Stubhub API Tutorial.ipynb

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   "source": [
    "# Intro\n",
    "\n",
    "This is a tutorial on using my Python wrapper for the listing scraper api API which enbales users to download pricing information from major ticket exchanges."
   ]
  },
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       "RendererRegistry.enable('mimetype')"
      ]
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     "execution_count": 11,
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   ],
   "source": [
    "from listing_scraper import st\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import altair as al\n",
    "al.themes.enable('dark') # set dark theme\n",
    "al.renderers.enable('mimetype')\n"
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   "source": [
    "# Single event\n",
    "To download listings for a single event use `get_listings` function which requires `eventid`. Lets get listings for the Nets at Lakers game on Novermber 13th 2022. You can find the event id in the event url on the stubhub website. "
   ]
  },
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      "                                                                              \r"
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      "text/plain": [
       "{'RatingMessage': None,\n",
       " 'RatingValue': 0.0,\n",
       " 'Id': 5625874291,\n",
       " 'EventId': 150339062,\n",
       " 'CssPostFix': '',\n",
       " 'TraderType': None,\n",
       " 'Section': 'LOT C',\n",
       " 'SectionId': None,\n",
       " 'SectionMapName': '',\n",
       " 'RowMapName': '$r',\n",
       " 'Row': None,\n",
       " 'Price': '$26',\n",
       " 'PriceWithFees': '$34',\n",
       " 'RawPrice': 26.39,\n",
       " 'QuantityRange': '1',\n",
       " 'IsAlreadySold': None,\n",
       " 'IsAlreadySoldTicketClass': None,\n",
       " 'LastInSectionString': None,\n",
       " 'LastInSectionDescriptionString': None,\n",
       " 'AboutToSellOut': None,\n",
       " 'AboutToSellOutExplanation': None,\n",
       " 'NumberOfTicketsString': None,\n",
       " 'AdmitXTicketsString': None,\n",
       " 'TicketsLeftInListingMessage': {'Message': '1 ticket remaining',\n",
       "  'Qualifier': 'in this listing',\n",
       "  'Disclaimer': None,\n",
       "  'HasValue': True,\n",
       "  'FeatureTrackingKey': '2fbb0992-0355-42c8-98d2-4f0616842abb'},\n",
       " 'ShowNumberOfTicketsString': False,\n",
       " 'LastTicketsInSectionMessage': None,\n",
       " 'NumberOfTicketsDisplayed': None,\n",
       " 'WillBeAnimated': False,\n",
       " 'LocationRank': 0.0,\n",
       " 'ListingRank': 0.0,\n",
       " 'PriceRank': 0.0,\n",
       " 'SectionColor': None,\n",
       " 'HasBestValue': False,\n",
       " 'IsPromotedListing': False,\n",
       " 'SwapMapInTile': False,\n",
       " 'ShowStandOrSitFreely': None,\n",
       " 'PreAllocatedSeats': None,\n",
       " 'PriceBucketColour': '#a3d165',\n",
       " 'PriceBucketColourClass': None,\n",
       " 'GeneralAdmissionExplanation': None,\n",
       " 'IsGeneralAdmission': False,\n",
       " 'FaceValueText': None,\n",
       " 'MaxQuantity': 1,\n",
       " 'SourceId': None,\n",
       " 'AvailableQuantities': [1],\n",
       " 'TicketTypeNotes': '',\n",
       " 'TicketTypeNotesAndListingNotesSeparator': '<br />',\n",
       " 'TicketClass': 0,\n",
       " 'TicketClassName': None,\n",
       " 'BuyUrl': '/secure/buy/Initialise?ListingID=5625874291&EventID=150339062&CategoryId=5973',\n",
       " 'PackageTitle': None,\n",
       " 'Package': None,\n",
       " 'Seats': None,\n",
       " 'IsRationalizedListing': True,\n",
       " 'ShowInstantDownloadBadge': False,\n",
       " 'RowId': None,\n",
       " 'IsUsersListing': False,\n",
       " 'TicketReservationContent': None,\n",
       " 'IsCheapestInSection': None,\n",
       " 'IsCheapestInTicketClass': False,\n",
       " 'IsBestValueInTicketClass': False,\n",
       " 'ShowStandingTogetherBadge': False,\n",
       " 'ShowReservedSeatsBadge': False,\n",
       " 'ShowPeopleViewed': False,\n",
       " 'PeopleViewedMessage': None,\n",
       " 'IsBestDealInSection': False,\n",
       " 'ShowLastTicketsAtThisPriceMessage': False,\n",
       " 'IsRecommendedByUs': False,\n",
       " 'SvgMapPngUrl': 'https://img.vggcdn.net/img/svgpng4/349165/tc_0.png',\n",
       " 'SvgMapPngTcFallbackUrl': 'https://img.vggcdn.net/img/svgpng4/349165/tc_0.png',\n",
       " 'HottestTicketMessage': None,\n",
       " 'PeopleViewingMessage': None,\n",
       " 'IsSellingFast': False,\n",
       " 'ShowExpressHomeDeliveryBadge': False,\n",
       " 'IsDiscounted': False,\n",
       " 'WillKickUser': False,\n",
       " 'TicketTypeId': 10,\n",
       " 'TicketTypesWithIcons': None,\n",
       " 'TicketTypeForPageEvent': None,\n",
       " 'ShowClickAndDownloadMessage': None,\n",
       " 'XOtherInterestedInBadgeText': None,\n",
       " 'XOthersInterestedInTicketsMessage': None,\n",
       " 'ListingNoteSectionTitle': None,\n",
       " 'IsReservedListing': False,\n",
       " 'NowAtPrice': None,\n",
       " 'othersViewingTheseTicketsMessage': None,\n",
       " 'CountryCode': None,\n",
       " 'HumanFilterIds': None,\n",
       " 'RestrictionListingNotes': None,\n",
       " 'RecommendReason': None,\n",
       " 'IsCheapest': False,\n",
       " 'RecentlyAddedString': None,\n",
       " 'RecentlySoldTicketCountString': None,\n",
       " 'SeatInfo': None,\n",
       " 'HasBadListingNotes': False,\n",
       " 'CanBeCompared': False,\n",
       " 'LocationRanking': 0.0,\n",
       " 'LocationRankingMessage': None,\n",
       " 'AlternativeListingMessage': None,\n",
       " 'ListingNotes': [{'ListingNoteId': 1582,\n",
       "   'ListingNoteContentId': 2447686,\n",
       "   'FormattedListingNoteContent': 'Parking Pass',\n",
       "   'ListingNoteTypeId': 3,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2447687,\n",
       "   'FormattedListingNoteDescription': 'Parking Pass',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1653,\n",
       "   'ListingNoteContentId': 2447828,\n",
       "   'FormattedListingNoteContent': 'VIP Parking Pass',\n",
       "   'ListingNoteTypeId': 3,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2447829,\n",
       "   'FormattedListingNoteDescription': 'VIP Parking Pass',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1036,\n",
       "   'ListingNoteContentId': 2446513,\n",
       "   'FormattedListingNoteContent': 'VIP parking',\n",
       "   'ListingNoteTypeId': 4,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2446514,\n",
       "   'FormattedListingNoteDescription': 'VIP parking',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1142,\n",
       "   'ListingNoteContentId': 2446805,\n",
       "   'FormattedListingNoteContent': 'VIP Lot parking',\n",
       "   'ListingNoteTypeId': 4,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2446806,\n",
       "   'FormattedListingNoteDescription': 'VIP Lot parking',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1268,\n",
       "   'ListingNoteContentId': 2447057,\n",
       "   'FormattedListingNoteContent': 'Includes premium parking',\n",
       "   'ListingNoteTypeId': 4,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2447058,\n",
       "   'FormattedListingNoteDescription': 'Includes premium parking',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': -1,\n",
       "   'ListingNoteContentId': 1523411,\n",
       "   'FormattedListingNoteContent': 'Unrestricted view.',\n",
       "   'ListingNoteTypeId': 3,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 1319276,\n",
       "   'FormattedListingNoteDescription': \"You'll have a clear view of the show.\",\n",
       "   'IconOverride': None}],\n",
       " 'PreRenderedTicketTypeNotesAndListingNotes': [{'ListingNoteId': 1582,\n",
       "   'ListingNoteContentId': 2447686,\n",
       "   'FormattedListingNoteContent': 'Parking Pass',\n",
       "   'ListingNoteTypeId': 3,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2447687,\n",
       "   'FormattedListingNoteDescription': 'Parking Pass',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1653,\n",
       "   'ListingNoteContentId': 2447828,\n",
       "   'FormattedListingNoteContent': 'VIP Parking Pass',\n",
       "   'ListingNoteTypeId': 3,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2447829,\n",
       "   'FormattedListingNoteDescription': 'VIP Parking Pass',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': -1,\n",
       "   'ListingNoteContentId': 1523411,\n",
       "   'FormattedListingNoteContent': 'Unrestricted view.',\n",
       "   'ListingNoteTypeId': 3,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 1319276,\n",
       "   'FormattedListingNoteDescription': \"You'll have a clear view of the show.\",\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1036,\n",
       "   'ListingNoteContentId': 2446513,\n",
       "   'FormattedListingNoteContent': 'VIP parking',\n",
       "   'ListingNoteTypeId': 4,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2446514,\n",
       "   'FormattedListingNoteDescription': 'VIP parking',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1142,\n",
       "   'ListingNoteContentId': 2446805,\n",
       "   'FormattedListingNoteContent': 'VIP Lot parking',\n",
       "   'ListingNoteTypeId': 4,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2446806,\n",
       "   'FormattedListingNoteDescription': 'VIP Lot parking',\n",
       "   'IconOverride': None},\n",
       "  {'ListingNoteId': 1268,\n",
       "   'ListingNoteContentId': 2447057,\n",
       "   'FormattedListingNoteContent': 'Includes premium parking',\n",
       "   'ListingNoteTypeId': 4,\n",
       "   'ShowToBuyer': True,\n",
       "   'HideInMock': False,\n",
       "   'FormattedListingNoteQualifier': None,\n",
       "   'ListingNoteDescriptionContentId': 2447058,\n",
       "   'FormattedListingNoteDescription': 'Includes premium parking',\n",
       "   'IconOverride': None}],\n",
       " 'MostPopularSectionDescription': None,\n",
       " 'ViewFromSectionUrl': None,\n",
       " 'ViewFromSeatUrl': None,\n",
       " 'ViewFromSectionDisclaimer': None,\n",
       " 'RemoveAnimation': False,\n",
       " 'DistinctPriceTicketContent': None,\n",
       " 'MaySoonChangePriceMessage': None,\n",
       " 'CheapestInSectionMessage': None,\n",
       " 'SectionSellingFastMessage': None,\n",
       " 'SeasonalDealMessage': None,\n",
       " 'SeasonalDealClass': None,\n",
       " 'SeasonalDealExplanation': None,\n",
       " 'MonthlyDealMessage': None,\n",
       " 'MonthlyDealDescription': None,\n",
       " 'SellerIsNearbyMessage': None,\n",
       " 'SoldXTimeAgoSiteMessage': None,\n",
       " 'RowContent': 'Row',\n",
       " 'XPercentageOfPeopleBoughtThisSection': None,\n",
       " 'XPercentageOfPeopleBoughtThisSectionExplanation': None,\n",
       " 'EmailDeliveryMessage': None,\n",
       " 'ExpiresMessage': None,\n",
       " 'ExpiresMessageDisclaimer': None,\n",
       " 'AlternativeOffer': None,\n",
       " 'SectionRecentlySoldCount': 0,\n",
       " 'ShowSeatedTogetherBulletAsChairs': False,\n",
       " 'IsSeatedTogether': False,\n",
       " 'PipelineEntryMessage': None,\n",
       " 'GoldenListingPowerUp': False,\n",
       " 'StarDecorationPowerUp': False,\n",
       " 'DisplayPrice': 26.0,\n",
       " 'IsLocked': False,\n",
       " 'LockedTimeSeconds': 0,\n",
       " 'NoParticipationLocked': False,\n",
       " 'HasSeatInformation': True,\n",
       " 'SectionLikelyToSellOutMessage': None,\n",
       " 'BestSellingInSectionMessage': None,\n",
       " 'CheapestInCityMessage': None,\n",
       " 'HasSpecialNotes': True,\n",
       " 'IsExpertChoice': False,\n",
       " 'IsStandardTicket': False,\n",
       " 'FaceValueMessage': None,\n",
       " 'SellerTrackingKey': 'e205966f-497c-4d4f-b5f6-6be10d8e63ba',\n",
       " 'FvTrackingKey': None,\n",
       " 'InvalidSeatsTrackingKey': None,\n",
       " 'TraderIconMessage': {'Message': None,\n",
       "  'Qualifier': None,\n",
       "  'Disclaimer': None,\n",
       "  'HasValue': False,\n",
       "  'FeatureTrackingKey': None},\n",
       " 'ConnectedSellerIconMessage': {'Message': None,\n",
       "  'Qualifier': None,\n",
       "  'Disclaimer': None,\n",
       "  'HasValue': False,\n",
       "  'FeatureTrackingKey': None},\n",
       " 'SeatDetailsIcon': {'Message': None,\n",
       "  'Qualifier': None,\n",
       "  'Disclaimer': None,\n",
       "  'HasValue': False,\n",
       "  'FeatureTrackingKey': None}}"
      ]
     },
     "execution_count": 12,
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   ],
   "source": [
    "listings = st.get_listings(150339062)\n",
    "listings[0]"
   ]
  },
  {
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       "1225"
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   "source": [
    "# Number of listings\n",
    "len(listings)"
   ]
  },
  {
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   "source": [
    "`listings` is a list of dictionaries one of which you can see above. You can convert it to a dataframe and save as a csv."
   ]
  },
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       "      <th></th>\n",
       "      <th>RatingMessage</th>\n",
       "      <th>RatingValue</th>\n",
       "      <th>Id</th>\n",
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       "      <td>False</td>\n",
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       "      <td>None</td>\n",
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       "      <td>5575812063</td>\n",
       "      <td>150339062</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>PARKINGING</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>$r</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>e205966f-497c-4d4f-b5f6-6be10d8e63ba</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5583031600</td>\n",
       "      <td>150339062</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>PARKING</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>$r</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>e205966f-497c-4d4f-b5f6-6be10d8e63ba</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  RatingMessage  RatingValue          Id    EventId CssPostFix TraderType  \\\n",
       "0          None          0.0  5625874291  150339062                  None   \n",
       "1          None          0.0  5640201492  150339062      blue2       None   \n",
       "2          None          0.0  5494985230  150339062      blue2       None   \n",
       "3          None          0.0  5575812063  150339062                  None   \n",
       "4          None          0.0  5583031600  150339062                  None   \n",
       "\n",
       "      Section  SectionId SectionMapName RowMapName  ... HasSpecialNotes  \\\n",
       "0       LOT C        NaN                        $r  ...            True   \n",
       "1        PR15  1504521.0                        $r  ...           False   \n",
       "2        PR05  1504159.0                        $r  ...           False   \n",
       "3  PARKINGING        NaN                        $r  ...            True   \n",
       "4     PARKING        NaN                        $r  ...            True   \n",
       "\n",
       "  IsExpertChoice IsStandardTicket  FaceValueMessage  \\\n",
       "0          False            False              None   \n",
       "1          False            False              None   \n",
       "2          False            False              None   \n",
       "3          False            False              None   \n",
       "4          False            False              None   \n",
       "\n",
       "                      SellerTrackingKey FvTrackingKey InvalidSeatsTrackingKey  \\\n",
       "0  e205966f-497c-4d4f-b5f6-6be10d8e63ba          None                    None   \n",
       "1                                  None          None                    None   \n",
       "2                                  None          None                    None   \n",
       "3  e205966f-497c-4d4f-b5f6-6be10d8e63ba          None                    None   \n",
       "4  e205966f-497c-4d4f-b5f6-6be10d8e63ba          None                    None   \n",
       "\n",
       "                                   TraderIconMessage  \\\n",
       "0  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "1  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "2  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "3  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "4  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "\n",
       "                          ConnectedSellerIconMessage  \\\n",
       "0  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "1  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "2  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "3  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "4  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "\n",
       "                                     SeatDetailsIcon  \n",
       "0  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "1  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "2  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "3  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "4  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "\n",
       "[5 rows x 148 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listings = pd.DataFrame(listings)\n",
    "listings.head()\n",
    "# listings.to_csv('SpursvsWarriors 2018-3-19.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 35,
        "width": 12
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "## Multiple events\n",
    "To download listings for multiple events use `get_listings` function that takes the `events` parameter which is a list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 21,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Let's get listings for some of the Nets home games in 2022-2023 season."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 0,
        "height": 4,
        "hidden": false,
        "row": 39,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                                              \r"
     ]
    }
   ],
   "source": [
    "events =[150339062,150339757,150341276,150337701,150341823,150339062]\n",
    "res = st.get_listings(events)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RatingMessage</th>\n",
       "      <th>RatingValue</th>\n",
       "      <th>Id</th>\n",
       "      <th>EventId</th>\n",
       "      <th>CssPostFix</th>\n",
       "      <th>TraderType</th>\n",
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       "      <th>SectionId</th>\n",
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       "      <th>RowMapName</th>\n",
       "      <th>...</th>\n",
       "      <th>HasSpecialNotes</th>\n",
       "      <th>IsExpertChoice</th>\n",
       "      <th>IsStandardTicket</th>\n",
       "      <th>FaceValueMessage</th>\n",
       "      <th>SellerTrackingKey</th>\n",
       "      <th>FvTrackingKey</th>\n",
       "      <th>InvalidSeatsTrackingKey</th>\n",
       "      <th>TraderIconMessage</th>\n",
       "      <th>ConnectedSellerIconMessage</th>\n",
       "      <th>SeatDetailsIcon</th>\n",
       "    </tr>\n",
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       "      <td>$r</td>\n",
       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>e205966f-497c-4d4f-b5f6-6be10d8e63ba</td>\n",
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       "      <td>None</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5575812063</td>\n",
       "      <td>150339062</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>PARKINGING</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>$r</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>e205966f-497c-4d4f-b5f6-6be10d8e63ba</td>\n",
       "      <td>None</td>\n",
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       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5583031600</td>\n",
       "      <td>150339062</td>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>PARKING</td>\n",
       "      <td>NaN</td>\n",
       "      <td></td>\n",
       "      <td>$r</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>e205966f-497c-4d4f-b5f6-6be10d8e63ba</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5640201492</td>\n",
       "      <td>150339062</td>\n",
       "      <td>blue2</td>\n",
       "      <td>None</td>\n",
       "      <td>PR15</td>\n",
       "      <td>1504521.0</td>\n",
       "      <td></td>\n",
       "      <td>$r</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5632535025</td>\n",
       "      <td>150339062</td>\n",
       "      <td>blue2</td>\n",
       "      <td>None</td>\n",
       "      <td>PR15</td>\n",
       "      <td>1504521.0</td>\n",
       "      <td></td>\n",
       "      <td>$r</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "      <td>{'Message': None, 'Qualifier': None, 'Disclaim...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 148 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  RatingMessage  RatingValue          Id    EventId CssPostFix TraderType  \\\n",
       "0          None          0.0  5625874291  150339062                  None   \n",
       "1          None          0.0  5575812063  150339062                  None   \n",
       "2          None          0.0  5583031600  150339062                  None   \n",
       "3          None          0.0  5640201492  150339062      blue2       None   \n",
       "4          None          0.0  5632535025  150339062      blue2       None   \n",
       "\n",
       "      Section  SectionId SectionMapName RowMapName  ... HasSpecialNotes  \\\n",
       "0       LOT C        NaN                        $r  ...            True   \n",
       "1  PARKINGING        NaN                        $r  ...            True   \n",
       "2     PARKING        NaN                        $r  ...            True   \n",
       "3        PR15  1504521.0                        $r  ...           False   \n",
       "4        PR15  1504521.0                        $r  ...           False   \n",
       "\n",
       "  IsExpertChoice IsStandardTicket  FaceValueMessage  \\\n",
       "0          False            False              None   \n",
       "1          False            False              None   \n",
       "2          False            False              None   \n",
       "3          False            False              None   \n",
       "4          False            False              None   \n",
       "\n",
       "                      SellerTrackingKey FvTrackingKey InvalidSeatsTrackingKey  \\\n",
       "0  e205966f-497c-4d4f-b5f6-6be10d8e63ba          None                    None   \n",
       "1  e205966f-497c-4d4f-b5f6-6be10d8e63ba          None                    None   \n",
       "2  e205966f-497c-4d4f-b5f6-6be10d8e63ba          None                    None   \n",
       "3                                  None          None                    None   \n",
       "4                                  None          None                    None   \n",
       "\n",
       "                                   TraderIconMessage  \\\n",
       "0  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "1  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "2  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "3  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "4  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "\n",
       "                          ConnectedSellerIconMessage  \\\n",
       "0  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "1  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "2  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "3  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "4  {'Message': None, 'Qualifier': None, 'Disclaim...   \n",
       "\n",
       "                                     SeatDetailsIcon  \n",
       "0  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "1  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "2  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "3  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "4  {'Message': None, 'Qualifier': None, 'Disclaim...  \n",
       "\n",
       "[5 rows x 148 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([pd.DataFrame(r) for r in res])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_loc = 'example_multiple_events.json'\n",
    "# df.to_json(save_loc, orient='records')\n",
    "df = pd.read_json(save_loc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
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        "row": 55,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150339062, 150339757, 150341276, 150337701, 150341823])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.unique(df['EventId'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 8,
        "hidden": false,
        "row": 55,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "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>RawPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EventId</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>150337701</th>\n",
       "      <td>333.957932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150339062</th>\n",
       "      <td>526.352839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150339757</th>\n",
       "      <td>143.927185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150341276</th>\n",
       "      <td>242.654522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150341823</th>\n",
       "      <td>304.837251</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             RawPrice\n",
       "EventId              \n",
       "150337701  333.957932\n",
       "150339062  526.352839\n",
       "150339757  143.927185\n",
       "150341276  242.654522\n",
       "150341823  304.837251"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_price = pd.pivot_table(df, values='RawPrice', index=['EventId'], aggfunc=np.mean)\n",
    "avg_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
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       "grid_default": {
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        "row": 60,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Average listing price by Event'}, ylabel='EventId'>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import style\n",
    "import matplotlib.pyplot as plt\n",
    "style.use('dark_background')\n",
    "\n",
    "avg_price.plot.barh(title='Average listing price by Event')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 4,
        "hidden": false,
        "row": 39,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Let's also look at the number of listings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 4,
        "height": 8,
        "hidden": false,
        "row": 63,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Number of listings by Event'}, ylabel='EventId'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "listings_number = pd.pivot_table(df, values='Id', index=['EventId'], aggfunc=len)\n",
    "listings_number.plot.barh(title='Number of listings by Event')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "hidden": true
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "source": [
    "Below is a good visual to see how spread out the prices by event are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "extensions": {
     "jupyter_dashboards": {
      "version": 1,
      "views": {
       "grid_default": {
        "col": 8,
        "height": 20,
        "hidden": false,
        "row": 63,
        "width": 4
       },
       "report_default": {
        "hidden": false
       }
      }
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'150337701'}>,\n",
       "        <AxesSubplot:title={'center':'150339062'}>],\n",
       "       [<AxesSubplot:title={'center':'150339757'}>,\n",
       "        <AxesSubplot:title={'center':'150341276'}>],\n",
       "       [<AxesSubplot:title={'center':'150341823'}>, <AxesSubplot:>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histogram of Ticket Prices under $1000 by Event\n",
    "temp = df[df.RawPrice < 1000]\n",
    "temp['RawPrice'].hist(by=temp['EventId'],alpha=0.8,bins=50,figsize=(12,10),color='red', range=[0,1000])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see some distributions are more similar than others"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_mixed_list(lst):\n",
    "    # sort mixed list\n",
    "    ret = []\n",
    "    for i in lst:\n",
    "        try:\n",
    "            ret.append(int(i))\n",
    "        except:\n",
    "            ret.append(str(i))\n",
    "    ret = sorted(ret, key=lambda x: (isinstance(x, str), x))\n",
    "    return [str(i) for i in ret]\n",
    "\n",
    "\n",
    "df = df.loc[(df['EventId']==150339062),:] # pick single event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Section</th>\n",
       "      <th>Row</th>\n",
       "      <th>RawPrice</th>\n",
       "      <th>QuantityRange</th>\n",
       "      <th>W_Avg_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>101</td>\n",
       "      <td>1</td>\n",
       "      <td>1972.196667</td>\n",
       "      <td>14</td>\n",
       "      <td>4.886859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>101</td>\n",
       "      <td>10</td>\n",
       "      <td>873.931111</td>\n",
       "      <td>38</td>\n",
       "      <td>5.877767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>101</td>\n",
       "      <td>11</td>\n",
       "      <td>661.980000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.468658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>101</td>\n",
       "      <td>12</td>\n",
       "      <td>686.480000</td>\n",
       "      <td>232</td>\n",
       "      <td>28.188205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101</td>\n",
       "      <td>13</td>\n",
       "      <td>836.275000</td>\n",
       "      <td>18</td>\n",
       "      <td>2.664239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>C42</td>\n",
       "      <td></td>\n",
       "      <td>230.300000</td>\n",
       "      <td>14</td>\n",
       "      <td>0.570655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>C43</td>\n",
       "      <td></td>\n",
       "      <td>230.780000</td>\n",
       "      <td>24</td>\n",
       "      <td>0.980304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>CRT</td>\n",
       "      <td>1</td>\n",
       "      <td>11645.546667</td>\n",
       "      <td>16</td>\n",
       "      <td>32.978539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>587</th>\n",
       "      <td>Loge Level 104</td>\n",
       "      <td>14</td>\n",
       "      <td>445.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.315398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>Loge Level 118</td>\n",
       "      <td>14</td>\n",
       "      <td>476.100000</td>\n",
       "      <td>8</td>\n",
       "      <td>0.674124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>589 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Section Row      RawPrice  QuantityRange  W_Avg_Price\n",
       "0               101   1   1972.196667             14     4.886859\n",
       "1               101  10    873.931111             38     5.877767\n",
       "2               101  11    661.980000              4     0.468658\n",
       "3               101  12    686.480000            232    28.188205\n",
       "4               101  13    836.275000             18     2.664239\n",
       "..              ...  ..           ...            ...          ...\n",
       "584             C42        230.300000             14     0.570655\n",
       "585             C43        230.780000             24     0.980304\n",
       "586             CRT   1  11645.546667             16    32.978539\n",
       "587  Loge Level 104  14    445.500000              4     0.315398\n",
       "588  Loge Level 118  14    476.100000              8     0.674124\n",
       "\n",
       "[589 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heat_data = df[~df.Section.str.contains('PR')] \\\n",
    "        .groupby(['Section','Row']) \\\n",
    "        .agg({'RawPrice': 'mean',\n",
    "              'QuantityRange': lambda x: sum([int(a[-1]) for a in x.str.split('-')])}) \\\n",
    "        .reset_index()\n",
    "heat_data['W_Avg_Price'] = heat_data['RawPrice']*heat_data['QuantityRange']/heat_data['QuantityRange'].sum()\n",
    "heat_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v4+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json",
       "config": {
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        "view": {
         "continuousHeight": 300,
         "continuousWidth": 400
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       },
       "data": {
        "name": "data-12a91202ae5a89c83e5013e0052d2ed3"
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          "Row": "10",
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          "Row": "11",
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          "",
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          "D",
          "E",
          "F",
          "G",
          "H",
          "J",
          "K",
          "L",
          "M",
          "None",
          "SRO"
         ],
         "title": null,
         "type": "nominal"
        },
        "y": {
         "field": "Section",
         "sort": [
          "101",
          "102",
          "103",
          "104",
          "105",
          "106",
          "107",
          "108",
          "109",
          "110",
          "111",
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          "329",
          "330",
          "331",
          "332",
          "333",
          "334",
          "A09",
          "A48",
          "B Level Suites",
          "B03",
          "B04",
          "B16",
          "B34",
          "B59",
          "C Level Suites",
          "C-40",
          "C-44",
          "C-6",
          "C03",
          "C04",
          "C29",
          "C30",
          "C32",
          "C37",
          "C41",
          "C42",
          "C43",
          "CRT",
          "LOT C",
          "Lexus Club Pass",
          "Loge Level 104",
          "Loge Level 118",
          "PARKING",
          "PARKINGING",
          "PR01",
          "PR02",
          "PR03",
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          "PR05",
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          "PR17",
          "PR18"
         ],
         "title": null,
         "type": "nominal"
        }
       },
       "height": 1000,
       "mark": "rect",
       "title": "Avg Price by Section and Row for Event 150339062",
       "usermeta": {
        "embedOptions": {
         "theme": "dark"
        }
       },
       "width": 750
      },
      "text/plain": [
       "<VegaLite 4 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heat_chart = al.Chart(heat_data, title=f'Avg Price by Section and Row for Event {150339062}').mark_rect().encode(\n",
    "    x=al.X('Row', title=None, sort=sort_mixed_list(df['Row'].unique()), axis=al.Axis(orient=\"top\",labelAngle=-45)),\n",
    "    y=al.Y('Section', title=None, sort=sort_mixed_list(df['Section'].unique())),\n",
    "    color=al.Color('W_Avg_Price', scale=al.Scale(type='log',scheme='greenblue')),\n",
    "    tooltip=['Section','Row','QuantityRange',al.Tooltip('RawPrice', format=\"$.0f\")]\n",
    ").properties(\n",
    "    width=750,\n",
    "    height=1000\n",
    ")\n",
    "# add colored labels \n",
    "text = heat_chart.mark_text(size=9).encode(\n",
    "    al.Text('RawPrice:Q', format=\"$.0f\"))\n",
    "\n",
    "heat_chart.configure(background='black')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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