From 2cd632b82dc744e224e15776ea6a724fa69d8872 Mon Sep 17 00:00:00 2001
From: DesireeWyrzylala <74858504+DesireeWyrzylala@users.noreply.github.com>
Date: Tue, 11 Feb 2025 16:52:15 +0100
Subject: [PATCH] order files create combined dataframe template create heatmap
 template

---
 .../{run_KMeansAD_U.csv => KMeansAD_U.csv}    |     0
 .../evaluation/{run_LSTMAD.csv => LSTMAD.csv} |     0
 docs/evaluation/{POLY2.csv => POLY.csv}       |     0
 .../VUS-PR_combined_by_group.csv              |     9 +
 .../auc_pr_combined_by_group.csv              |     9 +
 docs/evaluation/{ => temp}/run_Sub_PCA3.csv   |     0
 docs/evaluation/{ => temp}/run_Sub_PCA4.csv   |     0
 docs/evaluation/{ => temp}/run_Sub_PCA6.csv   |     0
 docs/evaluation/{ => temp}/run_Sub_PCA7.csv   |     0
 src/group_evaluation/create_heatmap.ipynb     | 41435 ++++++++++++++++
 .../generate_combined_dataframe.ipynb         |   589 +
 src/group_evaluation/test.png                 |   Bin 0 -> 45668 bytes
 src/group_evaluation/test_vus-pr.png          |   Bin 0 -> 45652 bytes
 13 files changed, 42042 insertions(+)
 rename docs/evaluation/{run_KMeansAD_U.csv => KMeansAD_U.csv} (100%)
 rename docs/evaluation/{run_LSTMAD.csv => LSTMAD.csv} (100%)
 rename docs/evaluation/{POLY2.csv => POLY.csv} (100%)
 create mode 100644 docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
 create mode 100644 docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv
 rename docs/evaluation/{ => temp}/run_Sub_PCA3.csv (100%)
 rename docs/evaluation/{ => temp}/run_Sub_PCA4.csv (100%)
 rename docs/evaluation/{ => temp}/run_Sub_PCA6.csv (100%)
 rename docs/evaluation/{ => temp}/run_Sub_PCA7.csv (100%)
 create mode 100644 src/group_evaluation/create_heatmap.ipynb
 create mode 100644 src/group_evaluation/generate_combined_dataframe.ipynb
 create mode 100644 src/group_evaluation/test.png
 create mode 100644 src/group_evaluation/test_vus-pr.png

diff --git a/docs/evaluation/run_KMeansAD_U.csv b/docs/evaluation/KMeansAD_U.csv
similarity index 100%
rename from docs/evaluation/run_KMeansAD_U.csv
rename to docs/evaluation/KMeansAD_U.csv
diff --git a/docs/evaluation/run_LSTMAD.csv b/docs/evaluation/LSTMAD.csv
similarity index 100%
rename from docs/evaluation/run_LSTMAD.csv
rename to docs/evaluation/LSTMAD.csv
diff --git a/docs/evaluation/POLY2.csv b/docs/evaluation/POLY.csv
similarity index 100%
rename from docs/evaluation/POLY2.csv
rename to docs/evaluation/POLY.csv
diff --git a/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv b/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
new file mode 100644
index 0000000..0fa2f2b
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/VUS-PR_combined_by_group.csv
@@ -0,0 +1,9 @@
+,Model,IOPS,SMAP,YAHOO,UCR,Daphnet,TODS,NAB,SVDB,CATSv2,TAO,LTDB,MSL,Power,SWaT,MITDB,WSD,Exathlon,OPPORTUNITY,NEK,SED,MGAB,SMD,Stock
+0,CNN,0.20582640755044848,0.3368175788334636,0.5120964005826543,0.06544275611727057,0.3984561332333746,0.575941184620618,0.24809064327969454,0.1997846992126922,0.2882929464081713,0.9942696053962238,0.40617143072486417,0.34635004588200624,0.0754866098747693,0.6807482076261672,0.14363186026434818,0.22492161972936892,0.6577570780079647,0.6327991152012451,0.7148450524753189,0.058416114173648566,0.0069880026447859105,0.5450449067296064,0.9399247021670545
+0,KMeansAD_U,0.1079882871672322,0.6577379563617795,0.78182374346563,0.48682179418842214,0.0397474048677725,0.7392574120062714,0.42747718824263387,0.8113847695158117,0.2292684024148795,0.7479454637274009,0.8355528232967653,0.4415431287006482,0.5241664765771313,0.1115190303707684,0.5908924511486201,0.17306040829201394,0.6180514515785507,0.3518276749123187,0.3179274541052912,0.8952913367248847,0.03664497423359471,0.2545373617228621,0.7381976474065515
+0,LSTMAD,0.2130441268776781,0.273213536450389,0.4174161081456283,0.023292741312739982,0.1330800298859931,0.5222277602405662,0.17373197532380166,0.12748852543364644,0.3290558627970383,0.9950444582863889,0.32982840878783637,0.30512791043157395,0.0672501040011019,0.669824054008361,0.11239751245844268,0.13026969854826323,0.735253231133451,0.5637720546144361,0.6896972412727194,0.056562170107036035,0.02541916388299069,0.48394405976309657,0.8179595058481393
+0,OmniAnomaly,0.1928374569986444,0.4178519668986689,0.21978699689042347,0.08955150737058691,0.1230261153960334,0.5381935047574228,0.34200742217457203,0.445289118136039,0.2822817568231803,0.9300201627687462,0.5247628039733387,0.4280475527739124,0.0670900075295742,0.366250384309729,0.16346484655995386,0.15761230831578144,0.8763722516375352,0.6499305118412632,0.8583489172841368,0.055985189981857066,0.005586448237557389,0.47874214848138164,0.7716583853918693
+0,PCA,0.2603723461255518,0.5668421229644969,0.1637287767285993,0.1526782948310656,0.4415678196838903,0.5528774654894228,0.5113983091235315,0.5239841979223827,0.2324174649783961,0.8402761677661017,0.5348733977584581,0.5233099809194475,0.0796163424706734,0.3755116281521825,0.32352578503655105,0.15536603262796655,0.9591725273812642,0.9245164887480816,0.9291709818321476,0.030269609217551566,0.005551113652017667,0.4672566855173588,0.8253823808338303
+0,POLY,0.36505488384910495,0.7270794225766999,0.3076533188880135,0.19877971115578724,0.5195156603941771,0.5864932357832445,0.58411151200798,0.4455308354706224,0.232251680558134,0.8084529309725733,0.5105066913376546,0.7342443893083611,0.1222940559336612,0.1033636962969695,0.3204245599085764,0.5311097529528253,0.8479450729850164,0.4263263694845811,0.7648360444393737,0.040175651318865566,0.008268735555270955,0.6631912450676738,0.8053753836898458
+0,Sub_KNN,0.11652716789354169,0.5582867850000348,0.5020311037216072,0.32974766955827883,0.0351848897952167,0.7802797461321267,0.3414322714780459,0.5914836251513862,0.3547303318013108,0.6841191787952137,0.6923549140356251,0.3687456862707051,0.1665616569474886,0.1108500020039233,0.5489948037487509,0.08320386434254905,0.5624491502491966,0.29539772741997733,0.13290842186836746,0.882987455118878,0.23861305462798313,0.16380278316830532,0.6915117129467715
+0,USAD,0.19281537411048083,0.3022119484350021,0.19236639692499613,0.08443712894111184,0.122991565905646,0.5298426171179035,0.30830310589567134,0.435722376807082,0.4068306405403881,0.9289872260925759,0.5122662584098864,0.2952083391272381,0.0670897271132299,0.3659224082416971,0.15835735364914574,0.15594955683658498,0.864300862495465,0.6512690843155523,0.8272831680533905,0.053374299505158866,0.005403198063337867,0.6294041253579502,0.7716118184030186
diff --git a/docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv b/docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv
new file mode 100644
index 0000000..82cf143
--- /dev/null
+++ b/docs/evaluation/groupedDataframes/auc_pr_combined_by_group.csv
@@ -0,0 +1,9 @@
+,Model,IOPS,SMAP,YAHOO,UCR,Daphnet,TODS,NAB,SVDB,CATSv2,TAO,LTDB,MSL,Power,SWaT,MITDB,WSD,Exathlon,OPPORTUNITY,NEK,SED,MGAB,SMD,Stock
+0,CNN,0.2658893118021881,0.3299309288297937,0.5615618691240216,0.06947703302366708,0.3814990500329981,0.3037856723254471,0.22334685517173777,0.19710262016040547,0.4118939698305733,0.9948566473921369,0.3544623948216704,0.30703296485567955,0.0723996101568745,0.7387181088941073,0.1601528087440646,0.30312825425966844,0.6543096941901486,0.6191602168303323,0.6855731430154801,0.029325049402745367,0.003854961812131611,0.565369591802724,0.9293442608636605
+0,KMeansAD_U,0.06871545205888992,0.6489127043129476,0.6994439310340633,0.48720584604063366,0.0364448974253113,0.4487028103262184,0.401504819120054,0.8090356381810129,0.4556114877404176,0.12914709845456196,0.7658193255633926,0.37143817671367996,0.507629476682699,0.1111516911833987,0.5838081485697818,0.14637226924693936,0.617689617530909,0.3653810197119157,0.3469725689956081,0.7105752596393731,0.08529855686124818,0.2609082130751905,0.11447566194398832
+0,LSTMAD,0.3201945495278017,0.2788270775280182,0.4439573166657559,0.028041338054278185,0.1322337577607795,0.20567074108559213,0.1533056834456119,0.12987864790676196,0.3580709697520762,0.9933974763498682,0.27673530608030433,0.23744155439224032,0.0647879297198734,0.7376946947277406,0.11306591669413261,0.20949249626949865,0.733874544363356,0.5455348153510139,0.6674208880035603,0.030497343412870064,0.04585981775047413,0.5398904963082672,0.5192324559661586
+0,OmniAnomaly,0.36816289562529086,0.38508194752955704,0.1481883731109026,0.08523551844806315,0.1920691814960085,0.17236897672776852,0.3322271540944519,0.4768352264302811,0.4378677976960084,0.344060221602637,0.4797110125580823,0.39428271688844074,0.0646648482156372,0.7576872065528726,0.20699912890412075,0.251057174157317,0.8745189065767695,0.6498671005600911,0.8361454306772048,0.031663480582993765,0.015268247817355054,0.5311401685436009,0.13364632394088924
+0,PCA,0.3728494779469234,0.4844163013142009,0.1171068241297201,0.1385328677464032,0.468647067072471,0.12432056705472291,0.48652829694101696,0.5588963799777975,0.4747050350102691,0.1491442049585928,0.4894113142147856,0.4933200185137532,0.0788664308035282,0.7567001273773122,0.3778533754675271,0.21654161409334477,0.9581020113822701,0.9231919377778752,0.891733187275396,0.0209941892398622,0.027122742026965137,0.4958507799477434,0.14842805522832
+0,POLY,0.3328661662243327,0.6048262003837879,0.0753954175292419,0.15685006774098753,0.5368267899294596,0.2390237154249407,0.5264388130929107,0.50600229760421,0.4764337820823609,0.1405259120954581,0.4553236691053208,0.629532587636957,0.2037595305274893,0.1231331999424481,0.3669341487796059,0.42738732029834203,0.844512912662751,0.4206576520060393,0.7094581583655936,0.03142142379603057,0.008995606099947298,0.6393941543831635,0.14659009081554358
+0,Sub_KNN,0.07059816232194295,0.4538159096946783,0.14444537624494838,0.2838297601145142,0.0338932937578225,0.41064853826364733,0.31109955913147375,0.5382699167825771,0.4821662398986781,0.10873282764385776,0.5898283428336337,0.313525013097462,0.1601672827617932,0.1103758314980202,0.500833656234517,0.044597067463582274,0.563481394957596,0.2983959722264093,0.11917378259905748,0.7074528685504887,0.32564999100566705,0.16297042908781798,0.0961032782898458
+0,USAD,0.40352305115361864,0.27315882208579156,0.11726577742217488,0.08839193854672213,0.1920565328650643,0.1639152198541356,0.30030480702906,0.4592092425862558,0.4816245755217306,0.3397807769750269,0.4613347441601701,0.26725560751582494,0.0646645958264565,0.7574841555865873,0.20180998220768165,0.2402758256128196,0.8623584859187469,0.651234598743209,0.8160210681956586,0.026230704465697034,0.032096908715701186,0.6657365297403594,0.13357031995417967
diff --git a/docs/evaluation/run_Sub_PCA3.csv b/docs/evaluation/temp/run_Sub_PCA3.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA3.csv
rename to docs/evaluation/temp/run_Sub_PCA3.csv
diff --git a/docs/evaluation/run_Sub_PCA4.csv b/docs/evaluation/temp/run_Sub_PCA4.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA4.csv
rename to docs/evaluation/temp/run_Sub_PCA4.csv
diff --git a/docs/evaluation/run_Sub_PCA6.csv b/docs/evaluation/temp/run_Sub_PCA6.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA6.csv
rename to docs/evaluation/temp/run_Sub_PCA6.csv
diff --git a/docs/evaluation/run_Sub_PCA7.csv b/docs/evaluation/temp/run_Sub_PCA7.csv
similarity index 100%
rename from docs/evaluation/run_Sub_PCA7.csv
rename to docs/evaluation/temp/run_Sub_PCA7.csv
diff --git a/src/group_evaluation/create_heatmap.ipynb b/src/group_evaluation/create_heatmap.ipynb
new file mode 100644
index 0000000..2a4e81e
--- /dev/null
+++ b/src/group_evaluation/create_heatmap.ipynb
@@ -0,0 +1,41435 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.graph_objects as go\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IOPS</th>\n",
+       "      <th>SMAP</th>\n",
+       "      <th>YAHOO</th>\n",
+       "      <th>UCR</th>\n",
+       "      <th>Daphnet</th>\n",
+       "      <th>TODS</th>\n",
+       "      <th>NAB</th>\n",
+       "      <th>SVDB</th>\n",
+       "      <th>CATSv2</th>\n",
+       "      <th>TAO</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SWaT</th>\n",
+       "      <th>MITDB</th>\n",
+       "      <th>WSD</th>\n",
+       "      <th>Exathlon</th>\n",
+       "      <th>OPPORTUNITY</th>\n",
+       "      <th>NEK</th>\n",
+       "      <th>SED</th>\n",
+       "      <th>MGAB</th>\n",
+       "      <th>SMD</th>\n",
+       "      <th>Stock</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Model</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>CNN</th>\n",
+       "      <td>0.205826</td>\n",
+       "      <td>0.336818</td>\n",
+       "      <td>0.512096</td>\n",
+       "      <td>0.065443</td>\n",
+       "      <td>0.398456</td>\n",
+       "      <td>0.575941</td>\n",
+       "      <td>0.248091</td>\n",
+       "      <td>0.199785</td>\n",
+       "      <td>0.288293</td>\n",
+       "      <td>0.994270</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.680748</td>\n",
+       "      <td>0.143632</td>\n",
+       "      <td>0.224922</td>\n",
+       "      <td>0.657757</td>\n",
+       "      <td>0.632799</td>\n",
+       "      <td>0.714845</td>\n",
+       "      <td>0.058416</td>\n",
+       "      <td>0.006988</td>\n",
+       "      <td>0.545045</td>\n",
+       "      <td>0.939925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>KMeansAD_U</th>\n",
+       "      <td>0.107988</td>\n",
+       "      <td>0.657738</td>\n",
+       "      <td>0.781824</td>\n",
+       "      <td>0.486822</td>\n",
+       "      <td>0.039747</td>\n",
+       "      <td>0.739257</td>\n",
+       "      <td>0.427477</td>\n",
+       "      <td>0.811385</td>\n",
+       "      <td>0.229268</td>\n",
+       "      <td>0.747945</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.111519</td>\n",
+       "      <td>0.590892</td>\n",
+       "      <td>0.173060</td>\n",
+       "      <td>0.618051</td>\n",
+       "      <td>0.351828</td>\n",
+       "      <td>0.317927</td>\n",
+       "      <td>0.895291</td>\n",
+       "      <td>0.036645</td>\n",
+       "      <td>0.254537</td>\n",
+       "      <td>0.738198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>LSTMAD</th>\n",
+       "      <td>0.213044</td>\n",
+       "      <td>0.273214</td>\n",
+       "      <td>0.417416</td>\n",
+       "      <td>0.023293</td>\n",
+       "      <td>0.133080</td>\n",
+       "      <td>0.522228</td>\n",
+       "      <td>0.173732</td>\n",
+       "      <td>0.127489</td>\n",
+       "      <td>0.329056</td>\n",
+       "      <td>0.995044</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.669824</td>\n",
+       "      <td>0.112398</td>\n",
+       "      <td>0.130270</td>\n",
+       "      <td>0.735253</td>\n",
+       "      <td>0.563772</td>\n",
+       "      <td>0.689697</td>\n",
+       "      <td>0.056562</td>\n",
+       "      <td>0.025419</td>\n",
+       "      <td>0.483944</td>\n",
+       "      <td>0.817960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>OmniAnomaly</th>\n",
+       "      <td>0.192837</td>\n",
+       "      <td>0.417852</td>\n",
+       "      <td>0.219787</td>\n",
+       "      <td>0.089552</td>\n",
+       "      <td>0.123026</td>\n",
+       "      <td>0.538194</td>\n",
+       "      <td>0.342007</td>\n",
+       "      <td>0.445289</td>\n",
+       "      <td>0.282282</td>\n",
+       "      <td>0.930020</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.366250</td>\n",
+       "      <td>0.163465</td>\n",
+       "      <td>0.157612</td>\n",
+       "      <td>0.876372</td>\n",
+       "      <td>0.649931</td>\n",
+       "      <td>0.858349</td>\n",
+       "      <td>0.055985</td>\n",
+       "      <td>0.005586</td>\n",
+       "      <td>0.478742</td>\n",
+       "      <td>0.771658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>PCA</th>\n",
+       "      <td>0.260372</td>\n",
+       "      <td>0.566842</td>\n",
+       "      <td>0.163729</td>\n",
+       "      <td>0.152678</td>\n",
+       "      <td>0.441568</td>\n",
+       "      <td>0.552877</td>\n",
+       "      <td>0.511398</td>\n",
+       "      <td>0.523984</td>\n",
+       "      <td>0.232417</td>\n",
+       "      <td>0.840276</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.375512</td>\n",
+       "      <td>0.323526</td>\n",
+       "      <td>0.155366</td>\n",
+       "      <td>0.959173</td>\n",
+       "      <td>0.924516</td>\n",
+       "      <td>0.929171</td>\n",
+       "      <td>0.030270</td>\n",
+       "      <td>0.005551</td>\n",
+       "      <td>0.467257</td>\n",
+       "      <td>0.825382</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 23 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 IOPS      SMAP     YAHOO       UCR   Daphnet      TODS  \\\n",
+       "Model                                                                     \n",
+       "CNN          0.205826  0.336818  0.512096  0.065443  0.398456  0.575941   \n",
+       "KMeansAD_U   0.107988  0.657738  0.781824  0.486822  0.039747  0.739257   \n",
+       "LSTMAD       0.213044  0.273214  0.417416  0.023293  0.133080  0.522228   \n",
+       "OmniAnomaly  0.192837  0.417852  0.219787  0.089552  0.123026  0.538194   \n",
+       "PCA          0.260372  0.566842  0.163729  0.152678  0.441568  0.552877   \n",
+       "\n",
+       "                  NAB      SVDB    CATSv2       TAO  ...      SWaT     MITDB  \\\n",
+       "Model                                                ...                       \n",
+       "CNN          0.248091  0.199785  0.288293  0.994270  ...  0.680748  0.143632   \n",
+       "KMeansAD_U   0.427477  0.811385  0.229268  0.747945  ...  0.111519  0.590892   \n",
+       "LSTMAD       0.173732  0.127489  0.329056  0.995044  ...  0.669824  0.112398   \n",
+       "OmniAnomaly  0.342007  0.445289  0.282282  0.930020  ...  0.366250  0.163465   \n",
+       "PCA          0.511398  0.523984  0.232417  0.840276  ...  0.375512  0.323526   \n",
+       "\n",
+       "                  WSD  Exathlon  OPPORTUNITY       NEK       SED      MGAB  \\\n",
+       "Model                                                                        \n",
+       "CNN          0.224922  0.657757     0.632799  0.714845  0.058416  0.006988   \n",
+       "KMeansAD_U   0.173060  0.618051     0.351828  0.317927  0.895291  0.036645   \n",
+       "LSTMAD       0.130270  0.735253     0.563772  0.689697  0.056562  0.025419   \n",
+       "OmniAnomaly  0.157612  0.876372     0.649931  0.858349  0.055985  0.005586   \n",
+       "PCA          0.155366  0.959173     0.924516  0.929171  0.030270  0.005551   \n",
+       "\n",
+       "                  SMD     Stock  \n",
+       "Model                            \n",
+       "CNN          0.545045  0.939925  \n",
+       "KMeansAD_U   0.254537  0.738198  \n",
+       "LSTMAD       0.483944  0.817960  \n",
+       "OmniAnomaly  0.478742  0.771658  \n",
+       "PCA          0.467257  0.825382  \n",
+       "\n",
+       "[5 rows x 23 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "path = '../../docs/evaluation/groupedDataframes/'\n",
+    "metric = 'VUS-PR'\n",
+    "file = metric +'_combined_by_group.csv'\n",
+    "file_path = path + file\n",
+    "df = pd.read_csv(file_path, index_col='Model')\n",
+    "df = df.drop(columns=['Unnamed: 0'])\n",
+    "df.head()"
+   ]
+  },
+  {
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+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
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+            "linecolor": "white",
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+            "gridcolor": "white",
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+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+           "yaxis": {
+            "backgroundcolor": "#E5ECF6",
+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
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+            "ticks": "",
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+            "gridcolor": "white",
+            "gridwidth": 2,
+            "linecolor": "white",
+            "showbackground": true,
+            "ticks": "",
+            "zerolinecolor": "white"
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+            "color": "#2a3f5f"
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+          "ternary": {
+           "aaxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "baxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           },
+           "bgcolor": "#E5ECF6",
+           "caxis": {
+            "gridcolor": "white",
+            "linecolor": "white",
+            "ticks": ""
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "white",
+           "linecolor": "white",
+           "ticks": "",
+           "title": {
+            "standoff": 15
+           },
+           "zerolinecolor": "white",
+           "zerolinewidth": 2
+          }
+         }
+        },
+        "title": {
+         "text": "Darkmint Heatmap der VUS-PRvon den einzelnen Modelle auf den jeweiligen Datensätzen"
+        },
+        "xaxis": {
+         "title": {
+          "text": "Datensätze"
+         }
+        },
+        "yaxis": {
+         "title": {
+          "text": "Modelle"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "color_scales = [\n",
+    "    \"Viridis\", \"Cividis\",\n",
+    "    \"Blues\", \"BuGn\", \"BuPu\", \"GnBu\", \"Greens\", \"Greys\", \"Oranges\", \"OrRd\",\n",
+    "    \"PuBu\", \"PuBuGn\", \"PuRd\", \"Purples\", \"RdPu\", \"Reds\", \"YlGn\", \"YlGnBu\",\n",
+    "    \"YlOrBr\", \"YlOrRd\", \"Aggrnyl\", \"Agsunset\", \"Solar\", \"Teal\", \"Tealgrn\",\n",
+    "    \"Temps\", \"Sunset\", \"Sunsetdark\", \"Mint\", \"Emrld\", \"Darkmint\",\n",
+    "]\n",
+    "\n",
+    "for scale in color_scales:\n",
+    "    fig = go.Figure(go.Heatmap(\n",
+    "        z= df.values,\n",
+    "        x= df.columns,  \n",
+    "        y= df.index,\n",
+    "        colorscale=scale,#'Viridis',\n",
+    "        text= df.values,\n",
+    "        texttemplate=\"%{text:.2f}\",\n",
+    "        textfont=dict(size=12)\n",
+    "    ))\n",
+    "\n",
+    "    fig.update_layout(\n",
+    "        title= scale+' Heatmap der ' +metric+ 'von den einzelnen Modelle auf den jeweiligen Datensätzen',\n",
+    "        xaxis=dict(title='Datensätze'),\n",
+    "        yaxis=dict(title='Modelle')\n",
+    "    )\n",
+    "\n",
+    "    fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/src/group_evaluation/generate_combined_dataframe.ipynb b/src/group_evaluation/generate_combined_dataframe.ipynb
new file mode 100644
index 0000000..fbd8b79
--- /dev/null
+++ b/src/group_evaluation/generate_combined_dataframe.ipynb
@@ -0,0 +1,589 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = '../../docs/evaluation/'\n",
+    "#get list of all groups\n",
+    "temp_df = pd.read_csv(path + 'PCA.csv')\n",
+    "keys = list(set(list(temp_df['group'])))\n",
+    "keys.insert(0,'Model')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>IOPS</th>\n",
+       "      <th>SMAP</th>\n",
+       "      <th>YAHOO</th>\n",
+       "      <th>UCR</th>\n",
+       "      <th>Daphnet</th>\n",
+       "      <th>TODS</th>\n",
+       "      <th>NAB</th>\n",
+       "      <th>SVDB</th>\n",
+       "      <th>CATSv2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SWaT</th>\n",
+       "      <th>MITDB</th>\n",
+       "      <th>WSD</th>\n",
+       "      <th>Exathlon</th>\n",
+       "      <th>OPPORTUNITY</th>\n",
+       "      <th>NEK</th>\n",
+       "      <th>SED</th>\n",
+       "      <th>MGAB</th>\n",
+       "      <th>SMD</th>\n",
+       "      <th>Stock</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>0 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Model, IOPS, SMAP, YAHOO, UCR, Daphnet, TODS, NAB, SVDB, CATSv2, TAO, LTDB, MSL, Power, SWaT, MITDB, WSD, Exathlon, OPPORTUNITY, NEK, SED, MGAB, SMD, Stock]\n",
+       "Index: []\n",
+       "\n",
+       "[0 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#create enpty dataframes with dataset group as column\n",
+    "data =  {key: [] for key in keys}\n",
+    "df = pd.DataFrame(data)\n",
+    "df.set_index('Model')\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found:  CNN.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Not correct file type:  groupedDataframes\n",
+      "Found:  KMeansAD_U.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Found:  LSTMAD.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Found:  OmniAnomaly.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Found:  PCA.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Found:  POLY.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Found:  Sub_KNN.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Not correct file type:  temp\n",
+      "Found:  USAD.csv\n",
+      "Model\n",
+      "IOPS\n",
+      "SMAP\n",
+      "YAHOO\n",
+      "UCR\n",
+      "Daphnet\n",
+      "TODS\n",
+      "NAB\n",
+      "SVDB\n",
+      "CATSv2\n",
+      "TAO\n",
+      "LTDB\n",
+      "MSL\n",
+      "Power\n",
+      "SWaT\n",
+      "MITDB\n",
+      "WSD\n",
+      "Exathlon\n",
+      "OPPORTUNITY\n",
+      "NEK\n",
+      "SED\n",
+      "MGAB\n",
+      "SMD\n",
+      "Stock\n",
+      "Not correct file type:  Vergleich der Ergebnisse.md\n"
+     ]
+    }
+   ],
+   "source": [
+    "col = 'VUS-PR'\n",
+    "\n",
+    "for file in os.listdir(path):\n",
+    "    suffix = file.split('.')\n",
+    "    if len(suffix) < 2 or suffix[1] != 'csv':\n",
+    "        print('Not correct file type: ', file)\n",
+    "        continue\n",
+    "    else:\n",
+    "        print('Found: ', file)\n",
+    "        #read Data\n",
+    "        df_tmp = pd.read_csv(path+file)\n",
+    "        #select cols\n",
+    "        df_tmp = df_tmp[['group',col]]\n",
+    "        #Group Dataframe and get mean for each group\n",
+    "        groupedMeans = df_tmp.groupby('group').mean()\n",
+    "        #turn group col from index into col\n",
+    "        groupedMeans = groupedMeans.reset_index()\n",
+    "        #merge key value paires for dict\n",
+    "        keys_temp = data.copy()\n",
+    "        for key in keys_temp:\n",
+    "            print(key)\n",
+    "            if key in groupedMeans['group'].values:\n",
+    "                value = groupedMeans.loc[groupedMeans['group'] == key, col].values[0]\n",
+    "                keys_temp[key] = [value]\n",
+    "            else:\n",
+    "                keys_temp[key] = []\n",
+    "        keys_temp['Model'] = suffix[0]\n",
+    "        df_tmp = pd.DataFrame(keys_temp)\n",
+    "        df = pd.concat([df,df_tmp])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Model</th>\n",
+       "      <th>IOPS</th>\n",
+       "      <th>SMAP</th>\n",
+       "      <th>YAHOO</th>\n",
+       "      <th>UCR</th>\n",
+       "      <th>Daphnet</th>\n",
+       "      <th>TODS</th>\n",
+       "      <th>NAB</th>\n",
+       "      <th>SVDB</th>\n",
+       "      <th>CATSv2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>SWaT</th>\n",
+       "      <th>MITDB</th>\n",
+       "      <th>WSD</th>\n",
+       "      <th>Exathlon</th>\n",
+       "      <th>OPPORTUNITY</th>\n",
+       "      <th>NEK</th>\n",
+       "      <th>SED</th>\n",
+       "      <th>MGAB</th>\n",
+       "      <th>SMD</th>\n",
+       "      <th>Stock</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>CNN</td>\n",
+       "      <td>0.205826</td>\n",
+       "      <td>0.336818</td>\n",
+       "      <td>0.512096</td>\n",
+       "      <td>0.065443</td>\n",
+       "      <td>0.398456</td>\n",
+       "      <td>0.575941</td>\n",
+       "      <td>0.248091</td>\n",
+       "      <td>0.199785</td>\n",
+       "      <td>0.288293</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.680748</td>\n",
+       "      <td>0.143632</td>\n",
+       "      <td>0.224922</td>\n",
+       "      <td>0.657757</td>\n",
+       "      <td>0.632799</td>\n",
+       "      <td>0.714845</td>\n",
+       "      <td>0.058416</td>\n",
+       "      <td>0.006988</td>\n",
+       "      <td>0.545045</td>\n",
+       "      <td>0.939925</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>KMeansAD_U</td>\n",
+       "      <td>0.107988</td>\n",
+       "      <td>0.657738</td>\n",
+       "      <td>0.781824</td>\n",
+       "      <td>0.486822</td>\n",
+       "      <td>0.039747</td>\n",
+       "      <td>0.739257</td>\n",
+       "      <td>0.427477</td>\n",
+       "      <td>0.811385</td>\n",
+       "      <td>0.229268</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.111519</td>\n",
+       "      <td>0.590892</td>\n",
+       "      <td>0.173060</td>\n",
+       "      <td>0.618051</td>\n",
+       "      <td>0.351828</td>\n",
+       "      <td>0.317927</td>\n",
+       "      <td>0.895291</td>\n",
+       "      <td>0.036645</td>\n",
+       "      <td>0.254537</td>\n",
+       "      <td>0.738198</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>LSTMAD</td>\n",
+       "      <td>0.213044</td>\n",
+       "      <td>0.273214</td>\n",
+       "      <td>0.417416</td>\n",
+       "      <td>0.023293</td>\n",
+       "      <td>0.133080</td>\n",
+       "      <td>0.522228</td>\n",
+       "      <td>0.173732</td>\n",
+       "      <td>0.127489</td>\n",
+       "      <td>0.329056</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.669824</td>\n",
+       "      <td>0.112398</td>\n",
+       "      <td>0.130270</td>\n",
+       "      <td>0.735253</td>\n",
+       "      <td>0.563772</td>\n",
+       "      <td>0.689697</td>\n",
+       "      <td>0.056562</td>\n",
+       "      <td>0.025419</td>\n",
+       "      <td>0.483944</td>\n",
+       "      <td>0.817960</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>OmniAnomaly</td>\n",
+       "      <td>0.192837</td>\n",
+       "      <td>0.417852</td>\n",
+       "      <td>0.219787</td>\n",
+       "      <td>0.089552</td>\n",
+       "      <td>0.123026</td>\n",
+       "      <td>0.538194</td>\n",
+       "      <td>0.342007</td>\n",
+       "      <td>0.445289</td>\n",
+       "      <td>0.282282</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.366250</td>\n",
+       "      <td>0.163465</td>\n",
+       "      <td>0.157612</td>\n",
+       "      <td>0.876372</td>\n",
+       "      <td>0.649931</td>\n",
+       "      <td>0.858349</td>\n",
+       "      <td>0.055985</td>\n",
+       "      <td>0.005586</td>\n",
+       "      <td>0.478742</td>\n",
+       "      <td>0.771658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>PCA</td>\n",
+       "      <td>0.260372</td>\n",
+       "      <td>0.566842</td>\n",
+       "      <td>0.163729</td>\n",
+       "      <td>0.152678</td>\n",
+       "      <td>0.441568</td>\n",
+       "      <td>0.552877</td>\n",
+       "      <td>0.511398</td>\n",
+       "      <td>0.523984</td>\n",
+       "      <td>0.232417</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.375512</td>\n",
+       "      <td>0.323526</td>\n",
+       "      <td>0.155366</td>\n",
+       "      <td>0.959173</td>\n",
+       "      <td>0.924516</td>\n",
+       "      <td>0.929171</td>\n",
+       "      <td>0.030270</td>\n",
+       "      <td>0.005551</td>\n",
+       "      <td>0.467257</td>\n",
+       "      <td>0.825382</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "         Model      IOPS      SMAP     YAHOO       UCR   Daphnet      TODS  \\\n",
+       "0          CNN  0.205826  0.336818  0.512096  0.065443  0.398456  0.575941   \n",
+       "0   KMeansAD_U  0.107988  0.657738  0.781824  0.486822  0.039747  0.739257   \n",
+       "0       LSTMAD  0.213044  0.273214  0.417416  0.023293  0.133080  0.522228   \n",
+       "0  OmniAnomaly  0.192837  0.417852  0.219787  0.089552  0.123026  0.538194   \n",
+       "0          PCA  0.260372  0.566842  0.163729  0.152678  0.441568  0.552877   \n",
+       "\n",
+       "        NAB      SVDB    CATSv2  ...      SWaT     MITDB       WSD  Exathlon  \\\n",
+       "0  0.248091  0.199785  0.288293  ...  0.680748  0.143632  0.224922  0.657757   \n",
+       "0  0.427477  0.811385  0.229268  ...  0.111519  0.590892  0.173060  0.618051   \n",
+       "0  0.173732  0.127489  0.329056  ...  0.669824  0.112398  0.130270  0.735253   \n",
+       "0  0.342007  0.445289  0.282282  ...  0.366250  0.163465  0.157612  0.876372   \n",
+       "0  0.511398  0.523984  0.232417  ...  0.375512  0.323526  0.155366  0.959173   \n",
+       "\n",
+       "   OPPORTUNITY       NEK       SED      MGAB       SMD     Stock  \n",
+       "0     0.632799  0.714845  0.058416  0.006988  0.545045  0.939925  \n",
+       "0     0.351828  0.317927  0.895291  0.036645  0.254537  0.738198  \n",
+       "0     0.563772  0.689697  0.056562  0.025419  0.483944  0.817960  \n",
+       "0     0.649931  0.858349  0.055985  0.005586  0.478742  0.771658  \n",
+       "0     0.924516  0.929171  0.030270  0.005551  0.467257  0.825382  \n",
+       "\n",
+       "[5 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df.to_csv(path+'/groupedDataframes/'+col+'_combined_by_group.csv')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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literal 0
HcmV?d00001

-- 
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