import pandas

mycons = {'Total Charges':lambda x:float(x.replace('$','')),
          'Length of Stay':lambda x:int(x.replace('+',''))}
s = pandas.read_csv('sparcs2014.csv',converters=mycons)
s.head(2)

for col in s.columns:
    print(col)

Health Service Area
Hospital County
Operating Certificate Number
Facility Id
Facility Name
Age Group
Zip Code - 3 digits
Gender
Race
Ethnicity
Length of Stay
Admit Day of Week
Type of Admission
Patient Disposition
Discharge Year
Discharge Day of Week
CCS Diagnosis Code
CCS Diagnosis Description
CCS Procedure Code
CCS Procedure Description
APR DRG Code
APR DRG Description
APR MDC Code
APR MDC Description
APR Severity of Illness Code
APR Severity of Illness Description
APR Risk of Mortality
APR Medical Surgical Description
Payment Typology 1
Payment Typology 2
Payment Typology 3
Attending Provider License Number
Operating Provider License Number
Other Provider License Number
Birth Weight
Abortion Edit Indicator
Emergency Department Indicator
Total Charges
Total Costs

mmc = s[ s['Facility Name']=='Maimonides Medical Center' ]
len(mmc)

43112

mmcbabies = mmc[ mmc['CCS Diagnosis Description'] == 'LIVEBORN' ]
len(mmcbabies)

8507

from numpy import *
vstack([ones(5),zeros(5)])

array([[ 1.,  1.,  1.,  1.,  1.],
       [ 0.,  0.,  0.,  0.,  0.]])

babylogcharges = log10(mmcbabies['Total Charges'])
babymax = babylogcharges.max()
babymin = babylogcharges.min()

from histogram1d import histogram1d

import matplotlib.pyplot as pl

lefts, counts = histogram1d(babylogcharges, babymin,babymax, 10000, rawcounts=True)
w = lefts[1]-lefts[0]
values = lefts + w/2
x = vstack([values,values])
y = vstack([zeros_like(counts), counts])
pl.figure(figsize=(12,6))
pl.plot(x,y,'b')
pl.show()

s['Patient Disposition'].value_counts()

Home or Self Care                        1581401
Home w/ Home Health Services              317027
Skilled Nursing Home                      223621
Expired                                    50913
Left Against Medical Advice                47012
Inpatient Rehabilitation Facility          41524
Short-term Hospital                        40817
Psychiatric Hospital or Unit of Hosp       12565
Hospice - Medical Facility                 12513
Hospice - Home                             10540
Another Type Not Listed                     8441
Facility w/ Custodial/Supportive Care       6268
Medicare Cert Long Term Care Hospital       3789
Court/Law Enforcement                       3731
Cancer Center or Children's Hospital        2504
Hosp Basd Medicare Approved Swing Bed       1544
Federal Health Care Facility                 725
Medicaid Cert Nursing Facility               152
Critical Access Hospital                     121
Name: Patient Disposition, dtype: int64

s['CCS Diagnosis Description'].value_counts()

LIVEBORN                   231293
SEPTICEMIA                  96517
OSTEOARTHRITIS              59366
MOOD DISORDERS              58624
CHF                         56848
PNEUMONIA                   46113
SCHIZ/OTH PSYCH DISORDR     43908
DYSRHYTHMIA                 43628
ALCOHOL-RELATD DISORDER     41824
OTH COMP BIRTH/PUERPRM      39211
DEVICE/IMPLNT/GRFT COMP     38947
SKIN/SUBCUT TISS INFECT     38645
SUBSTANCE-RLTD DISORDER     36649
DIABETES W/COMPL            35084
ACUTE CVD                   34874
ASTHMA                      34609
BACK PROBLEM                34541
ACUTE MYOCARDL INFARCT      33357
OTHR PREGNANCY COMPL        32975
COPD                        32330
PROLONGED PREGNANCY         32292
CORONARY ATHEROSCLER        31813
CHEST PAIN                  31438
REHAB/PROSTH FIT/ADJUST     30756
SURGCL/MED CARE COMPL       30731
URINARY TRACT INFECTION     30244
PREVIOUS C-SECTION          28651
OB PERINEAL/VULV TRAUMA     26319
FLUID/ELECTRLYT DISORDR     26041
EPILEPSY/CONVULSIONS        25750
                            ...  
MENOPAUSAL DISORDER           350
SEXUAL INFECTIONS             348
SHORT GEST/LOW BRTHWT         347
RETINAL PROBLEMS              339
ACQUIRD FOOT DEFORMITY        331
OTHER URINARY CANCER          292
OTHR HEMATOLOGIC COND         286
MEDICAL EXAM/EVALUATN         268
SKIN MELANOMAS                235
POSTABORTION COMPL            233
RESP DISTRSS SYNDROME         231
VARICOSE VEIN                 225
OTHR RESPIRATRY CANCER        192
DEVELOPMENTAL DISORDERS       167
FORCEPS DELIVERY              156
ADMIN/SOCIAL ADMISSION        149
IMMUNITY DISORDER             140
TESTICULAR CANCER             130
IMMUNIZATION/SCREENING         84
SHOCK                          84
BIRTH ASPHYXIA                 79
SUICIDE/SELF-INFLCT INJ        77
GLAUCOMA                       69
MALE GENITAL CANCER            66
HYPERLIPIDEMIA                 59
BIRTH TRAUMA                   33
CATARACT                       26
CONTRCPTV/PROCRTV MGT          26
OSTEOPOROSIS                   21
FEMALE INFERTILITY             10
Name: CCS Diagnosis Description, dtype: int64

sc = s[ s['CCS Diagnosis Description'].str.contains('CANCER') ]
sc['CCS Diagnosis Description'].value_counts()

BRONCHIAL/LUNG CANCER      9447
COLON CANCER               5902
BREAST CANCER              5736
PROSTATE CANCER            4334
KIDNEY/RENAL CANCER        3322
PANCREAS CANCER            2859
BRAIN/NERV SYST CANCER     2703
RECTAL/ANAL CANCER         2700
UTERINE CANCER             2426
BLADDER CANCER             2384
HEAD/NECK CANCER           2368
LIVER/BILE DUCT CANCER     2167
STOMACH CANCER             2019
THYROID CANCER             1717
OTHR GI/PERITONL CANCER    1629
OVARIAN CANCER             1618
BONE/CONN TISSU CANCER     1226
ESOPHAGEAL CANCER           879
CERVICAL CANCER             838
OTHER PRIMARY CANCER        607
NON-EPITHELIAL CANCER       474
FEMALE GENITAL CANCER       418
OTHER URINARY CANCER        292
OTHR RESPIRATRY CANCER      192
TESTICULAR CANCER           130
MALE GENITAL CANCER          66
Name: CCS Diagnosis Description, dtype: int64

g = s.groupby('Hospital County')
for name,group in g:
    print(name,len(group))

Albany 63461
Allegany 2052
Bronx 183847
Broome 28213
Cattaraugus 5262
Cayuga 4984
Chautauqua 11132
Chemung 15803
Chenango 1761
Clinton 9868
Columbia 5528
Cortland 3745
Delaware 762
Dutchess 30932
Erie 121301
Essex 410
Franklin 4710
Fulton 3076
Genesee 4412
Herkimer 748
Jefferson 11092
Kings 258712
Lewis 1641
Livingston 2023
Madison 4729
Manhattan 412188
Monroe 104802
Montgomery 6500
Nassau 180741
Niagara 17400
Oneida 29735
Onondaga 76495
Ontario 11487
Orange 39572
Orleans 1372
Oswego 5092
Otsego 12449
Putnam 6650
Queens 199374
Rensselaer 12476
Richmond 58876
Rockland 32211
Saratoga 9167
Schenectady 21963
Schoharie 562
Schuyler 651
St Lawrence 10705
Steuben 6438
Suffolk 159765
Sullivan 4442
Tompkins 7226
Ulster 11537
Warren 13939
Wayne 5315
Westchester 119384
Wyoming 2256
Yates 759

import numpy as np
g.agg({'Total Charges':['median',np.mean]})

g.agg({'Total Charges':'median'}).sort_values('Total Charges',ascending=False)

	Health Service Area	Hospital County	Operating Certificate Number	Facility Id	Facility Name	Age Group	Zip Code - 3 digits	Gender	Race	Ethnicity	...	Payment Typology 2	Payment Typology 3	Attending Provider License Number	Operating Provider License Number	Other Provider License Number	Birth Weight	Abortion Edit Indicator	Emergency Department Indicator	Total Charges	Total Costs
0	Western NY	Allegany	226700	37	Cuba Memorial Hospital Inc	30 to 49	147	F	White	Not Span/Hispanic	...	NaN	NaN	90335341	NaN	NaN	0	N	Y	9546.85	$12303.20
1	Western NY	Allegany	226700	37	Cuba Memorial Hospital Inc	50 to 69	147	F	White	Not Span/Hispanic	...	NaN	NaN	90335341	NaN	NaN	0	N	Y	11462.75	$10298.32

	Total Charges
	median	mean
Hospital County
Albany	24941.200	42824.678678
Allegany	6864.775	9447.230804
Bronx	20387.020	40888.858943
Broome	14152.400	23337.190761
Cattaraugus	8897.075	12605.036923
Cayuga	12146.000	15864.603355
Chautauqua	7425.375	10905.151219
Chemung	12829.090	19567.670297
Chenango	14201.500	19882.085014
Clinton	19832.975	29965.544606
Columbia	12700.515	18099.838461
Cortland	7211.710	9157.265872
Delaware	8687.275	10802.212703
Dutchess	24442.405	37470.757036
Erie	15248.480	28356.993957
Essex	7799.880	8424.805878
Franklin	11561.195	15563.635943
Fulton	16380.865	20709.369467
Genesee	10601.320	14939.391514
Herkimer	8466.495	9332.398008
Jefferson	11143.970	15556.550593
Kings	20466.985	35827.915035
Lewis	7115.270	9301.480792
Livingston	7820.470	11787.964726
Madison	13774.910	17657.331191
Manhattan	31191.865	57623.191722
Monroe	13732.395	24275.150814
Montgomery	12375.345	15840.527629
Nassau	33457.220	56269.047370
Niagara	9362.760	13473.650586
Oneida	14747.810	21626.534473
Onondaga	18779.020	32415.260638
Ontario	10369.530	14767.910544
Orange	27844.385	38516.229516
Orleans	8696.010	11967.996764
Oswego	10178.085	14588.237247
Otsego	12328.140	20015.032696
Putnam	25818.580	33308.905686
Queens	20376.820	36483.546173
Rensselaer	13882.760	20164.968626
Richmond	21551.195	36616.813506
Rockland	26242.630	39691.695802
Saratoga	21055.910	28686.658518
Schenectady	14969.120	25815.983324
Schoharie	9193.440	10310.857011
Schuyler	5909.140	6972.520369
St Lawrence	9022.900	11782.562507
Steuben	9896.750	13170.891367
Suffolk	31143.000	48742.260524
Sullivan	18411.120	26597.025905
Tompkins	8004.660	11897.976759
Ulster	18330.850	26800.900193
Warren	15494.660	23637.492441
Wayne	10740.780	15697.965737
Westchester	22476.255	47804.177764
Wyoming	6592.915	8619.636201
Yates	8105.930	9788.401818