pandas.DataFrame.shift#

DataFrame.shift(periods=1,freq=None,axis=0,fill_value=<no_default>,suffix=None)[source]#

Shift index by desired number of periods with an optional timefreq.

Whenfreq is not passed, shift the index without realigning the data.Iffreq is passed (in this case, the index must be date or datetime,or it will raise aNotImplementedError), the index will beincreased using the periods and thefreq.freq can be inferredwhen specified as “infer” as long as either freq or inferred_freqattribute is set in the index.

Parameters:

periodsint or Sequence

Number of periods to shift. Can be positive or negative.If an iterable of ints, the data will be shifted once by each int.This is equivalent to shifting by one value at a time andconcatenating all resulting frames. The resulting columns will havethe shift suffixed to their column names. For multiple periods,axis must not be 1.

freqDateOffset, tseries.offsets, timedelta, or str, optional

Offset to use from the tseries module or time rule (e.g. ‘EOM’).Iffreq is specified then the index values are shifted but thedata is not realigned. That is, usefreq if you would like toextend the index when shifting and preserve the original data.Iffreq is specified as “infer” then it will be inferred fromthe freq or inferred_freq attributes of the index. If neither ofthose attributes exist, a ValueError is thrown.

axis{0 or ‘index’, 1 or ‘columns’, None}, default None

Shift direction. ForSeries this parameter is unused and defaults to 0.

fill_valueobject, optional

The scalar value to use for newly introduced missing values.the default depends on the dtype ofself.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.

suffixstr, optional

If str and periods is an iterable, this is added after the columnname and before the shift value for each shifted column name.

Returns:

DataFrame

Copy of input object, shifted.

See also

Index.shift

Shift values of Index.

DatetimeIndex.shift

Shift values of DatetimeIndex.

PeriodIndex.shift

Shift values of PeriodIndex.

Examples

>>>df=pd.DataFrame({"Col1":[10,20,15,30,45],..."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  Col32020-01-01    10    13    172020-01-02    20    23    272020-01-03    15    18    222020-01-04    30    33    372020-01-05    45    48    52

>>>df.shift(periods=3)            Col1  Col2  Col32020-01-01   NaN   NaN   NaN2020-01-02   NaN   NaN   NaN2020-01-03   NaN   NaN   NaN2020-01-04  10.0  13.0  17.02020-01-05  20.0  23.0  27.0

>>>df.shift(periods=1,axis="columns")            Col1  Col2  Col32020-01-01   NaN    10    132020-01-02   NaN    20    232020-01-03   NaN    15    182020-01-04   NaN    30    332020-01-05   NaN    45    48

>>>df.shift(periods=3,fill_value=0)            Col1  Col2  Col32020-01-01     0     0     02020-01-02     0     0     02020-01-03     0     0     02020-01-04    10    13    172020-01-05    20    23    27

>>>df.shift(periods=3,freq="D")            Col1  Col2  Col32020-01-04    10    13    172020-01-05    20    23    272020-01-06    15    18    222020-01-07    30    33    372020-01-08    45    48    52

>>>df.shift(periods=3,freq="infer")            Col1  Col2  Col32020-01-04    10    13    172020-01-05    20    23    272020-01-06    15    18    222020-01-07    30    33    372020-01-08    45    48    52

>>>df['Col1'].shift(periods=[0,1,2])            Col1_0  Col1_1  Col1_22020-01-01      10     NaN     NaN2020-01-02      20    10.0     NaN2020-01-03      15    20.0    10.02020-01-04      30    15.0    20.02020-01-05      45    30.0    15.0