pandas.Series.rolling

Series.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=0, closed=None)

Provides rolling window calculations.

New in version 0.18.0.

Parameters:

window : int, or offset

Size of the moving window. This is the number of observations used for calculating the statistic. Each window will be a fixed size.

If its an offset then this will be the time period of each window. Each window will be a variable sized based on the observations included in the time-period. This is only valid for datetimelike indexes. This is new in 0.19.0

min_periods : int, default None

Minimum number of observations in window required to have a value (otherwise result is NA). For a window that is specified by an offset, min_periods will default to 1. Otherwise, min_periods will default to the size of the window.

center : bool, default False

Set the labels at the center of the window.

win_type : str, default None

Provide a window type. If None, all points are evenly weighted. See the notes below for further information.

on : str, optional

For a DataFrame, column on which to calculate the rolling window, rather than the index

axis : int or str, default 0

closed : str, default None

Make the interval closed on the ‘right’, ‘left’, ‘both’ or ‘neither’ endpoints. For offset-based windows, it defaults to ‘right’. For fixed windows, defaults to ‘both’. Remaining cases not implemented for fixed windows.

New in version 0.20.0.

Returns:

a Window or Rolling sub-classed for the particular operation

See also

expanding

Provides expanding transformations.

ewm

Provides exponential weighted functions.

Notes

By default, the result is set to the right edge of the window. This can be changed to the center of the window by setting center=True.

To learn more about the offsets & frequency strings, please see this link.

The recognized win_types are:

• boxcar
• triang
• blackman
• hamming
• bartlett
• parzen
• bohman
• blackmanharris
• nuttall
• barthann
• kaiser (needs beta)
• gaussian (needs std)
• general_gaussian (needs power, width)
• slepian (needs width).

If win_type=None all points are evenly weighted. To learn more about different window types see scipy.signal window functions.

Examples

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
>>> df
     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Rolling sum with a window length of 2, using the ‘triang’ window type.

>>> df.rolling(2, win_type='triang').sum()
     B
0  NaN
1  1.0
2  2.5
3  NaN
4  NaN

Rolling sum with a window length of 2, min_periods defaults to the window length.

>>> df.rolling(2).sum()
     B
0  NaN
1  1.0
2  3.0
3  NaN
4  NaN

Same as above, but explicitly set the min_periods

>>> df.rolling(2, min_periods=1).sum()
     B
0  0.0
1  1.0
2  3.0
3  2.0
4  4.0

A ragged (meaning not-a-regular frequency), time-indexed DataFrame

>>> df = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]},
...                   index = [pd.Timestamp('20130101 09:00:00'),
...                            pd.Timestamp('20130101 09:00:02'),
...                            pd.Timestamp('20130101 09:00:03'),
...                            pd.Timestamp('20130101 09:00:05'),
...                            pd.Timestamp('20130101 09:00:06')])

>>> df
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  2.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0

Contrasting to an integer rolling window, this will roll a variable length window corresponding to the time period. The default for min_periods is 1.

>>> df.rolling('2s').sum()
                       B
2013-01-01 09:00:00  0.0
2013-01-01 09:00:02  1.0
2013-01-01 09:00:03  3.0
2013-01-01 09:00:05  NaN
2013-01-01 09:00:06  4.0