shift(self, periods=1, freq: 'Frequency | None' = None, axis: 'Axis' = 0, fill_value=<no_default>) -> 'DataFrame'
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.generic.NDFrame.pct_change
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