title: Add trend line to pandas
body: <p>I have time-series data, as followed:</p>
  
  <pre><code>                  emplvl
  date                    
  2003-01-01  10955.000000
  2003-04-01  11090.333333
  2003-07-01  11157.000000
  2003-10-01  11335.666667
  2004-01-01  11045.000000
  2004-04-01  11175.666667
  2004-07-01  11135.666667
  2004-10-01  11480.333333
  2005-01-01  11441.000000
  2005-04-01  11531.000000
  2005-07-01  11320.000000
  2005-10-01  11516.666667
  2006-01-01  11291.000000
  2006-04-01  11223.000000
  2006-07-01  11230.000000
  2006-10-01  11293.000000
  2007-01-01  11126.666667
  2007-04-01  11383.666667
  2007-07-01  11535.666667
  2007-10-01  11567.333333
  2008-01-01  11226.666667
  2008-04-01  11342.000000
  2008-07-01  11201.666667
  2008-10-01  11321.000000
  2009-01-01  11082.333333
  2009-04-01  11099.000000
  2009-07-01  10905.666667
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/i28tJ.png" rel="noreferrer"><img src="https://i.stack.imgur.com/i28tJ.png" alt="time series graph"></a></p>
  
  <p>I would like to add, in the most simple way, a linear trend (with intercept) onto this graph. Also, I would like to compute this trend only conditional on data before, say, 2006.</p>
  
  <p>I've found some answers here, but they all include <code>statsmodels</code>. First of all, these answers might be not up to date: <code>pandas</code> improved, and now itself includes an OLS component. Second, <code>statsmodels</code> appears to estimate an individual fixed-effect for each time period, instead of a linear trend. I suppose I could recalculate a running-quarter variable, but there most be a more comfortable way of doing this?</p>
  
  <pre><code>                            OLS Regression Results                            
  ==============================================================================
  Dep. Variable:                 emplvl   R-squared:                       1.000
  Model:                            OLS   Adj. R-squared:                    nan
  Method:                 Least Squares   F-statistic:                     0.000
  Date:                tor, 14 apr 2016   Prob (F-statistic):                nan
  Time:                        17:17:43   Log-Likelihood:                 929.85
  No. Observations:                  40   AIC:                            -1780.
  Df Residuals:                       0   BIC:                            -1712.
  Df Model:                          39                                         
  Covariance Type:            nonrobust                                         
  ============================================================================================================
                                                 coef    std err          t      P&gt;|t|      [95.0% Conf. Int.]
  ------------------------------------------------------------------------------------------------------------
  Intercept                                 1.095e+04        inf          0        nan           nan       nan
  date[T.Timestamp('2003-04-01 00:00:00')]   135.3333        inf          0        nan           nan       nan
  date[T.Timestamp('2003-07-01 00:00:00')]   202.0000        inf          0        nan           nan       nan
  date[T.Timestamp('2003-10-01 00:00:00')]   380.6667        inf          0        nan           nan       nan
  date[T.Timestamp('2004-01-01 00:00:00')]    90.0000        inf          0        nan           nan       nan
  date[T.Timestamp('2004-04-01 00:00:00')]   220.6667        inf          0        nan           nan       nan
  </code></pre>
  
  <p>How do I, in the simplest way possible, estimate this trend and add the predicted values as a column to my data frame?</p>