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In [2]:

    
%matplotlib inline
import pandas as pd
import seaborn as sns
from collections import Counter
from pmareport import pmareport
import numpy as np
import matplotlib.pyplot as plt
import calendar



In [3]:

    
clinic = pmareport.Clinic()
df = clinic.df



In [4]:

    
df.head()









    Out[4]:






  
    
      
      PATIENT_ID
      PATIENT_CONDITION
      INSURANCE_TYPE
      AGE
      SEX
      PROVIDER_NAME
      date
      sched
      start
      end
      ...
      month
      num_appts
      appt_pos_overall
      appt_pos_doctor
      cond
      schedd
      startd
      endd
      arrived
      since_prev_sched
    
  
  
    
      0
      4591553
      Uncommon Cold
      BlueSword/BlueDiamond
      31
      M
      L. Jedermann
      2005-01-03
      1900-01-01 07:59:00
      1900-01-01 08:07:00
      1900-01-01 08:22:00
      ...
      1
      16
      0
      0
      uc
      479
      487
      502
      475
      NaN
    
    
      1
      4607440
      TV Personality Disorder
      Medifix
      48
      F
      I. Petrov
      2005-01-03
      1900-01-01 08:15:00
      1900-01-01 08:18:00
      1900-01-01 08:53:00
      ...
      1
      16
      1
      0
      tvp
      495
      498
      533
      495
      NaN
    
    
      2
      4590430
      TV Personality Disorder
      Medifix
      42
      M
      E. Medelsvensson
      2005-01-03
      1900-01-01 08:30:00
      1900-01-01 08:42:00
      1900-01-01 09:17:00
      ...
      1
      16
      2
      0
      tvp
      510
      522
      557
      506
      NaN
    
    
      3
      4619481
      Saturday Night Fever
      Self Insured
      22
      M
      J. Smith
      2005-01-03
      1900-01-01 09:00:00
      1900-01-01 09:11:00
      1900-01-01 09:39:00
      ...
      1
      16
      3
      0
      snf
      540
      551
      579
      543
      NaN
    
    
      4
      4610862
      Uncommon Cold
      Self Insured
      32
      M
      L. Jedermann
      2005-01-03
      1900-01-01 10:00:00
      1900-01-01 10:00:00
      1900-01-01 10:16:00
      ...
      1
      16
      4
      1
      uc
      600
      600
      616
      596
      121
    
  

5 rows × 24 columns



In [5]:

    
# Interesting plot
df.AGE.hist(bins=70)









    Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f3f3cdf5dd0>



In [35]:

    
# Nmber of appointments plot
df_month_doc = df.groupby(['month', 'PROVIDER_NAME'])
df_month_doc_cnt = df_month_doc.count()
for i in list(set(df[df.cond == 'tvp'].PROVIDER_NAME)):
    #plt.clf()
    data = df_month_doc_cnt.xs(i, level='PROVIDER_NAME')
    plt.plot(data.index, data.PATIENT_ID, label=i, linewidth=3)
plt.xticks(xrange(0, 13), calendar.month_abbr, size=13)
plt.xlim(xmin=1)
plt.yticks(size=13)
plt.title('Number of appointments per month by doctor', size=16)
plt.legend(fontsize=13)
plt.savefig('appts_per_month_doc_tpd2.png', bbox='tight', dpi=300)



In [7]:

    
df[df.cond == 'tvp'].groupby(['PROVIDER_NAME', 'month']).count()









    Out[7]:






  
    
      
      
      PATIENT_ID
      PATIENT_CONDITION
      INSURANCE_TYPE
      AGE
      SEX
      date
      sched
      start
      end
      arrive
      ...
      appt_time
      num_appts
      appt_pos_overall
      appt_pos_doctor
      cond
      schedd
      startd
      endd
      arrived
      since_prev_sched
    
    
      PROVIDER_NAME
      month
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      E. Medelsvensson
      1
      47
      47
      47
      47
      47
      47
      47
      47
      47
      47
      ...
      47
      47
      47
      47
      47
      47
      47
      47
      47
      26
    
    
      2
      55
      55
      55
      55
      55
      55
      55
      55
      55
      55
      ...
      55
      55
      55
      55
      55
      55
      55
      55
      55
      31
    
    
      3
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      ...
      54
      54
      54
      54
      54
      54
      54
      54
      54
      31
    
    
      4
      65
      65
      65
      65
      65
      65
      65
      65
      65
      65
      ...
      65
      65
      65
      65
      65
      65
      65
      65
      65
      41
    
    
      5
      71
      71
      71
      71
      71
      71
      71
      71
      71
      71
      ...
      71
      71
      71
      71
      71
      71
      71
      71
      71
      51
    
    
      6
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      ...
      54
      54
      54
      54
      54
      54
      54
      54
      54
      33
    
    
      7
      85
      85
      85
      85
      85
      85
      85
      85
      85
      85
      ...
      85
      85
      85
      85
      85
      85
      85
      85
      85
      63
    
    
      8
      60
      60
      60
      60
      60
      60
      60
      60
      60
      60
      ...
      60
      60
      60
      60
      60
      60
      60
      60
      60
      40
    
    
      9
      35
      35
      35
      35
      35
      35
      35
      35
      35
      35
      ...
      35
      35
      35
      35
      35
      35
      35
      35
      35
      15
    
    
      10
      48
      48
      48
      48
      48
      48
      48
      48
      48
      48
      ...
      48
      48
      48
      48
      48
      48
      48
      48
      48
      30
    
    
      11
      47
      47
      47
      47
      47
      47
      47
      47
      47
      47
      ...
      47
      47
      47
      47
      47
      47
      47
      47
      47
      26
    
    
      12
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      ...
      32
      32
      32
      32
      32
      32
      32
      32
      32
      18
    
    
      I. Petrov
      1
      40
      40
      40
      40
      40
      40
      40
      40
      40
      40
      ...
      40
      40
      40
      40
      40
      40
      40
      40
      40
      21
    
    
      2
      43
      43
      43
      43
      43
      43
      43
      43
      43
      43
      ...
      43
      43
      43
      43
      43
      43
      43
      43
      43
      24
    
    
      3
      57
      57
      57
      57
      57
      57
      57
      57
      57
      57
      ...
      57
      57
      57
      57
      57
      57
      57
      57
      57
      33
    
    
      4
      47
      47
      47
      47
      47
      47
      47
      47
      47
      47
      ...
      47
      47
      47
      47
      47
      47
      47
      47
      47
      28
    
    
      5
      78
      78
      78
      78
      78
      78
      78
      78
      78
      78
      ...
      78
      78
      78
      78
      78
      78
      78
      78
      78
      55
    
    
      6
      54
      54
      54
      54
      54
      54
      54
      54
      54
      54
      ...
      54
      54
      54
      54
      54
      54
      54
      54
      54
      33
    
    
      N. Fulano
      1
      32
      32
      32
      32
      32
      32
      32
      32
      32
      32
      ...
      32
      32
      32
      32
      32
      32
      32
      32
      32
      13
    
    
      2
      56
      56
      56
      56
      56
      56
      56
      56
      56
      56
      ...
      56
      56
      56
      56
      56
      56
      56
      56
      56
      34
    
    
      3
      62
      62
      62
      62
      62
      62
      62
      62
      62
      62
      ...
      62
      62
      62
      62
      62
      62
      62
      62
      62
      39
    
    
      4
      69
      69
      69
      69
      69
      69
      69
      69
      69
      69
      ...
      69
      69
      69
      69
      69
      69
      69
      69
      69
      45
    
    
      5
      72
      72
      72
      72
      72
      72
      72
      72
      72
      72
      ...
      72
      72
      72
      72
      72
      72
      72
      72
      72
      50
    
    
      6
      67
      67
      67
      67
      67
      67
      67
      67
      67
      67
      ...
      67
      67
      67
      67
      67
      67
      67
      67
      67
      44
    
    
      7
      93
      93
      93
      93
      93
      93
      93
      93
      93
      93
      ...
      93
      93
      93
      93
      93
      93
      93
      93
      93
      71
    
    
      8
      40
      40
      40
      40
      40
      40
      40
      40
      40
      40
      ...
      40
      40
      40
      40
      40
      40
      40
      40
      40
      20
    
    
      9
      38
      38
      38
      38
      38
      38
      38
      38
      38
      38
      ...
      38
      38
      38
      38
      38
      38
      38
      38
      38
      18
    
    
      10
      38
      38
      38
      38
      38
      38
      38
      38
      38
      38
      ...
      38
      38
      38
      38
      38
      38
      38
      38
      38
      22
    
    
      11
      41
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      41
      41
      41
      41
      41
      41
      41
      41
      ...
      41
      41
      41
      41
      41
      41
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      41
      41
      26
    
    
      12
      28
      28
      28
      28
      28
      28
      28
      28
      28
      28
      ...
      28
      28
      28
      28
      28
      28
      28
      28
      28
      15
    
  

30 rows × 22 columns



In [8]:

    
def change(a, b):
    return (b-a)/float(a)



In [9]:

    
change(54, 85), change(67, 93)









    Out[9]:





(0.5740740740740741, 0.3880597014925373)



In [10]:

    
set(df.PROVIDER_NAME)









    Out[10]:





{'C. Siu Ming',
 'E. Medelsvensson',
 'I. Petrov',
 'J. Smith',
 'K. van der Merwe',
 'L. Jedermann',
 'M. Dupont',
 'N. Fulano',
 'W. Plinge'}



In [11]:

    
df.groupby(['PROVIDER_NAME', 'PATIENT_CONDITION']).count().PATIENT_ID









    Out[11]:





PROVIDER_NAME     PATIENT_CONDITION      
C. Siu Ming       Uncommon Cold              461
E. Medelsvensson  TV Personality Disorder    653
I. Petrov         TV Personality Disorder    319
J. Smith          Saturday Night Fever       903
K. van der Merwe  Saturday Night Fever       871
L. Jedermann      Uncommon Cold              481
M. Dupont         Uncommon Cold              492
N. Fulano         TV Personality Disorder    636
W. Plinge         Saturday Night Fever       890
Name: PATIENT_ID, dtype: int64



In [15]:

    
# number of appointments by condition plot
df_month_cond = df.groupby(['month', 'PATIENT_CONDITION'])
df_month_cond_cnt = df_month_cond.count()
for i in set(df.PATIENT_CONDITION):
    #plt.clf()
    data = df_month_cond_cnt.xs(i, level='PATIENT_CONDITION')
    plt.plot(xrange(1,13), data.PATIENT_ID, label=i)
    plt.xticks(xrange(0, 13), calendar.month_abbr)
    plt.xlim(xmin=1)
plt.title('Number of appointments per month by condition')
plt.legend()
plt.savefig('appts_per_month_cond2.png', bbox='tight', dpi=200)



In [ ]:



In [29]:

    
# Average delay per month by condition
df_month_cond_mean = df.groupby(['month', 'PATIENT_CONDITION']).mean()
for i in ['Uncommon Cold', 'TV Personality Disorder', 'Saturday Night Fever']:
    #plt.clf()
    data = df_month_cond_mean.xs(i, level='PATIENT_CONDITION')
    plt.plot(data.index, data.delay, label=i, linewidth=3)
    plt.xticks(xrange(0, 13), calendar.month_abbr, size=13)
    plt.yticks(size=13)
    plt.xlim(xmin=1)
plt.title('Average patient wait time per month by condition', fontsize=16)
plt.ylabel('min', fontsize=13)
plt.legend(loc=2, fontsize=13)
plt.savefig('delay_per_month_cond.png', bbox='tight', dpi=300)



In [ ]:

	PATIENT_ID	PATIENT_CONDITION	INSURANCE_TYPE	AGE	SEX	PROVIDER_NAME	date	sched	start	end	...	month	num_appts	appt_pos_overall	appt_pos_doctor	cond	schedd	startd	endd	arrived	since_prev_sched
0	4591553	Uncommon Cold	BlueSword/BlueDiamond	31	M	L. Jedermann	2005-01-03	1900-01-01 07:59:00	1900-01-01 08:07:00	1900-01-01 08:22:00	...	1	16	0	0	uc	479	487	502	475	NaN
1	4607440	TV Personality Disorder	Medifix	48	F	I. Petrov	2005-01-03	1900-01-01 08:15:00	1900-01-01 08:18:00	1900-01-01 08:53:00	...	1	16	1	0	tvp	495	498	533	495	NaN
2	4590430	TV Personality Disorder	Medifix	42	M	E. Medelsvensson	2005-01-03	1900-01-01 08:30:00	1900-01-01 08:42:00	1900-01-01 09:17:00	...	1	16	2	0	tvp	510	522	557	506	NaN
3	4619481	Saturday Night Fever	Self Insured	22	M	J. Smith	2005-01-03	1900-01-01 09:00:00	1900-01-01 09:11:00	1900-01-01 09:39:00	...	1	16	3	0	snf	540	551	579	543	NaN
4	4610862	Uncommon Cold	Self Insured	32	M	L. Jedermann	2005-01-03	1900-01-01 10:00:00	1900-01-01 10:00:00	1900-01-01 10:16:00	...	1	16	4	1	uc	600	600	616	596	121

		PATIENT_ID	PATIENT_CONDITION	INSURANCE_TYPE	AGE	SEX	date	sched	start	end	arrive	...	appt_time	num_appts	appt_pos_overall	appt_pos_doctor	cond	schedd	startd	endd	arrived	since_prev_sched
PROVIDER_NAME	month
E. Medelsvensson	1	47	47	47	47	47	47	47	47	47	47	...	47	47	47	47	47	47	47	47	47	26
	2	55	55	55	55	55	55	55	55	55	55	...	55	55	55	55	55	55	55	55	55	31
	3	54	54	54	54	54	54	54	54	54	54	...	54	54	54	54	54	54	54	54	54	31
	4	65	65	65	65	65	65	65	65	65	65	...	65	65	65	65	65	65	65	65	65	41
	5	71	71	71	71	71	71	71	71	71	71	...	71	71	71	71	71	71	71	71	71	51
	6	54	54	54	54	54	54	54	54	54	54	...	54	54	54	54	54	54	54	54	54	33
	7	85	85	85	85	85	85	85	85	85	85	...	85	85	85	85	85	85	85	85	85	63
	8	60	60	60	60	60	60	60	60	60	60	...	60	60	60	60	60	60	60	60	60	40
	9	35	35	35	35	35	35	35	35	35	35	...	35	35	35	35	35	35	35	35	35	15
	10	48	48	48	48	48	48	48	48	48	48	...	48	48	48	48	48	48	48	48	48	30
	11	47	47	47	47	47	47	47	47	47	47	...	47	47	47	47	47	47	47	47	47	26
	12	32	32	32	32	32	32	32	32	32	32	...	32	32	32	32	32	32	32	32	32	18
I. Petrov	1	40	40	40	40	40	40	40	40	40	40	...	40	40	40	40	40	40	40	40	40	21
	2	43	43	43	43	43	43	43	43	43	43	...	43	43	43	43	43	43	43	43	43	24
	3	57	57	57	57	57	57	57	57	57	57	...	57	57	57	57	57	57	57	57	57	33
	4	47	47	47	47	47	47	47	47	47	47	...	47	47	47	47	47	47	47	47	47	28
	5	78	78	78	78	78	78	78	78	78	78	...	78	78	78	78	78	78	78	78	78	55
	6	54	54	54	54	54	54	54	54	54	54	...	54	54	54	54	54	54	54	54	54	33
N. Fulano	1	32	32	32	32	32	32	32	32	32	32	...	32	32	32	32	32	32	32	32	32	13
	2	56	56	56	56	56	56	56	56	56	56	...	56	56	56	56	56	56	56	56	56	34
	3	62	62	62	62	62	62	62	62	62	62	...	62	62	62	62	62	62	62	62	62	39
	4	69	69	69	69	69	69	69	69	69	69	...	69	69	69	69	69	69	69	69	69	45
	5	72	72	72	72	72	72	72	72	72	72	...	72	72	72	72	72	72	72	72	72	50
	6	67	67	67	67	67	67	67	67	67	67	...	67	67	67	67	67	67	67	67	67	44
	7	93	93	93	93	93	93	93	93	93	93	...	93	93	93	93	93	93	93	93	93	71
	8	40	40	40	40	40	40	40	40	40	40	...	40	40	40	40	40	40	40	40	40	20
	9	38	38	38	38	38	38	38	38	38	38	...	38	38	38	38	38	38	38	38	38	18
	10	38	38	38	38	38	38	38	38	38	38	...	38	38	38	38	38	38	38	38	38	22
	11	41	41	41	41	41	41	41	41	41	41	...	41	41	41	41	41	41	41	41	41	26
	12	28	28	28	28	28	28	28	28	28	28	...	28	28	28	28	28	28	28	28	28	15