In [2]:

    

step_1 = open("Sample_redone.txt", "r")


    

In [3]:

    

step_1


    

    
Out[3]:





<_io.TextIOWrapper name='Sample_redone.txt' mode='r' encoding='cp1252'>

In [4]:

    

step_2 = step_1.read()


    

In [31]:

    

step_3 = step_2.split(sep = "/m")
str(step_3)


    

    
Out[31]:





"['MSH|^~\\\\&|CERNER||PriorityHealth||||ORU^R01|Q479004375T431430612|P|2.3', 'PID|||001677980||SMITH^CURTIS||19680219|M||||||||||929645156318|123456789', 'PD1||||1234567890^LAST^FIRST^M^^^^^NPI', 'OBR|1|341856649^HNAM_ORDERID|000002006326002362|648088^Basic Metabolic Panel||', '20061122151600|||||||||1620^Hooker^Robert^L||||||20061122154733|||F|||||||||||20061122140000', 'OBX|1|NM|GLU^Glucose Lvl|59|mg/dL|65-99^65^99|L|||F|||20061122154733/n']"

In [33]:

    

step_4 = list(map(lambda x: x.split(sep="|"), step_3))


    

In [43]:

    

import pandas as pandas
step_4


    

    
Out[43]:





[['MSH',
  '^~\\&',
  'CERNER',
  '',
  'PriorityHealth',
  '',
  '',
  '',
  'ORU^R01',
  'Q479004375T431430612',
  'P',
  '2.3'],
 ['PID',
  '',
  '',
  '001677980',
  '',
  'SMITH^CURTIS',
  '',
  '19680219',
  'M',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '929645156318',
  '123456789'],
 ['PD1', '', '', '', '1234567890^LAST^FIRST^M^^^^^NPI'],
 ['OBR',
  '1',
  '341856649^HNAM_ORDERID',
  '000002006326002362',
  '648088^Basic Metabolic Panel',
  '',
  ''],
 ['20061122151600',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '1620^Hooker^Robert^L',
  '',
  '',
  '',
  '',
  '',
  '20061122154733',
  '',
  '',
  'F',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '',
  '20061122140000'],
 ['OBX',
  '1',
  'NM',
  'GLU^Glucose Lvl',
  '59',
  'mg/dL',
  '65-99^65^99',
  'L',
  '',
  '',
  'F',
  '',
  '',
  '20061122154733/n']]

In [44]:

    

dataframe = pandas.DataFrame(step_4)


    

In [45]:

    

dataframe


    

    
Out[45]:






  

    

      

      
0
      
1
      
2
      
3
      
4
      
5
      
6
      
7
      
8
      
9
      
...
      
20
      
21
      
22
      
23
      
24
      
25
      
26
      
27
      
28
      
29
    
  
  

    

      
0
      
MSH
      
^~\&
      
CERNER
      

      
PriorityHealth
      

      

      

      
ORU^R01
      
Q479004375T431430612
      
...
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
    
    

      
1
      
PID
      

      

      
001677980
      

      
SMITH^CURTIS
      

      
19680219
      
M
      

      
...
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
    
    

      
2
      
PD1
      

      

      

      
1234567890^LAST^FIRST^M^^^^^NPI
      
None
      
None
      
None
      
None
      
None
      
...
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
    
    

      
3
      
OBR
      
1
      
341856649^HNAM_ORDERID
      
000002006326002362
      
648088^Basic Metabolic Panel
      

      

      
None
      
None
      
None
      
...
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
    
    

      
4
      
20061122151600
      

      

      

      

      

      

      

      

      
1620^Hooker^Robert^L
      
...
      

      

      

      

      

      

      

      

      

      
20061122140000
    
    

      
5
      
OBX
      
1
      
NM
      
GLU^Glucose Lvl
      
59
      
mg/dL
      
65-99^65^99
      
L
      

      

      
...
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
      
None
    
  

6 rows × 30 columns

In [68]:

    

new = dataframe.rename(columns={'0':'a','1':'b'},inplace=True)


    

In [70]:

    

print(new)


    

    




None

In [ ]: