pandas.core.groupby.DataFrameGroupBy.shift#

DataFrameGroupBy.shift(periods=1, freq=None, axis=_NoDefault.no_default, fill_value=_NoDefault.no_default, suffix=None)[source]#

Shift each group by periods observations.

If freq is passed, the index will be increased using the periods and the freq.

Parameters:

periodsint | Sequence[int], default 1: Number of periods to shift. If a list of values, shift each group by each period.
freqstr, optional: Frequency string.
axisaxis to shift, default 0: Shift direction.

Deprecated since version 2.1.0: For axis=1, operate on the underlying object instead. Otherwise the axis keyword is not necessary.
fill_valueoptional: The scalar value to use for newly introduced missing values.

Changed in version 2.1.0: Will raise a ValueError if freq is provided too.
suffixstr, optional: A string to add to each shifted column if there are multiple periods. Ignored otherwise.

Returns:

Series or DataFrame: Object shifted within each group.

See also

Index.shift: Shift values of Index.

Examples

For SeriesGroupBy:

>>> lst = ['a', 'a', 'b', 'b']
>>> ser = pd.Series([1, 2, 3, 4], index=lst)
>>> ser
a    1
a    2
b    3
b    4
dtype: int64
>>> ser.groupby(level=0).shift(1)
a    NaN
a    1.0
b    NaN
b    3.0
dtype: float64

For DataFrameGroupBy:

>>> data = [[1, 2, 3], [1, 5, 6], [2, 5, 8], [2, 6, 9]]
>>> df = pd.DataFrame(data, columns=["a", "b", "c"],
...                   index=["tuna", "salmon", "catfish", "goldfish"])
>>> df
           a  b  c
    tuna   1  2  3
  salmon   1  5  6
 catfish   2  5  8
goldfish   2  6  9
>>> df.groupby("a").shift(1)
              b    c
    tuna    NaN  NaN
  salmon    2.0  3.0
 catfish    NaN  NaN
goldfish    5.0  8.0