shift(self, periods=1, freq=None, axis=0, fill_value=None) -> 'Series'
When :None:None:`freq` is not passed, shift the index without realigning the data. If :None:None:`freq` is passed (in this case, the index must be date or datetime, or it will raise a :None:None:`NotImplementedError`), the index will be increased using the periods and the :None:None:`freq`. :None:None:`freq` can be inferred when specified as "infer" as long as either freq or inferred_freq attribute is set in the index.
Number of periods to shift. Can be positive or negative.
Offset to use from the tseries module or time rule (e.g. 'EOM'). If :None:None:`freq` is specified then the index values are shifted but the data is not realigned. That is, use :None:None:`freq` if you would like to extend the index when shifting and preserve the original data. If :None:None:`freq` is specified as "infer" then it will be inferred from the freq or inferred_freq attributes of the index. If neither of those attributes exist, a ValueError is thrown.
Shift direction.
The scalar value to use for newly introduced missing values. the default depends on the dtype of :None:None:`self`. For numeric data, np.nan
is used. For datetime, timedelta, or period data, etc. NaT
is used. For extension dtypes, self.dtype.na_value
is used.
Copy of input object, shifted.
Shift index by desired number of periods with an optional time :None:None:`freq`.
DatetimeIndex.shift
Shift values of DatetimeIndex.
Index.shift
Shift values of Index.
PeriodIndex.shift
Shift values of PeriodIndex.
tshift
Shift the time index, using the index's frequency if available.
>>> df = pd.DataFrame({"Col1": [10, 20, 15, 30, 45],This example is valid syntax, but we were not able to check execution
... "Col2": [13, 23, 18, 33, 48],
... "Col3": [17, 27, 22, 37, 52]},
... index=pd.date_range("2020-01-01", "2020-01-05"))
... df Col1 Col2 Col3 2020-01-01 10 13 17 2020-01-02 20 23 27 2020-01-03 15 18 22 2020-01-04 30 33 37 2020-01-05 45 48 52
>>> df.shift(periods=3) Col1 Col2 Col3 2020-01-01 NaN NaN NaN 2020-01-02 NaN NaN NaN 2020-01-03 NaN NaN NaN 2020-01-04 10.0 13.0 17.0 2020-01-05 20.0 23.0 27.0This example is valid syntax, but we were not able to check execution
>>> df.shift(periods=1, axis="columns") Col1 Col2 Col3 2020-01-01 NaN 10 13 2020-01-02 NaN 20 23 2020-01-03 NaN 15 18 2020-01-04 NaN 30 33 2020-01-05 NaN 45 48This example is valid syntax, but we were not able to check execution
>>> df.shift(periods=3, fill_value=0) Col1 Col2 Col3 2020-01-01 0 0 0 2020-01-02 0 0 0 2020-01-03 0 0 0 2020-01-04 10 13 17 2020-01-05 20 23 27This example is valid syntax, but we were not able to check execution
>>> df.shift(periods=3, freq="D") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52This example is valid syntax, but we were not able to check execution
>>> df.shift(periods=3, freq="infer") Col1 Col2 Col3 2020-01-04 10 13 17 2020-01-05 20 23 27 2020-01-06 15 18 22 2020-01-07 30 33 37 2020-01-08 45 48 52See :
The following pages refer to to this document either explicitly or contain code examples using this.
pandas.core.series.Series.autocorr
pandas.core.generic.NDFrame.pct_change
pandas.core.indexes.base.Index.shift
pandas.core.series.Series.diff
Hover to see nodes names; edges to Self not shown, Caped at 50 nodes.
Using a canvas is more power efficient and can get hundred of nodes ; but does not allow hyperlinks; , arrows or text (beyond on hover)
SVG is more flexible but power hungry; and does not scale well to 50 + nodes.
All aboves nodes referred to, (or are referred from) current nodes; Edges from Self to other have been omitted (or all nodes would be connected to the central node "self" which is not useful). Nodes are colored by the library they belong to, and scaled with the number of references pointing them