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# pandas.Series.rolling

Series.rolling(window, min_periods=None, center=False, win_type=None, on=None, closed=None, step=None, method='single')

  Provide rolling window calculations.

  Parameters:
    window int, timedelta, str, offset, or BaseIndexer subclass

      Interval of the moving window.

      If an integer, the delta between the start and end of each window. The number of points in the window depends on the `closed` argument.

      If a timedelta, str, or offset, 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. To learn more about the offsets & frequency strings, please see this link.

      If a BaseIndexer subclass, the window boundaries based on the defined `get_window_bounds` method. Additional rolling keyword arguments, namely `min_periods`, `center`, `closed` and `step` will be passed to `get_window_bounds`.

    min_periods int, default None

      Minimum number of observations in window required to have a value; otherwise, result is `np.nan`.

      For a window that is specified by an offset, `min_periods` will default to 1.

      For a window that is specified by an integer, `min_periods` will default to the size of the window.

    center bool, default False

      If False, set the window labels as the right edge of the window index.

      If True, set the window labels as the center of the window index.

    win_type str, default None

      If `None`, all points are evenly weighted.

      If a string, it must be a valid scipy.signal window function.

      Certain Scipy window types require additional parameters to be passed in the aggregation function. The additional parameters must match the keywords specified in the Scipy window type method signature.

    on str, optional

      For a DataFrame, a column label or Index level on which to calculate the rolling window, rather than the DataFrame’s index.

      Provided integer column is ignored and excluded from result since an integer index is not used to calculate the rolling window.

    closed str, default None

      Determines the inclusivity of points in the window

      If `'right'`, uses the window (first, last] meaning the last point is included in the calculations.

      If `'left'`, uses the window [first, last) meaning the first point is included in the calculations.

      If `'both'`, uses the window [first, last] meaning all points in the window are included in the calculations.

      If `'neither'`, uses the window (first, last) meaning the first and last points in the window are excluded from calculations.

      () and [] are referencing open and closed set notation respetively.

      Default `None` (`'right'`).

    step int, default None

      Evaluate the window at every `step` result, equivalent to slicing as `[::step]`. `window` must be an integer. Using a step argument other than None or 1 will produce a result with a different shape than the input.

    method str {‘single’, ‘table’}, default ‘single’

      Execute the rolling operation per single column or row (`'single'`) or over the entire object (`'table'`).

      This argument is only implemented when specifying `engine='numba'` in the method call.

  Returns:
    pandas.api.typing.Window or pandas.api.typing.Rolling

      An instance of Window is returned if `win_type` is passed. Otherwise, an instance of Rolling is returned.

  See also

  `expanding`

    Provides expanding transformations.

  `ewm`

    Provides exponential weighted functions.

  Notes

  See Windowing Operations for further usage details and examples.

  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

  window

  Rolling sum with a window length of 2 observations.

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

  Rolling sum with a window span of 2 seconds.

      >>> df_time = 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_time
                             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

      >>> df_time.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

  Rolling sum with forward looking windows with 2 observations.

      >>> indexer = pd.api.indexers.FixedForwardWindowIndexer(window_size=2)
      >>> df.rolling(window=indexer, min_periods=1).sum()
           B
      0  1.0
      1  3.0
      2  2.0
      3  4.0
      4  4.0

  min_periods

  Rolling sum with a window length of 2 observations, but only needs a minimum of 1 observation to calculate a value.

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

  center

  Rolling sum with the result assigned to the center of the window index.

      >>> df.rolling(3, min_periods=1, center=True).sum()
           B
      0  1.0
      1  3.0
      2  3.0
      3  6.0
      4  4.0

      >>> df.rolling(3, min_periods=1, center=False).sum()
           B
      0  0.0
      1  1.0
      2  3.0
      3  3.0
      4  6.0

  step

  Rolling sum with a window length of 2 observations, minimum of 1 observation to calculate a value, and a step of 2.

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

  win_type

  Rolling sum with a window length of 2, using the Scipy `'gaussian'` window type. `std` is required in the aggregation function.

      >>> df.rolling(2, win_type="gaussian").sum(std=3)
                B
      0        NaN
      1   0.986207
      2   2.958621
      3        NaN
      4        NaN

  on

  Rolling sum with a window length of 2 days.

      >>> df = pd.DataFrame(
      ...     {
      ...         "A": [
      ...             pd.to_datetime("2020-01-01"),
      ...             pd.to_datetime("2020-01-01"),
      ...             pd.to_datetime("2020-01-02"),
      ...         ],
      ...         "B": [1, 2, 3],
      ...     },
      ...     index=pd.date_range("2020", periods=3),
      ... )

      >>> df
                          A  B
      2020-01-01 2020-01-01  1
      2020-01-02 2020-01-01  2
      2020-01-03 2020-01-02  3

      >>> df.rolling("2D", on="A").sum()
                          A    B
      2020-01-01 2020-01-01  1.0
      2020-01-02 2020-01-01  3.0
      2020-01-03 2020-01-02  6.0