maxframe.dataframe.groupby.GroupBy.rolling#

GroupBy.rolling(window, min_periods=None, *, center=False, win_type=None, on=None, axis=0, closed=None, shift=0, order_cols=None, ascending=True) → RollingGroupby#

Return a rolling grouper, providing rolling functionality per group.

Parameters:

  • window (int, timedelta, str, offset, or BaseIndexer subclass) –

    Size of the moving window.

    If an integer, the fixed number of observations used for each window.

    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.

  • axis (int or str, default 0) –

    If 0 or 'index', roll across the rows.

    If 1 or 'columns', roll across the columns.

    For Series this parameter is unused and defaults to 0.

  • closed (str, default None) –

    If 'right', the first point in the window is excluded from calculations.

    If 'left', the last point in the window is excluded from calculations.

    If 'both', no points in the window are excluded from calculations.

    If 'neither', the first and last points in the window are excluded from calculations.

    Default None ('right').

  • shift (int, default 0) – If specified, the window will be shifted by shift rows (or data will be shifted by -shift rows) before computing window function.

Returns:

Return a new grouper with our rolling appended.

Return type:

maxframe.dataframe.groupby.RollingGroupby

See also

Series.rolling

Calling object with Series data.

DataFrame.rolling

Calling object with DataFrames.

Series.groupby

Apply a function groupby to a Series.

DataFrame.groupby

Apply a function groupby.

Examples

>>> import maxframe.dataframe as md
>>> df = md.DataFrame({'A': [1, 1, 2, 2],
...                    'B': [1, 2, 3, 4],
...                    'C': [0.362, 0.227, 1.267, -0.562]})
>>> df.execute()
      A  B      C
0     1  1  0.362
1     1  2  0.227
2     2  3  1.267
3     2  4 -0.562

>>> df.groupby('A').rolling(2).sum().execute()
    B      C
A
1 0  NaN    NaN
  1  3.0  0.589
2 2  NaN    NaN
  3  7.0  0.705

>>> df.groupby('A').rolling(2, min_periods=1).sum().execute()
    B      C
A
1 0  1.0  0.362
  1  3.0  0.589
2 2  3.0  1.267
  3  7.0  0.705

>>> df.groupby('A').rolling(2, on='B').sum().execute()
    B      C
A
1 0  1    NaN
  1  2  0.589
2 2  3    NaN
  3  4  0.705