counts that are included in the "HVAC" selection
+df_cm = df_pmcm[(df_pmcm['prob_type'].str.contains('HVAC|BOILER|CHILLERS')==True)&(~df_pmcm['prob_type'].str.contains('PM')==True)]
+
+
+# In[16]:
+
+#total number of correective maintenance work orders
+CM = df_cm['count'].sum()
+PMCM = (PM/CM)*100
+
+
+# In[17]:
+
+print('In Fiscal Year '+ str(FYyear) + ' the ratio of preventative maintenance versus corrective maintenance was ' + str(round (PM/CM,2)) +', or ' + str(round (PMCM,2)) + '%')
+
+
+# In[ ]:
+
+
+