title: How to import messy .txt and drop timestamp using Pandas?
body: <p>I am trying to import a txt file with huge amounts of transaction data. Normally I could open the file in Excel and then save as CSV but the file is too large so I have been using Python 3:</p>
  
  <pre><code>df = pd.read_csv("file_name", sep = "\s+", dtype = str)
  </code></pre>
  
  <p>This gives a somewhat usable data frame but now I have some problems with the date and time stamp. </p>
  
  <p>In the txt file these are saved as:</p>
  
  <pre><code>2016-12-11 00:00:00.000 "XXXX"  "XXXX"  "XXXXX" "X"
  </code></pre>
  
  <p>The X's are other data. When I import this data into Python I get the date as an index, with the time stamp in the date column followed by the other variables:</p>
  
  <pre><code>                VAR1.           VAR2.   VAR3.   VAR4.   VAR5. 
  2016-12-11      00:00:00.000    XXXX    XXXX    XXXXX   X
  </code></pre>
  
  <p>I tried using the following to create a new index when I import the data:</p>
  
  <pre><code>df = pd.read_csv("file_name", sep = "\s+", dtype = str, index_col = False)
  </code></pre>
  
  <p>But this ended up pushing VAR5 off of the data frame and assigning the timestamp to VAR2.</p>
  
  <pre><code>       VAR1.           VAR2.       VAR3.     VAR4.   VAR5. 
  1   2016-12-11      00:00:00.000    XXXX    XXXX    XXXXX   
  </code></pre>
  
  <p>I would greatly appreciate any help with importing the data from a txt file into python, dropping the time stamp, and then applying datetime to the first column. </p>
  
  <p>The txt file has  some large white spaces between variables and each string is encapsulated with "" eg:</p>
  
  <pre><code>2016-12-11 00:00:00.000 "XXXX"  "XXX"   "XXXX"  "X"
  </code></pre>
  
  <p>Thanks! </p>