# staircase.Stairs¶

class staircase.Stairs(value=0, use_dates=<staircase.defaults.Defaults object>, tz=<staircase.defaults.Defaults object>)

An instance of a Stairs class is used to represent a step function.

The Stairs class encapsulates a SortedDict which is used to hold the points at which the step function changes, and by how much.

See the Stairs API for details of methods.

__init__(value=0, use_dates=<staircase.defaults.Defaults object>, tz=<staircase.defaults.Defaults object>)

Initialise a Stairs instance.

Parameters: value (float, default 0) – The value of the step function at negative infinity. use_dates (bool, default False) – Allows the step function to be defined with Pandas.Timestamp. Stairs

Methods

 __init__([value, use_dates, tz]) Initialise a Stairs instance. add(other) A binary operator facilitating the addition of step functions. clip([lower, upper]) Returns a copy of self which is zero-valued everywhere outside of [lower, upper) copy([deep]) Returns a deep copy of this Stairs instance corr(other[, lower, upper, lag, clip]) Calculates either correlation, autocorrelation or cross-correlation. cov(other[, lower, upper, lag, clip]) Calculates either covariance, autocovariance or cross-covariance. describe([lower, upper, percentiles]) Generate descriptive statistics. diff(delta) Returns a stairs instance corresponding to the difference between the step function corresponding to self and the same step-function translated by delta. divide(other) A binary operator facilitating the division of step functions. ecdf_stairs([lower, upper]) Calculates an empirical cumulative distribution function for the corresponding step function values (and returns the result as a Stairs instance) eq(other) Returns a boolean-valued step function indicating where self is equal to other. from_cumulative(cumulative[, use_dates, tz]) ge(other) Returns a boolean-valued step function indicating where self is greater than, or equal to, other. get_integral_and_mean([lower, upper]) Calculates the integral, and the mean of the step function. gt(other) Returns a boolean-valued step function indicating where self is strictly greater than other. hist([lower, upper, bin_edges, closed]) Calculates a histogram for the corresponding step function values hist_from_ecdf([bin_edges, closed]) Calculates a histogram from a Stairs instance corresponding to an empirical cumulative distribution function. identical(other) Returns True if self and other represent the same step functions. integrate([lower, upper]) Calculates the integral of the step function. invert() Returns a boolean-valued step function indicating where self is zero-valued. layer([start, end, value]) Changes the value of the step function. le(other) Returns a boolean-valued step function indicating where self is less than, or equal to, other. logical_and(other) Returns a boolean-valued step function indicating where self and other are non-zero. logical_or(other) Returns a boolean-valued step function indicating where self or other are non-zero. lt(other) Returns a boolean-valued step function indicating where self is strictly less than other. make_boolean() Returns a boolean-valued step function indicating where self is non-zero. max([lower, upper, lower_how, upper_how]) Calculates the maximum value of the step function mean([lower, upper]) Calculates the mean of the step function. median([lower, upper]) Calculates the median of the step function. min([lower, upper, lower_how, upper_how]) Calculates the minimum value of the step function mode([lower, upper]) Calculates the mode of the step function. multiply(other) A binary operator facilitating the multiplication of step functions. ne(other) Returns a boolean-valued step function indicating where self is not equal to other. negate() An operator which produces a new Stairs instance representing the multiplication of the step function by -1. number_of_steps() Calculates the number of step changes percentile(x[, lower, upper]) Calculates the x-th percentile of the step function. percentile_Stairs([lower, upper]) Deprecated. percentile_stairs([lower, upper]) Calculates a percentile function (and returns a corresponding Stairs instance) plot([ax]) Makes a step plot representing the finite intervals belonging to the Stairs instance. resample(x[, how, aggfunc, window, …]) Evaluates the value of the step function at one, or more, points and creates a new Stairs instance whose step changes occur at a subset of these points. rolling_mean([window, lower, upper]) Returns coordinates defining rolling mean sample(x[, how, aggfunc, window, lower_how, …]) Evaluates the value of the step function at one, or more, points. shift(delta) Returns a stairs instance corresponding to a horizontal translation by delta std([lower, upper]) Calculates the standard deviation of the step function. step_changes() Returns a dictionary of key, value pairs of indicating where step changes occur in the step function, and the change in value subtract(other) A binary operator facilitating the subtraction of step functions. to_dataframe() Returns a pandas.DataFrame with columns ‘start’, ‘end’ and ‘value’ values_in_range([lower, upper, lower_how, …]) Returns the range of the step function as a set of discrete values. var([lower, upper]) Calculates the variance of the step function.