title: Analysing Time Series in Python - pandas formatting error - statsmodels
body: <p>I am trying to analyse stars' data. I have light time series of the stars and I want to predict to which class (among 4 different types) they belong.
  I have light time series of those stars, and I want to analyse those time series by doing deseasonalisation, frequencies analysis and other potentially relevant studies. </p>
  
  <p>The object time_series is a panda DataFrame, including 10 columns : time_points_b, light_points_b (the b being for blue), etc...</p>
  
  <p>I first want to study the blue light time series.</p>
  
  <pre><code>import statsmodels.api as sm;
  import pandas as pd
  import matplotlib.pyplot as plt
  pd.options.display.mpl_style = 'default'
  %matplotlib inline
  
  def star_key(slab_id, star_id_b):
      return str(slab_id) + '_' + str(star_id_b)
  
  raw_time_series = pd.read_csv("data/public/train_varlength_features.csv.gz", index_col=0, compression='gzip')
  time_series = raw_time_series.applymap(csv_array_to_float)
  
  
  time_points = np.array(time_series.loc[star_key(patch_id, star_id_b)]['time_points_b'])
  light_points = np.array(time_series.loc[star_key(patch_id, star_id_b)]['light_points_b'])
  error_points = np.array(time_series.loc[star_key(patch_id, star_id_b)]['error_points_b'])
  
  light_data = pd.DataFrame({'time':time_points[:], 'light':light_points[:]})
  residuals = sm.tsa.seasonal_decompose(light_data);
  
  light_plt = residuals.plot()
  light_plt.set_size_inches(10, 5)
  light_plt.tight_layout()
  </code></pre>
  
  <p>This code gives me an attribute error when I apply the seasonal_decompose method :
  AttributeError: 'Int64Index' object has no attribute 'inferred_freq'</p>