Python - Statsmodels.tsa.seasonal_decompose - missing values in head and tail of dataframe
<p>I have the following dataframe, that I'm calling "sales_df":</p>

<pre><code>            Value
Date             
2004-01-01      0
2004-02-01    173
2004-03-01    225
2004-04-01    230
2004-05-01    349
2004-06-01    258
2004-07-01    270
2004-08-01    223
...           ...
2015-06-01    218
2015-07-01    215
2015-08-01    233
2015-09-01    258
2015-10-01    252
2015-11-01    256
2015-12-01    188
2016-01-01     70
</code></pre>

<p>I want to separate its trend from its seasonal component and for that I use statsmodels.tsa.seasonal_decompose through the following code:</p>

<pre><code>decomp=sm.tsa.seasonal_decompose(sales_df.Value)
df=pd.concat([sales_df,decomp.trend],axis=1)
df.columns=['sales','trend']
</code></pre>

<p>This is getting me this:</p>

<pre><code>            sales       trend
Date                         
2004-01-01      0         NaN
2004-02-01    173         NaN
2004-03-01    225         NaN
2004-04-01    230         NaN
2004-05-01    349         NaN
2004-06-01    258         NaN
2004-07-01    270  236.708333
2004-08-01    223  248.208333
2004-09-01    243  251.250000
...           ...         ...
2015-05-01    270  214.416667
2015-06-01    218  215.583333
2015-07-01    215  212.791667
2015-08-01    233         NaN
2015-09-01    258         NaN
2015-10-01    252         NaN
2015-11-01    256         NaN
2015-12-01    188         NaN
2016-01-01     70         NaN
</code></pre>

<p>Note that there are 6 NaN's in the start and in the end of the Trend's series.
So I ask, is that right? Why is that happening?</p>