text: Q:
  
  line terminator instead of blank space pandas read_csv
  
  I have a data set that has a line terminator  in my time field like below:
  Text file:
  num1  Name    Num   dan    pan   fran    time
  ----  ------  ----  ----   ----  -----   ----------------------------
  52345 john    12    True   True  False   <LF>
  43214 Matt    56    False  False True    2008-12-12 01:01:47 UTC+0000<LF>
  12345 Adam    13    True   True  False   <LF>
  54321 Sam     23    False  True  True    <LF>
  
  Current Code:
  I am currently attempting to read it into a pandas.DataFrame like below:
  pd.read_csv(file.txt, delim_white_space=True, skiprows=range(1,2)).fillna("NONE) 
  
  Current Result:
  I am getting an output that has my data shifted because I am not able to recognize the blank value in my time field like below: 
      num1  Name    Num   dan    pan   fran    time
      52345 john    12    True   True  False   <LF>
  43214 Matt    56    False  False True    2008-12-12 01:01:47 UTC+0000
      12345 Adam    13    True   True  False   <LF>
      54321 Sam     23    False  True  True    <LF>
  
  Question:
  How can I either pre-parse my data to make the time field contain blank space or "NONE" and then read into pandas appropriately or have pandas recognize there is a missing value?
  Expected output: 
  num1  Name    Num   dan    pan   fran    time
  52345 john    12    True   True  False   NONE
  43214 Matt    56    False  False True    2008-12-12 01:01:47 UTC+0000
  12345 Adam    13    True   True  False   NONE
  54321 Sam     23    False  True  True    NONE
  
  A:
  
  I would suggest pandas.read_fwf:
  Code:
  df = pd.read_fwf('file1', skiprows=range(1, 2)).fillna("NONE")
  
  Results:
      num1  Name  Num    dan    pan   fran                          time
  0  52345  john   12   True   True  False                          NONE
  1  43214  Matt   56  False  False   True  2008-12-12 01:01:47 UTC+0000
  2  12345  Adam   13   True   True  False                          NONE
  3  54321   Sam   23  False   True   True                          NONE
  
  