title: What's the fastest way to compare datetime in pandas?
body: <p>I have two big csv files with different number of rows which I am importing as follows:</p>
  
  <pre><code>tdata = pd.read_csv(tfilepath, sep=',', parse_dates=['date_1'])
  print(tdata.iloc[:, [0,3]])
  
         TBA    date_1
  0       0   2010-01-04
  1       9   2010-01-05
  2       0   2010-01-06    
  3       8   2010-01-07
  4       0   2010-01-08
  5       0   2010-01-09
  
  pdata = pd.read_csv(pfilepath, sep=',', parse_dates=['date_2'])
  print(pdata.iloc[:, [0,3]])
  
         TBA    date_2
  0       3   2011-01-04
  1       5   2010-01-09
  2       0   2012-02-03    
  3       9   2010-03-17
  4       1   2010-11-08
  5       2   2010-01-05
  </code></pre>
  
  <p>Now I want to replace TBA in the first dataframe with corresponding TBA in the second dataframe where the dates match. The default value would be 0. So I am iterating through rows as follows:</p>
  
  <pre><code> for i, row1 in tdata.iterrows():
       for j, row2 in pdata.iterrows():
           if row1['date_1'] == row2['date_2']:
               tdata.loc[i, 'TBA'] = row2['TBA']
               break
           else:
               tdata.loc[i, 'TBA'] = 0
  </code></pre>
  
  <p>Problem is this takes very long (around 11 minutes). I want to compare one csv with 160 other csv and further run some tree based models. I am a newbee with little coding background! Pardon me if this is a 'dirty' way. Any help would be appreciated. Thanks! </p>