meta_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "461a6522",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e8bff194",
   "metadata": {},
   "outputs": [],
   "source": [
    "comps = pd.read_csv('meta_data/Competitions.csv')\n",
    "tags = pd.read_csv('meta_data/CompetitionTags.csv')\n",
    "teams = pd.read_csv('meta_data/Teams.csv',low_memory=False)\n",
    "subs = pd.read_csv('meta_data/Submissions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e391c8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5576, 42)\n",
      "(793, 3)\n",
      "(6180319, 14)\n",
      "(12130309, 11)\n"
     ]
    }
   ],
   "source": [
    "print(comps.shape)\n",
    "print(tags.shape)\n",
    "print(teams.shape)\n",
    "print(subs.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6ef201ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "comps.DeadlineDate = pd.to_datetime(comps.DeadlineDate)\n",
    "comps['year'] = comps.DeadlineDate.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "abe33cda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2010       9\n",
       "2011      18\n",
       "2012      54\n",
       "2013      56\n",
       "2014      61\n",
       "2015      78\n",
       "2016     122\n",
       "2017     279\n",
       "2018     584\n",
       "2019     965\n",
       "2020    1385\n",
       "2021    1320\n",
       "2022     621\n",
       "2023      24\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps.groupby('year').count().Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25d61505",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Slug</th>\n",
       "      <th>Title</th>\n",
       "      <th>Subtitle</th>\n",
       "      <th>HostSegmentTitle</th>\n",
       "      <th>ForumId</th>\n",
       "      <th>OrganizationId</th>\n",
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       "      <th>DeadlineDate</th>\n",
       "      <th>ProhibitNewEntrantsDeadlineDate</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>TotalCompetitors</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>ValidationSetName</th>\n",
       "      <th>ValidationSetValue</th>\n",
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       "      <th>year</th>\n",
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       "    <tr>\n",
       "      <th>4001</th>\n",
       "      <td>26680</td>\n",
       "      <td>siim-covid19-detection</td>\n",
       "      <td>SIIM-FISABIO-RSNA COVID-19 Detection</td>\n",
       "      <td>Identify and localize COVID-19 abnormalities o...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1366504</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <td>1</td>\n",
       "      <td>2021</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 43 columns</p>\n",
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      ],
      "text/plain": [
       "         Id                    Slug                                 Title  \\\n",
       "4001  26680  siim-covid19-detection  SIIM-FISABIO-RSNA COVID-19 Detection   \n",
       "\n",
       "                                               Subtitle HostSegmentTitle  \\\n",
       "4001  Identify and localize COVID-19 abnormalities o...         Featured   \n",
       "\n",
       "      ForumId  OrganizationId          EnabledDate        DeadlineDate  \\\n",
       "4001  1366504          2734.0  05/18/2021 00:03:39 2021-08-09 23:59:00   \n",
       "\n",
       "     ProhibitNewEntrantsDeadlineDate  ... TotalTeams TotalCompetitors  \\\n",
       "4001             08/02/2021 23:59:00  ...       1305             1786   \n",
       "\n",
       "     TotalSubmissions  ValidationSetName  ValidationSetValue  \\\n",
       "4001            32307                NaN                 NaN   \n",
       "\n",
       "      EnableSubmissionModelHashes  EnableSubmissionModelAttachments  HostName  \\\n",
       "4001                        False                             False       NaN   \n",
       "\n",
       "      CompetitionTypeId  year  \n",
       "4001                  1  2021  \n",
       "\n",
       "[1 rows x 43 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps[comps.Title=='SIIM-FISABIO-RSNA COVID-19 Detection'] #Kaggle comp we are looking at, used to verify stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f899ee2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14221664275466284"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_df = pd.merge(comps, tags, left_on='Id', right_on='CompetitionId', how='inner', validate='m:m') #m:m a comp can have many tags, and a tag can belong to many comps\n",
    "comps.shape,j_df.shape #There are a considerable number of untagged competitions...\n",
    "793/5576"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee8fc466",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2013      3\n",
       "2014      3\n",
       "2015     80\n",
       "2016     78\n",
       "2017     85\n",
       "2018     60\n",
       "2019    104\n",
       "2020     98\n",
       "2021    120\n",
       "2022    111\n",
       "2023     51\n",
       "Name: Title, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_df.groupby('year').count().Title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8134d10a",
   "metadata": {},
   "outputs": [
    {
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       "      <td>SIIM-FISABIO-RSNA COVID-19 Detection</td>\n",
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       "      <td>siim-covid19-detection</td>\n",
       "      <td>SIIM-FISABIO-RSNA COVID-19 Detection</td>\n",
       "      <td>Identify and localize COVID-19 abnormalities o...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1366504</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>05/18/2021 00:03:39</td>\n",
       "      <td>2021-08-09 23:59:00</td>\n",
       "      <td>08/02/2021 23:59:00</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>830</td>\n",
       "      <td>26680</td>\n",
       "      <td>16636</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id_x                    Slug                                 Title  \\\n",
       "595  26680  siim-covid19-detection  SIIM-FISABIO-RSNA COVID-19 Detection   \n",
       "596  26680  siim-covid19-detection  SIIM-FISABIO-RSNA COVID-19 Detection   \n",
       "\n",
       "                                              Subtitle HostSegmentTitle  \\\n",
       "595  Identify and localize COVID-19 abnormalities o...         Featured   \n",
       "596  Identify and localize COVID-19 abnormalities o...         Featured   \n",
       "\n",
       "     ForumId  OrganizationId          EnabledDate        DeadlineDate  \\\n",
       "595  1366504          2734.0  05/18/2021 00:03:39 2021-08-09 23:59:00   \n",
       "596  1366504          2734.0  05/18/2021 00:03:39 2021-08-09 23:59:00   \n",
       "\n",
       "    ProhibitNewEntrantsDeadlineDate  ... ValidationSetName ValidationSetValue  \\\n",
       "595             08/02/2021 23:59:00  ...               NaN                NaN   \n",
       "596             08/02/2021 23:59:00  ...               NaN                NaN   \n",
       "\n",
       "    EnableSubmissionModelHashes  EnableSubmissionModelAttachments  HostName  \\\n",
       "595                       False                             False       NaN   \n",
       "596                       False                             False       NaN   \n",
       "\n",
       "     CompetitionTypeId  year  Id_y  CompetitionId  TagId  \n",
       "595                  1  2021   829          26680  14102  \n",
       "596                  1  2021   830          26680  16636  \n",
       "\n",
       "[2 rows x 46 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_df[j_df.Title=='SIIM-FISABIO-RSNA COVID-19 Detection'] #Expect 2: image, multilabel classification\n",
    "#There is a third tag online, but this seems to be a non-standard one, and doesn't appear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bc66d7f6",
   "metadata": {},
   "outputs": [
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       "      <th>year</th>\n",
       "      <th>Id_y</th>\n",
       "      <th>CompetitionId</th>\n",
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       "      <td>rsna-2022-cervical-spine-fracture-detection</td>\n",
       "      <td>RSNA 2022 Cervical Spine Fracture Detection</td>\n",
       "      <td>Identify cervical fractures from scans</td>\n",
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       "      <td>rsna-2022-cervical-spine-fracture-detection</td>\n",
       "      <td>RSNA 2022 Cervical Spine Fracture Detection</td>\n",
       "      <td>Identify cervical fractures from scans</td>\n",
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       "      <td>2022-10-27 23:59:00</td>\n",
       "      <td>10/20/2022 23:59:00</td>\n",
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       "      <td>rsna-2022-cervical-spine-fracture-detection</td>\n",
       "      <td>RSNA 2022 Cervical Spine Fracture Detection</td>\n",
       "      <td>Identify cervical fractures from scans</td>\n",
       "      <td>Featured</td>\n",
       "      <td>2147844</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>07/28/2022 17:49:51</td>\n",
       "      <td>2022-10-27 23:59:00</td>\n",
       "      <td>10/20/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>995</td>\n",
       "      <td>36363</td>\n",
       "      <td>14201</td>\n",
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      ],
      "text/plain": [
       "      Id_x                                         Slug  \\\n",
       "720  36363  rsna-2022-cervical-spine-fracture-detection   \n",
       "721  36363  rsna-2022-cervical-spine-fracture-detection   \n",
       "722  36363  rsna-2022-cervical-spine-fracture-detection   \n",
       "\n",
       "                                           Title  \\\n",
       "720  RSNA 2022 Cervical Spine Fracture Detection   \n",
       "721  RSNA 2022 Cervical Spine Fracture Detection   \n",
       "722  RSNA 2022 Cervical Spine Fracture Detection   \n",
       "\n",
       "                                   Subtitle HostSegmentTitle  ForumId  \\\n",
       "720  Identify cervical fractures from scans         Featured  2147844   \n",
       "721  Identify cervical fractures from scans         Featured  2147844   \n",
       "722  Identify cervical fractures from scans         Featured  2147844   \n",
       "\n",
       "     OrganizationId          EnabledDate        DeadlineDate  \\\n",
       "720          1817.0  07/28/2022 17:49:51 2022-10-27 23:59:00   \n",
       "721          1817.0  07/28/2022 17:49:51 2022-10-27 23:59:00   \n",
       "722          1817.0  07/28/2022 17:49:51 2022-10-27 23:59:00   \n",
       "\n",
       "    ProhibitNewEntrantsDeadlineDate  ... ValidationSetName ValidationSetValue  \\\n",
       "720             10/20/2022 23:59:00  ...               NaN                NaN   \n",
       "721             10/20/2022 23:59:00  ...               NaN                NaN   \n",
       "722             10/20/2022 23:59:00  ...               NaN                NaN   \n",
       "\n",
       "    EnableSubmissionModelHashes  EnableSubmissionModelAttachments  HostName  \\\n",
       "720                       False                             False       NaN   \n",
       "721                       False                             False       NaN   \n",
       "722                       False                             False       NaN   \n",
       "\n",
       "     CompetitionTypeId  year  Id_y  CompetitionId  TagId  \n",
       "720                  1  2022  1023          36363  13207  \n",
       "721                  1  2022   994          36363  14102  \n",
       "722                  1  2022   995          36363  14201  \n",
       "\n",
       "[3 rows x 46 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_df[j_df.Title=='RSNA 2022 Cervical Spine Fracture Detection'] #expect 3 tags: image, binary classification, computervision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "69367de4",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id_x</th>\n",
       "      <th>Slug</th>\n",
       "      <th>Title</th>\n",
       "      <th>Subtitle</th>\n",
       "      <th>HostSegmentTitle</th>\n",
       "      <th>ForumId</th>\n",
       "      <th>OrganizationId</th>\n",
       "      <th>EnabledDate</th>\n",
       "      <th>DeadlineDate</th>\n",
       "      <th>ProhibitNewEntrantsDeadlineDate</th>\n",
       "      <th>...</th>\n",
       "      <th>ValidationSetName</th>\n",
       "      <th>ValidationSetValue</th>\n",
       "      <th>EnableSubmissionModelHashes</th>\n",
       "      <th>EnableSubmissionModelAttachments</th>\n",
       "      <th>HostName</th>\n",
       "      <th>CompetitionTypeId</th>\n",
       "      <th>year</th>\n",
       "      <th>Id_y</th>\n",
       "      <th>CompetitionId</th>\n",
       "      <th>TagId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>6004</td>\n",
       "      <td>data-science-bowl-2017</td>\n",
       "      <td>Data Science Bowl 2017</td>\n",
       "      <td>Can you improve lung cancer detection?</td>\n",
       "      <td>Featured</td>\n",
       "      <td>2363</td>\n",
       "      <td>360.0</td>\n",
       "      <td>01/12/2017 14:00:00</td>\n",
       "      <td>2017-04-12 23:59:00</td>\n",
       "      <td>03/31/2017 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>96</td>\n",
       "      <td>6004</td>\n",
       "      <td>4202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>6004</td>\n",
       "      <td>data-science-bowl-2017</td>\n",
       "      <td>Data Science Bowl 2017</td>\n",
       "      <td>Can you improve lung cancer detection?</td>\n",
       "      <td>Featured</td>\n",
       "      <td>2363</td>\n",
       "      <td>360.0</td>\n",
       "      <td>01/12/2017 14:00:00</td>\n",
       "      <td>2017-04-12 23:59:00</td>\n",
       "      <td>03/31/2017 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>94</td>\n",
       "      <td>6004</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>6004</td>\n",
       "      <td>data-science-bowl-2017</td>\n",
       "      <td>Data Science Bowl 2017</td>\n",
       "      <td>Can you improve lung cancer detection?</td>\n",
       "      <td>Featured</td>\n",
       "      <td>2363</td>\n",
       "      <td>360.0</td>\n",
       "      <td>01/12/2017 14:00:00</td>\n",
       "      <td>2017-04-12 23:59:00</td>\n",
       "      <td>03/31/2017 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2017</td>\n",
       "      <td>97</td>\n",
       "      <td>6004</td>\n",
       "      <td>14201</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id_x                    Slug                   Title  \\\n",
       "186  6004  data-science-bowl-2017  Data Science Bowl 2017   \n",
       "187  6004  data-science-bowl-2017  Data Science Bowl 2017   \n",
       "188  6004  data-science-bowl-2017  Data Science Bowl 2017   \n",
       "\n",
       "                                   Subtitle HostSegmentTitle  ForumId  \\\n",
       "186  Can you improve lung cancer detection?         Featured     2363   \n",
       "187  Can you improve lung cancer detection?         Featured     2363   \n",
       "188  Can you improve lung cancer detection?         Featured     2363   \n",
       "\n",
       "     OrganizationId          EnabledDate        DeadlineDate  \\\n",
       "186           360.0  01/12/2017 14:00:00 2017-04-12 23:59:00   \n",
       "187           360.0  01/12/2017 14:00:00 2017-04-12 23:59:00   \n",
       "188           360.0  01/12/2017 14:00:00 2017-04-12 23:59:00   \n",
       "\n",
       "    ProhibitNewEntrantsDeadlineDate  ... ValidationSetName ValidationSetValue  \\\n",
       "186             03/31/2017 23:59:00  ...               NaN                NaN   \n",
       "187             03/31/2017 23:59:00  ...               NaN                NaN   \n",
       "188             03/31/2017 23:59:00  ...               NaN                NaN   \n",
       "\n",
       "    EnableSubmissionModelHashes  EnableSubmissionModelAttachments  HostName  \\\n",
       "186                       False                             False       NaN   \n",
       "187                       False                             False       NaN   \n",
       "188                       False                             False       NaN   \n",
       "\n",
       "     CompetitionTypeId  year  Id_y  CompetitionId  TagId  \n",
       "186                  1  2017    96           6004   4202  \n",
       "187                  1  2017    94           6004  14102  \n",
       "188                  1  2017    97           6004  14201  \n",
       "\n",
       "[3 rows x 46 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j_df[j_df.Title=='Data Science Bowl 2017'] #image comp that is not a code comp. tags: image, binaryclassification, healthcare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d7e269",
   "metadata": {},
   "source": [
    "So tag 14102 is for image competitions as it is present in all of these cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e42763bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(108, 46)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = j_df[j_df.TagId==14102].copy().reset_index(drop=True)\n",
    "image.shape #108 image-tagged competitions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "253971ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2017-01-07 00:00:00\n",
       "1     2017-01-07 00:00:00\n",
       "2     2015-03-16 23:59:00\n",
       "3     2015-07-27 23:59:00\n",
       "4     2015-10-05 23:59:00\n",
       "              ...        \n",
       "103   2022-06-02 23:59:00\n",
       "104   2022-05-30 23:59:00\n",
       "105   2022-10-27 23:59:00\n",
       "106   2022-10-10 23:59:00\n",
       "107   2022-10-05 23:59:00\n",
       "Name: DeadlineDate, Length: 108, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.DeadlineDate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b227ed8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#image['year'] = image.DeadlineDate.dt.year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b7febcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2015     3\n",
       "2016     7\n",
       "2017    19\n",
       "2018    13\n",
       "2019    19\n",
       "2020    16\n",
       "2021    17\n",
       "2022    14\n",
       "Name: CompetitionId, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.groupby('year').count().CompetitionId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a8095bea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2019, 2021, 2020, 2022])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image[image.OnlyAllowKernelSubmissions].year.unique()    ## Is a kernal only the same as a code competition?\n",
    "                                                ## The dates seem to match."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "36f1154c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2019     6\n",
       "2020     9\n",
       "2021    12\n",
       "2022     9\n",
       "Name: CompetitionId, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image[image.OnlyAllowKernelSubmissions].groupby('year').count().CompetitionId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "27915620",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2015     3\n",
       "2016     7\n",
       "2017    19\n",
       "2018    13\n",
       "2019    13\n",
       "2020     7\n",
       "2021     5\n",
       "2022     5\n",
       "Name: CompetitionId, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image[image.OnlyAllowKernelSubmissions== False].groupby('year').count().CompetitionId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "d1f395e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image = image[image.year>2018]\n",
    "image.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b0e871aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id_x</th>\n",
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       "      <th>ProhibitNewEntrantsDeadlineDate</th>\n",
       "      <th>...</th>\n",
       "      <th>ValidationSetName</th>\n",
       "      <th>ValidationSetValue</th>\n",
       "      <th>EnableSubmissionModelHashes</th>\n",
       "      <th>EnableSubmissionModelAttachments</th>\n",
       "      <th>HostName</th>\n",
       "      <th>CompetitionTypeId</th>\n",
       "      <th>year</th>\n",
       "      <th>Id_y</th>\n",
       "      <th>CompetitionId</th>\n",
       "      <th>TagId</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>10686</td>\n",
       "      <td>petfinder-adoption-prediction</td>\n",
       "      <td>PetFinder.my Adoption Prediction</td>\n",
       "      <td>How cute is that doggy in the shelter?</td>\n",
       "      <td>Featured</td>\n",
       "      <td>106880</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>12/27/2018 18:52:10</td>\n",
       "      <td>2019-04-10 01:09:00</td>\n",
       "      <td>03/21/2019 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>455</td>\n",
       "      <td>10686</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>13251</td>\n",
       "      <td>imet-2019-fgvc6</td>\n",
       "      <td>iMet Collection 2019 - FGVC6</td>\n",
       "      <td>Recognize artwork attributes from The Metropol...</td>\n",
       "      <td>Research</td>\n",
       "      <td>161849</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>03/28/2019 15:05:58</td>\n",
       "      <td>2019-06-10 22:09:06</td>\n",
       "      <td>05/28/2019 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>495</td>\n",
       "      <td>13251</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>13435</td>\n",
       "      <td>aerial-cactus-identification</td>\n",
       "      <td>Aerial Cactus Identification</td>\n",
       "      <td>Determine whether an image contains a columnar...</td>\n",
       "      <td>Playground</td>\n",
       "      <td>144645</td>\n",
       "      <td>4.0</td>\n",
       "      <td>03/08/2019 19:22:51</td>\n",
       "      <td>2019-07-08 23:59:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>484</td>\n",
       "      <td>13435</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>13836</td>\n",
       "      <td>cassava-leaf-disease-classification</td>\n",
       "      <td>Cassava Leaf Disease Classification</td>\n",
       "      <td>Identify the type of disease present on a Cass...</td>\n",
       "      <td>Research</td>\n",
       "      <td>1000771</td>\n",
       "      <td>3758.0</td>\n",
       "      <td>11/19/2020 21:12:46</td>\n",
       "      <td>2021-02-18 23:59:00</td>\n",
       "      <td>02/11/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>754</td>\n",
       "      <td>13836</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>14241</td>\n",
       "      <td>severstal-steel-defect-detection</td>\n",
       "      <td>Severstal: Steel Defect Detection</td>\n",
       "      <td>Can you detect and classify defects in steel?</td>\n",
       "      <td>Featured</td>\n",
       "      <td>288048</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>07/25/2019 21:10:14</td>\n",
       "      <td>2019-10-24 23:59:00</td>\n",
       "      <td>10/17/2019 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>538</td>\n",
       "      <td>14241</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>14774</td>\n",
       "      <td>aptos2019-blindness-detection</td>\n",
       "      <td>APTOS 2019 Blindness Detection</td>\n",
       "      <td>Detect diabetic retinopathy to stop blindness ...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>260503</td>\n",
       "      <td>2931.0</td>\n",
       "      <td>06/28/2019 01:15:11</td>\n",
       "      <td>2019-09-07 23:59:00</td>\n",
       "      <td>08/29/2019 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>528</td>\n",
       "      <td>14774</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>14897</td>\n",
       "      <td>bengaliai-cv19</td>\n",
       "      <td>Bengali.AI Handwritten Grapheme Classification</td>\n",
       "      <td>Classify the components of handwritten Bengali</td>\n",
       "      <td>Research</td>\n",
       "      <td>463791</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>12/19/2019 23:47:42</td>\n",
       "      <td>2020-03-16 23:59:00</td>\n",
       "      <td>03/09/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>586</td>\n",
       "      <td>14897</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>16017</td>\n",
       "      <td>Kannada-MNIST</td>\n",
       "      <td>Kannada MNIST</td>\n",
       "      <td>MNIST like datatset for Kannada handwritten di...</td>\n",
       "      <td>Playground</td>\n",
       "      <td>362849</td>\n",
       "      <td>4.0</td>\n",
       "      <td>09/18/2019 13:41:34</td>\n",
       "      <td>2019-12-17 23:59:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "      <td>550</td>\n",
       "      <td>16017</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>18278</td>\n",
       "      <td>flower-classification-with-tpus</td>\n",
       "      <td>Flower Classification with TPUs</td>\n",
       "      <td>Use TPUs to classify 104 types of flowers</td>\n",
       "      <td>Playground</td>\n",
       "      <td>522291</td>\n",
       "      <td>3734.0</td>\n",
       "      <td>02/10/2020 20:16:21</td>\n",
       "      <td>2020-05-11 23:59:00</td>\n",
       "      <td>05/04/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>596</td>\n",
       "      <td>18278</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>18647</td>\n",
       "      <td>prostate-cancer-grade-assessment</td>\n",
       "      <td>Prostate cANcer graDe Assessment (PANDA) Chall...</td>\n",
       "      <td>Prostate cancer diagnosis using the Gleason gr...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>628635</td>\n",
       "      <td>3741.0</td>\n",
       "      <td>04/21/2020 16:38:33</td>\n",
       "      <td>2020-07-22 23:59:00</td>\n",
       "      <td>07/15/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>655</td>\n",
       "      <td>18647</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>19231</td>\n",
       "      <td>landmark-recognition-2020</td>\n",
       "      <td>Google Landmark Recognition 2020</td>\n",
       "      <td>Label famous (and not-so-famous) landmarks in ...</td>\n",
       "      <td>Research</td>\n",
       "      <td>821168</td>\n",
       "      <td>855.0</td>\n",
       "      <td>07/29/2020 21:35:48</td>\n",
       "      <td>2020-09-29 23:59:00</td>\n",
       "      <td>09/22/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>699</td>\n",
       "      <td>19231</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>19233</td>\n",
       "      <td>landmark-retrieval-2020</td>\n",
       "      <td>Google Landmark Retrieval 2020</td>\n",
       "      <td>Given an image, can you find all of the same l...</td>\n",
       "      <td>Research</td>\n",
       "      <td>764051</td>\n",
       "      <td>855.0</td>\n",
       "      <td>06/30/2020 22:11:08</td>\n",
       "      <td>2020-08-17 23:59:00</td>\n",
       "      <td>08/10/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>701</td>\n",
       "      <td>19233</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>19989</td>\n",
       "      <td>global-wheat-detection</td>\n",
       "      <td>Global Wheat Detection</td>\n",
       "      <td>Can you help identify wheat heads using image ...</td>\n",
       "      <td>Research</td>\n",
       "      <td>650006</td>\n",
       "      <td>3743.0</td>\n",
       "      <td>05/04/2020 18:21:07</td>\n",
       "      <td>2020-08-19 01:59:00</td>\n",
       "      <td>07/28/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>669</td>\n",
       "      <td>19989</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>19990</td>\n",
       "      <td>lyft-motion-prediction-autonomous-vehicles</td>\n",
       "      <td>Lyft Motion Prediction for Autonomous Vehicles</td>\n",
       "      <td>Build motion prediction models for self-drivin...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>859090</td>\n",
       "      <td>3695.0</td>\n",
       "      <td>08/24/2020 20:13:59</td>\n",
       "      <td>2020-11-25 23:59:00</td>\n",
       "      <td>11/18/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>710</td>\n",
       "      <td>19990</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>20604</td>\n",
       "      <td>osic-pulmonary-fibrosis-progression</td>\n",
       "      <td>OSIC Pulmonary Fibrosis Progression</td>\n",
       "      <td>Predict lung function decline</td>\n",
       "      <td>Featured</td>\n",
       "      <td>777005</td>\n",
       "      <td>3745.0</td>\n",
       "      <td>07/07/2020 17:00:16</td>\n",
       "      <td>2020-10-06 23:59:00</td>\n",
       "      <td>09/29/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>693</td>\n",
       "      <td>20604</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>22307</td>\n",
       "      <td>rsna-str-pulmonary-embolism-detection</td>\n",
       "      <td>RSNA STR Pulmonary Embolism Detection</td>\n",
       "      <td>Classify Pulmonary Embolism cases in chest CT ...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>881661</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>09/10/2020 00:41:24</td>\n",
       "      <td>2020-10-26 23:59:00</td>\n",
       "      <td>10/19/2020 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "      <td>722</td>\n",
       "      <td>22307</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>22990</td>\n",
       "      <td>hubmap-kidney-segmentation</td>\n",
       "      <td>HuBMAP - Hacking the Kidney</td>\n",
       "      <td>Identify glomeruli in human kidney tissue images</td>\n",
       "      <td>Research</td>\n",
       "      <td>995088</td>\n",
       "      <td>3760.0</td>\n",
       "      <td>11/16/2020 22:11:08</td>\n",
       "      <td>2021-05-10 23:59:00</td>\n",
       "      <td>05/03/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>742</td>\n",
       "      <td>22990</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>23823</td>\n",
       "      <td>hpa-single-cell-image-classification</td>\n",
       "      <td>Human Protein Atlas - Single Cell Classification</td>\n",
       "      <td>Find individual human cell differences in micr...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1138531</td>\n",
       "      <td>1861.0</td>\n",
       "      <td>01/26/2021 23:56:29</td>\n",
       "      <td>2021-05-11 23:59:00</td>\n",
       "      <td>05/04/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>772</td>\n",
       "      <td>23823</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>23870</td>\n",
       "      <td>ranzcr-clip-catheter-line-classification</td>\n",
       "      <td>RANZCR CLiP - Catheter and Line Position Chall...</td>\n",
       "      <td>Classify the presence and correct placement of...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1052427</td>\n",
       "      <td>3765.0</td>\n",
       "      <td>12/14/2020 20:23:30</td>\n",
       "      <td>2021-03-16 23:59:00</td>\n",
       "      <td>03/08/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>763</td>\n",
       "      <td>23870</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>24286</td>\n",
       "      <td>shopee-product-matching</td>\n",
       "      <td>Shopee - Price Match Guarantee</td>\n",
       "      <td>Determine if two products are the same by thei...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1218334</td>\n",
       "      <td>3772.0</td>\n",
       "      <td>03/09/2021 00:34:45</td>\n",
       "      <td>2021-05-10 23:59:00</td>\n",
       "      <td>05/03/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>765</td>\n",
       "      <td>24286</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>25383</td>\n",
       "      <td>petfinder-pawpularity-score</td>\n",
       "      <td>PetFinder.my - Pawpularity Contest</td>\n",
       "      <td>Predict the popularity of shelter pet photos</td>\n",
       "      <td>Research</td>\n",
       "      <td>1627520</td>\n",
       "      <td>2170.0</td>\n",
       "      <td>09/23/2021 14:20:14</td>\n",
       "      <td>2022-01-14 23:59:00</td>\n",
       "      <td>01/06/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>876</td>\n",
       "      <td>25383</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>25563</td>\n",
       "      <td>plant-pathology-2021-fgvc8</td>\n",
       "      <td>Plant Pathology 2021 - FGVC8</td>\n",
       "      <td>Identify the category of foliar diseases in ap...</td>\n",
       "      <td>Research</td>\n",
       "      <td>1231234</td>\n",
       "      <td>3773.0</td>\n",
       "      <td>03/15/2021 18:08:38</td>\n",
       "      <td>2021-05-26 23:59:00</td>\n",
       "      <td>05/19/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>801</td>\n",
       "      <td>25563</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>25980</td>\n",
       "      <td>hotel-id-2021-fgvc8</td>\n",
       "      <td>Hotel-ID to Combat Human Trafficking 2021 - FGVC8</td>\n",
       "      <td>Recognizing hotels to aid Human trafficking in...</td>\n",
       "      <td>Research</td>\n",
       "      <td>1221987</td>\n",
       "      <td>3773.0</td>\n",
       "      <td>03/10/2021 22:24:34</td>\n",
       "      <td>2021-05-26 23:59:00</td>\n",
       "      <td>05/19/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>797</td>\n",
       "      <td>25980</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>26680</td>\n",
       "      <td>siim-covid19-detection</td>\n",
       "      <td>SIIM-FISABIO-RSNA COVID-19 Detection</td>\n",
       "      <td>Identify and localize COVID-19 abnormalities o...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1366504</td>\n",
       "      <td>2734.0</td>\n",
       "      <td>05/18/2021 00:03:39</td>\n",
       "      <td>2021-08-09 23:59:00</td>\n",
       "      <td>08/02/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>829</td>\n",
       "      <td>26680</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>27923</td>\n",
       "      <td>uw-madison-gi-tract-image-segmentation</td>\n",
       "      <td>UW-Madison GI Tract Image Segmentation</td>\n",
       "      <td>Track healthy organs in medical scans to impro...</td>\n",
       "      <td>Research</td>\n",
       "      <td>2110639</td>\n",
       "      <td>3793.0</td>\n",
       "      <td>04/14/2022 22:03:16</td>\n",
       "      <td>2022-07-14 23:59:00</td>\n",
       "      <td>07/07/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>905</td>\n",
       "      <td>27923</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>29653</td>\n",
       "      <td>rsna-miccai-brain-tumor-radiogenomic-classific...</td>\n",
       "      <td>RSNA-MICCAI Brain Tumor Radiogenomic Classific...</td>\n",
       "      <td>Predict the status of a genetic biomarker impo...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1485708</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>07/13/2021 22:39:36</td>\n",
       "      <td>2021-10-15 23:59:00</td>\n",
       "      <td>10/08/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>845</td>\n",
       "      <td>29653</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>29761</td>\n",
       "      <td>landmark-retrieval-2021</td>\n",
       "      <td>Google Landmark Retrieval 2021</td>\n",
       "      <td>Given an image, can you find all of the same l...</td>\n",
       "      <td>Research</td>\n",
       "      <td>1544967</td>\n",
       "      <td>855.0</td>\n",
       "      <td>08/11/2021 12:57:11</td>\n",
       "      <td>2021-10-01 23:59:00</td>\n",
       "      <td>09/24/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>863</td>\n",
       "      <td>29761</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>29762</td>\n",
       "      <td>landmark-recognition-2021</td>\n",
       "      <td>Google Landmark Recognition 2021</td>\n",
       "      <td>Label famous, and not-so-famous, landmarks in ...</td>\n",
       "      <td>Research</td>\n",
       "      <td>1544966</td>\n",
       "      <td>855.0</td>\n",
       "      <td>08/11/2021 12:56:59</td>\n",
       "      <td>2021-10-01 23:59:00</td>\n",
       "      <td>09/24/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>865</td>\n",
       "      <td>29762</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>30201</td>\n",
       "      <td>sartorius-cell-instance-segmentation</td>\n",
       "      <td>Sartorius - Cell Instance Segmentation</td>\n",
       "      <td>Detect single neuronal cells in microscopy images</td>\n",
       "      <td>Featured</td>\n",
       "      <td>1669380</td>\n",
       "      <td>3790.0</td>\n",
       "      <td>10/14/2021 19:57:51</td>\n",
       "      <td>2021-12-30 23:59:00</td>\n",
       "      <td>12/23/2021 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2021</td>\n",
       "      <td>888</td>\n",
       "      <td>30201</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>31703</td>\n",
       "      <td>tensorflow-great-barrier-reef</td>\n",
       "      <td>TensorFlow - Help Protect the Great Barrier Reef</td>\n",
       "      <td>Detect crown-of-thorns starfish in underwater ...</td>\n",
       "      <td>Research</td>\n",
       "      <td>1671784</td>\n",
       "      <td>1099.0</td>\n",
       "      <td>11/22/2021 18:53:57</td>\n",
       "      <td>2022-02-14 23:59:00</td>\n",
       "      <td>02/07/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>894</td>\n",
       "      <td>31703</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>34547</td>\n",
       "      <td>hubmap-organ-segmentation</td>\n",
       "      <td>HuBMAP + HPA - Hacking the Human Body</td>\n",
       "      <td>Segment multi-organ functional tissue units</td>\n",
       "      <td>Research</td>\n",
       "      <td>1960467</td>\n",
       "      <td>3808.0</td>\n",
       "      <td>06/22/2022 18:34:11</td>\n",
       "      <td>2022-09-22 23:59:00</td>\n",
       "      <td>09/15/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>980</td>\n",
       "      <td>34547</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>34970</td>\n",
       "      <td>image-matching-challenge-2022</td>\n",
       "      <td>Image Matching Challenge 2022</td>\n",
       "      <td>Register two images from different viewpoints</td>\n",
       "      <td>Research</td>\n",
       "      <td>1994860</td>\n",
       "      <td>1623.0</td>\n",
       "      <td>04/04/2022 16:59:28</td>\n",
       "      <td>2022-06-02 23:59:00</td>\n",
       "      <td>05/26/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>959</td>\n",
       "      <td>34970</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>35150</td>\n",
       "      <td>hotel-id-to-combat-human-trafficking-2022-fgvc9</td>\n",
       "      <td>Hotel-ID to Combat Human Trafficking 2022 - FGVC9</td>\n",
       "      <td>Recognizing hotels to aid Human trafficking in...</td>\n",
       "      <td>Research</td>\n",
       "      <td>2008649</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>03/16/2022 16:51:44</td>\n",
       "      <td>2022-05-30 23:59:00</td>\n",
       "      <td>05/23/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>954</td>\n",
       "      <td>35150</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>36363</td>\n",
       "      <td>rsna-2022-cervical-spine-fracture-detection</td>\n",
       "      <td>RSNA 2022 Cervical Spine Fracture Detection</td>\n",
       "      <td>Identify cervical fractures from scans</td>\n",
       "      <td>Featured</td>\n",
       "      <td>2147844</td>\n",
       "      <td>1817.0</td>\n",
       "      <td>07/28/2022 17:49:51</td>\n",
       "      <td>2022-10-27 23:59:00</td>\n",
       "      <td>10/20/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>994</td>\n",
       "      <td>36363</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>36414</td>\n",
       "      <td>google-universal-image-embedding</td>\n",
       "      <td>Google Universal Image Embedding</td>\n",
       "      <td>Create image representations that work across ...</td>\n",
       "      <td>Research</td>\n",
       "      <td>2153930</td>\n",
       "      <td>855.0</td>\n",
       "      <td>07/11/2022 17:09:06</td>\n",
       "      <td>2022-10-10 23:59:00</td>\n",
       "      <td>10/03/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>992</td>\n",
       "      <td>36414</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>37333</td>\n",
       "      <td>mayo-clinic-strip-ai</td>\n",
       "      <td>Mayo Clinic - STRIP AI</td>\n",
       "      <td>Image Classification of Stroke Blood Clot Origin</td>\n",
       "      <td>Research</td>\n",
       "      <td>2296551</td>\n",
       "      <td>3809.0</td>\n",
       "      <td>07/06/2022 20:34:00</td>\n",
       "      <td>2022-10-05 23:59:00</td>\n",
       "      <td>09/28/2022 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2022</td>\n",
       "      <td>984</td>\n",
       "      <td>37333</td>\n",
       "      <td>14102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Id_x                                               Slug  \\\n",
       "47   10686                      petfinder-adoption-prediction   \n",
       "49   13251                                    imet-2019-fgvc6   \n",
       "51   13435                       aerial-cactus-identification   \n",
       "54   13836                cassava-leaf-disease-classification   \n",
       "55   14241                   severstal-steel-defect-detection   \n",
       "60   14774                      aptos2019-blindness-detection   \n",
       "61   14897                                     bengaliai-cv19   \n",
       "64   16017                                      Kannada-MNIST   \n",
       "65   18278                    flower-classification-with-tpus   \n",
       "67   18647                   prostate-cancer-grade-assessment   \n",
       "70   19231                          landmark-recognition-2020   \n",
       "71   19233                            landmark-retrieval-2020   \n",
       "72   19989                             global-wheat-detection   \n",
       "73   19990         lyft-motion-prediction-autonomous-vehicles   \n",
       "76   20604                osic-pulmonary-fibrosis-progression   \n",
       "77   22307              rsna-str-pulmonary-embolism-detection   \n",
       "80   22990                         hubmap-kidney-segmentation   \n",
       "81   23823               hpa-single-cell-image-classification   \n",
       "82   23870           ranzcr-clip-catheter-line-classification   \n",
       "83   24286                            shopee-product-matching   \n",
       "86   25383                        petfinder-pawpularity-score   \n",
       "88   25563                         plant-pathology-2021-fgvc8   \n",
       "89   25980                                hotel-id-2021-fgvc8   \n",
       "90   26680                             siim-covid19-detection   \n",
       "91   27923             uw-madison-gi-tract-image-segmentation   \n",
       "92   29653  rsna-miccai-brain-tumor-radiogenomic-classific...   \n",
       "94   29761                            landmark-retrieval-2021   \n",
       "95   29762                          landmark-recognition-2021   \n",
       "96   30201               sartorius-cell-instance-segmentation   \n",
       "97   31703                      tensorflow-great-barrier-reef   \n",
       "102  34547                          hubmap-organ-segmentation   \n",
       "103  34970                      image-matching-challenge-2022   \n",
       "104  35150    hotel-id-to-combat-human-trafficking-2022-fgvc9   \n",
       "105  36363        rsna-2022-cervical-spine-fracture-detection   \n",
       "106  36414                   google-universal-image-embedding   \n",
       "107  37333                               mayo-clinic-strip-ai   \n",
       "\n",
       "                                                 Title  \\\n",
       "47                    PetFinder.my Adoption Prediction   \n",
       "49                        iMet Collection 2019 - FGVC6   \n",
       "51                        Aerial Cactus Identification   \n",
       "54                 Cassava Leaf Disease Classification   \n",
       "55                   Severstal: Steel Defect Detection   \n",
       "60                      APTOS 2019 Blindness Detection   \n",
       "61      Bengali.AI Handwritten Grapheme Classification   \n",
       "64                                       Kannada MNIST   \n",
       "65                     Flower Classification with TPUs   \n",
       "67   Prostate cANcer graDe Assessment (PANDA) Chall...   \n",
       "70                    Google Landmark Recognition 2020   \n",
       "71                      Google Landmark Retrieval 2020   \n",
       "72                              Global Wheat Detection   \n",
       "73      Lyft Motion Prediction for Autonomous Vehicles   \n",
       "76                 OSIC Pulmonary Fibrosis Progression   \n",
       "77               RSNA STR Pulmonary Embolism Detection   \n",
       "80                         HuBMAP - Hacking the Kidney   \n",
       "81    Human Protein Atlas - Single Cell Classification   \n",
       "82   RANZCR CLiP - Catheter and Line Position Chall...   \n",
       "83                      Shopee - Price Match Guarantee   \n",
       "86                  PetFinder.my - Pawpularity Contest   \n",
       "88                        Plant Pathology 2021 - FGVC8   \n",
       "89   Hotel-ID to Combat Human Trafficking 2021 - FGVC8   \n",
       "90                SIIM-FISABIO-RSNA COVID-19 Detection   \n",
       "91              UW-Madison GI Tract Image Segmentation   \n",
       "92   RSNA-MICCAI Brain Tumor Radiogenomic Classific...   \n",
       "94                      Google Landmark Retrieval 2021   \n",
       "95                    Google Landmark Recognition 2021   \n",
       "96              Sartorius - Cell Instance Segmentation   \n",
       "97    TensorFlow - Help Protect the Great Barrier Reef   \n",
       "102              HuBMAP + HPA - Hacking the Human Body   \n",
       "103                      Image Matching Challenge 2022   \n",
       "104  Hotel-ID to Combat Human Trafficking 2022 - FGVC9   \n",
       "105        RSNA 2022 Cervical Spine Fracture Detection   \n",
       "106                   Google Universal Image Embedding   \n",
       "107                             Mayo Clinic - STRIP AI   \n",
       "\n",
       "                                              Subtitle HostSegmentTitle  \\\n",
       "47              How cute is that doggy in the shelter?         Featured   \n",
       "49   Recognize artwork attributes from The Metropol...         Research   \n",
       "51   Determine whether an image contains a columnar...       Playground   \n",
       "54   Identify the type of disease present on a Cass...         Research   \n",
       "55       Can you detect and classify defects in steel?         Featured   \n",
       "60   Detect diabetic retinopathy to stop blindness ...         Featured   \n",
       "61      Classify the components of handwritten Bengali         Research   \n",
       "64   MNIST like datatset for Kannada handwritten di...       Playground   \n",
       "65           Use TPUs to classify 104 types of flowers       Playground   \n",
       "67   Prostate cancer diagnosis using the Gleason gr...         Featured   \n",
       "70   Label famous (and not-so-famous) landmarks in ...         Research   \n",
       "71   Given an image, can you find all of the same l...         Research   \n",
       "72   Can you help identify wheat heads using image ...         Research   \n",
       "73   Build motion prediction models for self-drivin...         Featured   \n",
       "76                       Predict lung function decline         Featured   \n",
       "77   Classify Pulmonary Embolism cases in chest CT ...         Featured   \n",
       "80    Identify glomeruli in human kidney tissue images         Research   \n",
       "81   Find individual human cell differences in micr...         Featured   \n",
       "82   Classify the presence and correct placement of...         Featured   \n",
       "83   Determine if two products are the same by thei...         Featured   \n",
       "86        Predict the popularity of shelter pet photos         Research   \n",
       "88   Identify the category of foliar diseases in ap...         Research   \n",
       "89   Recognizing hotels to aid Human trafficking in...         Research   \n",
       "90   Identify and localize COVID-19 abnormalities o...         Featured   \n",
       "91   Track healthy organs in medical scans to impro...         Research   \n",
       "92   Predict the status of a genetic biomarker impo...         Featured   \n",
       "94   Given an image, can you find all of the same l...         Research   \n",
       "95   Label famous, and not-so-famous, landmarks in ...         Research   \n",
       "96   Detect single neuronal cells in microscopy images         Featured   \n",
       "97   Detect crown-of-thorns starfish in underwater ...         Research   \n",
       "102        Segment multi-organ functional tissue units         Research   \n",
       "103      Register two images from different viewpoints         Research   \n",
       "104  Recognizing hotels to aid Human trafficking in...         Research   \n",
       "105             Identify cervical fractures from scans         Featured   \n",
       "106  Create image representations that work across ...         Research   \n",
       "107   Image Classification of Stroke Blood Clot Origin         Research   \n",
       "\n",
       "     ForumId  OrganizationId          EnabledDate        DeadlineDate  \\\n",
       "47    106880          2170.0  12/27/2018 18:52:10 2019-04-10 01:09:00   \n",
       "49    161849          2567.0  03/28/2019 15:05:58 2019-06-10 22:09:06   \n",
       "51    144645             4.0  03/08/2019 19:22:51 2019-07-08 23:59:00   \n",
       "54   1000771          3758.0  11/19/2020 21:12:46 2021-02-18 23:59:00   \n",
       "55    288048          2888.0  07/25/2019 21:10:14 2019-10-24 23:59:00   \n",
       "60    260503          2931.0  06/28/2019 01:15:11 2019-09-07 23:59:00   \n",
       "61    463791          1645.0  12/19/2019 23:47:42 2020-03-16 23:59:00   \n",
       "64    362849             4.0  09/18/2019 13:41:34 2019-12-17 23:59:00   \n",
       "65    522291          3734.0  02/10/2020 20:16:21 2020-05-11 23:59:00   \n",
       "67    628635          3741.0  04/21/2020 16:38:33 2020-07-22 23:59:00   \n",
       "70    821168           855.0  07/29/2020 21:35:48 2020-09-29 23:59:00   \n",
       "71    764051           855.0  06/30/2020 22:11:08 2020-08-17 23:59:00   \n",
       "72    650006          3743.0  05/04/2020 18:21:07 2020-08-19 01:59:00   \n",
       "73    859090          3695.0  08/24/2020 20:13:59 2020-11-25 23:59:00   \n",
       "76    777005          3745.0  07/07/2020 17:00:16 2020-10-06 23:59:00   \n",
       "77    881661          1817.0  09/10/2020 00:41:24 2020-10-26 23:59:00   \n",
       "80    995088          3760.0  11/16/2020 22:11:08 2021-05-10 23:59:00   \n",
       "81   1138531          1861.0  01/26/2021 23:56:29 2021-05-11 23:59:00   \n",
       "82   1052427          3765.0  12/14/2020 20:23:30 2021-03-16 23:59:00   \n",
       "83   1218334          3772.0  03/09/2021 00:34:45 2021-05-10 23:59:00   \n",
       "86   1627520          2170.0  09/23/2021 14:20:14 2022-01-14 23:59:00   \n",
       "88   1231234          3773.0  03/15/2021 18:08:38 2021-05-26 23:59:00   \n",
       "89   1221987          3773.0  03/10/2021 22:24:34 2021-05-26 23:59:00   \n",
       "90   1366504          2734.0  05/18/2021 00:03:39 2021-08-09 23:59:00   \n",
       "91   2110639          3793.0  04/14/2022 22:03:16 2022-07-14 23:59:00   \n",
       "92   1485708          1817.0  07/13/2021 22:39:36 2021-10-15 23:59:00   \n",
       "94   1544967           855.0  08/11/2021 12:57:11 2021-10-01 23:59:00   \n",
       "95   1544966           855.0  08/11/2021 12:56:59 2021-10-01 23:59:00   \n",
       "96   1669380          3790.0  10/14/2021 19:57:51 2021-12-30 23:59:00   \n",
       "97   1671784          1099.0  11/22/2021 18:53:57 2022-02-14 23:59:00   \n",
       "102  1960467          3808.0  06/22/2022 18:34:11 2022-09-22 23:59:00   \n",
       "103  1994860          1623.0  04/04/2022 16:59:28 2022-06-02 23:59:00   \n",
       "104  2008649          2567.0  03/16/2022 16:51:44 2022-05-30 23:59:00   \n",
       "105  2147844          1817.0  07/28/2022 17:49:51 2022-10-27 23:59:00   \n",
       "106  2153930           855.0  07/11/2022 17:09:06 2022-10-10 23:59:00   \n",
       "107  2296551          3809.0  07/06/2022 20:34:00 2022-10-05 23:59:00   \n",
       "\n",
       "    ProhibitNewEntrantsDeadlineDate  ... ValidationSetName ValidationSetValue  \\\n",
       "47              03/21/2019 23:59:00  ...               NaN                NaN   \n",
       "49              05/28/2019 23:59:00  ...               NaN                NaN   \n",
       "51                              NaN  ...               NaN                NaN   \n",
       "54              02/11/2021 23:59:00  ...               NaN                NaN   \n",
       "55              10/17/2019 23:59:00  ...               NaN                NaN   \n",
       "60              08/29/2019 23:59:00  ...               NaN                NaN   \n",
       "61              03/09/2020 23:59:00  ...               NaN                NaN   \n",
       "64                              NaN  ...               NaN                NaN   \n",
       "65              05/04/2020 23:59:00  ...               NaN                NaN   \n",
       "67              07/15/2020 23:59:00  ...               NaN                NaN   \n",
       "70              09/22/2020 23:59:00  ...               NaN                NaN   \n",
       "71              08/10/2020 23:59:00  ...               NaN                NaN   \n",
       "72              07/28/2020 23:59:00  ...               NaN                NaN   \n",
       "73              11/18/2020 23:59:00  ...               NaN                NaN   \n",
       "76              09/29/2020 23:59:00  ...               NaN                NaN   \n",
       "77              10/19/2020 23:59:00  ...               NaN                NaN   \n",
       "80              05/03/2021 23:59:00  ...               NaN                NaN   \n",
       "81              05/04/2021 23:59:00  ...               NaN                NaN   \n",
       "82              03/08/2021 23:59:00  ...               NaN                NaN   \n",
       "83              05/03/2021 23:59:00  ...               NaN                NaN   \n",
       "86              01/06/2022 23:59:00  ...               NaN                NaN   \n",
       "88              05/19/2021 23:59:00  ...               NaN                NaN   \n",
       "89              05/19/2021 23:59:00  ...               NaN                NaN   \n",
       "90              08/02/2021 23:59:00  ...               NaN                NaN   \n",
       "91              07/07/2022 23:59:00  ...               NaN                NaN   \n",
       "92              10/08/2021 23:59:00  ...               NaN                NaN   \n",
       "94              09/24/2021 23:59:00  ...               NaN                NaN   \n",
       "95              09/24/2021 23:59:00  ...               NaN                NaN   \n",
       "96              12/23/2021 23:59:00  ...               NaN                NaN   \n",
       "97              02/07/2022 23:59:00  ...               NaN                NaN   \n",
       "102             09/15/2022 23:59:00  ...               NaN                NaN   \n",
       "103             05/26/2022 23:59:00  ...               NaN                NaN   \n",
       "104             05/23/2022 23:59:00  ...               NaN                NaN   \n",
       "105             10/20/2022 23:59:00  ...               NaN                NaN   \n",
       "106             10/03/2022 23:59:00  ...               NaN                NaN   \n",
       "107             09/28/2022 23:59:00  ...               NaN                NaN   \n",
       "\n",
       "    EnableSubmissionModelHashes  EnableSubmissionModelAttachments  HostName  \\\n",
       "47                        False                             False       NaN   \n",
       "49                        False                             False       NaN   \n",
       "51                        False                             False       NaN   \n",
       "54                        False                             False       NaN   \n",
       "55                        False                             False       NaN   \n",
       "60                        False                             False       NaN   \n",
       "61                        False                             False       NaN   \n",
       "64                        False                             False       NaN   \n",
       "65                        False                             False       NaN   \n",
       "67                        False                             False       NaN   \n",
       "70                        False                             False       NaN   \n",
       "71                        False                             False       NaN   \n",
       "72                        False                             False       NaN   \n",
       "73                        False                             False       NaN   \n",
       "76                        False                             False       NaN   \n",
       "77                        False                             False       NaN   \n",
       "80                        False                             False       NaN   \n",
       "81                        False                             False       NaN   \n",
       "82                        False                             False       NaN   \n",
       "83                        False                             False       NaN   \n",
       "86                        False                             False       NaN   \n",
       "88                        False                             False       NaN   \n",
       "89                        False                             False       NaN   \n",
       "90                        False                             False       NaN   \n",
       "91                        False                             False       NaN   \n",
       "92                        False                             False       NaN   \n",
       "94                        False                             False       NaN   \n",
       "95                        False                             False       NaN   \n",
       "96                        False                             False       NaN   \n",
       "97                        False                             False       NaN   \n",
       "102                       False                             False       NaN   \n",
       "103                       False                             False       NaN   \n",
       "104                       False                             False       NaN   \n",
       "105                       False                             False       NaN   \n",
       "106                       False                             False       NaN   \n",
       "107                       False                             False       NaN   \n",
       "\n",
       "     CompetitionTypeId  year  Id_y  CompetitionId  TagId  \n",
       "47                   1  2019   455          10686  14102  \n",
       "49                   1  2019   495          13251  14102  \n",
       "51                   1  2019   484          13435  14102  \n",
       "54                   1  2021   754          13836  14102  \n",
       "55                   1  2019   538          14241  14102  \n",
       "60                   1  2019   528          14774  14102  \n",
       "61                   1  2020   586          14897  14102  \n",
       "64                   1  2019   550          16017  14102  \n",
       "65                   1  2020   596          18278  14102  \n",
       "67                   1  2020   655          18647  14102  \n",
       "70                   1  2020   699          19231  14102  \n",
       "71                   1  2020   701          19233  14102  \n",
       "72                   1  2020   669          19989  14102  \n",
       "73                   1  2020   710          19990  14102  \n",
       "76                   1  2020   693          20604  14102  \n",
       "77                   1  2020   722          22307  14102  \n",
       "80                   1  2021   742          22990  14102  \n",
       "81                   1  2021   772          23823  14102  \n",
       "82                   1  2021   763          23870  14102  \n",
       "83                   1  2021   765          24286  14102  \n",
       "86                   1  2022   876          25383  14102  \n",
       "88                   1  2021   801          25563  14102  \n",
       "89                   1  2021   797          25980  14102  \n",
       "90                   1  2021   829          26680  14102  \n",
       "91                   1  2022   905          27923  14102  \n",
       "92                   1  2021   845          29653  14102  \n",
       "94                   1  2021   863          29761  14102  \n",
       "95                   1  2021   865          29762  14102  \n",
       "96                   1  2021   888          30201  14102  \n",
       "97                   1  2022   894          31703  14102  \n",
       "102                  1  2022   980          34547  14102  \n",
       "103                  1  2022   959          34970  14102  \n",
       "104                  1  2022   954          35150  14102  \n",
       "105                  1  2022   994          36363  14102  \n",
       "106                  1  2022   992          36414  14102  \n",
       "107                  1  2022   984          37333  14102  \n",
       "\n",
       "[36 rows x 46 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image[image.OnlyAllowKernelSubmissions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "52bc6b2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>Slug</th>\n",
       "      <th>Title</th>\n",
       "      <th>Subtitle</th>\n",
       "      <th>HostSegmentTitle</th>\n",
       "      <th>ForumId</th>\n",
       "      <th>OrganizationId</th>\n",
       "      <th>EnabledDate</th>\n",
       "      <th>DeadlineDate</th>\n",
       "      <th>ProhibitNewEntrantsDeadlineDate</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>TotalCompetitors</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>ValidationSetName</th>\n",
       "      <th>ValidationSetValue</th>\n",
       "      <th>EnableSubmissionModelHashes</th>\n",
       "      <th>EnableSubmissionModelAttachments</th>\n",
       "      <th>HostName</th>\n",
       "      <th>CompetitionTypeId</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1087</th>\n",
       "      <td>10737</td>\n",
       "      <td>quora-insincere-questions-classification</td>\n",
       "      <td>Quora Insincere Questions Classification</td>\n",
       "      <td>Detect toxic content to improve online convers...</td>\n",
       "      <td>Featured</td>\n",
       "      <td>82935</td>\n",
       "      <td>407.0</td>\n",
       "      <td>11/06/2018 17:59:32</td>\n",
       "      <td>2019-02-13 23:09:00</td>\n",
       "      <td>01/29/2019 23:59:00</td>\n",
       "      <td>...</td>\n",
       "      <td>1396</td>\n",
       "      <td>1726</td>\n",
       "      <td>2506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3239</th>\n",
       "      <td>22518</td>\n",
       "      <td>AI5112020-quora</td>\n",
       "      <td>Quora Insincere Questions Classification</td>\n",
       "      <td>IIITB ML 2020 Project - 3</td>\n",
       "      <td>Community</td>\n",
       "      <td>894712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>09/18/2020 11:09:23</td>\n",
       "      <td>2020-12-18 23:59:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id                                      Slug  \\\n",
       "1087  10737  quora-insincere-questions-classification   \n",
       "3239  22518                           AI5112020-quora   \n",
       "\n",
       "                                         Title  \\\n",
       "1087  Quora Insincere Questions Classification   \n",
       "3239  Quora Insincere Questions Classification   \n",
       "\n",
       "                                               Subtitle HostSegmentTitle  \\\n",
       "1087  Detect toxic content to improve online convers...         Featured   \n",
       "3239                          IIITB ML 2020 Project - 3        Community   \n",
       "\n",
       "      ForumId  OrganizationId          EnabledDate        DeadlineDate  \\\n",
       "1087    82935           407.0  11/06/2018 17:59:32 2019-02-13 23:09:00   \n",
       "3239   894712             NaN  09/18/2020 11:09:23 2020-12-18 23:59:00   \n",
       "\n",
       "     ProhibitNewEntrantsDeadlineDate  ... TotalTeams TotalCompetitors  \\\n",
       "1087             01/29/2019 23:59:00  ...       1396             1726   \n",
       "3239                             NaN  ...          2                2   \n",
       "\n",
       "     TotalSubmissions  ValidationSetName  ValidationSetValue  \\\n",
       "1087             2506                NaN                 NaN   \n",
       "3239               14                NaN                 NaN   \n",
       "\n",
       "      EnableSubmissionModelHashes  EnableSubmissionModelAttachments  HostName  \\\n",
       "1087                        False                             False       NaN   \n",
       "3239                        False                             False       NaN   \n",
       "\n",
       "      CompetitionTypeId  year  \n",
       "1087                  1  2019  \n",
       "3239                  1  2020  \n",
       "\n",
       "[2 rows x 43 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comps[comps.Title=='Quora Insincere Questions Classification']\n",
    "#The first one here is the example comp linked to Code comps in their FAQ,\n",
    "# which uses the term 'kernel' rather than notebook. It finished in early-2019, so 4 years ago. \n",
    "#This is the oldest code competition I found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "54848343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Lnz/0+He37TnM0FkXp+PrANgJzLcx/gcGZx5syvEF3gt8q9XzdwzOJNk04wvcxOD4/o8ZvLu6dj3GcqU+xrl5Kb0kdaqHQyiSpGUY4JLUKQNckjplgEtSpwxwSeqUAS5JnTLAJalT/w8de9kIdq9hVAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(image.TotalSubmissions);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5582af21",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = image[['CompetitionId', 'Title', 'HostSegmentTitle', 'OnlyAllowKernelSubmissions', 'year', 'RewardQuantity', 'TotalTeams',  'TotalSubmissions' ]]\n",
    "image['subs_per_team'] = image.TotalSubmissions/image.TotalTeams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e6569f38",
   "metadata": {},
   "outputs": [],
   "source": [
    "image.describe().to_csv('meta_data/meta/all_image_descriptives.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f98f2b22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = [0, 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000]\n",
    "\n",
    "plt.hist(image[image.OnlyAllowKernelSubmissions==False].TotalSubmissions, alpha=0.5, bins=10, color='orange', density=True)\n",
    "plt.hist(image[image.OnlyAllowKernelSubmissions].TotalSubmissions, alpha=0.5, bins=10, color='blue', density=True);\n",
    "#Not sure this is meaningful actually."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "81090cb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(image[image.OnlyAllowKernelSubmissions].TotalSubmissions.copy()).reset_index(drop=True)\n",
    "tdf = pd.DataFrame(image[image.OnlyAllowKernelSubmissions==False].TotalSubmissions.copy()).reset_index(drop=True)\n",
    "\n",
    "df['Code'] = df['TotalSubmissions']\n",
    "df['Traditional'] = tdf['TotalSubmissions']\n",
    "df = df[['Code', 'Traditional']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "95c36176",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Code\n",
       "1  32307"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = pd.DataFrame(32307,[1], columns = ['Code'])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "65d45411",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plt.plot(a, '_', color='r', markersize=20, label='SIIM COVID-19 competition')\n",
    "df.plot(kind='box', title='Submissions by Competition Type', ax=ax)\n",
    "ax.set_xlabel(\"Competition Type\")\n",
    "ax.set_ylabel(\"Submissions\")\n",
    "fig.tight_layout()\n",
    "plt.legend()\n",
    "plt.savefig('img/box.png');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e404ec40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5454545454545454"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "36/66 #54% code comps in total, but they have only existed since 2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c16bcbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "image['subs_per_team'] = image.TotalSubmissions/image.TotalTeams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7cd406a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(image[image.OnlyAllowKernelSubmissions==False].subs_per_team, alpha=0.5, bins=10, hatch='.',fill=True, color='orange',label='Traditional')\n",
    "plt.hist(image[image.OnlyAllowKernelSubmissions].subs_per_team, alpha=0.5, bins=10, hatch='/',fill=True, color='blue', label='Code')\n",
    "plt.title('Average Submissions per Team per Competition')\n",
    "plt.legend()\n",
    "plt.xlabel('Average Number of Submissions')\n",
    "plt.ylabel('Number of Competitions')\n",
    "fig.tight_layout()\n",
    "plt.savefig('img/submission_dist.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "7145dbed",
   "metadata": {},
   "outputs": [],
   "source": [
    "ccomps = image[image.OnlyAllowKernelSubmissions].copy().reset_index(drop=True)\n",
    "ccomps = ccomps[['CompetitionId', 'Title', 'HostSegmentTitle', 'RewardQuantity', 'TotalTeams', 'TotalSubmissions', 'subs_per_team', ]]\n",
    "ccomps.to_csv('meta_data/meta/image_code_comps.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "743174a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "desc = ccomps.describe()\n",
    "desc.to_csv('meta_data/meta/image_code_descriptives.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9ebad194",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ccomps.TotalSubmissions, ccomps.RewardQuantity, marker='*', c=ccomps.subs_per_team);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f5bcbdf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(ccomps.TotalTeams, ccomps.RewardQuantity, marker='*', c=ccomps.subs_per_team);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6abfeded",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ccomps.RewardQuantity);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "91df92cc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>subs_per_team</th>\n",
       "      <th>RewardQuantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>39568</td>\n",
       "      <td>1175</td>\n",
       "      <td>33.674894</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>60934</td>\n",
       "      <td>2025</td>\n",
       "      <td>30.090864</td>\n",
       "      <td>150000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>34699</td>\n",
       "      <td>1200</td>\n",
       "      <td>28.915833</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>40956</td>\n",
       "      <td>1548</td>\n",
       "      <td>26.457364</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>6782</td>\n",
       "      <td>263</td>\n",
       "      <td>25.787072</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>18783</td>\n",
       "      <td>736</td>\n",
       "      <td>25.520380</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>19058</td>\n",
       "      <td>757</td>\n",
       "      <td>25.175694</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>32307</td>\n",
       "      <td>1305</td>\n",
       "      <td>24.756322</td>\n",
       "      <td>100000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>71433</td>\n",
       "      <td>2928</td>\n",
       "      <td>24.396516</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>8717</td>\n",
       "      <td>383</td>\n",
       "      <td>22.759791</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>14170</td>\n",
       "      <td>642</td>\n",
       "      <td>22.071651</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>32813</td>\n",
       "      <td>1505</td>\n",
       "      <td>21.802658</td>\n",
       "      <td>75000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>51874</td>\n",
       "      <td>2427</td>\n",
       "      <td>21.373712</td>\n",
       "      <td>120000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44505</td>\n",
       "      <td>2097</td>\n",
       "      <td>21.223176</td>\n",
       "      <td>55000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>51077</td>\n",
       "      <td>2426</td>\n",
       "      <td>21.053998</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>81524</td>\n",
       "      <td>3900</td>\n",
       "      <td>20.903590</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1712</td>\n",
       "      <td>82</td>\n",
       "      <td>20.878049</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>20984</td>\n",
       "      <td>1022</td>\n",
       "      <td>20.532290</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19723</td>\n",
       "      <td>1010</td>\n",
       "      <td>19.527723</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>38927</td>\n",
       "      <td>2059</td>\n",
       "      <td>18.905780</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>28112</td>\n",
       "      <td>1547</td>\n",
       "      <td>18.171946</td>\n",
       "      <td>50000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>27466</td>\n",
       "      <td>1555</td>\n",
       "      <td>17.663023</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10483</td>\n",
       "      <td>626</td>\n",
       "      <td>16.746006</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14900</td>\n",
       "      <td>935</td>\n",
       "      <td>15.935829</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>12871</td>\n",
       "      <td>883</td>\n",
       "      <td>14.576444</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11408</td>\n",
       "      <td>784</td>\n",
       "      <td>14.551020</td>\n",
       "      <td>30000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>47037</td>\n",
       "      <td>3537</td>\n",
       "      <td>13.298558</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10387</td>\n",
       "      <td>848</td>\n",
       "      <td>12.248821</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>1083</td>\n",
       "      <td>92</td>\n",
       "      <td>11.771739</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4983</td>\n",
       "      <td>541</td>\n",
       "      <td>9.210721</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11077</td>\n",
       "      <td>1212</td>\n",
       "      <td>9.139439</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6980</td>\n",
       "      <td>888</td>\n",
       "      <td>7.860360</td>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6776</td>\n",
       "      <td>1221</td>\n",
       "      <td>5.549550</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>8187</td>\n",
       "      <td>1742</td>\n",
       "      <td>4.699770</td>\n",
       "      <td>15000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3136</td>\n",
       "      <td>1788</td>\n",
       "      <td>1.753915</td>\n",
       "      <td>25000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>767</td>\n",
       "      <td>442</td>\n",
       "      <td>1.735294</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TotalSubmissions  TotalTeams  subs_per_team  RewardQuantity\n",
       "30             39568        1175      33.674894         60000.0\n",
       "29             60934        2025      30.090864        150000.0\n",
       "16             34699        1200      28.915833         60000.0\n",
       "24             40956        1548      26.457364         25000.0\n",
       "26              6782         263      25.787072             0.0\n",
       "10             18783         736      25.520380         25000.0\n",
       "17             19058         757      25.175694         25000.0\n",
       "23             32307        1305      24.756322        100000.0\n",
       "5              71433        2928      24.396516         50000.0\n",
       "27              8717         383      22.759791             0.0\n",
       "31             14170         642      22.071651         10000.0\n",
       "28             32813        1505      21.802658         75000.0\n",
       "4              51874        2427      21.373712        120000.0\n",
       "14             44505        2097      21.223176         55000.0\n",
       "19             51077        2426      21.053998         30000.0\n",
       "3              81524        3900      20.903590         18000.0\n",
       "32              1712          82      20.878049             0.0\n",
       "34             20984        1022      20.532290         50000.0\n",
       "9              19723        1010      19.527723         25000.0\n",
       "6              38927        2059      18.905780         10000.0\n",
       "18             28112        1547      18.171946         50000.0\n",
       "25             27466        1555      17.663023         30000.0\n",
       "21             10483         626      16.746006             0.0\n",
       "13             14900         935      15.935829         30000.0\n",
       "33             12871         883      14.576444         30000.0\n",
       "15             11408         784      14.551020         30000.0\n",
       "20             47037        3537      13.298558         25000.0\n",
       "8              10387         848      12.248821             NaN\n",
       "22              1083          92      11.771739             0.0\n",
       "11              4983         541       9.210721         25000.0\n",
       "7              11077        1212       9.139439             NaN\n",
       "35              6980         888       7.860360         10000.0\n",
       "2               6776        1221       5.549550             NaN\n",
       "12              8187        1742       4.699770         15000.0\n",
       "0               3136        1788       1.753915         25000.0\n",
       "1                767         442       1.735294             0.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps[['TotalSubmissions', 'TotalTeams', 'subs_per_team', 'RewardQuantity']].sort_values('subs_per_team', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "4c4a6fd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count       36.000000\n",
       "mean     24894.416667\n",
       "std      20839.013687\n",
       "min        767.000000\n",
       "25%       8584.500000\n",
       "50%      18920.500000\n",
       "75%      39087.250000\n",
       "max      81524.000000\n",
       "Name: TotalSubmissions, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps.TotalSubmissions.describe() #wide range here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "875caf19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ccomps.TotalSubmissions);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "69bca217",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    36.000000\n",
       "mean     18.075550\n",
       "std       7.912399\n",
       "min       1.735294\n",
       "25%      13.036124\n",
       "50%      20.030006\n",
       "75%      23.168972\n",
       "max      33.674894\n",
       "Name: subs_per_team, dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps.subs_per_team.describe() #mean lower than expected - do high scoring teams tend to submit more?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1c28cec0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ccomps.subs_per_team);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c520ac56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      36.000000\n",
       "mean     1336.972222\n",
       "std       896.393520\n",
       "min        82.000000\n",
       "25%       751.750000\n",
       "50%      1187.500000\n",
       "75%      1753.500000\n",
       "max      3900.000000\n",
       "Name: TotalTeams, dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps.TotalTeams.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "9ab6db16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>subs_per_team</th>\n",
       "      <th>RewardQuantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Cassava Leaf Disease Classification</td>\n",
       "      <td>3900</td>\n",
       "      <td>81524</td>\n",
       "      <td>20.90359</td>\n",
       "      <td>18000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title  TotalTeams  TotalSubmissions  \\\n",
       "3  Cassava Leaf Disease Classification        3900             81524   \n",
       "\n",
       "   subs_per_team  RewardQuantity  \n",
       "3       20.90359         18000.0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps[['Title', 'TotalTeams', 'TotalSubmissions', 'subs_per_team', 'RewardQuantity']][ccomps.TotalSubmissions==max(ccomps.TotalSubmissions)] #max total submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8a6cd695",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>subs_per_team</th>\n",
       "      <th>RewardQuantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iMet Collection 2019 - FGVC6</td>\n",
       "      <td>442</td>\n",
       "      <td>767</td>\n",
       "      <td>1.735294</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Title  TotalTeams  TotalSubmissions  subs_per_team  \\\n",
       "1  iMet Collection 2019 - FGVC6         442               767       1.735294   \n",
       "\n",
       "   RewardQuantity  \n",
       "1             0.0  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps[['Title', 'TotalTeams', 'TotalSubmissions', 'subs_per_team', 'RewardQuantity']][ccomps.TotalSubmissions==min(ccomps.TotalSubmissions)] #min total submissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c474ea9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>subs_per_team</th>\n",
       "      <th>RewardQuantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HuBMAP + HPA - Hacking the Human Body</td>\n",
       "      <td>1175</td>\n",
       "      <td>39568</td>\n",
       "      <td>33.674894</td>\n",
       "      <td>60000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Title  TotalTeams  TotalSubmissions  \\\n",
       "30  HuBMAP + HPA - Hacking the Human Body        1175             39568   \n",
       "\n",
       "    subs_per_team  RewardQuantity  \n",
       "30      33.674894         60000.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps[['Title', 'TotalTeams', 'TotalSubmissions', 'subs_per_team', 'RewardQuantity']][ccomps.subs_per_team==max(ccomps.subs_per_team)] #max avg submissions per team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "443144bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>TotalTeams</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "      <th>subs_per_team</th>\n",
       "      <th>RewardQuantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>iMet Collection 2019 - FGVC6</td>\n",
       "      <td>442</td>\n",
       "      <td>767</td>\n",
       "      <td>1.735294</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Title  TotalTeams  TotalSubmissions  subs_per_team  \\\n",
       "1  iMet Collection 2019 - FGVC6         442               767       1.735294   \n",
       "\n",
       "   RewardQuantity  \n",
       "1             0.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps[['Title', 'TotalTeams', 'TotalSubmissions', 'subs_per_team', 'RewardQuantity']][ccomps.subs_per_team==min(ccomps.subs_per_team)] #min avg submissions per team"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "462962fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>TotalTeams</th>\n",
       "      <th>TotalSubmissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>1305</td>\n",
       "      <td>32307</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    TotalTeams  TotalSubmissions\n",
       "23        1305             32307"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccomps[ccomps.CompetitionId==26680][['TotalTeams', 'TotalSubmissions']].head(1) #get values for the SIIM competition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "174d8beb",
   "metadata": {},
   "outputs": [],
   "source": [
    "subs['SubmissionId'] = subs['Id'] #rename and reduce this table\n",
    "subs = subs[['SubmissionId', 'TeamId', 'SourceKernelVersionId', 'SubmissionDate', 'IsAfterDeadline','PublicScoreLeaderboardDisplay','PrivateScoreLeaderboardDisplay']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a30c33c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "siimteams = teams[teams.CompetitionId==26680]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d066037c",
   "metadata": {},
   "outputs": [],
   "source": [
    "hope = pd.merge(siimteams, subs, left_on='Id', right_on='TeamId', validate='1:m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "693671bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32307\n",
      "1305\n"
     ]
    }
   ],
   "source": [
    "#isolated all submissions belonging to the SIIM.... competition\n",
    "finalSiim = hope[(hope.TeamLeaderId.isna()==False) & (hope.IsAfterDeadline==False) & (hope.LastSubmissionDate.isna()==False)].copy()\n",
    "print(finalSiim.shape[0]) #32307 submissions, which bloody matches finally!! Woot!\n",
    "print(len(finalSiim.TeamId.unique())) #1307 teams, which matches expected\n",
    "finalSiim.to_csv('meta_data/meta/SIIM_only_submissions.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be30c452",
   "metadata": {},
   "source": [
    "#### Now we have the relevant submissions, we would like to split them into teams that made a viable effort (and therefore had a higher carbon cost), ones that made some effort but maybe lacked the skills/tools/experience to do well, and teams that joined and didn't actually get very far, maybe were collecting data, or just didn't have time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "aa76a468",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalSiim[finalSiim.Medal.isna()];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b84a4dd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "finalSiim.loc[finalSiim[finalSiim.Medal.isna()].index, 'Medal'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1c27a08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in [0, 1., 2., 3.]:\n",
    "    temp_df = finalSiim[finalSiim.Medal == i]\n",
    "\n",
    "    plt.plot(temp_df.groupby('TeamId').max().PublicLeaderboardRank,temp_df.groupby(by='TeamId').count().Id, '.', label=f'Medal {i}')\n",
    "plt.ylabel('number of submissions')\n",
    "plt.xlabel('public leader board position')\n",
    "fig.tight_layout()\n",
    "plt.legend();\n",
    "#plt.xlim(0,200)\n",
    "#plt.ylim(0,50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d7584441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in [1., 2., 3., 0, ]:\n",
    "    temp_df = finalSiim[finalSiim.Medal == i]\n",
    "    if i == 0:\n",
    "        l = 'Others'\n",
    "    elif i == 3.0:\n",
    "        l = 'Top 10%'\n",
    "    elif i == 2.0:\n",
    "        l = 'Top 5%'\n",
    "    elif i == 1.0:\n",
    "        l = 'Top 12 teams'\n",
    "        \n",
    "    plt.plot(temp_df.groupby('TeamId').max().PrivateLeaderboardRank,temp_df.groupby(by='TeamId').count().Id, '.', label=l)\n",
    "plt.ylabel('Number of Submissions')\n",
    "plt.xlabel('Private Leaderboard Position')\n",
    "plt.title('Team Submissions and Final Private Leaderboard Position')\n",
    "plt.legend();\n",
    "fig.tight_layout()\n",
    "#plt.savefig('img/totalsubsVsPrivateleaderpos.png')\n",
    "#plt.xlim(0,200);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "6ec2234d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>CompetitionId</th>\n",
       "      <th>TeamLeaderId</th>\n",
       "      <th>TeamName</th>\n",
       "      <th>ScoreFirstSubmittedDate</th>\n",
       "      <th>LastSubmissionDate</th>\n",
       "      <th>PublicLeaderboardSubmissionId</th>\n",
       "      <th>PrivateLeaderboardSubmissionId</th>\n",
       "      <th>IsBenchmark</th>\n",
       "      <th>Medal</th>\n",
       "      <th>...</th>\n",
       "      <th>PublicLeaderboardRank</th>\n",
       "      <th>PrivateLeaderboardRank</th>\n",
       "      <th>WriteUpForumTopicId</th>\n",
       "      <th>SubmissionId</th>\n",
       "      <th>TeamId</th>\n",
       "      <th>SourceKernelVersionId</th>\n",
       "      <th>SubmissionDate</th>\n",
       "      <th>IsAfterDeadline</th>\n",
       "      <th>PublicScoreLeaderboardDisplay</th>\n",
       "      <th>PrivateScoreLeaderboardDisplay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>6815184</td>\n",
       "      <td>26680</td>\n",
       "      <td>2730705.0</td>\n",
       "      <td>BabaCondaRabbit -trust us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22239050.0</td>\n",
       "      <td>22246346.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>263662.0</td>\n",
       "      <td>21719039</td>\n",
       "      <td>6815184</td>\n",
       "      <td>66642850.0</td>\n",
       "      <td>06/25/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>6815184</td>\n",
       "      <td>26680</td>\n",
       "      <td>2730705.0</td>\n",
       "      <td>BabaCondaRabbit -trust us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22239050.0</td>\n",
       "      <td>22246346.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>263662.0</td>\n",
       "      <td>21719259</td>\n",
       "      <td>6815184</td>\n",
       "      <td>66645651.0</td>\n",
       "      <td>06/25/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>6815184</td>\n",
       "      <td>26680</td>\n",
       "      <td>2730705.0</td>\n",
       "      <td>BabaCondaRabbit -trust us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22239050.0</td>\n",
       "      <td>22246346.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>263662.0</td>\n",
       "      <td>22288461</td>\n",
       "      <td>6815184</td>\n",
       "      <td>70870429.0</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>6815184</td>\n",
       "      <td>26680</td>\n",
       "      <td>2730705.0</td>\n",
       "      <td>BabaCondaRabbit -trust us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22239050.0</td>\n",
       "      <td>22246346.0</td>\n",
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       "      <th>195</th>\n",
       "      <td>6815184</td>\n",
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       "      <td>BabaCondaRabbit -trust us</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>34910</th>\n",
       "      <td>7183047</td>\n",
       "      <td>26680</td>\n",
       "      <td>8049416.0</td>\n",
       "      <td>endiqq_our</td>\n",
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       "      <td>08/09/2021</td>\n",
       "      <td>22294074.0</td>\n",
       "      <td>22294074.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22280087</td>\n",
       "      <td>7183047</td>\n",
       "      <td>70748803.0</td>\n",
       "      <td>08/08/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34911</th>\n",
       "      <td>7183047</td>\n",
       "      <td>26680</td>\n",
       "      <td>8049416.0</td>\n",
       "      <td>endiqq_our</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22294074.0</td>\n",
       "      <td>22294074.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1132.0</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22281969</td>\n",
       "      <td>7183047</td>\n",
       "      <td>70772462.0</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34914</th>\n",
       "      <td>7186326</td>\n",
       "      <td>26680</td>\n",
       "      <td>8048215.0</td>\n",
       "      <td>covidX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22284466.0</td>\n",
       "      <td>22284466.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22180462</td>\n",
       "      <td>7186326</td>\n",
       "      <td>69720630.0</td>\n",
       "      <td>08/02/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34915</th>\n",
       "      <td>7186326</td>\n",
       "      <td>26680</td>\n",
       "      <td>8048215.0</td>\n",
       "      <td>covidX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22284466.0</td>\n",
       "      <td>22284466.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1102.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22180592</td>\n",
       "      <td>7186326</td>\n",
       "      <td>69721285.0</td>\n",
       "      <td>08/02/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34916</th>\n",
       "      <td>7186326</td>\n",
       "      <td>26680</td>\n",
       "      <td>8048215.0</td>\n",
       "      <td>covidX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22284466.0</td>\n",
       "      <td>22284466.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22222479</td>\n",
       "      <td>7186326</td>\n",
       "      <td>70046202.0</td>\n",
       "      <td>08/04/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3664 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Id  CompetitionId  TeamLeaderId                   TeamName  \\\n",
       "102    6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "103    6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "190    6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "192    6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "195    6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "...        ...            ...           ...                        ...   \n",
       "34910  7183047          26680     8049416.0                 endiqq_our   \n",
       "34911  7183047          26680     8049416.0                 endiqq_our   \n",
       "34914  7186326          26680     8048215.0                     covidX   \n",
       "34915  7186326          26680     8048215.0                     covidX   \n",
       "34916  7186326          26680     8048215.0                     covidX   \n",
       "\n",
       "       ScoreFirstSubmittedDate LastSubmissionDate  \\\n",
       "102                        NaN         08/09/2021   \n",
       "103                        NaN         08/09/2021   \n",
       "190                        NaN         08/09/2021   \n",
       "192                        NaN         08/09/2021   \n",
       "195                        NaN         08/09/2021   \n",
       "...                        ...                ...   \n",
       "34910                      NaN         08/09/2021   \n",
       "34911                      NaN         08/09/2021   \n",
       "34914                      NaN         08/09/2021   \n",
       "34915                      NaN         08/09/2021   \n",
       "34916                      NaN         08/09/2021   \n",
       "\n",
       "       PublicLeaderboardSubmissionId  PrivateLeaderboardSubmissionId  \\\n",
       "102                       22239050.0                      22246346.0   \n",
       "103                       22239050.0                      22246346.0   \n",
       "190                       22239050.0                      22246346.0   \n",
       "192                       22239050.0                      22246346.0   \n",
       "195                       22239050.0                      22246346.0   \n",
       "...                              ...                             ...   \n",
       "34910                     22294074.0                      22294074.0   \n",
       "34911                     22294074.0                      22294074.0   \n",
       "34914                     22284466.0                      22284466.0   \n",
       "34915                     22284466.0                      22284466.0   \n",
       "34916                     22284466.0                      22284466.0   \n",
       "\n",
       "       IsBenchmark  Medal  ... PublicLeaderboardRank  PrivateLeaderboardRank  \\\n",
       "102          False    2.0  ...                  25.0                    19.0   \n",
       "103          False    2.0  ...                  25.0                    19.0   \n",
       "190          False    2.0  ...                  25.0                    19.0   \n",
       "192          False    2.0  ...                  25.0                    19.0   \n",
       "195          False    2.0  ...                  25.0                    19.0   \n",
       "...            ...    ...  ...                   ...                     ...   \n",
       "34910        False    0.0  ...                1132.0                  1103.0   \n",
       "34911        False    0.0  ...                1132.0                  1103.0   \n",
       "34914        False    0.0  ...                1102.0                  1096.0   \n",
       "34915        False    0.0  ...                1102.0                  1096.0   \n",
       "34916        False    0.0  ...                1102.0                  1096.0   \n",
       "\n",
       "       WriteUpForumTopicId  SubmissionId   TeamId  SourceKernelVersionId  \\\n",
       "102               263662.0      21719039  6815184             66642850.0   \n",
       "103               263662.0      21719259  6815184             66645651.0   \n",
       "190               263662.0      22288461  6815184             70870429.0   \n",
       "192               263662.0      22270558  6815184             70626721.0   \n",
       "195               263662.0      22287927  6815184             70863850.0   \n",
       "...                    ...           ...      ...                    ...   \n",
       "34910                  NaN      22280087  7183047             70748803.0   \n",
       "34911                  NaN      22281969  7183047             70772462.0   \n",
       "34914                  NaN      22180462  7186326             69720630.0   \n",
       "34915                  NaN      22180592  7186326             69721285.0   \n",
       "34916                  NaN      22222479  7186326             70046202.0   \n",
       "\n",
       "       SubmissionDate IsAfterDeadline  PublicScoreLeaderboardDisplay  \\\n",
       "102        06/25/2021           False                            NaN   \n",
       "103        06/25/2021           False                            NaN   \n",
       "190        08/09/2021           False                            NaN   \n",
       "192        08/08/2021           False                            NaN   \n",
       "195        08/09/2021           False                            NaN   \n",
       "...               ...             ...                            ...   \n",
       "34910      08/08/2021           False                            NaN   \n",
       "34911      08/09/2021           False                            NaN   \n",
       "34914      08/02/2021           False                            NaN   \n",
       "34915      08/02/2021           False                            NaN   \n",
       "34916      08/04/2021           False                            NaN   \n",
       "\n",
       "       PrivateScoreLeaderboardDisplay  \n",
       "102                               NaN  \n",
       "103                               NaN  \n",
       "190                               NaN  \n",
       "192                               NaN  \n",
       "195                               NaN  \n",
       "...                               ...  \n",
       "34910                             NaN  \n",
       "34911                             NaN  \n",
       "34914                             NaN  \n",
       "34915                             NaN  \n",
       "34916                             NaN  \n",
       "\n",
       "[3664 rows x 21 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalSiim[finalSiim.PublicScoreLeaderboardDisplay.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "f10e4a1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "556 31751\n"
     ]
    },
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>6815184</td>\n",
       "      <td>26680</td>\n",
       "      <td>2730705.0</td>\n",
       "      <td>BabaCondaRabbit -trust us</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08/09/2021</td>\n",
       "      <td>22239050.0</td>\n",
       "      <td>22246346.0</td>\n",
       "      <td>False</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>263662.0</td>\n",
       "      <td>21222058</td>\n",
       "      <td>6815184</td>\n",
       "      <td>63680617.0</td>\n",
       "      <td>05/23/2021</td>\n",
       "      <td>False</td>\n",
       "      <td>0.230</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Id  CompetitionId  TeamLeaderId                   TeamName  \\\n",
       "12  6815181          26680      385905.0                    Bo Peng   \n",
       "13  6815181          26680      385905.0                    Bo Peng   \n",
       "14  6815181          26680      385905.0                    Bo Peng   \n",
       "15  6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "16  6815184          26680     2730705.0  BabaCondaRabbit -trust us   \n",
       "\n",
       "    ScoreFirstSubmittedDate LastSubmissionDate  PublicLeaderboardSubmissionId  \\\n",
       "12                      NaN         08/08/2021                     22279112.0   \n",
       "13                      NaN         08/08/2021                     22279112.0   \n",
       "14                      NaN         08/08/2021                     22279112.0   \n",
       "15                      NaN         08/09/2021                     22239050.0   \n",
       "16                      NaN         08/09/2021                     22239050.0   \n",
       "\n",
       "    PrivateLeaderboardSubmissionId  IsBenchmark  Medal  ...  \\\n",
       "12                      22279112.0        False    0.0  ...   \n",
       "13                      22279112.0        False    0.0  ...   \n",
       "14                      22279112.0        False    0.0  ...   \n",
       "15                      22246346.0        False    2.0  ...   \n",
       "16                      22246346.0        False    2.0  ...   \n",
       "\n",
       "   PublicLeaderboardRank  PrivateLeaderboardRank  WriteUpForumTopicId  \\\n",
       "12                 222.0                   223.0                  NaN   \n",
       "13                 222.0                   223.0                  NaN   \n",
       "14                 222.0                   223.0                  NaN   \n",
       "15                  25.0                    19.0             263662.0   \n",
       "16                  25.0                    19.0             263662.0   \n",
       "\n",
       "    SubmissionId   TeamId  SourceKernelVersionId  SubmissionDate  \\\n",
       "12      22279112  6815181             70733396.0      08/08/2021   \n",
       "13      22279114  6815181             70733380.0      08/08/2021   \n",
       "14      22278279  6815181             70714266.0      08/08/2021   \n",
       "15      21224783  6815184             63694447.0      05/23/2021   \n",
       "16      21222058  6815184             63680617.0      05/23/2021   \n",
       "\n",
       "   IsAfterDeadline  PublicScoreLeaderboardDisplay  \\\n",
       "12           False                          0.614   \n",
       "13           False                          0.613   \n",
       "14           False                          0.614   \n",
       "15           False                          0.230   \n",
       "16           False                          0.230   \n",
       "\n",
       "    PrivateScoreLeaderboardDisplay  \n",
       "12                           0.597  \n",
       "13                           0.596  \n",
       "14                           0.597  \n",
       "15                           0.000  \n",
       "16                           0.000  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#more50 = list(finalSiim.groupby(by='TeamId').count()[(finalSiim.groupby(by='TeamId').count().Id > 2) & (finalSiim.groupby(by='TeamId').count().Id <= 100)].index)\n",
    "\n",
    "#middle = finalSiim[finalSiim.TeamId.isin(more50)].copy()\n",
    "\n",
    "more100 = list(finalSiim.groupby(by='TeamId').count()[finalSiim.groupby(by='TeamId').count().Id > 2].index)\n",
    "\n",
    "serious = finalSiim[finalSiim.TeamId.isin(more100)].copy()\n",
    "\n",
    "print(len(finalSiim)- len(serious), len(serious))\n",
    "serious.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d186183d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = [0, 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300, 325, 350, 375]\n",
    "n = 12\n",
    "\n",
    "plt.hist(serious.groupby(by='TeamId').count().Id, bins=b, log=True, alpha=0.5, label='all results')\n",
    "plt.hist(serious[serious.PublicLeaderboardRank <=n].groupby('TeamId').count().Id, bins=b, log=True, alpha=0.7, label = f'top {n} public results')\n",
    "plt.title('Number of submissions per team (SIIM)')\n",
    "plt.xlabel('Number of submissions per team')\n",
    "plt.ylabel('Count of number of teams')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "bf406131",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9N4sWLSoYT5s2bTj33HPp2bMnBx10EIsXLwZgwIABnHPOOVRUVPCb3/yGSy+9lKuvvpo5c+bQr1+/3PwLFixg1113BWDkyJH07duXXr16MWzYMCKC8ePHU1lZycknn0yfPn34+OOPmTZtGgcccAB77rknhx56KG+99dYX4jrttNMYPnw4e+21Fz/+8Y+ZP38+hx12GHvuuSdf/epXmTNnDgB33303vXr1onfv3vTv3x9IktBZZ52Vq+vII49k0qRJtX4Ob731Fv3796dPnz706tWLJ598suD2qg8nBTOr0xVXXMH222/PjBkzGDVqFPfeey8zZsxg5syZPPbYY7kdFMDUqVO5/vrrefnll5k/fz733nvvWi3zlltu4fDDDy847cMPP6SiooKXXnqJAw44gMsuuyw37dNPP6WyspLzzz8/V/aVr3yFTz/9lNdeew2AcePGceKJSRPcWWedxXPPPcesWbP4+OOPeeCBBxg4cCAVFRWMHTuWGTNm0LJlS37wgx8wfvx4pk2bxumnn85Pf/rTgrEtWrSIp59+mmuuuYZhw4Zx/fXXM23aNK6++upcs8/IkSN55JFHmDlzJvfff3/mbVL9c7jzzjs59NBDc59Fnz59MtdVE/cpmFm9PfXUU5x00km0aNGCzTbbjAMOOIDnnnuOdu3a0a9fP7bbbjsATjrpJJ566qncfQ6yevzxx7nlllt46qmnCk7fYIMNcjv1U045heOOOy43raq8uhNOOIFx48ZxwQUXMG7cuNzRzeOPP85VV13FRx99xHvvvUfPnj056qij1pj3lVdeYdasWRx88MEAfPbZZ2yxxRYFlzNo0CBatGjB8uXLefrppxk0aFBu2ieffAIkzWennXYaJ5xwwhqx11ffvn05/fTTWblyJccee6yTgpmVH0m1vq7LCy+8wBlnnMHDDz+c+SY++cvIH6Y734knnsigQYM47rjjkMSOO+7IihUrOPPMM6msrGTrrbfm0ksvLTg8d0TQs2dPpkyZUmcsVctfvXo1HTp0YMaMGV94z0033cSzzz7Lgw8+yJ577sm0adPWGCIcsg0T3r9/fyZPnsyDDz7IaaedxnnnnbfWfShVmmTzkS9eM2tc+UNTA3z1q19l3LhxfPbZZyxevJjJkyfn2uynTp3Ka6+9xurVqxk3bhz7779/5uX8+9//5rjjjuP2229np51qHmBy9erVjB8/HoA777wz0zK23357WrRowc9//vPc0UTVjrdz584sX748V2f1dd55551ZvHhxLimsXLmSl156qdbltWvXju7du3P33XcDSWKZOXMmAPPnz2evvfZi5MiRdOnShYULF9KtWzdmzJiRG/p76tSpX6iz+ufw+uuvs9lmmzF06FDOOOOM3HDj66JJHilExARgQkVFxdBSx2JWEnWcQtrQOnXqxH777UevXr04/PDDueqqq5gyZQq9e/dGEldddRWbb745c+bMoW/fvpx11lnMmzePAw88kG9+85tfqG/06NFcddVVvP322+y2224cccQR/OEPf2DkyJEsWbIk1/besmVLCo2EvMkmmzB16lQuv/xyvvzlL+eagupy4oknMmLEiFzfQocOHRg6dCi9evVi8803p2/fvrn3VnUaf+lLX2LKlCmMHz+es88+m2XLlrFq1SrOOeccevbsWevyxo4dy/e+9z0uv/xyVq5cyeDBg+nduzcjRoxg7ty5RAQHHXRQ7rTb7t2706NHD3bZZRf22GOPOj+HXr16MWrUKFq1akWbNm247bbbMm2H2njo7AbkobOtWJrK0NmTJk3i6quv5oEHHijqctq0aeMb52TkobPNzGytNcnmIzMrTwMGDKjx7mgNyUcJxeMjBbMmoik39VpprM13xknBrAlo3bo1S5YscWKwzCKCJUuW0Lp163rN5+Yjsyaga9euLFq0KDecg1kWrVu3pmvXrvWax0nBrAlo1aoV3bt3L3UY1gy4+cjMzHKcFMzMLMdJwczMcppkUvDYR2ZmxdEkk0JETIiIYe3bty91KGZm65U6k4Kk/SRtkj4/RdI1krataz4zM2t6shwp3Ah8JKk3cD4wH1j3ofjMzKzsZEkKqyK5jPIY4IaI+C3QtrhhmZlZKWS5eO0DSRcCpwD9JW0AtCpuWGZmVgpZjhROBD4BhkTE20BXYFRRozIzs5Ko80ghTQTXSGonqSOwHCjuHTTMzKwk6kwKkr4LXAasAKqGaAxguyLGZWZmJZClT+FHQK+IeLfYwZiZWWll6VOYD3xU7EDMzKz0shwpXAg8LelZkg5nACLi7KJFZWZmJZElKfwv8A/gRWB1ccNp2q599F8NWt+5B+/UoPWZmdUlS1JoFRHnFT0SMzMruSx9Cg9LGiZpC0kdqx5Fj8zMzBpdliOFk9K/F+aV+ZRUM7P1UJaL13xjWDOzZiLLkQKSegE9gNZVZRHhkVLNzNYzWa5ovgQYQJIUHgIOB57Cw2ebma13snQ0DwQOAt6OiO8AvYGi3PJM0rGSfi9pnKRDirEMMzOrWZak8HFErAZWSWoHvANsnXUBkm6V9I6kWdXKD5P0iqR5ki4AiIj7ImIoMJxkdFYzM2tEWZJCpaQOwO+BacB0YEo9ljEGOCy/QFIL4LckTVE9gJMk9ch7y0XpdDMza0RZzj46M316k6S/A+0i4oWsC4iIyZK6VSvuB8yLiFcBJN0FHCNpNnAF8HBETC9Un6RhwDCAbbbZJmsYZmaWQZ1HCkqcIulnEbEAWCqp3zoudytgYd7rRWnZD4CvAwMlDS80Y0TcHBEVEVHRpUuXdQzDzMzyZTkl9XckYx59DRgJfADcA/Rt6GAiYjQwuqHrNTOzbLL0KewVEd8nuckOEfFfYMN1XO4brNlZ3TUty0TSUZJuXrZs2TqGYWZm+bIkhZVpx3AASOrCuo+W+hywo6TukjYEBgP3Z505IiZExLD27YtyZqyZWbOVJSmMBv4KfFnSL0guXPtV1gVI+jPJ2Uo7S1okaUhErALOAh4BZgN/iYiX6h29mZk1qCxnH42VNI3kAjYBx0bE7KwLiIiTaih/iOQKaTMzKxNZhrm4PSL+B5hToKwkJB0FHLXDDjuUKgQzs/VSluajnvkv0v6FPYsTTjbuUzAzK44ak4KkCyV9AOwm6f308QHJMBd/a7QIzcys0dSYFCLiVxHRFhgVEe3SR9uI6BQRF9Y0n5mZNV11Nh+VYwLwdQpmZsWRpU+h7LhPwcysOGrrU/BtOM3MmpnajhTGA0j6v0aKxczMSqy26xQ2kPT/gJ0knVd9YkRcU7ywzMysFGo7UhgMfEaSONoWeJSMO5rNzIpDEVH7G6TDI+LhRoqnXioqKqKysnKt5r320X81cDTl79yDdyp1CGZWBiRNi4iKQtOynH30tKRrJFWmj19L8mk/ZmbroSxJ4VaSG+uckD7eB/5YzKDMzKw0stx5bfuIOD7v9WWSZhQpHjMzK6EsRwofS9q/6oWk/YCPixdS3dzRbGZWHFmSwnDgt5IWSFoA3AB8t6hR1cFXNJuZFUeWm+zMBHpLape+fr/oUZmZWUlk6VMAnAzMzJqDJjkgnpmZFUetSUHSBpL2baxgzMystGpNChGxGvhtI8ViZmYllqX56P8kHS9JRY8mI5+SamZWHFmSwneBu4FPq+7TLKmknc4+JdXMrDiynJJa0hFRreE09CCAHmDPbP1T55GCEqdIujh9vbWkfsUPzczMGluW5qPfAfsA30pfL8edz2Zm66UsF6/tFRF7SHoeICL+K2nDIsdlZmYlkOVIYaWkFkAASOoCrC5qVGZmVhJZksJo4K/AZpJ+ATwF/LKoUZmZWUlkOftorKRpwEFp0bERMbu4YZmZWSlkHftoY6BF+v4vFS+cbHzxmplZcWQ5JfVnwJ+AjkBn4I+SLip2YLXxxWtmZsWR5eyjk4HeEbECQNIVwAzg8iLGZWZmJZCl+ehNoHXe642AN4oTjpmZlVKNRwqSric5DXUZ8JKkR9PXBwNTGyc8MzNrTLU1H1Wmf6eRnJJaZVLRojEzs5KqMSlExJ8aMxAzMyu9LGcfHSnpeUnvlcvQ2WZmVhxZzj66DjgOeDEiorjhWFPS0ENxg4fjNiu1LGcfLQRmOSGYma3/shwp/Bh4SNITwCdVhRFxTdGiMjOzksiSFH5Bcg+F1oCHzDYzW49lSQpbRkSvokdiZmYll6VP4SFJhxQ9knrwgHhmZsWhuvqPJX0AbELSn7ASEBAR0a744dWuoqIiKisr635jAcU4c8bKj89mMvsiSdMioqLQtCz3U2jb8CGZmVk5qjMpSOpfqDwiJjd8OGZmVkpZOppH5D1vDfQjGQ/pa0WJyMzMSiZL89FR+a8lbU1ylbOZma1nst6OM98iYJeGDsTMzEovS59C1X0VIEkifYDpRYzJzMxKJEufQv45n6uAP0fEP4sUj5mZlVCWPgXfV8HMrJnI0ny0H3ApsG36/qqL17YrbmhmZtbYsjQf3QKcS3Ia6mfFDcfMzEopS1JYFhEPFz0SMzMruSxJ4XFJo4B7WfN+Cj4DycxsPZMlKeyV/s0fPCnwFc1mZuudLGcfHdgYgZiZWemtzRXNZma2nnJSMDOznBqbjyQNioi7JXWPiNeKHYik7YCfAu0jYmCxl2fNQ0PfTMk37bH1XW1HChemf+9Z28ol3SrpHUmzqpUfJukVSfMkXQAQEa9GxJC1XZaZma272jqal0iaCHSXdH/1iRFxdIb6xwA3ALdVFUhqAfwWOJhkxNXnJN0fES/XJ3AzM2t4tSWFbwB7ALcDv16byiNisqRu1Yr7AfMi4lUASXcBxwCZkoKkYcAwgG222WZtwjIzsxrU2HwUEZ9GxDPAvhHxBMkwF9Mi4on09draCliY93oRsJWkTpJuAnaXdGHhWSEibo6Iioio6NKlyzqEYWZm1WW5eG2ztBmpIyBJi4FvR8SsOuarl4hYAgxvyDrNzKx+spySejNwXkRsGxHbAOenZWvrDWDrvNdd0zIzMyuxLEcKm0TE41UvImKSpE3WYZnPATtK6k6SDAYD36pPBZKOAo7aYYcd1iEMs/pr6FNcwae5WnnJcqTwqqSLJXVLHxcBr2apXNKfgSnAzpIWSRoSEauAs4BHgNnAXyLipfoEHRETImJY+/bt6zObmZnVIcuRwunAZSSjpAbwZFpWp4g4qYbyh4CHMsZoZmaNJMuAeP8Fzm6EWDJz85GZWXE0ybGP3HxkZlYcTTIpmJlZcdSZFCTtl6XMzMyaviwdzdeTDHdRV1mjcZ+CrU+KcZprQ/Ips81LbUNn7wPsC3SRdF7epHZAi2IHVpuImABMqKioGFrKOMzM1je1HSlsCLRJ39M2r/x9wPc7MDNbD9WYFNJB756QNCYiXm/EmMzMrESy9ClsJOlmoFv++yPia8UKyszMSiNLUrgbuAn4A/BZccPJxh3NZmbFkSUprIqIG4seST24o9nMrDiyXLw2QdKZkraQ1LHqUfTIzMys0WU5Uvh2+ndEXlkA2zV8OGZmVkpZBsTr3hiBmJlZ6dWZFCSdWqg8Im5r+HDMzKyUsjQf9c173ho4CJgOlCwp+OwjM7PiyNJ89IP815I6AHcVK6AsfPaRmVlxrM3Q2R8C7mcwM1sPZelTmEBythEkA+HtAvylmEGZmVlpZOlTuDrv+Srg9YhYVKR4zMyshOpsPkoHxptDMlLqpsCnxQ7KzMxKI8ud104ApgKDgBOAZyV56Gwzs/VQluajnwJ9I+IdAEldgMeA8cUMrDY+JdWyOublcxu0vr/1uLZB6zMrN1nOPtqgKiGklmScr2giYkJEDGvfvn0pwzAzW+9kOVL4u6RHgD+nr08EHi5eSGZmVipZLl4bIek4YP+06OaI+GtxwzIzs1KoMSlI2gHYLCL+GRH3Avem5ftL2j4i5jdWkGZm1jhq6xu4Dni/QPmydJqZma1naksKm0XEi9UL07JuRYvIzMxKprak0KGWaV9q4DjMzKwM1JYUKiV9YRRSSWcA04oXkpmZlUptZx+dA/xV0sl8ngQqgA2BbxY5rlr54jUzK6ZrH/1Xg9Z37sE7NWh9xVTjkUJE/Cci9gUuAxakj8siYp+IeLtxwqsxNl+8ZmZWBFmuU3gceLwRYjEzsxIr6XAVZmZWXpwUzMwsx0nBzMxynBTMzCzHScHMzHKcFMzMLMdJwczMcpwUzMwsx0nBzMxystyOs+w0xNhHDX1Dd/BN3a08NPR3+1oa9ntdjHGAGnqsooZWjPiKNZ5SkzxS8NhHZmbF0SSTgpmZFYeTgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVmOk4KZmeU4KZiZWY6TgpmZ5TgpmJlZjpOCmZnlOCmYmVlO2dyOU9ImwO+AT4FJETG2xCGZmTU7RT1SkHSrpHckzapWfpikVyTNk3RBWnwcMD4ihgJHFzMuMzMrrNjNR2OAw/ILJLUAfgscDvQATpLUA+gKLEzf9lmR4zIzswKK2nwUEZMldatW3A+YFxGvAki6CzgGWESSGGZQS7KSNAwYBrDNNts0fNDrsWNePrdB6/tbj2sbtL6moKG3IZT/dmzodb6W8l5faN7/K6XoaN6Kz48IIEkGWwH3AsdLuhGYUNPMEXFzRFREREWXLl2KG6mZWTNTNh3NEfEh8J1Sx2Fm1pyV4kjhDWDrvNdd07LMJB0l6eZly5Y1aGBmZs1dKZLCc8COkrpL2hAYDNxfnwoiYkJEDGvfvn1RAjQza66KfUrqn4EpwM6SFkkaEhGrgLOAR4DZwF8i4qVixmFmZtkU++yjk2oofwh4qJjLNjOz+muSw1y4T8HMrDiaZFJwn4KZWXE0yaRgZmbFoYgodQxrTdJi4PW1nL0z8G4DhlMM5R5juccHjrEhlHt8UP4xllt820ZEwat/m3RSWBeSKiOiotRx1KbcYyz3+MAxNoRyjw/KP8Zyjy+fm4/MzCzHScHMzHKac1K4udQBZFDuMZZ7fOAYG0K5xwflH2O5x5fTbPsUzMzsi5rzkYKZmVXjpGBmZjnNMinUcI/okpK0QNKLkmZIqkzLOkp6VNLc9O+mjRzTF+6xXVNMSoxOt+kLkvYoYYyXSnoj3ZYzJB2RN+3CNMZXJB3aCPFtLelxSS9LeknSD9PystiOtcRXTtuwtaSpkmamMV6WlneX9Gway7h01GUkbZS+npdO71bCGMdIei1vO/ZJy0vy/5JJRDSrB9ACmA9sB2wIzAR6lEFcC4DO1cquAi5In18AXNnIMfUH9gBm1RUTcATwMCBgb+DZEsZ4KfCjAu/tkX7eGwHd0+9BiyLHtwWwR/q8LfCvNI6y2I61xFdO21BAm/R5K+DZdNv8BRiclt8EfC99fiZwU/p8MDCuEb6HNcU4BhhY4P0l+X/J8miORwq5e0RHxKdA1T2iy9ExwJ/S538Cjm3MhUfEZOC9jDEdA9wWiWeADpK2KFGMNTkGuCsiPomI14B5JN+HoomItyJievr8A5Lh4reiTLZjLfHVpBTbMCJiefqyVfoI4GvA+LS8+jas2rbjgYMkqUQx1qQk/y9ZNMekUNM9okstgImSpkkalpZtFhFvpc/fBjYrTWhrqCmmctuuZ6WH5bfmNbuVNMa0GWN3kl+RZbcdq8UHZbQNJbWQNAN4B3iU5AhlaST3Z6keRy7GdPoyoFNjxxgRVdvxF+l2vFbSRtVjLBB/STXHpFCu9o+IPYDDge9L6p8/MZJjzrI6f7gcY0rdCGwP9AHeAn5d0mgASW2Ae4BzIuL9/GnlsB0LxFdW2zAiPouIPiS37+0HfKWU8RRSPUZJvYALSWLtC3QEflK6CLNpjklhne8RXQwR8Ub69x3gryRf/P9UHVKmf98pXYQ5NcVUNts1Iv6T/oOuBn7P580bJYlRUiuSHe7YiLg3LS6b7VgovnLbhlUiYinwOLAPSZNL1Y3C8uPIxZhObw8sKUGMh6XNcxERnwB/pEy2Y22aY1JY53tENzRJm0hqW/UcOASYlcb17fRt3wb+VpoI11BTTPcDp6ZnVewNLMtrHmlU1dpmv0myLSGJcXB6dkp3YEdgapFjEXALMDsirsmbVBbbsab4ymwbdpHUIX3+JeBgkr6Px4GB6duqb8OqbTsQ+Ed6NNbYMc7JS/wi6fPI345l8f/yBaXu6S7Fg6Tn/18k7ZI/LYN4tiM5o2Mm8FJVTCTtoP8HzAUeAzo2clx/Jmk6WEnS5jmkpphIzqL4bbpNXwQqShjj7WkML5D8822R9/6fpjG+AhzeCPHtT9I09AIwI30cUS7bsZb4ymkb7gY8n8YyC/hZWr4dSUKaB9wNbJSWt05fz0unb1fCGP+RbsdZwB18foZSSf5fsjw8zIWZmeU0x+YjMzOrgZOCmZnlOCmYmVmOk4KZmeU4KZiZWY6Tgn2BpJD067zXP5J0aQPVPUbSwLrfuc7LGSRptqTH17GedYpX0khJX6/nPBWSRq/tMhuTpA6Szix1HNZwnBSskE+A4yR1LnUg+fKuXs1iCDA0Ig4sVjxZRMTPIuKxes5TGRFnFyumdVHgM+hAMiqprSecFKyQVST3lD23+oTqv5wlLU//DpD0hKS/SXpV0hWSTk7HmH9R0vZ51XxdUqWkf0k6Mp2/haRRkp5LBw/7bl69T0q6H3i5QDwnpfXPknRlWvYzkouybpE0qtr7t5A0WcnY9rMkfTV/PdLnAyWNqSPe0yTdp+ReCAsknSXpPEnPS3pGUsfq2yvdJi+n63d1WjYojWOmpMl56/xA+rxjupwX0np3S8svVTJQ3aR0e5+dlm8i6cG0vlmSTiywzSZJ+k3eNuiXN++t6Wf2vKRj8tb1fkn/ILngLt8VwPZpXaPS94/I+xwvy1vufUoGfHxJnw/6iKTl6Wf/kqTHJPXLW6+jq8dvRVbqq+f8KL8HsBxoR3KPh/bAj4BL02ljyBsfHlie/h0ALCUZn38jknFcLkun/RC4Lm/+v5P8INmR5Crk1sAw4KL0PRsBlSTj9Q8APgS6F4hzS+DfQBegJcnVo8em0yZR4CpR4Hw+v2K8BdA2fz3S5wOBMXXEexrJFbNt0+UvA4an81xLMrBcbnuRXMH8Cp/fF71D+vdFYKtqZQOAB9Ln1wOXpM+/BsxIn18KPJ1uq84kY/u0Ao4Hfp+3Lu0LbINJVe8huR/FrPT5L4FTqmIhuep/k3RdF1HginqgG2vey+IQkh8USrfZA0D/dFrVVdtfIrnCt1P6OkivjCYZ92tiui69q9bXj8Z7+EjBCopkpMzbgPo0YzwXyQBgn5Bcvj8xLX+RZOdR5S8RsToi5gKvkowieQjJWDAzSIZu7kSyEwaYGsnY/dX1BSZFxOJIhkgeS7KTqzVG4DtK+kh2jeQeAnUpFC/A4xHxQUQsJkkKE2pYX9LpK0iOXo4DPkrL/wmMkTSUJElVtz/JkBNExD+ATpLapdMejOS+Bu+SDKi3WbrsgyVdKemrEbGshnX6c1rnZKCdknF7DgEuSD+DSSTJb5v0/Y9GRJb7VhySPp4HppNsq6rP8WxJM4FnSAaDqyr/lCTxksb/RESspPB2tCJzUrDaXEfSNr9JXtkq0u+NpA1I7l5X5ZO856vzXq8m+SVfpfrYKkHyy/IHEdEnfXSPiKqk8uG6rMQaC0p2gv1JjmTGSDq1QEytC8RX6HXW9SVNWv1IbvpyJOlOMCKGAxeR7CSnSarPuP/5y/8MaBkR/yK5E92LwOVpU1ohNX0Gx+d9BttExOx0etbPQMCv8urYISJukTQA+DqwT0T0JkkaVdt5ZURUxZPbjpGM0FqffiRrAE4KVqP0l+FfSBJDlQXAnunzo0kO8+trkKQN0n6G7UiaVR4BvqdkGGck7aRkxNjaTAUOkNRZUgvgJOCJ2maQtC3wn4j4PfAHkh0oJENZ75Imum9miLdelNyvoH1EPETSV9M7Ld8+Ip6NiJ8Bi1lzOGWAJ4GT0/cOAN6NavdjqLacLYGPIuIOYFTe+lV3Yvr+/UlG6FxG8hn8QEruUiZp9wyr9gFJE1qVR4DT0/VF0laSvkzSDPnfiPhI0ldIbkFpZchZ2Orya+CsvNe/B/6WNgP8nbX7Ff9vkh16O5J2+BWS/kDSVDA93Sktpo7bj0bEW5IuIBlCWSTNKXUNLz4AGCFpJUnfSdWRwgUk7d+LSfoz2tQRb7Y1/Vxbku3WOo31vLR8lKQd07L/Ixkp94C8+S4FbpX0AkmT07ep3a5pnatJRo79Xg3vWyHpeZKkfnpa9nOSo8MX0uT4GslRTY0iYomkf0qaBTwcESMk7QJMSbfRcuAUku/KcEmzSZLqM3Wsh5WIR0k1a2YkTQJ+FBGVpY7Fyo+bj8zMLMdHCmZmluMjBTMzy3FSMDOzHCcFMzPLcVIwM7McJwUzM8v5/1QYDr/NQkLtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(serious.groupby(by='TeamId').count().Id, bins=b, log=True, alpha=0.5, label='all results')\n",
    "plt.hist(serious[serious.Medal ==1.].groupby('TeamId').count().Id, bins=b, log=True, alpha=0.7, label = f'top {n} private results')\n",
    "plt.title('Number of submissions per team (SIIM)')\n",
    "plt.xlabel('Number of submissions per team')\n",
    "plt.ylabel('Count of number of teams')\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "5aafa468",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_307/3271615303.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  serious[serious.Medal==1.].groupby('TeamId').mean().sort_values('PrivateLeaderboardRank') ## These 12 medal==1\n"
     ]
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      ],
      "text/plain": [
       "                Id  CompetitionId  TeamLeaderId  ScoreFirstSubmittedDate  \\\n",
       "TeamId                                                                     \n",
       "6815503  6815503.0        26680.0     1393170.0                      NaN   \n",
       "6818936  6818936.0        26680.0     2545270.0                      NaN   \n",
       "6815267  6815267.0        26680.0     3460291.0                      NaN   \n",
       "6823597  6823597.0        26680.0     3574256.0                      NaN   \n",
       "6817202  6817202.0        26680.0     4712534.0                      NaN   \n",
       "6903817  6903817.0        26680.0     7574779.0                      NaN   \n",
       "6968613  6968613.0        26680.0     3613976.0                      NaN   \n",
       "6839557  6839557.0        26680.0     4940666.0                      NaN   \n",
       "6817029  6817029.0        26680.0     1424766.0                      NaN   \n",
       "6815527  6815527.0        26680.0      478989.0                      NaN   \n",
       "6861408  6861408.0        26680.0     6969136.0                      NaN   \n",
       "6815226  6815226.0        26680.0     1057275.0                      NaN   \n",
       "\n",
       "         PublicLeaderboardSubmissionId  PrivateLeaderboardSubmissionId  \\\n",
       "TeamId                                                                   \n",
       "6815503                     22115046.0                      22138936.0   \n",
       "6818936                     22271717.0                      22280976.0   \n",
       "6815267                     22286802.0                      22286806.0   \n",
       "6823597                     22280375.0                      22280375.0   \n",
       "6817202                     22154952.0                      22286450.0   \n",
       "6903817                     22274761.0                      22274761.0   \n",
       "6968613                     22285019.0                      22285019.0   \n",
       "6839557                     22285051.0                      22286492.0   \n",
       "6817029                     22285666.0                      22285666.0   \n",
       "6815527                     22286517.0                      22278286.0   \n",
       "6861408                     22260232.0                      22277236.0   \n",
       "6815226                     22286591.0                      22286591.0   \n",
       "\n",
       "         IsBenchmark  Medal  PublicLeaderboardRank  PrivateLeaderboardRank  \\\n",
       "TeamId                                                                       \n",
       "6815503          0.0    1.0                    1.0                     1.0   \n",
       "6818936          0.0    1.0                    6.0                     2.0   \n",
       "6815267          0.0    1.0                    4.0                     3.0   \n",
       "6823597          0.0    1.0                    2.0                     4.0   \n",
       "6817202          0.0    1.0                    3.0                     5.0   \n",
       "6903817          0.0    1.0                   31.0                     6.0   \n",
       "6968613          0.0    1.0                    5.0                     7.0   \n",
       "6839557          0.0    1.0                   28.0                     8.0   \n",
       "6817029          0.0    1.0                    9.0                     9.0   \n",
       "6815527          0.0    1.0                    8.0                    10.0   \n",
       "6861408          0.0    1.0                   17.0                    11.0   \n",
       "6815226          0.0    1.0                   13.0                    12.0   \n",
       "\n",
       "         WriteUpForumTopicId  SubmissionId  SourceKernelVersionId  \\\n",
       "TeamId                                                              \n",
       "6815503             263658.0  2.178476e+07           6.719075e+07   \n",
       "6818936             263674.0  2.184838e+07           6.759822e+07   \n",
       "6815267             263654.0  2.175123e+07           6.699764e+07   \n",
       "6823597             264243.0  2.182679e+07           6.751683e+07   \n",
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       "6903817             264858.0  2.196845e+07           6.846591e+07   \n",
       "6968613             263748.0  2.204544e+07           6.894520e+07   \n",
       "6839557             263734.0  2.195073e+07           6.829584e+07   \n",
       "6817029             265583.0  2.208444e+07           6.923010e+07   \n",
       "6815527             263683.0  2.222636e+07           7.024439e+07   \n",
       "6861408             263701.0  2.182266e+07           6.743162e+07   \n",
       "6815226             264311.0  2.200596e+07           6.866506e+07   \n",
       "\n",
       "         IsAfterDeadline  PublicScoreLeaderboardDisplay  \\\n",
       "TeamId                                                    \n",
       "6815503              0.0                       0.562220   \n",
       "6818936              0.0                       0.419434   \n",
       "6815267              0.0                       0.383267   \n",
       "6823597              0.0                       0.547498   \n",
       "6817202              0.0                       0.568304   \n",
       "6903817              0.0                       0.551867   \n",
       "6968613              0.0                       0.377543   \n",
       "6839557              0.0                       0.367388   \n",
       "6817029              0.0                       0.477181   \n",
       "6815527              0.0                       0.459409   \n",
       "6861408              0.0                       0.504584   \n",
       "6815226              0.0                       0.428816   \n",
       "\n",
       "         PrivateScoreLeaderboardDisplay  \n",
       "TeamId                                   \n",
       "6815503                        0.148776  \n",
       "6818936                        0.124403  \n",
       "6815267                        0.135102  \n",
       "6823597                        0.441562  \n",
       "6817202                        0.281775  \n",
       "6903817                        0.543400  \n",
       "6968613                        0.366318  \n",
       "6839557                        0.352347  \n",
       "6817029                        0.248916  \n",
       "6815527                        0.447909  \n",
       "6861408                        0.095474  \n",
       "6815226                        0.414952  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serious[serious.Medal==1.].groupby('TeamId').mean().sort_values('PrivateLeaderboardRank') ## These 12 medal==1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae066897",
   "metadata": {},
   "source": [
    "Some teams do well even though they make relatively few submissions ~ Guessing they are doing significant work offline?"
   ]
  }
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