diff --git a/examples/README.md b/examples/README.md
index 108c7dec84..c1bffb8509 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -32,6 +32,9 @@ ___
* titanic ([notebook](notebooks/titanic/Titanic.ipynb)/[Datalore](https://datalore.jetbrains.com/view/notebook/B5YeMMONSAR78FgKQ9yJyW)) –
Let's see how the new library will show itself on the famous Titanic dataset.
___
+* top 12 German companies ([notebook](notebooks/top%2012%20german%20companies)/[Datalore](https://datalore.jetbrains.com/report/static/KQKedA4jDrKu63O53gEN0z/MDg5pHcGvRdDVQnPLmwjuc)) –
+ Analyze key financial metrics for several major German companies.
+___
* wine ([notebook](notebooks/wine/WineNetWIthKotlinDL.ipynb)/[Datalore](https://datalore.jetbrains.com/view/notebook/aK9vYHH8pCA8H1KbKB5WsI)) –
Wine. Kotlin Dataframe. KotlinDL. What came out of this can be seen in this notebook.
___
diff --git a/examples/notebooks/top 12 german companies/Top 12 German Companies.ipynb b/examples/notebooks/top 12 german companies/Top 12 German Companies.ipynb
new file mode 100644
index 0000000000..7c1615981a
--- /dev/null
+++ b/examples/notebooks/top 12 german companies/Top 12 German Companies.ipynb
@@ -0,0 +1,8428 @@
+{
+ "cells": [
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "# Financial Analysis of Top German Companies\n",
+ "\n",
+ "In this notebook, we load and analyze key financial metrics for several major German companies.\n",
+ "We perform data transformations, compute statistical measures, group by business sector, and create various plots to visualize trends in\n",
+ "revenue, net income, return on assets (ROA), and return on equity (ROE)"
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:20.785123Z",
+ "start_time": "2025-01-28T22:25:19.106031Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "%useLatestDescriptors\n",
+ "%use dataframe, kandy"
+ ],
+ "outputs": [],
+ "execution_count": 1
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:21.402091Z",
+ "start_time": "2025-01-28T22:25:20.791844Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Read data from a CSV file into a DataFrame\n",
+ "val dataFrame = DataFrame.read(\"Top_12_German_Companies NEW.csv\")\n",
+ " .renameToCamelCase().rename(\"rOA(%)\", \"rOE(%)\").into(\"ROA\", \"ROE\")"
+ ],
+ "outputs": [],
+ "execution_count": 2
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:21.491563Z",
+ "start_time": "2025-01-28T22:25:21.408388Z"
+ }
+ },
+ "cell_type": "code",
+ "source": "dataFrame.head()",
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | company | period | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity |
|---|
| Volkswagen AG | 12/31/2017 | 9750496618 | 5168898184,000000 | 21354201295 | 54861302788 | 33507101493 | 942175618,000000 | 1542627668,000000 | 637303746,000000 | 0,00% |
| Siemens AG | 12/31/2017 | 19716237464 | 1276840007,000000 | 45009303223 | 75268101508 | 30258798286 | 1696389282,000000 | 4219731382,000000 | 1487478214,000000 | 283,68% |
| Allianz SE | 12/31/2017 | 19458831198 | 1600107100,000000 | 48538978480 | 69583711255 | 21044732775 | 2299542624,000000 | 7603361452,000000 | 2306466848,000000 | 329,65% |
| BMW AG | 12/31/2017 | 18808147150 | 9601843496,000000 | 35382107627 | 67327482638 | 31945375011 | 142614028,000000 | 3005706927,000000 | 1107581539,000000 | 0,00% |
| BASF SE | 12/31/2017 | 16895580815 | 1797081911,000000 | 28309420014 | 68036567115 | 39727147101 | 2641347127,000000 | 4523561449,000000 | 71259635,000000 | 634,80% |
\n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"company\",\"period\",\"revenue\",\"netIncome\",\"liabilities\",\"assets\",\"equity\",\"ROA\",\"ROE\",\"debtToEquity\",\"percentageDebtToEquity\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.String\"}],\"nrow\":5,\"ncol\":11},\"kotlin_dataframe\":[{\"company\":\"Volkswagen AG\",\"period\":\"12/31/2017\",\"revenue\":9750496618,\"netIncome\":5.168898184E9,\"liabilities\":21354201295,\"assets\":54861302788,\"equity\":33507101493,\"ROA\":9.42175618E8,\"ROE\":1.542627668E9,\"debtToEquity\":6.37303746E8,\"percentageDebtToEquity\":\"0,00%\"},{\"company\":\"Siemens AG\",\"period\":\"12/31/2017\",\"revenue\":19716237464,\"netIncome\":1.276840007E9,\"liabilities\":45009303223,\"assets\":75268101508,\"equity\":30258798286,\"ROA\":1.696389282E9,\"ROE\":4.219731382E9,\"debtToEquity\":1.487478214E9,\"percentageDebtToEquity\":\"283,68%\"},{\"company\":\"Allianz SE\",\"period\":\"12/31/2017\",\"revenue\":19458831198,\"netIncome\":1.6001071E9,\"liabilities\":48538978480,\"assets\":69583711255,\"equity\":21044732775,\"ROA\":2.299542624E9,\"ROE\":7.603361452E9,\"debtToEquity\":2.306466848E9,\"percentageDebtToEquity\":\"329,65%\"},{\"company\":\"BMW AG\",\"period\":\"12/31/2017\",\"revenue\":18808147150,\"netIncome\":9.601843496E9,\"liabilities\":35382107627,\"assets\":67327482638,\"equity\":31945375011,\"ROA\":1.42614028E8,\"ROE\":3.005706927E9,\"debtToEquity\":1.107581539E9,\"percentageDebtToEquity\":\"0,00%\"},{\"company\":\"BASF SE\",\"period\":\"12/31/2017\",\"revenue\":16895580815,\"netIncome\":1.797081911E9,\"liabilities\":28309420014,\"assets\":68036567115,\"equity\":39727147101,\"ROA\":2.641347127E9,\"ROE\":4.523561449E9,\"debtToEquity\":7.1259635E7,\"percentageDebtToEquity\":\"634,80%\"}]}"
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 3
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Data Preparation: Formatting and Categorization\n",
+ "\n",
+ "In this step, we prepare and clean the data for analysis.\n",
+ "\n",
+ "- Custom Date Format: We define a custom date format (MM/DD/YYYY) to parse the \"period\" column into `LocalDate` without zero-padding for months.\n",
+ "- Business Sectors: We create an `enum` to classify companies into sectors such as Automotive, Banking, IT, and others.\n",
+ "- Data Transformation:\n",
+ " - Convert the \"period\" column to `LocalDate` using the custom format.\n",
+ " - Parse the \"percentageDebtToEquity\" column by removing the percentage sign and converting it to a `Double`.\n",
+ " - Sort the data by \"company\" and \"period\".\n",
+ " - Add a new column, \"sector,\" which assigns companies to specific business sectors based on their names.\n",
+ "\n",
+ "\n",
+ "This step ensures the dataset is well-structured and categorized for further analysis."
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:22.082325Z",
+ "start_time": "2025-01-28T22:25:21.497270Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "import kotlinx.datetime.format.Padding\n",
+ "import kotlinx.datetime.format.char\n",
+ "\n",
+ "// Define a custom date format without zero-padding for the month,\n",
+ "// separating month/day/year with slashes\n",
+ "val format = LocalDate.Format {\n",
+ " monthNumber(Padding.NONE)\n",
+ " char('/')\n",
+ " dayOfMonth()\n",
+ " char('/')\n",
+ " year()\n",
+ "}\n",
+ "\n",
+ "// Enum of Business Sectors\n",
+ "enum class BusinessSector(val simpleName: String) {\n",
+ " AUTOMOTIVE(\"Automotive\"),\n",
+ " BANKING(\"Banking\"),\n",
+ " INDUSTRIAL_TECH(\"Industrial\"),\n",
+ " INSURANCE_FINANCE(\"Insurance\"),\n",
+ " TELECOMMUNICATIONS(\"Telecom\"),\n",
+ " IT_SOFTWARE(\"IT\"),\n",
+ " PHARMA_CHEMICAL(\"Pharma\"),\n",
+ " OTHER(\"Other\")\n",
+ "}\n",
+ "\n",
+ "// Create a new DataFrame by converting the \"period\" column to LocalDate using the custom format\n",
+ "// and converting \"percentageDebtToEquity\" column to Double,\n",
+ "// then sorting based on \"company\" and \"period\", and finally adding a \"sector\" column\n",
+ "// depending on the company name\n",
+ "val companiesDf = dataFrame\n",
+ " .convert { period }.with { LocalDate.parse(it, format) }\n",
+ " .convert { percentageDebtToEquity }.with { it.removeSuffix(\"%\").replace(',', '.').toDouble() }\n",
+ " .sortBy { company and period }\n",
+ " .add(\"sector\") {\n",
+ " when (company) {\n",
+ " \"Volkswagen AG\", \"BMW AG\", \"Daimler AG\", \"Porsche AG\" -> BusinessSector.AUTOMOTIVE\n",
+ " \"Siemens AG\", \"BASF SE\" -> BusinessSector.INDUSTRIAL_TECH\n",
+ " \"Allianz SE\" -> BusinessSector.INSURANCE_FINANCE\n",
+ " \"Deutsche Bank AG\" -> BusinessSector.BANKING\n",
+ " \"Deutsche Telekom AG\" -> BusinessSector.TELECOMMUNICATIONS\n",
+ " \"SAP SE\" -> BusinessSector.IT_SOFTWARE\n",
+ " \"Bayer AG\", \"Merck KGaA\" -> BusinessSector.PHARMA_CHEMICAL\n",
+ " else -> BusinessSector.OTHER\n",
+ " }\n",
+ " }"
+ ],
+ "outputs": [],
+ "execution_count": 4
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "### Aggregating Financial Data\n",
+ "\n",
+ "These steps group data by company and calculate key metrics (mean, median, std, min, max) for financial columns like revenue, net income, and ratios."
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:22.449336Z",
+ "start_time": "2025-01-28T22:25:22.157826Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "companiesDf.groupBy { company }.aggregate {\n",
+ " val financeColumns = it.select { revenue and netIncome and liabilities and assets and equity and ROA and ROE and debtToEquity and percentageDebtToEquity }\n",
+ " financeColumns.mean() into \"mean\"\n",
+ " financeColumns.median() into \"median\"\n",
+ " financeColumns.std() into \"std\"\n",
+ " financeColumns.min() into \"min\"\n",
+ " financeColumns.max() into \"max\"\n",
+ "}"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | company | mean | | | | | | | | | median | | | | | | | | | std | | | | | | | | | min | | | | | | | | | max | | | | | | | | |
|---|
| revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity |
|---|
| Allianz SE | 13015427603,000000 | 3087778508,000000 | 29927069416,812500 | 56971548896,812500 | 27044479479,843750 | 2086997152,625000 | 3437586364,281250 | 1139250883,937500 | 451,397500 | 13380302290 | 1850996481,500000 | 30751494230 | 56314634789 | 27995269353 | 1511668947,500000 | 3325694133,500000 | 1007508473,500000 | 369,450000 | 4031726568,538354 | 2757583327,017181 | 11841968487,721144 | 15082579450,801540 | 8964325169,429451 | 1886302018,904862 | 2043975922,447980 | 746056815,370577 | 497,898219 | 5318114486 | 431100966,000000 | 10477167725 | 30329911272 | 11830459986 | 174587839,000000 | 611965731,000000 | 13267702,000000 | 0,000000 | 19814938566 | 9810419526,000000 | 49692687138 | 93604660446 | 47947280377 | 7368255218,000000 | 7893783146,000000 | 2577992857,000000 | 2293,350000 |
| BASF SE | 13206929984,593750 | 2929323943,937500 | 28948414331,687500 | 63253275177,031250 | 34304860845,375000 | 2095734476,375000 | 3667010068,812500 | 840864888,375000 | 552,036250 | 12383084935 | 1609098495,500000 | 29078100833 | 59504915680 | 36459467650 | 1885690844,000000 | 3229014762,000000 | 636540242,500000 | 401,815000 | 3953606287,298842 | 2628554646,960894 | 12414635870,741161 | 17108154038,669016 | 11372819844,337757 | 1688294717,024260 | 2188317238,825261 | 708214344,257501 | 628,148363 | 6644620233 | 551583106,000000 | 10848294146 | 30031637051 | 14342060597 | 8183968,000000 | 314982347,000000 | 7718738,000000 | 0,000000 | 19846387618 | 9907447145,000000 | 49603599758 | 95477772055 | 49441756560 | 9156051924,000000 | 9827624651,000000 | 2977818543,000000 | 2617,560000 |
| BMW AG | 12793626967,750000 | 3479632204,468750 | 31641220583,968750 | 62541000370,843750 | 30899779786,968750 | 1970748924,500000 | 3279635339,500000 | 1197896958,750000 | 420,747813 | 12797309067 | 1933117782,000000 | 33479741513 | 60857899135 | 31338099384 | 1991201713,000000 | 2626961321,000000 | 1061258560,500000 | 311,305000 | 4594622507,890679 | 2898142809,555578 | 11501509839,387596 | 17802412071,598537 | 12847726142,863003 | 1488654231,552701 | 2331079366,967284 | 849448878,303844 | 510,167476 | 5647276212 | 742678238,000000 | 10865997336 | 31832927467 | 10560668658 | 142614028,000000 | 125152581,000000 | 73367419,000000 | 0,000000 | 19909637251 | 9624993221,000000 | 49769403556 | 95122657900 | 48801871894 | 7406783982,000000 | 8011078402,000000 | 3346213148,000000 | 1814,590000 |
| Bayer AG | 12280150040,531250 | 3491404274,156250 | 32207322936,625000 | 60858790576,218750 | 28651467639,718750 | 2074341115,531250 | 3907549735,156250 | 1143423648,031250 | 411,859375 | 12667494670 | 1598673537,000000 | 32258852641 | 60203666215 | 29501507476 | 1742836396,000000 | 3481261635,500000 | 983605350,500000 | 306,700000 | 3722908864,690987 | 3206795690,601987 | 11640892417,013212 | 18011890733,431713 | 11375096650,473440 | 1184037640,651491 | 2464055972,901899 | 890356368,256408 | 450,237144 | 5728072323 | 905684923,000000 | 10046371516 | 27849556029 | 10017618918 | 139484054,000000 | 47691775,000000 | 102121758,000000 | 0,000000 | 19647577142 | 9844936329,000000 | 49223539015 | 91140421051 | 48315458259 | 4910670497,000000 | 9607657241,000000 | 3948535083,000000 | 1818,110000 |
| Daimler AG | 12982945622,281250 | 3106719353,031250 | 33110683422,812500 | 61025135448,218750 | 27914452025,312500 | 2057640070,781250 | 3599838843,531250 | 1379945269,281250 | 384,516875 | 13278094462 | 1734753144,500000 | 34617753396 | 58368560660 | 27907274004 | 1847166935,000000 | 3296400653,000000 | 1038169386,000000 | 334,975000 | 4328582507,932568 | 2873779266,018796 | 10107899623,321072 | 14627955672,994179 | 12080334988,462170 | 1226544584,317596 | 2378725754,217035 | 1024583876,777378 | 419,087621 | 5152922484 | 482111535,000000 | 10787737144 | 31178454239 | 10659050238 | 55749485,000000 | 124523492,000000 | 114419599,000000 | 0,000000 | 19604996789 | 9831399769,000000 | 48085311015 | 86840936189 | 46368261566 | 4273603847,000000 | 9878142355,000000 | 4145214472,000000 | 1948,440000 |
| Deutsche Bank AG | 12552273759,250000 | 3852865310,187500 | 31804324927,843750 | 64484873493,968750 | 32680548566,156250 | 2042935983,156250 | 3542614134,843750 | 926108158,812500 | 398,719688 | 11868501230 | 2062260119,000000 | 33874325159 | 64508527196 | 32899597047 | 1688790297,000000 | 2806814710,500000 | 868387254,000000 | 293,870000 | 4429631424,406467 | 3287539872,477768 | 12815920189,273525 | 17260497429,425790 | 11041972482,317745 | 1230001154,483563 | 2106292761,582215 | 852175822,341487 | 486,555376 | 5220250485 | 663079894,000000 | 10546244209 | 28137106192 | 11728609038 | 107264457,000000 | 229299295,000000 | 73286514,000000 | 0,000000 | 19868905162 | 9802851594,000000 | 49199711543 | 92083567435 | 49507409035 | 4955368641,000000 | 8550802927,000000 | 3925086081,000000 | 2008,380000 |
| Deutsche Telekom AG | 12948836356,781250 | 3612449673,750000 | 29721302574,218750 | 62968327384,843750 | 33247024810,656250 | 1907479731,343750 | 3626682563,343750 | 887536951,062500 | 346,870937 | 14031055010 | 1656127217,000000 | 30653814592 | 62361230856 | 35293691238 | 1734117630,000000 | 2968449223,500000 | 689784846,500000 | 340,680000 | 4267095201,540025 | 3233861719,779653 | 11114012390,578995 | 15930592724,460455 | 12022516129,146206 | 1262589468,122650 | 2258582990,930430 | 723528112,694197 | 369,846773 | 5149849693 | 1007117429,000000 | 11494162751 | 32355610354 | 11183252715 | 178292287,000000 | 429978286,000000 | 31680151,000000 | 0,000000 | 19614896783 | 9904204744,000000 | 47810647914 | 96220600130 | 49229460503 | 6805417841,000000 | 9041825831,000000 | 2769301274,000000 | 1547,480000 |
| Merck KGaA | 13429626794,312500 | 2786067729,593750 | 34212478452,031250 | 62611554099,312500 | 28399075647,281250 | 2301233277,562500 | 3953880877,625000 | 1262105888,125000 | 458,833438 | 14463857538 | 1907924647,000000 | 34840616728 | 61671584787 | 28674185880 | 2304004777,000000 | 3730883412,500000 | 1014379621,000000 | 393,650000 | 4312918107,535037 | 2159186368,002079 | 10310890849,580265 | 14436585303,052738 | 11015177395,550402 | 1412484406,025840 | 2504604793,688944 | 895429432,369268 | 428,230486 | 5897253747 | 389688114,000000 | 12733925598 | 36970270841 | 11945417506 | 977949,000000 | 110168192,000000 | 127014176,000000 | 0,000000 | 19217681417 | 8683617029,000000 | 49354599469 | 95054854077 | 49110493703 | 6055903707,000000 | 9237712947,000000 | 3328702904,000000 | 1758,200000 |
| Porsche AG | 11801739996,500000 | 3749276327,562500 | 31933699799,437500 | 62902797570,750000 | 30969097771,437500 | 1812543408,500000 | 3138381255,406250 | 1138772302,468750 | 326,929375 | 11821233243 | 1495116491,500000 | 33611865718 | 63838811515 | 29516759934 | 1492638445,000000 | 2786218764,000000 | 1008560375,000000 | 307,740000 | 3643118507,061031 | 3417170583,079504 | 10551496082,676825 | 19091868224,734550 | 12568676517,076225 | 1532298279,541574 | 2250832283,751501 | 576038382,472852 | 365,118629 | 5536870879 | 1033867668,000000 | 11368621660 | 21773242219 | 10404620558 | 17386781,000000 | 226901839,000000 | 109265125,000000 | 0,000000 | 17963578177 | 9755568792,000000 | 49106113898 | 95043652065 | 49918115168 | 8532052149,000000 | 8202899676,000000 | 2761786516,000000 | 1634,060000 |
| SAP SE | 12416678253,562500 | 4944656066,156250 | 32416812456,343750 | 61494676430,937500 | 29077863974,656250 | 1803926622,593750 | 3279309301,281250 | 1168741765,500000 | 238,142187 | 12908509779 | 5359235366,000000 | 35197257099 | 56011046255 | 26407085362 | 1494215797,500000 | 2698095641,000000 | 976196092,000000 | 0,000000 | 4286554454,753746 | 3233555305,354313 | 12073688294,924797 | 19813718642,814350 | 12615671347,131495 | 1235422951,830178 | 2326900921,602219 | 983484952,491065 | 377,507489 | 5282345417 | 1027662516,000000 | 10102113588 | 23437806831 | 10018751320 | 96093011,000000 | 10929103,000000 | 79241434,000000 | 0,000000 | 19774628627 | 9899343613,000000 | 49206362475 | 96574017987 | 49752983577 | 4844445609,000000 | 8305270255,000000 | 4631348222,000000 | 1819,360000 |
| Siemens AG | 12662499402,906250 | 3833250447,968750 | 30351530970,843750 | 59315681225,187500 | 28964150254,375000 | 2084857343,156250 | 3508368718,656250 | 1037253234,531250 | 359,701875 | 12163469384 | 1832577182,500000 | 29454204370 | 58062958009 | 29594404315 | 1812769210,000000 | 3569433108,000000 | 923590815,500000 | 271,095000 | 4258721679,121483 | 3245786716,800676 | 11127809058,660181 | 16399014129,918648 | 11183817793,500856 | 1300402512,066721 | 2175553434,007928 | 841493177,404319 | 441,614520 | 5703877749 | 527700192,000000 | 10137976567 | 29444881442 | 10556038645 | 636667678,000000 | 45603762,000000 | 69724357,000000 | 0,000000 | 19833452617 | 9807152435,000000 | 47957169639 | 95650487131 | 48614290545 | 6802458781,000000 | 8696071832,000000 | 3948375437,000000 | 1868,110000 |
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+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 5
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:22.607447Z",
+ "start_time": "2025-01-28T22:25:22.462518Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Group by \"company\" and aggregate key financial columns\n",
+ "companiesDf.groupBy { sector }.aggregate {\n",
+ " val financeColumns = it.select { revenue and netIncome and liabilities and assets and equity and ROA and ROE and debtToEquity and percentageDebtToEquity }\n",
+ " financeColumns.mean() into \"mean\"\n",
+ " financeColumns.median() into \"median\"\n",
+ " financeColumns.std() into \"std\"\n",
+ " financeColumns.min() into \"min\"\n",
+ " financeColumns.max() into \"max\"\n",
+ "}"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | sector | mean | | | | | | | | | median | | | | | | | | | std | | | | | | | | | min | | | | | | | | | max | | | | | | | | |
|---|
| revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity | revenue | netIncome | liabilities | assets | equity | ROA | ROE | debtToEquity | percentageDebtToEquity |
|---|
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| TELECOMMUNICATIONS | 12948836356,781250 | 3612449673,750000 | 29721302574,218750 | 62968327384,843750 | 33247024810,656250 | 1907479731,343750 | 3626682563,343750 | 887536951,062500 | 346,870937 | 14031055010 | 1656127217,000000 | 30653814592 | 62361230856 | 35293691238 | 1734117630,000000 | 2968449223,500000 | 689784846,500000 | 340,680000 | 4267095201,540025 | 3233861719,779653 | 11114012390,578995 | 15930592724,460455 | 12022516129,146206 | 1262589468,122650 | 2258582990,930430 | 723528112,694197 | 369,846773 | 5149849693 | 1007117429,000000 | 11494162751 | 32355610354 | 11183252715 | 178292287,000000 | 429978286,000000 | 31680151,000000 | 0,000000 | 19614896783 | 9904204744,000000 | 47810647914 | 96220600130 | 49229460503 | 6805417841,000000 | 9041825831,000000 | 2769301274,000000 | 1547,480000 |
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\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}]}},\"median\":{\"data\":{\"revenue\":12908509779,\"netIncome\":5.359235366E9,\"liabilities\":35197257099,\"assets\":56011046255,\"equity\":26407085362,\"ROA\":1.4942157975E9,\"ROE\":2.698095641E9,\"debtToEquity\":9.76196092E8,\"percentageDebtToEquity\":0.0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"revenue\",\"netIncome\",\"liabilities\",\"assets\",\"equity\",\"ROA\",\"ROE\",\"debtToEquity\",\"percentageDebtToEquity\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}]}},\"std\":{\"data\":{\"revenue\":4.286554454753746E9,\"netIncome\":3.233555305354313E9,\"liabilities\":1.2073688294924797E10,\"assets\":1.981371864281435E10,\"equity\":1.2615671347131495E10,\"ROA\":1.235422951830178E9,\"ROE\":2.326900921602219E9,\"debtToEquity\":9.83484952491065E8,\"percentageDebtToEquity\":377.5074889984292},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"revenue\",\"netIncome\",\"liabilities\",\"assets\",\"equity\",\"ROA\",\"ROE\",\"debtToEquity\",\"percentageDebtToEquity\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}]}},\"min\":{\"data\":{\"revenue\":5282345417,\"netIncome\":1.027662516E9,\"liabilities\":10102113588,\"assets\":23437806831,\"equity\":10018751320,\"ROA\":9.6093011E7,\"ROE\":1.0929103E7,\"debtToEquity\":7.9241434E7,\"percentageDebtToEquity\":0.0},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"revenue\",\"netIncome\",\"liabilities\",\"assets\",\"equity\",\"ROA\",\"ROE\",\"debtToEquity\",\"percentageDebtToEquity\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}]}},\"max\":{\"data\":{\"revenue\":19774628627,\"netIncome\":9.899343613E9,\"liabilities\":49206362475,\"assets\":96574017987,\"equity\":49752983577,\"ROA\":4.844445609E9,\"ROE\":8.305270255E9,\"debtToEquity\":4.631348222E9,\"percentageDebtToEquity\":1819.36},\"metadata\":{\"kind\":\"ColumnGroup\",\"columns\":[\"revenue\",\"netIncome\",\"liabilities\",\"assets\",\"equity\",\"ROA\",\"ROE\",\"debtToEquity\",\"percentageDebtToEquity\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}]}}}]}"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 6
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:22.781495Z",
+ "start_time": "2025-01-28T22:25:22.681841Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Group by \"sector\" then calculate main metrics and sort by \"sector\"\n",
+ "companiesDf.groupBy { sector }.aggregate {\n",
+ " revenue.mean() into \"Avg revenue\"\n",
+ " revenue.sum() into \"Total revenue\"\n",
+ " netIncome.mean() into \"Avg Net Income\"\n",
+ " netIncome.sum() into \"Sum Net Income\"\n",
+ " ROA.mean() into \"Avg ROA\"\n",
+ " ROE.mean() into \"Avg ROE\"\n",
+ "}.sortBy { sector }"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | sector | Avg revenue | Total revenue | Avg Net Income | Sum Net Income | Avg ROA | Avg ROE |
|---|
| AUTOMOTIVE | 12501654488,507812 | 1600211774529 | 3259360840,460938 | 417198187579,000000 | 2118081428,039063 | 3235733994,031250 |
| BANKING | 12552273759,250000 | 401672760296 | 3852865310,187500 | 123291689926,000000 | 2042935983,156250 | 3542614134,843750 |
| INDUSTRIAL_TECH | 12934714693,750000 | 827821740400 | 3381287195,953125 | 216402380541,000000 | 2090295909,765625 | 3587689393,734375 |
| INSURANCE_FINANCE | 13015427603,000000 | 416493683296 | 3087778508,000000 | 98808912256,000000 | 2086997152,625000 | 3437586364,281250 |
| TELECOMMUNICATIONS | 12948836356,781250 | 414362763417 | 3612449673,750000 | 115598389560,000000 | 1907479731,343750 | 3626682563,343750 |
| IT_SOFTWARE | 12416678253,562500 | 397333704114 | 4944656066,156250 | 158228994117,000000 | 1803926622,593750 | 3279309301,281250 |
| PHARMA_CHEMICAL | 12854888417,421875 | 822712858715 | 3138736001,875000 | 200879104120,000000 | 2187787196,546875 | 3930715306,390625 |
\n",
+ " \n",
+ " \n",
+ " "
+ ],
+ "application/kotlindataframe+json": "{\"$version\":\"2.1.1\",\"metadata\":{\"columns\":[\"sector\",\"Avg revenue\",\"Total revenue\",\"Avg Net Income\",\"Sum Net Income\",\"Avg ROA\",\"Avg ROE\"],\"types\":[{\"kind\":\"ValueColumn\",\"type\":\"Line_14_jupyter.BusinessSector\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Long\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"},{\"kind\":\"ValueColumn\",\"type\":\"kotlin.Double\"}],\"nrow\":7,\"ncol\":7},\"kotlin_dataframe\":[{\"sector\":\"AUTOMOTIVE\",\"Avg revenue\":1.2501654488507812E10,\"Total revenue\":1600211774529,\"Avg Net Income\":3.2593608404609375E9,\"Sum Net Income\":4.17198187579E11,\"Avg ROA\":2.1180814280390625E9,\"Avg ROE\":3.23573399403125E9},{\"sector\":\"BANKING\",\"Avg revenue\":1.255227375925E10,\"Total revenue\":401672760296,\"Avg Net Income\":3.8528653101875E9,\"Sum Net Income\":1.23291689926E11,\"Avg ROA\":2.04293598315625E9,\"Avg ROE\":3.54261413484375E9},{\"sector\":\"INDUSTRIAL_TECH\",\"Avg revenue\":1.293471469375E10,\"Total revenue\":827821740400,\"Avg Net Income\":3.381287195953125E9,\"Sum Net Income\":2.16402380541E11,\"Avg ROA\":2.090295909765625E9,\"Avg ROE\":3.587689393734375E9},{\"sector\":\"INSURANCE_FINANCE\",\"Avg revenue\":1.3015427603E10,\"Total revenue\":416493683296,\"Avg Net Income\":3.087778508E9,\"Sum Net Income\":9.8808912256E10,\"Avg ROA\":2.086997152625E9,\"Avg ROE\":3.43758636428125E9},{\"sector\":\"TELECOMMUNICATIONS\",\"Avg revenue\":1.294883635678125E10,\"Total revenue\":414362763417,\"Avg Net Income\":3.61244967375E9,\"Sum Net Income\":1.1559838956E11,\"Avg ROA\":1.90747973134375E9,\"Avg ROE\":3.62668256334375E9},{\"sector\":\"IT_SOFTWARE\",\"Avg revenue\":1.24166782535625E10,\"Total revenue\":397333704114,\"Avg Net Income\":4.94465606615625E9,\"Sum Net Income\":1.58228994117E11,\"Avg ROA\":1.80392662259375E9,\"Avg ROE\":3.27930930128125E9},{\"sector\":\"PHARMA_CHEMICAL\",\"Avg revenue\":1.2854888417421875E10,\"Total revenue\":822712858715,\"Avg Net Income\":3.138736001875E9,\"Sum Net Income\":2.0087910412E11,\"Avg ROA\":2.187787196546875E9,\"Avg ROE\":3.930715306390625E9}]}"
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "execution_count": 7
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:23.118694Z",
+ "start_time": "2025-01-28T22:25:22.843507Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Group by \"period\" and \"sector\" then compute total revenue and net income\n",
+ "val timeSerDf = companiesDf.groupBy { period and sector }.aggregate {\n",
+ " revenue.sum() into \"totalRevenue\"\n",
+ " netIncome.sum() into \"totalNetIncome\"\n",
+ "}\n",
+ "\n",
+ "// List of business sectors\n",
+ "val listOfSectors = listOf(\n",
+ " BusinessSector.AUTOMOTIVE,\n",
+ " BusinessSector.BANKING,\n",
+ " BusinessSector.INSURANCE_FINANCE,\n",
+ " BusinessSector.INDUSTRIAL_TECH,\n",
+ " BusinessSector.TELECOMMUNICATIONS,\n",
+ " BusinessSector.IT_SOFTWARE,\n",
+ " BusinessSector.PHARMA_CHEMICAL\n",
+ ")\n",
+ "\n",
+ "// Matching colors for each sector\n",
+ "val listOfSectorColors = listOf(\n",
+ " Color.hex(\"#ffaf00\"),\n",
+ " Color.hex(\"#f46920\"),\n",
+ " Color.hex(\"#f53255\"),\n",
+ " Color.hex(\"#f857c1\"),\n",
+ " Color.hex(\"#29bdfd\"),\n",
+ " Color.hex(\"#00cbbf\"),\n",
+ " Color.hex(\"#01c159\")\n",
+ ")"
+ ],
+ "outputs": [],
+ "execution_count": 8
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## Visualizing Revenue and Net Income by Sector\n",
+ "\n",
+ "1. Revenue by Sector:\n",
+ " - A line chart shows total revenue over time, grouped by business sector.\n",
+ " - Points highlight specific values, and each sector is color-coded using a predefined palette.\n",
+ " - The chart includes a legend for sector identification.\n",
+ "2. Net Income by Sector:\n",
+ " - A similar line chart displays total net income over time for each sector.\n",
+ " - Points and color-coding are used to enhance clarity, with a legend indicating the sectors.\n",
+ "\n",
+ "These visualizations help analyze trends and compare financial performance across sectors over time."
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:23.852949Z",
+ "start_time": "2025-01-28T22:25:23.211657Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Plot total revenue by period and sector\n",
+ "timeSerDf.plot {\n",
+ " // Map the x-axis to the \"period\" column\n",
+ " x(period) { axis.name = \"Date\" }\n",
+ " // Map the y-axis to the aggregated \"totalRevenue\"\n",
+ " y(totalRevenue) { axis.name = \"Revenue\" }\n",
+ "\n",
+ " // Draw a line chart\n",
+ " line {\n",
+ " // Color lines by the \"sector\" column\n",
+ " color(sector) {\n",
+ " // Use a categorical color scale with predefined colors and sectors\n",
+ " scale = categorical(range = listOfSectorColors, domain = listOfSectors)\n",
+ " // Configure and label the legend\n",
+ " legend {\n",
+ " name = \"Sector\"\n",
+ " this.breaksLabeled(\n",
+ " BusinessSector.AUTOMOTIVE to BusinessSector.AUTOMOTIVE.simpleName,\n",
+ " BusinessSector.BANKING to BusinessSector.BANKING.simpleName,\n",
+ " BusinessSector.INSURANCE_FINANCE to BusinessSector.INSURANCE_FINANCE.simpleName,\n",
+ " BusinessSector.INDUSTRIAL_TECH to BusinessSector.INDUSTRIAL_TECH.simpleName,\n",
+ " BusinessSector.TELECOMMUNICATIONS to BusinessSector.TELECOMMUNICATIONS.simpleName,\n",
+ " BusinessSector.IT_SOFTWARE to BusinessSector.IT_SOFTWARE.simpleName,\n",
+ " BusinessSector.PHARMA_CHEMICAL to BusinessSector.PHARMA_CHEMICAL.simpleName\n",
+ " )\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " // Add points on top of the line chart\n",
+ " points {\n",
+ " size = 3.0\n",
+ " color(sector) { scale = categorical(range = listOfSectorColors, domain = listOfSectors) }\n",
+ " }\n",
+ "\n",
+ " // Adjust the layout and overall plot appearance\n",
+ " layout {\n",
+ " title = \"Revenue by Sector\"\n",
+ " size = 875 to 500\n",
+ " }\n",
+ "}"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " "
+ ],
+ "application/plot+json": {
+ "output_type": "lets_plot_spec",
+ "output": {
+ "ggtitle": {
+ "text": "Revenue by Sector"
+ },
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+ "metadata": {},
+ "output_type": "execute_result"
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+ ],
+ "execution_count": 9
+ },
+ {
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+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:24.012893Z",
+ "start_time": "2025-01-28T22:25:23.859061Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Plot total net income by period and sector\n",
+ "timeSerDf.plot {\n",
+ " // Map the x-axis to the \"period\" column\n",
+ " x(period) { axis.name = \"Date\" }\n",
+ " // Map the y-axis to the aggregated \"totalNetIncome\"\n",
+ " y(totalNetIncome) { axis.name = \"Net Income\" }\n",
+ "\n",
+ " // Draw a line chart\n",
+ " line {\n",
+ " // Color lines by the \"sector\" column\n",
+ " color(sector) {\n",
+ " // Use the same categorical color scale and sector list\n",
+ " scale = categorical(range = listOfSectorColors, domain = listOfSectors)\n",
+ " // Configure and label the legend\n",
+ " legend {\n",
+ " name = \"Sector\"\n",
+ " this.breaksLabeled(\n",
+ " BusinessSector.AUTOMOTIVE to BusinessSector.AUTOMOTIVE.simpleName,\n",
+ " BusinessSector.BANKING to BusinessSector.BANKING.simpleName,\n",
+ " BusinessSector.INSURANCE_FINANCE to BusinessSector.INSURANCE_FINANCE.simpleName,\n",
+ " BusinessSector.INDUSTRIAL_TECH to BusinessSector.INDUSTRIAL_TECH.simpleName,\n",
+ " BusinessSector.TELECOMMUNICATIONS to BusinessSector.TELECOMMUNICATIONS.simpleName,\n",
+ " BusinessSector.IT_SOFTWARE to BusinessSector.IT_SOFTWARE.simpleName,\n",
+ " BusinessSector.PHARMA_CHEMICAL to BusinessSector.PHARMA_CHEMICAL.simpleName\n",
+ " )\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " }\n",
+ "\n",
+ " // Add points on top of the line chart\n",
+ " points {\n",
+ " size = 3.0\n",
+ " color(sector) { scale = categorical(range = listOfSectorColors, domain = listOfSectors) }\n",
+ " }\n",
+ "\n",
+ " // Adjust the layout and overall plot appearance\n",
+ " layout {\n",
+ " title = \"Net Income by Sector\"\n",
+ " size = 875 to 500\n",
+ " }\n",
+ "}"
+ ],
+ "outputs": [
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+ "data": {
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+ " "
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+ "application/plot+json": {
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+ "INDUSTRIAL_TECH",
+ "INDUSTRIAL_TECH",
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+ "AUTOMOTIVE",
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+ "AUTOMOTIVE",
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+ "AUTOMOTIVE",
+ "AUTOMOTIVE",
+ "AUTOMOTIVE",
+ "AUTOMOTIVE",
+ "AUTOMOTIVE",
+ "PHARMA_CHEMICAL",
+ "PHARMA_CHEMICAL",
+ "PHARMA_CHEMICAL",
+ "PHARMA_CHEMICAL",
+ "PHARMA_CHEMICAL",
+ "PHARMA_CHEMICAL",
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+ "BANKING",
+ "BANKING",
+ "BANKING",
+ "BANKING",
+ "BANKING",
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+ "IT_SOFTWARE",
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+ "name": "Sector",
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+ "AUTOMOTIVE",
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+ "INDUSTRIAL_TECH",
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+ "Automotive",
+ "Banking",
+ "Insurance",
+ "Industrial",
+ "Telecom",
+ "IT",
+ "Pharma"
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+ "INDUSTRIAL_TECH",
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+ "IT_SOFTWARE",
+ "PHARMA_CHEMICAL"
+ ]
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+ ],
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+ {
+ "mapping": {
+ "x": "period",
+ "y": "totalNetIncome",
+ "color": "sector"
+ },
+ "stat": "identity",
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+ "position": "identity",
+ "geom": "line"
+ },
+ {
+ "mapping": {
+ "x": "period",
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+ ],
+ "execution_count": 10
+ },
+ {
+ "metadata": {},
+ "cell_type": "markdown",
+ "source": [
+ "## ROA and ROE Analysis by Sector\n",
+ "\n",
+ "1. Computing Averages and Standard Deviations:\n",
+ " - Group the data by sector and calculate the mean and standard deviation for Return on Assets (ROA) and Return on Equity (ROE).\n",
+ " - This creates a summarized dataset for sector-level performance comparison.\n",
+ "2. Visualizing ROA by Sector:\n",
+ " - A bar chart displays the average ROA for each sector.\n",
+ " - Error bars represent one standard deviation, showing the variability within each sector.\n",
+ "3. Visualizing ROE by Sector:\n",
+ " - A similar bar chart illustrates the average ROE across sectors.\n",
+ " - Error bars provide insight into the standard deviation of ROE within each sector.\n",
+ "\n",
+ "These charts help compare sector-level profitability metrics and assess consistency within sectors."
+ ]
+ },
+ {
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2025-01-28T22:25:24.186565Z",
+ "start_time": "2025-01-28T22:25:24.033014Z"
+ }
+ },
+ "cell_type": "code",
+ "source": [
+ "// Group data by sector to compute average and standard deviations of ROA and ROE\n",
+ "val roeAndRoaDf = companiesDf.groupBy { sector }.aggregate {\n",
+ " ROA.mean() into \"Avg ROA\"\n",
+ " ROA.std() into \"Std ROA\"\n",
+ " ROE.mean() into \"Avg ROE\"\n",
+ " ROE.std() into \"Std ROE\"\n",
+ "}\n",
+ "\n",
+ "roeAndRoaDf"
+ ],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " | sector | Avg ROA | Std ROA | Avg ROE | Std ROE |
|---|
| INSURANCE_FINANCE | 2086997152,625000 | 1886302018,904862 | 3437586364,281250 | 2043975922,447980 |
| INDUSTRIAL_TECH | 2090295909,765625 | 1494884669,783042 | 3587689393,734375 | 2166034306,212876 |
| AUTOMOTIVE | 2118081428,039063 | 1540731600,206889 | 3235733994,031250 | 2239985575,500741 |
| PHARMA_CHEMICAL | 2187787196,546875 | 1297937669,004892 | 3930715306,390625 | 2464727270,115643 |
| BANKING | 2042935983,156250 | 1230001154,483563 | 3542614134,843750 | 2106292761,582215 |
| TELECOMMUNICATIONS | 1907479731,343750 | 1262589468,122650 | 3626682563,343750 | 2258582990,930430 |
| IT_SOFTWARE | 1803926622,593750 | 1235422951,830178 | 3279309301,281250 | 2326900921,602219 |
\n",
+ " \n",
+ " \n",
+ " "
+ ],
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+ }
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+ "source": [
+ "// Plot average ROA by sector with error bars representing one standard deviation\n",
+ "roeAndRoaDf.plot {\n",
+ " // Set the x-axis to the sector names\n",
+ " x(sector.map { it.simpleName }) { axis.name = \"Sector of Business\" }\n",
+ "\n",
+ " bars {\n",
+ " // Use the \"Avg ROA\" column for the bar heights\n",
+ " y(`Avg ROA`) { scale = continuous(min = .0, max = 4.5e+9) }\n",
+ " // Fill bars with a chosen color\n",
+ " fillColor = Color.hex(\"#ffaf00\")\n",
+ " }\n",
+ " lineRanges {\n",
+ " // Calculate the min and max for the error bars (Std ROA)\n",
+ " yMin(`Avg ROA`.toList().zip(`Std ROA`.toList()).map { it.first - it.second })\n",
+ " yMax(`Avg ROA`.toList().zip(`Std ROA`.toList()).map { it.first + it.second })\n",
+ " // Color the line of the ranges\n",
+ " borderLine.color = Color.GREY\n",
+ " }\n",
+ "\n",
+ " // Adjust layout options such as title and overall size\n",
+ " layout {\n",
+ " title = \"Average ROA By Sector With Standard Deviation\"\n",
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+ "// Plot average ROE by sector with error bars representing one standard deviation\n",
+ "roeAndRoaDf.plot {\n",
+ " // Set the x-axis to the sector names\n",
+ " x(sector.map { it.simpleName }) { axis.name = \"Sector of Business\" }\n",
+ "\n",
+ " bars {\n",
+ " // Use the \"Avg ROE\" column for the bar heights\n",
+ " y(`Avg ROE`)\n",
+ " // Fill bars with a chosen color\n",
+ " fillColor = Color.hex(\"#ffaf00\")\n",
+ " }\n",
+ " lineRanges {\n",
+ " // Calculate the min and max for the error bars (Std ROE)\n",
+ " yMin(`Avg ROE`.toList().zip(`Std ROE`.toList()).map { it.first - it.second })\n",
+ " yMax(`Avg ROE`.toList().zip(`Std ROE`.toList()).map { it.first + it.second })\n",
+ " // Color the line of the ranges\n",
+ " borderLine.color = Color.GREY\n",
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+ " layout {\n",
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diff --git a/examples/notebooks/top 12 german companies/Top_12_German_Companies NEW.csv b/examples/notebooks/top 12 german companies/Top_12_German_Companies NEW.csv
new file mode 100644
index 0000000000..569e80fa38
--- /dev/null
+++ b/examples/notebooks/top 12 german companies/Top_12_German_Companies NEW.csv
@@ -0,0 +1,385 @@
+Company,Period,Revenue,Net Income,Liabilities,Assets,Equity,ROA (%),ROE (%),Debt to Equity,percentage Debt to Equity
+Volkswagen AG,12/31/2017,9750496618,516889818.4,21354201295,54861302788,33507101493,942.175.618,1.542.627.668,637.303.746,"0,00%"
+Siemens AG,12/31/2017,19716237464,1276840007,45009303223,75268101508,30258798286,1.696.389.282,4.219.731.382,1.487.478.214,"283,68%"
+Allianz SE,12/31/2017,19458831198,1600107100,48538978480,69583711255,21044732775,2.299.542.624,7.603.361.452,2.306.466.848,"329,65%"
+BMW AG,12/31/2017,18808147150,960184349.6,35382107627,67327482638,31945375011,142.614.028,3.005.706.927,1.107.581.539,"0,00%"
+BASF SE,12/31/2017,16895580815,1797081911,28309420014,68036567115,39727147101,2.641.347.127,4.523.561.449,71.259.635,"634,80%"
+Deutsche Telekom AG,12/31/2017,11505351408,1425682028,36680109736,61941763399,25261653663,2.301.649.081,5.643.660.732,1.452.007.467,"388,68%"
+Daimler AG,12/31/2017,17133317238,1743084807,19707492188,56381587968,36674095780,309.158.516,4.752.904.659,537.368.182,"884,48%"
+SAP SE,12/31/2017,17560385805,2276360916,40828269592,73785136305,32956866713,3.085.121.245,6.907.091.429,123.883.954,"557,55%"
+Bayer AG,12/31/2017,18251254610,2670535587,16524775630,29169852309,12645076680,915.512.207,2.111.917.274,130.681.498,"1616,08%"
+Deutsche Bank AG,12/31/2017,9318303083,958508333.7,11123712659,49692010679,38568298020,1.928.898.269,2.485.223.313,288.415.959,"0,00%"
+Porsche AG,12/31/2017,12119018742,894091080.3,28361763209,73909302825,45547539617,120.971.386,1.962.984.363,622.684.857,"0,00%"
+Merck KGaA,12/31/2017,11760953647,812341476,29296072481,51828662312,22532589831,1.567.359.526,3.605.184.677,130.016.446,"277,29%"
+Volkswagen AG,12/31/2018,15132692627,1296423798,22603216585,52612061095,30008844510,2.464.119.008,432.013.901,753.218.491,"573,56%"
+Siemens AG,12/31/2018,9738110535,1128540207,47796930083,86681431720,38884501637,1.301.939.971,2.902.287.954,1.229.202.589,"236,11%"
+Allianz SE,12/31/2018,11358222175,1091045445,25112465464,47584136480,22471671016,229.287.642,4.855.203.888,1.117.516.603,"434,46%"
+BMW AG,12/31/2018,6557505339,618883604.9,33315961378,60538144207,27222182829,1.022.303.562,2.273.453.267,1.223.853.414,"0,00%"
+BASF SE,12/31/2018,10270182691,708863277.1,42708053991,57050114588,14342060597,124.252.735,4.942.548.334,2.977.818.543,"0,00%"
+Deutsche Telekom AG,12/31/2018,15273880507,1698666925,35514608139,56589637247,21075029108,3.001.727.892,8.060.092.903,1.685.151.084,"478,30%"
+Daimler AG,12/31/2018,14737735881,1726421482,41539802392,60496305967,18956503575,2.853.763.473,9.107.278.014,2.191.321.951,"415,61%"
+SAP SE,12/31/2018,10184334320,1365996842,44932510908,90595603440,45663092533,1.507.795.953,299.146.809,98.400.061,"304,01%"
+Bayer AG,12/31/2018,18541451915,2242441772,28896911499,58238408654,29341497155,3.850.451.659,7.642.560.842,984.847.888,"776,01%"
+Deutsche Bank AG,12/31/2018,8808021584,1274941171,36770248755,55390246152,18619997397,2.301.743.104,6.847.160.844,197.477.196,"346,73%"
+Porsche AG,12/31/2018,7955302121,939711762.4,35954156213,65117507958,29163351745,1.443.101.543,3.222.235.121,1.232.854.047,"0,00%"
+Merck KGaA,12/31/2018,14241334382,1165009578,37401670447,71252783664,33851113217,1.635.037.283,3.441.569.472,1.104.887.458,"311,49%"
+Volkswagen AG,12/31/2019,15448794996,1760261975,43856467673,93429721556,49573253883,1.884.049.257,3.550.830.008,884.680.029,"401,37%"
+Siemens AG,12/31/2019,12841952642,1411970100,29126693633,55130546327,26003852694,2.561.139.322,5.429.849.634,1.120.091.472,"484,77%"
+Allianz SE,12/31/2019,15508664175,1417702229,18132341778,46620472331,28488130553,3.040.943.514,4.976.466.345,636.487.598,"781,86%"
+BMW AG,12/31/2019,8859340166,1144565565,40538252302,78168514026,37630261724,1.464.228.377,3.041.609.365,1.077.277.979,"282,34%"
+BASF SE,12/31/2019,17877114298,1224635136,31860731568,65500282723,33639551155,1.869.663.892,3.640.462.175,947.121.185,"384,37%"
+Deutsche Telekom AG,12/31/2019,15796654133,805016265.8,11494162751,47776069193,36281906442,1.684.978.022,2.218.781.604,31.680.151,"0,00%"
+Daimler AG,12/31/2019,10937481164,1145842379,19578678306,31178454239,11599775933,3.675.109.647,9.878.142.355,1.687.849.698,"585,25%"
+SAP SE,12/31/2019,8046709255,662165230.7,39424977655,89177961232,49752983577,742.521.158,1.330.905.572,79.241.434,"0,00%"
+Bayer AG,12/31/2019,17402979577,1387637302,31887265220,61548783018,29661517798,2.254.532.476,467.824.105,1.075.038.217,"435,17%"
+Deutsche Bank AG,12/31/2019,9814156310,576451352.5,25131659304,46301105258,21169445954,1.245.005.598,2.723.034.669,1.187.166.606,"0,00%"
+Porsche AG,12/31/2019,15806884536,845076735.9,34376508276,55201546863,20825038587,1.530.893.216,405.798.401,1.650.729.632,"0,00%"
+Merck KGaA,12/31/2019,17666094987,1547381835,47717175754,76466035769,28748860015,2.023.619.793,5.382.411.109,1.659.793.666,"324,28%"
+Volkswagen AG,12/31/2020,6074909806,875656602.5,42262965768,80569106208,38306140440,1.086.839.167,2.285.943.174,1.103.294.806,"0,00%"
+Siemens AG,12/31/2020,17731550761,1893885668,10137976567,39067986912,28930010345,4.847.666.382,6.546.439.648,350.431.142,"1868,11%"
+Allianz SE,12/31/2020,10098889889,1161225924,39497490703,77791788915,38294298213,1.492.735.853,3.032.372.908,1.031.419.625,"294,00%"
+BMW AG,12/31/2020,6940311223,962499322.1,29150494667,41331347093,12180852427,2.328.739.298,7.901.740.276,2.393.140.779,"0,00%"
+BASF SE,12/31/2020,11583572658,1407524593,37122614462,82673694404,45551079942,1.702.505.982,3.089.991.707,814.966.725,"379,16%"
+Deutsche Telekom AG,12/31/2020,19614896783,1231038198,45625478678,94598230353,48972751675,1.301.333.221,2.513.720.704,931.650.298,"269,81%"
+Daimler AG,12/31/2020,8582626126,482111535,40109942612,86478204177,46368261566,55.749.485,1.039.744.685,865.030.115,"120,20%"
+SAP SE,12/31/2020,9687560178,1027662516,40817136100,65594337613,24777201513,1.566.693.946,4.147.613.343,1.647.366.676,"251,77%"
+Bayer AG,12/31/2020,14954913916,1086100755,49223539015,87910824435,38687285420,1.235.457.365,2.807.384.243,1.272.344.091,"220,65%"
+Deutsche Bank AG,12/31/2020,16454038855,1761523797,29090502114,64555499757,35464997643,2.728.696.708,4.966.936.173,820.259.525,"605,53%"
+Porsche AG,12/31/2020,14994514428,1118453310,13089516203,26894911034,13805394830,4.158.605.723,8.101.566.983,948.145.009,"854,46%"
+Merck KGaA,12/31/2020,5897253747,625948791.2,38506883950,51548729961,13041846011,1.214.285.573,4.799.541.343,2.952.563.918,"0,00%"
+Volkswagen AG,12/31/2021,11544833810,1709962819,14660160673,40260511363,25600350690,4.247.245.654,6.679.450.762,572.654.682,"1166,40%"
+Siemens AG,12/31/2021,15389996263,923394181.6,21808491986,40095375980,18286883994,2.302.994.196,5.049.488.923,1.192.575.618,"0,00%"
+Allianz SE,12/31/2021,9380555380,1350100953,41223028779,68933524997,27710496218,195.855.493,4.872.164.475,1.487.632.284,"327,51%"
+BMW AG,12/31/2021,18321682480,1436804767,33230894855,73659285546,40428390691,1.950.609.154,355.394.994,821.969.272,"432,37%"
+BASF SE,12/31/2021,18793891125,2702710428,12423193661,54396450621,41973256961,4.968.541.876,6.439.124.871,295.978.787,"2175,54%"
+Deutsche Telekom AG,12/31/2021,18588737724,2116759905,15557689149,64787149652,49229460503,3.267.252.713,429.978.286,316.023.962,"1360,59%"
+Daimler AG,12/31/2021,19604996789,2406144332,39627936994,77954949919,38327012925,3.086.583.128,6.277.933.363,1.033.942.746,"607,18%"
+SAP SE,12/31/2021,12465229611,800983300.4,28257880783,51866957482,23609076699,1.544.303.617,3.392.692.186,1.196.907.492,"0,00%"
+Bayer AG,12/31/2021,13009192330,1451167596,31049801948,49482603845,18432801897,2.932.682.364,7.872.745.579,1.684.486.283,"467,37%"
+Deutsche Bank AG,12/31/2021,10414516344,1120888368,33962234582,80244374780,46282140198,1.396.843.544,2.421.859.412,733.808.645,"330,04%"
+Porsche AG,12/31/2021,14047287806,1489899027,14851641486,42561319258,27709677772,3.500.594.092,5.376.818.307,535.973.085,"1003,19%"
+Merck KGaA,12/31/2021,12917187369,1708731611,17319486583,43037893850,25718407267,3.970.295.613,664.400.246,673.427.651,"986,59%"
+Volkswagen AG,12/31/2022,11168902003,587168043.4,20072750684,67464272122,47391521437,870.339.255,1.238.972.765,423.551.515,"0,00%"
+Siemens AG,12/31/2022,16861000234,918419211.5,32873432607,81487723152,48614290545,1.127.064.515,1.889.195.957,676.209.243,"0,00%"
+Allianz SE,12/31/2022,16472180169,2043609431,34718393733,62464433746,27746040013,3.271.636.847,736.540.937,125.129.185,"588,62%"
+BMW AG,12/31/2022,7382434260,1093800017,35338248318,45898916976,10560668658,2.383.062.802,1.035.729.888,3.346.213.148,"309,52%"
+BASF SE,12/31/2022,13018762765,1704211990,11167034881,30031637051,18864602170,567.472.225,9.033.914.282,591.957.083,"1526,11%"
+Deutsche Telekom AG,12/31/2022,9420241101,990420474.4,12931047199,34081834188,21150786988,2.906.006.963,4.682.664.881,611.374.282,"0,00%"
+Daimler AG,12/31/2022,9363037363,704852259.1,34778269496,54701473950,19923204453,1.288.543.449,3.537.845.836,1.745.616.253,"0,00%"
+SAP SE,12/31/2022,16805465254,1837934274,10102113588,37939001132,27836887544,4.844.445.609,6.602.513.558,362.903.847,"1819,36%"
+Bayer AG,12/31/2022,19647577142,1826544478,10046371516,37513857859,27467486343,4.868.985.975,6.649.842.126,365.755.038,"1818,11%"
+Deutsche Bank AG,12/31/2022,7252204354,949551701.2,44858188981,86269206025,41411017045,110.068.441,229.299.295,1.083.242.871,"0,00%"
+Porsche AG,12/31/2022,5536870879,298030924.1,19445445005,30265348449,10819903444,984.726.558,275.446.935,1.797.192.101,"0,00%"
+Merck KGaA,12/31/2022,7610452133,536310750.1,48688712492,88688270941,39999558449,604.714.405,1.340.791.676,1.217.231.249,"0,00%"
+Volkswagen AG,12/31/2023,16782030250,1481049554,32384571623,78417189252,46032617629,1.888.679.725,3.217.391.559,70.351.358,"457,33%"
+Siemens AG,12/31/2023,8282175431,459390813.5,28551545477,64407150986,35855605509,713.260.572,1.281.224.531,796.292.381,"0,00%"
+Allianz SE,12/31/2023,13941085692,1722322782,32850325731,66248470463,33398144732,2.599.792.523,5.156.941.487,983.597.322,"524,29%"
+BMW AG,12/31/2023,8469619625,602960048.5,32716601950,78863289364,46146687414,764.563.656,1.306.616.103,708.969.674,"0,00%"
+BASF SE,12/31/2023,12852011317,684110085.9,18179739764,53040583203,34860843439,1.289.786.131,1.962.402.565,521.494.547,"0,00%"
+Deutsche Telekom AG,12/31/2023,12752580163,884167446.6,28470052842,53978751141,25508698298,1.637.991.669,3.466.140.986,1.116.091.951,"0,00%"
+Daimler AG,12/31/2023,5161220914,527374446.2,15838145692,51630867307,35792721615,1.021.432.476,1.473.412.533,442.496.267,"0,00%"
+SAP SE,12/31/2023,13414872686,1069408471,35679098743,80239095201,44559996459,1.332.777.331,2.399.929.435,800.697.971,"299,73%"
+Bayer AG,12/31/2023,15851738346,905684923,42371799830,52389418748,10017618918,1.728.755.433,9.040.920.106,422.972.766,"213,75%"
+Deutsche Bank AG,12/31/2023,12421650351,967097057.5,33786415736,79888234351,46101818615,1.210.562.563,2.097.741.665,73.286.514,"0,00%"
+Porsche AG,12/31/2023,16438581863,1419961878,19415830018,36726316966,17310486949,386.633.345,8.202.899.676,1.121.622.406,"731,34%"
+Merck KGaA,12/31/2023,17637490503,2103890014,44486109206,61628452463,17142343256,3.413.829.051,1.227.305.965,259.510.083,"472,93%"
+Volkswagen AG,12/31/2024,14186610709,2107123762,14632441063,32642980976,18010539913,6.455.059.247,1.169.939.253,812.437.669,"1440,04%"
+Siemens AG,12/31/2024,8451674463,530013312.6,43381572844,83248031989,39866459145,636.667.678,1.329.471.751,1.088.172.207,"0,00%"
+Allianz SE,12/31/2024,7203400075,631684017.9,23852825717,41830939577,17978113860,1.510.088.046,3.513.627.863,13.267.702,"0,00%"
+BMW AG,12/31/2024,6152045684,707968936.5,29243056689,57427208762,28184152074,1.232.810.982,2.511.939.811,1.037.570.923,"0,00%"
+BASF SE,12/31/2024,15113132584,1914494156,43259483243,88221659272,44962176029,2.170.095.385,4.258.010.455,962.130.552,"442,56%"
+Deutsche Telekom AG,12/31/2024,19139621006,2834016899,18313729720,41643539977,23329810257,6.805.417.841,1.214.762.087,784.992.656,"1547,48%"
+Daimler AG,12/31/2024,16456922307,1492220093,48085311015,79618978403,31533667388,1.874.201.507,4.732.148.895,1.524.888.001,"310,33%"
+SAP SE,12/31/2024,16754700510,923006607.2,49206362475,96053458782,46847096308,96.093.011,1.970.253.612,105.036.099,"0,00%"
+Bayer AG,12/31/2024,5728072323,604515777.3,34398569588,80121015584,45722445996,754.503.388,1.322.142.252,752.334.413,"0,00%"
+Deutsche Bank AG,12/31/2024,19852203756,2223753463,36451064643,76222443508,39771378864,2.917.452.342,5.591.341.126,916.514.983,"610,07%"
+Porsche AG,12/31/2024,13056746207,1500333956,33698449632,62560115073,28861665442,2.398.227.615,519.836.237,1.167.585.069,"445,22%"
+Merck KGaA,12/31/2024,18915504026,2413914962,30820597362,65357495789,34536898427,3.693.401.855,6.989.379.684,892.396.213,"783,21%"
+Volkswagen AG,3/31/2017,19695988685,1329397719,24520605687,58120450678,33599844991,2.287.314.883,3.956.559.082,729.783.298,"542,16%"
+Siemens AG,3/31/2017,19833452617,1519697640,16063730954,53272532961,37208802008,2.852.685.156,4.084.242.325,431.718.574,"946,04%"
+Allianz SE,3/31/2017,9164246774,981041952.6,19407851173,43999960378,24592109205,2.229.642.809,3.989.255.027,789.190.183,"0,00%"
+BMW AG,3/31/2017,18765462866,1081539894,34543581067,80126669599,45583088532,1.349.787.655,2.372.677.959,75.781.572,"313,09%"
+BASF SE,3/31/2017,16836243798,2100216671,19360624506,64506392372,45145767866,3.255.827.204,4.652.078.745,428.846.942,"1084,79%"
+Deutsche Telekom AG,3/31/2017,13951553720,1348710564,21828517855,62780698314,40952180459,2.148.288.567,3.293.379.129,533.024.557,"617,87%"
+Daimler AG,3/31/2017,7142318181,980884827.4,33400146455,69867319792,36467173338,1.403.925.083,2.689.774.769,915.896.227,"0,00%"
+SAP SE,3/31/2017,6568715630,719090971.2,18570025227,65782612977,47212587750,1.093.132.271,1.523.091.628,393.327.841,"0,00%"
+Bayer AG,3/31/2017,13027840283,1127343336,48157761467,85094142147,36936380680,1.324.818.968,3.052.121.825,1.303.802.933,"234,09%"
+Deutsche Bank AG,3/31/2017,14843015558,1680873545,14840428879,46770654578,31930225699,3.593.863.632,5.264.208.155,464.776.824,"1132,63%"
+Porsche AG,3/31/2017,5883512064,440184439.5,47041623953,76926443805,29884819852,572.214.726,1.472.936.567,1.574.097.625,"0,00%"
+Merck KGaA,3/31/2017,6974057939,389688114,31405828053,49969368014,18563539961,779.853.997,2.099.212.299,1.691.801.678,"124,08%"
+Volkswagen AG,3/31/2018,15989256775,1291419013,42659096630,53863933584,11204836955,2.397.557.934,1.152.554.935,3.807.203.693,"302,73%"
+Siemens AG,3/31/2018,10138254767,1262116952,45296373926,72972094010,27675720085,1.729.588.508,45.603.762,163.668.276,"278,64%"
+Allianz SE,3/31/2018,17610247426,2015329868,40259793264,68504291957,28244498693,294.190.307,7.135.300.541,1.425.403.003,"500,58%"
+BMW AG,3/31/2018,7335760537,743322627.7,48776154787,95122657900,46346503113,781.435.931,1.603.837.566,1.052.423.624,"0,00%"
+BASF SE,3/31/2018,16240083291,1330285061,20620789244,35863861269,15243072025,3.709.263.346,872.714.541,1.352.797.468,"645,12%"
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+SAP SE,9/30/2021,9000667547,1079598533,20844606335,48683375624,27838769289,2.217.591.773,3.878.039.728,748.761.776,"517,93%"
+Bayer AG,9/30/2021,13756386408,721617987.5,44559907041,88194004126,43634097086,818.216.606,1.653.793.789,102.121.758,"0,00%"
+Deutsche Bank AG,9/30/2021,12533912164,782840180.3,42843256371,86694386780,43851130409,902.988.313,1.785.222.349,977.016.008,"0,00%"
+Porsche AG,9/30/2021,7245971264,1033867668,43655777728,59462856760,15807079033,17.386.781,6.540.535.831,2.761.786.516,"236,82%"
+Merck KGaA,9/30/2021,14686380695,2175890454,36499903384,85610397087,49110493703,2.541.619.392,4.430.601.873,743.220.046,"596,14%"
+Volkswagen AG,9/30/2022,10260879440,889227522,10690275300,28159139854,17468864554,3.157.864.646,5.090.356.727,611.961.657,"831,81%"
+Siemens AG,9/30/2022,12664949131,649372783.1,35555990358,69522102895,33966112537,934.052.274,1.911.825.448,1.046.807.765,"0,00%"
+Allianz SE,9/30/2022,16931011156,2086994153,18754849198,52873275455,34118426257,3.947.162.597,6.116.912.125,549.698.543,"1112,78%"
+BMW AG,9/30/2022,18782382394,1614997271,25317192992,38617124033,13299931041,418.207.547,1.214.289.959,1.903.558.215,"637,91%"
+BASF SE,9/30/2022,8861717408,569761713.3,46662383110,69619249891,22956866781,8.183.968,2.481.879.251,2.032.611.138,"0,00%"
+Deutsche Telekom AG,9/30/2022,14873007184,1613587509,33957807568,82298368793,48340561225,1.960.655.518,3.337.957.748,70.247.028,"475,17%"
+Daimler AG,9/30/2022,18430272465,1278461027,44184049308,54843099546,10659050238,2.331.124.677,1.199.413.642,4.145.214.472,"289,35%"
+SAP SE,9/30/2022,18713092617,1701020228,36355439326,47077977464,10722538138,3.613.197.338,1.586.396.995,3.390.562.837,"467,89%"
+Bayer AG,9/30/2022,10642582906,1154447670,44364932131,75643621464,31278689333,1.526.166.579,3.690.844.131,1.418.375.676,"260,22%"
+Deutsche Bank AG,9/30/2022,11924228778,1074208571,47986876748,85149075647,37162198900,1.261.562.223,2.890.594.752,1.291.281.952,"223,85%"
+Porsche AG,9/30/2022,8621455856,1229669587,32376788972,47679877246,15303088274,2.579.011.647,803.543.419,2.115.702.948,"379,80%"
+Merck KGaA,9/30/2022,7888146905,698353200.2,33355799273,53175544683,19819745411,131.329.769,3.523.522.556,1.682.958.009,"0,00%"
+Volkswagen AG,9/30/2023,11664866018,1677667162,15998323030,33372541902,17374218871,5.027.088.338,965.607.245,92.080.819,"1048,65%"
+Siemens AG,9/30/2023,10185412162,754846740.3,24373958388,59331553465,34957595076,1.272.251.772,2.159.321.139,69.724.357,"0,00%"
+Allianz SE,9/30/2023,19814938566,1836312727,18499451286,30329911272,11830459986,6.054.461.257,1.552.190.472,1.563.713.609,"992,63%"
+BMW AG,9/30/2023,12729885504,1804875257,10865997336,56007965536,45141968200,3.222.533.152,3.998.220.124,240.707.213,"1661,03%"
+BASF SE,9/30/2023,8416753062,980336277,46036015495,95477772055,49441756560,1.026.769.117,1.982.810.372,931.116.099,"212,95%"
+Deutsche Telekom AG,9/30/2023,5149849693,750613275.1,17312068995,55772071679,38460002684,1.345.858.693,1.951.672.446,450.131.768,"0,00%"
+Daimler AG,9/30/2023,13104191262,1856279741,29165603027,45413408789,16247805762,4.087.514.659,1.142.480.264,1.795.048.726,"636,46%"
+SAP SE,9/30/2023,17167661800,1338393789,26063953489,52362126677,26298173188,2.556.034.053,5.089.303.273,991.093.689,"513,50%"
+Bayer AG,9/30/2023,9457609977,1131311165,40856047831,77252438398,36396390567,1.464.434.247,3.108.305.926,1.122.530.207,"276,90%"
+Deutsche Bank AG,9/30/2023,17914537947,1449472284,20633879462,43019367872,22385488410,3.369.348.169,6.475.053.201,92.175.248,"702,47%"
+Porsche AG,9/30/2023,12920254885,1147104411,35348848744,85266963912,49918115168,1.345.309.318,2.297.972.203,708.136.688,"324,51%"
+Merck KGaA,9/30/2023,9297896247,1145109250,49354599469,67631473649,18276874181,1.693.160.283,6.265.345.152,2.700.385.141,"232,02%"
+Volkswagen AG,9/30/2024,9752432683,970590418.5,31255665329,70790020629,39534355300,1.371.083.678,2.455.055.637,790.595.043,"0,00%"
+Siemens AG,9/30/2024,11691964631,1345578064,24477801438,72482874961,48005073523,1.856.408.241,2.802.991.363,509.900.301,"549,71%"
+Allianz SE,9/30/2024,16908879786,1197174003,48008203569,67570965703,19562762134,177.172.842,611.965.731,2.454.060.589,"249,37%"
+BMW AG,9/30/2024,17989542164,2639441820,33643521649,69399402463,35755880815,3.803.263.035,7.381.839.743,940.922.748,"784,53%"
+BASF SE,9/30/2024,16485330446,1727330520,41199120058,58775396940,17576276882,2.938.866.617,9.827.624.651,2.344.018.607,"419,26%"
+Deutsche Telekom AG,9/30/2024,6531276814,872767531.7,15526905086,49404344791,33877439705,1.766.580.521,2.576.249.974,458.325.813,"0,00%"
+Daimler AG,9/30/2024,14640665936,2101927829,10787737144,51913035226,41125298082,4.048.940.347,5.111.033.662,262.313.896,"1948,44%"
+SAP SE,9/30/2024,10824151396,830279996.2,42369695133,72342104340,29972409208,114.771.336,2.770.147.673,1.413.623.271,"0,00%"
+Bayer AG,9/30/2024,9332023907,746667545.1,23673143006,42677324477,19004181470,1.749.565.031,3.928.964.508,1.245.680.749,"0,00%"
+Deutsche Bank AG,9/30/2024,18549946525,2323022170,49199711543,64461554635,15261843092,3.603.732.773,1.522.111.161,3.223.707.074,"472,16%"
+Porsche AG,9/30/2024,15911174550,1241693861,36125130235,84583217359,48458087124,1.468.014.459,2.562.407.917,745.492.288,"343,72%"
+Merck KGaA,9/30/2024,15036344926,1704097607,30603212259,59444780752,28841568494,2.866.690.036,5.908.477.575,1.061.080.026,"556,84%"
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