staircase.Stairs

class staircase.Stairs(value=0, use_dates=<staircase.stairs.Default object>, tz=<staircase.stairs.Default 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.stairs.Default object>, tz=<staircase.stairs.Default 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)	An operator facilitating the addition of two 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)	An operator facilitating the division of one step function by another.
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.
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
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)	An operator facilitating the multiplication of one step function with another.
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’s values
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)	An operator facilitating the subtraction of one step function from another.
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.