Snapshotsurfer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "global file\n",
    "file = input('Selet a folder to save output') ##enter your file path here - the file is in the repo \"summary_stats.csv\".\n",
    "raw_file = file"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from datetime import date\n",
    "from subgrounds.subgraph import SyntheticField, FieldPath\n",
    "from subgrounds.subgrounds import Subgrounds\n",
    "import pandas as pd\n",
    "import  os as os\n",
    "import duckdb as db\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:05:52.756438Z",
     "end_time": "2023-04-04T11:05:52.894059Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "sg = Subgrounds()\n",
    "snapshot = sg.load_api('https://hub.snapshot.org/graphql')\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:05:52.895061Z",
     "end_time": "2023-04-04T11:05:53.235814Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "snapshot.Proposal.datetime = SyntheticField(\n",
    "  lambda timestamp: str(datetime.fromtimestamp(timestamp)),\n",
    "  SyntheticField.STRING,\n",
    "  snapshot.Proposal.end,\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:03.167341Z",
     "end_time": "2023-04-04T11:07:03.187366Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "spaces = snapshot.Query.spaces(\n",
    "    first=5000,\n",
    "    orderby = 'name',\n",
    "    orderdirection = 'asc'\n",
    ")\n",
    "print(spaces)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "spaces_list = sg.query_df([\n",
    "    spaces.name\n",
    "])\n",
    "spaces_list"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "olympusdao.eth\n"
     ]
    }
   ],
   "source": [
    "spacename = input('spacename plz?')\n",
    "# based on their url, for example \"curve.eth\" for  https://snapshot.org/#/curve.eth\n",
    "print  (spacename)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:17.650063Z",
     "end_time": "2023-04-04T11:07:25.389091Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "proposals = snapshot.Query.proposals(\n",
    "  orderBy='created',\n",
    "  orderDirection='desc',\n",
    "  first=1000,\n",
    "  where=[\n",
    "    snapshot.Proposal.space == spacename, ##'fuse.eth',\n",
    "    snapshot.Proposal.state == 'closed'\n",
    "    ##snapshot.Proposal.title == 'OIP-18: Reward rate framework and reduction',\n",
    "  ]\n",
    ")\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:34.585225Z",
     "end_time": "2023-04-04T11:07:34.606243Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "                                     proposals_title  proposals_created  \\\n0  OIP-136: Cross-chain native OHM as bonus proje...         1680108558   \n1                     TAP-22 - Treasury Segmentation         1680108259   \n2  OIP-135: Olympus Grants Program 1-Year Review ...         1679402793   \n3  AP-23 - Liquidity Incentives for Aura / Balanc...         1679325165   \n4  OIP 134: Hypernative as a security and risk de...         1679244244   \n5                     OIP-133: Staking Rate Strategy         1678428395   \n6       OIP-132: wstETH/OHM Boosted Liquidity Engine         1678427799   \n7  OIP 131: Boosted Liquidity Engine as a Key Pro...         1678126068   \n8  OIP-130: Lending AMO as a key DAO project for ...         1677691772   \n9   OOP-1 Funding request for 2023 Q1 and Q2 budget.         1677453933   \n\n                                        proposals_id  \\\n0  0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907...   \n1  0xebd3cf4c019ddf8eb637c816379c8b464fc20ee78284...   \n2  0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946...   \n3  0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5...   \n4  0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da1...   \n5  0x0fbdc3e8adbda28970646743492623e92e3a7c2336d4...   \n6  0x991058cca07d1e6f361217927a99951d200826e2ee57...   \n7  0xe02d0e0d6cb2ad8e6a6512ab1d138d9ec6b64919715a...   \n8  0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...   \n9  0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...   \n\n                                      proposals_body  proposals_start  \\\n0  ## Summary\\n\\nDesignate the “Cross-chain Nativ...       1680108526   \n1  ## Summary:\\nRestructure Olympus DAO Multisig ...       1680108233   \n2  Summary\\n\\nThis OIP seeks authority from the c...       1679402748   \n3  Summary:\\nEstablish an ongoing budget for voti...       1679325106   \n4  ### Summary\\n\\nBelow is a preliminary list of ...       1679244167   \n5  # Motivation - “how do we make 80% of OHM prod...       1678428364   \n6  # Summary\\nLaunch a Boosted Liquidity Engine f...       1678427767   \n7  Summary\\n\\nDesignate the “Boosted Liquidity En...       1678125946   \n8  # Summary\\nFollowing OIP-128 and the new budge...       1677691749   \n9  ## Summary\\nAfter the successful first quarter...       1677453900   \n\n   proposals_end  proposals_votes  \n0     1680367726               74  \n1     1680367433               63  \n2     1679686380               67  \n3     1679500800               50  \n4     1679503367               68  \n5     1678687564               76  \n6     1678686967               65  \n7     1678467600               80  \n8     1677950949               55  \n9     1677713100               60  ",
      "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>proposals_title</th>\n      <th>proposals_created</th>\n      <th>proposals_id</th>\n      <th>proposals_body</th>\n      <th>proposals_start</th>\n      <th>proposals_end</th>\n      <th>proposals_votes</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>OIP-136: Cross-chain native OHM as bonus proje...</td>\n      <td>1680108558</td>\n      <td>0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907...</td>\n      <td>## Summary\\n\\nDesignate the “Cross-chain Nativ...</td>\n      <td>1680108526</td>\n      <td>1680367726</td>\n      <td>74</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>TAP-22 - Treasury Segmentation</td>\n      <td>1680108259</td>\n      <td>0xebd3cf4c019ddf8eb637c816379c8b464fc20ee78284...</td>\n      <td>## Summary:\\nRestructure Olympus DAO Multisig ...</td>\n      <td>1680108233</td>\n      <td>1680367433</td>\n      <td>63</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>OIP-135: Olympus Grants Program 1-Year Review ...</td>\n      <td>1679402793</td>\n      <td>0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946...</td>\n      <td>Summary\\n\\nThis OIP seeks authority from the c...</td>\n      <td>1679402748</td>\n      <td>1679686380</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AP-23 - Liquidity Incentives for Aura / Balanc...</td>\n      <td>1679325165</td>\n      <td>0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5...</td>\n      <td>Summary:\\nEstablish an ongoing budget for voti...</td>\n      <td>1679325106</td>\n      <td>1679500800</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>OIP 134: Hypernative as a security and risk de...</td>\n      <td>1679244244</td>\n      <td>0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da1...</td>\n      <td>### Summary\\n\\nBelow is a preliminary list of ...</td>\n      <td>1679244167</td>\n      <td>1679503367</td>\n      <td>68</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>OIP-133: Staking Rate Strategy</td>\n      <td>1678428395</td>\n      <td>0x0fbdc3e8adbda28970646743492623e92e3a7c2336d4...</td>\n      <td># Motivation - “how do we make 80% of OHM prod...</td>\n      <td>1678428364</td>\n      <td>1678687564</td>\n      <td>76</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>OIP-132: wstETH/OHM Boosted Liquidity Engine</td>\n      <td>1678427799</td>\n      <td>0x991058cca07d1e6f361217927a99951d200826e2ee57...</td>\n      <td># Summary\\nLaunch a Boosted Liquidity Engine f...</td>\n      <td>1678427767</td>\n      <td>1678686967</td>\n      <td>65</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>OIP 131: Boosted Liquidity Engine as a Key Pro...</td>\n      <td>1678126068</td>\n      <td>0xe02d0e0d6cb2ad8e6a6512ab1d138d9ec6b64919715a...</td>\n      <td>Summary\\n\\nDesignate the “Boosted Liquidity En...</td>\n      <td>1678125946</td>\n      <td>1678467600</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>OIP-130: Lending AMO as a key DAO project for ...</td>\n      <td>1677691772</td>\n      <td>0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...</td>\n      <td># Summary\\nFollowing OIP-128 and the new budge...</td>\n      <td>1677691749</td>\n      <td>1677950949</td>\n      <td>55</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>OOP-1 Funding request for 2023 Q1 and Q2 budget.</td>\n      <td>1677453933</td>\n      <td>0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...</td>\n      <td>## Summary\\nAfter the successful first quarter...</td>\n      <td>1677453900</td>\n      <td>1677713100</td>\n      <td>60</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proposals_snapshots = sg.query_df([\n",
    "    proposals.title,\n",
    "    proposals.created,\n",
    "    proposals.id,\n",
    "    proposals.body,\n",
    "    proposals.start,\n",
    "    proposals.end,\n",
    "    proposals.votes\n",
    "])\n",
    "\n",
    "proposals_snapshots.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:35.377964Z",
     "end_time": "2023-04-04T11:07:35.963987Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250\n"
     ]
    }
   ],
   "source": [
    "total_snapshots = len(proposals_snapshots)\n",
    "print(total_snapshots)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:36.872801Z",
     "end_time": "2023-04-04T11:07:36.914850Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [],
   "source": [
    "proposals_choices = sg.query(proposals.choices)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:37.555476Z",
     "end_time": "2023-04-04T11:07:37.980765Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "proposals_choices = pd.DataFrame(proposals_choices, columns = ['option_1', 'option_2', 'option_3', 'option_4', 'option_5','option_6','option_7'])#,'option_8', 'option_9', 'option_10', 'option_11', 'option_12','option_13','option_14','option_15', 'option_16', 'option_17', 'option_18', 'option_19'])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:37.977765Z",
     "end_time": "2023-04-04T11:07:38.038229Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "                           option_1                          option_2  \\\n0     APPROVE OIP-136 bonus project      REJECT OIP-136 bonus project   \n1                    Approve TAP-22                     Reject TAP-22   \n2  Approve the changes as specified  Do not approve the changes as sp   \n3          Approve Incentive Budget                            Reject   \n4                               For                           Against   \n5                          Option 1                          Option 2   \n6                   Approve OIP-132                    Reject OIP-132   \n7        Approve BLE as Key Project                            Reject   \n8                   Approve OIP-130                    Reject OIP-130   \n9                     Approve OOP-1                      Reject OOP-1   \n\n   option_3           option_4 option_5 option_6 option_7  \n0      None               None     None     None     None  \n1      None               None     None     None     None  \n2      None               None     None     None     None  \n3      None               None     None     None     None  \n4      None               None     None     None     None  \n5  Option 3  None of the above     None     None     None  \n6      None               None     None     None     None  \n7      None               None     None     None     None  \n8      None               None     None     None     None  \n9      None               None     None     None     None  ",
      "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>option_1</th>\n      <th>option_2</th>\n      <th>option_3</th>\n      <th>option_4</th>\n      <th>option_5</th>\n      <th>option_6</th>\n      <th>option_7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>APPROVE OIP-136 bonus project</td>\n      <td>REJECT OIP-136 bonus project</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>Approve TAP-22</td>\n      <td>Reject TAP-22</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>Approve the changes as specified</td>\n      <td>Do not approve the changes as sp</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Approve Incentive Budget</td>\n      <td>Reject</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>For</td>\n      <td>Against</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Option 1</td>\n      <td>Option 2</td>\n      <td>Option 3</td>\n      <td>None of the above</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Approve OIP-132</td>\n      <td>Reject OIP-132</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Approve BLE as Key Project</td>\n      <td>Reject</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Approve OIP-130</td>\n      <td>Reject OIP-130</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>Approve OOP-1</td>\n      <td>Reject OOP-1</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proposals_choices.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:38.553926Z",
     "end_time": "2023-04-04T11:07:38.602472Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "250\n"
     ]
    }
   ],
   "source": [
    "total_choices = len(proposals_choices)\n",
    "print(total_choices)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:39.801894Z",
     "end_time": "2023-04-04T11:07:39.835125Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [],
   "source": [
    "olympus_governance_view = pd.concat([proposals_snapshots,proposals_choices], axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:40.373448Z",
     "end_time": "2023-04-04T11:07:40.382960Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "olympus_governance_view[\"proposal_date\"] = (datetime.fromtimestamp(olympus_governance_view[\"proposals_created\"]))"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "                                     proposals_title  proposals_created  \\\n0  OIP-136: Cross-chain native OHM as bonus proje...         1680108558   \n1                     TAP-22 - Treasury Segmentation         1680108259   \n2  OIP-135: Olympus Grants Program 1-Year Review ...         1679402793   \n3  AP-23 - Liquidity Incentives for Aura / Balanc...         1679325165   \n4  OIP 134: Hypernative as a security and risk de...         1679244244   \n\n                                        proposals_id  \\\n0  0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907...   \n1  0xebd3cf4c019ddf8eb637c816379c8b464fc20ee78284...   \n2  0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946...   \n3  0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5...   \n4  0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da1...   \n\n                                      proposals_body  proposals_start  \\\n0  ## Summary\\n\\nDesignate the “Cross-chain Nativ...       1680108526   \n1  ## Summary:\\nRestructure Olympus DAO Multisig ...       1680108233   \n2  Summary\\n\\nThis OIP seeks authority from the c...       1679402748   \n3  Summary:\\nEstablish an ongoing budget for voti...       1679325106   \n4  ### Summary\\n\\nBelow is a preliminary list of ...       1679244167   \n\n   proposals_end  proposals_votes                          option_1  \\\n0     1680367726               74     APPROVE OIP-136 bonus project   \n1     1680367433               63                    Approve TAP-22   \n2     1679686380               67  Approve the changes as specified   \n3     1679500800               50          Approve Incentive Budget   \n4     1679503367               68                               For   \n\n                           option_2 option_3 option_4 option_5 option_6  \\\n0      REJECT OIP-136 bonus project     None     None     None     None   \n1                     Reject TAP-22     None     None     None     None   \n2  Do not approve the changes as sp     None     None     None     None   \n3                            Reject     None     None     None     None   \n4                           Against     None     None     None     None   \n\n  option_7  \n0     None  \n1     None  \n2     None  \n3     None  \n4     None  ",
      "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>proposals_title</th>\n      <th>proposals_created</th>\n      <th>proposals_id</th>\n      <th>proposals_body</th>\n      <th>proposals_start</th>\n      <th>proposals_end</th>\n      <th>proposals_votes</th>\n      <th>option_1</th>\n      <th>option_2</th>\n      <th>option_3</th>\n      <th>option_4</th>\n      <th>option_5</th>\n      <th>option_6</th>\n      <th>option_7</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>OIP-136: Cross-chain native OHM as bonus proje...</td>\n      <td>1680108558</td>\n      <td>0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907...</td>\n      <td>## Summary\\n\\nDesignate the “Cross-chain Nativ...</td>\n      <td>1680108526</td>\n      <td>1680367726</td>\n      <td>74</td>\n      <td>APPROVE OIP-136 bonus project</td>\n      <td>REJECT OIP-136 bonus project</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>TAP-22 - Treasury Segmentation</td>\n      <td>1680108259</td>\n      <td>0xebd3cf4c019ddf8eb637c816379c8b464fc20ee78284...</td>\n      <td>## Summary:\\nRestructure Olympus DAO Multisig ...</td>\n      <td>1680108233</td>\n      <td>1680367433</td>\n      <td>63</td>\n      <td>Approve TAP-22</td>\n      <td>Reject TAP-22</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>OIP-135: Olympus Grants Program 1-Year Review ...</td>\n      <td>1679402793</td>\n      <td>0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946...</td>\n      <td>Summary\\n\\nThis OIP seeks authority from the c...</td>\n      <td>1679402748</td>\n      <td>1679686380</td>\n      <td>67</td>\n      <td>Approve the changes as specified</td>\n      <td>Do not approve the changes as sp</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>AP-23 - Liquidity Incentives for Aura / Balanc...</td>\n      <td>1679325165</td>\n      <td>0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5...</td>\n      <td>Summary:\\nEstablish an ongoing budget for voti...</td>\n      <td>1679325106</td>\n      <td>1679500800</td>\n      <td>50</td>\n      <td>Approve Incentive Budget</td>\n      <td>Reject</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>OIP 134: Hypernative as a security and risk de...</td>\n      <td>1679244244</td>\n      <td>0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da1...</td>\n      <td>### Summary\\n\\nBelow is a preliminary list of ...</td>\n      <td>1679244167</td>\n      <td>1679503367</td>\n      <td>68</td>\n      <td>For</td>\n      <td>Against</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##let's view the output just to make sure\n",
    "olympus_governance_view.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:41.769777Z",
     "end_time": "2023-04-04T11:07:41.825115Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "              DAO                                    proposals_title  \\\n0  olympusdao.eth  OIP-136: Cross-chain native OHM as bonus proje...   \n1  olympusdao.eth                     TAP-22 - Treasury Segmentation   \n2  olympusdao.eth  OIP-135: Olympus Grants Program 1-Year Review ...   \n3  olympusdao.eth  AP-23 - Liquidity Incentives for Aura / Balanc...   \n4  olympusdao.eth  OIP 134: Hypernative as a security and risk de...   \n5  olympusdao.eth                     OIP-133: Staking Rate Strategy   \n6  olympusdao.eth       OIP-132: wstETH/OHM Boosted Liquidity Engine   \n7  olympusdao.eth  OIP 131: Boosted Liquidity Engine as a Key Pro...   \n8  olympusdao.eth  OIP-130: Lending AMO as a key DAO project for ...   \n9  olympusdao.eth   OOP-1 Funding request for 2023 Q1 and Q2 budget.   \n\n   proposals_created                                       proposals_id  \\\n0         1680108558  0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907...   \n1         1680108259  0xebd3cf4c019ddf8eb637c816379c8b464fc20ee78284...   \n2         1679402793  0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946...   \n3         1679325165  0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5...   \n4         1679244244  0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da1...   \n5         1678428395  0x0fbdc3e8adbda28970646743492623e92e3a7c2336d4...   \n6         1678427799  0x991058cca07d1e6f361217927a99951d200826e2ee57...   \n7         1678126068  0xe02d0e0d6cb2ad8e6a6512ab1d138d9ec6b64919715a...   \n8         1677691772  0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...   \n9         1677453933  0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...   \n\n                                      proposals_body  proposals_start  \\\n0  ## Summary\\n\\nDesignate the “Cross-chain Nativ...       1680108526   \n1  ## Summary:\\nRestructure Olympus DAO Multisig ...       1680108233   \n2  Summary\\n\\nThis OIP seeks authority from the c...       1679402748   \n3  Summary:\\nEstablish an ongoing budget for voti...       1679325106   \n4  ### Summary\\n\\nBelow is a preliminary list of ...       1679244167   \n5  # Motivation - “how do we make 80% of OHM prod...       1678428364   \n6  # Summary\\nLaunch a Boosted Liquidity Engine f...       1678427767   \n7  Summary\\n\\nDesignate the “Boosted Liquidity En...       1678125946   \n8  # Summary\\nFollowing OIP-128 and the new budge...       1677691749   \n9  ## Summary\\nAfter the successful first quarter...       1677453900   \n\n   proposals_end  proposals_votes                          option_1  \\\n0     1680367726               74     APPROVE OIP-136 bonus project   \n1     1680367433               63                    Approve TAP-22   \n2     1679686380               67  Approve the changes as specified   \n3     1679500800               50          Approve Incentive Budget   \n4     1679503367               68                               For   \n5     1678687564               76                          Option 1   \n6     1678686967               65                   Approve OIP-132   \n7     1678467600               80        Approve BLE as Key Project   \n8     1677950949               55                   Approve OIP-130   \n9     1677713100               60                     Approve OOP-1   \n\n                           option_2  option_3           option_4 option_5  \\\n0      REJECT OIP-136 bonus project       NaN                NaN      NaN   \n1                     Reject TAP-22       NaN                NaN      NaN   \n2  Do not approve the changes as sp       NaN                NaN      NaN   \n3                            Reject       NaN                NaN      NaN   \n4                           Against       NaN                NaN      NaN   \n5                          Option 2  Option 3  None of the above      NaN   \n6                    Reject OIP-132       NaN                NaN      NaN   \n7                            Reject       NaN                NaN      NaN   \n8                    Reject OIP-130       NaN                NaN      NaN   \n9                      Reject OOP-1       NaN                NaN      NaN   \n\n  option_6 option_7 proposal_date  \n0      NaN      NaN    2023-03-29  \n1      NaN      NaN    2023-03-29  \n2      NaN      NaN    2023-03-21  \n3      NaN      NaN    2023-03-20  \n4      NaN      NaN    2023-03-19  \n5      NaN      NaN    2023-03-10  \n6      NaN      NaN    2023-03-10  \n7      NaN      NaN    2023-03-06  \n8      NaN      NaN    2023-03-01  \n9      NaN      NaN    2023-02-26  ",
      "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>DAO</th>\n      <th>proposals_title</th>\n      <th>proposals_created</th>\n      <th>proposals_id</th>\n      <th>proposals_body</th>\n      <th>proposals_start</th>\n      <th>proposals_end</th>\n      <th>proposals_votes</th>\n      <th>option_1</th>\n      <th>option_2</th>\n      <th>option_3</th>\n      <th>option_4</th>\n      <th>option_5</th>\n      <th>option_6</th>\n      <th>option_7</th>\n      <th>proposal_date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>olympusdao.eth</td>\n      <td>OIP-136: Cross-chain native OHM as bonus proje...</td>\n      <td>1680108558</td>\n      <td>0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907...</td>\n      <td>## Summary\\n\\nDesignate the “Cross-chain Nativ...</td>\n      <td>1680108526</td>\n      <td>1680367726</td>\n      <td>74</td>\n      <td>APPROVE OIP-136 bonus project</td>\n      <td>REJECT OIP-136 bonus project</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-29</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>olympusdao.eth</td>\n      <td>TAP-22 - Treasury Segmentation</td>\n      <td>1680108259</td>\n      <td>0xebd3cf4c019ddf8eb637c816379c8b464fc20ee78284...</td>\n      <td>## Summary:\\nRestructure Olympus DAO Multisig ...</td>\n      <td>1680108233</td>\n      <td>1680367433</td>\n      <td>63</td>\n      <td>Approve TAP-22</td>\n      <td>Reject TAP-22</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-29</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>olympusdao.eth</td>\n      <td>OIP-135: Olympus Grants Program 1-Year Review ...</td>\n      <td>1679402793</td>\n      <td>0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946...</td>\n      <td>Summary\\n\\nThis OIP seeks authority from the c...</td>\n      <td>1679402748</td>\n      <td>1679686380</td>\n      <td>67</td>\n      <td>Approve the changes as specified</td>\n      <td>Do not approve the changes as sp</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-21</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>olympusdao.eth</td>\n      <td>AP-23 - Liquidity Incentives for Aura / Balanc...</td>\n      <td>1679325165</td>\n      <td>0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5...</td>\n      <td>Summary:\\nEstablish an ongoing budget for voti...</td>\n      <td>1679325106</td>\n      <td>1679500800</td>\n      <td>50</td>\n      <td>Approve Incentive Budget</td>\n      <td>Reject</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-20</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>olympusdao.eth</td>\n      <td>OIP 134: Hypernative as a security and risk de...</td>\n      <td>1679244244</td>\n      <td>0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da1...</td>\n      <td>### Summary\\n\\nBelow is a preliminary list of ...</td>\n      <td>1679244167</td>\n      <td>1679503367</td>\n      <td>68</td>\n      <td>For</td>\n      <td>Against</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-19</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>olympusdao.eth</td>\n      <td>OIP-133: Staking Rate Strategy</td>\n      <td>1678428395</td>\n      <td>0x0fbdc3e8adbda28970646743492623e92e3a7c2336d4...</td>\n      <td># Motivation - “how do we make 80% of OHM prod...</td>\n      <td>1678428364</td>\n      <td>1678687564</td>\n      <td>76</td>\n      <td>Option 1</td>\n      <td>Option 2</td>\n      <td>Option 3</td>\n      <td>None of the above</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-10</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>olympusdao.eth</td>\n      <td>OIP-132: wstETH/OHM Boosted Liquidity Engine</td>\n      <td>1678427799</td>\n      <td>0x991058cca07d1e6f361217927a99951d200826e2ee57...</td>\n      <td># Summary\\nLaunch a Boosted Liquidity Engine f...</td>\n      <td>1678427767</td>\n      <td>1678686967</td>\n      <td>65</td>\n      <td>Approve OIP-132</td>\n      <td>Reject OIP-132</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-10</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>olympusdao.eth</td>\n      <td>OIP 131: Boosted Liquidity Engine as a Key Pro...</td>\n      <td>1678126068</td>\n      <td>0xe02d0e0d6cb2ad8e6a6512ab1d138d9ec6b64919715a...</td>\n      <td>Summary\\n\\nDesignate the “Boosted Liquidity En...</td>\n      <td>1678125946</td>\n      <td>1678467600</td>\n      <td>80</td>\n      <td>Approve BLE as Key Project</td>\n      <td>Reject</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-06</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>olympusdao.eth</td>\n      <td>OIP-130: Lending AMO as a key DAO project for ...</td>\n      <td>1677691772</td>\n      <td>0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...</td>\n      <td># Summary\\nFollowing OIP-128 and the new budge...</td>\n      <td>1677691749</td>\n      <td>1677950949</td>\n      <td>55</td>\n      <td>Approve OIP-130</td>\n      <td>Reject OIP-130</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-03-01</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>olympusdao.eth</td>\n      <td>OOP-1 Funding request for 2023 Q1 and Q2 budget.</td>\n      <td>1677453933</td>\n      <td>0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...</td>\n      <td>## Summary\\nAfter the successful first quarter...</td>\n      <td>1677453900</td>\n      <td>1677713100</td>\n      <td>60</td>\n      <td>Approve OOP-1</td>\n      <td>Reject OOP-1</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2023-02-26</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#let's remove duplicate rows the easy way, and add the name of the DAO to the table\n",
    "olympus_governance_view_clean = olympus_governance_view.copy(deep=True)\n",
    "olympus_governance_view_clean = db.query(\"select \"\n",
    "                                 \"  *\"\n",
    "                                 \"  , (to_timestamp((proposals_created::bigint)))::date proposal_date  \"\n",
    "                                 \"\"\n",
    "                                 \"from olympus_governance_view\").df()\n",
    "#make sure we know which DAO we are working with\n",
    "olympus_governance_view_clean.insert(0, 'DAO', spacename)\n",
    "olympus_governance_view_clean.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:43.117879Z",
     "end_time": "2023-04-04T11:07:43.190942Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "248"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_proposals = len(olympus_governance_view_clean)\n",
    "total_proposals"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "16"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_index = len(olympus_governance_view_clean.columns)\n",
    "max_index"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "path =file+'/'+spacename+'_proposals_table_'+str(date.today().strftime(\"%b-%d-%Y\"))+'_'+str(len(olympus_governance_view_clean))+'_proposals.csv'\n",
    "olympus_governance_view_clean.to_csv(path, index = False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "'0xe8117f18e79acf2e15f7254308c8d1c1138840f5c907b2e08101800196356cb0'"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proposal_id = olympus_governance_view_clean.iloc[0,3]\n",
    "proposal_id"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:47.594425Z",
     "end_time": "2023-04-04T11:07:47.621485Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "vote_proposals = snapshot.Query.votes(\n",
    "orderBy = 'created',\n",
    "orderDirection='desc',\n",
    "first=1000,\n",
    "where=[\n",
    "  snapshot.Vote.proposal == '0x058bda9a27ba8e0e154df38fdd6f41e59c640a44cd6252cacad965549d0994d7'\n",
    "]\n",
    ")\n",
    "print(vote_proposals)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [],
   "source": [
    "vote_tracker = snapshot.Query.votes(\n",
    "orderBy = 'created',\n",
    "orderDirection='desc',\n",
    "first=1000,\n",
    "where=[\n",
    "  snapshot.Vote.proposal == proposal_id\n",
    "]\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:49.799594Z",
     "end_time": "2023-04-04T11:07:49.836044Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "votes\n"
     ]
    }
   ],
   "source": [
    "print(vote_tracker)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:50.485209Z",
     "end_time": "2023-04-04T11:07:50.494234Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [],
   "source": [
    "voting_snapshots_list_pre = sg.query_df([\n",
    "    vote_tracker.id,\n",
    "    vote_tracker.voter,\n",
    "    vote_tracker.created,\n",
    "    vote_tracker.choice,\n",
    "    vote_tracker.vp\n",
    "])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:51.046988Z",
     "end_time": "2023-04-04T11:07:51.203786Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [],
   "source": [
    "\n",
    "voting_snapshots_list = db.query(\"select \"\n",
    "                                 \"  *\"\n",
    "                                 \"  , to_timestamp((votes_Created::bigint)) vote_timestamp  \"\n",
    "                                 \"\"\n",
    "                                 \"from voting_snapshots_list_pre order by votes_Created\").df()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:52.165875Z",
     "end_time": "2023-04-04T11:07:52.199066Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "                                             votes_id  \\\n0   0x34d6da8ff35ad6cb78fc3b2b1d380ee2dc6f017d21fb...   \n1   0xfc1d1c58909cd385fbc5d4f3c5795df4899edfc93d11...   \n2   0xe7bc54be971305ef2247c14a190ef56521a353d837b5...   \n3   0x9a3b282dc10d9598bdec64a0199cce24318c4956997b...   \n4   0xb6ba2e99d78ca66ac4261cbe3b05e3d6041418134fa0...   \n..                                                ...   \n60  0xa2a4050842f2dab2b4f0d630f1e504cdbcc732f98e7a...   \n61  0xe44b3977f50d16f73ab4d3953b3b3e3727b278c5d3ab...   \n62  0xa021b0aa8de98a71d6870cb88372d608d7cd30c395da...   \n63  0x35f2ed7a334b0f42939df96be21f9a1363d338624134...   \n64  0xd7a851b5f6a859887a5caa752cdd0d3f17e7a4c2df29...   \n\n                                   votes_voter  votes_created  votes_choice  \\\n0   0x51A7914e2B6246D9DA55849354EA692A370B0673     1680114321             1   \n1   0x36d9b6A89196042B5450b4687Aa7C5c315C22Aa9     1680115066             2   \n2   0x721Ca5428eC12b148b34458E0f8557F4C9624630     1680115459             1   \n3   0x4e99BfBEE1020138eACC68a32eD7Fc6DD2EFc08f     1680116656             1   \n4   0x4900EB80bD15E59D221f7ffd0307B808EC6699d8     1680116772             1   \n..                                         ...            ...           ...   \n60  0x3bD785eE00804A460ed896BEC65Bf71282948fC9     1680285938             1   \n61  0xaB5a193A11f779fe105E8edf9815a0b514B013CA     1680291741             1   \n62  0x5d6a3d249E6628f6614797A10afc9Ec85aa05DB7     1680294121             1   \n63  0x8a7F162dAaC546997cc36B6B7C528A21800507B9     1680295495             1   \n64  0xE9EC5DFaAD2D81A4827Ed58B3d064bcF39dd9E31     1680297725             1   \n\n       votes_vp      vote_timestamp  \n0     37.081379 2023-03-29 18:25:21  \n1      0.016506 2023-03-29 18:37:46  \n2   9633.538065 2023-03-29 18:44:19  \n3     54.283578 2023-03-29 19:04:16  \n4    379.423030 2023-03-29 19:06:12  \n..          ...                 ...  \n60     6.301165 2023-03-31 18:05:38  \n61   882.472121 2023-03-31 19:42:21  \n62   873.127356 2023-03-31 20:22:01  \n63  1958.628195 2023-03-31 20:44:55  \n64    28.928343 2023-03-31 21:22:05  \n\n[65 rows x 6 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>votes_id</th>\n      <th>votes_voter</th>\n      <th>votes_created</th>\n      <th>votes_choice</th>\n      <th>votes_vp</th>\n      <th>vote_timestamp</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0x34d6da8ff35ad6cb78fc3b2b1d380ee2dc6f017d21fb...</td>\n      <td>0x51A7914e2B6246D9DA55849354EA692A370B0673</td>\n      <td>1680114321</td>\n      <td>1</td>\n      <td>37.081379</td>\n      <td>2023-03-29 18:25:21</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0xfc1d1c58909cd385fbc5d4f3c5795df4899edfc93d11...</td>\n      <td>0x36d9b6A89196042B5450b4687Aa7C5c315C22Aa9</td>\n      <td>1680115066</td>\n      <td>2</td>\n      <td>0.016506</td>\n      <td>2023-03-29 18:37:46</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0xe7bc54be971305ef2247c14a190ef56521a353d837b5...</td>\n      <td>0x721Ca5428eC12b148b34458E0f8557F4C9624630</td>\n      <td>1680115459</td>\n      <td>1</td>\n      <td>9633.538065</td>\n      <td>2023-03-29 18:44:19</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0x9a3b282dc10d9598bdec64a0199cce24318c4956997b...</td>\n      <td>0x4e99BfBEE1020138eACC68a32eD7Fc6DD2EFc08f</td>\n      <td>1680116656</td>\n      <td>1</td>\n      <td>54.283578</td>\n      <td>2023-03-29 19:04:16</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0xb6ba2e99d78ca66ac4261cbe3b05e3d6041418134fa0...</td>\n      <td>0x4900EB80bD15E59D221f7ffd0307B808EC6699d8</td>\n      <td>1680116772</td>\n      <td>1</td>\n      <td>379.423030</td>\n      <td>2023-03-29 19:06:12</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>0xa2a4050842f2dab2b4f0d630f1e504cdbcc732f98e7a...</td>\n      <td>0x3bD785eE00804A460ed896BEC65Bf71282948fC9</td>\n      <td>1680285938</td>\n      <td>1</td>\n      <td>6.301165</td>\n      <td>2023-03-31 18:05:38</td>\n    </tr>\n    <tr>\n      <th>61</th>\n      <td>0xe44b3977f50d16f73ab4d3953b3b3e3727b278c5d3ab...</td>\n      <td>0xaB5a193A11f779fe105E8edf9815a0b514B013CA</td>\n      <td>1680291741</td>\n      <td>1</td>\n      <td>882.472121</td>\n      <td>2023-03-31 19:42:21</td>\n    </tr>\n    <tr>\n      <th>62</th>\n      <td>0xa021b0aa8de98a71d6870cb88372d608d7cd30c395da...</td>\n      <td>0x5d6a3d249E6628f6614797A10afc9Ec85aa05DB7</td>\n      <td>1680294121</td>\n      <td>1</td>\n      <td>873.127356</td>\n      <td>2023-03-31 20:22:01</td>\n    </tr>\n    <tr>\n      <th>63</th>\n      <td>0x35f2ed7a334b0f42939df96be21f9a1363d338624134...</td>\n      <td>0x8a7F162dAaC546997cc36B6B7C528A21800507B9</td>\n      <td>1680295495</td>\n      <td>1</td>\n      <td>1958.628195</td>\n      <td>2023-03-31 20:44:55</td>\n    </tr>\n    <tr>\n      <th>64</th>\n      <td>0xd7a851b5f6a859887a5caa752cdd0d3f17e7a4c2df29...</td>\n      <td>0xE9EC5DFaAD2D81A4827Ed58B3d064bcF39dd9E31</td>\n      <td>1680297725</td>\n      <td>1</td>\n      <td>28.928343</td>\n      <td>2023-03-31 21:22:05</td>\n    </tr>\n  </tbody>\n</table>\n<p>65 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "voting_snapshots_list.head(65)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "start_time": "2023-04-04T11:07:52.924253Z",
     "end_time": "2023-04-04T11:07:52.950893Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'votesDb' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[1;32mIn [25]\u001B[0m, in \u001B[0;36m<cell line: 18>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      9\u001B[0m voting_snapshots \u001B[38;5;241m=\u001B[39m sg\u001B[38;5;241m.\u001B[39mquery_df([\n\u001B[0;32m     10\u001B[0m vote_tracker\u001B[38;5;241m.\u001B[39mid,\n\u001B[0;32m     11\u001B[0m vote_tracker\u001B[38;5;241m.\u001B[39mvoter,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     14\u001B[0m vote_tracker\u001B[38;5;241m.\u001B[39mvp\n\u001B[0;32m     15\u001B[0m ])\n\u001B[0;32m     17\u001B[0m \u001B[38;5;66;03m#votesDb=pd.concat([vote_tracker, voteslist])\u001B[39;00m\n\u001B[1;32m---> 18\u001B[0m recordTimestamp1 \u001B[38;5;241m=\u001B[39m \u001B[43mvotesDb\u001B[49m\u001B[38;5;241m.\u001B[39miat[voteTicker,\u001B[38;5;241m2\u001B[39m]\n\u001B[0;32m     19\u001B[0m recordTimestamp \u001B[38;5;241m=\u001B[39m dt\u001B[38;5;241m.\u001B[39mdatetime\u001B[38;5;241m.\u001B[39mfromtimestamp( recordTimestamp1 )\n\u001B[0;32m     20\u001B[0m votesDbLength \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(votesDb)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'votesDb' is not defined"
     ]
    }
   ],
   "source": [
    "    vote_tracker = snapshot.Query.votes(\n",
    "        orderBy = 'created',\n",
    "        orderDirection='desc',\n",
    "        first=1000,\n",
    "        where=[\n",
    "          snapshot.Vote.proposal == proposal_id\n",
    "        ]\n",
    "    )\n",
    "    voting_snapshots = sg.query_df([\n",
    "    vote_tracker.id,\n",
    "    vote_tracker.voter,\n",
    "    vote_tracker.created,\n",
    "    vote_tracker.choice,\n",
    "    vote_tracker.vp\n",
    "    ])\n",
    "\n",
    "    #votesDb=pd.concat([vote_tracker, voteslist])\n",
    "    recordTimestamp1 = votesDb.iat[voteTicker,2]\n",
    "    recordTimestamp = dt.datetime.fromtimestamp( recordTimestamp1 )\n",
    "    votesDbLength = len(votesDb)\n",
    "    now = (int(dt.datetime.utcnow().timestamp()))\n",
    "    datediff=abs(int(now) - recordTimestamp1)\n",
    "    daysAgo = int(datediff/86400)\n",
    "    voteListLength = len(vote_tracker)\n",
    "    recordID = votesDb.iat[voteTicker,0]\n",
    "    vote = silovotesDb.iat[voteTicker,3]\n",
    "    print(\"iterations: \", voteTicker, \"Lines skipped: \",skipValue, \"records collected: \", silovoteListLength, \" - latest recordID: \",vote, \" \", recordID , \" - votes DB length: \", silovotesDbLength, \" - latest record from: \", recordTimestamp, daysAgo,\"days ago\")\n",
    "    #print(\"iterations: \", voteTicker, \"records collected: \", voteListLength, \" - userdb length: \", votesDbLength)\n",
    "    voteTicker = voteTicker+1"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ticker 0 proposal 0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946f73f128af10995288538 records: 67 DB size: 67 days ago: 701492\n",
      "ticker 1 proposal 0xde251686e7f312a5e524ba45f4115a887e31ab6a3cb5c2400a0f273d4db2a9c5 records: 50 DB size: 117 days ago: 887038\n",
      "ticker 2 proposal 0x58a7bc957b83e4a6424cb72d80bf3c3b0bd8025d0da11d4717fb0a657eafcbd8 records: 68 DB size: 185 days ago: 909494\n",
      "ticker 3 proposal 0x0fbdc3e8adbda28970646743492623e92e3a7c2336d453a07d6a090089c72ecf records: 76 DB size: 261 days ago: 1725663\n",
      "ticker 4 proposal 0x991058cca07d1e6f361217927a99951d200826e2ee5781a5c04d8f01e376167d records: 65 DB size: 326 days ago: 1727941\n",
      "ticker 5 proposal 0xe02d0e0d6cb2ad8e6a6512ab1d138d9ec6b64919715a3a5257536548b590a3b3 records: 80 DB size: 406 days ago: 1950470\n",
      "ticker 6 proposal 0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90b5627fb4fc6335492300 records: 55 DB size: 461 days ago: 2521283\n",
      "ticker 7 proposal 0xadd0850bb53fc31df236d61e2b53326748c80ef72f543ce8e997b1f6616dc8be records: 60 DB size: 521 days ago: 2695919\n",
      "ticker 8 proposal 0x7065c53c88cff153efefc4dc042f81d795011cfdfa29a367dfbf4b1916b327d9 records: 101 DB size: 622 days ago: 4060699\n",
      "ticker 9 proposal 0xe1f1a1eea2da4d8c74a5b2f840eb7dce8c8143926b35e61df38b332c4eabb9ed records: 96 DB size: 718 days ago: 4409242\n",
      "ticker 10 proposal 0x58f5e9709fd2a69c7695ab69d9aa3d6f29b354c0abd347f9d5a326fb658efd92 records: 93 DB size: 811 days ago: 4969775\n",
      "ticker 11 proposal 0x5a8c1550f2781d7c1587bf74a793a87f149774c6b5c3d3a743dd67741f91f4ef records: 92 DB size: 903 days ago: 5043694\n",
      "ticker 12 proposal 0x7e787218d495849b505d575088c7cf1c7c8de44b79fb81205dcf9f804b781c3a records: 83 DB size: 986 days ago: 5090089\n",
      "ticker 13 proposal 0x5c3b454300357b90e4dbe1866a20b02010dcaa7a74070814bb6f3ea1aacc630b records: 50 DB size: 1036 days ago: 6111998\n",
      "ticker 14 proposal 0x0aa9d7913eeaa0bde3b502b02aec8e407fd0898a773df7042475d392d0faaae5 records: 88 DB size: 1124 days ago: 6327020\n",
      "ticker 15 proposal 0xda3c3616164ae0674c1df2f54cc5734e8af86b99fe0c3b3075e2a49ca665aef1 records: 60 DB size: 1184 days ago: 6751568\n",
      "ticker 16 proposal 0x5b06e73b9744a5f224d5ec0ac0e4b3411fdcb5eaa19040b4091a91bbc6c6d504 records: 93 DB size: 1277 days ago: 6794471\n",
      "ticker 17 proposal 0x058bda9a27ba8e0e154df38fdd6f41e59c640a44cd6252cacad965549d0994d7 records: 90 DB size: 1367 days ago: 7456337\n",
      "ticker 18 proposal 0x1736efdc67a7cbca8feed50ff6350b2033bd4adecd79fc7d55ea68be22ddb3ff records: 84 DB size: 1451 days ago: 10088870\n",
      "ticker 19 proposal 0xd8f99606699e08ee9b07536d1d96cb0308a413fb9d6fe71784f7298b52cae6a2 records: 55 DB size: 1506 days ago: 10649142\n",
      "ticker 20 proposal 0x209512b332aa5ad2c42050976935c405fae183c045f38372840838fd8c335930 records: 173 DB size: 1679 days ago: 11689007\n",
      "ticker 21 proposal 0x850935de12eacef37e10e7b2a1644e3087c2e190d4d45e35965bc442e0c9b4d3 records: 100 DB size: 1779 days ago: 13390780\n",
      "ticker 22 proposal 0x0fb7276daaba81d453bd2fc37cf2b99fb7fea023ac943da2a59cd8b13e5f3888 records: 99 DB size: 1878 days ago: 14108911\n",
      "ticker 23 proposal 0x3236041d0857f7548c8da12f3890c6f590a016f02fd2f100230e1b8cbcfed078 records: 74 DB size: 1952 days ago: 14814286\n",
      "ticker 24 proposal 0x461aa948bcd9c889162cb740c5c878ae31133f75f7d12c0731232f9674c8aa50 records: 138 DB size: 2090 days ago: 16065451\n",
      "ticker 25 proposal 0xd931ce1682ebd4679fe9fe2d36e44459c492fa0f473ed1595f43d38a5bfce303 records: 82 DB size: 2172 days ago: 16589507\n",
      "ticker 26 proposal 0x10bf2ac29c8f722dd1af3d186f58ccbc17b82c2e518c304f3b055b7878ec2ab0 records: 121 DB size: 2293 days ago: 17968893\n",
      "ticker 27 proposal 0xa3efbf76f3638bc35898d61b68cb6dd404a61b3a634e2c6b6c0ef4f97551bc03 records: 113 DB size: 2406 days ago: 18003982\n",
      "ticker 28 proposal 0xe8a3395334f6ae564cf066b08b6930320fa19dcd6ac554381e6c918bc1152388 records: 96 DB size: 2502 days ago: 18223015\n",
      "ticker 29 proposal 0x16aac6c181fd1ecdabe46d573725bbc60b02ec79a16e209f0df4a9d088e2168f records: 91 DB size: 2593 days ago: 18526321\n",
      "ticker 30 proposal 0x010c55975344f063ed6a84267148561b90ca4a0ea1150f5a487cb639ce3d6e1b records: 87 DB size: 2680 days ago: 18531295\n",
      "ticker 31 proposal 0xd7356be885caad00613f1741e01179db9fac9ffb19a5278c88cc08e3b16ccdfa records: 81 DB size: 2761 days ago: 18539637\n",
      "ticker 32 proposal 0xe19c31a976a96a0915a35f88ace70d8daeffbf68cbd26dc12551f8c35806fb0f records: 80 DB size: 2841 days ago: 18539679\n",
      "ticker 33 proposal 0x63b803240532ab8edf7a1909e2ddd8d3b3be0f7111e11d970b73ae2c51600149 records: 57 DB size: 2898 days ago: 18865275\n",
      "ticker 34 proposal 0x917da121877c9428c5b23079348d09cab0e2ba90673d0b23d1529030c6d80872 records: 92 DB size: 2990 days ago: 19389994\n",
      "ticker 35 proposal 0x3ae572c2aa58597ba6eeee9d0073dab00209ddb16f3891071012bcccf6632d10 records: 76 DB size: 3066 days ago: 19454400\n",
      "ticker 36 proposal 0xeea3d150182e1b176e6ed37b8b8b7c071c7d8b5f8c9eb6d7146b49d1172eebcf records: 76 DB size: 3142 days ago: 19486055\n",
      "ticker 37 proposal 0x8cea5bd2f227366bada868363c5be5a68f0ab1bbb8d3654aabe9b89ec268552f records: 103 DB size: 3245 days ago: 19462549\n",
      "ticker 38 proposal 0xe6233a0b159f58a97ba41019bd09cb904bd0120f656dcaac3c7aa33d862abf72 records: 117 DB size: 3362 days ago: 21788856\n",
      "ticker 39 proposal 0xcce2dd72fa5bf12ddbe5a15ed23f6256f6cd841ed5720f858189acc73775a7a2 records: 145 DB size: 3507 days ago: 22635449\n",
      "ticker 40 proposal 0xebd05bd5896c472eecceb380b345689fc5afcd2958656b3ef2d282ff65abcde0 records: 157 DB size: 3664 days ago: 22896660\n",
      "ticker 41 proposal 0xfb69ecfd9d0f366635951c7b337cf44753ecb8c24b751593bc9f8fd55c31f083 records: 119 DB size: 3783 days ago: 23303476\n",
      "ticker 42 proposal 0x5e3963823e0664c71172e94bb8fea703474537a367b05f70848ac200e2d57124 records: 90 DB size: 3873 days ago: 23564200\n",
      "ticker 43 proposal 0x41607de83deda264c5f3ed567b148a71c2cd786124e298f6cbfc1e0c932e6871 records: 178 DB size: 4051 days ago: 23917800\n",
      "ticker 44 proposal 0xffdcf93b7a0c09c606bd72fd56b17ea63952627aa19e998535859cd71ba2038c records: 134 DB size: 4185 days ago: 24459956\n",
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      "ticker 86 proposal 0x893809b3725e9629201539c68f9c0f2a13cbe140d6deb1849e497db1f74d8d84 records: 931 DB size: 16021 days ago: 39501740\n",
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      "ticker 94 proposal 0xcc1eb1848f5a5d09fafab14493d3c0d822c84ac1ca18a55adee1db42a609bc8f records: 85 DB size: 16637 days ago: 36697832\n",
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      "ticker 98 proposal 0x3ca1a75688a2b13716f4d9c7340e5ef5029fe3e45cd59c56d6520fc90dbe2010 records: 55 DB size: 17088 days ago: 40745616\n",
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      "ticker 100 proposal 0x49f8bcf9b07eda70dcc376d82352d86b0d5cf3a4c13cfca9fd16970eef8fe4ff records: 112 DB size: 17313 days ago: 40953369\n",
      "ticker 101 proposal 0x18c942eb87705d0505e78d04d6de3e21d1be14b3a11711d575b9f1fe15c5915a records: 214 DB size: 17527 days ago: 41115506\n",
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      "ticker 103 proposal 0xdc5d3cfe87d48ad7130cc888264831a39eba1a0db8ea5e2ec4b0cacfd23d0fbf records: 251 DB size: 18143 days ago: 41071142\n",
      "ticker 104 proposal 0x62a3b7c94478e61e24ae38369cc4c60b084077caf18d72c539cd72f47c45336c records: 526 DB size: 18669 days ago: 41306276\n",
      "ticker 105 proposal 0x48897392d09a87f82b315c6d238dff38a5b08f7ba9acbfc53f99a9627eb49332 records: 75 DB size: 18744 days ago: 41226550\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'errors'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\client.py:165\u001B[0m, in \u001B[0;36mquery\u001B[1;34m(url, query_str, variables)\u001B[0m\n\u001B[0;32m    164\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 165\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mresp\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mdata\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m    166\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exn:\n",
      "\u001B[1;31mKeyError\u001B[0m: 'data'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Input \u001B[1;32mIn [26]\u001B[0m, in \u001B[0;36m<cell line: 12>\u001B[1;34m()\u001B[0m\n\u001B[0;32m     14\u001B[0m skipValue \u001B[38;5;241m=\u001B[39m (voteTicker)\u001B[38;5;241m*\u001B[39m(\u001B[38;5;241m1000\u001B[39m)\n\u001B[0;32m     15\u001B[0m vote_tracker \u001B[38;5;241m=\u001B[39m snapshot\u001B[38;5;241m.\u001B[39mQuery\u001B[38;5;241m.\u001B[39mvotes(\n\u001B[0;32m     16\u001B[0m     orderBy \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcreated\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m     17\u001B[0m     orderDirection\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdesc\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     21\u001B[0m     ]\n\u001B[0;32m     22\u001B[0m )\n\u001B[1;32m---> 23\u001B[0m voting_snapshots \u001B[38;5;241m=\u001B[39m \u001B[43msg\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\n\u001B[0;32m     24\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mid\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     25\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvoter\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     26\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreated\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     27\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mchoice\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     28\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvp\u001B[49m\n\u001B[0;32m     29\u001B[0m \u001B[43m\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     31\u001B[0m votesDb\u001B[38;5;241m=\u001B[39mpd\u001B[38;5;241m.\u001B[39mconcat([voting_snapshots, votesDb])\n\u001B[0;32m     32\u001B[0m votesDbLength \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(votesDb)\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:355\u001B[0m, in \u001B[0;36mSubgrounds.query_df\u001B[1;34m(self, fpaths, columns, concat, pagination_strategy)\u001B[0m\n\u001B[0;32m    284\u001B[0m \u001B[38;5;124;03m\"\"\"Same as :func:`Subgrounds.query` but formats the response data into a\u001B[39;00m\n\u001B[0;32m    285\u001B[0m \u001B[38;5;124;03mPandas DataFrame. If the response data cannot be flattened to a single query\u001B[39;00m\n\u001B[0;32m    286\u001B[0m \u001B[38;5;124;03m(e.g.: when querying multiple list fields that return different entities),\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    347\u001B[0m \u001B[38;5;124;03m    9       1643213196  2610.686563\u001B[39;00m\n\u001B[0;32m    348\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    349\u001B[0m fpaths \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(\n\u001B[0;32m    350\u001B[0m   [fpaths]\n\u001B[0;32m    351\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    352\u001B[0m   \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(FieldPath\u001B[38;5;241m.\u001B[39m_auto_select)\n\u001B[0;32m    353\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    354\u001B[0m )\n\u001B[1;32m--> 355\u001B[0m json_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_json\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfpaths\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpagination_strategy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpagination_strategy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    356\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df_of_json(json_data, fpaths, columns, concat)\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:249\u001B[0m, in \u001B[0;36mSubgrounds.query_json\u001B[1;34m(self, fpaths, pagination_strategy)\u001B[0m\n\u001B[0;32m    242\u001B[0m fpaths \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(\n\u001B[0;32m    243\u001B[0m   [fpaths]\n\u001B[0;32m    244\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    245\u001B[0m   \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(FieldPath\u001B[38;5;241m.\u001B[39m_auto_select)\n\u001B[0;32m    246\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    247\u001B[0m )\n\u001B[0;32m    248\u001B[0m req \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmk_request(fpaths)\n\u001B[1;32m--> 249\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpagination_strategy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpagination_strategy\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:174\u001B[0m, in \u001B[0;36mSubgrounds.execute\u001B[1;34m(self, req, pagination_strategy)\u001B[0m\n\u001B[0;32m    164\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    165\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    166\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(req\u001B[38;5;241m.\u001B[39mdocuments \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(\u001B[38;5;28;01mlambda\u001B[39;00m doc: transform_doc(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msubgraphs[doc\u001B[38;5;241m.\u001B[39murl]\u001B[38;5;241m.\u001B[39m_transforms, doc)))\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    169\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_req(rest, new_req)\n\u001B[0;32m    170\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(req, data)\n\u001B[0;32m    172\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m  \u001B[38;5;66;03m# Suppress mypy missing return statement warning\u001B[39;00m\n\u001B[1;32m--> 174\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtransform_req\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mglobal_transforms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreq\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:166\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_req\u001B[1;34m(transforms, req)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_req\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[RequestTransform], req: DataRequest) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mlist\u001B[39m[\u001B[38;5;28mdict\u001B[39m]:\n\u001B[0;32m    164\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    165\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[1;32m--> 166\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mreq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdocuments\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msubgraphs\u001B[49m\u001B[43m[\u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_transforms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    167\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    168\u001B[0m       new_req \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_request(req)\n\u001B[0;32m    169\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_req(rest, new_req)\n\u001B[0;32m    170\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(req, data)\n\u001B[0;32m    172\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:166\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_req.<locals>.<lambda>\u001B[1;34m(doc)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_req\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[RequestTransform], req: DataRequest) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mlist\u001B[39m[\u001B[38;5;28mdict\u001B[39m]:\n\u001B[0;32m    164\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    165\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[1;32m--> 166\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(req\u001B[38;5;241m.\u001B[39mdocuments \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(\u001B[38;5;28;01mlambda\u001B[39;00m doc: \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msubgraphs\u001B[49m\u001B[43m[\u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_transforms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m)\u001B[49m))\n\u001B[0;32m    167\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    168\u001B[0m       new_req \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_request(req)\n\u001B[0;32m    169\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_req(rest, new_req)\n\u001B[0;32m    170\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(req, data)\n\u001B[0;32m    172\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:158\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m execute_document(doc)\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[1;32m--> 158\u001B[0m       data \u001B[38;5;241m=\u001B[39m \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_doc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:158\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m execute_document(doc)\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[1;32m--> 158\u001B[0m       data \u001B[38;5;241m=\u001B[39m \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_doc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:158\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m execute_document(doc)\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[1;32m--> 158\u001B[0m       data \u001B[38;5;241m=\u001B[39m \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_doc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:155\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[1;32m--> 155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mexecute_document\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[0;32m    158\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_doc(rest, new_doc)\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:149\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.execute_document\u001B[1;34m(doc)\u001B[0m\n\u001B[0;32m    147\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m paginate(subgraph\u001B[38;5;241m.\u001B[39m_schema, doc, pagination_strategy\u001B[38;5;241m=\u001B[39mpagination_strategy)\n\u001B[0;32m    148\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 149\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgraphql\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvariables\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvariables\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\client.py:167\u001B[0m, in \u001B[0;36mquery\u001B[1;34m(url, query_str, variables)\u001B[0m\n\u001B[0;32m    165\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m resp[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m    166\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exn:\n\u001B[1;32m--> 167\u001B[0m   \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m(\u001B[43mresp\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43merrors\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mexn\u001B[39;00m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'errors'"
     ]
    }
   ],
   "source": [
    "##this captures the ENTIRE list of people who voteed OHM\n",
    "voteTicker = 0\n",
    "totalProposals = len(olympus_governance_view_clean)\n",
    "voteslist = pd.DataFrame()\n",
    "votesDb = pd.DataFrame()\n",
    "voteListLength = 1000\n",
    "datediff = 0\n",
    "now=0\n",
    "daysAgo=0\n",
    "daysLimit = 90\n",
    "exit = False\n",
    "while exit==False:\n",
    "    proposal_id = olympus_governance_view_clean.iloc[voteTicker,3]\n",
    "    skipValue = (voteTicker)*(1000)\n",
    "    vote_tracker = snapshot.Query.votes(\n",
    "        orderBy = 'created',\n",
    "        orderDirection='desc',\n",
    "        first=1000,\n",
    "        where=[\n",
    "          snapshot.Vote.proposal == proposal_id\n",
    "        ]\n",
    "    )\n",
    "    voting_snapshots = sg.query_df([\n",
    "    vote_tracker.id,\n",
    "    vote_tracker.voter,\n",
    "    vote_tracker.created,\n",
    "    vote_tracker.choice,\n",
    "    vote_tracker.vp\n",
    "    ])\n",
    "\n",
    "    votesDb=pd.concat([voting_snapshots, votesDb])\n",
    "    votesDbLength = len(votesDb)\n",
    "    voteListLength = len(voting_snapshots)\n",
    "    recordTimestamp1 = votesDb.iat[voteTicker,2]\n",
    "    recordTimestamp = dt.datetime.fromtimestamp( recordTimestamp1 )\n",
    "    now = (int(dt.datetime.utcnow().timestamp()))\n",
    "    datediff=abs(int(now) - recordTimestamp1)\n",
    "\n",
    "    if datediff> daysLimit: exit\n",
    "    if voteTicker== totalProposals: exit\n",
    "    print('ticker', voteTicker, 'proposal',proposal_id, 'records:',voteListLength, 'DB size:',votesDbLength, 'days ago:', datediff )\n",
    "    voteTicker = voteTicker+1\n",
    "\n",
    "votesDb.drop_duplicates\n",
    "print(\"Done. Total Records collected: \", votesDbLength)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   0   0x246ccd5ab718a160f03e3dbbd1280ebb5389d3637946f73f128af10995288538   2023-03-21 00:00:00\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'errors'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\client.py:165\u001B[0m, in \u001B[0;36mquery\u001B[1;34m(url, query_str, variables)\u001B[0m\n\u001B[0;32m    164\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 165\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mresp\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mdata\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m\n\u001B[0;32m    166\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exn:\n",
      "\u001B[1;31mKeyError\u001B[0m: 'data'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Input \u001B[1;32mIn [27]\u001B[0m, in \u001B[0;36m<cell line: 12>\u001B[1;34m()\u001B[0m\n\u001B[0;32m     15\u001B[0m \u001B[38;5;28mprint\u001B[39m(x,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m'\u001B[39m,progress,\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m'\u001B[39m, proposal_id, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m'\u001B[39m, proposal_date)\n\u001B[0;32m     16\u001B[0m vote_tracker \u001B[38;5;241m=\u001B[39m snapshot\u001B[38;5;241m.\u001B[39mQuery\u001B[38;5;241m.\u001B[39mvotes(\n\u001B[0;32m     17\u001B[0m orderBy \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mcreated\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m     18\u001B[0m orderDirection\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdesc\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     22\u001B[0m ]\n\u001B[0;32m     23\u001B[0m )\n\u001B[1;32m---> 24\u001B[0m voting_snapshots \u001B[38;5;241m=\u001B[39m \u001B[43msg\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43m[\u001B[49m\n\u001B[0;32m     25\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mid\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     26\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvoter\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     27\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcreated\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     28\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mchoice\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m     29\u001B[0m \u001B[43m\u001B[49m\u001B[43mvote_tracker\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvp\u001B[49m\n\u001B[0;32m     30\u001B[0m \u001B[43m\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m     32\u001B[0m voting_snapshots[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mProposal\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m=\u001B[39m proposal_id\n\u001B[0;32m     33\u001B[0m voting_snapshots_list\u001B[38;5;241m=\u001B[39mpd\u001B[38;5;241m.\u001B[39mconcat([voting_snapshots_list, voting_snapshots])\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:355\u001B[0m, in \u001B[0;36mSubgrounds.query_df\u001B[1;34m(self, fpaths, columns, concat, pagination_strategy)\u001B[0m\n\u001B[0;32m    284\u001B[0m \u001B[38;5;124;03m\"\"\"Same as :func:`Subgrounds.query` but formats the response data into a\u001B[39;00m\n\u001B[0;32m    285\u001B[0m \u001B[38;5;124;03mPandas DataFrame. If the response data cannot be flattened to a single query\u001B[39;00m\n\u001B[0;32m    286\u001B[0m \u001B[38;5;124;03m(e.g.: when querying multiple list fields that return different entities),\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    347\u001B[0m \u001B[38;5;124;03m    9       1643213196  2610.686563\u001B[39;00m\n\u001B[0;32m    348\u001B[0m \u001B[38;5;124;03m\"\"\"\u001B[39;00m\n\u001B[0;32m    349\u001B[0m fpaths \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(\n\u001B[0;32m    350\u001B[0m   [fpaths]\n\u001B[0;32m    351\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    352\u001B[0m   \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(FieldPath\u001B[38;5;241m.\u001B[39m_auto_select)\n\u001B[0;32m    353\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    354\u001B[0m )\n\u001B[1;32m--> 355\u001B[0m json_data \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery_json\u001B[49m\u001B[43m(\u001B[49m\u001B[43mfpaths\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpagination_strategy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpagination_strategy\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    356\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df_of_json(json_data, fpaths, columns, concat)\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:249\u001B[0m, in \u001B[0;36mSubgrounds.query_json\u001B[1;34m(self, fpaths, pagination_strategy)\u001B[0m\n\u001B[0;32m    242\u001B[0m fpaths \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(\n\u001B[0;32m    243\u001B[0m   [fpaths]\n\u001B[0;32m    244\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    245\u001B[0m   \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(FieldPath\u001B[38;5;241m.\u001B[39m_auto_select)\n\u001B[0;32m    246\u001B[0m   \u001B[38;5;241m|\u001B[39m traverse\n\u001B[0;32m    247\u001B[0m )\n\u001B[0;32m    248\u001B[0m req \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmk_request(fpaths)\n\u001B[1;32m--> 249\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mexecute\u001B[49m\u001B[43m(\u001B[49m\u001B[43mreq\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpagination_strategy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mpagination_strategy\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:174\u001B[0m, in \u001B[0;36mSubgrounds.execute\u001B[1;34m(self, req, pagination_strategy)\u001B[0m\n\u001B[0;32m    164\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    165\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    166\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(req\u001B[38;5;241m.\u001B[39mdocuments \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(\u001B[38;5;28;01mlambda\u001B[39;00m doc: transform_doc(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msubgraphs[doc\u001B[38;5;241m.\u001B[39murl]\u001B[38;5;241m.\u001B[39m_transforms, doc)))\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    169\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_req(rest, new_req)\n\u001B[0;32m    170\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(req, data)\n\u001B[0;32m    172\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m  \u001B[38;5;66;03m# Suppress mypy missing return statement warning\u001B[39;00m\n\u001B[1;32m--> 174\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mtransform_req\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mglobal_transforms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mreq\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:166\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_req\u001B[1;34m(transforms, req)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_req\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[RequestTransform], req: DataRequest) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mlist\u001B[39m[\u001B[38;5;28mdict\u001B[39m]:\n\u001B[0;32m    164\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    165\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[1;32m--> 166\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mlist\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mreq\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdocuments\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m|\u001B[39;49m\u001B[43m \u001B[49m\u001B[38;5;28;43mmap\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mlambda\u001B[39;49;00m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m:\u001B[49m\u001B[43m \u001B[49m\u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msubgraphs\u001B[49m\u001B[43m[\u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_transforms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    167\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    168\u001B[0m       new_req \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_request(req)\n\u001B[0;32m    169\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_req(rest, new_req)\n\u001B[0;32m    170\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(req, data)\n\u001B[0;32m    172\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:166\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_req.<locals>.<lambda>\u001B[1;34m(doc)\u001B[0m\n\u001B[0;32m    163\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_req\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[RequestTransform], req: DataRequest) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mlist\u001B[39m[\u001B[38;5;28mdict\u001B[39m]:\n\u001B[0;32m    164\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    165\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[1;32m--> 166\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mlist\u001B[39m(req\u001B[38;5;241m.\u001B[39mdocuments \u001B[38;5;241m|\u001B[39m \u001B[38;5;28mmap\u001B[39m(\u001B[38;5;28;01mlambda\u001B[39;00m doc: \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msubgraphs\u001B[49m\u001B[43m[\u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m]\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_transforms\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m)\u001B[49m))\n\u001B[0;32m    167\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    168\u001B[0m       new_req \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_request(req)\n\u001B[0;32m    169\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_req(rest, new_req)\n\u001B[0;32m    170\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(req, data)\n\u001B[0;32m    172\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:158\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m execute_document(doc)\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[1;32m--> 158\u001B[0m       data \u001B[38;5;241m=\u001B[39m \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_doc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:158\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m execute_document(doc)\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[1;32m--> 158\u001B[0m       data \u001B[38;5;241m=\u001B[39m \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_doc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:158\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[0;32m    155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m execute_document(doc)\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[1;32m--> 158\u001B[0m       data \u001B[38;5;241m=\u001B[39m \u001B[43mtransform_doc\u001B[49m\u001B[43m(\u001B[49m\u001B[43mrest\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnew_doc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:155\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.transform_doc\u001B[1;34m(transforms, doc)\u001B[0m\n\u001B[0;32m    151\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mtransform_doc\u001B[39m(transforms: \u001B[38;5;28mlist\u001B[39m[DocumentTransform], doc: Document) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28mdict\u001B[39m:\n\u001B[0;32m    152\u001B[0m   logger\u001B[38;5;241m.\u001B[39mdebug(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mexecute.transform_doc: doc = \u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;132;01m{\u001B[39;00mdoc\u001B[38;5;241m.\u001B[39mgraphql\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    153\u001B[0m   \u001B[38;5;28;01mmatch\u001B[39;00m transforms:\n\u001B[0;32m    154\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m []:\n\u001B[1;32m--> 155\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mexecute_document\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdoc\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    156\u001B[0m     \u001B[38;5;28;01mcase\u001B[39;00m [transform, \u001B[38;5;241m*\u001B[39mrest]:\n\u001B[0;32m    157\u001B[0m       new_doc \u001B[38;5;241m=\u001B[39m transform\u001B[38;5;241m.\u001B[39mtransform_document(doc)\n\u001B[0;32m    158\u001B[0m       data \u001B[38;5;241m=\u001B[39m transform_doc(rest, new_doc)\n\u001B[0;32m    159\u001B[0m       \u001B[38;5;28;01mreturn\u001B[39;00m transform\u001B[38;5;241m.\u001B[39mtransform_response(doc, data)\n\u001B[0;32m    161\u001B[0m   \u001B[38;5;28;01massert\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\subgrounds.py:149\u001B[0m, in \u001B[0;36mSubgrounds.execute.<locals>.execute_document\u001B[1;34m(doc)\u001B[0m\n\u001B[0;32m    147\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m paginate(subgraph\u001B[38;5;241m.\u001B[39m_schema, doc, pagination_strategy\u001B[38;5;241m=\u001B[39mpagination_strategy)\n\u001B[0;32m    148\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m--> 149\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mclient\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mquery\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43murl\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mgraphql\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvariables\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mdoc\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mvariables\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\subgrounds\\client.py:167\u001B[0m, in \u001B[0;36mquery\u001B[1;34m(url, query_str, variables)\u001B[0m\n\u001B[0;32m    165\u001B[0m   \u001B[38;5;28;01mreturn\u001B[39;00m resp[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdata\u001B[39m\u001B[38;5;124m'\u001B[39m]\n\u001B[0;32m    166\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m exn:\n\u001B[1;32m--> 167\u001B[0m   \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m(\u001B[43mresp\u001B[49m\u001B[43m[\u001B[49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43merrors\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m]\u001B[49m) \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mexn\u001B[39;00m\n",
      "\u001B[1;31mKeyError\u001B[0m: 'errors'"
     ]
    }
   ],
   "source": [
    "voting_snapshots_list.drop\n",
    "voting_snapshots_list = db.query(\"select \"\n",
    "                                 \"  *\"\n",
    "                                 \"  , to_timestamp((votes_Created::bigint)) vote_timestamp  \"\n",
    "                                 \"\"\n",
    "                                 \"from voting_snapshots_list_pre where to_timestamp((votes_Created::bigint))<'2022-01-01 00:00:01' order by vote_timestamp\"\n",
    "                                 \"\"\n",
    "                                  ).df()\n",
    "\n",
    "x=0\n",
    "progress=0\n",
    "while progress <100:\n",
    "    proposal_id = olympus_governance_view_clean.iloc[x,3]\n",
    "    proposal_date = olympus_governance_view_clean.iloc[x,15]\n",
    "    print(x,' ',progress,' ', proposal_id, ' ', proposal_date)\n",
    "    vote_tracker = snapshot.Query.votes(\n",
    "    orderBy = 'created',\n",
    "    orderDirection='desc',\n",
    "    first=1000,\n",
    "    where=[\n",
    "      snapshot.Vote.proposal == proposal_id\n",
    "    ]\n",
    "    )\n",
    "    voting_snapshots = sg.query_df([\n",
    "    vote_tracker.id,\n",
    "    vote_tracker.voter,\n",
    "    vote_tracker.created,\n",
    "    vote_tracker.choice,\n",
    "    vote_tracker.vp\n",
    "    ])\n",
    "\n",
    "    voting_snapshots['Proposal'] = proposal_id\n",
    "    voting_snapshots_list=pd.concat([voting_snapshots_list, voting_snapshots])\n",
    "\n",
    "    x=x+1\n",
    "    progress = 100*(round(x/total_proposals,4))\n",
    "\n",
    "\n",
    "print(len(voting_snapshots_list),' records')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [],
   "source": [
    "#spit out the file\n",
    "path =file+'/'+spacename+'_voting_snapshots_list_'+str(date.today().strftime(\"%b-%d-%Y\"))+'_'+str(len(olympus_governance_view_clean))+'.csv'\n",
    "voting_snapshots_list.to_csv(path, index = False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Proposal'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "Input \u001B[1;32mIn [30]\u001B[0m, in \u001B[0;36m<cell line: 2>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      1\u001B[0m \u001B[38;5;66;03m#I join these two tables to create my charts as it makes life easier. We are going to build the charts here now, so here we go\u001B[39;00m\n\u001B[1;32m----> 2\u001B[0m governance_data \u001B[38;5;241m=\u001B[39m \u001B[43mpd\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmerge\u001B[49m\u001B[43m(\u001B[49m\u001B[43mvoting_snapshots_list\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43molympus_governance_view_clean\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mhow\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43minner\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mleft_on\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mProposal\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mright_on\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mproposals_id\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m      3\u001B[0m \u001B[38;5;28;01mdel\u001B[39;00m governance_data[\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mproposals_body\u001B[39m\u001B[38;5;124m\"\u001B[39m] \u001B[38;5;66;03m#breaks the table with weird characters\u001B[39;00m\n\u001B[0;32m      4\u001B[0m \u001B[38;5;66;03m#governance_data[\"vote_value\"] = governance_data[\"votes_vp\"]*governance_data[\"usd_price\"]\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:107\u001B[0m, in \u001B[0;36mmerge\u001B[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[0;32m     90\u001B[0m \u001B[38;5;129m@Substitution\u001B[39m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;130;01m\\n\u001B[39;00m\u001B[38;5;124mleft : DataFrame or named Series\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m     91\u001B[0m \u001B[38;5;129m@Appender\u001B[39m(_merge_doc, indents\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m0\u001B[39m)\n\u001B[0;32m     92\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mmerge\u001B[39m(\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    105\u001B[0m     validate: \u001B[38;5;28mstr\u001B[39m \u001B[38;5;241m|\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m    106\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m DataFrame:\n\u001B[1;32m--> 107\u001B[0m     op \u001B[38;5;241m=\u001B[39m \u001B[43m_MergeOperation\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    108\u001B[0m \u001B[43m        \u001B[49m\u001B[43mleft\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    109\u001B[0m \u001B[43m        \u001B[49m\u001B[43mright\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    110\u001B[0m \u001B[43m        \u001B[49m\u001B[43mhow\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mhow\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    111\u001B[0m \u001B[43m        \u001B[49m\u001B[43mon\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mon\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    112\u001B[0m \u001B[43m        \u001B[49m\u001B[43mleft_on\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mleft_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    113\u001B[0m \u001B[43m        \u001B[49m\u001B[43mright_on\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mright_on\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    114\u001B[0m \u001B[43m        \u001B[49m\u001B[43mleft_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mleft_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    115\u001B[0m \u001B[43m        \u001B[49m\u001B[43mright_index\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mright_index\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    116\u001B[0m \u001B[43m        \u001B[49m\u001B[43msort\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msort\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    117\u001B[0m \u001B[43m        \u001B[49m\u001B[43msuffixes\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43msuffixes\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    118\u001B[0m \u001B[43m        \u001B[49m\u001B[43mcopy\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcopy\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    119\u001B[0m \u001B[43m        \u001B[49m\u001B[43mindicator\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mindicator\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    120\u001B[0m \u001B[43m        \u001B[49m\u001B[43mvalidate\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvalidate\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    121\u001B[0m \u001B[43m    \u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    122\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m op\u001B[38;5;241m.\u001B[39mget_result()\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:700\u001B[0m, in \u001B[0;36m_MergeOperation.__init__\u001B[1;34m(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001B[0m\n\u001B[0;32m    693\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_cross \u001B[38;5;241m=\u001B[39m cross_col\n\u001B[0;32m    695\u001B[0m \u001B[38;5;66;03m# note this function has side effects\u001B[39;00m\n\u001B[0;32m    696\u001B[0m (\n\u001B[0;32m    697\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mleft_join_keys,\n\u001B[0;32m    698\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mright_join_keys,\n\u001B[0;32m    699\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mjoin_names,\n\u001B[1;32m--> 700\u001B[0m ) \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_merge_keys\u001B[49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    702\u001B[0m \u001B[38;5;66;03m# validate the merge keys dtypes. We may need to coerce\u001B[39;00m\n\u001B[0;32m    703\u001B[0m \u001B[38;5;66;03m# to avoid incompatible dtypes\u001B[39;00m\n\u001B[0;32m    704\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_maybe_coerce_merge_keys()\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\pandas\\core\\reshape\\merge.py:1110\u001B[0m, in \u001B[0;36m_MergeOperation._get_merge_keys\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m   1108\u001B[0m     right_keys\u001B[38;5;241m.\u001B[39mappend(rk)\n\u001B[0;32m   1109\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m lk \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1110\u001B[0m     left_keys\u001B[38;5;241m.\u001B[39mappend(\u001B[43mleft\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_get_label_or_level_values\u001B[49m\u001B[43m(\u001B[49m\u001B[43mlk\u001B[49m\u001B[43m)\u001B[49m)\n\u001B[0;32m   1111\u001B[0m     join_names\u001B[38;5;241m.\u001B[39mappend(lk)\n\u001B[0;32m   1112\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1113\u001B[0m     \u001B[38;5;66;03m# work-around for merge_asof(left_index=True)\u001B[39;00m\n",
      "File \u001B[1;32m~\\PycharmProjects\\pythonProject\\venv\\lib\\site-packages\\pandas\\core\\generic.py:1840\u001B[0m, in \u001B[0;36mNDFrame._get_label_or_level_values\u001B[1;34m(self, key, axis)\u001B[0m\n\u001B[0;32m   1838\u001B[0m     values \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39maxes[axis]\u001B[38;5;241m.\u001B[39mget_level_values(key)\u001B[38;5;241m.\u001B[39m_values\n\u001B[0;32m   1839\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[1;32m-> 1840\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mKeyError\u001B[39;00m(key)\n\u001B[0;32m   1842\u001B[0m \u001B[38;5;66;03m# Check for duplicates\u001B[39;00m\n\u001B[0;32m   1843\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m values\u001B[38;5;241m.\u001B[39mndim \u001B[38;5;241m>\u001B[39m \u001B[38;5;241m1\u001B[39m:\n",
      "\u001B[1;31mKeyError\u001B[0m: 'Proposal'"
     ]
    }
   ],
   "source": [
    "#I join these two tables to create my charts as it makes life easier. We are going to build the charts here now, so here we go\n",
    "governance_data = pd.merge(voting_snapshots_list, olympus_governance_view_clean, how='inner', left_on='Proposal', right_on='proposals_id')\n",
    "del governance_data[\"proposals_body\"] #breaks the table with weird characters\n",
    "#governance_data[\"vote_value\"] = governance_data[\"votes_vp\"]*governance_data[\"usd_price\"]\n",
    "governance_data.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [],
   "source": [
    "#Spit out the file, but save it in its own folder for easy access\n",
    "final_file = file+'\\\\'+'final'\n",
    "final_raw_file = final_file\n",
    "os.makedirs(final_raw_file, exist_ok=True)\n",
    "final_path =file+'\\\\'+spacename+'governance_data_'+str(date.today().strftime(\"%b-%d-%Y\"))+'_'+str(len(governance_data))+'.csv'\n",
    "governance_data.to_csv(final_path, index = False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "              DAO                                           Proposal  \\\n0  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n1  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n2  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n3  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n4  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n5  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n6  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n7  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n8  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n9  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n\n                                  votes_voter  votes_choice     votes_vp  \\\n0  0x3F435ecb51C986CD2942c700e1E5076050D1Bb62             1  8410.354331   \n1  0x318A11FB434333DE60B3585428CC888eA1E22C67             1  8165.904205   \n2  0x318A11FB434333DE60B3585428CC888eA1E22C67             1  8165.904205   \n3  0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1  3312.725382   \n4  0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1  3312.725382   \n5  0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1  3272.832615   \n6  0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1  3272.832615   \n7  0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF             1  2791.835058   \n8  0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF             1  2791.835058   \n9  0x5bef46ac7f33b5E2FC2b6F26628884e7163eE96C             1  1850.886650   \n\n   votes_created                        proposals_title proposal_date  \\\n0     1646356214          TAP-6: Whitelist Frax Finance    2022-03-01   \n1     1646233183          TAP-6: Whitelist Frax Finance    2022-03-01   \n2     1646233293  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n3     1646377484  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n4     1646377527          TAP-6: Whitelist Frax Finance    2022-03-01   \n5     1646232431  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n6     1646232480          TAP-6: Whitelist Frax Finance    2022-03-01   \n7     1646285764  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n8     1646285798          TAP-6: Whitelist Frax Finance    2022-03-01   \n9     1646250915  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n\n      proposal_date_2  cumulative_vp      total_vp  percentage_of_total_vp  \\\n0 2022-03-04 01:10:14    8410.354331  53521.054419                0.157141   \n1 2022-03-02 14:59:43   16576.258536  53521.054419                0.152574   \n2 2022-03-02 15:01:33    8165.904205  43300.813626                0.188585   \n3 2022-03-04 07:04:44   11478.629586  43300.813626                0.076505   \n4 2022-03-04 07:05:27   19888.983917  53521.054419                0.061896   \n5 2022-03-02 14:47:11   14751.462201  43300.813626                0.075584   \n6 2022-03-02 14:48:00   23161.816532  53521.054419                0.061150   \n7 2022-03-03 05:36:04   17543.297259  43300.813626                0.064475   \n8 2022-03-03 05:36:38   25953.651590  53521.054419                0.052163   \n9 2022-03-02 19:55:15   19394.183910  43300.813626                0.042745   \n\n   cum_percentage_of_total_vp  cum_percentage_of_total_vp_stepped  \\\n0                    0.157141                                 0.0   \n1                    0.309715                                 0.0   \n2                    0.188585                                 0.0   \n3                    0.265090                                 0.0   \n4                    0.371610                                 0.0   \n5                    0.340674                                 0.0   \n6                    0.432761                                 0.0   \n7                    0.405149                                 0.0   \n8                    0.484924                                 0.0   \n9                    0.447894                                 0.0   \n\n   proposal_voter_rank  total_voters  percentage_voters_counted  \\\n0                    1             1                   0.006494   \n1                    2             2                   0.012987   \n2                    1             1                   0.006579   \n3                    2             2                   0.013158   \n4                    3             3                   0.019481   \n5                    3             3                   0.019737   \n6                    4             4                   0.025974   \n7                    4             4                   0.026316   \n8                    5             5                   0.032468   \n9                    5             5                   0.032895   \n\n   percentage_voters_counted_stepped  \n0                                1.0  \n1                                1.0  \n2                                1.0  \n3                                1.0  \n4                                2.0  \n5                                2.0  \n6                                3.0  \n7                                3.0  \n8                                3.0  \n9                                3.0  ",
      "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>DAO</th>\n      <th>Proposal</th>\n      <th>votes_voter</th>\n      <th>votes_choice</th>\n      <th>votes_vp</th>\n      <th>votes_created</th>\n      <th>proposals_title</th>\n      <th>proposal_date</th>\n      <th>proposal_date_2</th>\n      <th>cumulative_vp</th>\n      <th>total_vp</th>\n      <th>percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp_stepped</th>\n      <th>proposal_voter_rank</th>\n      <th>total_voters</th>\n      <th>percentage_voters_counted</th>\n      <th>percentage_voters_counted_stepped</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x3F435ecb51C986CD2942c700e1E5076050D1Bb62</td>\n      <td>1</td>\n      <td>8410.354331</td>\n      <td>1646356214</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 01:10:14</td>\n      <td>8410.354331</td>\n      <td>53521.054419</td>\n      <td>0.157141</td>\n      <td>0.157141</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.006494</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233183</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:59:43</td>\n      <td>16576.258536</td>\n      <td>53521.054419</td>\n      <td>0.152574</td>\n      <td>0.309715</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.012987</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233293</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 15:01:33</td>\n      <td>8165.904205</td>\n      <td>43300.813626</td>\n      <td>0.188585</td>\n      <td>0.188585</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.006579</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377484</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:04:44</td>\n      <td>11478.629586</td>\n      <td>43300.813626</td>\n      <td>0.076505</td>\n      <td>0.265090</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.013158</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377527</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:05:27</td>\n      <td>19888.983917</td>\n      <td>53521.054419</td>\n      <td>0.061896</td>\n      <td>0.371610</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019481</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232431</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:47:11</td>\n      <td>14751.462201</td>\n      <td>43300.813626</td>\n      <td>0.075584</td>\n      <td>0.340674</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019737</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232480</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:48:00</td>\n      <td>23161.816532</td>\n      <td>53521.054419</td>\n      <td>0.061150</td>\n      <td>0.432761</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.025974</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF</td>\n      <td>1</td>\n      <td>2791.835058</td>\n      <td>1646285764</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-03 05:36:04</td>\n      <td>17543.297259</td>\n      <td>43300.813626</td>\n      <td>0.064475</td>\n      <td>0.405149</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.026316</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF</td>\n      <td>1</td>\n      <td>2791.835058</td>\n      <td>1646285798</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-03 05:36:38</td>\n      <td>25953.651590</td>\n      <td>53521.054419</td>\n      <td>0.052163</td>\n      <td>0.484924</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.032468</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x5bef46ac7f33b5E2FC2b6F26628884e7163eE96C</td>\n      <td>1</td>\n      <td>1850.886650</td>\n      <td>1646250915</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 19:55:15</td>\n      <td>19394.183910</td>\n      <td>43300.813626</td>\n      <td>0.042745</td>\n      <td>0.447894</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.032895</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crunch_data = db.query(\"select \"\n",
    "                           \"Proposal\"\n",
    "                           \",votes_voter \"\n",
    "                           \",votes_choice\"\n",
    "                           \",votes_vp\"\n",
    "                           \",votes_created \"\n",
    "                       \"    ,proposals_title \"\n",
    "                       \"    ,proposal_date\"\n",
    "                    \"       , to_timestamp(min(votes_Created::bigint)) proposal_date \"\n",
    "                           \",sum(votes_vp) over (Partition by Proposal  order by votes_vp desc, votes_created asc) as cumulative_vp \"\n",
    "                           \",sum(votes_vp) over (Partition by Proposal) as total_vp \"\n",
    "                           \",(votes_vp::decimal/sum(votes_vp::decimal) over (Partition by Proposal)) as percentage_of_total_vp \"\n",
    "                           \",((sum(votes_vp) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/sum(votes_vp::decimal) over (Partition by Proposal)) as cum_percentage_of_total_vp \"\n",
    "                       \",round((sum(votes_vp) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/sum(votes_vp::decimal) over (Partition by Proposal)) as cum_percentage_of_total_vp_stepped \"\n",
    "                           \",row_number() over (Partition by Proposal order by votes_vp desc, votes_created asc) as proposal_voter_rank \"\n",
    "                           \",count(votes_voter) over (Partition by Proposal  order by votes_vp desc, votes_created asc) total_voters \"\n",
    "                           \",(count(*) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/(count(*) over (Partition by Proposal))::decimal percentage_voters_counted \"\n",
    "                           \",round(100*(count(*) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/(count(*) over (Partition by Proposal)))::decimal percentage_voters_counted_stepped \"\n",
    "                       \"from \"\n",
    "                       \"    governance_data  \"\n",
    "                       #\"where   to_timestamp((votes_Created::bigint))<'2023-01-01' \"\n",
    "                       \"\"\n",
    "                       \"Group by \"\n",
    "                       \"    Proposal\"\n",
    "                       \"    ,votes_voter\"\n",
    "                       \"    ,votes_choice\"\n",
    "                       \"    , votes_vp \"\n",
    "                       \"    , votes_created \"\n",
    "                       \"    , proposals_title \"\n",
    "                       \"    ,proposal_date \"\n",
    "                       \"\"\n",
    "                       \"Order by \"\n",
    "                       \"    7 asc, \"\n",
    "                       \"    votes_vp desc \"\n",
    "                       \"    , votes_created asc\"\n",
    "                       \"\").df()\n",
    "\n",
    "\n",
    "crunch_data.insert(0, 'DAO', spacename)\n",
    "crunch_data.head(n=10)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "                DAO                                           Proposal  \\\n0    olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n1    olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n2    olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n3    olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n4    olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n..              ...                                                ...   \n184  olympusdao.eth  0xe1f1a1eea2da4d8c74a5b2f840eb7dce8c8143926b35...   \n185  olympusdao.eth  0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...   \n186  olympusdao.eth  0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...   \n187  olympusdao.eth  0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...   \n188  olympusdao.eth  0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...   \n\n                                    votes_voter  votes_choice      votes_vp  \\\n0    0x318A11FB434333DE60B3585428CC888eA1E22C67             1   8165.904205   \n1    0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1   3312.725382   \n2    0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1   3312.725382   \n3    0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1   3272.832615   \n4    0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1   3272.832615   \n..                                          ...           ...           ...   \n184  0x489ce55616c1eBBe03c126742D0fb47032A1216b             1  35922.900766   \n185  0xca6aA0576c2ADc79d2c20e6D53c07f590f62932A             1  12412.901345   \n186  0x47af52916767f0550AEE8035b5b14A1ECe2B34f4             1  12312.131097   \n187  0xf10918Fc1725A65960db2b906b4868a0Fdbb6870             1  12548.221870   \n188  0x721Ca5428eC12b148b34458E0f8557F4C9624630             1   9319.250335   \n\n     votes_created                                    proposals_title  \\\n0       1646233183                      TAP-6: Whitelist Frax Finance   \n1       1646377484              OIP-83: Curve v2 Liquidity Deployment   \n2       1646377527                      TAP-6: Whitelist Frax Finance   \n3       1646232431              OIP-83: Curve v2 Liquidity Deployment   \n4       1646232480                      TAP-6: Whitelist Frax Finance   \n..             ...                                                ...   \n184     1675814814                        OIP 129 - Council Framework   \n185     1676065442               TAP-21 Pilot Clearing House Deposits   \n186     1676059742               TAP-21 Pilot Clearing House Deposits   \n187     1677514277   OOP-1 Funding request for 2023 Q1 and Q2 budget.   \n188     1677713759  OIP-130: Lending AMO as a key DAO project for ...   \n\n    proposal_date     proposal_date_2  cumulative_vp  ...  \\\n0      2022-03-01 2022-03-02 14:59:43   16576.258536  ...   \n1      2022-03-01 2022-03-04 07:04:44   11478.629586  ...   \n2      2022-03-01 2022-03-04 07:05:27   19888.983917  ...   \n3      2022-03-01 2022-03-02 14:47:11   14751.462201  ...   \n4      2022-03-01 2022-03-02 14:48:00   23161.816532  ...   \n..            ...                 ...            ...  ...   \n184    2023-02-06 2023-02-08 00:06:54  219780.130110  ...   \n185    2023-02-10 2023-02-10 21:44:02   26935.272543  ...   \n186    2023-02-10 2023-02-10 20:09:02   39247.403639  ...   \n187    2023-02-26 2023-02-27 16:11:17   37459.817672  ...   \n188    2023-03-01 2023-03-01 23:35:59   21873.977521  ...   \n\n     cum_percentage_of_total_vp  cum_percentage_of_total_vp_stepped  \\\n0                      0.309715                                 0.0   \n1                      0.265090                                 0.0   \n2                      0.371610                                 0.0   \n3                      0.340674                                 0.0   \n4                      0.432761                                 0.0   \n..                          ...                                 ...   \n184                    0.554837                                 1.0   \n185                    0.375321                                 0.0   \n186                    0.546880                                 1.0   \n187                    0.589748                                 1.0   \n188                    0.523450                                 1.0   \n\n     proposal_voter_rank  total_voters  percentage_voters_counted  \\\n0                      2             2                   0.012987   \n1                      2             2                   0.013158   \n2                      3             3                   0.019481   \n3                      3             3                   0.019737   \n4                      4             4                   0.025974   \n..                   ...           ...                        ...   \n184                    2             2                   0.020833   \n185                    2             2                   0.019802   \n186                    3             3                   0.029703   \n187                    2             2                   0.033333   \n188                    2             2                   0.036364   \n\n     percentage_voters_counted_stepped  \\\n0                                  1.0   \n1                                  1.0   \n2                                  2.0   \n3                                  2.0   \n4                                  3.0   \n..                                 ...   \n184                                2.0   \n185                                2.0   \n186                                3.0   \n187                                3.0   \n188                                4.0   \n\n                                            Proposal_2  \\\n0    0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n1    0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n2    0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n3    0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n4    0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n..                                                 ...   \n184  0xe1f1a1eea2da4d8c74a5b2f840eb7dce8c8143926b35...   \n185  0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...   \n186  0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...   \n187  0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...   \n188  0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...   \n\n                                  votes_voter_2 proposal_voter_rank_2  \\\n0    0x3F435ecb51C986CD2942c700e1E5076050D1Bb62                     1   \n1    0x318A11FB434333DE60B3585428CC888eA1E22C67                     1   \n2    0x318A11FB434333DE60B3585428CC888eA1E22C67                     2   \n3    0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F                     2   \n4    0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F                     3   \n..                                          ...                   ...   \n184  0xca5dF910F3e2226324707EFE7fa6d03A0F821353                     1   \n185  0x5E11A833d112e973661040E062F1aEdFB2BCEc1d                     1   \n186  0xca6aA0576c2ADc79d2c20e6D53c07f590f62932A                     2   \n187  0x31D960182A8e52Dd53cf910A6725aa6d66CAc570                     1   \n188  0xf10918Fc1725A65960db2b906b4868a0Fdbb6870                     1   \n\n    leader_rank  \n0             2  \n1             2  \n2             3  \n3             3  \n4             4  \n..          ...  \n184           2  \n185           2  \n186           3  \n187           2  \n188           2  \n\n[189 rows x 22 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>DAO</th>\n      <th>Proposal</th>\n      <th>votes_voter</th>\n      <th>votes_choice</th>\n      <th>votes_vp</th>\n      <th>votes_created</th>\n      <th>proposals_title</th>\n      <th>proposal_date</th>\n      <th>proposal_date_2</th>\n      <th>cumulative_vp</th>\n      <th>...</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp_stepped</th>\n      <th>proposal_voter_rank</th>\n      <th>total_voters</th>\n      <th>percentage_voters_counted</th>\n      <th>percentage_voters_counted_stepped</th>\n      <th>Proposal_2</th>\n      <th>votes_voter_2</th>\n      <th>proposal_voter_rank_2</th>\n      <th>leader_rank</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233183</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:59:43</td>\n      <td>16576.258536</td>\n      <td>...</td>\n      <td>0.309715</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.012987</td>\n      <td>1.0</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x3F435ecb51C986CD2942c700e1E5076050D1Bb62</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377484</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:04:44</td>\n      <td>11478.629586</td>\n      <td>...</td>\n      <td>0.265090</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.013158</td>\n      <td>1.0</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377527</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:05:27</td>\n      <td>19888.983917</td>\n      <td>...</td>\n      <td>0.371610</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019481</td>\n      <td>2.0</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232431</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:47:11</td>\n      <td>14751.462201</td>\n      <td>...</td>\n      <td>0.340674</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019737</td>\n      <td>2.0</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232480</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:48:00</td>\n      <td>23161.816532</td>\n      <td>...</td>\n      <td>0.432761</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.025974</td>\n      <td>3.0</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>184</th>\n      <td>olympusdao.eth</td>\n      <td>0xe1f1a1eea2da4d8c74a5b2f840eb7dce8c8143926b35...</td>\n      <td>0x489ce55616c1eBBe03c126742D0fb47032A1216b</td>\n      <td>1</td>\n      <td>35922.900766</td>\n      <td>1675814814</td>\n      <td>OIP 129 - Council Framework</td>\n      <td>2023-02-06</td>\n      <td>2023-02-08 00:06:54</td>\n      <td>219780.130110</td>\n      <td>...</td>\n      <td>0.554837</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.020833</td>\n      <td>2.0</td>\n      <td>0xe1f1a1eea2da4d8c74a5b2f840eb7dce8c8143926b35...</td>\n      <td>0xca5dF910F3e2226324707EFE7fa6d03A0F821353</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>185</th>\n      <td>olympusdao.eth</td>\n      <td>0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...</td>\n      <td>0xca6aA0576c2ADc79d2c20e6D53c07f590f62932A</td>\n      <td>1</td>\n      <td>12412.901345</td>\n      <td>1676065442</td>\n      <td>TAP-21 Pilot Clearing House Deposits</td>\n      <td>2023-02-10</td>\n      <td>2023-02-10 21:44:02</td>\n      <td>26935.272543</td>\n      <td>...</td>\n      <td>0.375321</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.019802</td>\n      <td>2.0</td>\n      <td>0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...</td>\n      <td>0x5E11A833d112e973661040E062F1aEdFB2BCEc1d</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>186</th>\n      <td>olympusdao.eth</td>\n      <td>0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...</td>\n      <td>0x47af52916767f0550AEE8035b5b14A1ECe2B34f4</td>\n      <td>1</td>\n      <td>12312.131097</td>\n      <td>1676059742</td>\n      <td>TAP-21 Pilot Clearing House Deposits</td>\n      <td>2023-02-10</td>\n      <td>2023-02-10 20:09:02</td>\n      <td>39247.403639</td>\n      <td>...</td>\n      <td>0.546880</td>\n      <td>1.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.029703</td>\n      <td>3.0</td>\n      <td>0x7065c53c88cff153efefc4dc042f81d795011cfdfa29...</td>\n      <td>0xca6aA0576c2ADc79d2c20e6D53c07f590f62932A</td>\n      <td>2</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>187</th>\n      <td>olympusdao.eth</td>\n      <td>0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...</td>\n      <td>0xf10918Fc1725A65960db2b906b4868a0Fdbb6870</td>\n      <td>1</td>\n      <td>12548.221870</td>\n      <td>1677514277</td>\n      <td>OOP-1 Funding request for 2023 Q1 and Q2 budget.</td>\n      <td>2023-02-26</td>\n      <td>2023-02-27 16:11:17</td>\n      <td>37459.817672</td>\n      <td>...</td>\n      <td>0.589748</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.033333</td>\n      <td>3.0</td>\n      <td>0xadd0850bb53fc31df236d61e2b53326748c80ef72f54...</td>\n      <td>0x31D960182A8e52Dd53cf910A6725aa6d66CAc570</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>188</th>\n      <td>olympusdao.eth</td>\n      <td>0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...</td>\n      <td>0x721Ca5428eC12b148b34458E0f8557F4C9624630</td>\n      <td>1</td>\n      <td>9319.250335</td>\n      <td>1677713759</td>\n      <td>OIP-130: Lending AMO as a key DAO project for ...</td>\n      <td>2023-03-01</td>\n      <td>2023-03-01 23:35:59</td>\n      <td>21873.977521</td>\n      <td>...</td>\n      <td>0.523450</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.036364</td>\n      <td>4.0</td>\n      <td>0x0e1173ac9585c4e46723d69ef1a4ef1995390d9d3f90...</td>\n      <td>0xf10918Fc1725A65960db2b906b4868a0Fdbb6870</td>\n      <td>1</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>189 rows × 22 columns</p>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#leaders = crunch_data.loc[crunch_data['proposal_voter_rank'] <=3]\n",
    "#leader_count = leaders.votes_voter.nunique()\n",
    "#leader_count\n",
    "#DONT ASK ME WHY THIS DIDNT WORK. HAVE TO DO IT THE UGLY WAY\n",
    "\n",
    "leader_ranks = db.query(\"with leader_ranks as \"\n",
    "                        \"(Select distinct \"\n",
    "                        \"   B.Proposal\"\n",
    "                        \"   ,B.votes_voter\"\n",
    "                        \"   ,B.proposal_voter_rank \"\n",
    "                        \"   ,(B.proposal_voter_rank +1) as leader_rank \"\n",
    "                        \"From \"\n",
    "                        \"   (select \"\n",
    "                               \"Proposal\"\n",
    "                               \",votes_voter \"\n",
    "                               \",votes_choice\"\n",
    "                               \",votes_vp\"\n",
    "                               \",votes_created  \"\n",
    "                               \",sum(votes_vp) over (Partition by Proposal  order by votes_vp desc, votes_created asc) as cumulative_vp\"\n",
    "                               \",sum(votes_vp) over (Partition by Proposal) as total_vp\"\n",
    "                               \",(votes_vp::decimal/sum(votes_vp::decimal) over (Partition by Proposal)) as percentage_of_total_vp \"\n",
    "                               \",((sum(votes_vp) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/sum(votes_vp::decimal) over (Partition by Proposal)) as cum_percentage_of_total_vp \"\n",
    "                           \"    ,round((sum(votes_vp) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/sum(votes_vp::decimal) over (Partition by Proposal)) as cum_percentage_of_total_vp_stepped \"\n",
    "                               \",row_number() over (Partition by Proposal order by votes_vp desc, votes_created asc) as proposal_voter_rank \"\n",
    "                               \",count(votes_voter) over (Partition by Proposal  order by votes_vp desc, votes_created asc) total_voters \"\n",
    "                               \",(count(*) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/(count(*) over (Partition by Proposal))::decimal percentage_voters_counted \"\n",
    "                               \",round(100*(count(*) over (Partition by Proposal  order by votes_vp desc, votes_created asc))::decimal/(count(*) over (Partition by Proposal)))::decimal percentage_voters_counted_stepped \"\n",
    "                           \"from \"\n",
    "                           \"    governance_data  \"\n",
    "                               \"\"\n",
    "                           \"Group by \"\n",
    "                           \"    Proposal\"\n",
    "                           \"    ,votes_voter\"\n",
    "                           \"    ,votes_choice\"\n",
    "                           \"    , votes_vp \"\n",
    "                           \"    , votes_created \"\n",
    "                           \"\"\n",
    "                           \"Order by \"\n",
    "                           \"    Proposal \"\n",
    "                           \"    ,votes_vp desc \"\n",
    "                           \"    , votes_created asc) B \"\n",
    "                        \"where \"\n",
    "                        \"   B.cum_percentage_of_total_vp<=0.5) \"\n",
    "                        \"\"\n",
    "                        \"Select \"\n",
    "                        \"   *\"\n",
    "                        \"From crunch_data A\"\n",
    "                        \"   Join leader_ranks B on A.proposal_voter_rank = B.leader_rank and A.Proposal = B.Proposal\"\n",
    "                        \"\"\n",
    "                    ).df()\n",
    "leader_ranks\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [],
   "source": [
    "#dao_members = db.query(\"Select count(distinct votes_voter) from crunch_data\")\n",
    "#dao_members"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016 3.72%\n"
     ]
    }
   ],
   "source": [
    "dao_members = crunch_data.groupby('DAO').votes_voter.nunique()\n",
    "dao_members = dao_members.iloc[0]\n",
    "leader_count =leader_ranks.votes_voter.nunique()\n",
    "elite = round((leader_count)/(dao_members),4)\n",
    "\n",
    "print(dao_members, \"{0:.2%}\".format(elite))\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "              DAO                                           Proposal  \\\n0  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n1  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n2  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n3  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n4  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n5  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n6  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n7  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n8  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n9  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n\n                                  votes_voter  votes_choice     votes_vp  \\\n0  0x3F435ecb51C986CD2942c700e1E5076050D1Bb62             1  8410.354331   \n1  0x318A11FB434333DE60B3585428CC888eA1E22C67             1  8165.904205   \n2  0x318A11FB434333DE60B3585428CC888eA1E22C67             1  8165.904205   \n3  0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1  3312.725382   \n4  0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1  3312.725382   \n5  0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1  3272.832615   \n6  0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1  3272.832615   \n7  0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF             1  2791.835058   \n8  0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF             1  2791.835058   \n9  0x5bef46ac7f33b5E2FC2b6F26628884e7163eE96C             1  1850.886650   \n\n   votes_created                        proposals_title proposal_date  \\\n0     1646356214          TAP-6: Whitelist Frax Finance    2022-03-01   \n1     1646233183          TAP-6: Whitelist Frax Finance    2022-03-01   \n2     1646233293  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n3     1646377484  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n4     1646377527          TAP-6: Whitelist Frax Finance    2022-03-01   \n5     1646232431  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n6     1646232480          TAP-6: Whitelist Frax Finance    2022-03-01   \n7     1646285764  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n8     1646285798          TAP-6: Whitelist Frax Finance    2022-03-01   \n9     1646250915  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n\n      proposal_date_2  cumulative_vp      total_vp  percentage_of_total_vp  \\\n0 2022-03-04 01:10:14    8410.354331  53521.054419                0.157141   \n1 2022-03-02 14:59:43   16576.258536  53521.054419                0.152574   \n2 2022-03-02 15:01:33    8165.904205  43300.813626                0.188585   \n3 2022-03-04 07:04:44   11478.629586  43300.813626                0.076505   \n4 2022-03-04 07:05:27   19888.983917  53521.054419                0.061896   \n5 2022-03-02 14:47:11   14751.462201  43300.813626                0.075584   \n6 2022-03-02 14:48:00   23161.816532  53521.054419                0.061150   \n7 2022-03-03 05:36:04   17543.297259  43300.813626                0.064475   \n8 2022-03-03 05:36:38   25953.651590  53521.054419                0.052163   \n9 2022-03-02 19:55:15   19394.183910  43300.813626                0.042745   \n\n   cum_percentage_of_total_vp  cum_percentage_of_total_vp_stepped  \\\n0                    0.157141                                 0.0   \n1                    0.309715                                 0.0   \n2                    0.188585                                 0.0   \n3                    0.265090                                 0.0   \n4                    0.371610                                 0.0   \n5                    0.340674                                 0.0   \n6                    0.432761                                 0.0   \n7                    0.405149                                 0.0   \n8                    0.484924                                 0.0   \n9                    0.447894                                 0.0   \n\n   proposal_voter_rank  total_voters  percentage_voters_counted  \\\n0                    1             1                   0.006494   \n1                    2             2                   0.012987   \n2                    1             1                   0.006579   \n3                    2             2                   0.013158   \n4                    3             3                   0.019481   \n5                    3             3                   0.019737   \n6                    4             4                   0.025974   \n7                    4             4                   0.026316   \n8                    5             5                   0.032468   \n9                    5             5                   0.032895   \n\n   percentage_voters_counted_stepped  \n0                                1.0  \n1                                1.0  \n2                                1.0  \n3                                1.0  \n4                                2.0  \n5                                2.0  \n6                                3.0  \n7                                3.0  \n8                                3.0  \n9                                3.0  ",
      "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>DAO</th>\n      <th>Proposal</th>\n      <th>votes_voter</th>\n      <th>votes_choice</th>\n      <th>votes_vp</th>\n      <th>votes_created</th>\n      <th>proposals_title</th>\n      <th>proposal_date</th>\n      <th>proposal_date_2</th>\n      <th>cumulative_vp</th>\n      <th>total_vp</th>\n      <th>percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp_stepped</th>\n      <th>proposal_voter_rank</th>\n      <th>total_voters</th>\n      <th>percentage_voters_counted</th>\n      <th>percentage_voters_counted_stepped</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x3F435ecb51C986CD2942c700e1E5076050D1Bb62</td>\n      <td>1</td>\n      <td>8410.354331</td>\n      <td>1646356214</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 01:10:14</td>\n      <td>8410.354331</td>\n      <td>53521.054419</td>\n      <td>0.157141</td>\n      <td>0.157141</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.006494</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233183</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:59:43</td>\n      <td>16576.258536</td>\n      <td>53521.054419</td>\n      <td>0.152574</td>\n      <td>0.309715</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.012987</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233293</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 15:01:33</td>\n      <td>8165.904205</td>\n      <td>43300.813626</td>\n      <td>0.188585</td>\n      <td>0.188585</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.006579</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377484</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:04:44</td>\n      <td>11478.629586</td>\n      <td>43300.813626</td>\n      <td>0.076505</td>\n      <td>0.265090</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.013158</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377527</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:05:27</td>\n      <td>19888.983917</td>\n      <td>53521.054419</td>\n      <td>0.061896</td>\n      <td>0.371610</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019481</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232431</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:47:11</td>\n      <td>14751.462201</td>\n      <td>43300.813626</td>\n      <td>0.075584</td>\n      <td>0.340674</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019737</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232480</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:48:00</td>\n      <td>23161.816532</td>\n      <td>53521.054419</td>\n      <td>0.061150</td>\n      <td>0.432761</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.025974</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF</td>\n      <td>1</td>\n      <td>2791.835058</td>\n      <td>1646285764</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-03 05:36:04</td>\n      <td>17543.297259</td>\n      <td>43300.813626</td>\n      <td>0.064475</td>\n      <td>0.405149</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.026316</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF</td>\n      <td>1</td>\n      <td>2791.835058</td>\n      <td>1646285798</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-03 05:36:38</td>\n      <td>25953.651590</td>\n      <td>53521.054419</td>\n      <td>0.052163</td>\n      <td>0.484924</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.032468</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x5bef46ac7f33b5E2FC2b6F26628884e7163eE96C</td>\n      <td>1</td>\n      <td>1850.886650</td>\n      <td>1646250915</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 19:55:15</td>\n      <td>19394.183910</td>\n      <td>43300.813626</td>\n      <td>0.042745</td>\n      <td>0.447894</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.032895</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crunch_data.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "       votes_choice      votes_vp  votes_created  cumulative_vp      total_vp  \\\ncount   8337.000000  8.337000e+03   8.337000e+03   8.337000e+03  8.337000e+03   \nmean       1.141658  1.636950e+03   1.656684e+09   2.658355e+05  2.808317e+05   \nstd        0.413847  9.750048e+03   9.034874e+06   2.619071e+05  2.710673e+05   \nmin        1.000000  1.168599e-16   1.646172e+09   4.005475e+03  2.269358e+04   \n25%        1.000000  1.090009e+01   1.649572e+09   8.218161e+04  9.675445e+04   \n50%        1.000000  8.764948e+01   1.654126e+09   1.524813e+05  1.542675e+05   \n75%        1.000000  5.103949e+02   1.662002e+09   3.428052e+05  3.598907e+05   \nmax        4.000000  3.418432e+05   1.677909e+09   1.013778e+06  1.013778e+06   \n\n       percentage_of_total_vp  cum_percentage_of_total_vp  \\\ncount             8337.000000                 8337.000000   \nmean                 0.007677                    0.938330   \nstd                  0.032214                    0.125684   \nmin                  0.000000                    0.097581   \n25%                  0.000062                    0.942831   \n50%                  0.000463                    0.991205   \n75%                  0.002802                    0.999256   \nmax                  0.828851                    1.000000   \n\n       cum_percentage_of_total_vp_stepped  proposal_voter_rank  total_voters  \\\ncount                         8337.000000          8337.000000   8337.000000   \nmean                             0.977330            86.150534     86.150534   \nstd                              0.148858            75.901802     75.901802   \nmin                              0.000000             1.000000      1.000000   \n25%                              1.000000            33.000000     33.000000   \n50%                              1.000000            67.000000     67.000000   \n75%                              1.000000           115.000000    115.000000   \nmax                              1.000000           449.000000    449.000000   \n\n       percentage_voters_counted  percentage_voters_counted_stepped  \ncount                8337.000000                        8337.000000  \nmean                    0.503838                          50.386230  \nstd                     0.288688                          28.873363  \nmin                     0.002227                           0.000000  \n25%                     0.253731                          25.000000  \n50%                     0.504132                          50.000000  \n75%                     0.753425                          75.000000  \nmax                     1.000000                         100.000000  ",
      "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>votes_choice</th>\n      <th>votes_vp</th>\n      <th>votes_created</th>\n      <th>cumulative_vp</th>\n      <th>total_vp</th>\n      <th>percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp_stepped</th>\n      <th>proposal_voter_rank</th>\n      <th>total_voters</th>\n      <th>percentage_voters_counted</th>\n      <th>percentage_voters_counted_stepped</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>8337.000000</td>\n      <td>8.337000e+03</td>\n      <td>8.337000e+03</td>\n      <td>8.337000e+03</td>\n      <td>8.337000e+03</td>\n      <td>8337.000000</td>\n      <td>8337.000000</td>\n      <td>8337.000000</td>\n      <td>8337.000000</td>\n      <td>8337.000000</td>\n      <td>8337.000000</td>\n      <td>8337.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.141658</td>\n      <td>1.636950e+03</td>\n      <td>1.656684e+09</td>\n      <td>2.658355e+05</td>\n      <td>2.808317e+05</td>\n      <td>0.007677</td>\n      <td>0.938330</td>\n      <td>0.977330</td>\n      <td>86.150534</td>\n      <td>86.150534</td>\n      <td>0.503838</td>\n      <td>50.386230</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>0.413847</td>\n      <td>9.750048e+03</td>\n      <td>9.034874e+06</td>\n      <td>2.619071e+05</td>\n      <td>2.710673e+05</td>\n      <td>0.032214</td>\n      <td>0.125684</td>\n      <td>0.148858</td>\n      <td>75.901802</td>\n      <td>75.901802</td>\n      <td>0.288688</td>\n      <td>28.873363</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000</td>\n      <td>1.168599e-16</td>\n      <td>1.646172e+09</td>\n      <td>4.005475e+03</td>\n      <td>2.269358e+04</td>\n      <td>0.000000</td>\n      <td>0.097581</td>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>0.002227</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>1.090009e+01</td>\n      <td>1.649572e+09</td>\n      <td>8.218161e+04</td>\n      <td>9.675445e+04</td>\n      <td>0.000062</td>\n      <td>0.942831</td>\n      <td>1.000000</td>\n      <td>33.000000</td>\n      <td>33.000000</td>\n      <td>0.253731</td>\n      <td>25.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1.000000</td>\n      <td>8.764948e+01</td>\n      <td>1.654126e+09</td>\n      <td>1.524813e+05</td>\n      <td>1.542675e+05</td>\n      <td>0.000463</td>\n      <td>0.991205</td>\n      <td>1.000000</td>\n      <td>67.000000</td>\n      <td>67.000000</td>\n      <td>0.504132</td>\n      <td>50.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.000000</td>\n      <td>5.103949e+02</td>\n      <td>1.662002e+09</td>\n      <td>3.428052e+05</td>\n      <td>3.598907e+05</td>\n      <td>0.002802</td>\n      <td>0.999256</td>\n      <td>1.000000</td>\n      <td>115.000000</td>\n      <td>115.000000</td>\n      <td>0.753425</td>\n      <td>75.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>4.000000</td>\n      <td>3.418432e+05</td>\n      <td>1.677909e+09</td>\n      <td>1.013778e+06</td>\n      <td>1.013778e+06</td>\n      <td>0.828851</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>449.000000</td>\n      <td>449.000000</td>\n      <td>1.000000</td>\n      <td>100.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crunch_data.describe()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [],
   "source": [
    "##spit out the file!\n",
    "crunch_data_path =final_file+'\\\\'+spacename+'_crunch_data_path'+str(date.today().strftime(\"%b-%d-%Y\"))+'_'+str(len(crunch_data))+'.csv'\n",
    "crunch_data.to_csv(crunch_data_path, index = False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [],
   "source": [
    "leader_rank_paths =final_file+'\\\\'+spacename+'_leader_ranks'+str(date.today().strftime(\"%b-%d-%Y\"))+'_'+str(len(crunch_data))+'.csv'\n",
    "leader_ranks.to_csv(leader_rank_paths, index = False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#calculate the p50 for each proposal\n",
    "p50_index = db.query(\"with p50s as \"\n",
    "                     \"  ( Select \"\n",
    "                     \"          \"\n",
    "                     \"      )\"\n",
    "                     \"\"\n",
    "                     \"\"\n",
    "                     \"select \"\n",
    "                     \"  A.DAO \"\n",
    "                     \"  , A.Proposal \"\n",
    "                     \"  , A.votes_voter \"\n",
    "                     \"  , A.leader_rank \"\n",
    "                     \"  , (select B.votes_voter from leader_ranks B where B.cum_percentage_of_total_vp>=0.5 and B.votes_voter=A.votes_voter order by cum_percentage_of_total_vp asc limit 1) as p50voter \"\n",
    "                     \"  , (select B.cum_percentage_of_total_vp from leader_ranks B where B.cum_percentage_of_total_vp>=0.5 and B.votes_voter=A.votes_voter order by cum_percentage_of_total_vp asc limit 1) as p50index \"\n",
    "                    \"from leader_ranks A \").df()\n",
    "p50_index"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ianfe\\AppData\\Local\\Temp\\ipykernel_52948\\105482612.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  data_means = crunch_data.groupby(\"percentage_voters_counted_stepped\")[\"cum_percentage_of_total_vp\", \"percentage_voters_counted\"].agg(\"mean\").reset_index()\n"
     ]
    },
    {
     "data": {
      "text/plain": "               DAO  percentage_voters_counted_stepped  \\\n0   olympusdao.eth                                0.0   \n1   olympusdao.eth                                1.0   \n2   olympusdao.eth                                2.0   \n3   olympusdao.eth                                3.0   \n4   olympusdao.eth                                4.0   \n5   olympusdao.eth                                5.0   \n6   olympusdao.eth                                6.0   \n7   olympusdao.eth                                7.0   \n8   olympusdao.eth                                8.0   \n9   olympusdao.eth                                9.0   \n10  olympusdao.eth                               10.0   \n11  olympusdao.eth                               11.0   \n12  olympusdao.eth                               12.0   \n13  olympusdao.eth                               13.0   \n14  olympusdao.eth                               14.0   \n15  olympusdao.eth                               15.0   \n16  olympusdao.eth                               16.0   \n17  olympusdao.eth                               17.0   \n18  olympusdao.eth                               18.0   \n19  olympusdao.eth                               19.0   \n20  olympusdao.eth                               20.0   \n21  olympusdao.eth                               21.0   \n22  olympusdao.eth                               22.0   \n23  olympusdao.eth                               23.0   \n24  olympusdao.eth                               24.0   \n25  olympusdao.eth                               25.0   \n26  olympusdao.eth                               26.0   \n27  olympusdao.eth                               27.0   \n28  olympusdao.eth                               28.0   \n29  olympusdao.eth                               29.0   \n30  olympusdao.eth                               30.0   \n31  olympusdao.eth                               31.0   \n32  olympusdao.eth                               32.0   \n33  olympusdao.eth                               33.0   \n34  olympusdao.eth                               34.0   \n35  olympusdao.eth                               35.0   \n36  olympusdao.eth                               36.0   \n37  olympusdao.eth                               37.0   \n38  olympusdao.eth                               38.0   \n39  olympusdao.eth                               39.0   \n40  olympusdao.eth                               40.0   \n41  olympusdao.eth                               41.0   \n42  olympusdao.eth                               42.0   \n43  olympusdao.eth                               43.0   \n44  olympusdao.eth                               44.0   \n45  olympusdao.eth                               45.0   \n46  olympusdao.eth                               46.0   \n47  olympusdao.eth                               47.0   \n48  olympusdao.eth                               48.0   \n49  olympusdao.eth                               49.0   \n50  olympusdao.eth                               50.0   \n\n    cum_percentage_of_total_vp  percentage_voters_counted  \n0                     0.240328                   0.003705  \n1                     0.331852                   0.010133  \n2                     0.479219                   0.020369  \n3                     0.545738                   0.030372  \n4                     0.617226                   0.040199  \n5                     0.656533                   0.050466  \n6                     0.708967                   0.060061  \n7                     0.730602                   0.069592  \n8                     0.756702                   0.079856  \n9                     0.778738                   0.089923  \n10                    0.804996                   0.100000  \n11                    0.819527                   0.109948  \n12                    0.834160                   0.119986  \n13                    0.848420                   0.130026  \n14                    0.860143                   0.140195  \n15                    0.869929                   0.149724  \n16                    0.880224                   0.159994  \n17                    0.891325                   0.170068  \n18                    0.897853                   0.180319  \n19                    0.904690                   0.190013  \n20                    0.913971                   0.200074  \n21                    0.912235                   0.210011  \n22                    0.924204                   0.219795  \n23                    0.928105                   0.230070  \n24                    0.935203                   0.240042  \n25                    0.938221                   0.250072  \n26                    0.939190                   0.259883  \n27                    0.948821                   0.269857  \n28                    0.947428                   0.280082  \n29                    0.953694                   0.289942  \n30                    0.954687                   0.300000  \n31                    0.959502                   0.309909  \n32                    0.961173                   0.319987  \n33                    0.964404                   0.329943  \n34                    0.964946                   0.340088  \n35                    0.968701                   0.350101  \n36                    0.970018                   0.360075  \n37                    0.971872                   0.369400  \n38                    0.973973                   0.379692  \n39                    0.976006                   0.389848  \n40                    0.977635                   0.399931  \n41                    0.977851                   0.409905  \n42                    0.981220                   0.419816  \n43                    0.981469                   0.430302  \n44                    0.982847                   0.439888  \n45                    0.983920                   0.449900  \n46                    0.985000                   0.459920  \n47                    0.986984                   0.469944  \n48                    0.986779                   0.479835  \n49                    0.988420                   0.489915  \n50                    0.988794                   0.500000  ",
      "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>DAO</th>\n      <th>percentage_voters_counted_stepped</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>percentage_voters_counted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>olympusdao.eth</td>\n      <td>0.0</td>\n      <td>0.240328</td>\n      <td>0.003705</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>olympusdao.eth</td>\n      <td>1.0</td>\n      <td>0.331852</td>\n      <td>0.010133</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>olympusdao.eth</td>\n      <td>2.0</td>\n      <td>0.479219</td>\n      <td>0.020369</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>olympusdao.eth</td>\n      <td>3.0</td>\n      <td>0.545738</td>\n      <td>0.030372</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>olympusdao.eth</td>\n      <td>4.0</td>\n      <td>0.617226</td>\n      <td>0.040199</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>olympusdao.eth</td>\n      <td>5.0</td>\n      <td>0.656533</td>\n      <td>0.050466</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>olympusdao.eth</td>\n      <td>6.0</td>\n      <td>0.708967</td>\n      <td>0.060061</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>olympusdao.eth</td>\n      <td>7.0</td>\n      <td>0.730602</td>\n      <td>0.069592</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>olympusdao.eth</td>\n      <td>8.0</td>\n      <td>0.756702</td>\n      <td>0.079856</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>olympusdao.eth</td>\n      <td>9.0</td>\n      <td>0.778738</td>\n      <td>0.089923</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>olympusdao.eth</td>\n      <td>10.0</td>\n      <td>0.804996</td>\n      <td>0.100000</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>olympusdao.eth</td>\n      <td>11.0</td>\n      <td>0.819527</td>\n      <td>0.109948</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>olympusdao.eth</td>\n      <td>12.0</td>\n      <td>0.834160</td>\n      <td>0.119986</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>olympusdao.eth</td>\n      <td>13.0</td>\n      <td>0.848420</td>\n      <td>0.130026</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>olympusdao.eth</td>\n      <td>14.0</td>\n      <td>0.860143</td>\n      <td>0.140195</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>olympusdao.eth</td>\n      <td>15.0</td>\n      <td>0.869929</td>\n      <td>0.149724</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>olympusdao.eth</td>\n      <td>16.0</td>\n      <td>0.880224</td>\n      <td>0.159994</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>olympusdao.eth</td>\n      <td>17.0</td>\n      <td>0.891325</td>\n      <td>0.170068</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>olympusdao.eth</td>\n      <td>18.0</td>\n      <td>0.897853</td>\n      <td>0.180319</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>olympusdao.eth</td>\n      <td>19.0</td>\n      <td>0.904690</td>\n      <td>0.190013</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>olympusdao.eth</td>\n      <td>20.0</td>\n      <td>0.913971</td>\n      <td>0.200074</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>olympusdao.eth</td>\n      <td>21.0</td>\n      <td>0.912235</td>\n      <td>0.210011</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>olympusdao.eth</td>\n      <td>22.0</td>\n      <td>0.924204</td>\n      <td>0.219795</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>olympusdao.eth</td>\n      <td>23.0</td>\n      <td>0.928105</td>\n      <td>0.230070</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>olympusdao.eth</td>\n      <td>24.0</td>\n      <td>0.935203</td>\n      <td>0.240042</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>olympusdao.eth</td>\n      <td>25.0</td>\n      <td>0.938221</td>\n      <td>0.250072</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>olympusdao.eth</td>\n      <td>26.0</td>\n      <td>0.939190</td>\n      <td>0.259883</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>olympusdao.eth</td>\n      <td>27.0</td>\n      <td>0.948821</td>\n      <td>0.269857</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>olympusdao.eth</td>\n      <td>28.0</td>\n      <td>0.947428</td>\n      <td>0.280082</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>olympusdao.eth</td>\n      <td>29.0</td>\n      <td>0.953694</td>\n      <td>0.289942</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>olympusdao.eth</td>\n      <td>30.0</td>\n      <td>0.954687</td>\n      <td>0.300000</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>olympusdao.eth</td>\n      <td>31.0</td>\n      <td>0.959502</td>\n      <td>0.309909</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>olympusdao.eth</td>\n      <td>32.0</td>\n      <td>0.961173</td>\n      <td>0.319987</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>olympusdao.eth</td>\n      <td>33.0</td>\n      <td>0.964404</td>\n      <td>0.329943</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>olympusdao.eth</td>\n      <td>34.0</td>\n      <td>0.964946</td>\n      <td>0.340088</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>olympusdao.eth</td>\n      <td>35.0</td>\n      <td>0.968701</td>\n      <td>0.350101</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>olympusdao.eth</td>\n      <td>36.0</td>\n      <td>0.970018</td>\n      <td>0.360075</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>olympusdao.eth</td>\n      <td>37.0</td>\n      <td>0.971872</td>\n      <td>0.369400</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>olympusdao.eth</td>\n      <td>38.0</td>\n      <td>0.973973</td>\n      <td>0.379692</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>olympusdao.eth</td>\n      <td>39.0</td>\n      <td>0.976006</td>\n      <td>0.389848</td>\n    </tr>\n    <tr>\n      <th>40</th>\n      <td>olympusdao.eth</td>\n      <td>40.0</td>\n      <td>0.977635</td>\n      <td>0.399931</td>\n    </tr>\n    <tr>\n      <th>41</th>\n      <td>olympusdao.eth</td>\n      <td>41.0</td>\n      <td>0.977851</td>\n      <td>0.409905</td>\n    </tr>\n    <tr>\n      <th>42</th>\n      <td>olympusdao.eth</td>\n      <td>42.0</td>\n      <td>0.981220</td>\n      <td>0.419816</td>\n    </tr>\n    <tr>\n      <th>43</th>\n      <td>olympusdao.eth</td>\n      <td>43.0</td>\n      <td>0.981469</td>\n      <td>0.430302</td>\n    </tr>\n    <tr>\n      <th>44</th>\n      <td>olympusdao.eth</td>\n      <td>44.0</td>\n      <td>0.982847</td>\n      <td>0.439888</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>olympusdao.eth</td>\n      <td>45.0</td>\n      <td>0.983920</td>\n      <td>0.449900</td>\n    </tr>\n    <tr>\n      <th>46</th>\n      <td>olympusdao.eth</td>\n      <td>46.0</td>\n      <td>0.985000</td>\n      <td>0.459920</td>\n    </tr>\n    <tr>\n      <th>47</th>\n      <td>olympusdao.eth</td>\n      <td>47.0</td>\n      <td>0.986984</td>\n      <td>0.469944</td>\n    </tr>\n    <tr>\n      <th>48</th>\n      <td>olympusdao.eth</td>\n      <td>48.0</td>\n      <td>0.986779</td>\n      <td>0.479835</td>\n    </tr>\n    <tr>\n      <th>49</th>\n      <td>olympusdao.eth</td>\n      <td>49.0</td>\n      <td>0.988420</td>\n      <td>0.489915</td>\n    </tr>\n    <tr>\n      <th>50</th>\n      <td>olympusdao.eth</td>\n      <td>50.0</td>\n      <td>0.988794</td>\n      <td>0.500000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_means = crunch_data.groupby(\"percentage_voters_counted_stepped\")[\"cum_percentage_of_total_vp\", \"percentage_voters_counted\"].agg(\"mean\").reset_index()\n",
    "data_means.insert(0, 'DAO', spacename)\n",
    "data_means.head(51)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "data_means_file =final_file+'\\\\'+spacename+'data_means_'+str(date.today().strftime(\"%b-%d-%Y\"))+'_'+str(len(crunch_data))+'.csv'\n",
    "crunch_data.to_csv(data_means_file, index = False)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ianfe\\AppData\\Local\\Temp\\ipykernel_52948\\467802046.py:4: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  data_means = crunch_data.groupby(\"percentage_voters_counted_stepped\")[\"cum_percentage_of_total_vp\",\"percentage_voters_counted\"].agg(\"mean\").reset_index()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "text/plain": "     percentage_voters_counted_stepped  cum_percentage_of_total_vp  \\\n0                                  0.0                    0.240328   \n1                                  1.0                    0.331852   \n2                                  2.0                    0.479219   \n3                                  3.0                    0.545738   \n4                                  4.0                    0.617226   \n..                                 ...                         ...   \n96                                96.0                    0.999993   \n97                                97.0                    0.999997   \n98                                98.0                    0.999999   \n99                                99.0                    1.000000   \n100                              100.0                    1.000000   \n\n     percentage_voters_counted  \n0                     0.003705  \n1                     0.010133  \n2                     0.020369  \n3                     0.030372  \n4                     0.040199  \n..                         ...  \n96                    0.959801  \n97                    0.969561  \n98                    0.979577  \n99                    0.989867  \n100                   0.999588  \n\n[101 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>percentage_voters_counted_stepped</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>percentage_voters_counted</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.0</td>\n      <td>0.240328</td>\n      <td>0.003705</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.0</td>\n      <td>0.331852</td>\n      <td>0.010133</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2.0</td>\n      <td>0.479219</td>\n      <td>0.020369</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3.0</td>\n      <td>0.545738</td>\n      <td>0.030372</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4.0</td>\n      <td>0.617226</td>\n      <td>0.040199</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>96</th>\n      <td>96.0</td>\n      <td>0.999993</td>\n      <td>0.959801</td>\n    </tr>\n    <tr>\n      <th>97</th>\n      <td>97.0</td>\n      <td>0.999997</td>\n      <td>0.969561</td>\n    </tr>\n    <tr>\n      <th>98</th>\n      <td>98.0</td>\n      <td>0.999999</td>\n      <td>0.979577</td>\n    </tr>\n    <tr>\n      <th>99</th>\n      <td>99.0</td>\n      <td>1.000000</td>\n      <td>0.989867</td>\n    </tr>\n    <tr>\n      <th>100</th>\n      <td>100.0</td>\n      <td>1.000000</td>\n      <td>0.999588</td>\n    </tr>\n  </tbody>\n</table>\n<p>101 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rc(\"figure\", figsize=(40, 20))\n",
    "#sns.set_style(\"whitegrid\")\n",
    "plt.rc(\"font\", size=25)\n",
    "data_means = crunch_data.groupby(\"percentage_voters_counted_stepped\")[\"cum_percentage_of_total_vp\",\"percentage_voters_counted\"].agg(\"mean\").reset_index()\n",
    "##print(data_means)\n",
    "plot_title = spacename + ' snapshots % of vote along population with Average as X'\n",
    "\n",
    "ax=sns.scatterplot(data=crunch_data, y=\"cum_percentage_of_total_vp\",x=\"percentage_voters_counted_stepped\").set(title=plot_title,xlabel='% of voters',ylabel='% of voting power')\n",
    "chart = sns.scatterplot(data=data_means,x=\"percentage_voters_counted_stepped\",y=\"cum_percentage_of_total_vp\",zorder=3, s=800,marker='X',color = 'orange')\n",
    "#and save the chart file, too\n",
    "plt.savefig(final_file+'\\\\'+spacename+' vote power distribution.png',transparent =False,  dpi=100)\n",
    "\n",
    "means_data_path =final_file+'\\\\'+spacename+'_means_'+'.csv'\n",
    "data_means.to_csv(means_data_path, index = False)\n",
    "\n",
    "print(chart)\n",
    "data_means"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The chart above shows what % of all possible votes has been cast (Y axis) as each incremental percent of the voting population casts their vote (X axis). Each line is a Proposal and has a unique color, so that a dot on each percent point represents what % of total voting power was accumulated by that group. The color represents which vote was cast.\n",
    "The Orange X shows the average % of power accumulated across all elections."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "voters_df = db.query(\"select  proposals_title, count(distinct votes_voter) total_voters,  min(proposal_date) proposal_date from crunch_data group by 1 order by 1 asc\").df()\n",
    "\n",
    "chart = sns.barplot(data=voters_df,x=\"proposal_date\",y=\"total_voters\", color = 'orange', ci=None)\n",
    "x_dates = voters_df['proposal_date'].dt.strftime('%Y-%m-%d').sort_values().unique()\n",
    "chart.set_xticklabels(labels=x_dates, rotation=45, ha='right')\n",
    "#and save the chart file, too\n",
    "\n",
    "print(chart)\n",
    "\n",
    "#and save the chart file, too\n",
    "plt.savefig(final_file+'\\\\'+spacename+'_proposal_participation.png',transparent =False,  dpi=100)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "voters_df = db.query(\"select  proposals_title, count(distinct votes_voter) total_voters,  min(proposal_date) proposal_date from crunch_data group by 1 order by 1 asc\").df()\n",
    "\n",
    "chart = sns.barplot(data=voters_df,x=\"proposals_title\",y=\"total_voters\", color = 'orange')\n",
    "proposal_titles =  voters_df['proposals_title'].sort_values().unique()\n",
    "chart.set_xticklabels(labels=proposal_titles, rotation=45, ha='right')\n",
    "#and save the chart file, too\n",
    "\n",
    "print(chart)\n",
    "plt.savefig(final_file+'\\\\'+spacename+'_proposal_participation_title.png',transparent =False,  dpi=100)\n",
    "\n",
    "means_data_path =final_file+'\\\\'+spacename+'proposal voters'+'.csv'\n",
    "voters_df.to_csv(means_data_path, index = False)\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On average, a proposal at  olympusdao.eth takes  3.04 % of the voting population.\n",
      "A total of  75 wallets have driven the result of all proposals at olympusdao.eth\n",
      "That's 3.72% of all DAO voters.\n"
     ]
    }
   ],
   "source": [
    "p50 = db.query(\"select min(percentage_voters_counted) \"\n",
    "               \"from data_means  where cum_percentage_of_total_vp>=0.5 \").df()\n",
    "p50display = round(100*(p50.iloc[0,0]),2)\n",
    "print('On average, a proposal at ', spacename, 'takes ',p50display,'% of the voting population.')\n",
    "\n",
    "print('A total of ',leader_count, 'wallets have driven the result of all proposals at',spacename,)\n",
    "print('That\\'s', (\"{0:.2%}\".format(elite)), 'of all DAO voters.')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [
    {
     "data": {
      "text/plain": "              DAO                                           Proposal  \\\n0  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n1  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n2  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n3  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n4  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n5  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n6  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n7  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n8  olympusdao.eth  0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...   \n9  olympusdao.eth  0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...   \n\n                                  votes_voter  votes_choice     votes_vp  \\\n0  0x3F435ecb51C986CD2942c700e1E5076050D1Bb62             1  8410.354331   \n1  0x318A11FB434333DE60B3585428CC888eA1E22C67             1  8165.904205   \n2  0x318A11FB434333DE60B3585428CC888eA1E22C67             1  8165.904205   \n3  0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1  3312.725382   \n4  0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F             1  3312.725382   \n5  0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1  3272.832615   \n6  0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6             1  3272.832615   \n7  0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF             1  2791.835058   \n8  0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF             1  2791.835058   \n9  0x5bef46ac7f33b5E2FC2b6F26628884e7163eE96C             1  1850.886650   \n\n   votes_created                        proposals_title proposal_date  \\\n0     1646356214          TAP-6: Whitelist Frax Finance    2022-03-01   \n1     1646233183          TAP-6: Whitelist Frax Finance    2022-03-01   \n2     1646233293  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n3     1646377484  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n4     1646377527          TAP-6: Whitelist Frax Finance    2022-03-01   \n5     1646232431  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n6     1646232480          TAP-6: Whitelist Frax Finance    2022-03-01   \n7     1646285764  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n8     1646285798          TAP-6: Whitelist Frax Finance    2022-03-01   \n9     1646250915  OIP-83: Curve v2 Liquidity Deployment    2022-03-01   \n\n      proposal_date_2  cumulative_vp      total_vp  percentage_of_total_vp  \\\n0 2022-03-04 01:10:14    8410.354331  53521.054419                0.157141   \n1 2022-03-02 14:59:43   16576.258536  53521.054419                0.152574   \n2 2022-03-02 15:01:33    8165.904205  43300.813626                0.188585   \n3 2022-03-04 07:04:44   11478.629586  43300.813626                0.076505   \n4 2022-03-04 07:05:27   19888.983917  53521.054419                0.061896   \n5 2022-03-02 14:47:11   14751.462201  43300.813626                0.075584   \n6 2022-03-02 14:48:00   23161.816532  53521.054419                0.061150   \n7 2022-03-03 05:36:04   17543.297259  43300.813626                0.064475   \n8 2022-03-03 05:36:38   25953.651590  53521.054419                0.052163   \n9 2022-03-02 19:55:15   19394.183910  43300.813626                0.042745   \n\n   cum_percentage_of_total_vp  cum_percentage_of_total_vp_stepped  \\\n0                    0.157141                                 0.0   \n1                    0.309715                                 0.0   \n2                    0.188585                                 0.0   \n3                    0.265090                                 0.0   \n4                    0.371610                                 0.0   \n5                    0.340674                                 0.0   \n6                    0.432761                                 0.0   \n7                    0.405149                                 0.0   \n8                    0.484924                                 0.0   \n9                    0.447894                                 0.0   \n\n   proposal_voter_rank  total_voters  percentage_voters_counted  \\\n0                    1             1                   0.006494   \n1                    2             2                   0.012987   \n2                    1             1                   0.006579   \n3                    2             2                   0.013158   \n4                    3             3                   0.019481   \n5                    3             3                   0.019737   \n6                    4             4                   0.025974   \n7                    4             4                   0.026316   \n8                    5             5                   0.032468   \n9                    5             5                   0.032895   \n\n   percentage_voters_counted_stepped  \n0                                1.0  \n1                                1.0  \n2                                1.0  \n3                                1.0  \n4                                2.0  \n5                                2.0  \n6                                3.0  \n7                                3.0  \n8                                3.0  \n9                                3.0  ",
      "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>DAO</th>\n      <th>Proposal</th>\n      <th>votes_voter</th>\n      <th>votes_choice</th>\n      <th>votes_vp</th>\n      <th>votes_created</th>\n      <th>proposals_title</th>\n      <th>proposal_date</th>\n      <th>proposal_date_2</th>\n      <th>cumulative_vp</th>\n      <th>total_vp</th>\n      <th>percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp</th>\n      <th>cum_percentage_of_total_vp_stepped</th>\n      <th>proposal_voter_rank</th>\n      <th>total_voters</th>\n      <th>percentage_voters_counted</th>\n      <th>percentage_voters_counted_stepped</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x3F435ecb51C986CD2942c700e1E5076050D1Bb62</td>\n      <td>1</td>\n      <td>8410.354331</td>\n      <td>1646356214</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 01:10:14</td>\n      <td>8410.354331</td>\n      <td>53521.054419</td>\n      <td>0.157141</td>\n      <td>0.157141</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.006494</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233183</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:59:43</td>\n      <td>16576.258536</td>\n      <td>53521.054419</td>\n      <td>0.152574</td>\n      <td>0.309715</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.012987</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x318A11FB434333DE60B3585428CC888eA1E22C67</td>\n      <td>1</td>\n      <td>8165.904205</td>\n      <td>1646233293</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 15:01:33</td>\n      <td>8165.904205</td>\n      <td>43300.813626</td>\n      <td>0.188585</td>\n      <td>0.188585</td>\n      <td>0.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.006579</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377484</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:04:44</td>\n      <td>11478.629586</td>\n      <td>43300.813626</td>\n      <td>0.076505</td>\n      <td>0.265090</td>\n      <td>0.0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0.013158</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xEA4B752083c7A386ff7556BB7C0b01876b4eE96F</td>\n      <td>1</td>\n      <td>3312.725382</td>\n      <td>1646377527</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-04 07:05:27</td>\n      <td>19888.983917</td>\n      <td>53521.054419</td>\n      <td>0.061896</td>\n      <td>0.371610</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019481</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232431</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:47:11</td>\n      <td>14751.462201</td>\n      <td>43300.813626</td>\n      <td>0.075584</td>\n      <td>0.340674</td>\n      <td>0.0</td>\n      <td>3</td>\n      <td>3</td>\n      <td>0.019737</td>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0xeAbed8538923d8B8E0616938F8Dc657F3CDF74c6</td>\n      <td>1</td>\n      <td>3272.832615</td>\n      <td>1646232480</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 14:48:00</td>\n      <td>23161.816532</td>\n      <td>53521.054419</td>\n      <td>0.061150</td>\n      <td>0.432761</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.025974</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF</td>\n      <td>1</td>\n      <td>2791.835058</td>\n      <td>1646285764</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-03 05:36:04</td>\n      <td>17543.297259</td>\n      <td>43300.813626</td>\n      <td>0.064475</td>\n      <td>0.405149</td>\n      <td>0.0</td>\n      <td>4</td>\n      <td>4</td>\n      <td>0.026316</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>olympusdao.eth</td>\n      <td>0xece47fbf9e8f9fe362ed3a7cc0bc11705ad2de5c4baa...</td>\n      <td>0x6716b6737620922ACC8F8B8A4c2A6D706F3224FF</td>\n      <td>1</td>\n      <td>2791.835058</td>\n      <td>1646285798</td>\n      <td>TAP-6: Whitelist Frax Finance</td>\n      <td>2022-03-01</td>\n      <td>2022-03-03 05:36:38</td>\n      <td>25953.651590</td>\n      <td>53521.054419</td>\n      <td>0.052163</td>\n      <td>0.484924</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.032468</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>olympusdao.eth</td>\n      <td>0x7d65f346d0285a10457c1f61fbfad1b2337c55e29114...</td>\n      <td>0x5bef46ac7f33b5E2FC2b6F26628884e7163eE96C</td>\n      <td>1</td>\n      <td>1850.886650</td>\n      <td>1646250915</td>\n      <td>OIP-83: Curve v2 Liquidity Deployment</td>\n      <td>2022-03-01</td>\n      <td>2022-03-02 19:55:15</td>\n      <td>19394.183910</td>\n      <td>43300.813626</td>\n      <td>0.042745</td>\n      <td>0.447894</td>\n      <td>0.0</td>\n      <td>5</td>\n      <td>5</td>\n      <td>0.032895</td>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crunch_data.head(10)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'diamonds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mNameError\u001B[0m                                 Traceback (most recent call last)",
      "Input \u001B[1;32mIn [44]\u001B[0m, in \u001B[0;36m<cell line: 9>\u001B[1;34m()\u001B[0m\n\u001B[0;32m      4\u001B[0m plot_title \u001B[38;5;241m=\u001B[39m spacename \u001B[38;5;241m+\u001B[39m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mVoting power dist\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[0;32m      6\u001B[0m \u001B[38;5;66;03m#ax=sns.scatterplot(data=crunch_data, y=\"cum_percentage_of_total_vp\",x=\"percentage_voters_counted_stepped\").set(title=plot_title,xlabel='% of voters',ylabel='% of voting power')\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;66;03m#chart = sns.distplot(crunch_data['votes_vp'], hist=True, kde=True,             bins=10,             color = 'darkblue',             hist_kws={'edgecolor':'black'},             kde_kws={'linewidth': 4})\u001B[39;00m\n\u001B[0;32m      9\u001B[0m chart \u001B[38;5;241m=\u001B[39msns\u001B[38;5;241m.\u001B[39mhistplot(\n\u001B[1;32m---> 10\u001B[0m     \u001B[43mdiamonds\u001B[49m,\n\u001B[0;32m     11\u001B[0m     x\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mprice\u001B[39m\u001B[38;5;124m\"\u001B[39m, hue\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mcut\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m     12\u001B[0m     multiple\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstack\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m     13\u001B[0m     palette\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mlight:m_r\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m     14\u001B[0m     edgecolor\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m.3\u001B[39m\u001B[38;5;124m\"\u001B[39m,\n\u001B[0;32m     15\u001B[0m     linewidth\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m.5\u001B[39m,\n\u001B[0;32m     16\u001B[0m     log_scale\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m,\n\u001B[0;32m     17\u001B[0m )\n\u001B[0;32m     20\u001B[0m \u001B[38;5;66;03m#\u001B[39;00m\n\u001B[0;32m     21\u001B[0m \u001B[38;5;66;03m#chart = sns.scatterplot(data=data_means,x=\"percentage_voters_counted_stepped\",y=\"cum_percentage_of_total_vp\",zorder=3, s=600,marker='X',color = 'orange')\u001B[39;00m\n\u001B[0;32m     22\u001B[0m \u001B[38;5;66;03m#and save the chart file, too\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m     25\u001B[0m \u001B[38;5;66;03m#means_data_path =final_file+'\\\\'+spacename+'_means_'+'.csv'\u001B[39;00m\n\u001B[0;32m     26\u001B[0m \u001B[38;5;66;03m#data_means.to_csv(means_data_path, index = False)\u001B[39;00m\n\u001B[0;32m     28\u001B[0m \u001B[38;5;28mprint\u001B[39m(chart)\n",
      "\u001B[1;31mNameError\u001B[0m: name 'diamonds' is not defined"
     ]
    }
   ],
   "source": [
    "plt.rc(\"figure\", figsize=(40, 20))\n",
    "sns.set_style(\"whitegrid\")\n",
    "plt.rc(\"font\", size=25)\n",
    "plot_title = spacename + 'Voting power dist'\n",
    "\n",
    "#ax=sns.scatterplot(data=crunch_data, y=\"cum_percentage_of_total_vp\",x=\"percentage_voters_counted_stepped\").set(title=plot_title,xlabel='% of voters',ylabel='% of voting power')\n",
    "#chart = sns.distplot(crunch_data['votes_vp'], hist=True, kde=True,             bins=10,             color = 'darkblue',             hist_kws={'edgecolor':'black'},             kde_kws={'linewidth': 4})\n",
    "\n",
    "chart =sns.histplot(\n",
    "    diamonds,\n",
    "    x=\"price\", hue=\"cut\",\n",
    "    multiple=\"stack\",\n",
    "    palette=\"light:m_r\",\n",
    "    edgecolor=\".3\",\n",
    "    linewidth=.5,\n",
    "    log_scale=True,\n",
    ")\n",
    "\n",
    "\n",
    "#\n",
    "#chart = sns.scatterplot(data=data_means,x=\"percentage_voters_counted_stepped\",y=\"cum_percentage_of_total_vp\",zorder=3, s=600,marker='X',color = 'orange')\n",
    "#and save the chart file, too\n",
    "#plt.savefig(final_file+'\\\\'+spacename+' vote power distribution.png', dpi=100)\n",
    "\n",
    "#means_data_path =final_file+'\\\\'+spacename+'_means_'+'.csv'\n",
    "#data_means.to_csv(means_data_path, index = False)\n",
    "\n",
    "print(chart)"
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