Stairs.
rolling_mean
(window=(0, 0), lower=inf, upper=inf)¶Returns coordinates defining rolling mean
The rolling mean of a step function is a continous piecewise linear function, hence it can
be described by a sequence of x,y coordinates which mark where function changes gradient. These
x,y coordinates are returned as a pandas.Series
which could then be used with
matplotlib.axes.Axes.plot()
, or equivalent, to visualise.
A rolling mean requires a window around a point x (referred to as the focal point) to be defined. In this implementation the window is defined by two values paired into an arraylike parameter called window. These two numbers are the distance from the focal point to the left boundary of the window, and the right boundary of the window respectively. This allows for trailing windows, leading windows and everything between (including a centred window).
If lower or upper is specified then only coordinates corresponding to windows contained within [lower, upper] are included.
Parameters: 


Returns:  
Return type: 

See also
Examples
>>> s2.rolling_mean(window=[0.5, 0.5])
0.5 0.0
0.5 0.5
1.5 0.5
2.5 0.0
3.5 1.0
5.0 1.0
6.0 0.0
dtype: float64
>>> series_list = [s2.rolling_mean(window=[0.5, 0.5]), s2.rolling_mean(window=[0.5, 0.5], lower=0, upper = 5.5)]
>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,5), sharey=True, sharex=True)
>>> for ax, title, series in zip(axes, ("default", "bounds supplied"), series_list):
... s2.plot(ax=ax)
... series.plot(ax=ax, label='rolling mean')
... ax.set_title(title)
... ax.legend()
>>> series_list = [s2.rolling_mean(window=[1, 0], lower=0, upper = 5.5), s2.rolling_mean(window=[0, 1], lower=0, upper = 5.5)]
>>> fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12,5), sharey=True, sharex=True)
>>> for ax, title, series in zip(axes, ("trailing window", "leading window"), series_list):
... s2.plot(ax=ax)
... series.plot(ax=ax, label='rolling mean')
... ax.set_title(title)
... ax.legend()