Triangle Slicing

import chainladder as cl

This example demonstrates the familiarity of the pandas API applied to a :class:Triangle instance.

# Load data
clrd = cl.load_sample('clrd')

# pandas-style Aggregations
clrd = clrd.groupby('LOB').sum()
clrd

	Triangle Summary
Valuation:	1997-12
Grain:	OYDY
Shape:	(6, 6, 10, 10)
Index:	[LOB]
Columns:	[IncurLoss, CumPaidLoss, BulkLoss, EarnedPremDIR, EarnedPremCeded, EarnedPremNet]

# pandas-style value/column slicing
clrd = clrd['CumPaidLoss']
# pandas loc-style index slicing
clrd = clrd.loc['medmal']
clrd

	12	24	36	48	60	72	84	96	108	120
1988	5,934	47,258	95,054	131,616	165,117	181,937	198,395	206,068	213,374	217,239
1989	9,433	51,855	98,976	149,169	175,588	197,590	211,242	217,986	222,707
1990	11,996	54,742	118,964	163,695	190,391	213,972	225,199	235,717
1991	9,517	73,420	146,347	199,262	244,987	260,333	275,923
1992	12,479	78,212	157,400	209,959	244,018	267,007
1993	18,229	90,710	166,325	227,891	276,235
1994	14,952	94,303	186,577	252,449
1995	17,995	110,181	209,222
1996	20,390	107,474
1997	20,361

import matplotlib.pyplot as plt
plt.style.use('ggplot')
%config InlineBackend.figure_format = 'retina'

# Plot
ax = clrd.link_ratio.T.plot(
    marker='o', 
    title='Medical Malpractice Link Ratios',
    ylabel='Link Ratio', xlabel='Accident Year');