{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "Em7RlIuuzk8Q" }, "source": [ "# SQL: Queries to Create Triangles" ] }, { "cell_type": "markdown", "metadata": { "id": "gzKPDBVF72a8" }, "source": [ "This notebook was created by Jacky Poon for the [Actuaries' Analytical Cookbook](https://actuariesinstitute.github.io/cookbook/docs/index.html).\n", "\n", "## Introduction and Setup\n", "\n", "This article describes a method with SQL to convert a transactional claims dataset to a triangle. With the source data often being in a data warehouse, by running queries in SQL we can efficiently extract a small summary set, rather than attempting to transfer what may be a large dataset of raw transactional data to our machine running Python or R. For this example, we will use Python for constructing our dummy dataset, and use ``duckdb`` as our SQL database, but basic concepts should apply similarly to other SQL databases.\n", "\n", "There is also a dual purpose for this article to serve as an introduction to ``duckdb``, a handy package that for running analytical SQL queries locally without having to use a data warehouse server. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "N1avYHra4_NZ", "outputId": "338198cf-2dbb-4d91-c09a-f568a7bbc3a8" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n", "Collecting duckdb\n", " Downloading duckdb-0.4.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (15.7 MB)\n", "\u001b[K |████████████████████████████████| 15.7 MB 7.1 MB/s \n", "\u001b[?25hRequirement already satisfied: numpy>=1.14 in /usr/local/lib/python3.7/dist-packages (from duckdb) (1.21.6)\n", "Installing collected packages: duckdb\n", "Successfully installed duckdb-0.4.0\n", "Name: duckdb\n", "Version: 0.4.0\n", "Summary: DuckDB embedded database\n", "Home-page: https://www.duckdb.org\n", "Author: None\n", "Author-email: None\n", "License: MIT\n", "Location: /usr/local/lib/python3.7/dist-packages\n", "Requires: numpy\n", "Required-by: \n" ] } ], "source": [ "!pip install duckdb\n", "!pip show duckdb" ] }, { "cell_type": "markdown", "metadata": { "id": "7OtHEPCn-AXr" }, "source": [ "Import the libraries:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "lLHnqIAg8HZl" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "import duckdb\n", "\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "OzMLKAFC49Wt" }, "outputs": [], "source": [ "# start an in-memory database\n", "con = duckdb.connect(database=':memory:')" ] }, { "cell_type": "markdown", "metadata": { "id": "NyR--K6WBKWc" }, "source": [ "## Transaction Data" ] }, { "cell_type": "markdown", "metadata": { "id": "Z0_2mQZF8QTZ" }, "source": [ "For the example reserving data, we use a simulated dataset from the [SynthETIC](https://arxiv.org/pdf/2008.05693.pdf) R package, with further adjustments to it to make it resemble a real dataset.\n", "\n", "DuckDB can [read and query CSVs directly from local files](https://duckdb.org/docs/data/csv) - but with CSV files from the internet it is easier to read it with Python in pandas." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 488 }, "id": "Yeb7wG-A8LbE", "outputId": "630be246-7d80-463b-846b-750a25fd26be" }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ " claim_no pmt_no occurrence_period occurrence_time claim_size \\\n", "0 1 1 1 0.623835 785870.789628 \n", "1 1 2 1 0.623835 785870.789628 \n", "2 1 3 1 0.623835 785870.789628 \n", "3 1 4 1 0.623835 785870.789628 \n", "4 1 5 1 0.623835 785870.789628 \n", "... ... ... ... ... ... \n", "18978 3624 2 40 39.767468 270737.291484 \n", "18979 3624 3 40 39.767468 270737.291484 \n", "18980 3624 4 40 39.767468 270737.291484 \n", "18981 3624 5 40 39.767468 270737.291484 \n", "18982 3624 6 40 39.767468 270737.291484 \n", "\n", " notidel setldel payment_time payment_period payment_size \\\n", "0 0.065163 18.228022 4.197594 5 25104.778182 \n", "1 0.065163 18.228022 7.096012 8 26176.620067 \n", "2 0.065163 18.228022 11.157697 12 26333.186750 \n", "3 0.065163 18.228022 14.445762 15 26341.097381 \n", "4 0.065163 18.228022 18.452453 19 592456.913866 \n", "... ... ... ... ... ... \n", "18978 0.666458 2.920804 41.622132 42 6586.081338 \n", "18979 0.666458 2.920804 42.081820 43 9716.975065 \n", "18980 0.666458 2.920804 42.407479 43 7770.338755 \n", "18981 0.666458 2.920804 43.066655 44 203618.760893 \n", "18982 0.666458 2.920804 43.354731 44 34908.748394 \n", "\n", " payment_inflated payment_delay \n", "0 25631.935128 3.508595 \n", "1 27112.545886 2.898418 \n", "2 27828.701791 4.061685 \n", "3 28293.903794 3.288065 \n", "4 649127.994604 4.006691 \n", "... ... ... \n", "18978 8093.128975 0.670541 \n", "18979 11967.648057 0.459688 \n", "18980 9585.568042 0.325659 \n", "18981 252007.199917 0.659176 \n", "18982 43266.205665 0.288075 \n", "\n", "[18983 rows x 12 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transactions = pd.read_csv(\n", " \"https://raw.githubusercontent.com/JackyP/SyntheticExports/main/synthetic_test_transaction_dataset.csv\"\n", ")\n", "transactions" ] }, { "cell_type": "markdown", "metadata": { "id": "71vnpC-249Ww" }, "source": [ "This is a transactional dataset with payments. The dataset has times are represented as arbitrary time period units rather than dates, and some additional calculated fields are already available. However, for this exercise, we want to demonstrate how to create these columns in real world situations where the raw datasets are unlikely to include them. So the time periods will be converted to date formats with months from a start date of 2000-01-01 and some columns hidden to create a dataset that resembles real datasets in practice." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "tj0m-7du49Wx" }, "outputs": [], "source": [ "# Feel free to skim through this part.\n", "import datetime\n", "from dateutil.relativedelta import relativedelta\n", "\n", "dummy_start_date = datetime.date(2000, 1, 1)\n", "\n", "transactions['occurrence_date'] = transactions.apply(\n", " lambda x: (dummy_start_date + \n", " relativedelta(months = int(x['occurrence_time'])) + \n", " relativedelta(days = int(x['occurrence_time'] % 1 * 28))\n", " ), \n", " axis = 1\n", ")\n", "\n", "transactions['payment_date'] = transactions.apply(\n", " lambda x: (dummy_start_date + \n", " relativedelta(months = int(x['payment_time'])) + \n", " relativedelta(days = int(x['payment_time'] % 1 * 28))\n", " ), \n", " axis = 1\n", ")\n", "transactions2 = transactions.loc[\n", " lambda df: df.payment_time <= 40, \n", " [\"claim_no\", \"pmt_no\", \"occurrence_date\", \"payment_date\", \"payment_size\"]\n", "]" ] }, { "cell_type": "markdown", "metadata": { "id": "3UBTPx5K49Wy" }, "source": [ "So we will register this pandas table in SQL and pretend we had a dataset in our data warehouse that looks like this:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "qCNzxYRE49Wy", "outputId": "4abb29f4-9133-4657-abfb-d75a81a30165" }, "outputs": [ { "data": { "text/html": [ "\n", "
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..................
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\n", " " ], "text/plain": [ " claim_no pmt_no occurrence_date payment_date payment_size\n", "0 1 1 2000-01-18 2000-05-06 25104.778182\n", "1 1 2 2000-01-18 2000-08-03 26176.620067\n", "2 1 3 2000-01-18 2000-12-05 26333.186750\n", "3 1 4 2000-01-18 2001-03-13 26341.097381\n", "4 1 5 2000-01-18 2001-07-13 592456.913866\n", "... ... ... ... ... ...\n", "18522 3540 1 2003-03-22 2003-04-15 7408.273603\n", "18523 3540 2 2003-03-22 2003-04-23 7557.339854\n", "18539 3543 1 2003-04-04 2003-04-25 11149.853130\n", "18691 3570 1 2003-04-07 2003-04-26 3354.405206\n", "18802 3588 1 2003-04-09 2003-04-26 2018.080359\n", "\n", "[14951 rows x 5 columns]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# register the table in sql\n", "con.register('transactions_view', transactions2)\n", "\n", "transactions2" ] }, { "cell_type": "markdown", "metadata": { "id": "Jym1qXXy49Wz" }, "source": [ "### Using SQL\n", "\n", "Here is the SQL query. It creates accident, development and payment/calendar periods from the dataset, and sums up at that triangle level. Uncomment ``claim_no`` and ``pmt_no`` to get a more detailed view - or for testing that the logic works." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "szfYHWOd49Wz", "outputId": "3091c20e-2a4f-429f-8686-8a97b23e0c83" }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ " occurrence_period payment_period development_period payments\n", "0 1 2 2 46985.029619\n", "1 1 3 3 392545.850638\n", "2 1 4 4 185946.113394\n", "3 1 5 5 700630.302735\n", "4 1 6 6 261024.509136\n", ".. ... ... ... ...\n", "765 37 40 4 338030.875016\n", "766 38 39 2 97489.963585\n", "767 38 40 3 259540.894156\n", "768 39 40 2 69383.491649\n", "769 40 40 1 16522.338695\n", "\n", "[770 rows x 4 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.execute(\"\"\"\n", " CREATE OR REPLACE VIEW triangle AS \n", " SELECT \n", " --claim_no,\n", " --pmt_no,\n", " DATE_DIFF('month', DATE '2000-01-01', STRPTIME(occurrence_date, '%Y-%m-%d')) + 1 as occurrence_period,\n", " DATE_DIFF('month', DATE '2000-01-01', STRPTIME(payment_date, '%Y-%m-%d')) + 1 as payment_period,\n", " DATE_DIFF('month', DATE '2000-01-01', STRPTIME(payment_date, '%Y-%m-%d')) - \n", " DATE_DIFF('month', DATE '2000-01-01', STRPTIME(occurrence_date, '%Y-%m-%d')) + 1 as development_period, \n", " SUM(payment_size) as payments\n", "\n", " FROM \n", " transactions_view\n", " GROUP BY \n", " --claim_no,\n", " --pmt_no, \n", " occurrence_period,\n", " development_period,\n", " payment_period\n", " \n", " ORDER BY\n", " --claim_no,\n", " --pmt_no, \n", " occurrence_period,\n", " development_period,\n", " payment_period\n", ";\n", " \n", " SELECT * FROM triangle;\n", "\"\"\"\n", ")\n", "triangle = con.fetchdf()\n", "triangle" ] }, { "cell_type": "markdown", "metadata": { "id": "YbTA47SG49W0" }, "source": [ "Whilst the above is perfect for further calculations or export, triangles are often displayed in the \"wide\" format as follows. Pivotting is easier in pandas in Python (or R with ``dplyr::pivot_wider``), than in SQL." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "nlswukcw49W0", "outputId": "f765f96b-e55a-48df-fafc-5d80e7c41114" }, "outputs": [ { "data": { "text/html": [ "\n", "
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40 rows × 39 columns

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NaN \n", "4 1.742546e+05 9.715882e+05 ... 4.688630e+05 \n", "5 7.504150e+05 2.611304e+05 ... 1.103665e+05 \n", "6 8.531432e+05 2.054070e+05 ... 6.795053e+04 \n", "7 7.314588e+05 6.290212e+05 ... 1.041520e+04 \n", "8 1.167906e+06 4.107145e+05 ... NaN \n", "9 5.535232e+05 5.008409e+05 ... 1.230771e+04 \n", "10 4.876399e+05 2.889569e+05 ... NaN \n", "11 5.397643e+05 4.108131e+05 ... 1.069290e+05 \n", "12 2.775127e+05 2.709438e+05 ... NaN \n", "13 3.248639e+05 2.372064e+05 ... NaN \n", "14 6.022564e+05 7.874864e+05 ... NaN \n", "15 1.432307e+06 4.311331e+05 ... NaN \n", "16 1.456116e+06 9.063431e+05 ... NaN \n", "17 1.066295e+06 3.610327e+05 ... NaN \n", "18 5.865144e+05 1.090865e+06 ... NaN \n", "19 1.351723e+06 1.012969e+06 ... NaN \n", "20 5.173210e+05 2.957905e+05 ... NaN \n", "21 2.055776e+06 4.470712e+05 ... NaN \n", "22 2.230668e+06 1.509081e+05 ... NaN \n", "23 3.514760e+05 3.263753e+05 ... NaN \n", "24 6.981326e+05 3.937930e+05 ... NaN \n", "25 7.713320e+05 5.950225e+05 ... NaN \n", "26 1.634412e+06 4.614016e+05 ... NaN \n", "27 6.043287e+05 6.298926e+05 ... NaN \n", "28 1.122428e+06 3.405180e+05 ... NaN \n", "29 9.524665e+05 8.048602e+05 ... NaN \n", "30 1.993302e+05 2.101516e+05 ... NaN \n", "31 7.195764e+05 4.164395e+05 ... NaN \n", "32 5.691857e+05 NaN ... NaN \n", "33 NaN NaN ... NaN \n", "34 NaN NaN ... NaN \n", "35 NaN NaN ... NaN \n", "36 NaN NaN ... NaN \n", "37 NaN NaN ... NaN \n", "38 NaN NaN ... NaN \n", "39 NaN NaN ... NaN \n", "40 NaN NaN ... NaN \n", "\n", "development_period 31 32 33 34 \\\n", "occurrence_period \n", "1 507751.595236 1.267975e+06 205780.737653 6226.894042 \n", "2 8745.529181 3.974022e+05 714117.521603 89465.857732 \n", "3 20938.965519 NaN NaN 19996.986384 \n", "4 NaN 6.155031e+05 197934.940768 NaN \n", "5 587681.103745 2.201814e+04 87364.263674 NaN \n", "6 NaN 1.900894e+06 NaN 189803.137289 \n", "7 40683.584878 4.737402e+05 NaN 874535.741565 \n", "8 493829.570893 3.123278e+05 58747.903282 NaN \n", "9 117212.190268 3.179059e+05 NaN NaN \n", "10 NaN NaN NaN NaN \n", "11 NaN NaN NaN NaN \n", "12 NaN NaN NaN NaN \n", "13 NaN NaN NaN NaN \n", "14 NaN NaN NaN NaN \n", "15 NaN NaN NaN NaN \n", "16 NaN NaN NaN NaN \n", "17 NaN NaN NaN NaN \n", "18 NaN NaN NaN NaN \n", "19 NaN NaN NaN NaN \n", "20 NaN NaN NaN NaN \n", "21 NaN NaN NaN NaN \n", "22 NaN NaN NaN NaN \n", "23 NaN NaN NaN NaN \n", "24 NaN NaN NaN NaN \n", "25 NaN NaN NaN NaN \n", "26 NaN NaN NaN NaN \n", "27 NaN NaN NaN NaN \n", "28 NaN NaN NaN NaN \n", "29 NaN NaN NaN NaN \n", "30 NaN NaN NaN NaN \n", "31 NaN NaN NaN NaN \n", "32 NaN NaN NaN NaN \n", "33 NaN NaN NaN NaN \n", "34 NaN NaN NaN NaN \n", "35 NaN NaN NaN NaN \n", "36 NaN NaN NaN NaN \n", "37 NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN \n", "39 NaN NaN NaN NaN \n", "40 NaN NaN NaN NaN \n", "\n", "development_period 35 36 37 38 \\\n", "occurrence_period \n", "1 NaN 5.087788e+05 55918.782513 NaN \n", "2 128332.630805 5.301655e+05 349790.454839 12587.59026 \n", "3 NaN NaN NaN NaN \n", "4 NaN 3.830643e+05 NaN NaN \n", "5 322793.116875 1.043557e+06 NaN NaN \n", "6 165066.293055 NaN NaN NaN \n", "7 NaN NaN NaN NaN \n", "8 NaN NaN NaN NaN \n", "9 NaN NaN NaN NaN \n", "10 NaN NaN NaN NaN \n", "11 NaN NaN NaN NaN \n", "12 NaN NaN NaN NaN \n", "13 NaN NaN NaN NaN \n", "14 NaN NaN NaN NaN \n", "15 NaN NaN NaN NaN \n", "16 NaN NaN NaN NaN \n", "17 NaN NaN NaN NaN \n", "18 NaN NaN NaN NaN \n", "19 NaN NaN NaN NaN \n", "20 NaN NaN NaN NaN \n", "21 NaN NaN NaN NaN \n", "22 NaN NaN NaN NaN \n", "23 NaN NaN NaN NaN \n", "24 NaN NaN NaN NaN \n", "25 NaN NaN NaN NaN \n", "26 NaN NaN NaN NaN \n", "27 NaN NaN NaN NaN \n", "28 NaN NaN NaN NaN \n", "29 NaN NaN NaN NaN \n", "30 NaN NaN NaN NaN \n", "31 NaN NaN NaN NaN \n", "32 NaN NaN NaN NaN \n", "33 NaN NaN NaN NaN \n", "34 NaN NaN NaN NaN \n", "35 NaN NaN NaN NaN \n", "36 NaN NaN NaN NaN \n", "37 NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN \n", "39 NaN NaN NaN NaN \n", "40 NaN NaN NaN NaN \n", "\n", "development_period 39 \n", "occurrence_period \n", "1 412516.399423 \n", "2 90955.620959 \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", "10 NaN \n", "11 NaN \n", "12 NaN \n", "13 NaN \n", "14 NaN \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "30 NaN \n", "31 NaN \n", "32 NaN \n", "33 NaN \n", "34 NaN \n", "35 NaN \n", "36 NaN \n", "37 NaN \n", "38 NaN \n", "39 NaN \n", "40 NaN \n", "\n", "[40 rows x 39 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "triangle.pivot(index=\"occurrence_period\", columns=\"development_period\", values=\"payments\")" ] }, { "cell_type": "markdown", "metadata": { "id": "MAK7SfG549W1" }, "source": [ "This can also be plotted easily with ``pandas``." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 434 }, "id": "OTRe3aBT49W1", "outputId": "f8d60856-60ce-491a-85d4-4c657117df2c", "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "(triangle\n", " .pivot(index=\"development_period\", columns=\"occurrence_period\", values=\"payments\")\n", " .plot(logy=True)\n", ")\n", "plt.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.8), ncol=5)" ] }, { "cell_type": "markdown", "metadata": { "id": "XiQJklV349W2" }, "source": [ "## Guaranteeing all cells\n", "\n", "With the above dataset, records will be missing if they do not have any claims transactions. This can be problematic if the models or calculations later on in the process flow rely on the dataset having every single accident/development period combination. To include these zero cells is not too difficult to implement in SQL." ] }, { "cell_type": "markdown", "metadata": { "id": "kO6ZLGcj49W3" }, "source": [ "The original dataset will be joined to a dummy dataset with the full range of accident/occurence and development periods." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "qYMNW8T249W3", "outputId": "3cd641d3-aa60-4255-fc81-2e5a4cef6443" }, "outputs": [ { "data": { "text/html": [ "\n", "
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\n", " " ], "text/plain": [ " occurrence_period development_period payment_period payments\n", "0 1 2 2 46985.029619\n", "1 1 3 3 392545.850638\n", "2 1 4 4 185946.113394\n", "3 1 5 5 700630.302735\n", "4 1 6 6 261024.509136\n", ".. ... ... ... ...\n", "815 33 1 33 0.000000\n", "816 34 1 34 0.000000\n", "817 35 1 35 0.000000\n", "818 38 1 38 0.000000\n", "819 39 1 39 0.000000\n", "\n", "[820 rows x 4 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "con.execute(\"\"\"\n", " CREATE OR REPLACE VIEW triangle_fill AS \n", " \n", " WITH full_tri as (\n", " SELECT \n", " o.occurrence_period,\n", " d.development_period,\n", " d.development_period + o.occurrence_period - 1 as payment_period\n", "\n", " FROM \n", " range_occurrence_view as o,\n", " range_development_view as d\n", " )\n", " SELECT \n", " full_tri.*, \n", " COALESCE(triangle.payments, 0) as payments\n", " FROM \n", " full_tri\n", "\n", " LEFT JOIN\n", " triangle\n", " ON\n", " full_tri.occurrence_period = triangle.occurrence_period\n", " AND full_tri.development_period = triangle.development_period\n", " AND full_tri.payment_period = triangle.payment_period\n", " \n", " WHERE\n", " full_tri.payment_period <= 40 \n", " -- if triangle is cut off at particular calendar period \n", ";\n", " \n", " SELECT * FROM triangle_fill;\n", "\"\"\"\n", ")\n", "triangle_fill = con.fetchdf()\n", "triangle_fill" ] }, { "cell_type": "markdown", "metadata": { "id": "7CJgupxN49W5" }, "source": [ "Again, here is the triangle. You can see the accident/development cells with no payments are now zero instead of null." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "OleYbVbI49W5", "outputId": "aee6b03a-90d8-40da-e68f-89291c673d2e" }, "outputs": [ { "data": { "text/html": [ "\n", "
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occurrence_period
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110.000000100416.983371292038.1983434.448307e+055.126842e+053.632974e+051.872042e+068.211516e+055.397643e+054.108131e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
120.00000071522.558157458885.3921981.747373e+052.250982e+054.301923e+053.891993e+051.278298e+062.775127e+052.709438e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
130.00000079064.667239254608.6174744.293419e+051.876411e+065.398142e+056.579055e+058.572567e+053.248639e+052.372064e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
140.00000096277.421753497171.0889931.677511e+055.025259e+053.275906e+055.593842e+054.950296e+056.022564e+057.874864e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
150.00000021066.291384131006.3027722.469400e+051.794551e+052.232539e+054.005883e+051.158106e+061.432307e+064.311331e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
160.00000040871.450031181169.9698465.136258e+059.165714e+052.441091e+056.139660e+058.670557e+051.456116e+069.063431e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1721559.05317098325.244742111475.3073666.413985e+056.213374e+057.985672e+051.252059e+069.138599e+051.066295e+063.610327e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
180.00000051441.830587135464.9675422.466380e+053.549195e+057.319261e+054.614343e+053.535514e+055.865144e+051.090865e+06...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
190.00000070653.792105453457.3165934.380374e+051.137775e+061.579018e+069.959784e+058.986285e+051.351723e+061.012969e+06...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
200.000000143003.416713780501.3761943.219736e+051.125306e+063.863964e+053.987071e+057.493505e+055.173210e+052.957905e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
21440.16601496436.510110932830.9204276.675994e+058.898485e+055.927916e+051.256132e+061.541598e+062.055776e+064.470712e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
225071.193962114720.424477407660.1077164.120153e+053.215467e+054.644149e+051.129388e+067.422531e+052.230668e+061.509081e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
230.00000067368.580343296994.6563151.742078e+056.093717e+056.336027e+054.269155e+051.150700e+063.514760e+053.263753e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
242538.22361160928.691028474989.5739242.655743e+053.890088e+057.493399e+051.231399e+066.559668e+056.981326e+053.937930e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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270.00000016208.391804129023.8207133.686506e+055.212379e+052.942529e+054.663017e+055.628172e+056.043287e+056.298926e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2815268.662911394082.054498221508.4228902.374703e+052.292117e+054.800493e+058.586132e+054.008364e+051.122428e+063.405180e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
293088.13024211868.998623171285.5696116.625948e+055.220645e+054.140340e+056.895261e+051.054986e+069.524665e+058.048602e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
305760.506354154821.974483655525.1506194.317146e+051.124190e+062.260838e+056.135078e+054.430098e+051.993302e+052.101516e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
314495.37050039937.684328156494.2230441.601225e+054.273681e+053.515268e+054.448231e+052.566694e+057.195764e+054.164395e+05...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
329929.17976174453.718579371373.6465861.127049e+068.716352e+057.048680e+054.415583e+059.356616e+055.691857e+05NaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
330.00000020348.062312170218.4597063.565538e+051.227990e+064.104408e+055.005702e+059.153730e+05NaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
340.00000060272.602312244223.4156056.362106e+057.611882e+054.975266e+051.682282e+06NaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
350.00000029157.569363140597.3742952.988598e+059.378148e+059.179868e+05NaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
361740.43945871028.928995159736.3507194.944280e+055.790932e+05NaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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4.644824e+05 9.741834e+05 \n", "10 6.072203e+05 4.012266e+05 1.216483e+06 1.395769e+06 \n", "11 5.126842e+05 3.632974e+05 1.872042e+06 8.211516e+05 \n", "12 2.250982e+05 4.301923e+05 3.891993e+05 1.278298e+06 \n", "13 1.876411e+06 5.398142e+05 6.579055e+05 8.572567e+05 \n", "14 5.025259e+05 3.275906e+05 5.593842e+05 4.950296e+05 \n", "15 1.794551e+05 2.232539e+05 4.005883e+05 1.158106e+06 \n", "16 9.165714e+05 2.441091e+05 6.139660e+05 8.670557e+05 \n", "17 6.213374e+05 7.985672e+05 1.252059e+06 9.138599e+05 \n", "18 3.549195e+05 7.319261e+05 4.614343e+05 3.535514e+05 \n", "19 1.137775e+06 1.579018e+06 9.959784e+05 8.986285e+05 \n", "20 1.125306e+06 3.863964e+05 3.987071e+05 7.493505e+05 \n", "21 8.898485e+05 5.927916e+05 1.256132e+06 1.541598e+06 \n", "22 3.215467e+05 4.644149e+05 1.129388e+06 7.422531e+05 \n", "23 6.093717e+05 6.336027e+05 4.269155e+05 1.150700e+06 \n", "24 3.890088e+05 7.493399e+05 1.231399e+06 6.559668e+05 \n", "25 5.405587e+05 6.677909e+05 1.147964e+06 1.547991e+06 \n", "26 6.651762e+05 1.773508e+06 2.409755e+05 3.835268e+05 \n", "27 5.212379e+05 2.942529e+05 4.663017e+05 5.628172e+05 \n", "28 2.292117e+05 4.800493e+05 8.586132e+05 4.008364e+05 \n", "29 5.220645e+05 4.140340e+05 6.895261e+05 1.054986e+06 \n", "30 1.124190e+06 2.260838e+05 6.135078e+05 4.430098e+05 \n", "31 4.273681e+05 3.515268e+05 4.448231e+05 2.566694e+05 \n", "32 8.716352e+05 7.048680e+05 4.415583e+05 9.356616e+05 \n", "33 1.227990e+06 4.104408e+05 5.005702e+05 9.153730e+05 \n", "34 7.611882e+05 4.975266e+05 1.682282e+06 NaN \n", "35 9.378148e+05 9.179868e+05 NaN NaN \n", "36 5.790932e+05 NaN NaN NaN \n", "37 NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN \n", "39 NaN NaN NaN NaN \n", "40 NaN NaN NaN NaN \n", "\n", "development_period 9 10 ... 31 \\\n", "occurrence_period ... \n", "1 6.057430e+05 1.434176e+05 ... 507751.595236 \n", "2 2.536358e+05 5.385814e+05 ... 8745.529181 \n", "3 7.212855e+05 8.362954e+05 ... 20938.965519 \n", "4 1.742546e+05 9.715882e+05 ... 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NaN \n", "37 NaN NaN ... NaN \n", "38 NaN NaN ... NaN \n", "39 NaN NaN ... NaN \n", "40 NaN NaN ... NaN \n", "\n", "development_period 32 33 34 35 \\\n", "occurrence_period \n", "1 1.267975e+06 205780.737653 6226.894042 0.000000 \n", "2 3.974022e+05 714117.521603 89465.857732 128332.630805 \n", "3 0.000000e+00 0.000000 19996.986384 0.000000 \n", "4 6.155031e+05 197934.940768 0.000000 0.000000 \n", "5 2.201814e+04 87364.263674 0.000000 322793.116875 \n", "6 1.900894e+06 0.000000 189803.137289 165066.293055 \n", "7 4.737402e+05 0.000000 874535.741565 NaN \n", "8 3.123278e+05 58747.903282 NaN NaN \n", "9 3.179059e+05 NaN NaN NaN \n", "10 NaN NaN NaN NaN \n", "11 NaN NaN NaN NaN \n", "12 NaN NaN NaN NaN \n", "13 NaN NaN NaN NaN \n", "14 NaN NaN NaN NaN \n", "15 NaN NaN NaN NaN \n", "16 NaN NaN NaN NaN \n", "17 NaN NaN NaN NaN \n", "18 NaN NaN NaN NaN \n", "19 NaN NaN NaN NaN \n", "20 NaN NaN NaN NaN \n", "21 NaN NaN NaN NaN \n", "22 NaN NaN NaN NaN \n", "23 NaN NaN NaN NaN \n", "24 NaN NaN NaN NaN \n", "25 NaN NaN NaN NaN \n", "26 NaN NaN NaN NaN \n", "27 NaN NaN NaN NaN \n", "28 NaN NaN NaN NaN \n", "29 NaN NaN NaN NaN \n", "30 NaN NaN NaN NaN \n", "31 NaN NaN NaN NaN \n", "32 NaN NaN NaN NaN \n", "33 NaN NaN NaN NaN \n", "34 NaN NaN NaN NaN \n", "35 NaN NaN NaN NaN \n", "36 NaN NaN NaN NaN \n", "37 NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN \n", "39 NaN NaN NaN NaN \n", "40 NaN NaN NaN NaN \n", "\n", "development_period 36 37 38 39 \\\n", "occurrence_period \n", "1 5.087788e+05 55918.782513 0.00000 412516.399423 \n", "2 5.301655e+05 349790.454839 12587.59026 90955.620959 \n", "3 0.000000e+00 0.000000 0.00000 NaN \n", "4 3.830643e+05 0.000000 NaN NaN \n", "5 1.043557e+06 NaN NaN NaN \n", "6 NaN NaN NaN NaN \n", "7 NaN NaN NaN NaN \n", "8 NaN NaN NaN NaN \n", "9 NaN NaN NaN NaN \n", "10 NaN NaN NaN NaN \n", "11 NaN NaN NaN NaN \n", "12 NaN NaN NaN NaN \n", "13 NaN NaN NaN NaN \n", "14 NaN NaN NaN NaN \n", "15 NaN NaN NaN NaN \n", "16 NaN NaN NaN NaN \n", "17 NaN NaN NaN NaN \n", "18 NaN NaN NaN NaN \n", "19 NaN NaN NaN NaN \n", "20 NaN NaN NaN NaN \n", "21 NaN NaN NaN NaN \n", "22 NaN NaN NaN NaN \n", "23 NaN NaN NaN NaN \n", "24 NaN NaN NaN NaN \n", "25 NaN NaN NaN NaN \n", "26 NaN NaN NaN NaN \n", "27 NaN NaN NaN NaN \n", "28 NaN NaN NaN NaN \n", "29 NaN NaN NaN NaN \n", "30 NaN NaN NaN NaN \n", "31 NaN NaN NaN NaN \n", "32 NaN NaN NaN NaN \n", "33 NaN NaN NaN NaN \n", "34 NaN NaN NaN NaN \n", "35 NaN NaN NaN NaN \n", "36 NaN NaN NaN NaN \n", "37 NaN NaN NaN NaN \n", "38 NaN NaN NaN NaN \n", "39 NaN NaN NaN NaN \n", "40 NaN NaN NaN NaN \n", "\n", "development_period 40 \n", "occurrence_period \n", "1 0.0 \n", "2 NaN \n", "3 NaN \n", "4 NaN \n", "5 NaN \n", "6 NaN \n", "7 NaN \n", "8 NaN \n", "9 NaN \n", "10 NaN \n", "11 NaN \n", "12 NaN \n", "13 NaN \n", "14 NaN \n", "15 NaN \n", "16 NaN \n", "17 NaN \n", "18 NaN \n", "19 NaN \n", "20 NaN \n", "21 NaN \n", "22 NaN \n", "23 NaN \n", "24 NaN \n", "25 NaN \n", "26 NaN \n", "27 NaN \n", "28 NaN \n", "29 NaN \n", "30 NaN \n", "31 NaN \n", "32 NaN \n", "33 NaN \n", "34 NaN \n", "35 NaN \n", "36 NaN \n", "37 NaN \n", "38 NaN \n", "39 NaN \n", "40 NaN \n", "\n", "[40 rows x 40 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "triangle_fill.pivot(index=\"occurrence_period\", columns=\"development_period\", values=\"payments\")" ] }, { "cell_type": "markdown", "metadata": { "id": "4z5pyKqi49W5" }, "source": [ "## Claims Ultimate Projections\n", "As demonstrated, it is fairly straightforward to transform claims data into the right format in SQL and create summaries for claims triangle projections. \n", "\n", "Where to from here for claims ultimates? For further analysis in Excel, in Python (similar tools exist in R), ``pandas`` can export to xlsx format with [``to_excel``](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_excel.html), or [xlwings](https://www.xlwings.org) can directly control Excel for additional VBA macro-style automation. \n", "\n", "Triangle methods can also be applied directly within Python without exporting to Excel or another tool. Consider for example, [chainladder](https://chainladder-python.readthedocs.io/en/latest/intro.html), a project led out of casact which provides a number of claims development models. Or, alternatively we can do further calculations using ``pandas``." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "LRmzk1cP7T1d" }, "outputs": [], "source": [ "# Define a dataframe from the generated triangle\n", "df_triangle_sort = triangle_fill.sort_values(['occurrence_period', 'development_period', 'payment_period'], \n", " ascending=[True, True, True]).reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "jQ4QTazO7Tyq" }, "outputs": [], "source": [ "# Add a column for cumulative payment\n", "\n", "df_triangle_sort[\"payments_cumulative\"] = \\\n", " df_triangle_sort.groupby(['occurrence_period'])['payments'] \\\n", " .cumsum(axis = 0)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "id": "Vpf_buye7TT6" }, "outputs": [], "source": [ "#df_triangle_sort.head()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "id": "Bl7LxETz7TQ-" }, "outputs": [], "source": [ "# Probably don't need this step but prints the triangle with cumulative payments\n", "# Useful for at least some life companies who manually do this in Excel\n", "\n", "IBNR_triangle_cumulative = \\\n", " df_triangle_sort.pivot(index = \"occurrence_period\", columns = \"development_period\", \n", " values = \"payments_cumulative\").fillna(0)\n", "\n", "#IBNR_triangle_cumulative" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 434 }, "id": "WiIS6ixocgWC", "outputId": "122e1166-79d3-45b6-f7b7-3216fb40b62e" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Inspect claims development by occurrence period\n", "\n", "(df_triangle_sort\n", " .pivot(index = \"development_period\", columns = \"occurrence_period\", values = \"payments_cumulative\")\n", " .plot(logy=True)\n", ")\n", "plt.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.8), ncol=5)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 434 }, "id": "tuv8vJjUcXSG", "outputId": "28698811-9db1-4e68-837d-1c4ffcd5a5e6" }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "(df_triangle_sort\n", " .pivot(index = \"development_period\", columns = \"occurrence_period\", values = \"payments_cumulative\")\n", " .plot(logy=True)\n", ")\n", "plt.legend(loc=\"lower center\", bbox_to_anchor=(0.5, -0.8), ncol=5)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "id": "k60RM8ri7TOM" }, "outputs": [], "source": [ "### 40 is the (hard-coded) cut-off as set out previously\n", "### Get the diagonals in the cumulative IBNR triangle, which represents cumulative payments for a particular\n", "### occurrence period\n", "\n", "triangle_diagonal_interim = df_triangle_sort['payments_cumulative'][df_triangle_sort['payment_period'] == 40] \\\n", " .reset_index(drop = True)\n", "\n", "triangle_diagonal_interim = pd.DataFrame(triangle_diagonal_interim).rename(columns = {'payments_cumulative': 'diagonal'})\n", "\n", "triangle_diagonal = triangle_diagonal_interim.iloc[::-1].reset_index(drop = True)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "id": "a5yO7IMX8Fo1" }, "outputs": [], "source": [ "### Sum cumulative payments by development period - to be used to calculate CDFs later\n", "\n", "development_period_sum_interim = df_triangle_sort.groupby(by = 'development_period').sum()\n", "development_period_sum = development_period_sum_interim['payments_cumulative'].reset_index(drop = True)\n", "\n", "development_period_sum = pd.DataFrame(development_period_sum).rename(columns = {'payments_cumulative': 'dev_period_sum'})" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "id": "J0fUHkwR8Fmc" }, "outputs": [], "source": [ "# Merge two dataframes\n", "\n", "df_cdf_interim = pd.concat([triangle_diagonal, development_period_sum], axis = 1)\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "7-VEb_vi8FkB" }, "outputs": [], "source": [ "#df_cdf_interim.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "id": "BvV2N2rb8FhR" }, "outputs": [], "source": [ "## dev_period_sum_alt column is to ensure the claims for two consecutive periods have the same number\n", "## of levels/elements (and division of these two claims columns give the CDF)\n", "\n", "df_cdf_interim['dev_period_sum_alt'] = df_cdf_interim['dev_period_sum'] - df_cdf_interim['diagonal']" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "m6aGa9P98Ozg", "outputId": "4bd95f55-38d9-47aa-fdb7-e727671b5854" }, "outputs": [ { "data": { "text/html": [ "\n", "
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diagonaldev_period_sumdev_period_sum_alt
016.522339185.889642169.367303
169.3834923175.2583093105.874818
2357.03085814478.49390814121.463050
3620.62116630945.25096930324.629803
41306.02693253178.45871751872.431785
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\n", " " ], "text/plain": [ " diagonal dev_period_sum dev_period_sum_alt\n", "0 16.522339 185.889642 169.367303\n", "1 69.383492 3175.258309 3105.874818\n", "2 357.030858 14478.493908 14121.463050\n", "3 620.621166 30945.250969 30324.629803\n", "4 1306.026932 53178.458717 51872.431785" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "### Show numbers in ,000 so they fit in the print\n", "\n", "df_cdf = df_cdf_interim / 1000\n", "df_cdf.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "id": "Fnb2SaEC8OxE" }, "outputs": [], "source": [ "### Calculate CDFs and IBNR\n", "\n", "df_cdf['dev_period_sum_shift'] = df_cdf['dev_period_sum'].shift(-1)\n", "df_cdf['CDF'] = df_cdf['dev_period_sum_shift'] / df_cdf['dev_period_sum_alt']\n", "df_cdf['IBNR'] = df_cdf['diagonal'] * df_cdf['CDF'] - df_cdf['diagonal']" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 671 }, "id": "tYRRgQsb8OuR", "outputId": "8dbb50c0-99d1-4721-cddd-13c8e7cfe457" }, "outputs": [ { "data": { "text/html": [ "\n", "
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diagonaldev_period_sumdev_period_sum_altdev_period_sum_shiftCDFIBNR
016.522339185.889642169.3673033175.25830918.7477642.932346e+02
169.3834923175.2583093105.87481814478.4939084.6616482.540579e+02
2357.03085814478.49390814121.46305030945.2509692.1913634.253533e+02
3620.62116630945.25096930324.62980353178.4587171.7536394.677244e+02
41306.02693253178.45871751872.43178573296.6021641.4130175.394107e+02
52324.41637373296.60216470972.18579196164.7256111.3549648.250831e+02
63881.70318696164.72561192283.022426117681.2502281.2752211.068326e+03
73601.493888117681.250228114079.756340140442.5474541.2310918.322724e+02
85105.714463140442.547454135336.832990150703.8029431.1135465.797340e+02
92977.453115150703.802943147726.349828170044.4434451.1510774.498255e+02
105956.335308170044.443445164088.108137183005.5770811.1152886.866969e+02
116137.892385183005.577081176867.684696194064.4767651.0972305.967854e+02
126525.720481194064.476765187538.756284204287.0608021.0893065.827849e+02
135723.672400204287.060802198563.388401215573.5721001.0856664.903256e+02
148773.672508215573.572100206799.899593218923.7722111.0586265.143662e+02
1511645.898736218923.772211207277.873475216004.1486861.0420994.902854e+02
1610863.724549216004.148686205140.424136216470.7692851.0552326.000268e+02
177327.527797216470.769285209143.241487220337.0772421.0535223.921864e+02
1811501.671667220337.077242208835.405575218513.2834931.0463425.330120e+02
1914660.109415218513.283493203853.174078215920.2406721.0591958.678036e+02
2011806.728149215920.240672204113.512524210996.7591701.0337233.981541e+02
2114524.793890210996.759170196471.965280205781.7270711.0473856.882528e+02
229737.849633205781.727071196043.877438203195.8940481.0364823.552534e+02
2315208.011233203195.894048187987.882815191860.4978211.0206003.132903e+02
2415112.028525191860.497821176748.469296179054.0113481.0130441.971243e+02
2512505.126742179054.011348166548.884606171642.6084081.0305843.824562e+02
2611949.795402171642.608408159692.813006162303.1665691.0163461.953325e+02
2710869.297362162303.166569151433.869207153292.8300321.0122761.334285e+02
2810278.079946153292.830032143014.750086145649.5563091.0184231.893563e+02
2913793.567522145649.556309131855.988788133632.8313271.0134761.858770e+02
3014555.869668133632.831327119076.961659124384.7283331.0445746.488170e+02
3115414.760743124384.728333108969.967590110233.9129571.0115991.787962e+02
3212014.925334110233.91295798218.98762399399.0162401.0120141.443505e+02
3315374.78641899399.01624084024.22982284640.4218631.0073341.127511e+02
3413050.85198084640.42186371589.56988374055.1350381.0344404.494751e+02
3513885.64116774055.13503860169.49387160575.2031081.0067439.362773e+01
3611566.35405660575.20310849008.84905249021.4366421.0002572.970740e+00
3713545.59025049021.43664235475.84639235979.3184121.0141921.922386e+02
3812844.80466635979.31841223134.51374623134.5137461.000000-1.818989e-12
3923134.51374623134.5137460.000000NaNNaNNaN
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\n", " " ], "text/plain": [ " diagonal dev_period_sum dev_period_sum_alt dev_period_sum_shift \\\n", "0 16.522339 185.889642 169.367303 3175.258309 \n", "1 69.383492 3175.258309 3105.874818 14478.493908 \n", "2 357.030858 14478.493908 14121.463050 30945.250969 \n", "3 620.621166 30945.250969 30324.629803 53178.458717 \n", "4 1306.026932 53178.458717 51872.431785 73296.602164 \n", "5 2324.416373 73296.602164 70972.185791 96164.725611 \n", "6 3881.703186 96164.725611 92283.022426 117681.250228 \n", "7 3601.493888 117681.250228 114079.756340 140442.547454 \n", "8 5105.714463 140442.547454 135336.832990 150703.802943 \n", "9 2977.453115 150703.802943 147726.349828 170044.443445 \n", "10 5956.335308 170044.443445 164088.108137 183005.577081 \n", "11 6137.892385 183005.577081 176867.684696 194064.476765 \n", "12 6525.720481 194064.476765 187538.756284 204287.060802 \n", "13 5723.672400 204287.060802 198563.388401 215573.572100 \n", "14 8773.672508 215573.572100 206799.899593 218923.772211 \n", "15 11645.898736 218923.772211 207277.873475 216004.148686 \n", "16 10863.724549 216004.148686 205140.424136 216470.769285 \n", "17 7327.527797 216470.769285 209143.241487 220337.077242 \n", "18 11501.671667 220337.077242 208835.405575 218513.283493 \n", "19 14660.109415 218513.283493 203853.174078 215920.240672 \n", "20 11806.728149 215920.240672 204113.512524 210996.759170 \n", "21 14524.793890 210996.759170 196471.965280 205781.727071 \n", "22 9737.849633 205781.727071 196043.877438 203195.894048 \n", "23 15208.011233 203195.894048 187987.882815 191860.497821 \n", "24 15112.028525 191860.497821 176748.469296 179054.011348 \n", "25 12505.126742 179054.011348 166548.884606 171642.608408 \n", "26 11949.795402 171642.608408 159692.813006 162303.166569 \n", "27 10869.297362 162303.166569 151433.869207 153292.830032 \n", "28 10278.079946 153292.830032 143014.750086 145649.556309 \n", "29 13793.567522 145649.556309 131855.988788 133632.831327 \n", "30 14555.869668 133632.831327 119076.961659 124384.728333 \n", "31 15414.760743 124384.728333 108969.967590 110233.912957 \n", "32 12014.925334 110233.912957 98218.987623 99399.016240 \n", "33 15374.786418 99399.016240 84024.229822 84640.421863 \n", "34 13050.851980 84640.421863 71589.569883 74055.135038 \n", "35 13885.641167 74055.135038 60169.493871 60575.203108 \n", "36 11566.354056 60575.203108 49008.849052 49021.436642 \n", "37 13545.590250 49021.436642 35475.846392 35979.318412 \n", "38 12844.804666 35979.318412 23134.513746 23134.513746 \n", "39 23134.513746 23134.513746 0.000000 NaN \n", "\n", " CDF IBNR \n", "0 18.747764 2.932346e+02 \n", "1 4.661648 2.540579e+02 \n", "2 2.191363 4.253533e+02 \n", "3 1.753639 4.677244e+02 \n", "4 1.413017 5.394107e+02 \n", "5 1.354964 8.250831e+02 \n", "6 1.275221 1.068326e+03 \n", "7 1.231091 8.322724e+02 \n", "8 1.113546 5.797340e+02 \n", "9 1.151077 4.498255e+02 \n", "10 1.115288 6.866969e+02 \n", "11 1.097230 5.967854e+02 \n", "12 1.089306 5.827849e+02 \n", "13 1.085666 4.903256e+02 \n", "14 1.058626 5.143662e+02 \n", "15 1.042099 4.902854e+02 \n", "16 1.055232 6.000268e+02 \n", "17 1.053522 3.921864e+02 \n", "18 1.046342 5.330120e+02 \n", "19 1.059195 8.678036e+02 \n", "20 1.033723 3.981541e+02 \n", "21 1.047385 6.882528e+02 \n", "22 1.036482 3.552534e+02 \n", "23 1.020600 3.132903e+02 \n", "24 1.013044 1.971243e+02 \n", "25 1.030584 3.824562e+02 \n", "26 1.016346 1.953325e+02 \n", "27 1.012276 1.334285e+02 \n", "28 1.018423 1.893563e+02 \n", "29 1.013476 1.858770e+02 \n", "30 1.044574 6.488170e+02 \n", "31 1.011599 1.787962e+02 \n", "32 1.012014 1.443505e+02 \n", "33 1.007334 1.127511e+02 \n", "34 1.034440 4.494751e+02 \n", "35 1.006743 9.362773e+01 \n", "36 1.000257 2.970740e+00 \n", "37 1.014192 1.922386e+02 \n", "38 1.000000 -1.818989e-12 \n", "39 NaN NaN " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_cdf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "T7on0vGgvqVT" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 28, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "2NLTKXpKxf7m", "outputId": "ac84e181-dfbe-42a7-8d01-118b7aeb80e9" }, "outputs": [ { "data": { "text/html": [ "\n", "
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190.071.0524.0962.02100.03679.04675.05574.06925.07938.09539.010685.011163.011861.012571.012899.013534.013790.013940.014312.014493.014525.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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270.016.0145.0514.01035.01329.01796.02358.02963.03593.03943.04129.04609.05724.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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293.015.0186.0849.01371.01785.02474.03529.04482.05287.05980.06138.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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314.044.0201.0361.0788.01140.01585.01841.02561.02977.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0
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8425.0 8455.0 \n", "14 7929.0 8424.0 8535.0 9656.0 9804.0 10104.0 \n", "15 7935.0 8432.0 8876.0 9410.0 9902.0 10008.0 \n", "16 9286.0 9738.0 9793.0 9850.0 10534.0 11440.0 \n", "17 8954.0 10040.0 10470.0 11418.0 11862.0 12955.0 \n", "18 7683.0 7796.0 7918.0 8170.0 8652.0 8699.0 \n", "19 12571.0 12899.0 13534.0 13790.0 13940.0 14312.0 \n", "20 8418.0 9080.0 9255.0 9523.0 10225.0 10584.0 \n", "21 11595.0 12560.0 13275.0 13541.0 14580.0 14660.0 \n", "22 8257.0 8712.0 8980.0 10412.0 11502.0 0.0 \n", "23 6393.0 6502.0 6930.0 7328.0 0.0 0.0 \n", "24 10066.0 10365.0 10864.0 0.0 0.0 0.0 \n", "25 11206.0 11646.0 0.0 0.0 0.0 0.0 \n", "26 8774.0 0.0 0.0 0.0 0.0 0.0 \n", "27 0.0 0.0 0.0 0.0 0.0 0.0 \n", "28 0.0 0.0 0.0 0.0 0.0 0.0 \n", "29 0.0 0.0 0.0 0.0 0.0 0.0 \n", "30 0.0 0.0 0.0 0.0 0.0 0.0 \n", "31 0.0 0.0 0.0 0.0 0.0 0.0 \n", "32 0.0 0.0 0.0 0.0 0.0 0.0 \n", "33 0.0 0.0 0.0 0.0 0.0 0.0 \n", "34 0.0 0.0 0.0 0.0 0.0 0.0 \n", "35 0.0 0.0 0.0 0.0 0.0 0.0 \n", "36 0.0 0.0 0.0 0.0 0.0 0.0 \n", "37 0.0 0.0 0.0 0.0 0.0 0.0 \n", "38 0.0 0.0 0.0 0.0 0.0 0.0 \n", "39 0.0 0.0 0.0 0.0 0.0 0.0 \n", "40 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "development_period 21 22 23 24 25 26 \\\n", "occurrence_period \n", "1 12255.0 13365.0 17229.0 17816.0 18258.0 18328.0 \n", "2 8812.0 8848.0 8861.0 9443.0 9509.0 9789.0 \n", "3 10539.0 10544.0 11024.0 11805.0 12693.0 12774.0 \n", "4 8504.0 8525.0 8941.0 9345.0 9840.0 9852.0 \n", "5 9562.0 10706.0 10807.0 11375.0 11412.0 11440.0 \n", "6 9862.0 9951.0 10073.0 10077.0 10277.0 10298.0 \n", "7 10179.0 10363.0 10527.0 12782.0 12922.0 13594.0 \n", "8 9313.0 9368.0 9951.0 10002.0 10190.0 10846.0 \n", "9 13234.0 13543.0 13620.0 13722.0 14403.0 14665.0 \n", "10 11107.0 11502.0 12665.0 13327.0 13918.0 14057.0 \n", "11 10511.0 10533.0 11141.0 11220.0 11228.0 11296.0 \n", "12 8516.0 8653.0 8683.0 9149.0 9196.0 9207.0 \n", "13 8517.0 8613.0 8750.0 8810.0 8838.0 8845.0 \n", "14 10214.0 10231.0 11440.0 11546.0 11559.0 11559.0 \n", "15 10280.0 12234.0 12383.0 12475.0 12505.0 12505.0 \n", "16 14453.0 15057.0 15086.0 15093.0 15112.0 0.0 \n", "17 14357.0 14847.0 14863.0 15208.0 0.0 0.0 \n", "18 9406.0 9590.0 9738.0 0.0 0.0 0.0 \n", "19 14493.0 14525.0 0.0 0.0 0.0 0.0 \n", "20 11807.0 0.0 0.0 0.0 0.0 0.0 \n", "21 0.0 0.0 0.0 0.0 0.0 0.0 \n", "22 0.0 0.0 0.0 0.0 0.0 0.0 \n", "23 0.0 0.0 0.0 0.0 0.0 0.0 \n", "24 0.0 0.0 0.0 0.0 0.0 0.0 \n", "25 0.0 0.0 0.0 0.0 0.0 0.0 \n", "26 0.0 0.0 0.0 0.0 0.0 0.0 \n", "27 0.0 0.0 0.0 0.0 0.0 0.0 \n", "28 0.0 0.0 0.0 0.0 0.0 0.0 \n", "29 0.0 0.0 0.0 0.0 0.0 0.0 \n", "30 0.0 0.0 0.0 0.0 0.0 0.0 \n", "31 0.0 0.0 0.0 0.0 0.0 0.0 \n", "32 0.0 0.0 0.0 0.0 0.0 0.0 \n", "33 0.0 0.0 0.0 0.0 0.0 0.0 \n", "34 0.0 0.0 0.0 0.0 0.0 0.0 \n", "35 0.0 0.0 0.0 0.0 0.0 0.0 \n", "36 0.0 0.0 0.0 0.0 0.0 0.0 \n", "37 0.0 0.0 0.0 0.0 0.0 0.0 \n", "38 0.0 0.0 0.0 0.0 0.0 0.0 \n", "39 0.0 0.0 0.0 0.0 0.0 0.0 \n", "40 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", "development_period 27 28 29 30 31 32 \\\n", "occurrence_period \n", "1 18358.0 18371.0 18398.0 20170.0 20677.0 21945.0 \n", "2 9800.0 9984.0 10437.0 10523.0 10532.0 10929.0 \n", "3 13191.0 13239.0 13505.0 13505.0 13526.0 13526.0 \n", "4 9901.0 9901.0 9901.0 10370.0 10370.0 10985.0 \n", "5 11440.0 11712.0 11712.0 11822.0 12410.0 12432.0 \n", "6 10352.0 10727.0 10727.0 10795.0 10795.0 12696.0 \n", "7 13897.0 13955.0 13975.0 13986.0 14027.0 14500.0 \n", "8 11112.0 11127.0 11150.0 11150.0 11644.0 11956.0 \n", "9 14792.0 14953.0 14967.0 14980.0 15097.0 15415.0 \n", "10 14427.0 14441.0 14556.0 14556.0 14556.0 0.0 \n", "11 11477.0 12838.0 13687.0 13794.0 0.0 0.0 \n", "12 10094.0 10186.0 10278.0 0.0 0.0 0.0 \n", "13 10852.0 10869.0 0.0 0.0 0.0 0.0 \n", "14 11950.0 0.0 0.0 0.0 0.0 0.0 \n", "15 0.0 0.0 0.0 0.0 0.0 0.0 \n", "16 0.0 0.0 0.0 0.0 0.0 0.0 \n", "17 0.0 0.0 0.0 0.0 0.0 0.0 \n", "18 0.0 0.0 0.0 0.0 0.0 0.0 \n", "19 0.0 0.0 0.0 0.0 0.0 0.0 \n", "20 0.0 0.0 0.0 0.0 0.0 0.0 \n", "21 0.0 0.0 0.0 0.0 0.0 0.0 \n", "22 0.0 0.0 0.0 0.0 0.0 0.0 \n", "23 0.0 0.0 0.0 0.0 0.0 0.0 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"code", "execution_count": null, "metadata": { "id": "794L3y7Yxf4J" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "collapsed_sections": [], "include_colab_link": true, "name": "SQL Query for Triangles_jp_jc.ipynb", "provenance": [], "toc_visible": true }, "gpuClass": "standard", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7" } }, "nbformat": 4, "nbformat_minor": 0 }