From 84d89eb9ea4fde41ae39aa245bd379b07d572fb8 Mon Sep 17 00:00:00 2001
From: David Fisher <dfisher@opensourcesolutions.com>
Date: Thu, 16 Jan 2025 12:23:34 -0500
Subject: [PATCH] fix a couple of notebook bugs

---
 jupyterlite/files/examples/Fleiss Kappa.ipynb |  366 +-
 .../examples/Jaccard and RBO Comparison.ipynb |  839 ++++-
 .../examples/Multiple Raters Analysis.ipynb   | 3043 ++++++++++++++---
 .../files/examples/Scoring Comparison.ipynb   |  420 ++-
 4 files changed, 3687 insertions(+), 981 deletions(-)

diff --git a/jupyterlite/files/examples/Fleiss Kappa.ipynb b/jupyterlite/files/examples/Fleiss Kappa.ipynb
index 2e1f9fe..255de25 100644
--- a/jupyterlite/files/examples/Fleiss Kappa.ipynb	
+++ b/jupyterlite/files/examples/Fleiss Kappa.ipynb	
@@ -1,151 +1,217 @@
 {
-  "metadata": {
-    "kernelspec": {
-      "name": "python",
-      "display_name": "Python (Pyodide)",
-      "language": "python"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "python",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8"
-    }
-  },
-  "nbformat_minor": 5,
-  "nbformat": 4,
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": "# Fleiss' Kappa \nTo understand how much your judges agree with each other.  It is meant to be used with more than two judges.\n\nRead https://www.datanovia.com/en/blog/kappa-coefficient-interpretation/ to learn more.\n\nPlease copy this example and customize it for your own purposes!",
-      "metadata": {},
-      "id": "bd7e4efa-eb00-451e-984d-ed6646d8e25f"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Imports",
-      "metadata": {},
-      "id": "e3412382"
-    },
-    {
-      "cell_type": "code",
-      "source": "import pandas as pd\nfrom js import fetch\nimport json\n\nfrom collections import defaultdict\nfrom statsmodels.stats.inter_rater import aggregate_raters\nfrom statsmodels.stats.inter_rater import fleiss_kappa\nfrom IPython.display import display, Markdown",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 1,
-      "outputs": [],
-      "id": "4972936a"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Step 0: Configuration",
-      "metadata": {},
-      "id": "6da26c5e"
-    },
-    {
-      "cell_type": "code",
-      "source": "QUEPID_BOOK_NUM = 25",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 2,
-      "outputs": [],
-      "id": "71803a49-4065-4adf-a69e-cb0fe2d00f22"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Step 1: Download the Quepid Book",
-      "metadata": {},
-      "id": "420416df-9e6a-41b4-987b-7a03c9dd38b3"
-    },
-    {
-      "cell_type": "code",
-      "source": "# Generic GET call to a JSON endpoint \nasync def get_json(url):\n    resp = await fetch(url)\n    resp_text = await resp.text()\n    return json.loads(resp_text)\n\n",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 3,
-      "outputs": [],
-      "id": "31193536-98eb-4b46-ab98-af04ee07c6d3"
-    },
-    {
-      "cell_type": "code",
-      "source": "data = await get_json(f'/api/export/books/{QUEPID_BOOK_NUM}')",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": null,
-      "outputs": [],
-      "id": "8fef6231-daa8-467f-ac57-13a144e8a356"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Step 2: Extract and Prepare Data",
-      "metadata": {},
-      "id": "79d985ad-cd11-44a9-a7e1-0851bc99aef3"
-    },
-    {
-      "cell_type": "code",
-      "source": "# Initialize a list to hold the tuples of (doc_id, rating, count)\nratings_data = []\n\n# Iterate through each query-doc pair\nfor pair in data['query_doc_pairs']:\n    # Initialize a dictionary to count the ratings for this pair\n    ratings_count = defaultdict(int)\n    \n    # Extract judgements and count the ratings\n    for judgement in pair['judgements']:\n        rating = judgement['rating']\n        ratings_count[rating] += 1\n\n    # Append the counts to the ratings_data list\n    for rating, count in ratings_count.items():\n        ratings_data.append((pair['doc_id'], rating, count))\n",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": null,
-      "outputs": [],
-      "id": "9a8561fd-2dbf-477e-9ac1-4df6d5ebdc91"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Step 3: Aggregate Raters' Data",
-      "metadata": {},
-      "id": "caf5632b-132a-4e1b-80fe-c8c5ab7f2f3a"
-    },
-    {
-      "cell_type": "code",
-      "source": "# Convert ratings_data to a DataFrame\ndf = pd.DataFrame(ratings_data, columns=['doc_id', 'rating', 'count'])\n\n# Use crosstab to create a contingency table\ndata_crosstab = pd.crosstab(index=df['doc_id'], columns=df['rating'], values=df['count'], aggfunc='sum')\n\n# Drop any rows missing judgements\ndata_crosstab = data_crosstab.dropna(how='any')\n\n# Convert the DataFrame to the format expected by aggregate_raters\ndata_for_aggregation = data_crosstab.values\n\n# Aggregate the raters' data\ntable, _ = aggregate_raters(data_for_aggregation)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": null,
-      "outputs": [],
-      "id": "a7598308-129b-4628-ad3a-fc3d703f8205"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Step 4: Compute Fleiss' Kappa",
-      "metadata": {},
-      "id": "25c79fbc"
-    },
-    {
-      "cell_type": "code",
-      "source": "kappa = fleiss_kappa(table, method='fleiss')\ndisplay(Markdown(f\"## Fleiss' Kappa: {kappa:.4f}\"))",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": null,
-      "outputs": [],
-      "id": "25a613f9"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "_This notebook was last updated 19-FEB-2024_",
-      "metadata": {},
-      "id": "5704579e-2321-4629-8de0-6608b428e2b6"
-    },
-    {
-      "cell_type": "code",
-      "source": "",
-      "metadata": {},
-      "execution_count": null,
-      "outputs": [],
-      "id": "7203f6cc-c068-4f75-a59a-1f49c5555319"
-    }
-  ]
-}
\ No newline at end of file
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bd7e4efa-eb00-451e-984d-ed6646d8e25f",
+   "metadata": {},
+   "source": [
+    "# Fleiss' Kappa \n",
+    "To understand how much your judges agree with each other.  It is meant to be used with more than two judges.\n",
+    "\n",
+    "Read https://www.datanovia.com/en/blog/kappa-coefficient-interpretation/ to learn more.\n",
+    "\n",
+    "Please copy this example and customize it for your own purposes!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3412382",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4972936a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from js import fetch\n",
+    "import json\n",
+    "\n",
+    "from collections import defaultdict\n",
+    "from statsmodels.stats.inter_rater import aggregate_raters\n",
+    "from statsmodels.stats.inter_rater import fleiss_kappa\n",
+    "from IPython.display import display, Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6da26c5e",
+   "metadata": {},
+   "source": [
+    "## Step 0: Configuration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "71803a49-4065-4adf-a69e-cb0fe2d00f22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "QUEPID_BOOK_NUM = 25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "420416df-9e6a-41b4-987b-7a03c9dd38b3",
+   "metadata": {},
+   "source": [
+    "## Step 1: Download the Quepid Book"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "31193536-98eb-4b46-ab98-af04ee07c6d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generic GET call to a JSON endpoint \n",
+    "async def get_json(url):\n",
+    "    resp = await fetch(url)\n",
+    "    resp_text = await resp.text()\n",
+    "    return json.loads(resp_text)\n",
+    "\n",
+    "async def get_text(url):\n",
+    "    resp = await fetch(url)\n",
+    "    resp_text = await resp.text()\n",
+    "    return resp_text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fef6231-daa8-467f-ac57-13a144e8a356",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = await get_text(f'/api/books/{QUEPID_BOOK_NUM}.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79d985ad-cd11-44a9-a7e1-0851bc99aef3",
+   "metadata": {},
+   "source": [
+    "## Step 2: Extract and Prepare Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9a8561fd-2dbf-477e-9ac1-4df6d5ebdc91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize a list to hold the tuples of (doc_id, rating, count)\n",
+    "ratings_data = []\n",
+    "\n",
+    "# Iterate through each query-doc pair\n",
+    "for pair in data['query_doc_pairs']:\n",
+    "    # Initialize a dictionary to count the ratings for this pair\n",
+    "    ratings_count = defaultdict(int)\n",
+    "    \n",
+    "    # Extract judgements and count the ratings\n",
+    "    for judgement in pair['judgements']:\n",
+    "        rating = judgement['rating']\n",
+    "        ratings_count[rating] += 1\n",
+    "\n",
+    "    # Append the counts to the ratings_data list\n",
+    "    for rating, count in ratings_count.items():\n",
+    "        ratings_data.append((pair['doc_id'], rating, count))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "caf5632b-132a-4e1b-80fe-c8c5ab7f2f3a",
+   "metadata": {},
+   "source": [
+    "## Step 3: Aggregate Raters' Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7598308-129b-4628-ad3a-fc3d703f8205",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert ratings_data to a DataFrame\n",
+    "df = pd.DataFrame(ratings_data, columns=['doc_id', 'rating', 'count'])\n",
+    "\n",
+    "# Use crosstab to create a contingency table\n",
+    "data_crosstab = pd.crosstab(index=df['doc_id'], columns=df['rating'], values=df['count'], aggfunc='sum')\n",
+    "\n",
+    "# Drop any rows missing judgements\n",
+    "data_crosstab = data_crosstab.dropna(how='any')\n",
+    "\n",
+    "# Convert the DataFrame to the format expected by aggregate_raters\n",
+    "data_for_aggregation = data_crosstab.values\n",
+    "\n",
+    "# Aggregate the raters' data\n",
+    "table, _ = aggregate_raters(data_for_aggregation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25c79fbc",
+   "metadata": {},
+   "source": [
+    "## Step 4: Compute Fleiss' Kappa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25a613f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kappa = fleiss_kappa(table, method='fleiss')\n",
+    "display(Markdown(f\"## Fleiss' Kappa: {kappa:.4f}\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5704579e-2321-4629-8de0-6608b428e2b6",
+   "metadata": {},
+   "source": [
+    "_This notebook was last updated 16_January_2025_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7203f6cc-c068-4f75-a59a-1f49c5555319",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb b/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb
index e86fc58..b806d64 100644
--- a/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb	
+++ b/jupyterlite/files/examples/Jaccard and RBO Comparison.ipynb	
@@ -1,173 +1,690 @@
 {
-  "metadata": {
-    "kernelspec": {
-      "name": "python",
-      "display_name": "Python (Pyodide)",
-      "language": "python"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "python",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Jaccard and RBO Comparison \n",
+    "To understand the magnatude of changes to your query result sets, you can compare multiple snapshots to each other.\n",
+    "\n",
+    "This notebook provides both Jaccard and Rank Biased Overlap (RBO) metrics.\n",
+    "\n",
+    "Please copy this example and customize it for your own purposes!"
+   ]
   },
-  "nbformat_minor": 4,
-  "nbformat": 4,
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": "# Jaccard and RBO Comparison \nTo understand the magnatude of changes to your query result sets, you can compare multiple snapshots to each other.\n\nThis notebook provides both Jaccard and Rank Biased Overlap (RBO) metrics.\n\nPlease copy this example and customize it for your own purposes!",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": "from js import fetch\nfrom typing import List, Optional, Union\n\nimport json\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nimport piplite\nawait piplite.install('seaborn')\nawait piplite.install('rbo')\n\nimport rbo\nimport seaborn as sns\n\nimport os",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 1,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": "# Generic GET call to a JSON endpoint \nasync def get_json(url):\n    resp = await fetch(url)\n    resp_text = await resp.text()\n    return json.loads(resp_text)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": "# Basic Quepid API client methods\n\nasync def get_snapshots(case):\n    response = await get_json(f'/api/cases/{case}/snapshots?shallow=true')\n    return [{'id': snapshot['id'], 'name': snapshot['name']} for snapshot in response['snapshots']]\n\nasync def get_cases_with_snapshots():\n    cases = await get_cases()\n    cases_with_snapshots = [{\n        'id': case['id'],\n        'name': case['name'],\n        'snapshots': [ {\n            'id': snapshot['id'],\n            'name': snapshot['name']\n        } for snapshot in (await get_snapshots(case['id'])) ]\n    } for case in cases]\n    return cases_with_snapshots\n",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 3,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "source": "# Load snapshot, return dict of queries and their (number of results, avg. score, [doc ids])\nasync def load_snapshot(case_id, snapshot_id):\n    snapshot = await get_json(f'/api/cases/{case_id}/snapshots/{snapshot_id}')\n    docs = snapshot['docs']\n    queries = snapshot['queries']\n    \n    # scores are a list of dicts, group them by query\n    scores_list = snapshot['scores']\n    scores = {}\n    for scores_dict in scores_list:\n        scores[scores_dict['query_id']] = scores_dict\n        \n    return pd.DataFrame({\n            \"num_results\": [scores[query['query_id']]['number_of_results'] for query in queries],\n            \"score\": [scores[query['query_id']]['score'] for query in queries],\n            \"docs\": [[doc['id'] for doc in docs[str(query['query_id'])] if doc['rated_only'] == False] for query in queries]\n        },\n        index=pd.Series(name='query', data=[query['query_text'] for query in queries])\n    )\n\nawait load_snapshot(case_id=6789, snapshot_id=2471)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "execution_count": 4,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                    num_results  score  \\\nquery                                    \nprojector screen              1    1.0   \nnotebook                      1    1.0   \niphone 8                      1    1.0   \nprinter                       1    1.0   \ncomputer                      1    1.0   \n...                         ...    ...   \nwindows 10                    1    1.0   \nmicrowave                     1    1.0   \nbluetooth speakers            1    1.0   \ncoffee                        1    1.0   \nvans                          1    1.0   \n\n                                                                 docs  \nquery                                                                  \nprojector screen    [1069226, 47471, 490523, 1229109, 1229118, 325...  \nnotebook            [3851056, 3959000, 1550833, 1684763, 1675257, ...  \niphone 8            [2048598, 1648546, 79524888, 1857711, 3613408,...  \nprinter             [3849563, 2225354, 1569761, 798960, 377837, 13...  \ncomputer            [560468, 532095, 560475, 523407, 693956, 56047...  \n...                                                               ...  \nwindows 10          [4481689, 3902727, 1560529, 1797902, 3155116, ...  \nmicrowave                       [79513345, 4020048, 1768856, 2936032]  \nbluetooth speakers  [1993197, 3537784, 279672, 2663204, 558184, 33...  \ncoffee              [1996660, 2102472, 79583150, 1357989, 656359, ...  \nvans                [78503576, 79118095, 77388459, 78322005, 79013...  \n\n[135 rows x 3 columns]",
-            "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>num_results</th>\n      <th>score</th>\n      <th>docs</th>\n    </tr>\n    <tr>\n      <th>query</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>projector screen</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1069226, 47471, 490523, 1229109, 1229118, 325...</td>\n    </tr>\n    <tr>\n      <th>notebook</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[3851056, 3959000, 1550833, 1684763, 1675257, ...</td>\n    </tr>\n    <tr>\n      <th>iphone 8</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[2048598, 1648546, 79524888, 1857711, 3613408,...</td>\n    </tr>\n    <tr>\n      <th>printer</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[3849563, 2225354, 1569761, 798960, 377837, 13...</td>\n    </tr>\n    <tr>\n      <th>computer</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[560468, 532095, 560475, 523407, 693956, 56047...</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>windows 10</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[4481689, 3902727, 1560529, 1797902, 3155116, ...</td>\n    </tr>\n    <tr>\n      <th>microwave</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[79513345, 4020048, 1768856, 2936032]</td>\n    </tr>\n    <tr>\n      <th>bluetooth speakers</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1993197, 3537784, 279672, 2663204, 558184, 33...</td>\n    </tr>\n    <tr>\n      <th>coffee</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1996660, 2102472, 79583150, 1357989, 656359, ...</td>\n    </tr>\n    <tr>\n      <th>vans</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[78503576, 79118095, 77388459, 78322005, 79013...</td>\n    </tr>\n  </tbody>\n</table>\n<p>135 rows × 3 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "source": "os.environ[\"TQDM_DISABLE\"] = \"1\"",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 5,
-      "outputs": []
-    },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from js import fetch\n",
+    "from typing import List, Optional, Union\n",
+    "\n",
+    "import json\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "import piplite\n",
+    "await piplite.install('seaborn')\n",
+    "await piplite.install('rbo')\n",
+    "\n",
+    "import rbo\n",
+    "import seaborn as sns\n",
+    "\n",
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generic GET call to a JSON endpoint \n",
+    "async def get_json(url):\n",
+    "    resp = await fetch(url)\n",
+    "    resp_text = await resp.text()\n",
+    "    return json.loads(resp_text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Basic Quepid API client methods\n",
+    "\n",
+    "async def get_snapshots(case):\n",
+    "    response = await get_json(f'/api/cases/{case}/snapshots?shallow=true')\n",
+    "    return [{'id': snapshot['id'], 'name': snapshot['name']} for snapshot in response['snapshots']]\n",
+    "\n",
+    "async def get_cases_with_snapshots():\n",
+    "    cases = await get_cases()\n",
+    "    cases_with_snapshots = [{\n",
+    "        'id': case['id'],\n",
+    "        'name': case['name'],\n",
+    "        'snapshots': [ {\n",
+    "            'id': snapshot['id'],\n",
+    "            'name': snapshot['name']\n",
+    "        } for snapshot in (await get_snapshots(case['id'])) ]\n",
+    "    } for case in cases]\n",
+    "    return cases_with_snapshots\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "def jaccard(l1, l2, max_n):\n    if len(l1) == 0 and len(l2) == 0:\n        return 1\n    max_len = min(len(l1), len(l2), max_n)\n    set1 = set(l1[:max_len])\n    set2 = set(l2[:max_len])\n    intersection = len(set1.intersection(set2))\n    union = len(set1) + len(set2) - intersection\n    return float(intersection) / union\n\nasync def load_snapshots(case_id1, snapshot_id1, case_id2, snapshot_id2):\n    df_a = await load_snapshot(case_id1, snapshot_id1)\n    df_b = await load_snapshot(case_id2, snapshot_id2)\n    return df_a.merge(df_b, on='query')\n\nasync def compare(case_id1, snapshot_id1, case_id2, snapshot_id2):\n    df = await load_snapshots(case_id1, snapshot_id1, case_id2, snapshot_id2)\n    \n    df['jaccard'] = df.apply(lambda row: jaccard(row['docs_x'], row['docs_y'], 10), axis=1)\n    df['rbo'] = df.apply(lambda row: rbo.RankingSimilarity(row['docs_x'], row['docs_y']).rbo(), axis=1)\n    df['score_delta'] = df['score_y'] - df['score_x']\n    df.name = f\"Case {case_id1} snapshot {snapshot_id1} vs. case {case_id1} snapshot {snapshot_id2}\"\n    return df\n\n\n\nawait compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2472)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 6,
-      "outputs": [
-        {
-          "name": "stderr",
-          "text": "/lib/python3.11/site-packages/rbo/rbo.py:129: TqdmMonitorWarning: tqdm:disabling monitor support (monitor_interval = 0) due to:\ncan't start new thread\n  for d in tqdm(range(1, k), disable=~self.verbose):\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 6,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                    num_results_x  score_x  \\\nquery                                        \nprojector screen                1      1.0   \nnotebook                        1      1.0   \niphone 8                        1      1.0   \nprinter                         1      1.0   \ncomputer                        1      1.0   \n...                           ...      ...   \nwindows 10                      1      1.0   \nmicrowave                       1      1.0   \nbluetooth speakers              1      1.0   \ncoffee                          1      1.0   \nvans                            1      1.0   \n\n                                                               docs_x  \\\nquery                                                                   \nprojector screen    [1069226, 47471, 490523, 1229109, 1229118, 325...   \nnotebook            [3851056, 3959000, 1550833, 1684763, 1675257, ...   \niphone 8            [2048598, 1648546, 79524888, 1857711, 3613408,...   \nprinter             [3849563, 2225354, 1569761, 798960, 377837, 13...   \ncomputer            [560468, 532095, 560475, 523407, 693956, 56047...   \n...                                                               ...   \nwindows 10          [4481689, 3902727, 1560529, 1797902, 3155116, ...   \nmicrowave                       [79513345, 4020048, 1768856, 2936032]   \nbluetooth speakers  [1993197, 3537784, 279672, 2663204, 558184, 33...   \ncoffee              [1996660, 2102472, 79583150, 1357989, 656359, ...   \nvans                [78503576, 79118095, 77388459, 78322005, 79013...   \n\n                    num_results_y  score_y  \\\nquery                                        \nprojector screen                1      1.0   \nnotebook                        1      1.0   \niphone 8                        1      1.0   \nprinter                         1      1.0   \ncomputer                        1      1.0   \n...                           ...      ...   \nwindows 10                      1      1.0   \nmicrowave                       1      1.0   \nbluetooth speakers              1      1.0   \ncoffee                          1      1.0   \nvans                            1      1.0   \n\n                                                               docs_y  \\\nquery                                                                   \nprojector screen    [1069226, 47471, 490523, 1229109, 1229118, 325...   \nnotebook            [3851056, 3959000, 1550833, 1684763, 1675257, ...   \niphone 8            [2048598, 1648546, 79524888, 1857711, 3613408,...   \nprinter             [3849563, 2225354, 1569761, 798960, 377837, 13...   \ncomputer            [560468, 532095, 560475, 523407, 693956, 56047...   \n...                                                               ...   \nwindows 10          [4481689, 3902727, 1560529, 1797902, 3155116, ...   \nmicrowave                       [79513345, 4020048, 1768856, 2936032]   \nbluetooth speakers  [1993197, 3537784, 279672, 2663204, 558184, 33...   \ncoffee              [1996660, 2102472, 79583150, 1357989, 656359, ...   \nvans                [78503576, 79118095, 77388459, 78322005, 79013...   \n\n                    jaccard  rbo  score_delta  \nquery                                          \nprojector screen        1.0  1.0          0.0  \nnotebook                1.0  1.0          0.0  \niphone 8                1.0  1.0          0.0  \nprinter                 1.0  1.0          0.0  \ncomputer                1.0  1.0          0.0  \n...                     ...  ...          ...  \nwindows 10              1.0  1.0          0.0  \nmicrowave               1.0  1.0          0.0  \nbluetooth speakers      1.0  1.0          0.0  \ncoffee                  1.0  1.0          0.0  \nvans                    1.0  1.0          0.0  \n\n[135 rows x 9 columns]",
-            "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>num_results_x</th>\n      <th>score_x</th>\n      <th>docs_x</th>\n      <th>num_results_y</th>\n      <th>score_y</th>\n      <th>docs_y</th>\n      <th>jaccard</th>\n      <th>rbo</th>\n      <th>score_delta</th>\n    </tr>\n    <tr>\n      <th>query</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>projector screen</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1069226, 47471, 490523, 1229109, 1229118, 325...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1069226, 47471, 490523, 1229109, 1229118, 325...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>notebook</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[3851056, 3959000, 1550833, 1684763, 1675257, ...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[3851056, 3959000, 1550833, 1684763, 1675257, ...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>iphone 8</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[2048598, 1648546, 79524888, 1857711, 3613408,...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[2048598, 1648546, 79524888, 1857711, 3613408,...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>printer</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[3849563, 2225354, 1569761, 798960, 377837, 13...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[3849563, 2225354, 1569761, 798960, 377837, 13...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>computer</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[560468, 532095, 560475, 523407, 693956, 56047...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[560468, 532095, 560475, 523407, 693956, 56047...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>windows 10</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[4481689, 3902727, 1560529, 1797902, 3155116, ...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[4481689, 3902727, 1560529, 1797902, 3155116, ...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>microwave</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[79513345, 4020048, 1768856, 2936032]</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[79513345, 4020048, 1768856, 2936032]</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>bluetooth speakers</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1993197, 3537784, 279672, 2663204, 558184, 33...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1993197, 3537784, 279672, 2663204, 558184, 33...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>coffee</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1996660, 2102472, 79583150, 1357989, 656359, ...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[1996660, 2102472, 79583150, 1357989, 656359, ...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>vans</th>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[78503576, 79118095, 77388459, 78322005, 79013...</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>[78503576, 79118095, 77388459, 78322005, 79013...</td>\n      <td>1.0</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>135 rows × 9 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
+     "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>num_results</th>\n",
+       "      <th>score</th>\n",
+       "      <th>docs</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>query</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>projector screen</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1069226, 47471, 490523, 1229109, 1229118, 325...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>notebook</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[3851056, 3959000, 1550833, 1684763, 1675257, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>iphone 8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[2048598, 1648546, 79524888, 1857711, 3613408,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>printer</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[3849563, 2225354, 1569761, 798960, 377837, 13...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>computer</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[560468, 532095, 560475, 523407, 693956, 56047...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>windows 10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[4481689, 3902727, 1560529, 1797902, 3155116, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>microwave</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[79513345, 4020048, 1768856, 2936032]</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bluetooth speakers</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1993197, 3537784, 279672, 2663204, 558184, 33...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coffee</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1996660, 2102472, 79583150, 1357989, 656359, ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vans</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[78503576, 79118095, 77388459, 78322005, 79013...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>135 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    num_results  score  \\\n",
+       "query                                    \n",
+       "projector screen              1    1.0   \n",
+       "notebook                      1    1.0   \n",
+       "iphone 8                      1    1.0   \n",
+       "printer                       1    1.0   \n",
+       "computer                      1    1.0   \n",
+       "...                         ...    ...   \n",
+       "windows 10                    1    1.0   \n",
+       "microwave                     1    1.0   \n",
+       "bluetooth speakers            1    1.0   \n",
+       "coffee                        1    1.0   \n",
+       "vans                          1    1.0   \n",
+       "\n",
+       "                                                                 docs  \n",
+       "query                                                                  \n",
+       "projector screen    [1069226, 47471, 490523, 1229109, 1229118, 325...  \n",
+       "notebook            [3851056, 3959000, 1550833, 1684763, 1675257, ...  \n",
+       "iphone 8            [2048598, 1648546, 79524888, 1857711, 3613408,...  \n",
+       "printer             [3849563, 2225354, 1569761, 798960, 377837, 13...  \n",
+       "computer            [560468, 532095, 560475, 523407, 693956, 56047...  \n",
+       "...                                                               ...  \n",
+       "windows 10          [4481689, 3902727, 1560529, 1797902, 3155116, ...  \n",
+       "microwave                       [79513345, 4020048, 1768856, 2936032]  \n",
+       "bluetooth speakers  [1993197, 3537784, 279672, 2663204, 558184, 33...  \n",
+       "coffee              [1996660, 2102472, 79583150, 1357989, 656359, ...  \n",
+       "vans                [78503576, 79118095, 77388459, 78322005, 79013...  \n",
+       "\n",
+       "[135 rows x 3 columns]"
       ]
-    },
-    {
-      "cell_type": "code",
-      "source": "import matplotlib\nmatplotlib.rc_file_defaults()\n\ndef plot_compare(df):\n    figure, axes = plt.subplots(1, 3, figsize=(10, 4))\n    figure.suptitle(df.name)\n\n    sns.barplot(ax=axes[0], x=df['score_delta'], y=df.index, width=0.3, color='darkgrey')\n    axes[0].set(xlim=(-1, 1))\n    axes[0].set_xlabel('Change in Score')\n    axes[0].set_ylabel('')\n    axes[0].set_facecolor((0.90, 0.90, 0.90))\n    axes[0].grid(True)\n    axes[0].spines['top'].set_visible(False)\n    axes[0].spines['right'].set_visible(False)\n    axes[0].spines['bottom'].set_visible(False)\n    axes[0].spines['left'].set_visible(False)\n    axes[0].set_axisbelow(True)\n    axes[0].xaxis.grid(color='w', linestyle='solid')\n    axes[0].yaxis.grid(color='w', linestyle='solid')\n    \n    sns.heatmap(df[['jaccard']], ax=axes[1], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n    axes[1].set_xlabel('Jaccard Similiarity')\n    axes[1].set_ylabel('')\n    \n    sns.heatmap(df[['rbo']], ax=axes[2], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n    axes[2].set_xlabel('Rank Biased Overlap')\n    axes[2].set_ylabel('')\n    \n    plt.show()\n    \ndf = await compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2473)\n",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 7,
-      "outputs": []
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load snapshot, return dict of queries and their (number of results, avg. score, [doc ids])\n",
+    "async def load_snapshot(case_id, snapshot_id):\n",
+    "    snapshot = await get_json(f'/api/cases/{case_id}/snapshots/{snapshot_id}')\n",
+    "    docs = snapshot['docs']\n",
+    "    queries = snapshot['queries']\n",
+    "    \n",
+    "    # scores are a list of dicts, group them by query\n",
+    "    scores_list = snapshot['scores']\n",
+    "    scores = {}\n",
+    "    for scores_dict in scores_list:\n",
+    "        scores[scores_dict['query_id']] = scores_dict\n",
+    "        \n",
+    "    return pd.DataFrame({\n",
+    "            \"num_results\": [scores[query['query_id']]['number_of_results'] for query in queries],\n",
+    "            \"score\": [scores[query['query_id']]['score'] for query in queries],\n",
+    "            \"docs\": [[doc['id'] for doc in docs[str(query['query_id'])] if doc['rated_only'] == False] for query in queries]\n",
+    "        },\n",
+    "        index=pd.Series(name='query', data=[query['query_text'] for query in queries])\n",
+    "    )\n",
+    "\n",
+    "await load_snapshot(case_id=6789, snapshot_id=2471)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.environ[\"TQDM_DISABLE\"] = \"1\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "## Overall Jaccard and RBO Scores",
-      "metadata": {}
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lib/python3.11/site-packages/rbo/rbo.py:129: TqdmMonitorWarning: tqdm:disabling monitor support (monitor_interval = 0) due to:\n",
+      "can't start new thread\n",
+      "  for d in tqdm(range(1, k), disable=~self.verbose):\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": "print(f\"Overall Jaccard Score: {df['jaccard'].mean()}\\nOverall RBO Score: {df['rbo'].mean()}\")",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 8,
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Overall Jaccard Score: 1.0\nOverall RBO Score: 1.0\n",
-          "output_type": "stream"
-        }
+     "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>num_results_x</th>\n",
+       "      <th>score_x</th>\n",
+       "      <th>docs_x</th>\n",
+       "      <th>num_results_y</th>\n",
+       "      <th>score_y</th>\n",
+       "      <th>docs_y</th>\n",
+       "      <th>jaccard</th>\n",
+       "      <th>rbo</th>\n",
+       "      <th>score_delta</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>query</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>projector screen</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1069226, 47471, 490523, 1229109, 1229118, 325...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1069226, 47471, 490523, 1229109, 1229118, 325...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>notebook</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[3851056, 3959000, 1550833, 1684763, 1675257, ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[3851056, 3959000, 1550833, 1684763, 1675257, ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>iphone 8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[2048598, 1648546, 79524888, 1857711, 3613408,...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[2048598, 1648546, 79524888, 1857711, 3613408,...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>printer</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[3849563, 2225354, 1569761, 798960, 377837, 13...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[3849563, 2225354, 1569761, 798960, 377837, 13...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>computer</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[560468, 532095, 560475, 523407, 693956, 56047...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[560468, 532095, 560475, 523407, 693956, 56047...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>windows 10</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[4481689, 3902727, 1560529, 1797902, 3155116, ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[4481689, 3902727, 1560529, 1797902, 3155116, ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>microwave</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[79513345, 4020048, 1768856, 2936032]</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[79513345, 4020048, 1768856, 2936032]</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>bluetooth speakers</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1993197, 3537784, 279672, 2663204, 558184, 33...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1993197, 3537784, 279672, 2663204, 558184, 33...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>coffee</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1996660, 2102472, 79583150, 1357989, 656359, ...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[1996660, 2102472, 79583150, 1357989, 656359, ...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>vans</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[78503576, 79118095, 77388459, 78322005, 79013...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>[78503576, 79118095, 77388459, 78322005, 79013...</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>135 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    num_results_x  score_x  \\\n",
+       "query                                        \n",
+       "projector screen                1      1.0   \n",
+       "notebook                        1      1.0   \n",
+       "iphone 8                        1      1.0   \n",
+       "printer                         1      1.0   \n",
+       "computer                        1      1.0   \n",
+       "...                           ...      ...   \n",
+       "windows 10                      1      1.0   \n",
+       "microwave                       1      1.0   \n",
+       "bluetooth speakers              1      1.0   \n",
+       "coffee                          1      1.0   \n",
+       "vans                            1      1.0   \n",
+       "\n",
+       "                                                               docs_x  \\\n",
+       "query                                                                   \n",
+       "projector screen    [1069226, 47471, 490523, 1229109, 1229118, 325...   \n",
+       "notebook            [3851056, 3959000, 1550833, 1684763, 1675257, ...   \n",
+       "iphone 8            [2048598, 1648546, 79524888, 1857711, 3613408,...   \n",
+       "printer             [3849563, 2225354, 1569761, 798960, 377837, 13...   \n",
+       "computer            [560468, 532095, 560475, 523407, 693956, 56047...   \n",
+       "...                                                               ...   \n",
+       "windows 10          [4481689, 3902727, 1560529, 1797902, 3155116, ...   \n",
+       "microwave                       [79513345, 4020048, 1768856, 2936032]   \n",
+       "bluetooth speakers  [1993197, 3537784, 279672, 2663204, 558184, 33...   \n",
+       "coffee              [1996660, 2102472, 79583150, 1357989, 656359, ...   \n",
+       "vans                [78503576, 79118095, 77388459, 78322005, 79013...   \n",
+       "\n",
+       "                    num_results_y  score_y  \\\n",
+       "query                                        \n",
+       "projector screen                1      1.0   \n",
+       "notebook                        1      1.0   \n",
+       "iphone 8                        1      1.0   \n",
+       "printer                         1      1.0   \n",
+       "computer                        1      1.0   \n",
+       "...                           ...      ...   \n",
+       "windows 10                      1      1.0   \n",
+       "microwave                       1      1.0   \n",
+       "bluetooth speakers              1      1.0   \n",
+       "coffee                          1      1.0   \n",
+       "vans                            1      1.0   \n",
+       "\n",
+       "                                                               docs_y  \\\n",
+       "query                                                                   \n",
+       "projector screen    [1069226, 47471, 490523, 1229109, 1229118, 325...   \n",
+       "notebook            [3851056, 3959000, 1550833, 1684763, 1675257, ...   \n",
+       "iphone 8            [2048598, 1648546, 79524888, 1857711, 3613408,...   \n",
+       "printer             [3849563, 2225354, 1569761, 798960, 377837, 13...   \n",
+       "computer            [560468, 532095, 560475, 523407, 693956, 56047...   \n",
+       "...                                                               ...   \n",
+       "windows 10          [4481689, 3902727, 1560529, 1797902, 3155116, ...   \n",
+       "microwave                       [79513345, 4020048, 1768856, 2936032]   \n",
+       "bluetooth speakers  [1993197, 3537784, 279672, 2663204, 558184, 33...   \n",
+       "coffee              [1996660, 2102472, 79583150, 1357989, 656359, ...   \n",
+       "vans                [78503576, 79118095, 77388459, 78322005, 79013...   \n",
+       "\n",
+       "                    jaccard  rbo  score_delta  \n",
+       "query                                          \n",
+       "projector screen        1.0  1.0          0.0  \n",
+       "notebook                1.0  1.0          0.0  \n",
+       "iphone 8                1.0  1.0          0.0  \n",
+       "printer                 1.0  1.0          0.0  \n",
+       "computer                1.0  1.0          0.0  \n",
+       "...                     ...  ...          ...  \n",
+       "windows 10              1.0  1.0          0.0  \n",
+       "microwave               1.0  1.0          0.0  \n",
+       "bluetooth speakers      1.0  1.0          0.0  \n",
+       "coffee                  1.0  1.0          0.0  \n",
+       "vans                    1.0  1.0          0.0  \n",
+       "\n",
+       "[135 rows x 9 columns]"
       ]
-    },
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def jaccard(l1, l2, max_n):\n",
+    "    if len(l1) == 0 and len(l2) == 0:\n",
+    "        return 1\n",
+    "    max_len = min(len(l1), len(l2), max_n)\n",
+    "    set1 = set(l1[:max_len])\n",
+    "    set2 = set(l2[:max_len])\n",
+    "    intersection = len(set1.intersection(set2))\n",
+    "    union = len(set1) + len(set2) - intersection\n",
+    "    return float(intersection) / union\n",
+    "\n",
+    "async def load_snapshots(case_id1, snapshot_id1, case_id2, snapshot_id2):\n",
+    "    df_a = await load_snapshot(case_id1, snapshot_id1)\n",
+    "    df_b = await load_snapshot(case_id2, snapshot_id2)\n",
+    "    return df_a.merge(df_b, on='query')\n",
+    "\n",
+    "async def compare(case_id1, snapshot_id1, case_id2, snapshot_id2):\n",
+    "    df = await load_snapshots(case_id1, snapshot_id1, case_id2, snapshot_id2)\n",
+    "    \n",
+    "    df['jaccard'] = df.apply(lambda row: jaccard(row['docs_x'], row['docs_y'], 10), axis=1)\n",
+    "    df['rbo'] = df.apply(lambda row: rbo.RankingSimilarity(row['docs_x'], row['docs_y']).rbo(), axis=1)\n",
+    "    df['score_delta'] = df['score_y'] - df['score_x']\n",
+    "    df.name = f\"Case {case_id1} snapshot {snapshot_id1} vs. case {case_id1} snapshot {snapshot_id2}\"\n",
+    "    return df\n",
+    "\n",
+    "\n",
+    "\n",
+    "await compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2472)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.rc_file_defaults()\n",
+    "\n",
+    "def plot_compare(df):\n",
+    "    figure, axes = plt.subplots(1, 3, figsize=(10, 4))\n",
+    "    figure.suptitle(df.name)\n",
+    "\n",
+    "    sns.barplot(ax=axes[0], x=df['score_delta'], y=df.index, width=0.3, color='darkgrey')\n",
+    "    axes[0].set(xlim=(-1, 1))\n",
+    "    axes[0].set_xlabel('Change in Score')\n",
+    "    axes[0].set_ylabel('')\n",
+    "    axes[0].set_facecolor((0.90, 0.90, 0.90))\n",
+    "    axes[0].grid(True)\n",
+    "    axes[0].spines['top'].set_visible(False)\n",
+    "    axes[0].spines['right'].set_visible(False)\n",
+    "    axes[0].spines['bottom'].set_visible(False)\n",
+    "    axes[0].spines['left'].set_visible(False)\n",
+    "    axes[0].set_axisbelow(True)\n",
+    "    axes[0].xaxis.grid(color='w', linestyle='solid')\n",
+    "    axes[0].yaxis.grid(color='w', linestyle='solid')\n",
+    "    \n",
+    "    sns.heatmap(df[['jaccard']], ax=axes[1], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n",
+    "    axes[1].set_xlabel('Jaccard Similiarity')\n",
+    "    axes[1].set_ylabel('')\n",
+    "    \n",
+    "    sns.heatmap(df[['rbo']], ax=axes[2], cmap='crest', annot=True, xticklabels=False, yticklabels=False)\n",
+    "    axes[2].set_xlabel('Rank Biased Overlap')\n",
+    "    axes[2].set_ylabel('')\n",
+    "    \n",
+    "    plt.show()\n",
+    "    \n",
+    "df = await compare(case_id1=6789, snapshot_id1=2471, case_id2=6789, snapshot_id2=2473)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overall Jaccard and RBO Scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "## Query Level Jaccard and RBO Scores",
-      "metadata": {}
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Overall Jaccard Score: 1.0\n",
+      "Overall RBO Score: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Overall Jaccard Score: {df['jaccard'].mean()}\\nOverall RBO Score: {df['rbo'].mean()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Query Level Jaccard and RBO Scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "plot_compare(df)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 9,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 1000x400 with 5 Axes>",
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 1000x400 with 5 Axes>"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": "_This notebook was last updated 19-FEB-2024_",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": "",
-      "metadata": {},
-      "execution_count": null,
-      "outputs": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "plot_compare(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_This notebook was last updated 19-FEB-2024_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/jupyterlite/files/examples/Multiple Raters Analysis.ipynb b/jupyterlite/files/examples/Multiple Raters Analysis.ipynb
index 53c3283..990138e 100644
--- a/jupyterlite/files/examples/Multiple Raters Analysis.ipynb	
+++ b/jupyterlite/files/examples/Multiple Raters Analysis.ipynb	
@@ -1,581 +1,2532 @@
 {
-  "metadata": {
-    "kernelspec": {
-      "name": "python",
-      "display_name": "Python (Pyodide)",
-      "language": "python"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "python",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5aa3a7af",
+   "metadata": {},
+   "source": [
+    "# Analysis of Judgements with Multiple Raters\n",
+    "\n",
+    "This notebook is an example of how we can analyze query / document pairs judgements in the case of multiple judges.\n",
+    "\n",
+    "**Why?**\n",
+    "When we have one single rater for our judgements, we have no other choice than trusting the rating of that judge. However when we have multiple judges rating the same pairs, this gives us much more information about the actual rating of the pair.  For example if all judges disagree, it might be a good indicator that the pair is hard to judge, for example because the intent is not 100% clear.  Conversely, if all judges aree, we can have much higher confidence in this rating.\n",
+    "\n",
+    "**Goal**\n",
+    "Analyze agreement between raters to measure confidence about each rating, in particular we would like to:\n",
+    "\n",
+    "  - analyze the distribution of grades\n",
+    "  - measure raters's consistency\n",
+    "  - identify suspicious ratings in other to clean the dataset and make sure we only keep <query,document> for which we have enough confidence in the rating.\n",
+    "\n",
+    "\n",
+    "**Data**\n",
+    "For this we need a dataset with following information:\n",
+    "  \n",
+    "  - <query,document> pairs \n",
+    "  - Each pair is rated by 3 different judges. In this example we consider a rating grade between 0 and 3:\n",
+    "      - `0`: document relevance is **poor**\n",
+    "      - `1`: document relevance is **fair**\n",
+    "      - `2`: document relevance is **good**\n",
+    "      - `3`: document relevance is **perfect**\n",
+    "      \n",
+    "The analysis would also work with other grade scale with minor changes in the code.\n",
+    "\n",
+    "To get this data, we will directly get judgement book from Quepid, via the API. Any external data could also be used.\n",
+    "\n",
+    " \n"
+   ]
   },
-  "nbformat_minor": 5,
-  "nbformat": 4,
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": "# Analysis of Judgements with Multiple Raters\n\nThis notebook is an example of how we can analyze query / document pairs judgements in the case of multiple judges.\n\n**Why?**\nWhen we have one single rater for our judgements, we have no other choice than trusting the rating of that judge. However when we have multiple judges rating the same pairs, this gives us much more information about the actual rating of the pair.  For example if all judges disagree, it might be a good indicator that the pair is hard to judge, for example because the intent is not 100% clear.  Conversely, if all judges aree, we can have much higher confidence in this rating.\n\n**Goal**\nAnalyze agreement between raters to measure confidence about each rating, in particular we would like to:\n\n  - analyze the distribution of grades\n  - measure raters's consistency\n  - identify suspicious ratings in other to clean the dataset and make sure we only keep <query,document> for which we have enough confidence in the rating.\n\n\n**Data**\nFor this we need a dataset with following information:\n  \n  - <query,document> pairs \n  - Each pair is rated by 3 different judges. In this example we consider a rating grade between 0 and 3:\n      - `0`: document relevance is **poor**\n      - `1`: document relevance is **fair**\n      - `2`: document relevance is **good**\n      - `3`: document relevance is **perfect**\n      \nThe analysis would also work with other grade scale with minor changes in the code.\n\nTo get this data, we will directly get judgement book from Quepid, via the API. Any external data could also be used.\n\n \n",
-      "metadata": {},
-      "id": "5aa3a7af"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Setup\nBasic libraries needed in this analysis",
-      "metadata": {},
-      "id": "77cc28f8"
-    },
-    {
-      "cell_type": "code",
-      "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pylab as plt\nimport random\nimport string\nfrom js import fetch\nfrom datetime import datetime\nimport random\nfrom matplotlib import pyplot\nfrom io import StringIO\n%matplotlib inline",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 1,
-      "outputs": [],
-      "id": "ce97f3d0"
-    },
-    {
-      "cell_type": "code",
-      "source": "ratings = ['0-Bad', '1-Fair', '2-Good', '3-Perfect']",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 2,
-      "outputs": [],
-      "id": "d985176c-95c1-4d29-bc03-06f03cf77ed4"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Get Ratings From Books API",
-      "metadata": {},
-      "id": "b57a896c-416b-4992-980d-2870de7cc120"
-    },
-    {
-      "cell_type": "code",
-      "source": "# You need to get your book_id from Quepid UI. You should be able to see it's content if you open <your-quepid-url>/api/books/1.json\nBOOK_ID = 25",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 3,
-      "outputs": [],
-      "id": "ed1f99f3-ff72-4e9d-81cd-3afef204cbef"
-    },
-    {
-      "cell_type": "code",
-      "source": "# Get content of the book in CSV format (could also use JSON)\nres = await fetch(f'/api/books/{BOOK_ID}.csv')\nres_str = await res.text()\ndf = pd.read_csv(StringIO(res_str))\ndf",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 66,
-      "outputs": [
-        {
-          "execution_count": 66,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                 query    docid  charlie@flax.co.uk  \\\n0     projector screen   325961                 NaN   \n1     projector screen    47471                 NaN   \n2     projector screen   126679                 NaN   \n3     projector screen   254441                 NaN   \n4     projector screen   325958                 NaN   \n...                ...      ...                 ...   \n2415      power supply  1667352                 NaN   \n2416      power supply  1667804                 NaN   \n2417      power supply  1667752                 NaN   \n2418      power supply  1667821                 NaN   \n2419      power supply  1667357                 NaN   \n\n      epugh@opensourceconnections.com  eschramma@cas.org  dtaivpp@gmail.com  \\\n0                                 3.0                NaN                3.0   \n1                                 3.0                NaN                3.0   \n2                                 3.0                NaN                3.0   \n3                                 3.0                NaN                NaN   \n4                                 3.0                NaN                NaN   \n...                               ...                ...                ...   \n2415                              0.0                NaN                NaN   \n2416                              0.0                NaN                NaN   \n2417                              0.0                NaN                NaN   \n2418                              0.0                NaN                NaN   \n2419                              0.0                NaN                NaN   \n\n      aarora@opensourceconnections.com  cmcollier@gmail.com  \\\n0                                  NaN                  NaN   \n1                                  NaN                  NaN   \n2                                  NaN                  NaN   \n3                                  NaN                  NaN   \n4                                  NaN                  NaN   \n...                                ...                  ...   \n2415                               NaN                  NaN   \n2416                               NaN                  NaN   \n2417                               NaN                  NaN   \n2418                               NaN                  NaN   \n2419                               NaN                  NaN   \n\n      ben.w.trent@gmail.com  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n0                       NaN           NaN                               NaN   \n1                       NaN           NaN                               NaN   \n2                       NaN           NaN                               NaN   \n3                       NaN           NaN                               NaN   \n4                       NaN           NaN                               NaN   \n...                     ...           ...                               ...   \n2415                    NaN           NaN                               NaN   \n2416                    NaN           NaN                               NaN   \n2417                    NaN           NaN                               NaN   \n2418                    NaN           NaN                               NaN   \n2419                    NaN           NaN                               NaN   \n\n      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n0                  NaN                        NaN                      NaN   \n1                  NaN                        NaN                      NaN   \n2                  NaN                        NaN                      NaN   \n3                  NaN                        NaN                      NaN   \n4                  NaN                        NaN                      NaN   \n...                ...                        ...                      ...   \n2415               NaN                        NaN                      NaN   \n2416               NaN                        NaN                      NaN   \n2417               NaN                        NaN                      NaN   \n2418               NaN                        NaN                      NaN   \n2419               NaN                        NaN                      NaN   \n\n      ryan.finley@ferguson.com  \n0                          NaN  \n1                          NaN  \n2                          NaN  \n3                          NaN  \n4                          NaN  \n...                        ...  \n2415                       NaN  \n2416                       NaN  \n2417                       NaN  \n2418                       NaN  \n2419                       NaN  \n\n[2420 rows x 15 columns]",
-            "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>query</th>\n      <th>docid</th>\n      <th>charlie@flax.co.uk</th>\n      <th>epugh@opensourceconnections.com</th>\n      <th>eschramma@cas.org</th>\n      <th>dtaivpp@gmail.com</th>\n      <th>aarora@opensourceconnections.com</th>\n      <th>cmcollier@gmail.com</th>\n      <th>ben.w.trent@gmail.com</th>\n      <th>jeff@vin.com</th>\n      <th>cmarino@enterprise-knowledge.com</th>\n      <th>msfroh@gmail.com</th>\n      <th>peter@searchintuition.com</th>\n      <th>maximilian.werk@jina.ai</th>\n      <th>ryan.finley@ferguson.com</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>projector screen</td>\n      <td>325961</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>projector screen</td>\n      <td>47471</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>projector screen</td>\n      <td>126679</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>projector screen</td>\n      <td>254441</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>projector screen</td>\n      <td>325958</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2415</th>\n      <td>power supply</td>\n      <td>1667352</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2416</th>\n      <td>power supply</td>\n      <td>1667804</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2417</th>\n      <td>power supply</td>\n      <td>1667752</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2418</th>\n      <td>power supply</td>\n      <td>1667821</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2419</th>\n      <td>power supply</td>\n      <td>1667357</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>2420 rows × 15 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "30ce3dc2-c049-436c-8049-58c0d8f4e46f"
-    },
-    {
-      "cell_type": "code",
-      "source": "if df.shape[0] == 0:\n    print('It Looks like your book is empty or does not exists')",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 28,
-      "outputs": [],
-      "id": "98ad3844-d67f-44a2-8bae-034223de6c68"
-    },
-    {
-      "cell_type": "code",
-      "source": "#df.dropna(inplace=True)\ndf.shape",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 6,
-      "outputs": [
-        {
-          "execution_count": 6,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "(0, 15)"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "79ce92af-e034-4e7d-bc8d-3a96c0fcd14f"
-    },
-    {
-      "cell_type": "code",
-      "source": "df.loc[df['docid'] == '325961']",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 29,
-      "outputs": [
-        {
-          "execution_count": 29,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "Empty DataFrame\nColumns: [query, docid, charlie@flax.co.uk, epugh@opensourceconnections.com, eschramma@cas.org, dtaivpp@gmail.com, aarora@opensourceconnections.com, cmcollier@gmail.com, ben.w.trent@gmail.com, jeff@vin.com, cmarino@enterprise-knowledge.com, msfroh@gmail.com, peter@searchintuition.com, maximilian.werk@jina.ai, ryan.finley@ferguson.com]\nIndex: []",
-            "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>query</th>\n      <th>docid</th>\n      <th>charlie@flax.co.uk</th>\n      <th>epugh@opensourceconnections.com</th>\n      <th>eschramma@cas.org</th>\n      <th>dtaivpp@gmail.com</th>\n      <th>aarora@opensourceconnections.com</th>\n      <th>cmcollier@gmail.com</th>\n      <th>ben.w.trent@gmail.com</th>\n      <th>jeff@vin.com</th>\n      <th>cmarino@enterprise-knowledge.com</th>\n      <th>msfroh@gmail.com</th>\n      <th>peter@searchintuition.com</th>\n      <th>maximilian.werk@jina.ai</th>\n      <th>ryan.finley@ferguson.com</th>\n    </tr>\n  </thead>\n  <tbody>\n  </tbody>\n</table>\n</div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "25006e0e-2013-4384-a34f-8fa920579614"
-    },
-    {
-      "cell_type": "code",
-      "source": "raters = list(df.columns[2:])\nraters",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 30,
-      "outputs": [
-        {
-          "execution_count": 30,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "['charlie@flax.co.uk',\n 'epugh@opensourceconnections.com',\n 'eschramma@cas.org',\n 'dtaivpp@gmail.com',\n 'aarora@opensourceconnections.com',\n 'cmcollier@gmail.com',\n 'ben.w.trent@gmail.com',\n 'jeff@vin.com',\n 'cmarino@enterprise-knowledge.com',\n 'msfroh@gmail.com',\n 'peter@searchintuition.com',\n 'maximilian.werk@jina.ai',\n 'ryan.finley@ferguson.com']"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "72d4481e-ae12-4fff-bbbd-1888a894f69a"
-    },
-    {
-      "cell_type": "code",
-      "source": "# We need to filter to raters that we THINK might have some overlap\nraters = [\n 'epugh@opensourceconnections.com',\n 'aarora@opensourceconnections.com',\n 'ben.w.trent@gmail.com'\n]",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 67,
-      "outputs": [],
-      "id": "dcd60629-44fc-4122-95dd-98fe2558489d"
-    },
-    {
-      "cell_type": "code",
-      "source": "nb_raters = len(raters)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 68,
-      "outputs": [],
-      "id": "4c1fc91f-cda6-4e76-8372-3062e6975adb"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "We just transform a bit the data so that it's easier to process:",
-      "metadata": {},
-      "id": "d704b517-aea3-4177-bb49-88f8d57ce647"
-    },
+  {
+   "cell_type": "markdown",
+   "id": "77cc28f8",
+   "metadata": {},
+   "source": [
+    "## Setup\n",
+    "Basic libraries needed in this analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ce97f3d0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "import random\n",
+    "import string\n",
+    "from js import fetch\n",
+    "from datetime import datetime\n",
+    "import random\n",
+    "from matplotlib import pyplot\n",
+    "from io import StringIO\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d985176c-95c1-4d29-bc03-06f03cf77ed4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ratings = ['0-Bad', '1-Fair', '2-Good', '3-Perfect']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b57a896c-416b-4992-980d-2870de7cc120",
+   "metadata": {},
+   "source": [
+    "## Get Ratings From Books API"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ed1f99f3-ff72-4e9d-81cd-3afef204cbef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# You need to get your book_id from Quepid UI. You should be able to see it's content if you open <your-quepid-url>/api/books/1.json\n",
+    "BOOK_ID = 25"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "30ce3dc2-c049-436c-8049-58c0d8f4e46f",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "df.rename(columns={rn:f'rating_{i}' for i,rn in enumerate(raters)}, inplace=True)\nfor i, rn in enumerate(raters):\n    df[f'rater_{i}'] = rn\ndf",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 69,
-      "outputs": [
-        {
-          "execution_count": 69,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                 query    docid  charlie@flax.co.uk  rating_0  \\\n0     projector screen   325961                 NaN       3.0   \n1     projector screen    47471                 NaN       3.0   \n2     projector screen   126679                 NaN       3.0   \n3     projector screen   254441                 NaN       3.0   \n4     projector screen   325958                 NaN       3.0   \n...                ...      ...                 ...       ...   \n2415      power supply  1667352                 NaN       0.0   \n2416      power supply  1667804                 NaN       0.0   \n2417      power supply  1667752                 NaN       0.0   \n2418      power supply  1667821                 NaN       0.0   \n2419      power supply  1667357                 NaN       0.0   \n\n      eschramma@cas.org  dtaivpp@gmail.com  rating_1  cmcollier@gmail.com  \\\n0                   NaN                3.0       NaN                  NaN   \n1                   NaN                3.0       NaN                  NaN   \n2                   NaN                3.0       NaN                  NaN   \n3                   NaN                NaN       NaN                  NaN   \n4                   NaN                NaN       NaN                  NaN   \n...                 ...                ...       ...                  ...   \n2415                NaN                NaN       NaN                  NaN   \n2416                NaN                NaN       NaN                  NaN   \n2417                NaN                NaN       NaN                  NaN   \n2418                NaN                NaN       NaN                  NaN   \n2419                NaN                NaN       NaN                  NaN   \n\n      rating_2  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n0          NaN           NaN                               NaN   \n1          NaN           NaN                               NaN   \n2          NaN           NaN                               NaN   \n3          NaN           NaN                               NaN   \n4          NaN           NaN                               NaN   \n...        ...           ...                               ...   \n2415       NaN           NaN                               NaN   \n2416       NaN           NaN                               NaN   \n2417       NaN           NaN                               NaN   \n2418       NaN           NaN                               NaN   \n2419       NaN           NaN                               NaN   \n\n      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n0                  NaN                        NaN                      NaN   \n1                  NaN                        NaN                      NaN   \n2                  NaN                        NaN                      NaN   \n3                  NaN                        NaN                      NaN   \n4                  NaN                        NaN                      NaN   \n...                ...                        ...                      ...   \n2415               NaN                        NaN                      NaN   \n2416               NaN                        NaN                      NaN   \n2417               NaN                        NaN                      NaN   \n2418               NaN                        NaN                      NaN   \n2419               NaN                        NaN                      NaN   \n\n      ryan.finley@ferguson.com                          rater_0  \\\n0                          NaN  epugh@opensourceconnections.com   \n1                          NaN  epugh@opensourceconnections.com   \n2                          NaN  epugh@opensourceconnections.com   \n3                          NaN  epugh@opensourceconnections.com   \n4                          NaN  epugh@opensourceconnections.com   \n...                        ...                              ...   \n2415                       NaN  epugh@opensourceconnections.com   \n2416                       NaN  epugh@opensourceconnections.com   \n2417                       NaN  epugh@opensourceconnections.com   \n2418                       NaN  epugh@opensourceconnections.com   \n2419                       NaN  epugh@opensourceconnections.com   \n\n                               rater_1                rater_2  \n0     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n1     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n2     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n3     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n4     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n...                                ...                    ...  \n2415  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n2416  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n2417  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n2418  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n2419  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n\n[2420 rows x 18 columns]",
-            "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>query</th>\n      <th>docid</th>\n      <th>charlie@flax.co.uk</th>\n      <th>rating_0</th>\n      <th>eschramma@cas.org</th>\n      <th>dtaivpp@gmail.com</th>\n      <th>rating_1</th>\n      <th>cmcollier@gmail.com</th>\n      <th>rating_2</th>\n      <th>jeff@vin.com</th>\n      <th>cmarino@enterprise-knowledge.com</th>\n      <th>msfroh@gmail.com</th>\n      <th>peter@searchintuition.com</th>\n      <th>maximilian.werk@jina.ai</th>\n      <th>ryan.finley@ferguson.com</th>\n      <th>rater_0</th>\n      <th>rater_1</th>\n      <th>rater_2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>projector screen</td>\n      <td>325961</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>projector screen</td>\n      <td>47471</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>projector screen</td>\n      <td>126679</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>projector screen</td>\n      <td>254441</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>projector screen</td>\n      <td>325958</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2415</th>\n      <td>power supply</td>\n      <td>1667352</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>2416</th>\n      <td>power supply</td>\n      <td>1667804</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>2417</th>\n      <td>power supply</td>\n      <td>1667752</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>2418</th>\n      <td>power supply</td>\n      <td>1667821</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>2419</th>\n      <td>power supply</td>\n      <td>1667357</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n  </tbody>\n</table>\n<p>2420 rows × 18 columns</p>\n</div>"
-          },
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+       "      <th></th>\n",
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+       "      <th>ryan.finley@ferguson.com</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
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+       "      <th>0</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>47471</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>126679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>3</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>254441</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>325958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "    <tr>\n",
+       "      <th>2415</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667352</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2416</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667804</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2417</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667752</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2418</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667821</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2419</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667357</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2420 rows × 15 columns</p>\n",
+       "</div>"
       ],
-      "id": "e81fbcd6-f2aa-476b-9b81-2ae7a950fe99"
-    },
+      "text/plain": [
+       "                 query    docid  charlie@flax.co.uk  \\\n",
+       "0     projector screen   325961                 NaN   \n",
+       "1     projector screen    47471                 NaN   \n",
+       "2     projector screen   126679                 NaN   \n",
+       "3     projector screen   254441                 NaN   \n",
+       "4     projector screen   325958                 NaN   \n",
+       "...                ...      ...                 ...   \n",
+       "2415      power supply  1667352                 NaN   \n",
+       "2416      power supply  1667804                 NaN   \n",
+       "2417      power supply  1667752                 NaN   \n",
+       "2418      power supply  1667821                 NaN   \n",
+       "2419      power supply  1667357                 NaN   \n",
+       "\n",
+       "      epugh@opensourceconnections.com  eschramma@cas.org  dtaivpp@gmail.com  \\\n",
+       "0                                 3.0                NaN                3.0   \n",
+       "1                                 3.0                NaN                3.0   \n",
+       "2                                 3.0                NaN                3.0   \n",
+       "3                                 3.0                NaN                NaN   \n",
+       "4                                 3.0                NaN                NaN   \n",
+       "...                               ...                ...                ...   \n",
+       "2415                              0.0                NaN                NaN   \n",
+       "2416                              0.0                NaN                NaN   \n",
+       "2417                              0.0                NaN                NaN   \n",
+       "2418                              0.0                NaN                NaN   \n",
+       "2419                              0.0                NaN                NaN   \n",
+       "\n",
+       "      aarora@opensourceconnections.com  cmcollier@gmail.com  \\\n",
+       "0                                  NaN                  NaN   \n",
+       "1                                  NaN                  NaN   \n",
+       "2                                  NaN                  NaN   \n",
+       "3                                  NaN                  NaN   \n",
+       "4                                  NaN                  NaN   \n",
+       "...                                ...                  ...   \n",
+       "2415                               NaN                  NaN   \n",
+       "2416                               NaN                  NaN   \n",
+       "2417                               NaN                  NaN   \n",
+       "2418                               NaN                  NaN   \n",
+       "2419                               NaN                  NaN   \n",
+       "\n",
+       "      ben.w.trent@gmail.com  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n",
+       "0                       NaN           NaN                               NaN   \n",
+       "1                       NaN           NaN                               NaN   \n",
+       "2                       NaN           NaN                               NaN   \n",
+       "3                       NaN           NaN                               NaN   \n",
+       "4                       NaN           NaN                               NaN   \n",
+       "...                     ...           ...                               ...   \n",
+       "2415                    NaN           NaN                               NaN   \n",
+       "2416                    NaN           NaN                               NaN   \n",
+       "2417                    NaN           NaN                               NaN   \n",
+       "2418                    NaN           NaN                               NaN   \n",
+       "2419                    NaN           NaN                               NaN   \n",
+       "\n",
+       "      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n",
+       "0                  NaN                        NaN                      NaN   \n",
+       "1                  NaN                        NaN                      NaN   \n",
+       "2                  NaN                        NaN                      NaN   \n",
+       "3                  NaN                        NaN                      NaN   \n",
+       "4                  NaN                        NaN                      NaN   \n",
+       "...                ...                        ...                      ...   \n",
+       "2415               NaN                        NaN                      NaN   \n",
+       "2416               NaN                        NaN                      NaN   \n",
+       "2417               NaN                        NaN                      NaN   \n",
+       "2418               NaN                        NaN                      NaN   \n",
+       "2419               NaN                        NaN                      NaN   \n",
+       "\n",
+       "      ryan.finley@ferguson.com  \n",
+       "0                          NaN  \n",
+       "1                          NaN  \n",
+       "2                          NaN  \n",
+       "3                          NaN  \n",
+       "4                          NaN  \n",
+       "...                        ...  \n",
+       "2415                       NaN  \n",
+       "2416                       NaN  \n",
+       "2417                       NaN  \n",
+       "2418                       NaN  \n",
+       "2419                       NaN  \n",
+       "\n",
+       "[2420 rows x 15 columns]"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Get content of the book in CSV format (could also use JSON)\n",
+    "res = await fetch(f'/api/books/{BOOK_ID}.csv')\n",
+    "res_str = await res.text()\n",
+    "df = pd.read_csv(StringIO(res_str))\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "98ad3844-d67f-44a2-8bae-034223de6c68",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if df.shape[0] == 0:\n",
+    "    print('It Looks like your book is empty or does not exists')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "79ce92af-e034-4e7d-bc8d-3a96c0fcd14f",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "We flatten the data to have 1 rating per row:",
-      "metadata": {},
-      "id": "c0396b4d-eb54-4786-939e-b8ad69d335ab"
-    },
+     "data": {
+      "text/plain": [
+       "(0, 15)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#df.dropna(inplace=True)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "25006e0e-2013-4384-a34f-8fa920579614",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "df_overall = pd.concat([\n    df[['query','docid',f'rating_{i}', f'rater_{i}']].rename(\n        columns={f'rating_{i}':'rating', f'rater_{i}':'rater'}) for i in range(nb_raters)]).reset_index(drop=True)\ndf_overall.sort_values(by=['docid'], inplace=True)\ndf_overall",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 70,
-      "outputs": [
-        {
-          "execution_count": 70,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "           query     docid  rating                             rater\n4063   iphone 11      1423     NaN  aarora@opensourceconnections.com\n6483   iphone 11      1423     NaN             ben.w.trent@gmail.com\n1643   iphone 11      1423     NaN   epugh@opensourceconnections.com\n4065   iphone 11      1424     NaN  aarora@opensourceconnections.com\n6485   iphone 11      1424     NaN             ben.w.trent@gmail.com\n...          ...       ...     ...                               ...\n2383  windows 10  79583170     NaN   epugh@opensourceconnections.com\n4803  windows 10  79583170     NaN  aarora@opensourceconnections.com\n5879     samsung  79659021     NaN             ben.w.trent@gmail.com\n3459     samsung  79659021     3.0  aarora@opensourceconnections.com\n1039     samsung  79659021     3.0   epugh@opensourceconnections.com\n\n[7260 rows x 4 columns]",
-            "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>query</th>\n      <th>docid</th>\n      <th>rating</th>\n      <th>rater</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>4063</th>\n      <td>iphone 11</td>\n      <td>1423</td>\n      <td>NaN</td>\n      <td>aarora@opensourceconnections.com</td>\n    </tr>\n    <tr>\n      <th>6483</th>\n      <td>iphone 11</td>\n      <td>1423</td>\n      <td>NaN</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>1643</th>\n      <td>iphone 11</td>\n      <td>1423</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n    </tr>\n    <tr>\n      <th>4065</th>\n      <td>iphone 11</td>\n      <td>1424</td>\n      <td>NaN</td>\n      <td>aarora@opensourceconnections.com</td>\n    </tr>\n    <tr>\n      <th>6485</th>\n      <td>iphone 11</td>\n      <td>1424</td>\n      <td>NaN</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2383</th>\n      <td>windows 10</td>\n      <td>79583170</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n    </tr>\n    <tr>\n      <th>4803</th>\n      <td>windows 10</td>\n      <td>79583170</td>\n      <td>NaN</td>\n      <td>aarora@opensourceconnections.com</td>\n    </tr>\n    <tr>\n      <th>5879</th>\n      <td>samsung</td>\n      <td>79659021</td>\n      <td>NaN</td>\n      <td>ben.w.trent@gmail.com</td>\n    </tr>\n    <tr>\n      <th>3459</th>\n      <td>samsung</td>\n      <td>79659021</td>\n      <td>3.0</td>\n      <td>aarora@opensourceconnections.com</td>\n    </tr>\n    <tr>\n      <th>1039</th>\n      <td>samsung</td>\n      <td>79659021</td>\n      <td>3.0</td>\n      <td>epugh@opensourceconnections.com</td>\n    </tr>\n  </tbody>\n</table>\n<p>7260 rows × 4 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
+     "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>query</th>\n",
+       "      <th>docid</th>\n",
+       "      <th>charlie@flax.co.uk</th>\n",
+       "      <th>epugh@opensourceconnections.com</th>\n",
+       "      <th>eschramma@cas.org</th>\n",
+       "      <th>dtaivpp@gmail.com</th>\n",
+       "      <th>aarora@opensourceconnections.com</th>\n",
+       "      <th>cmcollier@gmail.com</th>\n",
+       "      <th>ben.w.trent@gmail.com</th>\n",
+       "      <th>jeff@vin.com</th>\n",
+       "      <th>cmarino@enterprise-knowledge.com</th>\n",
+       "      <th>msfroh@gmail.com</th>\n",
+       "      <th>peter@searchintuition.com</th>\n",
+       "      <th>maximilian.werk@jina.ai</th>\n",
+       "      <th>ryan.finley@ferguson.com</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "id": "97b0bc0c-a20e-49b2-a65a-ef09eb7e6a58"
-    },
-    {
-      "cell_type": "code",
-      "source": "df_overall.dropna(inplace=True)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 71,
-      "outputs": [],
-      "id": "d58d14a6-6bfb-4c77-8145-514c0030bc53"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Rating distribution per query\nHe we just want to plot the distribution of ratings for each query:\n",
-      "metadata": {},
-      "id": "ff2dcb29-e376-4621-9ecf-f3374e71f464"
-    },
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [query, docid, charlie@flax.co.uk, epugh@opensourceconnections.com, eschramma@cas.org, dtaivpp@gmail.com, aarora@opensourceconnections.com, cmcollier@gmail.com, ben.w.trent@gmail.com, jeff@vin.com, cmarino@enterprise-knowledge.com, msfroh@gmail.com, peter@searchintuition.com, maximilian.werk@jina.ai, ryan.finley@ferguson.com]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.loc[df['docid'] == '325961']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "72d4481e-ae12-4fff-bbbd-1888a894f69a",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "df_overall[['query', 'rating']].groupby('query').agg(['count', 'mean', 'std'])",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 72,
-      "outputs": [
-        {
-          "execution_count": 72,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                    rating                    \n                     count      mean       std\nquery                                         \n120v power supply        2  0.000000  0.000000\naa                      10  1.800000  1.549193\naa battery              10  1.800000  1.135292\naaa                      7  1.285714  1.603567\nadapter                 10  1.300000  0.674949\n...                    ...       ...       ...\nwireless headphones      5  0.800000  1.303840\nwireless mouse          14  2.000000  1.109400\nxbox                    15  0.000000  0.000000\nxbox one                 7  0.428571  0.786796\nyoutube                 12  0.000000  0.000000\n\n[137 rows x 3 columns]",
-            "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"3\" halign=\"left\">rating</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n    <tr>\n      <th>query</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>120v power supply</th>\n      <td>2</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>aa</th>\n      <td>10</td>\n      <td>1.800000</td>\n      <td>1.549193</td>\n    </tr>\n    <tr>\n      <th>aa battery</th>\n      <td>10</td>\n      <td>1.800000</td>\n      <td>1.135292</td>\n    </tr>\n    <tr>\n      <th>aaa</th>\n      <td>7</td>\n      <td>1.285714</td>\n      <td>1.603567</td>\n    </tr>\n    <tr>\n      <th>adapter</th>\n      <td>10</td>\n      <td>1.300000</td>\n      <td>0.674949</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>wireless headphones</th>\n      <td>5</td>\n      <td>0.800000</td>\n      <td>1.303840</td>\n    </tr>\n    <tr>\n      <th>wireless mouse</th>\n      <td>14</td>\n      <td>2.000000</td>\n      <td>1.109400</td>\n    </tr>\n    <tr>\n      <th>xbox</th>\n      <td>15</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>xbox one</th>\n      <td>7</td>\n      <td>0.428571</td>\n      <td>0.786796</td>\n    </tr>\n    <tr>\n      <th>youtube</th>\n      <td>12</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>137 rows × 3 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "50d46a70"
-    },
+     "data": {
+      "text/plain": [
+       "['charlie@flax.co.uk',\n",
+       " 'epugh@opensourceconnections.com',\n",
+       " 'eschramma@cas.org',\n",
+       " 'dtaivpp@gmail.com',\n",
+       " 'aarora@opensourceconnections.com',\n",
+       " 'cmcollier@gmail.com',\n",
+       " 'ben.w.trent@gmail.com',\n",
+       " 'jeff@vin.com',\n",
+       " 'cmarino@enterprise-knowledge.com',\n",
+       " 'msfroh@gmail.com',\n",
+       " 'peter@searchintuition.com',\n",
+       " 'maximilian.werk@jina.ai',\n",
+       " 'ryan.finley@ferguson.com']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raters = list(df.columns[2:])\n",
+    "raters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "dcd60629-44fc-4122-95dd-98fe2558489d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# We need to filter to raters that we THINK might have some overlap\n",
+    "raters = [\n",
+    "    'Eric Pugh',\n",
+    "     'Atita Arora',\n",
+    "    'Benjamin Trent'\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "4c1fc91f-cda6-4e76-8372-3062e6975adb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nb_raters = len(raters)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d704b517-aea3-4177-bb49-88f8d57ce647",
+   "metadata": {},
+   "source": [
+    "We just transform a bit the data so that it's easier to process:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "e81fbcd6-f2aa-476b-9b81-2ae7a950fe99",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "fig, axes = plt.subplots()\nqueries = df_overall['query'].unique()\ndataset = [df_overall[df_overall['query'] == q][\"rating\"] for q in queries]\n\nnb_queries = len(queries)\n\naxes.violinplot(dataset = dataset, showmeans=True, bw_method=0.05)\naxes.set_xlabel('query')\naxes.set_ylabel('ratings')\naxes.yaxis.grid(True)\naxes.set_xticks(range(1,nb_queries+1))\naxes.set_xticklabels(queries,rotation=45)\nplt.title('Rating distribution per query')\nplt.show()",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 73,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 640x480 with 1 Axes>",
-            "image/png": 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\n"
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-          "metadata": {}
-        }
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>query</th>\n",
+       "      <th>docid</th>\n",
+       "      <th>charlie@flax.co.uk</th>\n",
+       "      <th>rating_0</th>\n",
+       "      <th>eschramma@cas.org</th>\n",
+       "      <th>dtaivpp@gmail.com</th>\n",
+       "      <th>rating_1</th>\n",
+       "      <th>cmcollier@gmail.com</th>\n",
+       "      <th>rating_2</th>\n",
+       "      <th>jeff@vin.com</th>\n",
+       "      <th>cmarino@enterprise-knowledge.com</th>\n",
+       "      <th>msfroh@gmail.com</th>\n",
+       "      <th>peter@searchintuition.com</th>\n",
+       "      <th>maximilian.werk@jina.ai</th>\n",
+       "      <th>ryan.finley@ferguson.com</th>\n",
+       "      <th>rater_0</th>\n",
+       "      <th>rater_1</th>\n",
+       "      <th>rater_2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>325961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>47471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>126679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>254441</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>325958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2415</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667352</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2416</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667804</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2417</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667752</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2418</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667821</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2419</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667357</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2420 rows × 18 columns</p>\n",
+       "</div>"
       ],
-      "id": "70b93e5d-425d-4925-97fd-1b062f7c373f"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Rating distribution per rater\n\nAssuming raters have seen the same <query,document> pairs and that they had the same guidelines, their ratings should be overall calibrated.  This is what we want to check here.   \nWe could easily detect outlier raters that rate very different to others, for example because they haven't really understood the guidelines.",
-      "metadata": {},
-      "id": "fa14bfdd-ea38-4fcc-9532-4019306410b1"
-    },
+      "text/plain": [
+       "                 query    docid  charlie@flax.co.uk  rating_0  \\\n",
+       "0     projector screen   325961                 NaN       3.0   \n",
+       "1     projector screen    47471                 NaN       3.0   \n",
+       "2     projector screen   126679                 NaN       3.0   \n",
+       "3     projector screen   254441                 NaN       3.0   \n",
+       "4     projector screen   325958                 NaN       3.0   \n",
+       "...                ...      ...                 ...       ...   \n",
+       "2415      power supply  1667352                 NaN       0.0   \n",
+       "2416      power supply  1667804                 NaN       0.0   \n",
+       "2417      power supply  1667752                 NaN       0.0   \n",
+       "2418      power supply  1667821                 NaN       0.0   \n",
+       "2419      power supply  1667357                 NaN       0.0   \n",
+       "\n",
+       "      eschramma@cas.org  dtaivpp@gmail.com  rating_1  cmcollier@gmail.com  \\\n",
+       "0                   NaN                3.0       NaN                  NaN   \n",
+       "1                   NaN                3.0       NaN                  NaN   \n",
+       "2                   NaN                3.0       NaN                  NaN   \n",
+       "3                   NaN                NaN       NaN                  NaN   \n",
+       "4                   NaN                NaN       NaN                  NaN   \n",
+       "...                 ...                ...       ...                  ...   \n",
+       "2415                NaN                NaN       NaN                  NaN   \n",
+       "2416                NaN                NaN       NaN                  NaN   \n",
+       "2417                NaN                NaN       NaN                  NaN   \n",
+       "2418                NaN                NaN       NaN                  NaN   \n",
+       "2419                NaN                NaN       NaN                  NaN   \n",
+       "\n",
+       "      rating_2  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n",
+       "0          NaN           NaN                               NaN   \n",
+       "1          NaN           NaN                               NaN   \n",
+       "2          NaN           NaN                               NaN   \n",
+       "3          NaN           NaN                               NaN   \n",
+       "4          NaN           NaN                               NaN   \n",
+       "...        ...           ...                               ...   \n",
+       "2415       NaN           NaN                               NaN   \n",
+       "2416       NaN           NaN                               NaN   \n",
+       "2417       NaN           NaN                               NaN   \n",
+       "2418       NaN           NaN                               NaN   \n",
+       "2419       NaN           NaN                               NaN   \n",
+       "\n",
+       "      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n",
+       "0                  NaN                        NaN                      NaN   \n",
+       "1                  NaN                        NaN                      NaN   \n",
+       "2                  NaN                        NaN                      NaN   \n",
+       "3                  NaN                        NaN                      NaN   \n",
+       "4                  NaN                        NaN                      NaN   \n",
+       "...                ...                        ...                      ...   \n",
+       "2415               NaN                        NaN                      NaN   \n",
+       "2416               NaN                        NaN                      NaN   \n",
+       "2417               NaN                        NaN                      NaN   \n",
+       "2418               NaN                        NaN                      NaN   \n",
+       "2419               NaN                        NaN                      NaN   \n",
+       "\n",
+       "      ryan.finley@ferguson.com                          rater_0  \\\n",
+       "0                          NaN  epugh@opensourceconnections.com   \n",
+       "1                          NaN  epugh@opensourceconnections.com   \n",
+       "2                          NaN  epugh@opensourceconnections.com   \n",
+       "3                          NaN  epugh@opensourceconnections.com   \n",
+       "4                          NaN  epugh@opensourceconnections.com   \n",
+       "...                        ...                              ...   \n",
+       "2415                       NaN  epugh@opensourceconnections.com   \n",
+       "2416                       NaN  epugh@opensourceconnections.com   \n",
+       "2417                       NaN  epugh@opensourceconnections.com   \n",
+       "2418                       NaN  epugh@opensourceconnections.com   \n",
+       "2419                       NaN  epugh@opensourceconnections.com   \n",
+       "\n",
+       "                               rater_1                rater_2  \n",
+       "0     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "1     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "2     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "3     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "4     aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "...                                ...                    ...  \n",
+       "2415  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "2416  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "2417  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "2418  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "2419  aarora@opensourceconnections.com  ben.w.trent@gmail.com  \n",
+       "\n",
+       "[2420 rows x 18 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.rename(columns={rn:f'rating_{i}' for i,rn in enumerate(raters)}, inplace=True)\n",
+    "for i, rn in enumerate(raters):\n",
+    "    df[f'rater_{i}'] = rn\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0396b4d-eb54-4786-939e-b8ad69d335ab",
+   "metadata": {},
+   "source": [
+    "We flatten the data to have 1 rating per row:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "97b0bc0c-a20e-49b2-a65a-ef09eb7e6a58",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "fig, axes = plt.subplots()\nraters = df_overall['rater'].unique()\ndataset = [df_overall[df_overall['rater'] == r][\"rating\"] for r in raters]\n\naxes.violinplot(dataset = dataset, showmeans=True, bw_method=0.05)\naxes.set_xlabel('rater')\naxes.set_ylabel('ratings')\naxes.yaxis.grid(True)\naxes.set_xticks(range(1,nb_raters+1))\naxes.set_xticklabels(raters)\nplt.title('Rating distribution per rater')\nplt.show()",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 74,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 640x480 with 1 Axes>",
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "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>query</th>\n",
+       "      <th>docid</th>\n",
+       "      <th>rating</th>\n",
+       "      <th>rater</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4063</th>\n",
+       "      <td>iphone 11</td>\n",
+       "      <td>1423</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6483</th>\n",
+       "      <td>iphone 11</td>\n",
+       "      <td>1423</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1643</th>\n",
+       "      <td>iphone 11</td>\n",
+       "      <td>1423</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4065</th>\n",
+       "      <td>iphone 11</td>\n",
+       "      <td>1424</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6485</th>\n",
+       "      <td>iphone 11</td>\n",
+       "      <td>1424</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2383</th>\n",
+       "      <td>windows 10</td>\n",
+       "      <td>79583170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4803</th>\n",
+       "      <td>windows 10</td>\n",
+       "      <td>79583170</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5879</th>\n",
+       "      <td>samsung</td>\n",
+       "      <td>79659021</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3459</th>\n",
+       "      <td>samsung</td>\n",
+       "      <td>79659021</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1039</th>\n",
+       "      <td>samsung</td>\n",
+       "      <td>79659021</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7260 rows × 4 columns</p>\n",
+       "</div>"
       ],
-      "id": "f954846d-54a9-4cf1-a9da-ab0faaa46df9"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Ratings Analysis\n\nWe now focus on the ratings themselves.  We first plot the overall rating distributions",
-      "metadata": {},
-      "id": "6168e768"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Overall rating distribution",
-      "metadata": {},
-      "id": "92f38adf"
-    },
+      "text/plain": [
+       "           query     docid  rating                             rater\n",
+       "4063   iphone 11      1423     NaN  aarora@opensourceconnections.com\n",
+       "6483   iphone 11      1423     NaN             ben.w.trent@gmail.com\n",
+       "1643   iphone 11      1423     NaN   epugh@opensourceconnections.com\n",
+       "4065   iphone 11      1424     NaN  aarora@opensourceconnections.com\n",
+       "6485   iphone 11      1424     NaN             ben.w.trent@gmail.com\n",
+       "...          ...       ...     ...                               ...\n",
+       "2383  windows 10  79583170     NaN   epugh@opensourceconnections.com\n",
+       "4803  windows 10  79583170     NaN  aarora@opensourceconnections.com\n",
+       "5879     samsung  79659021     NaN             ben.w.trent@gmail.com\n",
+       "3459     samsung  79659021     3.0  aarora@opensourceconnections.com\n",
+       "1039     samsung  79659021     3.0   epugh@opensourceconnections.com\n",
+       "\n",
+       "[7260 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_overall = pd.concat([\n",
+    "    df[['query','docid',f'rating_{i}', f'rater_{i}']].rename(\n",
+    "        columns={f'rating_{i}':'rating', f'rater_{i}':'rater'}) for i in range(nb_raters)]).reset_index(drop=True)\n",
+    "df_overall.sort_values(by=['docid'], inplace=True)\n",
+    "df_overall"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "d58d14a6-6bfb-4c77-8145-514c0030bc53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_overall.dropna(inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ff2dcb29-e376-4621-9ecf-f3374e71f464",
+   "metadata": {},
+   "source": [
+    "### Rating distribution per query\n",
+    "He we just want to plot the distribution of ratings for each query:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "50d46a70",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "from matplotlib.ticker import MaxNLocator\nplt.hist(df_overall['rating'])\nplt.title('Overall ratings')\nplt.xticks(range(len(ratings)), ratings,\n       rotation=60) \nNone\nplt.ylabel('nb_ratings')\nplt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))\n\nplt.grid()",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 75,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 640x480 with 1 Axes>",
-            "image/png": 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2CfQW2n7VoUMHKBQKscshiWC/IlMwZb/SnqEpDNFPab3K0dERtWvXxh9//IEKFSrA0tISdevW1ZunTp06uoHNrq6uyM3NRXp6OhwdHXXzpKWlvXFcEAAolUoolcoC7QqFwmR/5CqNzKweLcGNnXkwZZ+ldxf7FZmCKfpVUZYn+lVar3r+/Dlu3LiBSpUqwcrKCk2aNEFKSorePL///juqVq0KAPD19YVCocDBgwd101NSUnD37l34+fmVaO1ERERUeol6hGfChAno0qULqlatitTUVMyYMQMWFhbo06cPAGDixIno1asXPvjgA90YnoSEBBw5cgQA4ODggGHDhiE8PBzly5eHvb09wsLC4Ofnxyu0iIiISEfUwPPnn3+iT58+ePLkCZydneHv74+kpCQ4OzsDAD7++GPExsYiKioKn332Gby8vLB9+3b4+/vrlrF48WLI5XKEhIRApVIhKCgIy5cvF+sjERERUSkkauCJj4//x3mGDh2KoUOHvnG6tbU1YmJiEBMTY8zSiIiISEJK1RgeIiIiIlNg4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJKzWBZ968eZDJZBg3bpyu7fnz5xgzZgyqVKmCMmXKoG7duoiNjdV7X05ODkJDQ+Hk5AQ7OzuEhIQgLS2thKsnIiKi0qxUBJ4zZ85g5cqVaNiwoV57eHg49u7di++++w5Xr17FuHHjMGbMGOzatUs3z/jx45GQkICtW7fi6NGjSE1NRXBwcEl/BCIiIirFRA88z58/R79+/bB69WqUK1dOb9qpU6cwaNAgtGnTBtWqVcOIESPQqFEj/PLLLwCAjIwMrF27FosWLUK7du3g6+uLuLg4nDp1CklJSWJ8HCIiIiqFLMUuIDQ0FJ07d0ZAQADmzp2rN61FixbYtWsXhg4dCjc3Nxw5cgS///47Fi9eDABITk6GWq1GQECA7j3e3t7w8PDA6dOn0bx589euU6VSQaVS6V5nZmYCANRqNdRqtVE/n3Z5Srlg1OWamrF/DmRc2t8Pf09kTOxXZAqm7FdFWaaogSc+Ph7nzp3DmTNnXjt96dKlGDFiBKpUqQJLS0vI5XKsXr0aH3zwAQDg4cOHsLKygqOjo977XFxc8PDhwzeuNyoqCrNmzSrQvn//ftjY2Bj+gd5iTmONSZZrKrt37xa7BCqExMREsUsgCWK/IlMwRb/Kzs4u9LyiBZ579+5h7NixSExMhLW19WvnWbp0KZKSkrBr1y5UrVoVx44dQ2hoKNzc3PSO6hRVREQEwsPDda8zMzPh7u6OwMBA2NvbG7zc11Gr1UhMTETkWTlUGplRl21Kl2cGiV0CvYW2X3Xo0AEKhULsckgi2K/IFEzZr7RnaApDtMCTnJyMR48e4f3339e15efn49ixY1i2bBkyMjIwZcoU7NixA507dwYANGzYEBcuXMDXX3+NgIAAuLq6Ijc3F+np6XpHedLS0uDq6vrGdSuVSiiVygLtCoXCZH/kKo0MqnzzCTzc2JkHU/ZZenexX5EpmKJfFWV5og1abt++PS5duoQLFy7o/jVu3Bj9+vXDhQsXkJ+fD7VaDblcv0QLCwtoNC9PD/n6+kKhUODgwYO66SkpKbh79y78/PxK9PMQERFR6SXaEZ6yZcuifv36em22trZwcnLStbdu3RoTJ05EmTJlULVqVRw9ehTr16/HokWLAAAODg4YNmwYwsPDUb58edjb2yMsLAx+fn5vHLBMRERE7x7Rr9J6m/j4eERERKBfv354+vQpqlatii+++AIjR47UzbN48WLI5XKEhIRApVIhKCgIy5cvF7FqIiIiKm1KVeA5cuSI3mtXV1fExcW99T3W1taIiYlBTEyMCSsjIiIicyb6jQeJiIiITI2Bh4iIiCSPgYeIiIgkj4GHiIiIJK9UDVomIiKit6s2+SexSygSpYWABU3FroJHeIiIiOgdwMBDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJJXagLPvHnzIJPJMG7cOF2bIAiYPn06KlWqhDJlyiAgIADXr1/Xe19OTg5CQ0Ph5OQEOzs7hISEIC0trYSrJyIiotKsVASeM2fOYOXKlWjYsKFe+4IFC/DNN98gNjYWP//8M2xtbREUFIScnBzdPOPHj0dCQgK2bt2Ko0ePIjU1FcHBwSX9EYiIiKgUEz3wPH/+HP369cPq1atRrlw5XbsgCIiOjsa0adPQrVs3NGzYEOvXr0dqaip27twJAMjIyMDatWuxaNEitGvXDr6+voiLi8OpU6eQlJQk0iciIiKi0sZS7AJCQ0PRuXNnBAQEYO7cubr2W7du4eHDhwgICNC1OTg4oFmzZjh9+jR69+6N5ORkqNVqvXm8vb3h4eGB06dPo3nz5q9dp0qlgkql0r3OzMwEAKjVaqjVaqN+Pu3ylHLBqMs1NWP/HMi4tL8f/p7ImNivzIPSwrz2J9r9nyn6VVGWKWrgiY+Px7lz53DmzJkC0x4+fAgAcHFx0Wt3cXHRTXv48CGsrKzg6Oj4xnleJyoqCrNmzSrQvn//ftjY2BT1YxTKnMYakyzXVHbv3i12CVQIiYmJYpdAEsR+VbotaCp2BYYxRb/Kzs4u9LyiBZ579+5h7NixSExMhLW1dYmuOyIiAuHh4brXmZmZcHd3R2BgIOzt7Y26LrVajcTERESelUOlkRl12aZ0eWaQ2CXQW2j7VYcOHaBQKMQuhySC/co81J+5T+wSikQpFzCnscYk/Up7hqYwRAs8ycnJePToEd5//31dW35+Po4dO4Zly5YhJSUFAJCWloZKlSrp5klLS4OPjw8AwNXVFbm5uUhPT9c7ypOWlgZXV9c3rlupVEKpVBZoVygUJvsjV2lkUOWbT+Dhxs48mLLP0ruL/ap0M6d9yatM0a+KsjzRBi23b98ely5dwoULF3T/GjdujH79+uHChQuoXr06XF1dcfDgQd17MjMz8fPPP8PPzw8A4OvrC4VCoTdPSkoK7t69q5uHiIiISLQjPGXLlkX9+vX12mxtbeHk5KRrHzduHObOnYtatWrB09MTkZGRcHNzQ/fu3QG8HMQ8bNgwhIeHo3z58rC3t0dYWBj8/PzeOGCZiIiI3j2iX6X1Np9//jmysrIwYsQIpKenw9/fH3v37tUb87N48WLI5XKEhIRApVIhKCgIy5cvF7FqIiIiKm1KVeA5cuSI3muZTIbZs2dj9uzZb3yPtbU1YmJiEBMTY+LqiIiIyFyJfuNBIiIiIlNj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyTMo8Lx48QLZ2dm613fu3EF0dDT2799vtMKIiIiIjMWgwNOtWzesX78eAJCeno5mzZph4cKF6NatG1asWGHUAomIiIiKy6DAc+7cObRq1QoAsG3bNri4uODOnTtYv349vvnmG6MWSERERFRcBgWe7OxslC1bFgCwf/9+BAcHQy6Xo3nz5rhz545RCyQiIiIqLoMCT82aNbFz507cu3cP+/btQ2BgIADg0aNHsLe3N2qBRERERMVlUOCZPn06JkyYgGrVqqFZs2bw8/MD8PJoz3vvvWfUAomIiIiKy9KQN33yySfw9/fHgwcP0KhRI117+/bt8fHHHxutOCIiIiJjMCjwAICrqytcXV312po2bVrsgoiIiIiMzaDA8/HHH0MmkxVol8lksLa2Rs2aNdG3b194eXkVu0AiIiKi4jJoDI+DgwMOHTqEc+fOQSaTQSaT4fz58zh06BDy8vKwefNmNGrUCCdPnjR2vURERERFZtARHldXV/Tt2xfLli2DXP4yM2k0GowdOxZly5ZFfHw8Ro4ciUmTJuHEiRNGLZiIiIioqAw6wrN27VqMGzdOF3YAQC6XIywsDKtWrYJMJsOYMWNw+fJloxVKREREZCiDAk9eXh6uXbtWoP3atWvIz88HAFhbW792nA8RERFRSTPolNaAAQMwbNgwTJkyBU2aNAEAnDlzBl9++SUGDhwIADh69Cjq1atnvEqJiIiIDGRQ4Fm8eDFcXFywYMECpKWlAQBcXFwwfvx4TJo0CQAQGBiIjh07Gq9SIiIiIgMZFHgsLCwwdepUTJ06FZmZmQBQ4JESHh4exa+OiIiIyAgMvvGgFp+dRURERKWdQYOW09LSMGDAALi5ucHS0hIWFhZ6/wprxYoVaNiwIezt7WFvbw8/Pz/s2bMHAKBWqzFp0iQ0aNAAtra2cHNzw8CBA5Gamqq3jJycHISGhsLJyQl2dnYICQnRnWYjIiIiAgw8wjN48GDcvXsXkZGRqFSpksFXY1WpUgXz5s1DrVq1IAgC/vvf/6Jbt244f/48qlSpgnPnziEyMhKNGjXC33//jbFjx6Jr1644e/asbhnjx4/HTz/9hK1bt8LBwQFjxoxBcHAwb3pIREREOgYFnhMnTuD48ePw8fEp1sq7dOmi9/qLL77AihUrkJSUhGHDhiExMVFv+rJly9C0aVPcvXsXHh4eyMjIwNq1a7Fx40a0a9cOABAXF4c6deogKSkJzZs3L1Z9REREJA0GBR53d3cIgmDUQvLz87F161ZkZWXBz8/vtfNkZGRAJpPB0dERAJCcnAy1Wo2AgADdPN7e3vDw8MDp06ffGHhUKhVUKpXutXbgtVqthlqtNtIngm6ZAKCUG/fnZWrG/jmQcWl/P/w9kTGxX5kHpYV57U+0+z9T9KuiLNOgwBMdHY3Jkydj5cqVqFatmiGL0Ll06RL8/PyQk5MDOzs77NixA3Xr1i0wX05ODiZNmoQ+ffroBko/fPgQVlZWugCk5eLigocPH75xnVFRUZg1a1aB9v3798PGxqZYn+dN5jTWmGS5prJ7926xS6BC+N+joETGwH5Vui1oKnYFhjFFv8rOzi70vAYFnl69eiE7Oxs1atSAjY0NFAqF3vSnT58WelleXl64cOECMjIysG3bNgwaNAhHjx7VCz1qtRo9e/aEIAhYsWKFISXriYiIQHh4uO51ZmYm3N3dERgYaPSrztRqNRITExF5Vg6VxnzuPH15ZpDYJdBbaPtVhw4dCvz9ERmK/co81J+5T+wSikQpFzCnscYk/Up7hqYwDD7CYyxWVlaoWbMmAMDX1xdnzpzBkiVLsHLlSgD/F3bu3LmDQ4cO6QUSV1dX5ObmIj09Xe8oT1paGlxdXd+4TqVSCaVSWaBdoVCY7I9cpZFBlW8+gYcbO/Ngyj5L7y72q9LNnPYlrzJFvyrK8gwKPIMGDTLkbYWi0Wh042u0Yef69es4fPgwnJyc9Ob19fWFQqHAwYMHERISAgBISUnB3bt33zgOiIiIiN49hQ48mZmZuqMr/3QIqbCnhSIiItCpUyd4eHjg2bNn2LhxI44cOYJ9+/ZBrVbjk08+wblz5/Djjz8iPz9fNy6nfPnysLKygoODA4YNG4bw8HCUL18e9vb2CAsLg5+fH6/QIiIiIp1CB55y5crhwYMHqFixIhwdHV977x1BECCTyXRPTP8njx49wsCBA/HgwQM4ODigYcOG2LdvHzp06IDbt29j165dAFDg8vfDhw+jTZs2AF4+10sulyMkJAQqlQpBQUFYvnx5YT8WERERvQMKHXgOHTqE8uXLA3gZOIxh7dq1b5xWrVq1Ql36bm1tjZiYGMTExBilJiIiIpKeQgee1q1b6/7v6ekJd3f3Akd5BEHAvXv3jFcdERERkREY9CwtT09P/PXXXwXanz59Ck9Pz2IXRURERGRMBgUe7Vid//X8+XNYW1sXuygiIiIiYyrSZenam/XJZDJERkbq3ZU4Pz8fP//8c7Gfr0VERERkbEUKPOfPnwfw8gjPpUuXYGVlpZtmZWWFRo0aYcKECcatkIiIiKiYihR4tFdnDRkyBEuWLDH6YxiIiIiITMGgOy3HxcUZuw4iIiIikzEo8ADA2bNnsWXLFty9exe5ubl6077//vtiF0ZERERkLAZdpRUfH48WLVrg6tWr2LFjB9RqNa5cuYJDhw7BwcHB2DUSERERFYtBgefLL7/E4sWLkZCQACsrKyxZsgTXrl1Dz5494eHhYewaiYiIiIrFoMBz48YNdO7cGcDLq7OysrIgk8kwfvx4rFq1yqgFEhERERWXQYGnXLlyePbsGQCgcuXKuHz5MgAgPT0d2dnZxquOiIiIyAgMGrT8wQcfIDExEQ0aNECPHj0wduxYHDp0CImJiWjfvr2xayQiIiIqFoMCz7Jly5CTkwMAmDp1KhQKBU6dOoWQkBBMmzbNqAUSERERFVeRA09eXh5+/PFHBAUFAQDkcjkmT55s9MKIiIiIjKXIY3gsLS0xcuRI3REeIiIiotLOoEHLTZs2xYULF4xcChEREZFpGDSGZ/To0QgPD8e9e/fg6+sLW1tbvekNGzY0SnFERERExmBQ4OnduzcA4LPPPtO1yWQyCIIAmUyG/Px841RHREREZAQGBZ5bt24Zuw4iIiIikzEo8FStWrVQ83Xu3Blr1qxBpUqVDFkNERERkVEYNGi5sI4dO4YXL16YchVERERE/8ikgYeIiIioNGDgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIskzaeCZMmUKypcvb8pVEBEREf0jg+7DAwApKSlYunQprl69CgCoU6cOwsLC4OXlpZsnIiKi+BUSERERFZNBR3i2b9+O+vXrIzk5GY0aNUKjRo1w7tw51K9fH9u3bzd2jURERETFYtARns8//xwRERGYPXu2XvuMGTPw+eefIyQkxCjFERERERmDQUd4Hjx4gIEDBxZo79+/Px48eFDsooiIiIiMyaDA06ZNGxw/frxA+4kTJ9CqVatiF0VERERkTIU+pbVr1y7d/7t27YpJkyYhOTkZzZs3BwAkJSVh69atmDVrlvGrJCIiIiqGQgee7t27F2hbvnw5li9frtcWGhqKkSNHFrswIiIiImMpdODRaDSmrIOIiIjIZAy+D8/Bgwdx8OBBPHr0SC8MyWQyrF271ijFERERERmDQYFn1qxZmD17Nho3boxKlSpBJpMZuy4iIiIiozHoKq3Y2FisW7cOP//8M3bu3IkdO3bo/SusqKgoNGnSBGXLlkXFihXRvXt3pKSkvHH+kSNHQiaTITo6Wq89JycHoaGhcHJygp2dHUJCQpCWlmbIRyMiIiIJMijw5ObmokWLFsVe+dGjRxEaGoqkpCQkJiZCrVYjMDAQWVlZBebdsWMHkpKS4ObmVmDa+PHjkZCQgK1bt+Lo0aNITU1FcHBwsesjIiIiaTAo8PzrX//Cxo0bi73yvXv3YvDgwahXrx4aNWqEdevW4e7du0hOTtab7/79+wgLC8OGDRugUCj0pmVkZGDt2rVYtGgR2rVrB19fX8TFxeHUqVNISkoqdo1ERERk/gwaw5OTk4NVq1bhwIEDaNiwYYEQsmjRIoOKycjIAAC9J6xrNBoMGDAAEydORL169Qq8Jzk5GWq1GgEBAbo2b29veHh44PTp07r7BL1KpVJBpVLpXmdmZgIA1Go11Gq1QbW/iXZ5Srlg1OWamrF/DvR69WfuM+h9SrmAOY0B39l7odKU7Bi6yzODSnR9VHK0f/f8+y/dlBbmtT/R7v9M0a+KskyDAs/Fixfh4+MDALh8+bLeNEMHMGs0GowbNw4tW7ZE/fr1de3z58+HpaUlPvvss9e+7+HDh7CysoKjo6Neu4uLCx4+fPja90RFRb32Bon79++HjY2NQfX/kzmNzeuy/t27d4tdwjthQdPivV+MfsW+IX2JiYlil0BvUdzthlhM0a+ys7MLPa9Bgefw4cOGvO2tQkNDcfnyZZw4cULXlpycjCVLluDcuXNGvRIsIiIC4eHhuteZmZlwd3dHYGAg7O3tjbYe4GX6TExMRORZeYl/Ey8OfosvGcU7wqMRpV+xb0iXdnvVoUOHAkfuqfQwdLshFu32yhT9SnuGpjAMvg+PMY0ZMwY//vgjjh07hipVqujajx8/jkePHsHDw0PXlp+fj3//+9+Ijo7G7du34erqitzcXKSnp+sd5UlLS4Orq+tr16dUKqFUKgu0KxQKk/2RqzQyqPLNJ/BwY1cyitsnxOhX7BvSZ8ptIRWfOe1LXmWKflWU5Rk0aNlYBEHAmDFjsGPHDhw6dAienp560wcMGICLFy/iwoULun9ubm6YOHEi9u17mXB9fX2hUChw8OBB3ftSUlJw9+5d+Pn5lejnISIiotJJ1CM8oaGh2LhxI3744QeULVtWN+bGwcEBZcqUgZOTE5ycnPTeo1Ao4OrqCi8vL928w4YNQ3h4OMqXLw97e3uEhYXBz8/vtQOWiYiI6N0jauBZsWIFAKBNmzZ67XFxcRg8eHChl7N48WLI5XKEhIRApVIhKCiowENNiYiI6N0lauARhKJfWnf79u0CbdbW1oiJiUFMTIwRqiIiIiKpEXUMDxEREVFJYOAhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4CEiIiLJY+AhIiIiyRM18Bw7dgxdunSBm5sbZDIZdu7cWWCeq1evomvXrnBwcICtrS2aNGmCu3fv6qbn5OQgNDQUTk5OsLOzQ0hICNLS0krwUxAREVFpJ2rgycrKQqNGjRATE/Pa6Tdu3IC/vz+8vb1x5MgRXLx4EZGRkbC2ttbNM378eCQkJGDr1q04evQoUlNTERwcXFIfgYiIiMyApZgr79SpEzp16vTG6VOnTsWHH36IBQsW6Npq1Kih+39GRgbWrl2LjRs3ol27dgCAuLg41KlTB0lJSWjevLnpiiciIiKzIWrgeRuNRoOffvoJn3/+OYKCgnD+/Hl4enoiIiIC3bt3BwAkJydDrVYjICBA9z5vb294eHjg9OnTbww8KpUKKpVK9zozMxMAoFaroVarjfo5tMtTygWjLtfUjP1zoNdTWhjWL7T9SYx+xb4hXdrfLX/HpZuh2w2xaLdTpuhXRVlmqQ08jx49wvPnzzFv3jzMnTsX8+fPx969exEcHIzDhw+jdevWePjwIaysrODo6Kj3XhcXFzx8+PCNy46KisKsWbMKtO/fvx82NjbG/igAgDmNNSZZrqns3r1b7BLeCQuaFu/9YvQr9g3pS0xMFLsEeovibjfEYop+lZ2dXeh5S23g0Whebsi7deuG8ePHAwB8fHxw6tQpxMbGonXr1gYvOyIiAuHh4brXmZmZcHd3R2BgIOzt7YtX+P9Qq9VITExE5Fk5VBqZUZdtSpdnBoldwjuh/sx9Br1PKRcwp7FGlH7FviFd2u1Vhw4doFAoxC6H3sDQ7YZYtNsrU/Qr7Rmawii1gadChQqwtLRE3bp19drr1KmDEydOAABcXV2Rm5uL9PR0vaM8aWlpcHV1feOylUollEplgXaFQmGyP3KVRgZVvvkEHm7sSkZx+4QY/Yp9Q/pMuS2k4jOnfcmrTNGvirK8UnsfHisrKzRp0gQpKSl67b///juqVq0KAPD19YVCocDBgwd101NSUnD37l34+fmVaL1ERERUeol6hOf58+f4448/dK9v3bqFCxcuoHz58vDw8MDEiRPRq1cvfPDBB2jbti327t2LhIQEHDlyBADg4OCAYcOGITw8HOXLl4e9vT3CwsLg5+fHK7SIiIhIR9TAc/bsWbRt21b3WjuuZtCgQVi3bh0+/vhjxMbGIioqCp999hm8vLywfft2+Pv7696zePFiyOVyhISEQKVSISgoCMuXLy/xz0JERESll6iBp02bNhCEt19eN3ToUAwdOvSN062trRETE/PGmxcSERERldoxPERERETGwsBDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREksfAQ0RERJLHwENERESSx8BDREREkifqs7SIiOj1qk3+qcTXqbQQsKApUH/mPqjyZUV+/+15nU1QFZFx8AgPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUleqQ88+fn5iIyMhKenJ8qUKYMaNWpgzpw5EARBN48gCJg+fToqVaqEMmXKICAgANevXxexaiIiIipNSn3gmT9/PlasWIFly5bh6tWrmD9/PhYsWIClS5fq5lmwYAG++eYbxMbG4ueff4atrS2CgoKQk5MjYuVERERUWliKXcA/OXXqFLp164bOnTsDAKpVq4ZNmzbhl19+AfDy6E50dDSmTZuGbt26AQDWr18PFxcX7Ny5E7179xatdiIiIiodSn3gadGiBVatWoXff/8dtWvXxq+//ooTJ05g0aJFAIBbt27h4cOHCAgI0L3HwcEBzZo1w+nTp18beFQqFVQqle51ZmYmAECtVkOtVhu1fu3ylHLhH+YsXYz9c6DXU1oY1i+0/UmMfsW+UTIM7RvFWmcx+xX7RskQo28Uh7Y/maJ/FGWZMuHVwTClkEajwZQpU7BgwQJYWFggPz8fX3zxBSIiIgC8PALUsmVLpKamolKlSrr39ezZEzKZDJs3by6wzJkzZ2LWrFkF2jdu3AgbGxvTfRgiIiIymuzsbPTt2xcZGRmwt7d/67yl/gjPli1bsGHDBmzcuBH16tXDhQsXMG7cOLi5uWHQoEEGLTMiIgLh4eG615mZmXB3d0dgYOA//sCKSq1WIzExEZFn5VBpZEZdtildnhkkdgnvhPoz9xn0PqVcwJzGGlH6FftGyTC0bxRHcfsV+0bJEKNvFIe2X3Xo0AEKhcKoy9aeoSmMUh94Jk6ciMmTJ+tOTTVo0AB37txBVFQUBg0aBFdXVwBAWlqa3hGetLQ0+Pj4vHaZSqUSSqWyQLtCoTD6L0NLpZFBlW8+gcdUPwfSV9w+IUa/Yt8oGWJuLwztV+wbJcOc9iWvMsU+tijLK/VXaWVnZ0Mu1y/TwsICGo0GAODp6QlXV1ccPHhQNz0zMxM///wz/Pz8SrRWIiIiKp1K/RGeLl264IsvvoCHhwfq1auH8+fPY9GiRRg6dCgAQCaTYdy4cZg7dy5q1aoFT09PREZGws3NDd27dxe3eCIiIioVSn3gWbp0KSIjIzF69Gg8evQIbm5u+PTTTzF9+nTdPJ9//jmysrIwYsQIpKenw9/fH3v37oW1tbWIlRMREVFpUeoDT9myZREdHY3o6Og3ziOTyTB79mzMnj275AojIiIis1Hqx/AQERERFRcDDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUkeAw8RERFJHgMPERERSR4DDxEREUmeZAJPTEwMqlWrBmtrazRr1gy//PKL2CURERFRKSGJwLN582aEh4djxowZOHfuHBo1aoSgoCA8evRI7NKIiIioFJBE4Fm0aBGGDx+OIUOGoG7duoiNjYWNjQ3+85//iF0aERERlQKWYhdQXLm5uUhOTkZERISuTS6XIyAgAKdPn37te1QqFVQqle51RkYGAODp06dQq9VGrU+tViM7OxuWajnyNTKjLtuUnjx5InYJ7wTLvCzD3qcRkJ2tEaVfsW+UDEP7RrHWWcx+xb5RMsToG8Wh7VdPnjyBQqEw6rKfPXsGABAE4Z/rMOqaRfD48WPk5+fDxcVFr93FxQXXrl177XuioqIwa9asAu2enp4mqdEcVVgodgX0T/qKtF72DWkrTr9i36A3MfX26tmzZ3BwcHjrPGYfeAwRERGB8PBw3WuNRoOnT5/CyckJMplxvy1nZmbC3d0d9+7dg729vVGXTe8u9isyBfYrMgVT9itBEPDs2TO4ubn947xmH3gqVKgACwsLpKWl6bWnpaXB1dX1te9RKpVQKpV6bY6OjqYqEQBgb2/PDQgZHfsVmQL7FZmCqfrVPx3Z0TL7QctWVlbw9fXFwYMHdW0ajQYHDx6En5+fiJURERFRaWH2R3gAIDw8HIMGDULjxo3RtGlTREdHIysrC0OGDBG7NCIiIioFJBF4evXqhb/++gvTp0/Hw4cP4ePjg7179xYYyCwGpVKJGTNmFDiFRlQc7FdkCuxXZAqlpV/JhMJcy0VERERkxsx+DA8RERHRP2HgISIiIslj4CEiIiLJY+AhIiIiyWPgISIiIslj4ClBr14Qx4vjiMicPH78WOwSiIqFgacEjRgxApMnTwYAoz+zi+hVDNRkTPHx8Rg+fDh27dqFnJwcscshidJoNCZdPgNPCcnLy0PVqlWxZs0aVK9eHdu2bdNN486Jiis/Px8A8OuvvyI9PZ2BmowqOzsbDx48wPz58xEZGYmTJ0+KXRJJkFz+MpJs27ZNt00z6vKNvkR6LUtLS0yaNAmnT59Gly5d0LNnT4SEhODKlSu6nRODDxnKwsICANClSxf8+OOPIldDUjN06FDs2bMHbdu2xalTpxAZGYlFixbhxo0bYpdGZk673zty5AgAIDY2FgMGDEBeXp7R18XAU0IEQYBCoUCtWrXQqFEj+Pj4YMeOHWjQoAE+/fRTZGRkMPiQQbT9Ze/evfD29kavXr1MfmiY3h0ajQb5+fkoV64cmjVrBoVCgXPnzmH69OmYOHEi1q9fj7///lvsMslMyWQyXLp0CaNHj8bo0aMxefJkrF69WvcYCmNuyxh4Soj2lzZlyhTExcUhMjISR44cwTfffIPExER4enoiLi4OAMf3UOEIggCNRgOZTIasrCycPn0aNjY2kMvlkMvlDD1kFIIgwMLCAvHx8Rg1ahRGjRqFW7duYc2aNcjIyMCUKVMwadIkJCQkQK1Wi10umaHq1atj5MiR+OGHH5Cbm4ubN2/i/PnzAKC3LXvx4kWx1sNnaZWgzMxMVK9eHbGxsfjkk08AAGq1GleuXMGwYcNw/vx5uLu74/LlyyhbtqzI1ZI5Wbx4Mb744gtkZWXhq6++wtChQ2FjYwPg5Q6LIZqKq127dmjWrBmioqJ0bWq1GqNHj8bmzZtRvXp1/Oc//8H7778vYpVkbl7dPrVp0waVK1fG77//jooVK6Jjx47o3r073N3dcffuXXTq1AknTpxAuXLlDFqXJJ6Wbi60A5efPHmia1MoFPDx8cGAAQPg7e2NLl26MOzQP4qMjMTHH3+s27mEhYXB3t4e69atQ2xsLO7evYvg4GA0b96cYYeKRbtD8vDwwK+//orc3FwoFArk5eVBoVBgyJAhuHr1Kjp16sSwQ0Wm3T5lZGRg69atcHZ2xuHDh7FixQrExcXh7NmzqFu3Lvbt24fq1asbHHYAntIqUeXLl8d7772HBQsW4MCBA3pjdapUqYInT54gJCRExArJHNy5cwdXrlxBrVq1AABXrlyBpaUlhg0bhs2bN+Ojjz7CoUOHMH36dCxcuBDXrl0TuWIyZ9odUvv27XH+/Hn89NNPkMlkUCgUAAA3NzdYWVlh2LBhYpZJZkg7MPnYsWPo1q0bzpw5A0EQ0LZtW2zZsgXjx4/HkydP8MMPP8DS0hKbN28u1vp4SquEaL8l3bx5E2PHjkVOTg6aNGmCjh07Ij09HZ999hkGDx6MmTNnil0qmYGnT5+ifPny2L9/P8LDw9GzZ08MHjwYHh4eAICff/4ZsbGxOH78OD788EN88803IldM5uh/T4eGhYVh+fLl6N69O0aNGoWUlBR8//33AICDBw+KVSaZOS8vLwQHByM0NBRVqlRBXl4eLC1fnoB6/vw50tPTUa5cOdja2hZrPQw8JvTqxkKj0ejuMXD58mWsXLkSFy5cwKVLl2Bvbw9/f39s3LhRzHLJDJ0+fRqrV6/G9evXUbZsWXz88ccYOHCg7gqHb7/9Ft7e3mjSpInIlZI50W6vtLuHGzduoGbNmgCAPXv2YM6cObh27RpcXV3h7e2N2NhYVKxYUcySyUxt3LgRU6ZMwZUrV2BjYwOZTKbrf48fP0aFChWMti4GHhN5Nex8//33OHDgANRqNSZMmAAvLy8AwLVr11CuXDk8e/YMHh4esLKyErNkMlMqlQrbt2/Hrl27cPv2bVSvXh0DBgxAp06dxC6NzNyCBQuQkJCAR48eITc3F5GRkRg6dCiAlyHIzs4OTk5Oum/jREW1fv16rFu3DomJibCwsNA7OLB27Vo8fPgQEydONMr+kYHHRPLz82FhYYGpU6di27ZtaNKkCa5du4Zr166hT58+mDJlCjw9PcUuk8zM/55iePHiBcqUKQMAuH//PrZs2YIDBw7g8ePH8PX1RXR0NIM0FYl22/Xf//4XU6ZMweDBg1G/fn388ssvWLVqFXx9fbF9+3Y4OzvzCkAqtj179qBz587YtWsXPvroI71pQ4YMQU5ODjZt2mSUdTHwmIA2od66dQsNGzbEnj174O/vj759++L69ev4+++/8eLFC0yaNAn9+vWDk5OT2CWTmdm9ezd27NgBKysrODs7Y8SIEXBzcwMAXLhwAatXr0adOnUwZswYkSslc1W7dm2EhoZi7Nixurbz589jyJAhCAgIwNdffy1idSQVeXl5GDBgAO7cuYNRo0ahdevWcHV1xZYtW/Cvf/0LFy9eRO3atY2yLgYeE5o8eTLu3LmDTZs24eTJk+jSpQvOnDmDnJwcNGvWDNnZ2ZgzZw6mTp0qdqlkBrTfvPft24dRo0ahXr16qFatGjZt2oTKlStj4MCBGD9+vO5wML99k6GePHmCbt26YfTo0ejbty+A//siN336dOzatQuHDx8u1iXCRFp37tzBZ599hj/++APW1tb4448/UKNGDXTr1g0zZsww2np44tVEVCoVatWqBW9vbwBAdHQ0+vfvjxo1aiA7OxsDBgxA79690bJlS5ErJXOhfV7WuHHjMHDgQMycOROrV6/Gli1b0KhRI0RFRWH//v0YMGAA+vfvL3K1ZM7s7e1RpkwZLFq0CO3bt4eLi4suSHfs2BHx8fHIzs5m4KEi0X5pu3HjBvbt24fdu3ejfv36aNWqFX744Qf88MMP+Ouvv6BWq9G+fXujHdnRYuAxEaVSiX79+uHRo0cAXu6sHBwcALx8kOjJkyfRsWNHDvajIvnpp59QpkwZREREIC8vD3PnzsXChQvxySefoHPnzkhOToaDgwP69+/PoztkMIVCgejoaAwePBhjx45Fly5d0KtXL9y+fRuzZs1CgwYNULlyZbHLJDOifUQJAISEhMDT0xM1atTA5s2bceHCBQQFBaFbt24mrYF7WyP666+/cOvWLVSqVAkuLi6wtrbW3RfFwcEB33zzDWxtbZGUlITnz5+b/JdL5k17CmHr1q1ITk7GvHnzULlyZbRp0wY5OTnYvHkzKleujA8//BDW1tbo2rUrGjRogMmTJ4tdOpmZ153+rFevHubMmYOlS5diwYIFurt5V6pUCfHx8SJVSuZK28fmz58PtVqN7du3Qy6XY/369Rg8eDAsLS1x9uxZ5OXloWnTprojisbEwFNM2p3SoUOHMG/ePBw4cABVq1bFN998gy5duujmW7lyJSwtLbFo0SJ06NCBGwz6R9o/+NGjR2PatGkAAB8fH9SvXx+WlpbIyclBfn4+rK2tAQDnzp1DhQoV4OrqKlrNZJ60YefEiRPYvHkzlEolqlevju7du2Pbtm04dOiQ7mZwzZo146ksKjK5XI78/HwkJyejb9++kMvlGDZsGHx8fNC7d29oNBocPXoUqampaNCgQbFvMvg6HLRsJJ6enujevTv+9a9/4euvv8a1a9ewbt06/P7773j+/Dn69OkDAMjNzUV+fr7uUmKi19Ge654yZQp++OEHXLlyRTdt165d6Ny5M5KSkjBw4ED4+PjAwcEBmzdvxsWLF1GjRg0RKydzow0ya9euxZw5c3R3us3Ly0OFChUwYsQIBAcHi10mmaHU1FTMmzcPixYtAvByOMeUKVOgVqsRERGBqlWrYu/evbqxrD179kTFihWxbNky0xQkkMHy8vIEQRCEyZMnC3Xr1hXUarUgCILw559/ClWrVhXee+89wdHRUXB2dhZ69+4t3L9/X8xyyUxoNBpBEAThyZMngqWlpXD8+HHdtKioKKFVq1bCixcvhOzsbGHp0qVC165dhS5dughxcXEiVUzmTqPRCOXLlxfWrFmja9u3b58QHBws1KlTR7h9+7aI1ZG56tKli9C+fXtBEF5uzwRBEHbt2iWUK1dOqFSpkhAaGioIwst96d69ewWlUmnS/SQDTzE9ffpUkMlkwoULF3RtX3/9teDs7CycPHlSSElJESIjIwUrKyth7969IlZK5mbQoEFCpUqVhN9++00QhJc7JScnJ+G7777Tm+/vv/8WoTqSkv379wteXl7Cn3/+WWBanTp1hFGjRolQFZmzR48eCfXr1xfWrl0rCIIgdOjQQdixY4cgCIKwdu1aoXbt2oKnp6ewePFioUePHoKPj48QGRlp0pr4tPRi0j44b+XKlcjJyQEAREVFITo6Gi1atEDt2rUxYcIE+Pr64rfffhOzVDIzNWrUgFwux+jRo7F582b07t0b7733Hvr16wfg/5407OjoCAC65x4RFVWdOnWQk5OjewBoXl4e8vPzAQCffPIJUlNTkZubK2aJZGacnZ3x4YcfYt68efjkk09w5swZdO/eHQDQq1cvxMTEoH379li1ahWAl/etmz17tmmLMmmcegf8/vvvwooVK4T33ntPcHFxERo0aCAEBgYKgvDyG7lKpRIyMjKEatWqCVu3bhW5WjI3N2/eFPr06SO4uroKMplMmD9/vt4RHe3pL6LiePHihdCzZ0/B3d1dOHTokN60tm3b8ggPFZlGoxH++OMPYdKkSYJMJhO8vLyETZs2vXabVVLbMQ5aNgK1Wo2rV69i27Zt2LZtG7KysrB8+XJ07twZABAZGYktW7YgJSVF5ErJXGg0GgD/d6XWsWPHMHPmTNy5cwedO3dGSEgImjRpAhsbGzHLJDMj/P9Lg+/evYukpCS4u7vDz89PN61Pnz7Yvn072rVrh1q1auHKlSu4efMmrl69yr5GRaLta4sWLUJ8fDzq1q2LixcvwtPTE6NGjUJAQIBuXu3AeVNj4DGijIwMXLhwAWvXrsWuXbvQsmVLTJw4EV26dMHOnTvRvn17sUskM6NWq6FQKHSvV6xYgejoaNjb2yMwMBCjRo1ClSpVRKyQzIV2B3Tz5k18/vnnKFOmDGbMmIGaNWsiJSUFXl5eAICDBw8iOjoaGo0Gvr6++PDDD9G8eXORqydzor1dy927d9GtWzds2rQJzs7OiI+Px969e3H//n20bNkSYWFhRr+b8tsw8BiBdqf0119/ITMzE/b29jhy5AhWr16NAwcOoGPHjti9e7fYZZIZW7NmDQYMGAClUonnz59j6tSp2LlzJy5evKi7gzdRYQQEBKBu3boICwtDrVq1cPv2bfj7+6NFixaYMmUKfHx8AADPnz+HnZ2duMWSWRs+fDgyMjKwYcMG3Re3ixcvYseOHTh+/Dhu376NZcuWoWPHjiVSDwOPEWjvmVK/fn106NABixcvhiAI+OOPP5CYmIi+ffvqBpYSFZb2MO+WLVvQr18//Pnnn3B2dtad5nr8+DEqVKggcpVkDrTfuHfs2IERI0bg6tWrur4TFBSEhw8fonLlyrhz5w46dOiAadOmsW+RQbR97dGjR1i4cCF8fHzQp08f5ObmwsrKCsDLo42JiYnYs2cP5syZU2LBmndaNoB2R5SdnQ0bGxtYWFhg06ZNePjwISZMmADg5Z1La9WqBU9PTz4viwyi7Tfjxo3DggUL4OLiAuBlwJbL5dwhUaFpQ/KaNWswdOhQXd85c+YMbt26hf379+Pp06fYv38/vvrqK7i6uvIRJWQQbV/78ssvsX37dt2Nd62srKDRaKDRaGBpaYnAwEC0adNGF4JKAvfEhaQ9/x0fH4+dO3fit99+g4+PD+rVq4dhw4bh119/xZdffql7oJ52foYdehvtt6G///4bubm5ulCjdfz4cbRt2xajRo3StWkfwEdUWIIgICsrC2q1GkqlEsDLvufl5YVvv/0W1apV0/27dOkSVCrVa5+vRVQYmZmZkMvlcHNzw7p162Bra4vPPvsMVapUgVwuR15eHuRyeYmGHYCntArl1edlhYSEoHfv3ihXrhxSUlKQmpqKvLw8tGzZEl999ZXeAFOiwmrbti3Kly+Pf//733jvvfd0jx5RqVR49uyZ3tEc7ojIUH5+fqhfvz5Wr15dYJp2O9euXTt069YNY8eOFaFCkpKff/4Z3333HX755Rc4OTnhk08+Qf/+/Us86Ggx8BSBv78/PvjgA3z55ZcAXg7qO3r0KBISEvDrr7+ic+fOiIiIgFwu5w6J/pE2uGzYsAEjR45ExYoVkZaWhtDQUAwZMgQ1atTQC9CvngMnKgptmPnqq68wdepUrFixAgMHDizwBW3t2rWYOHEiHj9+bJKnVZN0vfpF7Pbt26hSpQosLS2Rn5+P77//Htu3b8e9e/dQrlw5zJ49G++//36J18geXUjPnz+Ho6Oj7nAwANjZ2aFz586YNm0a2rRpg+joaBw+fJhhhwpF20+OHTuG4cOH48aNG1i8eDFWrlyJTp06Yc2aNUhNTQXwcmMyY8YMHD9+XMySyUxpw0uPHj3g5+eHGTNmYPbs2Thz5gyAl/1ry5YtmDt3Lr788kuGHSqS/Px8yGQyPHjwAKNHj0ZAQAAqV66Mnj174vr16+jRoweWLFmCHj16ICcnR7TxhzzCUwRTpkzBTz/9hI0bN6Ju3boFgk3Hjh3h4eGhu1U20T/Jy8vDnj17dAP7gJffxseOHYuYmBi0bNkSU6dOxa1btzB27Fg8fvwY9vb2IldN5uzu3bsYNWoU9uzZg1q1asHW1hb5+fl49uwZunTpgiVLlohdIpmpjh07Qi6XY+7cufjpp58wb948XLp0CdWrV9fN8+DBA1SqVEmU+hh4iuDy5cvo3bs3nJyc8NVXX6Fx48Z634Tmzp2LgwcPIjExkYOVqdAEQUBeXh4UCoXeaaubN29ixIgROH78ONRqNWbOnInp06eLXC2ZM+0tNABg//792Lp1K7KysmBtbY3Ro0ejbt26vKMyGeTkyZPo3r07rl+/DkdHR/j7+6Nly5aYP38+rly5gv3792PYsGGifmHjXrkI6tevj4SEBPTv3x9t2rTBiBEj0Lt3bzg7O0OlUuG7775Dv379GHaoSGQymW4sxauXblavXh0HDhxAr169cPHiRYYdKhZt2Llz5w5ycnIQGBiIwMBAvXm0jzQhKqq0tDQ0aNAAjo6OWL58Oe7fv4+IiAgAQE5ODjZt2gR/f380adJEtBq5Zy4iT09PnDx5Ev/5z38wc+ZMbN26FdbW1sjLy4OPjw8iIyPFLpHMnFwuh1wuh0ajwfXr17F161Zs3rxZ7LLIzGmP7HTp0gVt27bVnbrSDmgGwLE7ZLA6derg6tWrSEpKwrx58xAVFaW74e7evXuh0WhEDTsAT2kV248//ghBEODq6oratWvzNv9kVBcvXsR3332HBQsWiF0KmSHtlTPaoztbtmzBmDFjcOHCBbi5uYldHpmhV+8ddu7cOTx9+hROTk5o164dpk+fjtjYWCiVSly6dAmOjo5ITExEr169sH79enz00Uei1s7AQ1TKvfoNnKiwXvcsrCFDhsDX1xdjxowRqSqSiu7du+Ps2bNITU1FxYoVERoaim7duuHbb7/FwYMH8ffff0OpVEKpVOKDDz7A0qVLxS6ZgYeISAq0wfj69evYsGED4uLi4OXlhZkzZ6JFixYAgJSUFNSsWZN36yaDaI8UTps2DTt27MCSJUtQt25dRERE4Ntvv8WpU6fg6+uLH3/8EWlpaUhNTUXfvn3h6empd0sXsTDwEBFJSMuWLVG2bFm0atUKSUlJOHbsGH788Ue0atVK7NLIjGlPjz59+hQuLi44evSoLkjn5+fj/fffR3BwMGbMmPHG94qNg5aJiMycdoeyatUqPHz4EPv27dOdzurUqRN27dqFVq1alZodD5kfbb8JDw+Hs7MznJycoFKpoFQqdVf/eXl5AXh5tFEmk+neU1r6HAcGEBGZOZlMBkEQsGPHDowZMwZ2dnbIy8sDAPTu3Rvbt2/X7YQA4Pvvv8e9e/fELJnMVI0aNSCXyzFq1CisWbMGWVlZWLx4MVxcXNC7d28IglBqH6/EwENEJAHZ2dlwcHCASqUCAN39wAICApCbm4sTJ04AAHbv3q17ADJRUUVGRuL48eOoVKkSoqKi0L9/f8ycORMTJkwA8H/huzRi4CEikgBbW1ts2LABw4cPBwDdTqdy5cqoW7cukpOTAQD//ve/MWHChAJXcBH9E+1NUT09PXUD4zMyMqBWq3Hp0iUcO3YM2dnZpfLoDsBBy0REkqUdszNt2jTcvHkT7du3R0REBB49eiR2aWTG1Gq17u7wALBq1SosXLgQ9vb2CAwMxKhRo1ClShURK3w9HuEhIpIo7Tftjh07Ys+ePRg+fDiWL18uclVk7rRhZ82aNVCpVBgxYgSSk5PRokULfPfddyhbtqzIFb4ej/AQEUlcZmYmPDw8UK9ePZw8eVLscsiMaY/ubNmyBf369cOff/4JZ2dn3c1RHz9+jAoVKohc5esx8BARvQNyc3ORmZlZandGVDrl5ubiwYMHKFOmDMqVK6c7uuPm5oYJEyYgPDwcwMt78ZTWq7O0eEqLiOgdYGVlxbBDhaLRaAAAv/76Kz799FPUrl0bH374IY4fPw4AOH78OAIDAxEaGqqb18LColSHHYCBh4iIiF6hPT01ePBg5OfnY8eOHahcuTKmT5+O58+fo1q1ahg2bBiUSqVZPeePp7SIiIgIwP89ky02NhYLFy7Er7/+ChsbGzx69AjBwcEoU6YMbt26BRcXF/Tv3x8jR44s9Ud2tMwnmhEREZFJyeVyCIKALVu2ICwsDDY2NgCAhIQEnD9/Hj169MCyZcvg5eWFr7/+Grdv3xa34CLgs7SIiIhIJzs7Gx9//DGaNm2qa5s2bRrmzp2LESNGAHj5iIkjR47gwYMH8PT0FKvUImHgISIiIh1bW1uMGTNG9zy23NxcbN++Hc2aNQPw8oosOzs7ODo6IiMjQ8xSi4SBh4iIiPTIZDLdJehWVlZo0aKFbpqFhQXi4+ORlZWFTp06iVVikTHwEBERUaGo1WokJSXh66+/xjfffCN2OUXCq7SIiIioUFJTUzFmzBhUrFgRsbGxYpdTJAw8REREVGj5+fnIzc1FmTJlxC6lSBh4iIiISPJ4Hx4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpI8Bh4iIiKSPAYeIiIikjwGHiIiIpK8/weqQL++drfKEgAAAABJRU5ErkJggg==\n"
-          },
-          "metadata": {}
-        }
+     "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 tr th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead tr:last-of-type th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th colspan=\"3\" halign=\"left\">rating</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th></th>\n",
+       "      <th>count</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>query</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>120v power supply</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aa</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>1.549193</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aa battery</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.800000</td>\n",
+       "      <td>1.135292</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>aaa</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1.285714</td>\n",
+       "      <td>1.603567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>adapter</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1.300000</td>\n",
+       "      <td>0.674949</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wireless headphones</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.800000</td>\n",
+       "      <td>1.303840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>wireless mouse</th>\n",
+       "      <td>14</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.109400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xbox</th>\n",
+       "      <td>15</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>xbox one</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.428571</td>\n",
+       "      <td>0.786796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>youtube</th>\n",
+       "      <td>12</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>137 rows × 3 columns</p>\n",
+       "</div>"
       ],
-      "id": "078955ba"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Agreements between raters\nIn this section we want to check how much raters agree or disagree.\n\n  - If all raters agreeing on a rating will give us confidence about the rating\n  - If all raters disagree, for example, 1 rating `Poor` another one rating `Fair` and the third one rating `Perfect`, then something draws our attention. Either the intent was not clear or raters did not rate as expected. ",
-      "metadata": {},
-      "id": "cf354a46-d693-47e5-8148-fbef8e8d8b25"
-    },
+      "text/plain": [
+       "                    rating                    \n",
+       "                     count      mean       std\n",
+       "query                                         \n",
+       "120v power supply        2  0.000000  0.000000\n",
+       "aa                      10  1.800000  1.549193\n",
+       "aa battery              10  1.800000  1.135292\n",
+       "aaa                      7  1.285714  1.603567\n",
+       "adapter                 10  1.300000  0.674949\n",
+       "...                    ...       ...       ...\n",
+       "wireless headphones      5  0.800000  1.303840\n",
+       "wireless mouse          14  2.000000  1.109400\n",
+       "xbox                    15  0.000000  0.000000\n",
+       "xbox one                 7  0.428571  0.786796\n",
+       "youtube                 12  0.000000  0.000000\n",
+       "\n",
+       "[137 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_overall[['query', 'rating']].groupby('query').agg(['count', 'mean', 'std'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "70b93e5d-425d-4925-97fd-1b062f7c373f",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "vals = []\nlabs = []\ncols = []\nfor agent,col in (('rating_0','blue'),\n                  ('rating_1','red'),\n                  ('rating_2', 'green')\n                 ):\n    vals.append(df[agent])\n    labs.append(agent)\n    cols.append(col)\nplt.hist(vals, color=cols, label=labs)\n\nplt.legend()\nplt.title('Overall ratings')\nplt.xticks(range(len(ratings)), ratings, rotation=60) \nNone",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 76,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 640x480 with 1 Axes>",
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "cf6869d0-3498-4d44-9bf6-d55e580916d1"
-    },
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots()\n",
+    "queries = df_overall['query'].unique()\n",
+    "dataset = [df_overall[df_overall['query'] == q][\"rating\"] for q in queries]\n",
+    "\n",
+    "nb_queries = len(queries)\n",
+    "\n",
+    "axes.violinplot(dataset = dataset, showmeans=True, bw_method=0.05)\n",
+    "axes.set_xlabel('query')\n",
+    "axes.set_ylabel('ratings')\n",
+    "axes.yaxis.grid(True)\n",
+    "axes.set_xticks(range(1,nb_queries+1))\n",
+    "axes.set_xticklabels(queries,rotation=45)\n",
+    "plt.title('Rating distribution per query')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa14bfdd-ea38-4fcc-9532-4019306410b1",
+   "metadata": {},
+   "source": [
+    "### Rating distribution per rater\n",
+    "\n",
+    "Assuming raters have seen the same <query,document> pairs and that they had the same guidelines, their ratings should be overall calibrated.  This is what we want to check here.   \n",
+    "We could easily detect outlier raters that rate very different to others, for example because they haven't really understood the guidelines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "f954846d-54a9-4cf1-a9da-ab0faaa46df9",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "def nb_distinct_ratings(r):\n    return len({r['rating_0'], r['rating_1'], r['rating_2']})\ndf['nb_distinct_ratings'] = df.apply(nb_distinct_ratings, axis=1)\ndf['nb_distinct_ratings'].hist()\nplt.xticks([1, 2, 3], ['3 Agree', '2 Agree', 'All disagree'],\n       rotation=45) \nplt.ylabel('nb cases')\nplt.title('Agents agreements')",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 77,
-      "outputs": [
-        {
-          "execution_count": 77,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "Text(0.5, 1.0, 'Agents agreements')"
-          },
-          "metadata": {}
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 640x480 with 1 Axes>",
-            "image/png": 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TDndQAkBBIGgBxdCVK1cc7qrLzs5W+/bttWfPHiUkJOR5x11R8OfjTk9P14MPPqjs7Gz9/PPPTqwMQFHFHC2gGBo1apSuXLmiJk2aKCMjQ+vWrdOOHTv06quvFtmQJV2dpF6pUiXVr19fycnJ+uCDD3To0CG+fBuAaTijBRRDK1as0OzZs3X06FGlp6erWrVqevrpp/9y4nlhN2/ePC1atEgnTpxQdna2atasqfHjx6tv377OLg1AEUXQAgAAMAnraAEAAJiEoAUAAGASJsPfJJvNptOnT6tUqVI3tbAhAABwPsMwdOnSJZUvX14uLnf+/BJB6yadPn3a/oWxAACgcPntt99077333vH9ErRuUs7XSxw/flwBAQFOrgbArbBardq0aZPat28vNzc3Z5cD4BbkdxynpKSoYsWKTvuaKILWTcq5XFiqVCn5+vo6uRoAt8Jqtcrb21u+vr4ELaCQutVx7KxpP0yGBwAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJOUcHYBAACg6Ljv+f+aun0PV0OzwkzdRYHijBYAAIBJCFoAAAAmIWgBAACYhKAFAABgEoIWAACASQhaAAAAJiFoAQAAmISgBQAAYBKCFgAAgEkIWgAAACYhaAEAAJiEoAUAAGASghYAAIBJCFoAAAAmIWgBAACYhKAFAABgEoIWAACASQhaAAAAJiFoAQAAmMSpQWvmzJl66KGHVKpUKZUrV049evTQ4cOHHfoYhqHJkycrODhYXl5eCg8P15EjRxz6pKena+TIkSpTpoxKliypiIgIJSYmOvS5ePGi+vfvL19fX/n7+2vo0KG6fPmy6ccIAACKL6cGrW3btmnkyJHauXOn4uLiZLVa1b59e6Wmptr7zJo1S/Pnz1dMTIx27dolHx8fdejQQenp6fY+Y8eO1SeffKLVq1dr27ZtOn36tHr16uWwr/79++unn35SXFycNmzYoO3bt2v48OF37FgBAEDxYzEMw3B2ETnOnTuncuXKadu2bWrRooUMw1D58uU1btw4Pfvss5Kk5ORkBQYGatmyZYqMjFRycrLKli2rFStWqHfv3pKkQ4cOKTQ0VPHx8WrcuLEOHjyomjVravfu3WrYsKEkaePGjercubNOnTql8uXL/2VtKSkp8vPz0/nz51WmTBnzfgkATGO1WvXpp5+qc+fOcnNzc3Y5QJF03/P/NXX7Hq6GZoVl3/Q4zvn3Ozk5Wb6+vqbWlpcSd3yPN5CcnCxJCggIkCQdP35cCQkJCg8Pt/fx8/NTo0aNFB8fr8jISO3du1dWq9WhT40aNVSpUiV70IqPj5e/v789ZElSeHi4XFxctGvXLvXs2TNXLRkZGcrIyLA/TklJkXT1g9pqtRbsgQO4I3LGLmMYMI+Hq7nnbzxcrm7/Zsexs8f7XRO0bDabxowZo6ZNm6p27dqSpISEBElSYGCgQ9/AwED7cwkJCXJ3d5e/v/8N+5QrV87h+RIlSiggIMDe589mzpypadOm5WrfunWrvL2983+AAO4acXFxzi4BKLJmhd2Z/dzsOE5LSzO5khu7a4LWyJEjtX//fn311VfOLkWSNHHiREVHR9sfp6SkqGLFimrdujWXDoFCymq1Ki4uTu3atePSIWCS2lM/N3X7Hi6GXmlou+lxnHNFylnuiqAVFRVln6B+77332tuDgoIkSYmJiQoODra3JyYmqn79+vY+mZmZSkpKcjirlZiYaH99UFCQzp4967DPrKwsXbx40d7nzzw8POTh4ZGr3c3NjQ9ooJBjHAPmyci23JH93Ow4dvZYd+pdh4ZhKCoqSh999JG2bNmiKlWqODxfpUoVBQUFafPmzfa2lJQU7dq1S02aNJEkNWjQQG5ubg59Dh8+rJMnT9r7NGnSRElJSdq7d6+9z5YtW2Sz2dSoUSMzDxEAABRjTj2jNXLkSK1YsULr169XqVKl7POl/Pz85OXlJYvFojFjxmj69OkKCQlRlSpVNGnSJJUvX149evSw9x06dKiio6MVEBAgX19fjRo1Sk2aNFHjxo0lSaGhoerYsaOGDRummJgYWa1WRUVFKTIy8qbuOAQAALgVTg1ab7/9tiSpVatWDu1Lly7VE088IUkaP368UlNTNXz4cCUlJalZs2bauHGjPD097f3nzp0rFxcXRUREKCMjQx06dNDChQsdtrl8+XJFRUWpbdu29r7z58839fgAAEDxdleto3U3Yx0toPBjHS3AfKyj5YjvOgQAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJM4NWht375d3bp1U/ny5WWxWBQbG+vw/BNPPCGLxeLwp2PHjg590tPTNXLkSJUpU0YlS5ZURESEEhMTHfpcvHhR/fv3l6+vr/z9/TV06FBdvnzZ7MMDAADFnFODVmpqqurVq6cFCxZct0/Hjh115swZ+5///Oc/Ds+PHTtWn3zyiVavXq1t27bp9OnT6tWrl0Of/v3766efflJcXJw2bNig7du3a/jw4aYcEwAAQI4Sztx5p06d1KlTpxv28fDwUFBQUJ7PJScna/HixVqxYoXatGkjSVq6dKlCQ0O1c+dONW7cWAcPHtTGjRu1e/duNWzYUJL05ptvqnPnznr99ddVvnz5gj0oAACA/8+pQetmfPnllypXrpxKly6tNm3aaPr06SpTpowkae/evbJarQoPD7f3r1GjhipVqqT4+Hg1btxY8fHx8vf3t4csSQoPD5eLi4t27dqlnj175rnfjIwMZWRk2B+npKRIkqxWq6xWqxmHCsBkOWOXMQyYx8PVMHf7Lle3f7Pj2Nnj/a4OWh07dlSvXr1UpUoVHTt2TC+88II6deqk+Ph4ubq6KiEhQe7u7vL393d4XWBgoBISEiRJCQkJKleunMPzJUqUUEBAgL1PXmbOnKlp06blat+6dau8vb1v/+AAOE1cXJyzSwCKrFlhd2Y/NzuO09LSTK7kxu7qoBUZGWn/uU6dOqpbt66qVq2qL7/8Um3btjV13xMnTlR0dLT9cUpKiipWrKjWrVvbz6gBKFysVqvi4uLUrl07ubm5ObscoEiqPfVzU7fv4WLolYa2mx7HOVeknOWuDlp/dv/99+uee+7R0aNH1bZtWwUFBSkzM1NJSUkOZ7USExPt87qCgoJ09uxZh+1kZWXp4sWL1537JV2dG+bh4ZGr3c3NjQ9ooJBjHAPmyci23JH93Ow4dvZYL1TraJ06dUoXLlxQcHCwJKlBgwZyc3PT5s2b7X0OHz6skydPqkmTJpKkJk2aKCkpSXv37rX32bJli2w2mxo1anRnDwAAABQrTj2jdfnyZR09etT++Pjx4/ruu+8UEBCggIAATZs2TREREQoKCtKxY8c0fvx4VatWTR06dJAk+fn5aejQoYqOjlZAQIB8fX01atQoNWnSRI0bN5YkhYaGqmPHjho2bJhiYmJktVoVFRWlyMhI7jgEAACmcmrQ2rNnj1q3bm1/nDMnatCgQXr77bf1ww8/6L333lNSUpLKly+v9u3b65VXXnG4pDd37ly5uLgoIiJCGRkZ6tChgxYuXOiwn+XLlysqKkpt27a1950/f/6dOUgAAFBsOTVotWrVSoZx/dtAP//8ryfUeXp6asGCBTdc9DQgIEArVqy4pRoBAABuVaGaowUAAFCYELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACT3HbQSklJUWxsrA4ePFgQ9QAAABQZ+Q5affr00VtvvSVJunLliho2bKg+ffqobt26Wrt2bYEXCAAAUFjlO2ht375dzZs3lyR99NFHMgxDSUlJmj9/vqZPn17gBQIAABRW+Q5aycnJCggIkCRt3LhRERER8vb2VpcuXXTkyJECLxAAAKCwynfQqlixouLj45WamqqNGzeqffv2kqQ//vhDnp6eBV4gAABAYVUivy8YM2aM+vfvr5IlS6pSpUpq1aqVpKuXFOvUqVPQ9QEAABRa+Q5azzzzjMLCwvTbb7+pXbt2cnG5elLs/vvvZ44WAADANfIdtCSpYcOGqlu3ro4fP66qVauqRIkS6tKlS0HXBgAAUKjle45WWlqahg4dKm9vb9WqVUsnT56UJI0aNUqvvfZagRcIAABQWOU7aE2cOFHff/+9vvzyS4fJ7+Hh4frwww8LtDgAAIDCLN+XDmNjY/Xhhx+qcePGslgs9vZatWrp2LFjBVocAABAYZbvM1rnzp1TuXLlcrWnpqY6BC8AAIDiLt9Bq2HDhvrvf/9rf5wTrhYtWqQmTZoUXGUAAACFXL4vHb766qvq1KmTDhw4oKysLL3xxhs6cOCAduzYoW3btplRIwAAQKGU7zNazZo103fffaesrCzVqVNHmzZtUrly5RQfH68GDRqYUSMAAEChdEvraFWtWlXvvvtuQdcCAABQpOT7jNa+ffv0448/2h+vX79ePXr00AsvvKDMzMwCLQ4AAKAwy3fQeuqpp/Tzzz9Lkn755Rf17dtX3t7eWr16tcaPH1/gBQIAABRW+Q5aP//8s+rXry9JWr16tVq2bKkVK1Zo2bJlWrt2bUHXBwAAUGjlO2gZhiGbzSZJ+uKLL9S5c2dJUsWKFXX+/PmCrQ4AAKAQu6V1tKZPn673339f27Zts3+Z9PHjxxUYGFjgBQIAABRW+Q5a8+bN0759+xQVFaUXX3xR1apVkyStWbNGDz/8cIEXCAAAUFjle3mHunXrOtx1mOOf//ynXF1dC6QoAACAouCW1tHKi6enZ0FtCgAAoEjId9DKzs7W3LlztWrVKp08eTLX2lkXL14ssOIAAAAKs3zP0Zo2bZrmzJmjvn37Kjk5WdHR0erVq5dcXFw0depUE0oEAAAonPIdtJYvX653331X48aNU4kSJfTYY49p0aJFmjx5snbu3GlGjQAAAIVSvoNWQkKC6tSpI0kqWbKkkpOTJUldu3bVf//734KtDgAAoBDLd9C69957debMGUlXv1x606ZNkqTdu3fLw8OjYKsDAAAoxPIdtHr27KnNmzdLkkaNGqVJkyYpJCREAwcO1JAhQwq8QAAAgMIq33cdvvbaa/af+/btq8qVK2vHjh0KCQlRt27dCrQ4AACAwuy219Fq3LixGjduXBC1AAAAFCn5vnQ4c+ZMLVmyJFf7kiVL9I9//KNAigIAACgK8h20/vWvf6lGjRq52mvVqqWYmJgCKQoAAKAouKXlHYKDg3O1ly1b1n43IgAAAG4haFWsWFFff/11rvavv/5a5cuXL5CiAAAAioJ8T4YfNmyYxowZI6vVqjZt2kiSNm/erPHjx2vcuHEFXiAAAEBhle+g9dxzz+nChQt65pln7F8o7enpqQkTJmjixIkFXiAAAEBhle+gZbFY9I9//EOTJk3SwYMH5eXlpZCQEFaFBwAA+JNbXkerZMmSeuihhwqyFgAAgCIl35PhAQAAcHMIWgAAACYhaAEAAJiEoAUAAGCSW5oMf/jwYb355ps6ePCgJCk0NFSjRo1S9erVC7Q4AACAwizfZ7TWrl2r2rVra+/evapXr57q1aunffv2qXbt2lq7dq0ZNQIAABRK+Q5a48eP18SJExUfH685c+Zozpw52rFjh1544QWNHz8+X9vavn27unXrpvLly8tisSg2NtbhecMwNHnyZAUHB8vLy0vh4eE6cuSIQ5/09HSNHDlSZcqUUcmSJRUREaHExESHPhcvXlT//v3l6+srf39/DR06VJcvX87voQMAAORLvoPWmTNnNHDgwFztjz/+eL6/VDo1NVX16tXTggUL8nx+1qxZmj9/vmJiYrRr1y75+PioQ4cOSk9Pt/cZO3asPvnkE61evVrbtm3T6dOn1atXL4ft9O/fXz/99JPi4uK0YcMGbd++XcOHD89XrQAAAPmV7zlarVq10v/+9z9Vq1bNof2rr75S8+bN87WtTp06qVOnTnk+ZxiG5s2bp5deekmPPPKIJOnf//63AgMDFRsbq8jISCUnJ2vx4sVasWKF/XsXly5dqtDQUO3cuVONGzfWwYMHtXHjRu3evVsNGzaUJL355pvq3LmzXn/9db4IGwAAmOamgtbHH39s/7l79+6aMGGC9u7dq8aNG0uSdu7cqdWrV2vatGkFVtjx48eVkJCg8PBwe5ufn58aNWqk+Ph4RUZGau/evbJarQ59atSooUqVKik+Pl6NGzdWfHy8/P397SFLksLDw+Xi4qJdu3apZ8+eee4/IyNDGRkZ9scpKSmSJKvVKqvVWmDHCeDOyRm7jGHAPB6uhrnbd7m6/Zsdx84e7zcVtHr06JGrbeHChVq4cKFD28iRIzVixIgCKSwhIUGSFBgY6NAeGBhofy4hIUHu7u7y9/e/YZ9y5co5PF+iRAkFBATY++Rl5syZeQbHrVu3ytvbO9/HA+DuERcX5+wSgCJrVtid2c/NjuO0tDSTK7mxmwpaNpvN7DruOhMnTlR0dLT9cUpKiipWrKjWrVurTJkyTqwMwK2yWq2Ki4tTu3bt5Obm5uxygCKp9tTPTd2+h4uhVxrabnoc51yRcpZb/lJpswUFBUmSEhMTFRwcbG9PTExU/fr17X0yMzOVlJTkcFYrMTHR/vqgoCCdPXvWYdtZWVm6ePGivU9ePDw85OHhkavdzc2ND2igkGMcA+bJyLbckf3c7Dh29li/paC1efNmbd68WWfPns11tmvJkiUFUliVKlUUFBSkzZs324NVSkqKdu3apaefflqS1KBBA7m5uWnz5s2KiIiQdHUx1ZMnT6pJkyaSpCZNmigpKUl79+5VgwYNJElbtmyRzWZTo0aNCqRWAACAvOQ7aE2bNk0vv/yyGjZsqODgYFkst55cL1++rKNHj9ofHz9+XN99950CAgJUqVIljRkzRtOnT1dISIiqVKmiSZMmqXz58vY5Y35+fho6dKiio6MVEBAgX19fjRo1Sk2aNLFP1A8NDVXHjh01bNgwxcTEyGq1KioqSpGRkdxxCAAATJXvoBUTE6Nly5ZpwIABt73zPXv2qHXr1vbHOXOiBg0apGXLlmn8+PFKTU3V8OHDlZSUpGbNmmnjxo3y9PS0v2bu3LlycXFRRESEMjIy1KFDh1yT9JcvX66oqCi1bdvW3nf+/Pm3XT8AAMCNWAzDyNd9mGXKlNE333yjqlWrmlXTXSklJUV+fn46f/48k+GBQspqterTTz9V586dnT5vAyiq7nv+v6Zu38PV0Kyw7Jsexzn/ficnJ8vX19fU2vKS75Xhn3zySa1YscKMWgAAAIqUfF86TE9P1zvvvKMvvvhCdevWzZUm58yZU2DFAQAAFGb5Dlo//PCD/S7A/fv3Ozx3OxPjAQAAipp8B62tW7eaUQcAAECRk+85WgAAALg5BC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAk9zVQWvq1KmyWCwOf2rUqGF/3jAMTZ48WcHBwfLy8lJ4eLiOHDnisI309HSNHDlSZcqUUcmSJRUREaHExMQ7fSgAAKAYuquDliTVqlVLZ86csf/56quv7M/NmjVL8+fPV0xMjHbt2iUfHx916NBB6enp9j5jx47VJ598otWrV2vbtm06ffq0evXq5YxDAQAAxUwJZxfwV0qUKKGgoKBc7YZhaN68eXrppZf0yCOPSJL+/e9/KzAwULGxsYqMjFRycrIWL16sFStWqE2bNpKkpUuXKjQ0VDt37lTjxo3v6LEAAIDi5a4/o3XkyBGVL19e999/v/r376+TJ09Kko4fP66EhASFh4fb+/r5+alRo0aKj4+XJO3du1dWq9WhT40aNVSpUiV7HwAAALPc1We0GjVqpGXLlql69eo6c+aMpk2bpubNm2v//v1KSEiQJAUGBjq8JjAw0P5cQkKC3N3d5e/vf90+15ORkaGMjAz745SUFEmS1WqV1Wq93UMD4AQ5Y5cxDJjHw9Uwd/suV7d/s+PY2eP9rg5anTp1sv9ct25dNWrUSJUrV9aqVasUGhpq6r5nzpypadOm5WrfunWrvL29Td03AHPFxcU5uwSgyJoVdmf2c7PjOC0tzeRKbuyuDlp/5u/vrwceeEBHjx5V69atJUmJiYkKDg6290lMTFT9+vUlSUFBQcrMzFRSUpLDWa3ExMQ8531da+LEiYqOjrY/TklJUcWKFdW6dWuVKVOm4A4KwB1jtVoVFxendu3ayc3NzdnlAEVS7amfm7p9DxdDrzS03fQ4zrki5SyFKmhdvnxZx44d04ABA1SlShUFBQVp8+bN9mCVkpKiXbt26emnn5YkNWjQQG5ubtq8ebMiIiIkSYcPH9bJkyfVpEmTG+7Lw8NDHh4eudrd3Nz4gAYKOcYxYJ6MbMsd2c/NjmNnj/W7Omg9++yz6tatmypXrqzTp09rypQpcnV11WOPPSaLxaIxY8Zo+vTpCgkJUZUqVTRp0iSVL19ePXr0kHR1cvzQoUMVHR2tgIAA+fr6atSoUWrSpAl3HAIAANPd1UHr1KlTeuyxx3ThwgWVLVtWzZo1086dO1W2bFlJ0vjx45Wamqrhw4crKSlJzZo108aNG+Xp6Wnfxty5c+Xi4qKIiAhlZGSoQ4cOWrhwobMOCQAAFCMWwzDMvT2giEhJSZGfn5/Onz/PHC2gkLJarfr000/VuXNnp19OAIqq+57/r6nb93A1NCss+6bHcc6/38nJyfL19TW1trzc9etoAQAAFFYELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0AAAATELQAgAAMAlBCwAAwCQELQAAAJMQtAAAAExC0AIAADAJQQsAAMAkBC0AAACTELQAAABMUsLZBQDAnVZ76ufKyLaYsu0Tr3UxZbsACifOaAEAAJiEoAUAAGASghYAAIBJCFoAAAAmIWgBAACYhKAFAABgEoIWAACASQhaAAAAJiFoAQAAmISgBQAAYBKCFgAAgEkIWgAAACYhaAEAAJiEoAUAAGASghYAAIBJCFoAAAAmIWgBAACYhKAFAABgEoIWAACASQhaAAAAJiFoAQAAmISgBQAAYBKCFgAAgEkIWgAAACYhaAEAAJiEoAUAAGASghYAAIBJCFoAAAAmIWgBAACYhKAFAABgEoIWAACASQhaAAAAJiFoAQAAmISgBQAAYBKCFgAAgEkIWgAAACYhaAEAAJiEoAUAAGASghYAAIBJilXQWrBgge677z55enqqUaNG+uabb5xdEgAAKMKKTdD68MMPFR0drSlTpmjfvn2qV6+eOnTooLNnzzq7NAAAUEQVm6A1Z84cDRs2TIMHD1bNmjUVExMjb29vLVmyxNmlAQCAIqpYBK3MzEzt3btX4eHh9jYXFxeFh4crPj7eiZUBAICirISzC7gTzp8/r+zsbAUGBjq0BwYG6tChQ3m+JiMjQxkZGfbHycnJkqSLFy+aVygAU1mtVqWlpamE1UXZNosp+7hw4YIp2wUKixJZqeZu32YoLc2mCxcuyM3N7S/7X7p0SZJkGIapdV1PsQhat2LmzJmaNm1arvYHHnjACdUAKCzume3sCoCir98tvObSpUvy8/Mr8Fr+SrEIWvfcc49cXV2VmJjo0J6YmKigoKA8XzNx4kRFR0fbHyclJaly5co6efKkU94oALcvJSVFFStW1G+//SZfX19nlwPgFuR3HBuGoUuXLql8+fJ3oLrcikXQcnd3V4MGDbR582b16NFDkmSz2bR582ZFRUXl+RoPDw95eHjkavfz8+MDGijkfH19GcdAIZefcezMEyTFImhJUnR0tAYNGqSGDRsqLCxM8+bNU2pqqgYPHuzs0gAAQBFVbIJW3759de7cOU2ePFkJCQmqX7++Nm7cmGuCPAAAQEEpNkFLkqKioq57qfCveHh4aMqUKXleTgRQODCOgcKvsI1ji+Gs+x0BAACKuGKxYCkAAIAzELQAAABMQtACAAAwCUELAADAJAQtAAAAkxC0THTtDZ3c3AkUToxjoHiw2WymbLdYraN1J9lsNrm4XM2xGRkZysjI4Cs/gEKGcQwUD9eO9R9++EHnz59X5cqVVa5cOZUqVeq2tk3QMsG1b9isWbO0ZcsWnThxQs2bN9dTTz2lBg0ayGKxOLlKADfCOAaKB8Mw7GN94sSJ+vjjj/XHH38oJCREfn5+iomJua0vpObSoQly3rCXXnpJc+fOVffu3bV48WKtWLFCU6ZMUUJCgpMrBPBXGMdA8ZDzH6Z58+Zp8eLFiomJ0enTp1W3bl1t3rxZBw8evK3tE7RMcvjwYa1fv17vv/++nnnmGbm6uspms6lXr14KDg52dnkAbgLjGCj6DMNQRkaGduzYoUmTJql58+b69NNPtWzZMs2bN09t27bVlStXlJaWdkvbJ2iZJCMjQxaLReHh4YqNjVW7du00d+5cDR06VJcuXVJsbKyys7OdXSaAG2AcA0WfxWKRh4eHrly5ogceeECffvqp+vbtq3/+858aNmyYrFarVqxYoS+++OKWboghaBWAvH7xAQEBSk1N1fPPP68nnnhC//znPzVixAhJ0qFDh/TGG2/o22+/vdOlArgOxjFQPOR1d6FhGPL09NSYMWPUv39/zZ492z7Wz507p5UrV+r06dO3NC+TL5W+TddOmL106ZI8PDyUlZUlb29vjR49WosWLdKgQYO0cOFCSVf/h9y7d29ZLBbFxsbaXwvAeRjHQPFw7Vjfs2ePbDabsrOz1aRJEyUmJqp9+/bKzs7WN998I5vNpvT0dA0cOFApKSnatm2bXF1d871PgtZt+PNdSV9//bVOnTqlsLAwjR49Wj4+Pho1apQOHDigPn36yMPDQ9u2bVNiYqL27dsnNzc3h20AuPMYx0DxYBiG/YzUiy++qDVr1sjd3V2nTp3So48+qsmTJ+vo0aPq27evAgIC5OrqKj8/P6Wnp2vnzp1yc3NTdnZ2vsMWQasAvPDCC3rnnXc0d+5cZWVlaf78+bp06ZIOHz6sb7/9Vps2bdJ7772n6tWrq1KlSpo3b55KlCihrKwslSjBChvA3YBxDBQPs2fP1muvvaZPPvlEjRs31tSpU/Xyyy9rz549+tvf/qbk5GQtW7ZM2dnZKl++vB599FG5urre+lg3cFsOHjxoPPjgg8b27dsNwzCMTz/91ChVqpQRExPj0C8tLc3hcVZW1h2rEcCNMY6Bostmszn8/dhjjxlvvfWWYRiGsXr1asPf399YuHChYRiGkZqamuc2bmesc647n66dRGcYhrKzs5WYmKiwsDB9/PHH6tOnj2bNmqWnnnpKqampWrp0qc6dOycvLy+H193KdV4ABYNxDBQPxjWXC/fv3y9J2r17t4KDg7Vjxw4NHjxYM2fO1NNPPy2r1aopU6boyy+/zLWd2xnrBK18ypmHMX36dP373/+WYRiqWbOmFi5cqAEDBuj111+336lw4MABxcXF6eTJkw7bYDVpwLkYx0DRd23ImjhxogYPHqzMzEw9/vjj+sc//qE2bdpo/vz59rGekpKib7/9Vt99912B1kHQuknX/g949erVev311/Xggw+qdu3acnNz07hx4zRu3Dg99dRTkqQrV65oypQpunz5sh588EFnlQ3gGoxjoPjICVm7du3S3r179dZbb8nd3V1hYWHKyspSo0aN1KpVK0nS2bNnNXDgQF25ckWjRo0q2DoMg8nw+bFmzRqdPn1akvT3v/9d0tVbvVu0aKHk5GT17dtXXl5e2rRpk86ePatvv/2Wu5KAuwzjGCgePvjgA3300UeyWq32uwwl6d1339WSJUv022+/qUKFCsrOzpbFYtGOHTtu+e7C6yFo5cMff/yh+++/X8nJyRo7dqxmz55tPzV55coVRUVF6ZdffpG7u7uqV6+uOXPmcFcScJdhHANFV85Yzvl74sSJWrp0qdzd3bVjxw7de++99r779u3T/v37derUKd1///23f3fhdRC0buDa67tWq1Vubm46cuSI+vTpI5vNptjYWFWpUsWhX0ZGhlxdXe1vEh/OgHMxjoHi58cff1SdOnUkXf2y6Pnz56tTp056/vnnVbFixeu+riDPZOXgHPh12Gw2+4fua6+9pkWLFiklJUUhISFatWqVUlJSNGTIECUmJtrTsyR5eHjYP5ANw+DDGXAixjFQPFw7//Lzzz/XgAEDtHLlSknSmDFj9OSTTyo+Pl5vvPGGTp06JSnvr90y405iglYerp2Hce7cOW3cuFGTJ09WbGysUlNTFRISok2bNun48ePq16+fzp49m+cdSNyVBDgP4xgoHq4d6x999JHWrFmjX3/9Va+99ppWrVol6eqCxBEREdq6davmz5+vX3/99Y6NbYJWHnLesHHjxqlHjx4KDAxU6dKlNWLECH344Yf2D+m4uDidOHFC4eHh+uOPP5xcNYBrMY6B4iFnrI8fP15RUVF64IEHNHr0aKWnp2vevHlavny5pKtfu9O7d2+tWLFCGzZsuHMF3vJSp0XcypUrDV9fX2Pfvn3G5cuXjYyMDOOZZ54x3N3djcWLFxuXLl0yDMMwDhw4YPTq1YsVooG7EOMYKB72799v3HfffcZnn31mb9u5c6fRs2dPo2HDhsbq1avt7cuWLbujY52JB9dx4cIFVa9eXaGhoXJ3d5eLi4sWLFigzMxMRUdHq0SJEurVq5dCQ0O1du1aSeZMogNw6xjHQPFQsmRJpaWlKS0tzd7WqFEjPf/88+rYsaNmzJghq9Wqxx57TIMGDZJ058Z6sb90aPz/yXDGnybF2Ww2/fzzz7JYLHJxcVF6erokadiwYbp06ZKio6O1adMmSVffLMmcSXQA/hrjGCi+jP9/x3BgYKAOHjyorKws+2dBWFiYGjVqJC8vL73//vvatWuX/XV3aqwX+6CVs2hhzqS4rKwsSVK/fv1UpUoVRUREKDMzU56enpIkT09PPfvss+rZs6eeeuopnT17lg9mwMkYx0DxcO3dhTn/ObJYLKpUqZIGDhyoKVOm6IMPPlBGRoYk6fLly/Lz89PAgQN15MgRbdu27Y7XXKyD1vLly1WxYkVNnTpV7733niTZb+P29fXVCy+8oMTERLVr107fffed4uPjNWHCBP3++++aPn26DMPQZ5995sxDAIo9xjFQPFx7d+E777yjJ598UoMHD9aiRYskSc8++6wmTJigYcOGafjw4RozZow6d+6sI0eOaMSIEapXr56++uqrPJd1MFOxDlqnT59WQECAzp8/r9WrV6tu3bpat26dDh8+rBIlSuiRRx7Rq6++quzsbDVt2lSPPfaYLl68qGXLlsnd3V1lypRRUFCQsw8DKNYYx0DxkBOyJkyYoEmTJqls2bLKysrSggULNHr0aEnSjBkz9O6778owDH3//fe6//77tWPHDklSamqqataseeeXbLlj0+7vQgcPHjQGDx5sfPnll4bNZjNGjBhhDBw40KhcubKxYMEC48cff7T3/f77742ff/7ZyM7ONgzDMCZOnGjUqFHD+O2335xVPgCDcQwUJ0uXLjVCQkKM3bt3G4ZhGKtWrTLc3d2NypUrG0OGDLH3u3Lliv3n5ORk48UXXzTKli1rHDx48I7XXKy/gic7O1s9e/aUj4+P/vOf/0iSfvnlF4WGhiooKEjBwcGqUaOGnn76adWtW1deXl7au3evFi9erP/85z/aunWr6tev79yDAIo5xjFQfMyfP19nzpzRzJkztX79eg0ePFgvvfSSLl++rDlz5mjQoEF644037P1///13vfrqq9qwYYPWr1/vnLF+x6PdXSLnf7SHDh0yqlevbk/HdevWNTp16mTs3r3b+PDDD43y5csbERERhs1mMwzDMPbu3Wu89tprTknFABwxjoGiK2d8G4ZhpKam2n8+efKkkZCQYNSrV8+YNWuWYRiG8fPPPxtBQUFGyZIljVdeecXe12azGQcPHjR+/fXXO1f4nxTrM1qSlJSUpOHDh6t69eqKjY2Vv7+/1q5dq3Llytn7XDsBT/q/L6YFcHdgHANFy7XjdeHChZKkrl27qlKlSpKk7du364knnlBcXJyqVq2qH374QTNmzFDv3r0VERHhMNad7e6pxEn8/f3Vu3dvzZgxQ6VKlVJcXJz9wznn1lEXFxf7z5L4cAbuMoxjoGi59mt1pk6dKh8fH7m7u9uf9/X1lSS9//77OnbsmJ5//nm5u7urd+/euca6s7EyvKTu3bsrMjJSgYGB9tvCJcfFzFhjB3A+4/8vTJgXxjFQtLzzzjv64IMP9MUXX6hu3bqSrp6JTk1NVb169RQZGalFixZp0aJFqlChgtavXy+LxSLDMO6qsV6kz2glJSUpISHhL/t5enqqVq1a2rBhgy5fvnwHKgNws7Kzs+0rut/otmzGMVB4NW/eXFu2bHFoO3r0qMLDw1W3bl0dO3ZMS5cuVVhYmHr16qUPPvhAr776qrZu3arly5drx44dcnNzU1ZW1p1fvuEvFNmg9Z///EcRERFq0KCBevToob179+bZL2eK2osvvqiEhASHuxUAOFdsbKwGDhyoZs2aKTo6WomJiXn2YxwDhVdaWpq6d++upk2b2tuys7OVmZmpQ4cOafz48Xr88ce1YcMGPfzwwwoNDdXrr7+uhIQEhYSEqGXLlnJ1dVV2drbD2ey7RZEMWsuWLdNTTz2lTp06ae7cudq3b59iYmIc+uR8MFssFtlsNmVlZendd9/VSy+95IySAfzJsmXLNGTIEAUGBqpdu3ZasmSJJk6cmGdfxjFQOKWlpcnb21vPPfecPDw8NGPGDK1cuVKurq4aN26cHnjgAX399deKjIzU1KlTtWDBArVt21a+vr7y8vJy2NbddLnwWkXursOtW7dqwIABmj17tvr27StJevvtt3XixAk988wzKlu2rLy9vSXlvgspx536Rm8Aefvqq680aNAgTZ06VQMGDJAk/e9//1P37t21fft21alT5y+3wTgG7m5DhgzR4cOHtWHDBpUuXVqZmZkaOXKkFi9erNWrVysiIkIZGRnKzMxUqVKlJEmZmZnq1auX3NzctG7durvuMmFeitQZrezsbP36668aPXq0unbtam9fvXq11q1bp/r166tbt2565ZVXJOm6t3/y4Qw4T3Z2tr7++mvVr19fjzzyiKSr/ym67777VKpUKYcvlb0RxjFwdxsxYoR++eUXDRkyRElJSXJ3d9fs2bM1duxY9e3bV+vWrZOHh4dKlSqllJQULVu2TD179tTJkye1atUq+5nsu12RClqurq7q1auXHn30Ufn4+EiSevbsqaNHj+qtt97Spk2bVLNmTX388cc6ePCgk6sFkBdXV1f17t1bjzzyiP0WbovFosDAQHl7eys1NdXJFQK4XVlZWQoLC9PGjRu1a9cuDR06VBcuXJCvr6+mTp2qUaNGqU+fPoqNjbX337FjhwIDA7Vv3z77xPe7ab2s67n7Zo3dJl9fX/uHc2Zmpnr27Km5c+fqvvvukyT5+flpwYIFOnr0qEJDQ51YKYDrqVq1qqpWrWp/bLFY5OrqqvT0dF28eNHe/uqrr6pLly6qV6+eM8oEcAtsNpt90vrly5c1duxYTZgwQd7e3nrzzTfl7++vadOmSZL69OmjlStXqlevXpozZ458fHxksVju2onveSkcVd4id3d3DRw40KHtypUratKkiT14Abj7GYahrKwseXh4qEyZMpKkDh066NChQ5owYYKTqwOQHzlnoSZMmKCVK1eqX79+6tq1q9avX6+UlBS999578vf318svvywXFxf17t1bW7ZsUatWrSTprlsn668U6aAlOS5wmJGRocmTJ8vPz0+1atVycmUAblbOHYU+Pj72L5E+efKkjh49KldX1+ve2ALg7vTNN9/o3Xff1Zo1a9SmTRv73MyIiAgNHjxYS5YsUenSpTV58mRVrlxZzZo1s7+2MEyAv1aR/2SyWCxKTU3Vxx9/rN69e+uXX37R+vXr5eLiUigm0QG4Om/Lw8ND6enpateunX766Sf98MMPhWqeBoD/k5aWJi8vL/tJDxcXF7Vo0ULLli3Thg0bNH78eJ0/f15+fn76+9//rhIlSigrK8vJVd+aYvHplJqaqlWrVsnb21vffvstH85AIZSWlqY//vhDVatW1YEDB+zjuLDM0wDwf6pVq6bk5GR9+umnkv7vLFWtWrUUHBysxYsXa9asWQ6vKaxjvcito3U9f/zxh/z9/e2T6ArT9V0AVx07dkyVK1e2/++2sH7wAsVFXv/e2mw2ZWdn67nnnlN8fLyee+459e7dW9LVf6ufe+45jRgxQg8++GCR+Le62AStHMzlAAo/QhZwdxsyZIiefPJJPfzww9f9d3ffvn2aN2+edu3apZ49e6pGjRp6//33lZqaqvj4+CJzYqTYBS0AAGCekydPasiQIfrpp5/0ySefqGHDhtcNWwcOHNBnn32m+fPnKygoSKVLl9Ynn3wiNzc3h5vZCjOCFgAAKFD79+/X9OnTtXXrVm3YsEEPPfTQDa8opaen2+8stlgsReqsNUELAADctsjISJUoUUIffPCBJOnHH3/UK6+8om3btt0wbP25raicycrBZCUAAHBbbDabOnbsqI8++kijRo2SJNWpU0eTJk1Sy5Yt1bVrV+3evTvPpZX+HLyKUsiSisGCpQAAwFwuLi4aMGCAvL299cQTT8gwDL311lv2sCVJXbt2vanLiEUNQQsAANyynEt9rq6uioiIkGEYGjx4sCTlClvdu3e3T5AvapcIr6d4xEkAAFDgbDabLBaL/XKgq6urevfuraVLl2rJkiWKioqS5HgZMSwsTIcOHSoWIUvijBYAALgFK1eu1KZNm/T888+rQoUK8vHxkXQ1bPXq1UuScp3ZGj9+vEJCQhQSEuK0uu807joEAAD5kpKSor/97W9KSUlRUFCQwsLC1Lx5cw0aNMjeJyMjQ7GxsRo8eLCefPJJzZ8/32EbRWEx0pvBGS0AAJAvPj4+6tOnjypXrqyHHnpIW7Zs0ZgxY7Rp0ybVqlVLzz33nDw8PNS3b18ZhqF+/fqpcuXKGjdunH0bxSFkSZzRAgAAt+Czzz5T37599dVXX6lu3bpKT0/Xq6++qunTp6t+/fqKjIxU586dVbt2bW3ZskUtWrQoMouQ5gdBCwAA3JKRI0dKkhYsWCBJqlWrlh544AFVq1ZN33//vb744gstXbrUfkmxKK34frOK19ECAIAC87e//U1Lly7VH3/8obZt26p06dJ677335Ovrq99//13/+9//1Lt3b3v/4hayJM5oAQCA2xAWFqY9e/aoRYsWWrdunQICAnL1KY5nsnKwjhYAAMi3nPM0f//731WrVi3Nnj1bAQEByuv8TXENWRJBCwAA3IKcBUdbt26tCxcuKC4uzqEdVxG0AADALatQoYImTpyo119/XQcOHHB2OXed4nsuDwAAFIjOnTtrz549qlGjhrNLueswGR4AANy2nC+JLi4rvt8sghYAAIBJmKMFAABgEoIWAACASQhaAAAAJiFoAQAAmISgBQAAYBKCFgAAgEkIWgAAACYhaAEAAJiEoAUAAGASghYAAIBJ/h9yUrnY7yXnUgAAAABJRU5ErkJggg==\n"
-          },
-          "metadata": {}
-        }
-      ],
-      "id": "fbbb3ed9-dcab-4c20-8a08-fa689b62db5d"
-    },
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots()\n",
+    "raters = df_overall['rater'].unique()\n",
+    "dataset = [df_overall[df_overall['rater'] == r][\"rating\"] for r in raters]\n",
+    "\n",
+    "axes.violinplot(dataset = dataset, showmeans=True, bw_method=0.05)\n",
+    "axes.set_xlabel('rater')\n",
+    "axes.set_ylabel('ratings')\n",
+    "axes.yaxis.grid(True)\n",
+    "axes.set_xticks(range(1,nb_raters+1))\n",
+    "axes.set_xticklabels(raters)\n",
+    "plt.title('Rating distribution per rater')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6168e768",
+   "metadata": {},
+   "source": [
+    "## Ratings Analysis\n",
+    "\n",
+    "We now focus on the ratings themselves.  We first plot the overall rating distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92f38adf",
+   "metadata": {},
+   "source": [
+    "### Overall rating distribution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "078955ba",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "Some suspicious cases we will further investigate later on:",
-      "metadata": {},
-      "id": "7fb17be1-df26-44fc-82d3-88c5255ca5ee"
-    },
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from matplotlib.ticker import MaxNLocator\n",
+    "plt.hist(df_overall['rating'])\n",
+    "plt.title('Overall ratings')\n",
+    "plt.xticks(range(len(ratings)), ratings,\n",
+    "       rotation=60) \n",
+    "None\n",
+    "plt.ylabel('nb_ratings')\n",
+    "plt.gca().yaxis.set_major_locator(MaxNLocator(integer=True))\n",
+    "\n",
+    "plt.grid()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf354a46-d693-47e5-8148-fbef8e8d8b25",
+   "metadata": {},
+   "source": [
+    "### Agreements between raters\n",
+    "In this section we want to check how much raters agree or disagree.\n",
+    "\n",
+    "  - If all raters agreeing on a rating will give us confidence about the rating\n",
+    "  - If all raters disagree, for example, 1 rating `Poor` another one rating `Fair` and the third one rating `Perfect`, then something draws our attention. Either the intent was not clear or raters did not rate as expected. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "cf6869d0-3498-4d44-9bf6-d55e580916d1",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "print('All disagree:')\ndf[df['nb_distinct_ratings']==3]",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 78,
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "All disagree:\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 78,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                 query    docid  charlie@flax.co.uk  rating_0  \\\n0     projector screen   325961                 NaN       3.0   \n1     projector screen    47471                 NaN       3.0   \n2     projector screen   126679                 NaN       3.0   \n3     projector screen   254441                 NaN       3.0   \n4     projector screen   325958                 NaN       3.0   \n...                ...      ...                 ...       ...   \n2415      power supply  1667352                 NaN       0.0   \n2416      power supply  1667804                 NaN       0.0   \n2417      power supply  1667752                 NaN       0.0   \n2418      power supply  1667821                 NaN       0.0   \n2419      power supply  1667357                 NaN       0.0   \n\n      eschramma@cas.org  dtaivpp@gmail.com  rating_1  cmcollier@gmail.com  \\\n0                   NaN                3.0       NaN                  NaN   \n1                   NaN                3.0       NaN                  NaN   \n2                   NaN                3.0       NaN                  NaN   \n3                   NaN                NaN       NaN                  NaN   \n4                   NaN                NaN       NaN                  NaN   \n...                 ...                ...       ...                  ...   \n2415                NaN                NaN       NaN                  NaN   \n2416                NaN                NaN       NaN                  NaN   \n2417                NaN                NaN       NaN                  NaN   \n2418                NaN                NaN       NaN                  NaN   \n2419                NaN                NaN       NaN                  NaN   \n\n      rating_2  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n0          NaN           NaN                               NaN   \n1          NaN           NaN                               NaN   \n2          NaN           NaN                               NaN   \n3          NaN           NaN                               NaN   \n4          NaN           NaN                               NaN   \n...        ...           ...                               ...   \n2415       NaN           NaN                               NaN   \n2416       NaN           NaN                               NaN   \n2417       NaN           NaN                               NaN   \n2418       NaN           NaN                               NaN   \n2419       NaN           NaN                               NaN   \n\n      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n0                  NaN                        NaN                      NaN   \n1                  NaN                        NaN                      NaN   \n2                  NaN                        NaN                      NaN   \n3                  NaN                        NaN                      NaN   \n4                  NaN                        NaN                      NaN   \n...                ...                        ...                      ...   \n2415               NaN                        NaN                      NaN   \n2416               NaN                        NaN                      NaN   \n2417               NaN                        NaN                      NaN   \n2418               NaN                        NaN                      NaN   \n2419               NaN                        NaN                      NaN   \n\n      ryan.finley@ferguson.com                          rater_0  \\\n0                          NaN  epugh@opensourceconnections.com   \n1                          NaN  epugh@opensourceconnections.com   \n2                          NaN  epugh@opensourceconnections.com   \n3                          NaN  epugh@opensourceconnections.com   \n4                          NaN  epugh@opensourceconnections.com   \n...                        ...                              ...   \n2415                       NaN  epugh@opensourceconnections.com   \n2416                       NaN  epugh@opensourceconnections.com   \n2417                       NaN  epugh@opensourceconnections.com   \n2418                       NaN  epugh@opensourceconnections.com   \n2419                       NaN  epugh@opensourceconnections.com   \n\n                               rater_1                rater_2  \\\n0     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n1     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n2     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n3     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n4     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n...                                ...                    ...   \n2415  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n2416  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n2417  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n2418  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n2419  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n\n      nb_distinct_ratings  \n0                       3  \n1                       3  \n2                       3  \n3                       3  \n4                       3  \n...                   ...  \n2415                    3  \n2416                    3  \n2417                    3  \n2418                    3  \n2419                    3  \n\n[2148 rows x 19 columns]",
-            "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>query</th>\n      <th>docid</th>\n      <th>charlie@flax.co.uk</th>\n      <th>rating_0</th>\n      <th>eschramma@cas.org</th>\n      <th>dtaivpp@gmail.com</th>\n      <th>rating_1</th>\n      <th>cmcollier@gmail.com</th>\n      <th>rating_2</th>\n      <th>jeff@vin.com</th>\n      <th>cmarino@enterprise-knowledge.com</th>\n      <th>msfroh@gmail.com</th>\n      <th>peter@searchintuition.com</th>\n      <th>maximilian.werk@jina.ai</th>\n      <th>ryan.finley@ferguson.com</th>\n      <th>rater_0</th>\n      <th>rater_1</th>\n      <th>rater_2</th>\n      <th>nb_distinct_ratings</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>projector screen</td>\n      <td>325961</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>projector screen</td>\n      <td>47471</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>projector screen</td>\n      <td>126679</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>projector screen</td>\n      <td>254441</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>projector screen</td>\n      <td>325958</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2415</th>\n      <td>power supply</td>\n      <td>1667352</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2416</th>\n      <td>power supply</td>\n      <td>1667804</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2417</th>\n      <td>power supply</td>\n      <td>1667752</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2418</th>\n      <td>power supply</td>\n      <td>1667821</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2419</th>\n      <td>power supply</td>\n      <td>1667357</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>2148 rows × 19 columns</p>\n</div>"
-          },
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-        }
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-    },
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vals = []\n",
+    "labs = []\n",
+    "cols = []\n",
+    "for agent,col in (('rating_0','blue'),\n",
+    "                  ('rating_1','red'),\n",
+    "                  ('rating_2', 'green')\n",
+    "                 ):\n",
+    "    vals.append(df[agent])\n",
+    "    labs.append(agent)\n",
+    "    cols.append(col)\n",
+    "plt.hist(vals, color=cols, label=labs)\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.title('Overall ratings')\n",
+    "plt.xticks(range(len(ratings)), ratings, rotation=60) \n",
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "fbbb3ed9-dcab-4c20-8a08-fa689b62db5d",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "Some cases where all raters agree, we can have good confidence in the rating:",
-      "metadata": {},
-      "id": "b4a60ac4-4c2b-4477-b7c9-285fb29f843c"
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Agents agreements')"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "source": "# We have none that everyone agrees on\nprint('All agree:')\ndf[df['nb_distinct_ratings']==1].sample(5)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 79,
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "All agree:\n",
-          "output_type": "stream"
-        },
-        {
-          "ename": "<class 'ValueError'>",
-          "evalue": "a must be greater than 0 unless no samples are taken",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "Cell \u001b[0;32mIn[79], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# We have none that everyone agrees on\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAll agree:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnb_distinct_ratings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/generic.py:5773\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m   5770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5771\u001b[0m     weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5773\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5774\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/sample.py:150\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m    151\u001b[0m     np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    152\u001b[0m )\n",
-            "File \u001b[0;32mmtrand.pyx:928\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mValueError\u001b[0m: a must be greater than 0 unless no samples are taken"
-          ],
-          "output_type": "error"
-        }
-      ],
-      "id": "8b065a8e-c76c-4e8b-ab5e-e1ec25db6a60"
-    },
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def nb_distinct_ratings(r):\n",
+    "    return len({r['rating_0'], r['rating_1'], r['rating_2']})\n",
+    "df['nb_distinct_ratings'] = df.apply(nb_distinct_ratings, axis=1)\n",
+    "df['nb_distinct_ratings'].hist()\n",
+    "plt.xticks([1, 2, 3], ['3 Agree', '2 Agree', 'All disagree'],\n",
+    "       rotation=45) \n",
+    "plt.ylabel('nb cases')\n",
+    "plt.title('Agents agreements')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7fb17be1-df26-44fc-82d3-88c5255ca5ee",
+   "metadata": {},
+   "source": [
+    "Some suspicious cases we will further investigate later on:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "id": "7d9b3876-17b5-42a0-97de-d9007b777aad",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "Not perfect but 2 / 3 raters agreed on the rating value:",
-      "metadata": {},
-      "id": "294c4445-1c81-4c73-96ac-6001f3f83ff2"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All disagree:\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": "# We have none\nprint('Majority agree:')\ndf[df['nb_distinct_ratings']==2]",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 80,
-      "outputs": [
-        {
-          "name": "stdout",
-          "text": "Majority agree:\n",
-          "output_type": "stream"
-        },
-        {
-          "execution_count": 80,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "                 query     docid  charlie@flax.co.uk  rating_0  \\\n6     projector screen    549808                 NaN       3.0   \n19              laptop  77031393                 NaN       3.0   \n20            iphone 8  79283963                 NaN       0.0   \n21            iphone 8  79284190                 NaN       0.0   \n24            iphone 8  77911774                 NaN       0.0   \n...                ...       ...                 ...       ...   \n1330            coffee    656359                 NaN       3.0   \n1331            coffee  77265396                 NaN       2.0   \n1334            coffee   2102472                 NaN       2.0   \n1340              vans  77129498                 NaN       0.0   \n1342              vans  77388459                 NaN       0.0   \n\n      eschramma@cas.org  dtaivpp@gmail.com  rating_1  cmcollier@gmail.com  \\\n6                   NaN                NaN       3.0                  NaN   \n19                  NaN                NaN       NaN                  NaN   \n20                  NaN                NaN       NaN                  NaN   \n21                  NaN                NaN       NaN                  NaN   \n24                  NaN                NaN       0.0                  NaN   \n...                 ...                ...       ...                  ...   \n1330                NaN                NaN       3.0                  NaN   \n1331                NaN                NaN       2.0                  NaN   \n1334                NaN                NaN       NaN                  NaN   \n1340                NaN                NaN       0.0                  NaN   \n1342                NaN                NaN       NaN                  NaN   \n\n      rating_2  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n6          NaN           NaN                               NaN   \n19         3.0           NaN                               NaN   \n20         0.0           NaN                               NaN   \n21         0.0           NaN                               NaN   \n24         NaN           NaN                               NaN   \n...        ...           ...                               ...   \n1330       NaN           NaN                               NaN   \n1331       NaN           NaN                               NaN   \n1334       2.0           NaN                               NaN   \n1340       NaN           NaN                               NaN   \n1342       0.0           NaN                               NaN   \n\n      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n6                  NaN                        NaN                      NaN   \n19                 NaN                        NaN                      NaN   \n20                 NaN                        NaN                      NaN   \n21                 NaN                        NaN                      NaN   \n24                 NaN                        NaN                      NaN   \n...                ...                        ...                      ...   \n1330               NaN                        NaN                      NaN   \n1331               NaN                        NaN                      NaN   \n1334               NaN                        NaN                      NaN   \n1340               NaN                        NaN                      NaN   \n1342               NaN                        NaN                      NaN   \n\n      ryan.finley@ferguson.com                          rater_0  \\\n6                          NaN  epugh@opensourceconnections.com   \n19                         NaN  epugh@opensourceconnections.com   \n20                         NaN  epugh@opensourceconnections.com   \n21                         NaN  epugh@opensourceconnections.com   \n24                         NaN  epugh@opensourceconnections.com   \n...                        ...                              ...   \n1330                       NaN  epugh@opensourceconnections.com   \n1331                       NaN  epugh@opensourceconnections.com   \n1334                       NaN  epugh@opensourceconnections.com   \n1340                       NaN  epugh@opensourceconnections.com   \n1342                       NaN  epugh@opensourceconnections.com   \n\n                               rater_1                rater_2  \\\n6     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n19    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n20    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n21    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n24    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n...                                ...                    ...   \n1330  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n1331  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n1334  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n1340  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n1342  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n\n      nb_distinct_ratings  \n6                       2  \n19                      2  \n20                      2  \n21                      2  \n24                      2  \n...                   ...  \n1330                    2  \n1331                    2  \n1334                    2  \n1340                    2  \n1342                    2  \n\n[272 rows x 19 columns]",
-            "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>query</th>\n      <th>docid</th>\n      <th>charlie@flax.co.uk</th>\n      <th>rating_0</th>\n      <th>eschramma@cas.org</th>\n      <th>dtaivpp@gmail.com</th>\n      <th>rating_1</th>\n      <th>cmcollier@gmail.com</th>\n      <th>rating_2</th>\n      <th>jeff@vin.com</th>\n      <th>cmarino@enterprise-knowledge.com</th>\n      <th>msfroh@gmail.com</th>\n      <th>peter@searchintuition.com</th>\n      <th>maximilian.werk@jina.ai</th>\n      <th>ryan.finley@ferguson.com</th>\n      <th>rater_0</th>\n      <th>rater_1</th>\n      <th>rater_2</th>\n      <th>nb_distinct_ratings</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>6</th>\n      <td>projector screen</td>\n      <td>549808</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>laptop</td>\n      <td>77031393</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>iphone 8</td>\n      <td>79283963</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>iphone 8</td>\n      <td>79284190</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>iphone 8</td>\n      <td>77911774</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1330</th>\n      <td>coffee</td>\n      <td>656359</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1331</th>\n      <td>coffee</td>\n      <td>77265396</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1334</th>\n      <td>coffee</td>\n      <td>2102472</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1340</th>\n      <td>vans</td>\n      <td>77129498</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1342</th>\n      <td>vans</td>\n      <td>77388459</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>0.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>epugh@opensourceconnections.com</td>\n      <td>aarora@opensourceconnections.com</td>\n      <td>ben.w.trent@gmail.com</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>272 rows × 19 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>query</th>\n",
+       "      <th>docid</th>\n",
+       "      <th>charlie@flax.co.uk</th>\n",
+       "      <th>rating_0</th>\n",
+       "      <th>eschramma@cas.org</th>\n",
+       "      <th>dtaivpp@gmail.com</th>\n",
+       "      <th>rating_1</th>\n",
+       "      <th>cmcollier@gmail.com</th>\n",
+       "      <th>rating_2</th>\n",
+       "      <th>jeff@vin.com</th>\n",
+       "      <th>cmarino@enterprise-knowledge.com</th>\n",
+       "      <th>msfroh@gmail.com</th>\n",
+       "      <th>peter@searchintuition.com</th>\n",
+       "      <th>maximilian.werk@jina.ai</th>\n",
+       "      <th>ryan.finley@ferguson.com</th>\n",
+       "      <th>rater_0</th>\n",
+       "      <th>rater_1</th>\n",
+       "      <th>rater_2</th>\n",
+       "      <th>nb_distinct_ratings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>325961</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>47471</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>126679</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>254441</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>325958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2415</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667352</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2416</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667804</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2417</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667752</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2418</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667821</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2419</th>\n",
+       "      <td>power supply</td>\n",
+       "      <td>1667357</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2148 rows × 19 columns</p>\n",
+       "</div>"
       ],
-      "id": "2b300fa0-4876-46c6-9526-91afe73b1d7a"
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Big discrepancies\n\nThos cases are the most suspicious ones. Some raters rated as `3-Perfect` some other rated as '1-Fair' or '0-Bad' (or at least a difference of 2 grades).\nThere can be several reasons for that:\n  - there is an issue with the pair: query not clear, document not clear\n  - guidelines not well specified: a very common case is when the guidelines are not 100% clear to the raters. For example, what happens if the image of the results is relevant but the text is not? Or if some document fields are missing?\n  - no clear intent.  Was the intent of the query clear enough? This can cause confusion to the raters.  It's important to give an opportunity to the rater to say `I don't know how to rate this pair!`\n",
-      "metadata": {},
-      "id": "a2b7880d-4707-4cf0-ad2e-20f5ee1f912e"
-    },
+      "text/plain": [
+       "                 query    docid  charlie@flax.co.uk  rating_0  \\\n",
+       "0     projector screen   325961                 NaN       3.0   \n",
+       "1     projector screen    47471                 NaN       3.0   \n",
+       "2     projector screen   126679                 NaN       3.0   \n",
+       "3     projector screen   254441                 NaN       3.0   \n",
+       "4     projector screen   325958                 NaN       3.0   \n",
+       "...                ...      ...                 ...       ...   \n",
+       "2415      power supply  1667352                 NaN       0.0   \n",
+       "2416      power supply  1667804                 NaN       0.0   \n",
+       "2417      power supply  1667752                 NaN       0.0   \n",
+       "2418      power supply  1667821                 NaN       0.0   \n",
+       "2419      power supply  1667357                 NaN       0.0   \n",
+       "\n",
+       "      eschramma@cas.org  dtaivpp@gmail.com  rating_1  cmcollier@gmail.com  \\\n",
+       "0                   NaN                3.0       NaN                  NaN   \n",
+       "1                   NaN                3.0       NaN                  NaN   \n",
+       "2                   NaN                3.0       NaN                  NaN   \n",
+       "3                   NaN                NaN       NaN                  NaN   \n",
+       "4                   NaN                NaN       NaN                  NaN   \n",
+       "...                 ...                ...       ...                  ...   \n",
+       "2415                NaN                NaN       NaN                  NaN   \n",
+       "2416                NaN                NaN       NaN                  NaN   \n",
+       "2417                NaN                NaN       NaN                  NaN   \n",
+       "2418                NaN                NaN       NaN                  NaN   \n",
+       "2419                NaN                NaN       NaN                  NaN   \n",
+       "\n",
+       "      rating_2  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n",
+       "0          NaN           NaN                               NaN   \n",
+       "1          NaN           NaN                               NaN   \n",
+       "2          NaN           NaN                               NaN   \n",
+       "3          NaN           NaN                               NaN   \n",
+       "4          NaN           NaN                               NaN   \n",
+       "...        ...           ...                               ...   \n",
+       "2415       NaN           NaN                               NaN   \n",
+       "2416       NaN           NaN                               NaN   \n",
+       "2417       NaN           NaN                               NaN   \n",
+       "2418       NaN           NaN                               NaN   \n",
+       "2419       NaN           NaN                               NaN   \n",
+       "\n",
+       "      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n",
+       "0                  NaN                        NaN                      NaN   \n",
+       "1                  NaN                        NaN                      NaN   \n",
+       "2                  NaN                        NaN                      NaN   \n",
+       "3                  NaN                        NaN                      NaN   \n",
+       "4                  NaN                        NaN                      NaN   \n",
+       "...                ...                        ...                      ...   \n",
+       "2415               NaN                        NaN                      NaN   \n",
+       "2416               NaN                        NaN                      NaN   \n",
+       "2417               NaN                        NaN                      NaN   \n",
+       "2418               NaN                        NaN                      NaN   \n",
+       "2419               NaN                        NaN                      NaN   \n",
+       "\n",
+       "      ryan.finley@ferguson.com                          rater_0  \\\n",
+       "0                          NaN  epugh@opensourceconnections.com   \n",
+       "1                          NaN  epugh@opensourceconnections.com   \n",
+       "2                          NaN  epugh@opensourceconnections.com   \n",
+       "3                          NaN  epugh@opensourceconnections.com   \n",
+       "4                          NaN  epugh@opensourceconnections.com   \n",
+       "...                        ...                              ...   \n",
+       "2415                       NaN  epugh@opensourceconnections.com   \n",
+       "2416                       NaN  epugh@opensourceconnections.com   \n",
+       "2417                       NaN  epugh@opensourceconnections.com   \n",
+       "2418                       NaN  epugh@opensourceconnections.com   \n",
+       "2419                       NaN  epugh@opensourceconnections.com   \n",
+       "\n",
+       "                               rater_1                rater_2  \\\n",
+       "0     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "1     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "2     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "3     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "4     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "...                                ...                    ...   \n",
+       "2415  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "2416  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "2417  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "2418  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "2419  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "\n",
+       "      nb_distinct_ratings  \n",
+       "0                       3  \n",
+       "1                       3  \n",
+       "2                       3  \n",
+       "3                       3  \n",
+       "4                       3  \n",
+       "...                   ...  \n",
+       "2415                    3  \n",
+       "2416                    3  \n",
+       "2417                    3  \n",
+       "2418                    3  \n",
+       "2419                    3  \n",
+       "\n",
+       "[2148 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print('All disagree:')\n",
+    "df[df['nb_distinct_ratings']==3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4a60ac4-4c2b-4477-b7c9-285fb29f843c",
+   "metadata": {},
+   "source": [
+    "Some cases where all raters agree, we can have good confidence in the rating:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "8b065a8e-c76c-4e8b-ab5e-e1ec25db6a60",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "def big_discrepancy(r):\n    \"\"\"returns 1 if there is at least one 2 grades between 1 rating and another, 0 otherwise\"\"\"\n    ratings = [r[f'rating_{i}'] for i in range(nb_raters)]\n    return 1 if  max(ratings) - min(ratings) >=2 else 0\ndf['big_discrepancy'] = df.apply(big_discrepancy, axis=1)\ndf[df['big_discrepancy']==1].sample(2)",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 81,
-      "outputs": [
-        {
-          "ename": "<class 'ValueError'>",
-          "evalue": "a must be greater than 0 unless no samples are taken",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "Cell \u001b[0;32mIn[81], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m  \u001b[38;5;28mmax\u001b[39m(ratings) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mmin\u001b[39m(ratings) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m      5\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbig_discrepancy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(big_discrepancy, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbig_discrepancy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/generic.py:5773\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m   5770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5771\u001b[0m     weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5773\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5774\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/sample.py:150\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m    151\u001b[0m     np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    152\u001b[0m )\n",
-            "File \u001b[0;32mmtrand.pyx:928\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
-            "\u001b[0;31mValueError\u001b[0m: a must be greater than 0 unless no samples are taken"
-          ],
-          "output_type": "error"
-        }
-      ],
-      "id": "a784ae64-cb70-43dd-8bca-459a60663987"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "All agree:\n"
+     ]
     },
     {
-      "cell_type": "markdown",
-      "source": "### Overall confusion between raters\nConfusion analysis allows to understand the types of disagreements between raters. \nIn a health rating setup we would expect to have most of the confusions between `0` and `1` or `1` and `2`.\n",
-      "metadata": {},
-      "id": "9df4a62c-df0d-46fd-bfa0-c96c9158542c"
-    },
+     "ename": "<class 'ValueError'>",
+     "evalue": "a must be greater than 0 unless no samples are taken",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[79], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# We have none that everyone agrees on\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAll agree:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 3\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mnb_distinct_ratings\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/generic.py:5773\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m   5770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5771\u001b[0m     weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5773\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5774\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/sample.py:150\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m    151\u001b[0m     np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    152\u001b[0m )\n",
+      "File \u001b[0;32mmtrand.pyx:928\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: a must be greater than 0 unless no samples are taken"
+     ]
+    }
+   ],
+   "source": [
+    "# We have none that everyone agrees on\n",
+    "print('All agree:')\n",
+    "df[df['nb_distinct_ratings']==1].sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "294c4445-1c81-4c73-96ac-6001f3f83ff2",
+   "metadata": {},
+   "source": [
+    "Not perfect but 2 / 3 raters agreed on the rating value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "2b300fa0-4876-46c6-9526-91afe73b1d7a",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "y1 = []\ny2 = []\nfor i,r in df.iterrows():\n    #12\n    y1.append(r['rating_0'])\n    y2.append(r['rating_1'])\n\n    ",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 82,
-      "outputs": [],
-      "id": "6d8a99b9-f823-4829-aed8-9e376a0dfa73"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Majority agree:\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "source": "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\ncm = confusion_matrix(y1, y2, labels=range(len(ratings)))\ndisp = ConfusionMatrixDisplay(confusion_matrix=cm,\n                              display_labels=ratings)\nplt.figure(figsize=(8,8))\ndisp.plot(xticks_rotation=45, colorbar=False, cmap=plt.cm.Blues, ax=plt.gca(), values_format='d')\nplt.xlabel('');plt.ylabel('')\nplt.gca().xaxis.tick_top()",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 83,
-      "outputs": [
-        {
-          "ename": "<class 'ValueError'>",
-          "evalue": "Input y_true contains NaN.",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-            "Cell \u001b[0;32mIn[83], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m confusion_matrix, ConfusionMatrixDisplay\n\u001b[0;32m----> 2\u001b[0m cm \u001b[38;5;241m=\u001b[39m \u001b[43mconfusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43my1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mratings\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix\u001b[38;5;241m=\u001b[39mcm,\n\u001b[1;32m      4\u001b[0m                               display_labels\u001b[38;5;241m=\u001b[39mratings)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/metrics/_classification.py:317\u001b[0m, in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconfusion_matrix\u001b[39m(\n\u001b[1;32m    233\u001b[0m     y_true, y_pred, \u001b[38;5;241m*\u001b[39m, labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    234\u001b[0m ):\n\u001b[1;32m    235\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute confusion matrix to evaluate the accuracy of a classification.\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \n\u001b[1;32m    237\u001b[0m \u001b[38;5;124;03m    By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[38;5;124;03m    (0, 2, 1, 1)\u001b[39;00m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 317\u001b[0m     y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    319\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/metrics/_classification.py:87\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \n\u001b[1;32m     62\u001b[0m \u001b[38;5;124;03mThis converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;124;03my_pred : array or indicator matrix\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     86\u001b[0m check_consistent_length(y_true, y_pred)\n\u001b[0;32m---> 87\u001b[0m type_true \u001b[38;5;241m=\u001b[39m \u001b[43mtype_of_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my_true\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m type_pred \u001b[38;5;241m=\u001b[39m type_of_target(y_pred, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_pred\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     90\u001b[0m y_type \u001b[38;5;241m=\u001b[39m {type_true, type_pred}\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/utils/multiclass.py:381\u001b[0m, in \u001b[0;36mtype_of_target\u001b[0;34m(y, input_name)\u001b[0m\n\u001b[1;32m    379\u001b[0m     data \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mif\u001b[39;00m issparse(y) \u001b[38;5;28;01melse\u001b[39;00m y\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39many(data \u001b[38;5;241m!=\u001b[39m data\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)):\n\u001b[0;32m--> 381\u001b[0m         \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    382\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuous\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m suffix\n\u001b[1;32m    384\u001b[0m \u001b[38;5;66;03m# Check multiclass\u001b[39;00m\n",
-            "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    146\u001b[0m     \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    147\u001b[0m     msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    149\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m     )\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n",
-            "\u001b[0;31mValueError\u001b[0m: Input y_true contains NaN."
-          ],
-          "output_type": "error"
-        }
+     "data": {
+      "text/html": [
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>query</th>\n",
+       "      <th>docid</th>\n",
+       "      <th>charlie@flax.co.uk</th>\n",
+       "      <th>rating_0</th>\n",
+       "      <th>eschramma@cas.org</th>\n",
+       "      <th>dtaivpp@gmail.com</th>\n",
+       "      <th>rating_1</th>\n",
+       "      <th>cmcollier@gmail.com</th>\n",
+       "      <th>rating_2</th>\n",
+       "      <th>jeff@vin.com</th>\n",
+       "      <th>cmarino@enterprise-knowledge.com</th>\n",
+       "      <th>msfroh@gmail.com</th>\n",
+       "      <th>peter@searchintuition.com</th>\n",
+       "      <th>maximilian.werk@jina.ai</th>\n",
+       "      <th>ryan.finley@ferguson.com</th>\n",
+       "      <th>rater_0</th>\n",
+       "      <th>rater_1</th>\n",
+       "      <th>rater_2</th>\n",
+       "      <th>nb_distinct_ratings</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>549808</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>laptop</td>\n",
+       "      <td>77031393</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>iphone 8</td>\n",
+       "      <td>79283963</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>iphone 8</td>\n",
+       "      <td>79284190</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>iphone 8</td>\n",
+       "      <td>77911774</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1330</th>\n",
+       "      <td>coffee</td>\n",
+       "      <td>656359</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1331</th>\n",
+       "      <td>coffee</td>\n",
+       "      <td>77265396</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1334</th>\n",
+       "      <td>coffee</td>\n",
+       "      <td>2102472</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1340</th>\n",
+       "      <td>vans</td>\n",
+       "      <td>77129498</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1342</th>\n",
+       "      <td>vans</td>\n",
+       "      <td>77388459</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>epugh@opensourceconnections.com</td>\n",
+       "      <td>aarora@opensourceconnections.com</td>\n",
+       "      <td>ben.w.trent@gmail.com</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>272 rows × 19 columns</p>\n",
+       "</div>"
       ],
-      "id": "1a4f7484-185c-42a3-8931-32a28a6d6964"
-    },
+      "text/plain": [
+       "                 query     docid  charlie@flax.co.uk  rating_0  \\\n",
+       "6     projector screen    549808                 NaN       3.0   \n",
+       "19              laptop  77031393                 NaN       3.0   \n",
+       "20            iphone 8  79283963                 NaN       0.0   \n",
+       "21            iphone 8  79284190                 NaN       0.0   \n",
+       "24            iphone 8  77911774                 NaN       0.0   \n",
+       "...                ...       ...                 ...       ...   \n",
+       "1330            coffee    656359                 NaN       3.0   \n",
+       "1331            coffee  77265396                 NaN       2.0   \n",
+       "1334            coffee   2102472                 NaN       2.0   \n",
+       "1340              vans  77129498                 NaN       0.0   \n",
+       "1342              vans  77388459                 NaN       0.0   \n",
+       "\n",
+       "      eschramma@cas.org  dtaivpp@gmail.com  rating_1  cmcollier@gmail.com  \\\n",
+       "6                   NaN                NaN       3.0                  NaN   \n",
+       "19                  NaN                NaN       NaN                  NaN   \n",
+       "20                  NaN                NaN       NaN                  NaN   \n",
+       "21                  NaN                NaN       NaN                  NaN   \n",
+       "24                  NaN                NaN       0.0                  NaN   \n",
+       "...                 ...                ...       ...                  ...   \n",
+       "1330                NaN                NaN       3.0                  NaN   \n",
+       "1331                NaN                NaN       2.0                  NaN   \n",
+       "1334                NaN                NaN       NaN                  NaN   \n",
+       "1340                NaN                NaN       0.0                  NaN   \n",
+       "1342                NaN                NaN       NaN                  NaN   \n",
+       "\n",
+       "      rating_2  jeff@vin.com  cmarino@enterprise-knowledge.com  \\\n",
+       "6          NaN           NaN                               NaN   \n",
+       "19         3.0           NaN                               NaN   \n",
+       "20         0.0           NaN                               NaN   \n",
+       "21         0.0           NaN                               NaN   \n",
+       "24         NaN           NaN                               NaN   \n",
+       "...        ...           ...                               ...   \n",
+       "1330       NaN           NaN                               NaN   \n",
+       "1331       NaN           NaN                               NaN   \n",
+       "1334       2.0           NaN                               NaN   \n",
+       "1340       NaN           NaN                               NaN   \n",
+       "1342       0.0           NaN                               NaN   \n",
+       "\n",
+       "      msfroh@gmail.com  peter@searchintuition.com  maximilian.werk@jina.ai  \\\n",
+       "6                  NaN                        NaN                      NaN   \n",
+       "19                 NaN                        NaN                      NaN   \n",
+       "20                 NaN                        NaN                      NaN   \n",
+       "21                 NaN                        NaN                      NaN   \n",
+       "24                 NaN                        NaN                      NaN   \n",
+       "...                ...                        ...                      ...   \n",
+       "1330               NaN                        NaN                      NaN   \n",
+       "1331               NaN                        NaN                      NaN   \n",
+       "1334               NaN                        NaN                      NaN   \n",
+       "1340               NaN                        NaN                      NaN   \n",
+       "1342               NaN                        NaN                      NaN   \n",
+       "\n",
+       "      ryan.finley@ferguson.com                          rater_0  \\\n",
+       "6                          NaN  epugh@opensourceconnections.com   \n",
+       "19                         NaN  epugh@opensourceconnections.com   \n",
+       "20                         NaN  epugh@opensourceconnections.com   \n",
+       "21                         NaN  epugh@opensourceconnections.com   \n",
+       "24                         NaN  epugh@opensourceconnections.com   \n",
+       "...                        ...                              ...   \n",
+       "1330                       NaN  epugh@opensourceconnections.com   \n",
+       "1331                       NaN  epugh@opensourceconnections.com   \n",
+       "1334                       NaN  epugh@opensourceconnections.com   \n",
+       "1340                       NaN  epugh@opensourceconnections.com   \n",
+       "1342                       NaN  epugh@opensourceconnections.com   \n",
+       "\n",
+       "                               rater_1                rater_2  \\\n",
+       "6     aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "19    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "20    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "21    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "24    aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "...                                ...                    ...   \n",
+       "1330  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "1331  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "1334  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "1340  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "1342  aarora@opensourceconnections.com  ben.w.trent@gmail.com   \n",
+       "\n",
+       "      nb_distinct_ratings  \n",
+       "6                       2  \n",
+       "19                      2  \n",
+       "20                      2  \n",
+       "21                      2  \n",
+       "24                      2  \n",
+       "...                   ...  \n",
+       "1330                    2  \n",
+       "1331                    2  \n",
+       "1334                    2  \n",
+       "1340                    2  \n",
+       "1342                    2  \n",
+       "\n",
+       "[272 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# We have none\n",
+    "print('Majority agree:')\n",
+    "df[df['nb_distinct_ratings']==2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a2b7880d-4707-4cf0-ad2e-20f5ee1f912e",
+   "metadata": {},
+   "source": [
+    "### Big discrepancies\n",
+    "\n",
+    "Thos cases are the most suspicious ones. Some raters rated as `3-Perfect` some other rated as '1-Fair' or '0-Bad' (or at least a difference of 2 grades).\n",
+    "There can be several reasons for that:\n",
+    "  - there is an issue with the pair: query not clear, document not clear\n",
+    "  - guidelines not well specified: a very common case is when the guidelines are not 100% clear to the raters. For example, what happens if the image of the results is relevant but the text is not? Or if some document fields are missing?\n",
+    "  - no clear intent.  Was the intent of the query clear enough? This can cause confusion to the raters.  It's important to give an opportunity to the rater to say `I don't know how to rate this pair!`\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "a784ae64-cb70-43dd-8bca-459a60663987",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "source": "This work has been provided by Wallapop Search team (http://www.wallapop.com/).\n\n_This notebook was last updated 17-FEB-2024_",
-      "metadata": {},
-      "id": "7c08fac4-ea2e-4aae-92e8-cde6739dc131"
-    },
+     "ename": "<class 'ValueError'>",
+     "evalue": "a must be greater than 0 unless no samples are taken",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[81], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m  \u001b[38;5;28mmax\u001b[39m(ratings) \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mmin\u001b[39m(ratings) \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m      5\u001b[0m df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbig_discrepancy\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mapply(big_discrepancy, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbig_discrepancy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/generic.py:5773\u001b[0m, in \u001b[0;36mNDFrame.sample\u001b[0;34m(self, n, frac, replace, weights, random_state, axis, ignore_index)\u001b[0m\n\u001b[1;32m   5770\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m weights \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5771\u001b[0m     weights \u001b[38;5;241m=\u001b[39m sample\u001b[38;5;241m.\u001b[39mpreprocess_weights(\u001b[38;5;28mself\u001b[39m, weights, axis)\n\u001b[0;32m-> 5773\u001b[0m sampled_indices \u001b[38;5;241m=\u001b[39m \u001b[43msample\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5774\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtake(sampled_indices, axis\u001b[38;5;241m=\u001b[39maxis)\n\u001b[1;32m   5776\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ignore_index:\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/pandas/core/sample.py:150\u001b[0m, in \u001b[0;36msample\u001b[0;34m(obj_len, size, replace, weights, random_state)\u001b[0m\n\u001b[1;32m    147\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInvalid weights: weights sum to zero\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mrandom_state\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchoice\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj_len\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreplace\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mastype(\n\u001b[1;32m    151\u001b[0m     np\u001b[38;5;241m.\u001b[39mintp, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    152\u001b[0m )\n",
+      "File \u001b[0;32mmtrand.pyx:928\u001b[0m, in \u001b[0;36mnumpy.random.mtrand.RandomState.choice\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: a must be greater than 0 unless no samples are taken"
+     ]
+    }
+   ],
+   "source": [
+    "def big_discrepancy(r):\n",
+    "    \"\"\"returns 1 if there is at least one 2 grades between 1 rating and another, 0 otherwise\"\"\"\n",
+    "    ratings = [r[f'rating_{i}'] for i in range(nb_raters)]\n",
+    "    return 1 if  max(ratings) - min(ratings) >=2 else 0\n",
+    "df['big_discrepancy'] = df.apply(big_discrepancy, axis=1)\n",
+    "df[df['big_discrepancy']==1].sample(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9df4a62c-df0d-46fd-bfa0-c96c9158542c",
+   "metadata": {},
+   "source": [
+    "### Overall confusion between raters\n",
+    "Confusion analysis allows to understand the types of disagreements between raters. \n",
+    "In a health rating setup we would expect to have most of the confusions between `0` and `1` or `1` and `2`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "6d8a99b9-f823-4829-aed8-9e376a0dfa73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y1 = []\n",
+    "y2 = []\n",
+    "for i,r in df.iterrows():\n",
+    "    #12\n",
+    "    y1.append(r['rating_0'])\n",
+    "    y2.append(r['rating_1'])\n",
+    "\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "1a4f7484-185c-42a3-8931-32a28a6d6964",
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "",
-      "metadata": {},
-      "execution_count": null,
-      "outputs": [],
-      "id": "349421e4-dab9-42ef-afba-c2d9df1a9929"
+     "ename": "<class 'ValueError'>",
+     "evalue": "Input y_true contains NaN.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[83], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m confusion_matrix, ConfusionMatrixDisplay\n\u001b[0;32m----> 2\u001b[0m cm \u001b[38;5;241m=\u001b[39m \u001b[43mconfusion_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43my1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my2\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mratings\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m disp \u001b[38;5;241m=\u001b[39m ConfusionMatrixDisplay(confusion_matrix\u001b[38;5;241m=\u001b[39mcm,\n\u001b[1;32m      4\u001b[0m                               display_labels\u001b[38;5;241m=\u001b[39mratings)\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m8\u001b[39m,\u001b[38;5;241m8\u001b[39m))\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/metrics/_classification.py:317\u001b[0m, in \u001b[0;36mconfusion_matrix\u001b[0;34m(y_true, y_pred, labels, sample_weight, normalize)\u001b[0m\n\u001b[1;32m    232\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconfusion_matrix\u001b[39m(\n\u001b[1;32m    233\u001b[0m     y_true, y_pred, \u001b[38;5;241m*\u001b[39m, labels\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, sample_weight\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, normalize\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m    234\u001b[0m ):\n\u001b[1;32m    235\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Compute confusion matrix to evaluate the accuracy of a classification.\u001b[39;00m\n\u001b[1;32m    236\u001b[0m \n\u001b[1;32m    237\u001b[0m \u001b[38;5;124;03m    By definition a confusion matrix :math:`C` is such that :math:`C_{i, j}`\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    315\u001b[0m \u001b[38;5;124;03m    (0, 2, 1, 1)\u001b[39;00m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 317\u001b[0m     y_type, y_true, y_pred \u001b[38;5;241m=\u001b[39m \u001b[43m_check_targets\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_pred\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y_type \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m (\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbinary\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmulticlass\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m    319\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m is not supported\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m y_type)\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/metrics/_classification.py:87\u001b[0m, in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     60\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Check that y_true and y_pred belong to the same classification task.\u001b[39;00m\n\u001b[1;32m     61\u001b[0m \n\u001b[1;32m     62\u001b[0m \u001b[38;5;124;03mThis converts multiclass or binary types to a common shape, and raises a\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[38;5;124;03my_pred : array or indicator matrix\u001b[39;00m\n\u001b[1;32m     85\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     86\u001b[0m check_consistent_length(y_true, y_pred)\n\u001b[0;32m---> 87\u001b[0m type_true \u001b[38;5;241m=\u001b[39m \u001b[43mtype_of_target\u001b[49m\u001b[43m(\u001b[49m\u001b[43my_true\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43my_true\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     88\u001b[0m type_pred \u001b[38;5;241m=\u001b[39m type_of_target(y_pred, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my_pred\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     90\u001b[0m y_type \u001b[38;5;241m=\u001b[39m {type_true, type_pred}\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/utils/multiclass.py:381\u001b[0m, in \u001b[0;36mtype_of_target\u001b[0;34m(y, input_name)\u001b[0m\n\u001b[1;32m    379\u001b[0m     data \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;28;01mif\u001b[39;00m issparse(y) \u001b[38;5;28;01melse\u001b[39;00m y\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m xp\u001b[38;5;241m.\u001b[39many(data \u001b[38;5;241m!=\u001b[39m data\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)):\n\u001b[0;32m--> 381\u001b[0m         \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    382\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontinuous\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m suffix\n\u001b[1;32m    384\u001b[0m \u001b[38;5;66;03m# Check multiclass\u001b[39;00m\n",
+      "File \u001b[0;32m/lib/python3.11/site-packages/sklearn/utils/validation.py:161\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m    144\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m    145\u001b[0m     \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m    146\u001b[0m     \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m    147\u001b[0m     msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    148\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    149\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    159\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    160\u001b[0m     )\n\u001b[0;32m--> 161\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n",
+      "\u001b[0;31mValueError\u001b[0m: Input y_true contains NaN."
+     ]
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
+    "cm = confusion_matrix(y1, y2, labels=range(len(ratings)))\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
+    "                              display_labels=ratings)\n",
+    "plt.figure(figsize=(8,8))\n",
+    "disp.plot(xticks_rotation=45, colorbar=False, cmap=plt.cm.Blues, ax=plt.gca(), values_format='d')\n",
+    "plt.xlabel('');plt.ylabel('')\n",
+    "plt.gca().xaxis.tick_top()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c08fac4-ea2e-4aae-92e8-cde6739dc131",
+   "metadata": {},
+   "source": [
+    "This work has been provided by Wallapop Search team (http://www.wallapop.com/).\n",
+    "\n",
+    "_This notebook was last updated 16_January_2025_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "349421e4-dab9-42ef-afba-c2d9df1a9929",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/jupyterlite/files/examples/Scoring Comparison.ipynb b/jupyterlite/files/examples/Scoring Comparison.ipynb
index c5c5df6..b7e779a 100644
--- a/jupyterlite/files/examples/Scoring Comparison.ipynb	
+++ b/jupyterlite/files/examples/Scoring Comparison.ipynb	
@@ -1,131 +1,303 @@
 {
-  "metadata": {
-    "language_info": {
-      "codemirror_mode": {
-        "name": "python",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.8"
-    },
-    "kernelspec": {
-      "name": "python",
-      "display_name": "Python (Pyodide)",
-      "language": "python"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Scoring Comparison\n",
+    "To understand the magnatude of changes, you can compare multiple snapshots of a case against each other.  The final visualization shows you a histogram of your snapshots overlaid with each other so you can see how your individual query scorers changed.\n",
+    "This visualization assumes you are using the same scorer and query set for all the snapshots.   It also assumes the snapshots come from the same case!\n",
+    "\n",
+    "Please copy this example and customize it for your own purposes!"
+   ]
   },
-  "nbformat_minor": 4,
-  "nbformat": 4,
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "source": "# Scoring Comparison\nTo understand the magnatude of changes, you can compare multiple snapshots of a case against each other.  The final visualization shows you a histogram of your snapshots overlaid with each other so you can see how your individual query scorers changed.\nThis visualization assumes you are using the same scorer and query set for all the snapshots.   It also assumes the snapshots come from the same case!\n\nPlease copy this example and customize it for your own purposes!",
-      "metadata": {}
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Imports",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": "from js import fetch\nimport pandas as pd\nfrom datetime import datetime\nimport random\nfrom matplotlib import pyplot\n%matplotlib inline",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 1,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Define the Data You Want",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": "CASE_ID = 6789   # Your Case\nSNAPSHOT_IDS = [2471,2473]   # Your Snapshots.  Use the Compare Snapshot function in Quepid to see what the specific ID's are of your snapshots.",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 2,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Pull data directly from Quepid's snapshot repository",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": "\n# Retrieve from Quepid API all the snapshots\nsnapshots = []\nfor snapshot_id in SNAPSHOT_IDS:\n    res = await fetch(f'/api/cases/{CASE_ID}/snapshots/{snapshot_id}.json')\n    snapshots.append(await res.json())",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 3,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "source": "### Read in data to a dataframe",
-      "metadata": {}
-    },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from js import fetch\n",
+    "import pandas as pd\n",
+    "from datetime import datetime\n",
+    "import random\n",
+    "from matplotlib import pyplot\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define the Data You Want"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "CASE_ID = 6789   # Your Case\n",
+    "SNAPSHOT_IDS = [2471,2473]   # Your Snapshots.  Use the Compare Snapshot function in Quepid to see what the specific ID's are of your snapshots."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pull data directly from Quepid's snapshot repository"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Retrieve from Quepid API all the snapshots\n",
+    "snapshots = []\n",
+    "for snapshot_id in SNAPSHOT_IDS:\n",
+    "    res = await fetch(f'/api/cases/{CASE_ID}/snapshots/{snapshot_id}.json')\n",
+    "    snapshots.append(await res.json())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Read in data to a dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "queryData = []\nsnapshotNames = {}\n\nfor snapshot in snapshots:\n    queryToScoreDict = {}\n    queryToNumFoundDict = {}\n    snapshotNames[snapshot.id] = snapshot.name\n    for snapshotScore in snapshot.scores:\n        queryToScoreDict[snapshotScore.query_id] = snapshotScore.score\n        queryToNumFoundDict[snapshotScore.query_id] = snapshotScore.number_of_results\n    for snapshotQuery in snapshot.queries:\n        queryData.append({'snapshot_id':snapshot.id, 'query':snapshotQuery.query_text, 'numfound':queryToNumFoundDict[snapshotQuery.query_id], 'score':queryToScoreDict[snapshotQuery.query_id]})\n    \ndf = pd.DataFrame(queryData)\ndf",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 4,
-      "outputs": [
-        {
-          "execution_count": 4,
-          "output_type": "execute_result",
-          "data": {
-            "text/plain": "     snapshot_id               query  numfound  score\n0           2471    projector screen         1      1\n1           2471            notebook         1      1\n2           2471            iphone 8         1      1\n3           2471             printer         1      1\n4           2471            computer         1      1\n..           ...                 ...       ...    ...\n265         2473          windows 10         1      1\n266         2473           microwave         1      1\n267         2473  bluetooth speakers         1      1\n268         2473              coffee         1      1\n269         2473                vans         1      1\n\n[270 rows x 4 columns]",
-            "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>snapshot_id</th>\n      <th>query</th>\n      <th>numfound</th>\n      <th>score</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2471</td>\n      <td>projector screen</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2471</td>\n      <td>notebook</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2471</td>\n      <td>iphone 8</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2471</td>\n      <td>printer</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2471</td>\n      <td>computer</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>265</th>\n      <td>2473</td>\n      <td>windows 10</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>266</th>\n      <td>2473</td>\n      <td>microwave</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>267</th>\n      <td>2473</td>\n      <td>bluetooth speakers</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>268</th>\n      <td>2473</td>\n      <td>coffee</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>269</th>\n      <td>2473</td>\n      <td>vans</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n<p>270 rows × 4 columns</p>\n</div>"
-          },
-          "metadata": {}
-        }
+     "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>snapshot_id</th>\n",
+       "      <th>query</th>\n",
+       "      <th>numfound</th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2471</td>\n",
+       "      <td>projector screen</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2471</td>\n",
+       "      <td>notebook</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2471</td>\n",
+       "      <td>iphone 8</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2471</td>\n",
+       "      <td>printer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2471</td>\n",
+       "      <td>computer</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>2473</td>\n",
+       "      <td>windows 10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>2473</td>\n",
+       "      <td>microwave</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>2473</td>\n",
+       "      <td>bluetooth speakers</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>268</th>\n",
+       "      <td>2473</td>\n",
+       "      <td>coffee</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>269</th>\n",
+       "      <td>2473</td>\n",
+       "      <td>vans</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>270 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     snapshot_id               query  numfound  score\n",
+       "0           2471    projector screen         1      1\n",
+       "1           2471            notebook         1      1\n",
+       "2           2471            iphone 8         1      1\n",
+       "3           2471             printer         1      1\n",
+       "4           2471            computer         1      1\n",
+       "..           ...                 ...       ...    ...\n",
+       "265         2473          windows 10         1      1\n",
+       "266         2473           microwave         1      1\n",
+       "267         2473  bluetooth speakers         1      1\n",
+       "268         2473              coffee         1      1\n",
+       "269         2473                vans         1      1\n",
+       "\n",
+       "[270 rows x 4 columns]"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": "## Create a histogram to compare snapshots\n\nThe snapshots that are represented in fill color.",
-      "metadata": {}
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "queryData = []\n",
+    "snapshotNames = {}\n",
+    "\n",
+    "for snapshot in snapshots:\n",
+    "    queryToScoreDict = {}\n",
+    "    queryToNumFoundDict = {}\n",
+    "    snapshotNames[snapshot.id] = snapshot.name\n",
+    "    for snapshotScore in snapshot.scores:\n",
+    "        queryToScoreDict[snapshotScore.query_id] = snapshotScore.score\n",
+    "        queryToNumFoundDict[snapshotScore.query_id] = snapshotScore.number_of_results\n",
+    "    for snapshotQuery in snapshot.queries:\n",
+    "        queryData.append({'snapshot_id':snapshot.id, 'query':snapshotQuery.query_text, 'numfound':queryToNumFoundDict[snapshotQuery.query_id], 'score':queryToScoreDict[snapshotQuery.query_id]})\n",
+    "    \n",
+    "df = pd.DataFrame(queryData)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a histogram to compare snapshots\n",
+    "\n",
+    "The snapshots that are represented in fill color."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "source": "for snapshot_id in SNAPSHOT_IDS:\n    pyplot.hist(df.loc[df['snapshot_id'] == snapshot_id]['score'], 20, alpha=0.5, label=f'{snapshotNames[snapshot_id]} ({snapshot_id})')\npyplot.legend(loc='upper right')\npyplot.show()",
-      "metadata": {
-        "trusted": true
-      },
-      "execution_count": 5,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/plain": "<Figure size 640x480 with 1 Axes>",
-            "image/png": 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\n"
-          },
-          "metadata": {}
-        }
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
-    {
-      "cell_type": "markdown",
-      "source": "_This notebook was last updated 16-FEB-2024_",
-      "metadata": {}
-    },
-    {
-      "cell_type": "code",
-      "source": "",
-      "metadata": {},
-      "execution_count": null,
-      "outputs": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ]
-}
\ No newline at end of file
+   ],
+   "source": [
+    "for snapshot_id in SNAPSHOT_IDS:\n",
+    "    pyplot.hist(df.loc[df['snapshot_id'] == snapshot_id]['score'], 20, alpha=0.5, label=f'{snapshotNames[snapshot_id]} ({snapshot_id})')\n",
+    "pyplot.legend(loc='upper right')\n",
+    "pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "_This notebook was last updated 16-FEB-2024_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}