Time-based Data Analysis with Window Method in Pandas

Syntax : DataFrame.expanding(min_periods=1, axis=_NoDefault.no_default , method='single')

min_periods: int, default 1

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

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.

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.

DataFrame.rolling(window, min_periods=None, center=False, win_type=None, on=None, axis=_NoDefault.no_default, closed=None, step=None, method='single')

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 date time like indexes.

If a Base Indexer 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 Data Frame, a column label or Index level on which to calculate the rolling window, rather than the Data Frame’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', the 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').

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.

DataFrame.ewm(com=None, span=None, halflife=None, alpha=None, min_periods=0, adjust=True, ignore_na=False, axis=_NoDefault.no_default, times=None, method='single')

Parameters:

com: float, optional

Specify decay in terms of center of mass

span: float, optional

Specify decay in terms of span

half life: float, str, time delta, optional

If times is specified, then  time delta convertible unit over which an observation decays to half its value.

Only applicable to mean(), and half life value will not apply to the other functions.

alpha: float, optional

Specify smoothing factor directly

min_periods: int, default 0

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

adjust: bool, default True

Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings (viewing EWMA as a moving average).

• When adjust=True (default), the EW function is calculated using weights

  
  

• When adjust=False, the exponentially weighted function is calculated recursively

ignore_na: bool, default False

Ignore missing values when calculating weights.

• When ignore_na=False (default), weights are based on absolute positions.

• When ignore_na=True, weights are based on relative positions.

axis{0, 1}, default 0

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

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

For Series this parameter is unused and defaults to 0.

times: np.ndarray, Series, default None

Only applicable to mean().

Times corresponding to the observations. Must be monotonically increasing and datetime64[ns] dtype.

If 1-D array like, a sequence with the same shape as the observations.

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.

Only applicable to mean()