Analysing Time Series in Python - pandas formatting error - statsmodels
Question: 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.

The object time_series is a panda DataFrame, including 10 columns :
time_points_b, light_points_b (the b being for blue), etc...

I first want to study the blue light time series.

    
    
    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()
    

This code gives me an attribute error when I apply the seasonal_decompose
method : AttributeError: 'Int64Index' object has no attribute 'inferred_freq'


Answer: `seasonal_decompose()` expects a `DateTimeIndex` on your DataFrame. Here's an
example:

[![enter image description
here](http://i.stack.imgur.com/lZoUt.png)](http://i.stack.imgur.com/lZoUt.png)

