title: cleaning big data using python
body: <p>I have to clean a input data file in python. Due to typo error, the datafield may have strings instead of numbers. I would like to identify all fields which are a string and fill these with NaN using pandas. Also, I would like to log the index of those fields.</p>
  
  <p>One of the crudest way is to loop through each and every field and checking whether it is a number or not, but this consumes lot of time if the data is big. </p>
  
  <p><em>My csv file contains data similar to the following table:</em></p>
  
  <pre><code>Country  Count  Sales
  USA         1   65000
  UK          3    4000
  IND         8       g
  SPA         3    9000
  NTH         5   80000
  </code></pre>
  
  <p>....
  Assume that i have 60,000 such rows in the data.</p>
  
  <p>Ideally I would like to identify that row IND has an invalid value under SALES column. Any suggestions on how to do this efficiently?</p>