Logo Questions Linux Laravel Mysql Ubuntu Git Menu
HTML CSS JAVASCRIPT SQL PYTHON PHP BOOTSTRAP JAVA JQUERY R React Kotlin
 

How to use statsmodels.tsa.seasonal.seasonal_decompose with a pandas dataframe

Tags:

python

pandas

dataframe

statsmodels

from statsmodels.tsa.seasonal import seasonal_decompose
      def seasonal_decomp(df, model="additive"):
        seasonal_df = None
        seasonal_df = seasonal_decompose(df, model='additive')
        return seasonal_df
    
 seasonal_decomp(df)

Error

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-93-00543113a58a> in <module>
----> 1 seasonal_decompose(df, model='additive')

e:\Anaconda3\lib\site-packages\pandas\util\_decorators.py in wrapper(*args, **kwargs)
    197                 else:
    198                     kwargs[new_arg_name] = new_arg_value
--> 199             return func(*args, **kwargs)
    200 
    201         return cast(F, wrapper)

e:\Anaconda3\lib\site-packages\statsmodels\tsa\seasonal.py in seasonal_decompose(x, model, filt, period, two_sided, extrapolate_trend)
    185     for s, name in zip((seasonal, trend, resid, x),
    186                        ('seasonal', 'trend', 'resid', None)):
--> 187         results.append(pw.wrap(s.squeeze(), columns=name))
    188     return DecomposeResult(seasonal=results[0], trend=results[1],
    189                            resid=results[2], observed=results[3])

e:\Anaconda3\lib\site-packages\statsmodels\tools\validation\validation.py in wrap(self, obj, columns, append, trim_start, trim_end)
    216                     new.append(append if c is None else str(c) + '_' + append)
    217                 columns = new
--> 218             return pd.DataFrame(obj, columns=columns, index=index)
    219         else:
    220             raise ValueError('Can only wrap 1 or 2-d array_like')

e:\Anaconda3\lib\site-packages\pandas\core\frame.py in __init__(self, data, index, columns, dtype, copy)
    495                 mgr = init_dict({data.name: data}, index, columns, dtype=dtype)
    496             else:
--> 497                 mgr = init_ndarray(data, index, columns, dtype=dtype, copy=copy)
    498 
    499         # For data is list-like, or Iterable (will consume into list)

e:\Anaconda3\lib\site-packages\pandas\core\internals\construction.py in init_ndarray(values, index, columns, dtype, copy)
    201 
    202     # _prep_ndarray ensures that values.ndim == 2 at this point
--> 203     index, columns = _get_axes(
    204         values.shape[0], values.shape[1], index=index, columns=columns
    205     )

e:\Anaconda3\lib\site-packages\pandas\core\internals\construction.py in _get_axes(N, K, index, columns)
    460         columns = ibase.default_index(K)
    461     else:
--> 462         columns = ensure_index(columns)
    463     return index, columns
    464 

e:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in ensure_index(index_like, copy)
   5612             index_like = copy_func(index_like)
   5613 
-> 5614     return Index(index_like)
   5615 
   5616 

e:\Anaconda3\lib\site-packages\pandas\core\indexes\base.py in __new__(cls, data, dtype, copy, name, tupleize_cols, **kwargs)
    409 
    410         elif data is None or is_scalar(data):
--> 411             raise cls._scalar_data_error(data)
    412         elif hasattr(data, "__array__"):
    413             return Index(np.asarray(data), dtype=dtype, copy=copy, name=name, **kwargs)

TypeError: Index(...) must be called with a collection of some kind, 'seasonal' was passed

Test Data

df = pd.DataFrame.from_dict(data, orient='index')

data = {pd.Timestamp('2020-01-23 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-24 00:00:00'): {'LA': 1.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-25 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-26 00:00:00'): {'LA': 3.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-27 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-28 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-29 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-30 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 1.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-01-31 00:00:00'): {'LA': 2.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 2.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-01 00:00:00'): {'LA': 1.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-02 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 1.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-03 00:00:00'): {'LA': 3.0,
  'NY': 0.0,
  'Miami': 1.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-04 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-05 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-06 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-07 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-08 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-09 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-10 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-11 00:00:00'): {'LA': 1.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-12 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-13 00:00:00'): {'LA': 1.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-14 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-15 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-16 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-17 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-18 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-19 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-20 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-21 00:00:00'): {'LA': 2.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-22 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-23 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-24 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-25 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-26 00:00:00'): {'LA': 0.0,
  'NY': 1.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-27 00:00:00'): {'LA': 1.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-28 00:00:00'): {'LA': 0.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-02-29 00:00:00'): {'LA': 8.0,
  'NY': 1.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-01 00:00:00'): {'LA': 6.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-02 00:00:00'): {'LA': 23.0,
  'NY': 0.0,
  'Miami': 2.0,
  'Seattle': 1.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-03 00:00:00'): {'LA': 20.0,
  'NY': 0.0,
  'Miami': 0.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-04 00:00:00'): {'LA': 31.0,
  'NY': 2.0,
  'Miami': 23.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-05 00:00:00'): {'LA': 70.0,
  'NY': 0.0,
  'Miami': 2.0,
  'Seattle': 1.0,
  'San Diego': 1.0},
 pd.Timestamp('2020-03-06 00:00:00'): {'LA': 48.0,
  'NY': 9.0,
  'Miami': 1.0,
  'Seattle': 9.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-07 00:00:00'): {'LA': 115.0,
  'NY': 0.0,
  'Miami': 3.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-08 00:00:00'): {'LA': 114.0,
  'NY': 7.0,
  'Miami': 5.0,
  'Seattle': 4.0,
  'San Diego': 2.0},
 pd.Timestamp('2020-03-09 00:00:00'): {'LA': 68.0,
  'NY': 5.0,
  'Miami': 4.0,
  'Seattle': 0.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-10 00:00:00'): {'LA': 192.0,
  'NY': 6.0,
  'Miami': 13.0,
  'Seattle': 3.0,
  'San Diego': 4.0},
 pd.Timestamp('2020-03-11 00:00:00'): {'LA': 398.0,
  'NY': 7.0,
  'Miami': 6.0,
  'Seattle': 0.0,
  'San Diego': 6.0},
 pd.Timestamp('2020-03-12 00:00:00'): {'LA': 452.0,
  'NY': 14.0,
  'Miami': 11.0,
  'Seattle': 8.0,
  'San Diego': 4.0},
 pd.Timestamp('2020-03-13 00:00:00'): {'LA': 596.0,
  'NY': 99.0,
  'Miami': 9.0,
  'Seattle': 17.0,
  'San Diego': 7.0},
 pd.Timestamp('2020-03-14 00:00:00'): {'LA': 713.0,
  'NY': 0.0,
  'Miami': 20.0,
  'Seattle': 14.0,
  'San Diego': 14.0},
 pd.Timestamp('2020-03-15 00:00:00'): {'LA': 98.0,
  'NY': 11.0,
  'Miami': 11.0,
  'Seattle': 4.0,
  'San Diego': 13.0},
 pd.Timestamp('2020-03-16 00:00:00'): {'LA': 1392.0,
  'NY': 38.0,
  'Miami': 6.0,
  'Seattle': 27.0,
  'San Diego': 11.0},
 pd.Timestamp('2020-03-17 00:00:00'): {'LA': 1781.0,
  'NY': 121.0,
  'Miami': 23.0,
  'Seattle': 24.0,
  'San Diego': 0.0},
 pd.Timestamp('2020-03-18 00:00:00'): {'LA': 2776.0,
  'NY': 51.0,
  'Miami': 14.0,
  'Seattle': 33.0,
  'San Diego': 54.0},
 pd.Timestamp('2020-03-19 00:00:00'): {'LA': 5240.0,
  'NY': 249.0,
  'Miami': 38.0,
  'Seattle': 52.0,
  'San Diego': 34.0},
 pd.Timestamp('2020-03-20 00:00:00'): {'LA': 5322.0,
  'NY': 172.0,
  'Miami': 50.0,
  'Seattle': 54.0,
  'San Diego': 52.0},
 pd.Timestamp('2020-03-21 00:00:00'): {'LA': 6346.0,
  'NY': 228.0,
  'Miami': 86.0,
  'Seattle': 53.0,
  'San Diego': 38.0},
 pd.Timestamp('2020-03-22 00:00:00'): {'LA': 7936.0,
  'NY': 525.0,
  'Miami': 66.0,
  'Seattle': 61.0,
  'San Diego': 34.0}}
like image 842
New_to_Python Avatar asked Oct 15 '22 00:10

New_to_Python

1 Answers

  • The issue is here, seasonal_decompose(df, model='additive'), the entire dataframe is being passed to seasonal_decompose, but you may only pass one column, and a datetime index.
  • The function has been updated to use a list comprehension to calculate the .trend for each column, and then combine the data into a single dataframe with pandas.concat.
from statsmodels.tsa.seasonal import seasonal_decompose
import pandas as pd

# dataframe from sample; in this case the index is already a datetime
df = pd.DataFrame.from_dict(data, orient='index')

# if the index is not a datetime format
df.index = pd.to_datetime(df.index)

# perform seasonal decompose in a list comprehension on each column, return dataframe
def season_decom(df, model='additive'):    
    return pd.concat([pd.DataFrame({col: seasonal_decompose(df[col], model=model).trend}) for col in df.columns], axis=1)


# call function
df_seasonal = season_decom(df)

# df_seasonal.head()
                  LA   NY     Miami   Seattle  San Diego
2020-01-23       NaN  NaN       NaN       NaN        NaN
2020-01-24       NaN  NaN       NaN       NaN        NaN
2020-01-25       NaN  NaN       NaN       NaN        NaN
2020-01-26  0.571429  0.0  0.000000  0.000000        0.0
2020-01-27  0.571429  0.0  0.142857  0.000000        0.0
2020-01-28  0.714286  0.0  0.142857  0.285714        0.0
2020-01-29  0.857143  0.0  0.142857  0.285714        0.0
2020-01-30  0.428571  0.0  0.285714  0.285714        0.0
2020-01-31  0.857143  0.0  0.428571  0.285714        0.0
2020-02-01  0.857143  0.0  0.428571  0.285714        0.0

Simplified Version

  • apply seasonal_decompose to each column with .apply 
df_seasonal = df.apply(lambda x: seasonal_decompose(x, model='additive').trend)
like image 169
Trenton McKinney Avatar answered Oct 19 '22 00:10

Trenton McKinney
Sign in to Comment

Related questions

Python: Plotly 3D Surface Plot
RuntimeWarning: coroutine 'qr.<locals>.confirm' was never awaited
How to combine tensor elements with similar values of specific column?
Python type hints with Protocol and TypeVar to specify arbitrary dataclasses
How change sizes of subplots in Matplotlib?
Python Plotly How to remove datetime gaps in candle stick chart?
Cython: How to sort the contents of one vector by another vector?
Memory leak in Google App Engine / Datastore / Flask / Python app
regex: change "white space" chracter and - character to null
Custom Criterion for DecisionTreeRegressor in sklearn
Keras custom loss function per tensor group
My image is not transparent when I am using ImageTk
Pandas DatetimeIndex TypeError
CNN model is overfitting to data after reaching 50% accuracy
Pandas dataframe - how to eliminate duplicate words in a column
Check if a date lies in first half of month or second in pandas
Pytest: Using fixtures in setup_method
Allow only one instance of a model in Django
Install numpy in Python virtualenv
Pandas DataFrame from Dictionary with Lists

Donate For Us

If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!