How can I convert back? [duplicate]
This question already has an answer here:
Pandas reverse of diff()
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I converted my timeseries into stationary time series with differentiation
data['consumption_diff'] = data.consumption-data.consumption.shift(1)
How can I convert consumption_diff back into consumption?
python python-3.x pandas series
edited Nov 26 '18 at 16:20
jpp
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asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
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marked as duplicate by jpp python
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Nov 26 '18 at 16:19
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This question already has an answer here:
Pandas reverse of diff()
1 answer
I converted my timeseries into stationary time series with differentiation
data['consumption_diff'] = data.consumption-data.consumption.shift(1)
How can I convert consumption_diff back into consumption?
python python-3.x pandas series
edited Nov 26 '18 at 16:20
jpp
100k2162111
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
marked as duplicate by jpp python
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Nov 26 '18 at 16:19
This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.
add a comment |
This question already has an answer here:
Pandas reverse of diff()
1 answer
I converted my timeseries into stationary time series with differentiation
data['consumption_diff'] = data.consumption-data.consumption.shift(1)
How can I convert consumption_diff back into consumption?
python python-3.x pandas series
edited Nov 26 '18 at 16:20
jpp
100k2162111
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
This question already has an answer here:
Pandas reverse of diff()
1 answer
I converted my timeseries into stationary time series with differentiation
data['consumption_diff'] = data.consumption-data.consumption.shift(1)
How can I convert consumption_diff back into consumption?
This question already has an answer here:
Pandas reverse of diff()
1 answer
python python-3.x pandas series
python python-3.x pandas series
edited Nov 26 '18 at 16:20
jpp
100k2162111
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
edited Nov 26 '18 at 16:20
jpp
100k2162111
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
edited Nov 26 '18 at 16:20
jpp
100k2162111
edited Nov 26 '18 at 16:20
jpp
100k2162111
edited Nov 26 '18 at 16:20
jpp
100k2162111
100k2162111
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
asked Nov 26 '18 at 15:46
Nikita PolozokNikita Polozok
183
183
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1 Answer
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You can use numpy's "r_" object which concatenates and flattens arrays and the "cumsum()" function which cumulatively sums values.
import numpy as np
undiffed = np.r_[data.consumption.iloc[0], data.consumption_diff.iloc[1:]].cumsum()
That is how you can undiff timeseries data and can be helpful if you've done a prediction into future dates that you need to undiff. However, you already have the undiffed values: data.consumption are your original undifffed data.
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
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1 Answer
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1 Answer
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You can use numpy's "r_" object which concatenates and flattens arrays and the "cumsum()" function which cumulatively sums values.
import numpy as np
undiffed = np.r_[data.consumption.iloc[0], data.consumption_diff.iloc[1:]].cumsum()
That is how you can undiff timeseries data and can be helpful if you've done a prediction into future dates that you need to undiff. However, you already have the undiffed values: data.consumption are your original undifffed data.
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
add a comment |
You can use numpy's "r_" object which concatenates and flattens arrays and the "cumsum()" function which cumulatively sums values.
import numpy as np
undiffed = np.r_[data.consumption.iloc[0], data.consumption_diff.iloc[1:]].cumsum()
That is how you can undiff timeseries data and can be helpful if you've done a prediction into future dates that you need to undiff. However, you already have the undiffed values: data.consumption are your original undifffed data.
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
add a comment |
You can use numpy's "r_" object which concatenates and flattens arrays and the "cumsum()" function which cumulatively sums values.
import numpy as np
undiffed = np.r_[data.consumption.iloc[0], data.consumption_diff.iloc[1:]].cumsum()
That is how you can undiff timeseries data and can be helpful if you've done a prediction into future dates that you need to undiff. However, you already have the undiffed values: data.consumption are your original undifffed data.
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
You can use numpy's "r_" object which concatenates and flattens arrays and the "cumsum()" function which cumulatively sums values.
import numpy as np
undiffed = np.r_[data.consumption.iloc[0], data.consumption_diff.iloc[1:]].cumsum()
That is how you can undiff timeseries data and can be helpful if you've done a prediction into future dates that you need to undiff. However, you already have the undiffed values: data.consumption are your original undifffed data.
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
answered Nov 26 '18 at 16:15
Ollie in PGHOllie in PGH
1,2501915
1,2501915
add a comment |
add a comment |
