AAVE analysis quick.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9630262c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import json\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d5ded0e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"aaveExample.csv\")\n",
    "\n",
    "# Clean Up dates\n",
    "\n",
    "df['FirstDeposit'] = df['FirstDeposit'].astype('datetime64[ns]')\n",
    "df['LastWithdraw'] = df['LastWithdraw'].astype('datetime64[ns]')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d75ba75e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>user</th>\n",
       "      <th>FirstDeposit</th>\n",
       "      <th>FirstBorrow</th>\n",
       "      <th>LastWithdraw</th>\n",
       "      <th>LastLiquidation</th>\n",
       "      <th>TotalUSDSupplied</th>\n",
       "      <th>TotalDepositsWETH</th>\n",
       "      <th>TotalWithdrawalsWETH</th>\n",
       "      <th>TotalLiquidationsWETH</th>\n",
       "      <th>...</th>\n",
       "      <th>TotalLiquidationsLUSD</th>\n",
       "      <th>TotalBorrowsLUSD</th>\n",
       "      <th>TotalRepaymentsLUSD</th>\n",
       "      <th>Total CollateralLUSD</th>\n",
       "      <th>Total Collateral USDLUSD</th>\n",
       "      <th>Total Collateral USD</th>\n",
       "      <th>End Date</th>\n",
       "      <th>TotalDaysSinceEntry</th>\n",
       "      <th>DaysUntilExit</th>\n",
       "      <th>Exit</th>\n",
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       "      <td>2021-06-29 05:41:10</td>\n",
       "      <td>2021-06-29 05:41:10</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.350787</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>24.405800</td>\n",
       "      <td>2022-12-31 23:59:11</td>\n",
       "      <td>550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-21.703572</td>\n",
       "      <td>2022-12-31 23:59:11</td>\n",
       "      <td>210</td>\n",
       "      <td>32.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2022-10-28 11:08:59</td>\n",
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       "      <td>False</td>\n",
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       "<p>5 rows × 271 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                        user        FirstDeposit  \\\n",
       "0           0  0x1ad3b6b2df0046cef9bbbc3441790c3a600fd989 2021-03-30 05:57:28   \n",
       "1           1  0x2c3e64f5077f5670caff4177fca1e182dedead3f 2022-08-25 02:31:15   \n",
       "2           2  0xa8b7a33f25490b8eebb10312e82403e8e9013663 2021-06-29 05:41:10   \n",
       "3           3  0x80d4ea495d4a02d84486c50bb197ba3bd3622859 2022-06-04 06:15:27   \n",
       "4           4  0x55d23b6a55ebac344a1a2e664e803a6880b46c83 2022-10-19 08:27:47   \n",
       "\n",
       "           FirstBorrow        LastWithdraw LastLiquidation  TotalUSDSupplied  \\\n",
       "0  2021-03-30 05:57:28 2021-06-12 09:30:28             NaN     509487.013270   \n",
       "1  2022-08-25 02:31:15                 NaT             NaN          1.057695   \n",
       "2  2021-06-29 05:41:10                 NaT             NaN         42.350787   \n",
       "3  2022-06-04 06:15:27 2022-07-06 14:15:09             NaN      32435.890847   \n",
       "4  2022-10-19 08:27:47 2022-10-28 11:08:59             NaN         11.957777   \n",
       "\n",
       "   TotalDepositsWETH  TotalWithdrawalsWETH  TotalLiquidationsWETH  ...  \\\n",
       "0             114.90                   0.0                    0.0  ...   \n",
       "1               0.00                   0.0                    0.0  ...   \n",
       "2               0.02                   0.0                    0.0  ...   \n",
       "3               0.00                   0.0                    0.0  ...   \n",
       "4               0.00                   0.0                    0.0  ...   \n",
       "\n",
       "   TotalLiquidationsLUSD  TotalBorrowsLUSD  TotalRepaymentsLUSD  \\\n",
       "0                    0.0               0.0                  0.0   \n",
       "1                    0.0               0.0                  0.0   \n",
       "2                    0.0               0.0                  0.0   \n",
       "3                    0.0               0.0                  0.0   \n",
       "4                    0.0               0.0                  0.0   \n",
       "\n",
       "   Total CollateralLUSD  Total Collateral USDLUSD  Total Collateral USD  \\\n",
       "0                   0.0                       0.0         164979.744755   \n",
       "1                   0.0                       0.0              0.780861   \n",
       "2                   0.0                       0.0             24.405800   \n",
       "3                   0.0                       0.0            -21.703572   \n",
       "4                   0.0                       0.0              6.355920   \n",
       "\n",
       "              End Date  TotalDaysSinceEntry  DaysUntilExit   Exit  \n",
       "0  2022-12-31 23:59:11                  641           74.0  False  \n",
       "1  2022-12-31 23:59:11                  128            NaN  False  \n",
       "2  2022-12-31 23:59:11                  550            NaN  False  \n",
       "3  2022-12-31 23:59:11                  210           32.0   True  \n",
       "4  2022-12-31 23:59:11                   73            9.0  False  \n",
       "\n",
       "[5 rows x 271 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d77377e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calc retention\n",
    "\n",
    "# Filters - applied when calculating retention \n",
    "AssetDeposited = ['WETH']\n",
    "minDepositValue = 10000\n",
    "\n",
    "\n",
    "Days = range(500)\n",
    "reducedDf = df[df['TotalUSDSupplied'] > minDepositValue]\n",
    "retentionList = []\n",
    "\n",
    "for day in Days :\n",
    "    reducedDf = reducedDf[reducedDf['TotalDaysSinceEntry'] > day]\n",
    "    n = len(reducedDf) \n",
    "    exits = (reducedDf[reducedDf.Exit == True])\n",
    "    retention = 1 - sum(exits['DaysUntilExit'] < day) / len(reducedDf)\n",
    "    retentionList.append(retention)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b025e620",
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(retentionList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d8697b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "date = pd.Timestamp('2022-10-01')\n",
    "# New Users \n",
    "minDepositValue = 100000\n",
    "\n",
    "newUsers = list(df[(df['FirstDeposit'] > date) & (df['TotalUSDSupplied'] >= minDepositValue) & (df['Exit'] == False)]['user'])\n",
    "lostUsers = list(df[(df['LastWithdraw'] > date) & (df['TotalUSDSupplied'] >= minDepositValue) & (df['Exit'] == True)]['user'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c7129f7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "userDf = pd.read_csv(\"AAVEusers.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e86e1f02",
   "metadata": {},
   "outputs": [],
   "source": [
    "### New User Portfolios\n",
    "\n",
    "newApps = userDf[userDf.User.isin(newUsers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc63a52f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "66e3c9d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>User</th>\n",
       "      <th>Name</th>\n",
       "      <th>Chain</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>5226</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>GrizzlyFi</td>\n",
       "      <td>bsc</td>\n",
       "      <td>920688.112760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5227</th>\n",
       "      <td>5227</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Venus</td>\n",
       "      <td>bsc</td>\n",
       "      <td>84016.050593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5228</th>\n",
       "      <td>5228</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Compound</td>\n",
       "      <td>eth</td>\n",
       "      <td>6.657349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5229</th>\n",
       "      <td>5229</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Damm Finance</td>\n",
       "      <td>eth</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5230</th>\n",
       "      <td>5230</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>DeFi Franc</td>\n",
       "      <td>eth</td>\n",
       "      <td>0.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>5231</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Geist</td>\n",
       "      <td>ftm</td>\n",
       "      <td>461379.629425</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                        User          Name  \\\n",
       "5226        5226  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75     GrizzlyFi   \n",
       "5227        5227  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75         Venus   \n",
       "5228        5228  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75      Compound   \n",
       "5229        5229  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75  Damm Finance   \n",
       "5230        5230  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75    DeFi Franc   \n",
       "5231        5231  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75         Geist   \n",
       "\n",
       "     Chain          Value  \n",
       "5226   bsc  920688.112760  \n",
       "5227   bsc   84016.050593  \n",
       "5228   eth       6.657349  \n",
       "5229   eth       0.000000  \n",
       "5230   eth       0.011439  \n",
       "5231   ftm  461379.629425  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Lost User Portfolios\n",
    "\n",
    "userDf[userDf.User == lostUsers[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "61f602f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def totalTVL(row):\n",
    "    return userDf[userDf.User == row['user']]['Value'].sum()\n",
    "\n",
    "def totalTVLAAVE(row):\n",
    "    return userDf[(userDf.User == row['user']) & (userDf.Name == 'Aave V2')]['Value'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3ff321ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['totalTVL'] = df.apply(totalTVL, axis=1)\n",
    "df['totalTVLAAVE'] = df.apply(totalTVLAAVE, axis=1)\n",
    "\n",
    "df['Percent In AAVE'] = df['totalTVLAAVE'] / df['totalTVL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1b71e831",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filters - applied when running analysis retention \n",
    "minTVL = 10000\n",
    "\n",
    "reducedDf = df[df['totalTVL'] > minTVL]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ba32662",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c9428724",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>FirstDeposit</th>\n",
       "      <th>totalTVLAAVE</th>\n",
       "      <th>totalTVL</th>\n",
       "      <th>Percent In AAVE</th>\n",
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       "      <td>0x645c4c0c95c1aa6ef25d12f4a25038ca9b0c6cc7</td>\n",
       "      <td>2022-11-07 01:40:23</td>\n",
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       "      <td>2022-04-12 21:39:34</td>\n",
       "      <td>6.302461e+06</td>\n",
       "      <td>6.302461e+06</td>\n",
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       "      <td>2022-06-25 00:17:10</td>\n",
       "      <td>4.867078e+06</td>\n",
       "      <td>2.742179e+07</td>\n",
       "      <td>0.177489</td>\n",
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       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>0xdc77b2c59db1b02f988e8e08a60fbe9139447cc3</td>\n",
       "      <td>2022-08-13 18:27:10</td>\n",
       "      <td>3.490881e+06</td>\n",
       "      <td>5.992164e+06</td>\n",
       "      <td>0.582574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>0xa5d223c176daab154a3134369d1c0478c5e6fecf</td>\n",
       "      <td>2021-05-30 13:36:56</td>\n",
       "      <td>3.250865e+06</td>\n",
       "      <td>1.986928e+07</td>\n",
       "      <td>0.163613</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           user        FirstDeposit  \\\n",
       "406  0x645c4c0c95c1aa6ef25d12f4a25038ca9b0c6cc7 2022-11-07 01:40:23   \n",
       "877  0xf89a2234f442ce862443f01c3998701a4cc4a3e3 2022-04-12 21:39:34   \n",
       "21   0x13e382dfe53207e9ce2eeeab330f69da2794179e 2022-06-25 00:17:10   \n",
       "556  0xdc77b2c59db1b02f988e8e08a60fbe9139447cc3 2022-08-13 18:27:10   \n",
       "996  0xa5d223c176daab154a3134369d1c0478c5e6fecf 2021-05-30 13:36:56   \n",
       "\n",
       "     totalTVLAAVE      totalTVL  Percent In AAVE  \n",
       "406  1.722756e+07  1.722756e+07         1.000000  \n",
       "877  6.302461e+06  6.302461e+06         1.000000  \n",
       "21   4.867078e+06  2.742179e+07         0.177489  \n",
       "556  3.490881e+06  5.992164e+06         0.582574  \n",
       "996  3.250865e+06  1.986928e+07         0.163613  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topUsers = df.nlargest(5,['totalTVLAAVE'])\n",
    "cols = ['user', 'FirstDeposit', 'totalTVLAAVE' ,'totalTVL', 'Percent In AAVE']\n",
    "topUsers[cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "78b18b5d",
   "metadata": {},
   "outputs": [
    {
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       "      <th>FirstDeposit</th>\n",
       "      <th>totalTVLAAVE</th>\n",
       "      <th>totalTVL</th>\n",
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       "      <td>2022-10-04 20:26:59</td>\n",
       "      <td>0.188882</td>\n",
       "      <td>1.888820e-01</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>412</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>7.573103e+01</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>783</th>\n",
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       "      <td>2022-10-06 16:13:23</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "                                           user        FirstDeposit  \\\n",
       "80   0x7a0af73e4c08a4a46513e8b7fea726fb8d499614 2022-10-04 20:26:59   \n",
       "412  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee 2022-12-12 19:30:47   \n",
       "561  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75 2022-09-30 23:18:59   \n",
       "783  0x49ae763ed846db9a69a639732caa320fad1d5799 2022-10-06 16:13:23   \n",
       "\n",
       "     totalTVLAAVE      totalTVL  Percent In AAVE  \n",
       "80       0.188882  1.888820e-01              1.0  \n",
       "412      0.000000  7.573103e+01              0.0  \n",
       "561      0.000000  1.466090e+06              0.0  \n",
       "783      0.000000  1.569588e+04              0.0  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.user.isin(lostUsers)][cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "7030d2ed",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user</th>\n",
       "      <th>FirstDeposit</th>\n",
       "      <th>totalTVLAAVE</th>\n",
       "      <th>totalTVL</th>\n",
       "      <th>Percent In AAVE</th>\n",
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       "  </thead>\n",
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       "      <th>357</th>\n",
       "      <td>0xf7626459234e9249808a06aa08dc6b67c8e0a2fc</td>\n",
       "      <td>2022-11-14 17:41:47</td>\n",
       "      <td>1.613568e-15</td>\n",
       "      <td>2.807902e+06</td>\n",
       "      <td>5.746526e-22</td>\n",
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       "      <th>406</th>\n",
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       "      <td>1.722756e+07</td>\n",
       "      <td>1.722756e+07</td>\n",
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       "      <td>2022-11-24 03:59:47</td>\n",
       "      <td>1.170800e-02</td>\n",
       "      <td>1.170800e-02</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "      <th>518</th>\n",
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       "      <td>2022-11-13 21:35:23</td>\n",
       "      <td>1.017033e+05</td>\n",
       "      <td>1.747232e+05</td>\n",
       "      <td>5.820827e-01</td>\n",
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       "      <td>6.038481e+05</td>\n",
       "      <td>6.038481e+05</td>\n",
       "      <td>1.000000e+00</td>\n",
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       "      <td>2022-10-05 15:19:23</td>\n",
       "      <td>5.228891e+05</td>\n",
       "      <td>5.234382e+05</td>\n",
       "      <td>9.989510e-01</td>\n",
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       "      <th>917</th>\n",
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       "      <td>2022-12-03 08:24:35</td>\n",
       "      <td>1.776000e-02</td>\n",
       "      <td>6.143192e+00</td>\n",
       "      <td>2.891005e-03</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                           user        FirstDeposit  \\\n",
       "357  0xf7626459234e9249808a06aa08dc6b67c8e0a2fc 2022-11-14 17:41:47   \n",
       "406  0x645c4c0c95c1aa6ef25d12f4a25038ca9b0c6cc7 2022-11-07 01:40:23   \n",
       "478  0x42c0eeb504d374d7cff5865c982cee7d4a59d77e 2022-11-24 03:59:47   \n",
       "518  0x064d3230f753d8c14e06cf491fe2f1a3d2ccd5dd 2022-11-13 21:35:23   \n",
       "587  0x63158ffa98f89a49a50a6e70279d8fc55692daed 2022-11-05 17:46:23   \n",
       "667  0x096232202dd7b86fddff038189dfe0f1861bb028 2022-10-05 15:19:23   \n",
       "917  0x0960cbefa076b248664c079174126ff9554ce809 2022-12-03 08:24:35   \n",
       "\n",
       "     totalTVLAAVE      totalTVL  Percent In AAVE  \n",
       "357  1.613568e-15  2.807902e+06     5.746526e-22  \n",
       "406  1.722756e+07  1.722756e+07     1.000000e+00  \n",
       "478  1.170800e-02  1.170800e-02     1.000000e+00  \n",
       "518  1.017033e+05  1.747232e+05     5.820827e-01  \n",
       "587  6.038481e+05  6.038481e+05     1.000000e+00  \n",
       "667  5.228891e+05  5.234382e+05     9.989510e-01  \n",
       "917  1.776000e-02  6.143192e+00     2.891005e-03  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.user.isin(newUsers)][cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e7b056e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7da8c6b970>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(reducedDf['totalTVL'], reducedDf['Percent In AAVE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "87978353",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([89.,  8.,  7.,  6.,  4.,  2.,  2.,  5.,  3., 26.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXAAAAD4CAYAAAD1jb0+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAMiUlEQVR4nO3dfYyl9VmH8etbVkRqWyg7EgR0aErVDcZAJkhDUrU0BsGwJBJCY3U1GzetWquYWLR/1Og/kGhrTYi6KdXVtBXERjbiSyqFEBtZHQqWN9uuFOgiL1MFfIsW0ts/zmPZLrPMw8552RuuT7LZ8/Kcee7fzuy1zzznnNlUFZKkfl616AEkSUfGgEtSUwZckpoy4JLUlAGXpKa2zHNnW7dureXl5XnuUpLau/POO79cVUuH3j7XgC8vL7O6ujrPXUpSe0keXu92T6FIUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSU3N9J+ZmLF9180L2+9DVFy9kv5K0EY/AJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDU1KuBJfiHJfUnuTfLxJMclOSPJviT7k1yf5NhZDytJet6GAU9yKvBzwEpVnQUcA1wBXAN8sKreCDwF7JzloJKkrzf2FMoW4JuSbAGOBx4D3grcONy/B7h06tNJkg5rw4BX1aPAbwCPMAn3M8CdwNNV9dyw2QHg1FkNKUl6oTGnUE4EtgNnAN8KvBq4cOwOkuxKsppkdW1t7YgHlSR9vTGnUN4GfLGq1qrqWeATwPnACcMpFYDTgEfXe3BV7a6qlapaWVpamsrQkqRxAX8EOC/J8UkCXADcD9wKXDZsswO4aTYjSpLWM+Yc+D4mT1Z+BrhneMxu4L3AlUn2AycB181wTknSIbZsvAlU1fuB9x9y84PAuVOfSJI0iu/ElKSmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTowKe5IQkNyb5pyQPJHlzktcn+WSSLwy/nzjrYSVJzxt7BP4h4K+q6juB7wEeAK4CbqmqM4FbhuuSpDnZMOBJXge8BbgOoKq+UlVPA9uBPcNme4BLZzOiJGk9Y47AzwDWgN9PcleSDyd5NXByVT02bPM4cPJ6D06yK8lqktW1tbXpTC1JGhXwLcA5wO9U1dnAf3HI6ZKqKqDWe3BV7a6qlapaWVpa2uy8kqTBmIAfAA5U1b7h+o1Mgv5EklMAht+fnM2IkqT1bBjwqnoc+FKS7xhuugC4H9gL7Bhu2wHcNJMJJUnr2jJyu3cDH01yLPAg8JNM4n9Dkp3Aw8DlsxlRkrSeUQGvqruBlXXuumCq00iSRvOdmJLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqanTAkxyT5K4kfz5cPyPJviT7k1yf5NjZjSlJOtRLOQJ/D/DAQdevAT5YVW8EngJ2TnMwSdKLGxXwJKcBFwMfHq4HeCtw47DJHuDSGcwnSTqMsUfgvwX8EvDV4fpJwNNV9dxw/QBw6noPTLIryWqS1bW1tc3MKkk6yIYBT/LDwJNVdeeR7KCqdlfVSlWtLC0tHcmHkCStY8uIbc4HLklyEXAc8FrgQ8AJSbYMR+GnAY/ObkxJ0qE2PAKvql+uqtOqahm4AvhUVf0ocCtw2bDZDuCmmU0pSXqBzbwO/L3AlUn2Mzknft10RpIkjTHmFMrXVNVtwG3D5QeBc6c/kiRpDN+JKUlNGXBJasqAS1JTBlySmjLgktSUAZekpgy4JDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJamrLogeQpHlZvurmhez3oasvnsnH9Qhckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmjLgktTUhgFPcnqSW5Pcn+S+JO8Zbn99kk8m+cLw+4mzH1eS9P/GHIE/B/xiVW0DzgN+Jsk24Crglqo6E7hluC5JmpMNA15Vj1XVZ4bL/wE8AJwKbAf2DJvtAS6d0YySpHW8pHPgSZaBs4F9wMlV9dhw1+PAyYd5zK4kq0lW19bWNjOrJOkgowOe5JuBPwV+vqr+/eD7qqqAWu9xVbW7qlaqamVpaWlTw0qSnjcq4Em+gUm8P1pVnxhufiLJKcP9pwBPzmZESdJ6xrwKJcB1wANV9YGD7toL7Bgu7wBumv54kqTDGfM/8pwP/BhwT5K7h9t+BbgauCHJTuBh4PKZTChJWteGAa+qvwVymLsvmO44kqSxfCemJDVlwCWpKQMuSU0ZcElqyoBLUlMGXJKaMuCS1JQBl6SmDLgkNWXAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLUlAGXpKYMuCQ1ZcAlqSkDLklNGXBJasqAS1JTBlySmtqy6AGOdstX3bywfT909cUL27eko59H4JLUlAGXpKY8hXIUW9TpG0/dSD14BC5JTXkErhfwiVupB4/AJakpj8ClVyi/0+rPI3BJasojcB1VfOWNNJ5H4JLUlAGXpKY8hSJp7hb5BOrLiUfgktSUR+ASHhGqJ4/AJampTQU8yYVJPpdkf5KrpjWUJGljRxzwJMcA1wI/BGwD3p5k27QGkyS9uM0cgZ8L7K+qB6vqK8AfA9unM5YkaSObeRLzVOBLB10/AHzvoRsl2QXsGq7+Z5LPHeH+tgJfPsLHduWaXxlc88tcrtn0er99vRtn/iqUqtoN7N7sx0myWlUrUxipDdf8yuCaX/5mtd7NnEJ5FDj9oOunDbdJkuZgMwH/B+DMJGckORa4Atg7nbEkSRs54lMoVfVckp8F/ho4BvhIVd03tcleaNOnYRpyza8MrvnlbybrTVXN4uNKkmbMd2JKUlMGXJKaOuoCvtHb85N8Y5Lrh/v3JVlewJhTNWLNVya5P8lnk9ySZN3XhHYy9scwJPmRJJWk9UvOxqw3yeXD5/m+JB+b94zTNuLr+tuS3JrkruFr+6JFzDlNST6S5Mkk9x7m/iT57eHP5LNJztnUDqvqqPnF5MnQfwbeABwL/COw7ZBtfhr43eHyFcD1i557Dmv+AeD44fK7XglrHrZ7DXA7cAewsui5Z/w5PhO4CzhxuP4ti557DmveDbxruLwNeGjRc09h3W8BzgHuPcz9FwF/CQQ4D9i3mf0dbUfgY96evx3YM1y+EbggSeY447RtuOaqurWq/nu4egeT19x3NvbHMPw6cA3wP/McbgbGrPengGur6imAqnpyzjNO25g1F/Da4fLrgH+Z43wzUVW3A//2IptsB/6wJu4ATkhyypHu72gL+Hpvzz/1cNtU1XPAM8BJc5luNsas+WA7mfwL3tmGax6+tTy9ql4OP6h7zOf4TcCbknw6yR1JLpzbdLMxZs2/CrwjyQHgL4B3z2e0hXqpf99flP+hQyNJ3gGsAN+36FlmKcmrgA8AP7HgUeZpC5PTKN/P5Dus25N8d1U9vcihZuztwB9U1W8meTPwR0nOqqqvLnqwLo62I/Axb8//2jZJtjD51utf5zLdbIz6kQRJ3ga8D7ikqv53TrPNykZrfg1wFnBbkoeYnCvc2/iJzDGf4wPA3qp6tqq+CHyeSdC7GrPmncANAFX1d8BxTH7I1cvZVH8EydEW8DFvz98L7BguXwZ8qoZnB5racM1JzgZ+j0m8u58bhQ3WXFXPVNXWqlquqmUm5/0vqarVxYy7aWO+rv+MydE3SbYyOaXy4BxnnLYxa34EuAAgyXcxCfjaXKecv73Ajw+vRjkPeKaqHjvij7boZ20P8yzt55k8g/2+4bZfY/IXGCaf5D8B9gN/D7xh0TPPYc1/AzwB3D382rvomWe95kO2vY3Gr0IZ+TkOk9NG9wP3AFcseuY5rHkb8Gkmr1C5G/jBRc88hTV/HHgMeJbJd1U7gXcC7zzo83zt8Gdyz2a/rn0rvSQ1dbSdQpEkjWTAJakpAy5JTRlwSWrKgEtSUwZckpoy4JLU1P8BuW0YXoHKkq0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(reducedDf['Percent In AAVE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ff7e7cde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0xf7626459234e9249808a06aa08dc6b67c8e0a2fc',\n",
       " '0x645c4c0c95c1aa6ef25d12f4a25038ca9b0c6cc7',\n",
       " '0x42c0eeb504d374d7cff5865c982cee7d4a59d77e',\n",
       " '0x064d3230f753d8c14e06cf491fe2f1a3d2ccd5dd',\n",
       " '0x63158ffa98f89a49a50a6e70279d8fc55692daed',\n",
       " '0x096232202dd7b86fddff038189dfe0f1861bb028',\n",
       " '0x0960cbefa076b248664c079174126ff9554ce809']"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "newUsers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9cbf90fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "otherApps = list(userDf['Name'].unique())\n",
    "\n",
    "exclude = ['Aave V2', 'Aave V3']\n",
    "for item in exclude : \n",
    "    otherApps.remove(item)\n",
    "\n",
    "compDf = pd.DataFrame({'Name' : otherApps})\n",
    "\n",
    "lostUserDf = userDf[userDf.User.isin(lostUsers)]\n",
    "newUserDf = userDf[userDf.User.isin(newUsers)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "35e012ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function __main__.lostUsers(row)>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lostUsers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "034aaa64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>TVLFromSharedUsers</th>\n",
       "      <th>nUsers</th>\n",
       "      <th>tvlInAAVE</th>\n",
       "      <th>lostTVL</th>\n",
       "      <th>compTVLGained</th>\n",
       "      <th>lostUsers</th>\n",
       "      <th>newUsers</th>\n",
       "      <th>compTVLLost</th>\n",
       "      <th>compTVLNew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GMX</td>\n",
       "      <td>3.155064e+05</td>\n",
       "      <td>149</td>\n",
       "      <td>208676.485661</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wonderland</td>\n",
       "      <td>2.117823e+05</td>\n",
       "      <td>63</td>\n",
       "      <td>598416.167394</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venus</td>\n",
       "      <td>2.006059e+05</td>\n",
       "      <td>53</td>\n",
       "      <td>181617.447387</td>\n",
       "      <td>3.582859e+06</td>\n",
       "      <td>84016.051002</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adamant</td>\n",
       "      <td>1.810417e+03</td>\n",
       "      <td>21</td>\n",
       "      <td>12724.704626</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Klima DAO</td>\n",
       "      <td>2.861680e+03</td>\n",
       "      <td>81</td>\n",
       "      <td>15431.465657</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>Voltage</td>\n",
       "      <td>2.649618e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Unifi Protocol</td>\n",
       "      <td>6.376570e-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Dot Dot Finance</td>\n",
       "      <td>7.528391e+01</td>\n",
       "      <td>1</td>\n",
       "      <td>46.555261</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Valas</td>\n",
       "      <td>2.480448e-01</td>\n",
       "      <td>1</td>\n",
       "      <td>46.555261</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Rally</td>\n",
       "      <td>7.255349e-14</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>553 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name  TVLFromSharedUsers  nUsers      tvlInAAVE       lostTVL  \\\n",
       "0                GMX        3.155064e+05     149  208676.485661  0.000000e+00   \n",
       "1         Wonderland        2.117823e+05      63  598416.167394  0.000000e+00   \n",
       "2              Venus        2.006059e+05      53  181617.447387  3.582859e+06   \n",
       "3            Adamant        1.810417e+03      21   12724.704626  0.000000e+00   \n",
       "4         Klima DAO         2.861680e+03      81   15431.465657  0.000000e+00   \n",
       "..               ...                 ...     ...            ...           ...   \n",
       "548          Voltage        2.649618e-06       1       0.000000  0.000000e+00   \n",
       "549   Unifi Protocol        6.376570e-06       1       0.000000  0.000000e+00   \n",
       "550  Dot Dot Finance        7.528391e+01       1      46.555261  0.000000e+00   \n",
       "551            Valas        2.480448e-01       1      46.555261  0.000000e+00   \n",
       "552            Rally        7.255349e-14       1       0.000000  0.000000e+00   \n",
       "\n",
       "     compTVLGained  lostUsers  newUsers  compTVLLost  compTVLNew  \n",
       "0         0.000000          0         0          0.0         0.0  \n",
       "1         0.000000          0         0          0.0         0.0  \n",
       "2     84016.051002          2         0          0.0         0.0  \n",
       "3         0.000000          0         0          0.0         0.0  \n",
       "4         0.000000          0         0          0.0         0.0  \n",
       "..             ...        ...       ...          ...         ...  \n",
       "548       0.000000          0         0          0.0         0.0  \n",
       "549       0.000000          0         0          0.0         0.0  \n",
       "550       0.000000          0         0          0.0         0.0  \n",
       "551       0.000000          0         0          0.0         0.0  \n",
       "552       0.000000          0         0          0.0         0.0  \n",
       "\n",
       "[553 rows x 10 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "compDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "a193d520",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>user</th>\n",
       "      <th>FirstDeposit</th>\n",
       "      <th>FirstBorrow</th>\n",
       "      <th>LastWithdraw</th>\n",
       "      <th>LastLiquidation</th>\n",
       "      <th>TotalUSDSupplied</th>\n",
       "      <th>TotalDepositsWETH</th>\n",
       "      <th>TotalWithdrawalsWETH</th>\n",
       "      <th>TotalLiquidationsWETH</th>\n",
       "      <th>...</th>\n",
       "      <th>Total CollateralLUSD</th>\n",
       "      <th>Total Collateral USDLUSD</th>\n",
       "      <th>Total Collateral USD</th>\n",
       "      <th>End Date</th>\n",
       "      <th>TotalDaysSinceEntry</th>\n",
       "      <th>DaysUntilExit</th>\n",
       "      <th>Exit</th>\n",
       "      <th>totalTVL</th>\n",
       "      <th>totalTVLAAVE</th>\n",
       "      <th>Percent In AAVE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>0x7a0af73e4c08a4a46513e8b7fea726fb8d499614</td>\n",
       "      <td>2022-10-04 20:26:59</td>\n",
       "      <td>2022-10-04 20:26:59</td>\n",
       "      <td>2022-11-08 23:16:35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.727655e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3588.055189</td>\n",
       "      <td>2022-12-31 23:59:11</td>\n",
       "      <td>88</td>\n",
       "      <td>35.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.888820e-01</td>\n",
       "      <td>0.188882</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>412</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>2022-12-12 19:30:47</td>\n",
       "      <td>2022-12-12 19:30:47</td>\n",
       "      <td>2022-12-31 00:34:23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.652763e+05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-388.039204</td>\n",
       "      <td>2022-12-31 23:59:11</td>\n",
       "      <td>19</td>\n",
       "      <td>18.0</td>\n",
       "      <td>True</td>\n",
       "      <td>7.573103e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>561</th>\n",
       "      <td>561</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>2022-09-30 23:18:59</td>\n",
       "      <td>2022-09-30 23:18:59</td>\n",
       "      <td>2022-11-04 04:36:47</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.917583e+06</td>\n",
       "      <td>560.75</td>\n",
       "      <td>560.829691</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-333.628124</td>\n",
       "      <td>2022-12-31 23:59:11</td>\n",
       "      <td>92</td>\n",
       "      <td>34.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.466090e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>783</th>\n",
       "      <td>783</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>2022-10-06 16:13:23</td>\n",
       "      <td>2022-10-06 16:13:23</td>\n",
       "      <td>2022-11-09 00:29:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.364006e+06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-587.456076</td>\n",
       "      <td>2022-12-31 23:59:11</td>\n",
       "      <td>86</td>\n",
       "      <td>33.0</td>\n",
       "      <td>True</td>\n",
       "      <td>1.569588e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 274 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Unnamed: 0                                        user  \\\n",
       "80           80  0x7a0af73e4c08a4a46513e8b7fea726fb8d499614   \n",
       "412         412  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee   \n",
       "561         561  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75   \n",
       "783         783  0x49ae763ed846db9a69a639732caa320fad1d5799   \n",
       "\n",
       "           FirstDeposit          FirstBorrow        LastWithdraw  \\\n",
       "80  2022-10-04 20:26:59  2022-10-04 20:26:59 2022-11-08 23:16:35   \n",
       "412 2022-12-12 19:30:47  2022-12-12 19:30:47 2022-12-31 00:34:23   \n",
       "561 2022-09-30 23:18:59  2022-09-30 23:18:59 2022-11-04 04:36:47   \n",
       "783 2022-10-06 16:13:23  2022-10-06 16:13:23 2022-11-09 00:29:59   \n",
       "\n",
       "    LastLiquidation  TotalUSDSupplied  TotalDepositsWETH  \\\n",
       "80              NaN      7.727655e+05               0.00   \n",
       "412             NaN      6.652763e+05               0.00   \n",
       "561             NaN      2.917583e+06             560.75   \n",
       "783             NaN      9.364006e+06               0.00   \n",
       "\n",
       "     TotalWithdrawalsWETH  TotalLiquidationsWETH  ...  Total CollateralLUSD  \\\n",
       "80               0.000000                    0.0  ...                   0.0   \n",
       "412              0.000000                    0.0  ...                   0.0   \n",
       "561            560.829691                    0.0  ...                   0.0   \n",
       "783              0.000000                    0.0  ...                   0.0   \n",
       "\n",
       "     Total Collateral USDLUSD  Total Collateral USD             End Date  \\\n",
       "80                        0.0          -3588.055189  2022-12-31 23:59:11   \n",
       "412                       0.0           -388.039204  2022-12-31 23:59:11   \n",
       "561                       0.0           -333.628124  2022-12-31 23:59:11   \n",
       "783                       0.0           -587.456076  2022-12-31 23:59:11   \n",
       "\n",
       "     TotalDaysSinceEntry  DaysUntilExit  Exit      totalTVL  totalTVLAAVE  \\\n",
       "80                    88           35.0  True  1.888820e-01      0.188882   \n",
       "412                   19           18.0  True  7.573103e+01      0.000000   \n",
       "561                   92           34.0  True  1.466090e+06      0.000000   \n",
       "783                   86           33.0  True  1.569588e+04      0.000000   \n",
       "\n",
       "     Percent In AAVE  \n",
       "80               1.0  \n",
       "412              0.0  \n",
       "561              0.0  \n",
       "783              0.0  \n",
       "\n",
       "[4 rows x 274 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.user.isin(lostUsers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "5212a5c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>User</th>\n",
       "      <th>Name</th>\n",
       "      <th>Chain</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>5226</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>GrizzlyFi</td>\n",
       "      <td>bsc</td>\n",
       "      <td>920688.112760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>5231</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Geist</td>\n",
       "      <td>ftm</td>\n",
       "      <td>461379.629425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5227</th>\n",
       "      <td>5227</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Venus</td>\n",
       "      <td>bsc</td>\n",
       "      <td>84016.050593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>6992</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>Convex</td>\n",
       "      <td>eth</td>\n",
       "      <td>12325.918568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>6989</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>Aave V3</td>\n",
       "      <td>avax</td>\n",
       "      <td>3369.748955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                        User       Name Chain  \\\n",
       "5226        5226  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75  GrizzlyFi   bsc   \n",
       "5231        5231  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75      Geist   ftm   \n",
       "5227        5227  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75      Venus   bsc   \n",
       "6992        6992  0x49ae763ed846db9a69a639732caa320fad1d5799     Convex   eth   \n",
       "6989        6989  0x49ae763ed846db9a69a639732caa320fad1d5799    Aave V3  avax   \n",
       "\n",
       "              Value  \n",
       "5226  920688.112760  \n",
       "5231  461379.629425  \n",
       "5227   84016.050593  \n",
       "6992   12325.918568  \n",
       "6989    3369.748955  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lostUserDf.nlargest(5, ['Value'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "383a2373",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compTVL(row):\n",
    "    return userDf[userDf.Name == row['Name']]['Value'].sum()\n",
    "\n",
    "def compUsers(row):\n",
    "    return len(userDf[userDf.Name == row['Name']])\n",
    "\n",
    "def tvlInAAVE(row):\n",
    "    userList = list(userDf[userDf.Name == row['Name']]['User'])\n",
    "    return userDf[(userDf.User.isin(userList)) & (userDf.Name.isin(exclude))]['Value'].sum()\n",
    "\n",
    "def lostTVL(row): \n",
    "    userList = list(lostUserDf[lostUserDf.Name == row['Name']]['User'])\n",
    "    return df[(df.user.isin(userList))]['TotalUSDSupplied'].sum()\n",
    "    \n",
    "def compTVLGained(row):    \n",
    "    return lostUserDf[lostUserDf.Name == row['Name']]['Value'].sum()\n",
    "    \n",
    "def lostUsers(row):\n",
    "    return len(lostUserDf[lostUserDf.Name == row['Name']])\n",
    "    \n",
    "\n",
    "def compTVLLost(row):    \n",
    "    return newUserDf[newUserDf.Name == row['Name']]['Value'].sum()\n",
    "    \n",
    "def countNewUsers(row):\n",
    "    return len(newUserDf[newUserDf.Name == row['Name']])\n",
    "        \n",
    "    \n",
    "    \n",
    "compDf['TVLFromSharedUsers'] =  compDf.apply(compTVL, axis=1)      \n",
    "compDf['nUsers'] =  compDf.apply(compUsers, axis=1)      \n",
    "compDf['tvlInAAVE'] = compDf.apply(tvlInAAVE, axis=1) \n",
    "\n",
    "compDf['lostTVL'] =  compDf.apply(lostTVL, axis=1) \n",
    "compDf['compTVLGained'] =  compDf.apply(compTVLGained, axis=1) \n",
    "compDf['lostUsers'] =  compDf.apply(lostUsers, axis=1) \n",
    "\n",
    "compDf['newUsers'] =  compDf.apply(countNewUsers, axis=1) \n",
    "compDf['compTVLNew'] =  compDf.apply(compTVLLost, axis=1) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8c6e90e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Of competitors what are N largest protocols that users are using (by TVL)\n",
    "largestComps = compDf.nlargest(5,['compTVLGained'])\n",
    "\n",
    "\n",
    "plt.bar(largestComps['Name'], largestComps['compTVLGained'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ea11b2ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Of competitors what are N largest protocols that users are using (by TVL)\n",
    "largestComps = compDf.nlargest(5,['lostUsers'])\n",
    "\n",
    "\n",
    "plt.bar(largestComps['Name'], largestComps['lostUsers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "6fefd03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From New Users What are Largest Protocols being Used \n",
    "largestComps = compDf.nlargest(5,['compTVLNew'])\n",
    "\n",
    "\n",
    "plt.bar(largestComps['Name'], largestComps['compTVLNew'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "6313f183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OF new users what are most common apps being used\n",
    "largestComps = compDf.nlargest(5,['newUsers'])\n",
    "\n",
    "\n",
    "plt.bar(largestComps['Name'], largestComps['newUsers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8099d1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Of competitors what are N largest protocols that users are using (by TVL)\n",
    "largestComps = compDf.nlargest(5,['lostUsers'])\n",
    "\n",
    "\n",
    "plt.bar(largestComps['Name'], largestComps['lostUsers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "afd3745f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "largestShared = compDf.nlargest(5,['TVLFromSharedUsers'])\n",
    "\n",
    "plt.bar(largestShared['Name'], largestShared['TVLFromSharedUsers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "650e1eef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 5 artists>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "largestShared = compDf.nlargest(5,['nUsers'])\n",
    "\n",
    "plt.bar(largestShared['Name'], largestShared['nUsers'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7e3559a6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['New Users', 'Lost Users'], [len(newUsers), len(lostUsers)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "d6758af8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>User</th>\n",
       "      <th>Name</th>\n",
       "      <th>Chain</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>521</td>\n",
       "      <td>0x7a0af73e4c08a4a46513e8b7fea726fb8d499614</td>\n",
       "      <td>Aave V2</td>\n",
       "      <td>eth</td>\n",
       "      <td>1.888820e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>522</td>\n",
       "      <td>0x7a0af73e4c08a4a46513e8b7fea726fb8d499614</td>\n",
       "      <td>LIDO</td>\n",
       "      <td>eth</td>\n",
       "      <td>1.616860e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3898</th>\n",
       "      <td>3898</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>Badger DAO</td>\n",
       "      <td>eth</td>\n",
       "      <td>6.364489e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3899</th>\n",
       "      <td>3899</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>AutoFarm</td>\n",
       "      <td>boba</td>\n",
       "      <td>1.429303e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3900</th>\n",
       "      <td>3900</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>Venus</td>\n",
       "      <td>bsc</td>\n",
       "      <td>4.086105e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3901</th>\n",
       "      <td>3901</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>Compound</td>\n",
       "      <td>eth</td>\n",
       "      <td>8.623277e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3902</th>\n",
       "      <td>3902</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>SpiritSwap</td>\n",
       "      <td>ftm</td>\n",
       "      <td>7.093745e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3903</th>\n",
       "      <td>3903</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>Uniswap V2</td>\n",
       "      <td>eth</td>\n",
       "      <td>4.129660e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3904</th>\n",
       "      <td>3904</td>\n",
       "      <td>0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee</td>\n",
       "      <td>xToken</td>\n",
       "      <td>eth</td>\n",
       "      <td>1.779806e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>5226</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>GrizzlyFi</td>\n",
       "      <td>bsc</td>\n",
       "      <td>9.206881e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5227</th>\n",
       "      <td>5227</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Venus</td>\n",
       "      <td>bsc</td>\n",
       "      <td>8.401605e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5228</th>\n",
       "      <td>5228</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Compound</td>\n",
       "      <td>eth</td>\n",
       "      <td>6.657349e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5229</th>\n",
       "      <td>5229</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Damm Finance</td>\n",
       "      <td>eth</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5230</th>\n",
       "      <td>5230</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>DeFi Franc</td>\n",
       "      <td>eth</td>\n",
       "      <td>1.143865e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>5231</td>\n",
       "      <td>0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75</td>\n",
       "      <td>Geist</td>\n",
       "      <td>ftm</td>\n",
       "      <td>4.613796e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6989</th>\n",
       "      <td>6989</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>Aave V3</td>\n",
       "      <td>avax</td>\n",
       "      <td>3.369749e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6990</th>\n",
       "      <td>6990</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>BENQI</td>\n",
       "      <td>avax</td>\n",
       "      <td>7.193645e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6991</th>\n",
       "      <td>6991</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>Compound V3</td>\n",
       "      <td>eth</td>\n",
       "      <td>2.081538e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6992</th>\n",
       "      <td>6992</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>Convex</td>\n",
       "      <td>eth</td>\n",
       "      <td>1.232592e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6993</th>\n",
       "      <td>6993</td>\n",
       "      <td>0x49ae763ed846db9a69a639732caa320fad1d5799</td>\n",
       "      <td>LIDO</td>\n",
       "      <td>eth</td>\n",
       "      <td>1.614300e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0                                        User          Name  \\\n",
       "521          521  0x7a0af73e4c08a4a46513e8b7fea726fb8d499614       Aave V2   \n",
       "522          522  0x7a0af73e4c08a4a46513e8b7fea726fb8d499614          LIDO   \n",
       "3898        3898  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee    Badger DAO   \n",
       "3899        3899  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee      AutoFarm   \n",
       "3900        3900  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee         Venus   \n",
       "3901        3901  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee      Compound   \n",
       "3902        3902  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee    SpiritSwap   \n",
       "3903        3903  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee    Uniswap V2   \n",
       "3904        3904  0x64bade3ea5ffc64cfb776f55cf82086b9429a5ee        xToken   \n",
       "5226        5226  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75     GrizzlyFi   \n",
       "5227        5227  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75         Venus   \n",
       "5228        5228  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75      Compound   \n",
       "5229        5229  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75  Damm Finance   \n",
       "5230        5230  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75    DeFi Franc   \n",
       "5231        5231  0xd35db3b19c8c2a60b8613e5b30aadcd290c99e75         Geist   \n",
       "6989        6989  0x49ae763ed846db9a69a639732caa320fad1d5799       Aave V3   \n",
       "6990        6990  0x49ae763ed846db9a69a639732caa320fad1d5799         BENQI   \n",
       "6991        6991  0x49ae763ed846db9a69a639732caa320fad1d5799   Compound V3   \n",
       "6992        6992  0x49ae763ed846db9a69a639732caa320fad1d5799        Convex   \n",
       "6993        6993  0x49ae763ed846db9a69a639732caa320fad1d5799          LIDO   \n",
       "\n",
       "     Chain         Value  \n",
       "521    eth  1.888820e-01  \n",
       "522    eth  1.616860e-15  \n",
       "3898   eth  6.364489e+01  \n",
       "3899  boba  1.429303e-16  \n",
       "3900   bsc  4.086105e-04  \n",
       "3901   eth  8.623277e-01  \n",
       "3902   ftm  7.093745e+00  \n",
       "3903   eth  4.129660e+00  \n",
       "3904   eth  1.779806e-12  \n",
       "5226   bsc  9.206881e+05  \n",
       "5227   bsc  8.401605e+04  \n",
       "5228   eth  6.657349e+00  \n",
       "5229   eth  0.000000e+00  \n",
       "5230   eth  1.143865e-02  \n",
       "5231   ftm  4.613796e+05  \n",
       "6989  avax  3.369749e+03  \n",
       "6990  avax  7.193645e-03  \n",
       "6991   eth  2.081538e-01  \n",
       "6992   eth  1.232592e+04  \n",
       "6993   eth  1.614300e-15  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "userDf[userDf.User.isin(lostUsers)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "49bba707",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(['New Users', 'Lost Users'], [len(newUsers), len(lostUsers)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "caee50bc",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'numpy.ndarray'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [84]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcompDf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnlargest\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mTVLFromSharedUsers\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/pyplot.py:2769\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2767\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m   2768\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\u001b[38;5;241m*\u001b[39margs, scalex\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, scaley\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m-> 2769\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2770\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2771\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\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\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_axes.py:1632\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1390\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m   1392\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1629\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m   1630\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1631\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1632\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m   1633\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m   1634\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py:312\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m     this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m    311\u001b[0m     args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 312\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axes/_base.py:495\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, tup, kwargs, return_kwargs)\u001b[0m\n\u001b[1;32m    493\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39mxaxis\u001b[38;5;241m.\u001b[39mupdate_units(x)\n\u001b[1;32m    494\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes\u001b[38;5;241m.\u001b[39myaxis \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[0;32m--> 495\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxes\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    497\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[1;32m    498\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    499\u001b[0m                      \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/axis.py:1449\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1447\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[1;32m   1448\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[0;32m-> 1449\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   1451\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/category.py:116\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[0;34m(data, axis)\u001b[0m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 116\u001b[0m     axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    117\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    118\u001b[0m     axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/category.py:192\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    190\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[1;32m    191\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \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[0;32m--> 192\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.local/lib/python3.8/site-packages/matplotlib/category.py:225\u001b[0m, in \u001b[0;36mUnitData.update\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[1;32m    224\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m--> 225\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    226\u001b[0m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[1;32m    227\u001b[0m     _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[1;32m    228\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[1;32m    229\u001b[0m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD8CAYAAAB0IB+mAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAANT0lEQVR4nO3cYYjkd33H8ffHO1NpjKb0VpC706T00njYQtIlTRFqirZc8uDugUXuIFgleGAbKVWEFEuU+MiGWhCu1ZOKVdAYfSALntwDjQTEC7chNXgXItvTeheFrDHNk6Ax7bcPZtKdrneZf3Zndy/7fb/gYP7/+e3Mlx97752d2ZlUFZKk7e8VWz2AJGlzGHxJasLgS1ITBl+SmjD4ktSEwZekJqYGP8lnkzyZ5PuXuD5JPplkKcmjSW6c/ZiSpPUa8gj/c8CBF7n+VmDf+N9R4F/WP5YkadamBr+qHgR+/iJLDgGfr5FTwNVJXj+rASVJs7FzBrexGzg/cXxhfO6nqxcmOcrotwCuvPLKP7z++utncPeS1MfDDz/8s6qaW8vXziL4g1XVceA4wPz8fC0uLm7m3UvSy16S/1zr187ir3SeAPZOHO8Zn5MkXUZmEfwF4F3jv9a5GXimqn7t6RxJ0taa+pROki8BtwC7klwAPgK8EqCqPgWcAG4DloBngfds1LCSpLWbGvyqOjLl+gL+emYTSZI2hO+0laQmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqYlBwU9yIMnjSZaS3HWR69+Q5IEkjyR5NMltsx9VkrQeU4OfZAdwDLgV2A8cSbJ/1bK/B+6vqhuAw8A/z3pQSdL6DHmEfxOwVFXnquo54D7g0Ko1BbxmfPm1wE9mN6IkaRaGBH83cH7i+ML43KSPArcnuQCcAN5/sRtKcjTJYpLF5eXlNYwrSVqrWb1oewT4XFXtAW4DvpDk1267qo5X1XxVzc/Nzc3oriVJQwwJ/hPA3onjPeNzk+4A7geoqu8CrwJ2zWJASdJsDAn+aWBfkmuTXMHoRdmFVWt+DLwNIMmbGAXf52wk6TIyNfhV9TxwJ3ASeIzRX+OcSXJPkoPjZR8E3pvke8CXgHdXVW3U0JKkl27nkEVVdYLRi7GT5+6euHwWeMtsR5MkzZLvtJWkJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNTEo+EkOJHk8yVKSuy6x5p1JziY5k+SLsx1TkrReO6ctSLIDOAb8GXABOJ1koarOTqzZB/wd8JaqejrJ6zZqYEnS2gx5hH8TsFRV56rqOeA+4NCqNe8FjlXV0wBV9eRsx5QkrdeQ4O8Gzk8cXxifm3QdcF2S7yQ5leTAxW4oydEki0kWl5eX1zaxJGlNZvWi7U5gH3ALcAT4TJKrVy+qquNVNV9V83NzczO6a0nSEEOC/wSwd+J4z/jcpAvAQlX9qqp+CPyA0Q8ASdJlYkjwTwP7klyb5ArgMLCwas3XGD26J8kuRk/xnJvdmJKk9Zoa/Kp6HrgTOAk8BtxfVWeS3JPk4HjZSeCpJGeBB4APVdVTGzW0JOmlS1VtyR3Pz8/X4uLilty3JL1cJXm4qubX8rW+01aSmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmBgU/yYEkjydZSnLXi6x7R5JKMj+7ESVJszA1+El2AMeAW4H9wJEk+y+y7irgb4CHZj2kJGn9hjzCvwlYqqpzVfUccB9w6CLrPgZ8HPjFDOeTJM3IkODvBs5PHF8Yn/s/SW4E9lbV11/shpIcTbKYZHF5efklDytJWrt1v2ib5BXAJ4APTltbVcerar6q5ufm5tZ715Kkl2BI8J8A9k4c7xmfe8FVwJuBbyf5EXAzsOALt5J0eRkS/NPAviTXJrkCOAwsvHBlVT1TVbuq6pqqugY4BRysqsUNmViStCZTg19VzwN3AieBx4D7q+pMknuSHNzoASVJs7FzyKKqOgGcWHXu7kusvWX9Y0mSZs132kpSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmhgU/CQHkjyeZCnJXRe5/gNJziZ5NMk3k7xx9qNKktZjavCT7ACOAbcC+4EjSfavWvYIMF9VfwB8FfiHWQ8qSVqfIY/wbwKWqupcVT0H3AccmlxQVQ9U1bPjw1PAntmOKUlaryHB3w2cnzi+MD53KXcA37jYFUmOJllMsri8vDx8SknSus30RdsktwPzwL0Xu76qjlfVfFXNz83NzfKuJUlT7Byw5glg78TxnvG5/yfJ24EPA2+tql/OZjxJ0qwMeYR/GtiX5NokVwCHgYXJBUluAD4NHKyqJ2c/piRpvaYGv6qeB+4ETgKPAfdX1Zkk9yQ5OF52L/Bq4CtJ/j3JwiVuTpK0RYY8pUNVnQBOrDp398Tlt894LknSjPlOW0lqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpow+JLUhMGXpCYMviQ1YfAlqQmDL0lNGHxJasLgS1ITBl+SmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElqwuBLUhMGX5KaMPiS1ITBl6QmDL4kNWHwJakJgy9JTRh8SWrC4EtSEwZfkpoYFPwkB5I8nmQpyV0Xuf43knx5fP1DSa6Z+aSSpHWZGvwkO4BjwK3AfuBIkv2rlt0BPF1Vvwv8E/DxWQ8qSVqfIY/wbwKWqupcVT0H3AccWrXmEPBv48tfBd6WJLMbU5K0XjsHrNkNnJ84vgD80aXWVNXzSZ4Bfhv42eSiJEeBo+PDXyb5/lqG3oZ2sWqvGnMvVrgXK9yLFb+31i8cEvyZqarjwHGAJItVNb+Z93+5ci9WuBcr3IsV7sWKJItr/dohT+k8AeydON4zPnfRNUl2Aq8FnlrrUJKk2RsS/NPAviTXJrkCOAwsrFqzAPzl+PJfAN+qqprdmJKk9Zr6lM74Ofk7gZPADuCzVXUmyT3AYlUtAP8KfCHJEvBzRj8Upjm+jrm3G/dihXuxwr1Y4V6sWPNexAfiktSD77SVpCYMviQ1seHB92MZVgzYiw8kOZvk0STfTPLGrZhzM0zbi4l170hSSbbtn+QN2Ysk7xx/b5xJ8sXNnnGzDPg/8oYkDyR5ZPz/5LatmHOjJflskicv9V6ljHxyvE+PJrlx0A1X1Yb9Y/Qi738AvwNcAXwP2L9qzV8BnxpfPgx8eSNn2qp/A/fiT4HfHF9+X+e9GK+7CngQOAXMb/XcW/h9sQ94BPit8fHrtnruLdyL48D7xpf3Az/a6rk3aC/+BLgR+P4lrr8N+AYQ4GbgoSG3u9GP8P1YhhVT96KqHqiqZ8eHpxi952E7GvJ9AfAxRp/L9IvNHG6TDdmL9wLHquppgKp6cpNn3CxD9qKA14wvvxb4ySbOt2mq6kFGf/F4KYeAz9fIKeDqJK+fdrsbHfyLfSzD7kutqarngRc+lmG7GbIXk+5g9BN8O5q6F+NfUfdW1dc3c7AtMOT74jrguiTfSXIqyYFNm25zDdmLjwK3J7kAnADevzmjXXZeak+ATf5oBQ2T5HZgHnjrVs+yFZK8AvgE8O4tHuVysZPR0zq3MPqt78Ekv19V/7WVQ22RI8Dnquofk/wxo/f/vLmq/merB3s52OhH+H4sw4ohe0GStwMfBg5W1S83abbNNm0vrgLeDHw7yY8YPUe5sE1fuB3yfXEBWKiqX1XVD4EfMPoBsN0M2Ys7gPsBquq7wKsYfbBaN4N6stpGB9+PZVgxdS+S3AB8mlHst+vztDBlL6rqmaraVVXXVNU1jF7POFhVa/7QqMvYkP8jX2P06J4kuxg9xXNuE2fcLEP24sfA2wCSvIlR8Jc3dcrLwwLwrvFf69wMPFNVP532RRv6lE5t3McyvOwM3It7gVcDXxm/bv3jqjq4ZUNvkIF70cLAvTgJ/HmSs8B/Ax+qqm33W/DAvfgg8Jkkf8voBdx3b8cHiEm+xOiH/K7x6xUfAV4JUFWfYvT6xW3AEvAs8J5Bt7sN90qSdBG+01aSmjD4ktSEwZekJgy+JDVh8CWpCYMvSU0YfElq4n8BzPZculjwdYoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(compDf.nlargest(5,['TVLFromSharedUsers']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7a058dfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GMX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Wonderland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Venus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adamant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Klima DAO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>548</th>\n",
       "      <td>Voltage</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>Unifi Protocol</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>550</th>\n",
       "      <td>Dot Dot Finance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551</th>\n",
       "      <td>Valas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Rally</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>553 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name\n",
       "0                GMX\n",
       "1         Wonderland\n",
       "2              Venus\n",
       "3            Adamant\n",
       "4         Klima DAO \n",
       "..               ...\n",
       "548          Voltage\n",
       "549   Unifi Protocol\n",
       "550  Dot Dot Finance\n",
       "551            Valas\n",
       "552            Rally\n",
       "\n",
       "[553 rows x 1 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# where have lost users gone ??? \n",
    "\n",
    "compDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ef4f1c6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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