Analysing Time Series in Python - pandas formatting error - statsmodels
19,764
seasonal_decompose()
expects a DateTimeIndex
on your DataFrame. Here's an example:
Comments
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Robin almost 2 years
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'
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Irene about 8 yearsWhere is the documentation of this "seasonal_decompose" function? How did you know it is expecting a DateTimeIndex? I tried to find it online, but couldnt, only tiny examples of use. And I would like to see the inputs and outputs this function expects/produces. Thanks!
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Randy about 8 yearsYou can get the basics of usage from the function's docstring (there's several ways to do that, but one way is
print sm.tsa.seasonal_decompose.__doc__
). As for the DateTimeIndex, I had to piece that together from the'Int64Index' object has no attribute 'inferred_freq'
error. A quick Google search turned up that it's a part of pandas' DateTimeIndex, so just gave that a shot and it worked.