import pandas as pd
import numpy as np

data_folder = '/home/kate/Research/Property/Data/'

dataset = pd.read_csv(data_folder+'property_water_claims_non_cat_fs_v5.csv', error_bad_lines=False, index_col=False)

dataset = dataset[dataset.cova_il_nc_water >=100].copy()
dataset = dataset[dataset.cova_il_nc_water<dataset.cova_il_nc_water.quantile(0.95)].copy()

featureset  = [
 'stories', 
 'units', 
 'multipolicyind', 
 'functionalreplacementcost', 
 'landlordind', 
 'burglaryalarmtype', 
 'propertymanager', 
 'gatedcommunityind', 
 'replacementcostdwellingind', 
 'equipmentbreakdown', 
 'cova_deductible', 
 'water_risk_sev_3_blk', 
 'fixture_leak_3_blk', 
 'rep_cost_3_blk', 
 'sqft', 
 'waterded', 
 'constructioncd_encd', 
 'multipolicyindumbrella', 
 'usagetype_encd', 
 'homegardcreditind', 
 'rentersinsurance', 
 'waterdetectiondevice', 
 'safeguardplusind', 
 'deadboltind', 
 'replacementvalueind', 
 'numberoffamilies', 
 'water_risk_fre_3_blk', 
 'pipe_froze_3_blk', 
 'ustructure_fail_3_blk', 
 'customer_cnt_active_policies_binned', 
 'ecy', 
 'yearbuilt', 
 'roofcd_encd', 
 'occupancy_encd', 
 'protectionclass', 
 'fire_risk_model_score', 
 'earthquakeumbrellaind', 
 'ordinanceorlawpct', 
 'sprinklersystem', 
 'firealarmtype', 
 'neighborhoodcrimewatchind', 
 'kitchenfireextinguisherind', 
 'poolind', 
 'serviceline', 
 'cova_limit', 
 'water_risk_3_blk', 
 'appl_fail_3_blk', 
 'plumb_leak_3_blk', 
 'waterh_fail_3_blk'
]

target_column= 'cova_il_nc_water' 

print('Original Size = %s'%len(dataset))

Original Size = 13384

EDA_FI_dataset = dataset.drop_duplicates(featureset + [target_column])[featureset + [target_column]]

EDA_FI_dataset.head()

	stories	units	multipolicyind	functionalreplacementcost	landlordind	burglaryalarmtype	propertymanager	gatedcommunityind	replacementcostdwellingind	equipmentbreakdown	...	neighborhoodcrimewatchind	kitchenfireextinguisherind	poolind	serviceline	cova_limit	water_risk_3_blk	appl_fail_3_blk	plumb_leak_3_blk	waterh_fail_3_blk	cova_il_nc_water
42	1	1	0	0	0	0	0	0	1	0	...	0	1	0	0	300000	270	5	5	1	6298.46
316	1	1	0	0	0	1	0	0	1	0	...	0	1	0	0	200000	238	5	5	4	5456.67
330	1	1	0	0	0	0	0	0	1	0	...	0	0	0	0	200000	164	5	4	4	14917.51
594	1	1	0	0	0	0	0	0	0	0	...	0	0	0	0	900000	76	3	1	0	1139.50
650	1	1	0	0	0	0	0	0	0	0	...	0	0	0	0	300000	121	5	1	0	1863.13

5 rows × 50 columns

print('Size of unique values for EDA of Feature Importance = %s'%len(EDA_FI_dataset))

Size of unique values for EDA of Feature Importance = 10957

EDA_FI_dataset.to_csv('%sEDA_Severity_FI_dataset.csv'%(data_folder),header=True,index=False)