title: Python Seasonal decompose Freq paramater determination
body: <p>Although the question seems to have been tackled a lot, I cannot figure out why seasonal decompose doesn't work in my case although I am giving as input a dataframe with a Datetime Index. Here is an example of my dataset:</p>
  
  <pre><code>    Customer order actual date  Sales Volumes
  0   01/01/1900                           300
  1   10/03/2008                          3000
  2   15/11/2013                            10
  3   23/12/2013                           200
  4   04/03/2014                             5
  5   17/03/2014                            30
  6   22/04/2014                             1
  7   26/06/2014                           290
  8   30/06/2014                            40
  </code></pre>
  
  <p>the code snippet is shown below:</p>
  
  <pre><code>from statsmodels.tsa.seasonal import seasonal_decompose
  df_agg['Customer order actual date'] = pd.to_datetime(df_agg['Customer order actual date'])
  df_agg = df_agg.set_index('Customer order actual date')
  df_agg.reset_index().sort_values('Customer order actual date', ascending=True)
  decomposition = seasonal_decompose(np.asarray(df_agg['Sales Volumes'] ), model = 'multiplicative')
  </code></pre>
  
  <p>But I get systematically the following error:</p>
  
  <blockquote>
    <p>: You must specify a freq or x must be a pandas object with a timeseries index witha freq not set to None</p>
  </blockquote>
  
  <p>Could you please explain why I should give a freq input although I am using a dataframe with Datetime Index? Does it make sense to give a frequency as an input paramater whereas I am looking for the seasonality as an output of seasonal_decompose? </p>