SARIMAX predicted_mean output
I am using SARIMAX to do a forecast model and i would like to add column titles to the output but i'm having trouble. I can work around it by doing .to_csv then reading it back in as a new dataframe before adding the labels but i'd like to be able to do it in one step rather than writing another file.
mod = sm.tsa.statespace.SARIMAX(y,
order=(0, 1, 1),
seasonal_order=(0, 1, 1, 12),
enforce_stationarity=False,
enforce_invertibility=False)
results = mod.fit()
pred_uc = results.get_forecast(steps = 48)
pred_ci = pred_uc.conf_int()
forecast = pd.DataFrame(pred_uc.predicted_mean, columns = ['TIME', 'column_2'])
ValueError: Wrong number of items passed 1, placement implies 2
ValueError: Shape of passed values is (1, 48), indices imply (2, 48)
python pandas statsmodels
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
add a comment |
I am using SARIMAX to do a forecast model and i would like to add column titles to the output but i'm having trouble. I can work around it by doing .to_csv then reading it back in as a new dataframe before adding the labels but i'd like to be able to do it in one step rather than writing another file.
mod = sm.tsa.statespace.SARIMAX(y,
order=(0, 1, 1),
seasonal_order=(0, 1, 1, 12),
enforce_stationarity=False,
enforce_invertibility=False)
results = mod.fit()
pred_uc = results.get_forecast(steps = 48)
pred_ci = pred_uc.conf_int()
forecast = pd.DataFrame(pred_uc.predicted_mean, columns = ['TIME', 'column_2'])
ValueError: Wrong number of items passed 1, placement implies 2
ValueError: Shape of passed values is (1, 48), indices imply (2, 48)
python pandas statsmodels
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
add a comment |
I am using SARIMAX to do a forecast model and i would like to add column titles to the output but i'm having trouble. I can work around it by doing .to_csv then reading it back in as a new dataframe before adding the labels but i'd like to be able to do it in one step rather than writing another file.
mod = sm.tsa.statespace.SARIMAX(y,
order=(0, 1, 1),
seasonal_order=(0, 1, 1, 12),
enforce_stationarity=False,
enforce_invertibility=False)
results = mod.fit()
pred_uc = results.get_forecast(steps = 48)
pred_ci = pred_uc.conf_int()
forecast = pd.DataFrame(pred_uc.predicted_mean, columns = ['TIME', 'column_2'])
ValueError: Wrong number of items passed 1, placement implies 2
ValueError: Shape of passed values is (1, 48), indices imply (2, 48)
python pandas statsmodels
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
I am using SARIMAX to do a forecast model and i would like to add column titles to the output but i'm having trouble. I can work around it by doing .to_csv then reading it back in as a new dataframe before adding the labels but i'd like to be able to do it in one step rather than writing another file.
mod = sm.tsa.statespace.SARIMAX(y,
order=(0, 1, 1),
seasonal_order=(0, 1, 1, 12),
enforce_stationarity=False,
enforce_invertibility=False)
results = mod.fit()
pred_uc = results.get_forecast(steps = 48)
pred_ci = pred_uc.conf_int()
forecast = pd.DataFrame(pred_uc.predicted_mean, columns = ['TIME', 'column_2'])
ValueError: Wrong number of items passed 1, placement implies 2
ValueError: Shape of passed values is (1, 48), indices imply (2, 48)
python pandas statsmodels
python pandas statsmodels
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
asked Dec 23 '18 at 4:39
Paul ClarkePaul Clarke
83
83
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
pred_uc.predicted_mean is a pandas Series object, associated with an index (e.g. a date index).
You can convert it to a DataFrame, with e.g. pred_uc.predicted_mean.to_frame(), but it will still only have one column and an index.
If for some reason you must have it in the form you showed, you could do e.g. the following:
pred_uc.predicted_mean.to_frame('column_2').rename_axis('time').reset_index()
which would give something like:
time column_2
0 2009Q4 3.791093
1 2010Q1 1.864600
2 2010Q2 0.680030
3 2010Q3 2.299168
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
add a comment |
After a bit of reading i ended up finding a solution that suited best because this is iterating and creating a new column for the results each time.
compiled_df[column_2] = forecast[0]
compiled_df.index.name = 'TIME'
Thank you for your help
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
pred_uc.predicted_mean is a pandas Series object, associated with an index (e.g. a date index).
You can convert it to a DataFrame, with e.g. pred_uc.predicted_mean.to_frame(), but it will still only have one column and an index.
If for some reason you must have it in the form you showed, you could do e.g. the following:
pred_uc.predicted_mean.to_frame('column_2').rename_axis('time').reset_index()
which would give something like:
time column_2
0 2009Q4 3.791093
1 2010Q1 1.864600
2 2010Q2 0.680030
3 2010Q3 2.299168
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
add a comment |
pred_uc.predicted_mean is a pandas Series object, associated with an index (e.g. a date index).
You can convert it to a DataFrame, with e.g. pred_uc.predicted_mean.to_frame(), but it will still only have one column and an index.
If for some reason you must have it in the form you showed, you could do e.g. the following:
pred_uc.predicted_mean.to_frame('column_2').rename_axis('time').reset_index()
which would give something like:
time column_2
0 2009Q4 3.791093
1 2010Q1 1.864600
2 2010Q2 0.680030
3 2010Q3 2.299168
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
add a comment |
pred_uc.predicted_mean is a pandas Series object, associated with an index (e.g. a date index).
You can convert it to a DataFrame, with e.g. pred_uc.predicted_mean.to_frame(), but it will still only have one column and an index.
If for some reason you must have it in the form you showed, you could do e.g. the following:
pred_uc.predicted_mean.to_frame('column_2').rename_axis('time').reset_index()
which would give something like:
time column_2
0 2009Q4 3.791093
1 2010Q1 1.864600
2 2010Q2 0.680030
3 2010Q3 2.299168
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
pred_uc.predicted_mean is a pandas Series object, associated with an index (e.g. a date index).
You can convert it to a DataFrame, with e.g. pred_uc.predicted_mean.to_frame(), but it will still only have one column and an index.
If for some reason you must have it in the form you showed, you could do e.g. the following:
pred_uc.predicted_mean.to_frame('column_2').rename_axis('time').reset_index()
which would give something like:
time column_2
0 2009Q4 3.791093
1 2010Q1 1.864600
2 2010Q2 0.680030
3 2010Q3 2.299168
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
answered Dec 27 '18 at 0:30
cfultoncfulton
26613
26613
add a comment |
add a comment |
After a bit of reading i ended up finding a solution that suited best because this is iterating and creating a new column for the results each time.
compiled_df[column_2] = forecast[0]
compiled_df.index.name = 'TIME'
Thank you for your help
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
add a comment |
After a bit of reading i ended up finding a solution that suited best because this is iterating and creating a new column for the results each time.
compiled_df[column_2] = forecast[0]
compiled_df.index.name = 'TIME'
Thank you for your help
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
add a comment |
After a bit of reading i ended up finding a solution that suited best because this is iterating and creating a new column for the results each time.
compiled_df[column_2] = forecast[0]
compiled_df.index.name = 'TIME'
Thank you for your help
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
After a bit of reading i ended up finding a solution that suited best because this is iterating and creating a new column for the results each time.
compiled_df[column_2] = forecast[0]
compiled_df.index.name = 'TIME'
Thank you for your help
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
answered Dec 28 '18 at 6:01
Paul ClarkePaul Clarke
83
83
add a comment |
add a comment |
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