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.

Returns:

Return type:

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.