title: How to check and replace date values conditionally in pandas dataframe?
body: <p>I am working with Python 2.7 and pandas and I have the following dataframe:</p>
  
  <pre><code>    col1    col2    col3    date_col
  0   123     0       foo     9999-12-31
  1   456     1       bar     2017-09-15
  2   789     1       psi     9999-12-31  
  </code></pre>
  
  <p>When I try to work with this dataframe I get the following error: </p>
  
  <blockquote>
    <p>pandas._libs.tslibs.OutOfBoundsDattime: Out of bounds nanosecond timestamp: 9999-12-31 00:00:00</p>
  </blockquote>
  
  <p>I am aware that this is because pandas has a limited window of timestamps it can represent.</p>
  
  <p><strong>My question is:</strong> How can I replace all the values in the column date_col which are exceeding the bound of the date window with a default value (for example 2000-01-01)? 
  Also in my real dataframe I only know the indices of the columns that contain dates, so I <em>cannot</em> use the column names.</p>
  
  <p>Any help is appreciated!</p>