govglads.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import time\n",
    "import pandas as pd\n",
    "\n",
    "import os\n",
    "from os.path import join, dirname\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "\n",
    "dotenv_path = join(dirname(os.path.dirname('__file__')), '.env')\n",
    "load_dotenv(dotenv_path)\n",
    "\n",
    "from sqlalchemy import create_engine\n",
    "\n",
    "# build connecting string from .env\n",
    "DATABASE_NAME = os.getenv(\"SNAPSHOT_PUBLIC_DB_NAME\")\n",
    "DATABASE_USER = os.getenv(\"SNAPSHOT_PUBLIC_DB_USER\")\n",
    "DATABASE_PASSWORD = os.getenv(\"SNAPSHOT_PUBLIC_DB_PASSWORD\")\n",
    "DATABASE_HOST = os.getenv(\"SNAPSHOT_PUBLIC_DB_HOST\")\n",
    "DATABASE_PORT = os.getenv(\"SNAPSHOT_PUBLIC_DB_PORT\")\n",
    "\n",
    "connection = \"postgresql://\"+DATABASE_USER+\":\"+DATABASE_PASSWORD+\"@\"+DATABASE_HOST+\":\"+DATABASE_PORT+\"/\"+DATABASE_NAME\n",
    "\n",
    "conn = create_engine(connection) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1009,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 1009,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "engine_govglads = create_engine(os.getenv(\"GOVGLADS_DB_URL\")) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>proposal</th>\n",
       "      <th>voter</th>\n",
       "      <th>vp</th>\n",
       "      <th>choice</th>\n",
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      ],
      "text/plain": [
       "                                                  proposal  \\\n",
       "316      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "711      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
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       "929      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "980      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "...                                                    ...   \n",
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       "2897351  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...   \n",
       "\n",
       "                                              voter             vp choice  \\\n",
       "316      0x1FD8271f95b32EcFFAdE04a2C93C7009b6Fe7d5F   11310.000000      1   \n",
       "711      0x8D470921050899284b451d6a65f4204Eee4aaf8f   63627.000000      1   \n",
       "825      0x8719A15457C5107417966873d2A4b16b8478B873   15000.177205      1   \n",
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       "980      0xE4b217F63fA4b9b10C87d2eae0D4a1065c6549a7   21179.000000      1   \n",
       "...                                             ...            ...    ...   \n",
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       "2897351  0xF6bc0e3662a840dc1Fd939652dCC793c3EDfAA1e   17122.000000      2   \n",
       "\n",
       "              created  \n",
       "316      1.679955e+09  \n",
       "711      1.679955e+09  \n",
       "825      1.679955e+09  \n",
       "929      1.679955e+09  \n",
       "980      1.679955e+09  \n",
       "...               ...  \n",
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       "\n",
       "[11500 rows x 5 columns]"
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     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# arbitrumfoundation.eth\n",
    "import numpy as np\n",
    "\n",
    "arbitrum_proposal_query = \"SELECT * FROM snapshot.proposallist WHERE dao_id = 'arbitrumfoundation.eth'\"\n",
    "arbitrum_proposal_df = pd.read_sql_query(arbitrum_proposal_query, conn)\n",
    "arbitrum_votes = f\"\"\"\n",
    "    SELECT * from snapshot.votelist where proposal IN {tuple(arbitrum_proposal_df['proposal_id'])};\n",
    "\"\"\"\n",
    "arbitrum_votes_df = pd.read_sql_query(arbitrum_votes, conn)\n",
    "\n",
    "arbitrum_votes_df['choice'] = arbitrum_votes_df['choice'].astype(str)\n",
    "arbitrum_votes_grouped = arbitrum_votes_df.groupby(['proposal', 'choice']).agg({'vp': 'sum'}).reset_index()\n",
    "\n",
    "# new df with proposal, and percent of majority choice and the majority choice\n",
    "arbitrum_votes_grouped['percent'] = arbitrum_votes_grouped.groupby('proposal')['vp'].transform(lambda x: x / x.sum())\n",
    "arbitrum_votes_grouped['percent'] = arbitrum_votes_grouped['percent'].replace(np.nan, 0)\n",
    "arbitrum_votes_grouped = arbitrum_votes_grouped[arbitrum_votes_grouped['percent'] > 0.5]\n",
    "arbitrum_votes_grouped['majority_choice'] = arbitrum_votes_grouped['choice']\n",
    "arbitrum_votes_grouped = arbitrum_votes_grouped.drop(columns=['choice'])\n",
    "\n",
    "arbitrum_votes = f\"\"\"\n",
    "    SELECT * from snapshot.votelist where proposal IN {tuple(arbitrum_votes_grouped['proposal'])};\n",
    "\"\"\"\n",
    "arbitrum_votes_df = pd.read_sql_query(arbitrum_votes, conn)\n",
    "\n",
    "# remove vote_id, app, reason, typename\n",
    "arbitrum_votes_df = arbitrum_votes_df.drop(columns=['vote_id', 'app', 'reason', 'typename'])\n",
    "# join proposals on proposal and proposal_id\n",
    "arbitrum_votes_and_proposals = pd.merge(arbitrum_votes_df, arbitrum_proposal_df, left_on='proposal', right_on='proposal_id')\n",
    "# drop proposal_id, plugins, discussion, ipfs, app, link, privacy, flagged\n",
    "arbitrum_votes_and_proposals = arbitrum_votes_and_proposals.drop(columns=['proposal_id', 'plugins', 'discussion', 'ipfs', 'app', 'link', 'privacy', 'flagged'])\n",
    "# drop created_x; rename created_y to created\n",
    "arbitrum_votes_and_proposals = arbitrum_votes_and_proposals.drop(columns=['created_x'])\n",
    "arbitrum_votes_and_proposals = arbitrum_votes_and_proposals.rename(columns={'created_y':'created'})\n",
    "# drop vp bellow 10000\n",
    "arbitrum_votes_and_proposals = arbitrum_votes_and_proposals[arbitrum_votes_and_proposals['vp'] > 10000]\n",
    "# clone df with proposal_id, voter, vp, choice, created\n",
    "arbitrum_votes_and_proposals_clone = arbitrum_votes_and_proposals.copy()\n",
    "arbitrum_votes_shallow = arbitrum_votes_and_proposals_clone[['proposal', 'voter', 'vp', 'choice', 'created']]\n",
    "arbitrum_votes_shallow\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {},
   "outputs": [
    {
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       "      <th>proposal</th>\n",
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       "      <td>0x087DD253ec7d42e3008ee8C0eA06D1237f08B99C</td>\n",
       "      <td>382.054233</td>\n",
       "      <td>1</td>\n",
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       "      <td>119752.889219</td>\n",
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      "text/plain": [
       "                                                proposal  \\\n",
       "3         QmU5rSEMyb9qpPqWUoaJBoTiH6XjBknnojUsXirFxeRjHr   \n",
       "4         QmU5rSEMyb9qpPqWUoaJBoTiH6XjBknnojUsXirFxeRjHr   \n",
       "5         QmU5rSEMyb9qpPqWUoaJBoTiH6XjBknnojUsXirFxeRjHr   \n",
       "6         QmU5rSEMyb9qpPqWUoaJBoTiH6XjBknnojUsXirFxeRjHr   \n",
       "7         QmU5rSEMyb9qpPqWUoaJBoTiH6XjBknnojUsXirFxeRjHr   \n",
       "...                                                  ...   \n",
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       "11079  0xd22d83201c9b65eb42e889ed2b6416c0e0331345ab9e...   \n",
       "\n",
       "                                            voter             vp  choice  \\\n",
       "3      0x1e92842D072dD2c71183af24458B013F678F9D3d    1000.000000       1   \n",
       "4      0x76Da9F88e63a85Ba8BFA19F3821D5A271753D3dc     253.272266       1   \n",
       "5      0x3ab5c2ac327C3044776A2c229Cb9c16bA9dCDBEe     173.986372       1   \n",
       "6      0x23Ed05AaD698cD4bE7c9c5F2C6F8c04ed1827793     462.606722       1   \n",
       "7      0xE86e28a0653736e76B78eaD692F0c287709D2Dd9   39990.000090       1   \n",
       "...                                           ...            ...     ...   \n",
       "11055  0x986D37258f86299E62eC0D208E2d46caA0298dBF    7486.641202       1   \n",
       "11063  0x087DD253ec7d42e3008ee8C0eA06D1237f08B99C     382.054233       1   \n",
       "11065  0x8F0F70c8828801d3d88D3B53e2f65B82de5acCab  119752.889219       3   \n",
       "11072  0xf6542152B0e4113375BA1FbCed8913270EdfBd6c     331.931877       1   \n",
       "11079  0x5762F3074605df17AebE3f5BC8FC7f8702aca752  310932.875793       3   \n",
       "\n",
       "            created  \n",
       "3      1.627828e+09  \n",
       "4      1.627828e+09  \n",
       "5      1.627828e+09  \n",
       "6      1.627828e+09  \n",
       "7      1.627828e+09  \n",
       "...             ...  \n",
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       "11063  1.699871e+09  \n",
       "11065  1.699871e+09  \n",
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       "\n",
       "[3658 rows x 5 columns]"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1inch.eth\n",
    "import numpy as np\n",
    "\n",
    "oneinch_proposal_query = \"SELECT * FROM snapshot.proposallist WHERE dao_id = '1inch.eth'\"\n",
    "oneinch_proposal_df = pd.read_sql_query(oneinch_proposal_query, conn)\n",
    "oneinch_votes = f\"\"\"\n",
    "    SELECT * from snapshot.votelist where proposal IN {tuple(oneinch_proposal_df['proposal_id'])};\n",
    "\"\"\"\n",
    "oneinch_votes_df = pd.read_sql_query(oneinch_votes, conn)\n",
    "\n",
    "oneinch_votes_df['choice'] = oneinch_votes_df['choice'].astype(str)\n",
    "oneinch_votes_grouped = oneinch_votes_df.groupby(['proposal', 'choice']).agg({'vp': 'sum'}).reset_index()\n",
    "\n",
    "# new df with proposal, and percent of majority choice and the majority choice\n",
    "oneinch_votes_grouped['percent'] = oneinch_votes_grouped.groupby('proposal')['vp'].transform(lambda x: x / x.sum())\n",
    "oneinch_votes_grouped['percent'] = oneinch_votes_grouped['percent'].replace(np.nan, 0)\n",
    "oneinch_votes_grouped = oneinch_votes_grouped[oneinch_votes_grouped['percent'] > 0.5]\n",
    "oneinch_votes_grouped['majority_choice'] = oneinch_votes_grouped['choice']\n",
    "oneinch_votes_grouped = oneinch_votes_grouped.drop(columns=['choice'])\n",
    "\n",
    "oneinch_votes = f\"\"\"\n",
    "    SELECT * from snapshot.votelist where proposal IN {tuple(oneinch_votes_grouped['proposal'])};\n",
    "\"\"\"\n",
    "oneinch_votes_df = pd.read_sql_query(oneinch_votes, conn)\n",
    "\n",
    "# remove vote_id, app, reason, typename\n",
    "oneinch_votes_df = oneinch_votes_df.drop(columns=['vote_id', 'app', 'reason', 'typename'])\n",
    "# join proposals on proposal and proposal_id\n",
    "oneinch_votes_and_proposals = pd.merge(oneinch_votes_df, oneinch_proposal_df, left_on='proposal', right_on='proposal_id')\n",
    "# drop proposal_id, plugins, discussion, ipfs, app, link, privacy, flagged\n",
    "oneinch_votes_and_proposals = oneinch_votes_and_proposals.drop(columns=['proposal_id', 'plugins', 'discussion', 'ipfs', 'app', 'link', 'privacy', 'flagged'])\n",
    "# drop created_x; rename created_y to created\n",
    "oneinch_votes_and_proposals = oneinch_votes_and_proposals.drop(columns=['created_x'])\n",
    "oneinch_votes_and_proposals = oneinch_votes_and_proposals.rename(columns={'created_y':'created'})\n",
    "# drop vp bellow 10000\n",
    "oneinch_votes_and_proposals = oneinch_votes_and_proposals[oneinch_votes_and_proposals['vp'] > 100]\n",
    "# clone df with proposal_id, voter, vp, choice, created\n",
    "oneinch_votes_and_proposals_clone = oneinch_votes_and_proposals.copy()\n",
    "oneinch_votes_shallow = oneinch_votes_and_proposals_clone[['proposal', 'voter', 'vp', 'choice', 'created']]\n",
    "oneinch_votes_shallow\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                                                 proposal  \\\n",
       "2       0x538c49304ef485ba4ea09ebce3dfd0548e6e230b5d33...   \n",
       "3       0x538c49304ef485ba4ea09ebce3dfd0548e6e230b5d33...   \n",
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       "7       0x538c49304ef485ba4ea09ebce3dfd0548e6e230b5d33...   \n",
       "...                                                   ...   \n",
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       "102104  0xda81167bae092cbc66bfe5a35abfbf12d96c2577155e...   \n",
       "\n",
       "                                             voter            vp  choice  \\\n",
       "2       0x9AaE5b185f764c5F1E06Ea3967bbaE3ADf39b0f5  3.527469e+03       2   \n",
       "3       0xa22D6bd7BFeca97C60063087265a9Fb2BB8cCd1b  5.749248e+03       2   \n",
       "4       0x2395ecF5e4BE53A155CCecA77a42Ac225491D9f0  2.406802e+02       2   \n",
       "5       0x5A4B1AA79B8AEAf34a2B4bE442616D09De12f01e  3.147655e+02       2   \n",
       "7       0x7E128fff8Eb2252f86b0A04F3BdBd03cE1aFEf6d  2.086067e+03       1   \n",
       "...                                            ...           ...     ...   \n",
       "102095  0xF81C87fB76F3Acf458eBd29df60c92F4b7Ce8Db3  4.306000e+04       2   \n",
       "102100  0x388AF224c9778830Eb545Cf9922AB6EdE9cB2999  1.052760e+06       2   \n",
       "102102  0xf69a6473F65C674e58B58644f074c536cA7bf458  3.245663e+05       2   \n",
       "102103  0xf9f9f29f4c5b43F9566BCC91f9CceBfC7D40e099  1.577265e+02       1   \n",
       "102104  0xcC83B391f66E7E7A1014CCc0EcE579b95D96b18B  2.347927e+03       3   \n",
       "\n",
       "             created  \n",
       "2       1.653491e+09  \n",
       "3       1.653491e+09  \n",
       "4       1.653491e+09  \n",
       "5       1.653491e+09  \n",
       "7       1.653491e+09  \n",
       "...              ...  \n",
       "102095  1.707967e+09  \n",
       "102100  1.707967e+09  \n",
       "102102  1.707967e+09  \n",
       "102103  1.707967e+09  \n",
       "102104  1.707967e+09  \n",
       "\n",
       "[27556 rows x 5 columns]"
      ]
     },
     "execution_count": 654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apecoin.eth\n",
    "import numpy as np\n",
    "\n",
    "apecoin_proposal_query = \"SELECT * FROM snapshot.proposallist WHERE dao_id = 'apecoin.eth'\"\n",
    "apecoin_proposal_df = pd.read_sql_query(apecoin_proposal_query, conn)\n",
    "apecoin_votes = f\"\"\"\n",
    "    SELECT * from snapshot.votelist where proposal IN {tuple(apecoin_proposal_df['proposal_id'])};\n",
    "\"\"\"\n",
    "apecoin_votes_df = pd.read_sql_query(apecoin_votes, conn)\n",
    "\n",
    "apecoin_votes_df['choice'] = apecoin_votes_df['choice'].astype(str)\n",
    "apecoin_votes_grouped = apecoin_votes_df.groupby(['proposal', 'choice']).agg({'vp': 'sum'}).reset_index()\n",
    "\n",
    "# new df with proposal, and percent of majority choice and the majority choice\n",
    "apecoin_votes_grouped['percent'] = apecoin_votes_grouped.groupby('proposal')['vp'].transform(lambda x: x / x.sum())\n",
    "apecoin_votes_grouped['percent'] = apecoin_votes_grouped['percent'].replace(np.nan, 0)\n",
    "apecoin_votes_grouped = apecoin_votes_grouped[apecoin_votes_grouped['percent'] > 0.5]\n",
    "apecoin_votes_grouped['majority_choice'] = apecoin_votes_grouped['choice']\n",
    "apecoin_votes_grouped = apecoin_votes_grouped.drop(columns=['choice'])\n",
    "\n",
    "apecoin_votes = f\"\"\"\n",
    "    SELECT * from snapshot.votelist where proposal IN {tuple(apecoin_votes_grouped['proposal'])};\n",
    "\"\"\"\n",
    "apecoin_votes_df = pd.read_sql_query(apecoin_votes, conn)\n",
    "\n",
    "# remove vote_id, app, reason, typename\n",
    "apecoin_votes_df = apecoin_votes_df.drop(columns=['vote_id', 'app', 'reason', 'typename'])\n",
    "# join proposals on proposal and proposal_id\n",
    "apecoin_votes_and_proposals = pd.merge(apecoin_votes_df, apecoin_proposal_df, left_on='proposal', right_on='proposal_id')\n",
    "# drop proposal_id, plugins, discussion, ipfs, app, link, privacy, flagged\n",
    "apecoin_votes_and_proposals = apecoin_votes_and_proposals.drop(columns=['proposal_id', 'plugins', 'discussion', 'ipfs', 'app', 'link', 'privacy', 'flagged'])\n",
    "# drop created_x; rename created_y to created\n",
    "apecoin_votes_and_proposals = apecoin_votes_and_proposals.drop(columns=['created_x'])\n",
    "apecoin_votes_and_proposals = apecoin_votes_and_proposals.rename(columns={'created_y':'created'})\n",
    "# drop vp bellow 10000\n",
    "apecoin_votes_and_proposals = apecoin_votes_and_proposals[apecoin_votes_and_proposals['vp'] > 100]\n",
    "# clone df with proposal_id, voter, vp, choice, created\n",
    "apecoin_votes_and_proposals_clone = apecoin_votes_and_proposals.copy()\n",
    "apecoin_votes_shallow = apecoin_votes_and_proposals_clone[['proposal', 'voter', 'vp', 'choice', 'created']]\n",
    "apecoin_votes_shallow\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 959,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>131968</th>\n",
       "      <td>0x8b7d79a679c10bed518fe905d764b4b7667f24de</td>\n",
       "      <td>0x4bca130d4d1387ec00705dc24e8eb63488136e71</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131969</th>\n",
       "      <td>0x8b7d79a679c10bed518fe905d764b4b7667f24de</td>\n",
       "      <td>0x24902fc7a499784a48aa8737a3162bf1171b716f</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>131386 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             voter  \\\n",
       "0       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "1       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "2       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "3       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "4       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "...                                            ...   \n",
       "131965  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "131966  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "131967  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "131968  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "131969  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "\n",
       "                                          co-voter  similar_votes  \\\n",
       "0       0x067b9bbbbc42d2ab9b9ab6bb62646daf7f344a76              4   \n",
       "1       0x4d88535301f606c534531415b8a7b3c5675c1fa3              4   \n",
       "2       0x85c7d244c6057d42c770ad85ae198fc5f47957fc              4   \n",
       "3       0xe5aedd6520c4d4e0cb4ee78784a0187d34d55adc              4   \n",
       "4       0xb92792552e590339a7dbf1e0d6114fbc7395c86b              4   \n",
       "...                                            ...            ...   \n",
       "131965  0x941db8a7404c725897a5e8b09a37db8cd4c55b74              1   \n",
       "131966  0xda24ee2c4b4b1ea69de3aa9c820438c7069d7d83              1   \n",
       "131967  0x030fb4444e3077bf4b62f7ee37ac724c31359477              1   \n",
       "131968  0x4bca130d4d1387ec00705dc24e8eb63488136e71              1   \n",
       "131969  0x24902fc7a499784a48aa8737a3162bf1171b716f              1   \n",
       "\n",
       "        total_votes  similarity_ratio  \n",
       "0                 4               1.0  \n",
       "1                 4               1.0  \n",
       "2                 4               1.0  \n",
       "3                 4               1.0  \n",
       "4                 4               1.0  \n",
       "...             ...               ...  \n",
       "131965            1               1.0  \n",
       "131966            1               1.0  \n",
       "131967            1               1.0  \n",
       "131968            1               1.0  \n",
       "131969            1               1.0  \n",
       "\n",
       "[131386 rows x 5 columns]"
      ]
     },
     "execution_count": 959,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edges_df = \"\"\"\n",
    "    SELECT * from nd;\n",
    "\"\"\"\n",
    "data_edges_df = pd.read_sql_query(data_edges_df, engine_govglads)\n",
    "data_edges_df = data_edges_df[data_edges_df['voter'] != data_edges_df['co-voter']]\n",
    "data_edges_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 961,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    131386.000000\n",
       "mean          0.814079\n",
       "std           0.232409\n",
       "min           0.011364\n",
       "25%           0.666667\n",
       "50%           1.000000\n",
       "75%           1.000000\n",
       "max           1.000000\n",
       "Name: similarity_ratio, dtype: float64"
      ]
     },
     "execution_count": 961,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_edges_df['similarity_ratio'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 941,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                                             voter  \\\n",
       "0       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "282     0x8d470921050899284b451d6a65f4204eee4aaf8f   \n",
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       "...                                            ...   \n",
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       "131841  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "\n",
       "                                          co-voter  similar_votes  \\\n",
       "0       0x067b9bbbbc42d2ab9b9ab6bb62646daf7f344a76              4   \n",
       "282     0x2f8f5adb3a7ea90eb1dd0cd7d4df33736d235909              4   \n",
       "580     0xd92b50e95faa39fada60735432435e86eb936f0c              1   \n",
       "649     0x70f8a0a356e4391c90721355158cca09e38b265e              5   \n",
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       "...                                            ...            ...   \n",
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       "131841  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065              1   \n",
       "\n",
       "        total_votes  similarity_ratio  similarity  \n",
       "0                 4               1.0    0.798168  \n",
       "282               4               1.0    0.806208  \n",
       "580               1               1.0    1.000000  \n",
       "649               5               1.0    0.776230  \n",
       "947              98               1.0    0.666575  \n",
       "...             ...               ...         ...  \n",
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       "131480            1               1.0    1.000000  \n",
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       "131725            1               1.0    1.000000  \n",
       "131841            1               1.0    1.000000  \n",
       "\n",
       "[584 rows x 6 columns]"
      ]
     },
     "execution_count": 941,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_q = \"\"\"\n",
    "    SELECT * from nd;\n",
    "\"\"\"\n",
    "data_df = pd.read_sql_query(data_q, engine_govglads)\n",
    "data_df\n",
    "\n",
    "# average similarity by voter sum similarity by voter and then divide\n",
    "data_df['similarity'] = data_df.groupby('voter')['similarity_ratio'].transform(lambda x: x.sum() / x.count())\n",
    "# drop duples\n",
    "data_df = data_df.drop_duplicates(subset=['voter'])\n",
    "data_df['similarity'] = data_df['similarity'].replace(np.nan, 0)\n",
    "data_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 942,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove lines where voter = co-voter\n",
    "data_df = data_df[data_df['voter'] != data_df['co-voter']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 943,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    363.0\n",
       "mean       1.0\n",
       "std        0.0\n",
       "min        1.0\n",
       "25%        1.0\n",
       "50%        1.0\n",
       "75%        1.0\n",
       "max        1.0\n",
       "Name: similarity_ratio, dtype: float64"
      ]
     },
     "execution_count": 943,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_df['similarity_ratio'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 944,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>19600.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.680919e+09</td>\n",
       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11460</th>\n",
       "      <td>0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...</td>\n",
       "      <td>0xb5ab290e152290b6bd5bdb04e23236d3f9d4aaec</td>\n",
       "      <td>17279.437411</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.680919e+09</td>\n",
       "      <td>0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11465</th>\n",
       "      <td>0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...</td>\n",
       "      <td>0x8f5c7cef4d9c6dae6fa645ca41b3a8c6ca2bafb8</td>\n",
       "      <td>22679.655114</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.680919e+09</td>\n",
       "      <td>0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11479</th>\n",
       "      <td>0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...</td>\n",
       "      <td>0x8b7d79a679c10bed518fe905d764b4b7667f24de</td>\n",
       "      <td>10250.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.680919e+09</td>\n",
       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>584 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                proposal  \\\n",
       "0      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "1      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "2      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "3      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "4      0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...   \n",
       "...                                                  ...   \n",
       "11445  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...   \n",
       "11449  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...   \n",
       "11460  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...   \n",
       "11465  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...   \n",
       "11479  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...   \n",
       "\n",
       "                                            voter            vp  choice  \\\n",
       "0      0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f  11310.000000     1.0   \n",
       "1      0x8d470921050899284b451d6a65f4204eee4aaf8f  63627.000000     1.0   \n",
       "2      0x8719a15457c5107417966873d2a4b16b8478b873  15000.177205     1.0   \n",
       "3      0x70f8a0a356e4391c90721355158cca09e38b265e  23640.000000     1.0   \n",
       "4      0xe4b217f63fa4b9b10c87d2eae0d4a1065c6549a7  21179.000000     1.0   \n",
       "...                                           ...           ...     ...   \n",
       "11445  0x090928b9327258f9b12ed9dcb9f64fb00e37c872  25747.118974     1.0   \n",
       "11449  0xc38da808d0865005312fe99387f5228881539e9f  19600.000000     1.0   \n",
       "11460  0xb5ab290e152290b6bd5bdb04e23236d3f9d4aaec  17279.437411     2.0   \n",
       "11465  0x8f5c7cef4d9c6dae6fa645ca41b3a8c6ca2bafb8  22679.655114     2.0   \n",
       "11479  0x8b7d79a679c10bed518fe905d764b4b7667f24de  10250.000000     1.0   \n",
       "\n",
       "            created                                    co-voter  \\\n",
       "0      1.679955e+09  0x067b9bbbbc42d2ab9b9ab6bb62646daf7f344a76   \n",
       "1      1.679955e+09  0x2f8f5adb3a7ea90eb1dd0cd7d4df33736d235909   \n",
       "2      1.679955e+09  0xd92b50e95faa39fada60735432435e86eb936f0c   \n",
       "3      1.679955e+09  0x70f8a0a356e4391c90721355158cca09e38b265e   \n",
       "4      1.679955e+09  0xe4b217f63fa4b9b10c87d2eae0d4a1065c6549a7   \n",
       "...             ...                                         ...   \n",
       "11445  1.680919e+09  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065   \n",
       "11449  1.680919e+09  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065   \n",
       "11460  1.680919e+09  0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e   \n",
       "11465  1.680919e+09  0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e   \n",
       "11479  1.680919e+09  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065   \n",
       "\n",
       "       similar_votes  total_votes  similarity  \n",
       "0                  4            4    0.798168  \n",
       "1                  4            4    0.806208  \n",
       "2                  1            1    1.000000  \n",
       "3                  5            5    0.776230  \n",
       "4                 98           98    0.666575  \n",
       "...              ...          ...         ...  \n",
       "11445              1            1    1.000000  \n",
       "11449              1            1    1.000000  \n",
       "11460              1            1    1.000000  \n",
       "11465              1            1    1.000000  \n",
       "11479              1            1    1.000000  \n",
       "\n",
       "[584 rows x 9 columns]"
      ]
     },
     "execution_count": 944,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop duplicates arbitrum_votes_shallow\n",
    "arbitrum_votes_shallow_x = arbitrum_votes_shallow.drop_duplicates(subset=['voter'])\n",
    "arbitrum_votes_shallow_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 945,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>voter</th>\n",
       "      <th>co-voter_x</th>\n",
       "      <th>similar_votes_x</th>\n",
       "      <th>total_votes_x</th>\n",
       "      <th>similarity_ratio</th>\n",
       "      <th>similarity_x</th>\n",
       "      <th>proposal</th>\n",
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       "      <th>created</th>\n",
       "      <th>co-voter_y</th>\n",
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       "      <td>0x67aa1a10f3b5fe18df6359456ed7b92f09e2cf59</td>\n",
       "      <td>0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f</td>\n",
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       "      <td>0x090928b9327258f9b12ed9dcb9f64fb00e37c872</td>\n",
       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
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       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0xc38da808d0865005312fe99387f5228881539e9f</td>\n",
       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
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       "      <td>19600.000000</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>0xb5ab290e152290b6bd5bdb04e23236d3f9d4aaec</td>\n",
       "      <td>0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...</td>\n",
       "      <td>17279.437411</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>361</th>\n",
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       "      <td>0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...</td>\n",
       "      <td>22679.655114</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.680919e+09</td>\n",
       "      <td>0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>0x8b7d79a679c10bed518fe905d764b4b7667f24de</td>\n",
       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...</td>\n",
       "      <td>10250.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.680919e+09</td>\n",
       "      <td>0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>363 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          voter  \\\n",
       "0    0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "1    0x8d470921050899284b451d6a65f4204eee4aaf8f   \n",
       "2    0x8719a15457c5107417966873d2a4b16b8478b873   \n",
       "3    0x67aa1a10f3b5fe18df6359456ed7b92f09e2cf59   \n",
       "4    0x88cc33242d5ced328ce19b5c75e2ed0e13781299   \n",
       "..                                          ...   \n",
       "358  0x090928b9327258f9b12ed9dcb9f64fb00e37c872   \n",
       "359  0xc38da808d0865005312fe99387f5228881539e9f   \n",
       "360  0xb5ab290e152290b6bd5bdb04e23236d3f9d4aaec   \n",
       "361  0x8f5c7cef4d9c6dae6fa645ca41b3a8c6ca2bafb8   \n",
       "362  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "\n",
       "                                     co-voter_x  similar_votes_x  \\\n",
       "0    0x067b9bbbbc42d2ab9b9ab6bb62646daf7f344a76                4   \n",
       "1    0x2f8f5adb3a7ea90eb1dd0cd7d4df33736d235909                4   \n",
       "2    0xd92b50e95faa39fada60735432435e86eb936f0c                1   \n",
       "3    0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f                2   \n",
       "4    0x1f99b2df41969c0b88517b55bf0426dd2ecf977c                3   \n",
       "..                                          ...              ...   \n",
       "358  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065                1   \n",
       "359  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065                1   \n",
       "360  0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e                1   \n",
       "361  0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e                1   \n",
       "362  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065                1   \n",
       "\n",
       "     total_votes_x  similarity_ratio  similarity_x  \\\n",
       "0                4               1.0      0.798168   \n",
       "1                4               1.0      0.806208   \n",
       "2                1               1.0      1.000000   \n",
       "3                2               1.0      0.848077   \n",
       "4                3               1.0      0.694969   \n",
       "..             ...               ...           ...   \n",
       "358              1               1.0      1.000000   \n",
       "359              1               1.0      1.000000   \n",
       "360              1               1.0      1.000000   \n",
       "361              1               1.0      1.000000   \n",
       "362              1               1.0      1.000000   \n",
       "\n",
       "                                              proposal            vp  choice  \\\n",
       "0    0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...  11310.000000     1.0   \n",
       "1    0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...  63627.000000     1.0   \n",
       "2    0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...  15000.177205     1.0   \n",
       "3    0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...  10250.000000     1.0   \n",
       "4    0x3be7368a662d1cf12fa4da768d626edbc013be0dc7b9...  16632.462621     1.0   \n",
       "..                                                 ...           ...     ...   \n",
       "358  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...  25747.118974     1.0   \n",
       "359  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...  19600.000000     1.0   \n",
       "360  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...  17279.437411     2.0   \n",
       "361  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...  22679.655114     2.0   \n",
       "362  0x3ae4c725f7cb82d450b6fe8517178b2a0a51d1105bea...  10250.000000     1.0   \n",
       "\n",
       "          created                                  co-voter_y  \\\n",
       "0    1.679955e+09  0x067b9bbbbc42d2ab9b9ab6bb62646daf7f344a76   \n",
       "1    1.679955e+09  0x2f8f5adb3a7ea90eb1dd0cd7d4df33736d235909   \n",
       "2    1.679955e+09  0xd92b50e95faa39fada60735432435e86eb936f0c   \n",
       "3    1.679955e+09  0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "4    1.679955e+09  0x1f99b2df41969c0b88517b55bf0426dd2ecf977c   \n",
       "..            ...                                         ...   \n",
       "358  1.680919e+09  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065   \n",
       "359  1.680919e+09  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065   \n",
       "360  1.680919e+09  0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e   \n",
       "361  1.680919e+09  0xf6bc0e3662a840dc1fd939652dcc793c3edfaa1e   \n",
       "362  1.680919e+09  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065   \n",
       "\n",
       "     similar_votes_y  total_votes_y  similarity_y  \n",
       "0                  4              4      0.798168  \n",
       "1                  4              4      0.806208  \n",
       "2                  1              1      1.000000  \n",
       "3                  2              2      0.848077  \n",
       "4                  3              3      0.694969  \n",
       "..               ...            ...           ...  \n",
       "358                1              1      1.000000  \n",
       "359                1              1      1.000000  \n",
       "360                1              1      1.000000  \n",
       "361                1              1      1.000000  \n",
       "362                1              1      1.000000  \n",
       "\n",
       "[363 rows x 14 columns]"
      ]
     },
     "execution_count": 945,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# augment voter with voting power from arbitrum_votes_shallow\n",
    "data_df_merged = data_df.merge(arbitrum_votes_shallow_x, left_on='voter', right_on='voter', how='left')\n",
    "data_df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 946,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>voter</th>\n",
       "      <th>vp</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f</td>\n",
       "      <td>11310.000000</td>\n",
       "      <td>0.798168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0x8d470921050899284b451d6a65f4204eee4aaf8f</td>\n",
       "      <td>63627.000000</td>\n",
       "      <td>0.806208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0x8719a15457c5107417966873d2a4b16b8478b873</td>\n",
       "      <td>15000.177205</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0x67aa1a10f3b5fe18df6359456ed7b92f09e2cf59</td>\n",
       "      <td>10250.000000</td>\n",
       "      <td>0.848077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0x88cc33242d5ced328ce19b5c75e2ed0e13781299</td>\n",
       "      <td>16632.462621</td>\n",
       "      <td>0.694969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>0x090928b9327258f9b12ed9dcb9f64fb00e37c872</td>\n",
       "      <td>25747.118974</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>0xc38da808d0865005312fe99387f5228881539e9f</td>\n",
       "      <td>19600.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>0xb5ab290e152290b6bd5bdb04e23236d3f9d4aaec</td>\n",
       "      <td>17279.437411</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>0x8f5c7cef4d9c6dae6fa645ca41b3a8c6ca2bafb8</td>\n",
       "      <td>22679.655114</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>0x8b7d79a679c10bed518fe905d764b4b7667f24de</td>\n",
       "      <td>10250.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>363 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          voter            vp  similarity\n",
       "0    0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f  11310.000000    0.798168\n",
       "1    0x8d470921050899284b451d6a65f4204eee4aaf8f  63627.000000    0.806208\n",
       "2    0x8719a15457c5107417966873d2a4b16b8478b873  15000.177205    1.000000\n",
       "3    0x67aa1a10f3b5fe18df6359456ed7b92f09e2cf59  10250.000000    0.848077\n",
       "4    0x88cc33242d5ced328ce19b5c75e2ed0e13781299  16632.462621    0.694969\n",
       "..                                          ...           ...         ...\n",
       "358  0x090928b9327258f9b12ed9dcb9f64fb00e37c872  25747.118974    1.000000\n",
       "359  0xc38da808d0865005312fe99387f5228881539e9f  19600.000000    1.000000\n",
       "360  0xb5ab290e152290b6bd5bdb04e23236d3f9d4aaec  17279.437411    1.000000\n",
       "361  0x8f5c7cef4d9c6dae6fa645ca41b3a8c6ca2bafb8  22679.655114    1.000000\n",
       "362  0x8b7d79a679c10bed518fe905d764b4b7667f24de  10250.000000    1.000000\n",
       "\n",
       "[363 rows x 3 columns]"
      ]
     },
     "execution_count": 946,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reduce to voter, vp, similarity_x\n",
    "data_df_red = data_df_merged[['voter', 'vp', 'similarity_x']]\n",
    "data_df_red = data_df_red.rename(columns={'similarity_x':'similarity'})\n",
    "data_df_red\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 971,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    6.433400e+04\n",
       "mean     1.118892e+05\n",
       "std      4.444302e+05\n",
       "min      9.091745e+02\n",
       "25%      1.049900e+04\n",
       "50%      1.558267e+04\n",
       "75%      4.035129e+04\n",
       "max      8.240974e+06\n",
       "Name: similarity_vp, dtype: float64"
      ]
     },
     "execution_count": 971,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiply similarity_ratio in data_edges_df with vp from data_df_red on voter = voter\n",
    "data_edges_df_merged = data_edges_df.merge(data_df_red, left_on='voter', right_on='voter', how='left')\n",
    "data_edges_df_merged['similarity_vp'] = data_edges_df_merged['similarity_ratio'] * data_edges_df_merged['vp']\n",
    "data_edges_df_merged['similarity_vp'].describe()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 947,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create a plut that shows the similarity ratio and the vp where vp is te size of the circle\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = pd.DataFrame(data_df_red)\n",
    "df['index'] = df.index\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.scatterplot(data=df, x='index', y='similarity', size='vp', sizes=(20, 200), legend=None)\n",
    "\n",
    "plt.title('VP vs Similarity')\n",
    "plt.xlabel('Index')\n",
    "plt.ylabel('Similarity')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 949,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                                          voter            vp  similarity\n",
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       "                                             voter  \\\n",
       "0       0x1fd8271f95b32ecffade04a2c93c7009b6fe7d5f   \n",
       "282     0x8d470921050899284b451d6a65f4204eee4aaf8f   \n",
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       "1615    0x88cc33242d5ced328ce19b5c75e2ed0e13781299   \n",
       "...                                            ...   \n",
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       "131841  0x8b7d79a679c10bed518fe905d764b4b7667f24de   \n",
       "\n",
       "                                          co-voter  similar_votes  \\\n",
       "0       0x067b9bbbbc42d2ab9b9ab6bb62646daf7f344a76              4   \n",
       "282     0x2f8f5adb3a7ea90eb1dd0cd7d4df33736d235909              4   \n",
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       "1615    0x1f99b2df41969c0b88517b55bf0426dd2ecf977c              3   \n",
       "...                                            ...            ...   \n",
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       "131841  0xc8bdec0f09a4cf86d0245021bc0cae9ab24a2065              1   \n",
       "\n",
       "        total_votes  similarity_ratio  similarity  \n",
       "0                 4               1.0    0.798168  \n",
       "282               4               1.0    0.806208  \n",
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       "...             ...               ...         ...  \n",
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       "\n",
       "[363 rows x 6 columns]"
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     "text": [
      "posx and posy should be finite values\n",
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      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
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      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
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      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
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      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
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      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
      "posx and posy should be finite values\n",
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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Load your data\n",
    "\n",
    "# Create DataFrames\n",
    "df_nodes = pd.DataFrame(data_df_red)\n",
    "df_edges = pd.DataFrame(data_edges_df_merged)\n",
    "\n",
    "# Create the graph\n",
    "G = nx.Graph()\n",
    "\n",
    "# Add nodes with size attribute\n",
    "for idx, row in df_nodes.iterrows():\n",
    "    G.add_node(row['voter'], size=row['vp'], similarity=row['similarity'])\n",
    "\n",
    "# Add edges with weight attribute\n",
    "for idx, row in df_edges.iterrows():\n",
    "    # Ensure both nodes exist in the graph\n",
    "    if row['voter'] not in G.nodes:\n",
    "        G.add_node(row['voter'], size=1, similarity=0)  # Default size and similarity\n",
    "    if row['co-voter'] not in G.nodes:\n",
    "        G.add_node(row['co-voter'], size=1, similarity=0)  # Default size and similarity\n",
    "    G.add_edge(row['voter'], row['co-voter'], weight=row['similarity_vp'])\n",
    "\n",
    "# Check for any missing values in node positions and sizes\n",
    "for node in G.nodes:\n",
    "    if 'size' not in G.nodes[node]:\n",
    "        G.nodes[node]['size'] = 1\n",
    "    if 'similarity' not in G.nodes[node]:\n",
    "        G.nodes[node]['similarity'] = 0\n",
    "\n",
    "# Draw the graph\n",
    "plt.figure(figsize=(12, 12))\n",
    "pos = nx.spring_layout(G, k=0.1)  # Adjust k for spacing between nodes\n",
    "\n",
    "# Draw nodes with transparency\n",
    "sizes = [G.nodes[node]['size'] / 1000 for node in G.nodes]  # Adjust node size\n",
    "nx.draw_networkx_nodes(G, pos, node_size=sizes, node_color='blue', alpha=0.6)\n",
    "\n",
    "# Draw edges with different color and adjusted width\n",
    "edges = G.edges(data=True)\n",
    "weights = [np.log(edge[2]['weight'] * 2) or 0 for edge in edges]  # Scale edge weights for better visibility\n",
    "nx.draw_networkx_edges(G, pos, width=weights, alpha=0.5, edge_color='red')\n",
    "\n",
    "# Draw labels with reduced font size\n",
    "nx.draw_networkx_labels(G, pos, font_size=1, font_color='black')\n",
    "\n",
    "plt.title('Graph Network of Voters with VP as Node Size and Similarity Ratio as Edge Weight')\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}