title: Decompose hourly time-series python
body: <p>I have an hourly dataset below. I use sm.tsa.seasonal_decompose this dataset to season, trend and residuals.</p>
  <pre><code>dataset = np.random.randint(100,500,143)
  
  start='2020-03-01 00:00:00'
  end='2020-03-06 22:00:00'
  index=pd.date_range(start=start,end=end, freq='H')
  
  df = pd.DataFrame(data=dataset, index=index, columns=['value'])
  decomposition = sm.tsa.seasonal_decompose(df, model='multiplicative', extrapolate_trend='freq')
  
  d={'observed': decomposition.observed, 'seas': decomposition.seasonal, 'trend': decomposition.trend, 'resid': decomposition.resid}
  df = pd.DataFrame(d)
  print(df.iloc[0:5])
                       observed      seas       trend     resid
  2020-03-01 00:00:00     240.0  1.235192  312.418454  0.621928
  2020-03-01 01:00:00     143.0  0.568295  312.827657  0.804372
  2020-03-01 02:00:00     110.0  0.731564  313.236860  0.480029
  2020-03-01 03:00:00     494.0  1.324896  313.646063  1.188790
  2020-03-01 04:00:00     146.0  0.636560  314.055266  0.730310
  </code></pre>
  <p>However, I tried this without creating a panda dataframe, and I am getting a different results.</p>
  <pre><code>index=pd.date_range(start=start,end=, freq='H')
  endog = pd.Series(dataset, index=index)
  result=sm.tsa.seasonal_decompose(endog, freq=24, model='multiplicative')
  
  d2={'observed': result.observed, 'seas': result.seasonal, 'trend': result.trend, 'resid': result.resid}
  df2 = pd.DataFrame(d2)
  print(df2.iloc[0:5])
  
                        observed      seas  trend  resid
  2020-03-01 00:00:00     240.0  1.330074    NaN    NaN
  2020-03-01 01:00:00     143.0  0.591202    NaN    NaN
  2020-03-01 02:00:00     110.0  0.808533    NaN    NaN
  2020-03-01 03:00:00     494.0  1.276380    NaN    NaN
  2020-03-01 04:00:00     146.0  0.671652    NaN    NaN
  </code></pre>