chainladder.Triangle

class chainladder.Triangle(data=None, origin=None, development=None, columns=None, index=None, origin_format=None, development_format=None, cumulative=None, array_backend=None, pattern=False, trailing=True, *args, **kwargs)

The core data structure of the chainladder package

Parameters:

data: DataFrame

A single dataframe that contains columns represeting all other arguments to the Triangle constructor

origin: str or list

A representation of the accident, reporting or more generally the origin period of the triangle that will map to the Origin dimension

development: str or list

A representation of the development/valuation periods of the triangle that will map to the Development dimension

columns: str or list

A representation of the numeric data of the triangle that will map to the columns dimension. If None, then a single ‘Total’ key will be generated.

index: str or list or None

A representation of the index of the triangle that will map to the index dimension. If None, then a single ‘Total’ key will be generated.

origin_format: optional str

A string representation of the date format of the origin arg. If omitted then date format will be inferred by pandas.

development_format: optional str

A string representation of the date format of the development arg. If omitted then date format will be inferred by pandas.

cumulative: bool

Whether the triangle is cumulative or incremental. This attribute is required to use the grain and dev_to_val methods and will be automatically set when invoking cum_to_incr or incr_to_cum methods.

trailing: bool

When partial origin periods are present, setting trailing to True will ensure the most recent origin period is a full period and the oldest origin is partial. If full origin periods are present in the data, then trailing has no effect.

Attributes:

index: Series

Represents all available levels of the index dimension.

columns: Series

Represents all available levels of the value dimension.

origin: DatetimeIndex

Represents all available levels of the origin dimension.

development: Series

Represents all available levels of the development dimension.

key_labels: list

Represents the index axis labels

virtual_columns: Series

Represents the subset of columns of the triangle that are virtual.

valuation: DatetimeIndex

Represents all valuation dates of each cell in the Triangle.

origin_grain: str

The grain of the origin vector (‘Y’, ‘S’, ‘Q’, ‘M’)

development_grain: str

The grain of the development vector (‘Y’, ‘S’, ‘Q’, ‘M’)

shape: tuple

The 4D shape of the triangle instance with axes corresponding to (index, columns, origin, development)

link_ratio, age_to_age

Displays age-to-age ratios for the triangle.

valuation_datedate

The latest valuation date of the data

loc: Triangle

pandas-style loc accessor

iloc: Triangle

pandas-style iloc accessor

latest_diagonal: Triangle

The latest diagonal of the triangle

is_cumulative: bool

Whether the triangle is cumulative or not

is_ultimate: bool

Whether the triangle has an ultimate valuation

is_full: bool

Whether lower half of Triangle has been filled in

is_val_tri:

Whether the triangle development period is expressed as valuation periods.

values: array

4D numpy array underlying the Triangle instance

T: Triangle

Transpose index and columns of object. Only available when Triangle is convertible to DataFrame.

Methods

append(other)	Append rows of other to the end of caller, returning a new object.
astype(dtype[, inplace])	Copy of the array, cast to a specified type.
cum_to_incr([inplace])	Method to convert an cumlative triangle into a incremental triangle.
cumsum([axis])	Refer to pandas for cumsum functionality.
describe(*args, **kwargs)	Refer to pandas for describe functionality.
dev_to_val([inplace])	Converts triangle from a development lag triangle to a valuation triangle.
development_correlation([p_critical])	Mack (1997) test for correlations between subsequent development factors.
diff([axis])	Refer to pandas for diff functionality.
drop([labels, axis])	Drop specified labels from rows or columns.
drop_duplicates(*args, **kwargs)	Refer to pandas for drop_duplicates functionality.
dropna()	Method that removes orgin/development vectors from edge of a triangle that are all missing values.
fillna([value, inplace])	Fill nan with 'value' by axis. Parameters ---------- value: single value or array-like values, default = None Value(s) to fill across the axis.
grain([grain, trailing, inplace])	Changes the grain of a cumulative triangle.
groupby(by[, axis])	Group Triangle by index values.
heatmap([cmap, low, high, axis, subset])	Color the background in a gradient according to the data in each column (optionally row).
hvplot(*args, **kwargs)	Passthrough of pandas functionality
incr_to_cum([inplace])	Method to convert an incremental triangle into a cumulative triangle.
max([axis])	Refer to pandas for max functionality.
mean([axis])	Refer to pandas for mean functionality.
median([axis])	Refer to pandas for median functionality.
melt(*args, **kwargs)	Refer to pandas for melt functionality.
min([axis])	Refer to pandas for min functionality.
pct_chg(*args, **kwargs)	Refer to pandas for pct_chg functionality.
pivot(*args, **kwargs)	Refer to pandas for pivot functionality.
plot(*args, **kwargs)	Passthrough of pandas functionality
prod([axis])	Refer to pandas for prod functionality.
quantile([axis])	Refer to pandas for quantile functionality.
rename(axis, value)	Alter axes labels.
set_backend(backend[, inplace, deep])	Converts triangle array_backend.
set_index(value[, inplace])	Sets the index of the Triangle
shift([periods, axis])	Shift elements along an axis by desired number of periods.
sort_axis(axis)	Method to sort a Triangle along a given axis
std([axis])	Refer to pandas for std functionality.
sum([axis])	Refer to pandas for sum functionality.
to_clipboard(*args, **kwargs)	Refer to pandas for to_clipboard functionality.
to_csv(*args, **kwargs)	Refer to pandas for to_csv functionality.
to_dict(*args, **kwargs)	Refer to pandas for to_dict functionality.
to_excel(*args, **kwargs)	Refer to pandas for to_excel functionality.
to_frame([origin_as_datetime, keepdims, ...])	Converts a triangle to a pandas.DataFrame. Parameters ---------- origin_as_datetime : bool Whether the origin vector should be converted from PeriodIndex into a datetime dtype. Default is False. keepdims : bool If True, the triangle will be converted to a DataFrame with all dimensions intact. The argument will force a consistent DataFrame format regardless of whether any dimensions are of length 1. implicit_axis : bool When keepdims is True, this denotes whether to include the implicit valuation axis in addition to the origin and development. Returns ------- pandas.DataFrame representation of the Triangle.
to_html(*args, **kwargs)	Refer to pandas for to_html functionality.
to_json()	Serializes triangle object to json format
to_pickle(path[, protocol])	Serializes triangle object to pickle.
trend([trend, axis, start, end, ultimate_lag])	Allows for the trending of a Triangle object along either a valuation or origin axis.
unstack(*args, **kwargs)	Refer to pandas for unstack functionality.
val_to_dev([inplace])	Converts triangle from a valuation triangle to a development lag triangle.
valuation_correlation([p_critical, total])	Mack test for calendar year effect A calendar period has impact across developments if the probability of the number of small (or large) development factors in that period occurring randomly is less than p_critical
var([axis])	Refer to pandas for var functionality.

broadcast_axis
compute
copy
exp
get_array_module
head
log
maximum
minimum
pipe
round
sort_index
sqrt
tail