title: Parsing a large txt file with Pandas
body: <p>I have a large .txt file, close to 2gb. first i tried this 
  <code>pd.read_csv("large_text_file.txt", header=0, delim_whitespace=True)</code>
  which keep throwing a <code>Error tokenizing data. C error: Expected 32 fields in line 3,  saw 36</code> error </p>
  
  <p>then i tried this: <code>pd.read_csv("wspace.csv", header=0, sep=r"\s+")</code> but because its not technically a <code>csv</code> file and some of the content such as "names" have spaces in them, the output turns out really bad. and using pandas <code>error_bad_lines=False</code> is not ideal as it just skips over ALL the rows for this particular table. is there any way to format a large txt file into csv? i know you can change the extension from <code>.txt</code> to <code>.csv</code> but without the commas int he right place i get a poor output. example data to illustrate my pain below.</p>
  
  <p>instead of showing something like this:
  <code>
  ╔══════════════════════════════════╦═════════╦════════════════════════╗
  ║               Col1               ║  Col2   ║          Col3          ║
  ╠══════════════════════════════════╬═════════╬════════════════════════╣
  ║ Value 1                          ║ Value 2 ║ 123                    ║
  ║ Separate                         ║ cols    ║ with a tab or 4 spaces ║
  ║ This is a row with only one cell ║         ║                        ║
  ╚══════════════════════════════════╩═════════╩════════════════════════╝
  </code>
  it shows this, which causes the "token error"
  <code>
  ╔════════════════╦════════════════════╦════════════════════════╦═══╦══╦═════╗
  ║ Col1 Col2 Col3 ║                    ║                        ║   ║  ║     ║
  ╠════════════════╬════════════════════╬════════════════════════╬═══╬══╬═════╣
  ║ Value          ║ 1                  ║ Value                  ║ 2 ║  ║ 123 ║
  ║ Separate       ║ cols               ║ with a tab or 4 spaces ║   ║  ║     ║
  ║ This is a row  ║ with only one cell ║                        ║   ║  ║     ║
  ╚════════════════╩════════════════════╩════════════════════════╩═══╩══╩═════╝
  </code></p>