FinCENSAR-Skill-Assess-William-Nsambu.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Objective\n",
    "- Match suspicious bank transactions to blockchain transaction hashes:\n",
    "\n",
    "## II. Technical Objective\n",
    "    1. Import libraries\n",
    "    2. Import the data\n",
    "    3. Clean Data\n",
    "    4. Merge Data (match suspecious bank trans)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries and Setting Up Defaults \n",
    "### In this section I am\n",
    "- Importing all my libraries\n",
    "- Setting my configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
    "warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
    "\n",
    "pd.set_option('display.max_columns',500) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "b'Skipping line 12137: expected 5 fields, saw 9\\n'\n",
      "/Users/williamnsambu/opt/anaconda3/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3071: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "#importing bank connections and transactions datasets  \n",
    "bank_connections = pd.read_csv('/Users/williamnsambu/Documents/Dev/ProffessionalAdvencement/DataScience/JupyterNotebooks/data/bank_connections.csv', index_col = None, header=0, encoding='latin-1')\n",
    "\n",
    "transactions_map = pd.read_csv('/Users/williamnsambu/Documents/Dev/ProffessionalAdvencement/DataScience/JupyterNotebooks/data/transactions_map.csv', index_col = None, header=0, encoding='latin-1')\n",
    "\n",
    "btc_tx = pd.read_csv('/Users/williamnsambu/Documents/Dev/ProffessionalAdvencement/DataScience/JupyterNotebooks/data/btc-tx.csv', error_bad_lines=False, index_col = None, header=0, encoding='latin-1')\n",
    "\n",
    "btc_usd_2021 = pd.read_csv('/Users/williamnsambu/Documents/Dev/ProffessionalAdvencement/DataScience/JupyterNotebooks/data/btc_usd_2021.csv', index_col = None, header=0, encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleansing\n",
    "There are different steps in the data cleasing but I will only use one which is removing nulls (NaN) in our datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 4507 entries, 0 to 4506\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   id                        4507 non-null   int64  \n",
      " 1   icij_sar_id               4507 non-null   int64  \n",
      " 2   filer_org_name_id         4507 non-null   object \n",
      " 3   filer_org_name            4507 non-null   object \n",
      " 4   begin_date                4501 non-null   object \n",
      " 5   end_date                  4501 non-null   object \n",
      " 6   originator_bank_id        4507 non-null   object \n",
      " 7   originator_bank           4507 non-null   object \n",
      " 8   originator_bank_country   4507 non-null   object \n",
      " 9   originator_iso            4507 non-null   object \n",
      " 10  beneficiary_bank_id       4507 non-null   object \n",
      " 11  beneficiary_bank          4507 non-null   object \n",
      " 12  beneficiary_bank_country  4507 non-null   object \n",
      " 13  beneficiary_iso           4507 non-null   object \n",
      " 14  number_transactions       4396 non-null   float64\n",
      " 15  amount_transactions       4507 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 563.5+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1427711 entries, 0 to 1427710\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count    Dtype \n",
      "---  ------                  --------------    ----- \n",
      " 0   time                    1427711 non-null  object\n",
      " 1   Hash                    460943 non-null   object\n",
      " 2   Sender                  460943 non-null   object\n",
      " 3   Receiver                299857 non-null   object\n",
      " 4   Transaction_amount_BTC  299857 non-null   object\n",
      "dtypes: object(5)\n",
      "memory usage: 54.5+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#displaying details about the data\n",
    "#display(bank_connections.info(verbose=True))\n",
    "display(transactions_map.info(verbose=True))\n",
    "display(btc_tx.info(verbose=True))\n",
    "#btc_usd_2021.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dropping nulls (NaN)\n",
    "#bank_connections_clean = bank_connections.dropna()\n",
    "transactions_map_clean = transactions_map.dropna()\n",
    "btc_tx_clean = btc_tx.dropna()\n",
    "#btc_usd_2021_clean = btc_usd_2021.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4390 entries, 0 to 4506\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   id                        4390 non-null   int64  \n",
      " 1   icij_sar_id               4390 non-null   int64  \n",
      " 2   filer_org_name_id         4390 non-null   object \n",
      " 3   filer_org_name            4390 non-null   object \n",
      " 4   begin_date                4390 non-null   object \n",
      " 5   end_date                  4390 non-null   object \n",
      " 6   originator_bank_id        4390 non-null   object \n",
      " 7   originator_bank           4390 non-null   object \n",
      " 8   originator_bank_country   4390 non-null   object \n",
      " 9   originator_iso            4390 non-null   object \n",
      " 10  beneficiary_bank_id       4390 non-null   object \n",
      " 11  beneficiary_bank          4390 non-null   object \n",
      " 12  beneficiary_bank_country  4390 non-null   object \n",
      " 13  beneficiary_iso           4390 non-null   object \n",
      " 14  number_transactions       4390 non-null   float64\n",
      " 15  amount_transactions       4390 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(12)\n",
      "memory usage: 583.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 299857 entries, 0 to 1427710\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count   Dtype \n",
      "---  ------                  --------------   ----- \n",
      " 0   time                    299857 non-null  object\n",
      " 1   Hash                    299857 non-null  object\n",
      " 2   Sender                  299857 non-null  object\n",
      " 3   Receiver                299857 non-null  object\n",
      " 4   Transaction_amount_BTC  299857 non-null  object\n",
      "dtypes: object(5)\n",
      "memory usage: 13.7+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#display(bank_connections_clean.info(verbose=True))\n",
    "display(transactions_map_clean.info(verbose=True))\n",
    "display(btc_tx_clean.info(verbose=True))\n",
    "#btc_usd_2021_clean.info(verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>icij_sar_id</th>\n",
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       "      <td>SWE</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.464435e+04</td>\n",
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       "      <th>95</th>\n",
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       "      <td>2924</td>\n",
       "      <td>the-bank-of-new-york-mellon-corp</td>\n",
       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>Jun 20, 2012</td>\n",
       "      <td>Jun 20, 2012</td>\n",
       "      <td>barclays-bank-plc-ho-uk</td>\n",
       "      <td>Barclays Bank Plc Ho UK</td>\n",
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       "      <td>skandinaviska-enskilda-banken-stockholm-sweden...</td>\n",
       "      <td>Skandinaviska Enskilda Banken</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>SWE</td>\n",
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       "      <td>4.000000e+04</td>\n",
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       "      <td>barclays-bank-plc-ho-uk</td>\n",
       "      <td>Barclays Bank Plc Ho UK</td>\n",
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       "      <td>SWE</td>\n",
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       "        id  icij_sar_id                 filer_org_name_id  \\\n",
       "0   223254         3297  the-bank-of-new-york-mellon-corp   \n",
       "1   223255         3297  the-bank-of-new-york-mellon-corp   \n",
       "94  223350         2924  the-bank-of-new-york-mellon-corp   \n",
       "95  223351         2924  the-bank-of-new-york-mellon-corp   \n",
       "96  223352         2924  the-bank-of-new-york-mellon-corp   \n",
       "\n",
       "                       filer_org_name    begin_date      end_date  \\\n",
       "0   The Bank of New York Mellon Corp.  Mar 25, 2015  Sep 25, 2015   \n",
       "1   The Bank of New York Mellon Corp.  Mar 30, 2015  Sep 25, 2015   \n",
       "94  The Bank of New York Mellon Corp.  Jul 17, 2012  Jul 17, 2012   \n",
       "95  The Bank of New York Mellon Corp.  Jun 20, 2012  Jun 20, 2012   \n",
       "96  The Bank of New York Mellon Corp.  May 24, 2012  May 24, 2012   \n",
       "\n",
       "         originator_bank_id          originator_bank originator_bank_country  \\\n",
       "0          cimb-bank-berhad         CIMB Bank Berhad               Singapore   \n",
       "1          cimb-bank-berhad         CIMB Bank Berhad               Singapore   \n",
       "94  barclays-bank-plc-ho-uk  Barclays Bank Plc Ho UK          United Kingdom   \n",
       "95  barclays-bank-plc-ho-uk  Barclays Bank Plc Ho UK          United Kingdom   \n",
       "96  barclays-bank-plc-ho-uk  Barclays Bank Plc Ho UK          United Kingdom   \n",
       "\n",
       "   originator_iso                                beneficiary_bank_id  \\\n",
       "0             SGP               barclays-bank-plc-london-england-gbr   \n",
       "1             SGP               barclays-bank-plc-london-england-gbr   \n",
       "94            GBR  skandinaviska-enskilda-banken-stockholm-sweden...   \n",
       "95            GBR  skandinaviska-enskilda-banken-stockholm-sweden...   \n",
       "96            GBR  skandinaviska-enskilda-banken-stockholm-sweden...   \n",
       "\n",
       "                 beneficiary_bank beneficiary_bank_country beneficiary_iso  \\\n",
       "0               Barclays Bank Plc           United Kingdom             GBR   \n",
       "1               Barclays Bank Plc           United Kingdom             GBR   \n",
       "94  Skandinaviska Enskilda Banken                   Sweden             SWE   \n",
       "95  Skandinaviska Enskilda Banken                   Sweden             SWE   \n",
       "96  Skandinaviska Enskilda Banken                   Sweden             SWE   \n",
       "\n",
       "    number_transactions  amount_transactions  \n",
       "0                  68.0         5.689852e+07  \n",
       "1                 118.0         1.162384e+08  \n",
       "94                  1.0         3.464435e+04  \n",
       "95                  1.0         4.000000e+04  \n",
       "96                  1.0         3.685587e+04  "
      ]
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       "      <th>time</th>\n",
       "      <th>Hash</th>\n",
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       "      <th>0</th>\n",
       "      <td>2015-03-31</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>68a4c74ebb211c5c61caa0296d9eae5b0336e0c7b7c68a...</td>\n",
       "      <td>1NE3QFkf4aFUgCNTvGjiZsY59yDsGratiU 1Bst4GwKrN2...</td>\n",
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       "      <td>4509.87714053</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-03-31</td>\n",
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       "      <th>3</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>1866b030f8a3a6f83393fa23215759c05d13a2bf30d716...</td>\n",
       "      <td>18uvUoZuDFXaPjKBDHu8w2PNYqY6dXDs8G</td>\n",
       "      <td>1HoiNQf18RwzeLhBmcGM8Pqqyqc9KCSiYE</td>\n",
       "      <td>4458.36429747</td>\n",
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       "      <th>4</th>\n",
       "      <td>2015-03-31</td>\n",
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       "      <td>14LPd6dcAZpJD3jfz8FPJcgdL8G1hMWZTq</td>\n",
       "      <td>8000</td>\n",
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      "text/plain": [
       "         time                                               Hash  \\\n",
       "0  2015-03-31  16f27194b06bcf1f61b1531a2a861fdcd027a232428090...   \n",
       "1  2015-03-31  68a4c74ebb211c5c61caa0296d9eae5b0336e0c7b7c68a...   \n",
       "2  2015-03-31  ddf989c7797164150de0764e364f7e01b7253538de753f...   \n",
       "3  2015-03-31  1866b030f8a3a6f83393fa23215759c05d13a2bf30d716...   \n",
       "4  2015-03-31  1866b030f8a3a6f83393fa23215759c05d13a2bf30d716...   \n",
       "\n",
       "                                              Sender  \\\n",
       "0                 1BtNH4rtj3PVe6PWYsziQMnXofZmoPmGHu   \n",
       "1  1NE3QFkf4aFUgCNTvGjiZsY59yDsGratiU 1Bst4GwKrN2...   \n",
       "2                 15HgmxddgExHKTfkXwacQgKf9nx8PZEDri   \n",
       "3                 18uvUoZuDFXaPjKBDHu8w2PNYqY6dXDs8G   \n",
       "4                 18uvUoZuDFXaPjKBDHu8w2PNYqY6dXDs8G   \n",
       "\n",
       "                             Receiver Transaction_amount_BTC  \n",
       "0  1PrGFmmFFeD6v9ihS74ewjAzSMmQxsQAuw              5272.9999  \n",
       "1  18zXyBVHCU3hu3ivszZNh3NqA1eA1jVBsm          4509.87714053  \n",
       "2  1EdyFyyzgxWsV3P1x41vKDrEeDDyz9QAMe          4138.09424942  \n",
       "3  1HoiNQf18RwzeLhBmcGM8Pqqyqc9KCSiYE          4458.36429747  \n",
       "4  14LPd6dcAZpJD3jfz8FPJcgdL8G1hMWZTq                   8000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(transactions_map_clean.head())\n",
    "display(btc_tx_clean.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-7-4ed6d761e0e3>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  transactions_map_clean['begin_date'] = pd.to_datetime(transactions_map_clean['begin_date'])\n",
      "<ipython-input-7-4ed6d761e0e3>:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  btc_tx_clean['time'] = pd.to_datetime(btc_tx_clean['time'])\n"
     ]
    }
   ],
   "source": [
    "#converting columns 'time' and 'begin_date' from object to date time data type\n",
    "transactions_map_clean['begin_date'] = pd.to_datetime(transactions_map_clean['begin_date'])\n",
    "btc_tx_clean['time'] = pd.to_datetime(btc_tx_clean['time'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>icij_sar_id</th>\n",
       "      <th>filer_org_name_id</th>\n",
       "      <th>filer_org_name</th>\n",
       "      <th>begin_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>originator_bank_id</th>\n",
       "      <th>originator_bank</th>\n",
       "      <th>originator_bank_country</th>\n",
       "      <th>originator_iso</th>\n",
       "      <th>beneficiary_bank_id</th>\n",
       "      <th>beneficiary_bank</th>\n",
       "      <th>beneficiary_bank_country</th>\n",
       "      <th>beneficiary_iso</th>\n",
       "      <th>number_transactions</th>\n",
       "      <th>amount_transactions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>223254</td>\n",
       "      <td>3297</td>\n",
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       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>Sep 25, 2015</td>\n",
       "      <td>cimb-bank-berhad</td>\n",
       "      <td>CIMB Bank Berhad</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>barclays-bank-plc-london-england-gbr</td>\n",
       "      <td>Barclays Bank Plc</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GBR</td>\n",
       "      <td>68.0</td>\n",
       "      <td>56898523.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  icij_sar_id                 filer_org_name_id  \\\n",
       "0  223254         3297  the-bank-of-new-york-mellon-corp   \n",
       "\n",
       "                      filer_org_name begin_date      end_date  \\\n",
       "0  The Bank of New York Mellon Corp. 2015-03-25  Sep 25, 2015   \n",
       "\n",
       "  originator_bank_id   originator_bank originator_bank_country originator_iso  \\\n",
       "0   cimb-bank-berhad  CIMB Bank Berhad               Singapore            SGP   \n",
       "\n",
       "                    beneficiary_bank_id   beneficiary_bank  \\\n",
       "0  barclays-bank-plc-london-england-gbr  Barclays Bank Plc   \n",
       "\n",
       "  beneficiary_bank_country beneficiary_iso  number_transactions  \\\n",
       "0           United Kingdom             GBR                 68.0   \n",
       "\n",
       "   amount_transactions  \n",
       "0          56898523.47  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>Hash</th>\n",
       "      <th>Sender</th>\n",
       "      <th>Receiver</th>\n",
       "      <th>Transaction_amount_BTC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>16f27194b06bcf1f61b1531a2a861fdcd027a232428090...</td>\n",
       "      <td>1BtNH4rtj3PVe6PWYsziQMnXofZmoPmGHu</td>\n",
       "      <td>1PrGFmmFFeD6v9ihS74ewjAzSMmQxsQAuw</td>\n",
       "      <td>5272.9999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time                                               Hash  \\\n",
       "0 2015-03-31  16f27194b06bcf1f61b1531a2a861fdcd027a232428090...   \n",
       "\n",
       "                               Sender                            Receiver  \\\n",
       "0  1BtNH4rtj3PVe6PWYsziQMnXofZmoPmGHu  1PrGFmmFFeD6v9ihS74ewjAzSMmQxsQAuw   \n",
       "\n",
       "  Transaction_amount_BTC  \n",
       "0              5272.9999  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4390 entries, 0 to 4506\n",
      "Data columns (total 16 columns):\n",
      " #   Column                    Non-Null Count  Dtype         \n",
      "---  ------                    --------------  -----         \n",
      " 0   id                        4390 non-null   int64         \n",
      " 1   icij_sar_id               4390 non-null   int64         \n",
      " 2   filer_org_name_id         4390 non-null   object        \n",
      " 3   filer_org_name            4390 non-null   object        \n",
      " 4   begin_date                4390 non-null   datetime64[ns]\n",
      " 5   end_date                  4390 non-null   object        \n",
      " 6   originator_bank_id        4390 non-null   object        \n",
      " 7   originator_bank           4390 non-null   object        \n",
      " 8   originator_bank_country   4390 non-null   object        \n",
      " 9   originator_iso            4390 non-null   object        \n",
      " 10  beneficiary_bank_id       4390 non-null   object        \n",
      " 11  beneficiary_bank          4390 non-null   object        \n",
      " 12  beneficiary_bank_country  4390 non-null   object        \n",
      " 13  beneficiary_iso           4390 non-null   object        \n",
      " 14  number_transactions       4390 non-null   float64       \n",
      " 15  amount_transactions       4390 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(2), object(11)\n",
      "memory usage: 583.0+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 299857 entries, 0 to 1427710\n",
      "Data columns (total 5 columns):\n",
      " #   Column                  Non-Null Count   Dtype         \n",
      "---  ------                  --------------   -----         \n",
      " 0   time                    299857 non-null  datetime64[ns]\n",
      " 1   Hash                    299857 non-null  object        \n",
      " 2   Sender                  299857 non-null  object        \n",
      " 3   Receiver                299857 non-null  object        \n",
      " 4   Transaction_amount_BTC  299857 non-null  object        \n",
      "dtypes: datetime64[ns](1), object(4)\n",
      "memory usage: 13.7+ MB\n"
     ]
    }
   ],
   "source": [
    "display(transactions_map_clean.head(1))\n",
    "display(btc_tx_clean.head(1))\n",
    "display(transactions_map_clean.info(verbose=True))\n",
    "btc_tx_clean.info(verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Match suspicious bank transactions to blockchain transaction hashes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Match suspicious bank transactions to blockchain transaction hashes\n",
    "suspicious_bank_and_blockchain_transactions = transactions_map_clean.merge(btc_tx_clean, left_on='begin_date', right_on='time', how ='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
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       "      <td>Sep 10, 2015</td>\n",
       "      <td>dbs-bank</td>\n",
       "      <td>DBS Bank</td>\n",
       "      <td>China</td>\n",
       "      <td>CHN</td>\n",
       "      <td>bank-of-east-asia-ltd-hong-kong-hkg</td>\n",
       "      <td>Bank of East Asia, Ltd</td>\n",
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       "      <td>HKG</td>\n",
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       "      <td>wells-fargo-bank-na-miami-fl-usa</td>\n",
       "      <td>Wells Fargo Bank Na</td>\n",
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       "      <td>USA</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>13z8KW7TBTMdcq7JHZUZVN79P7rJu4fNA3</td>\n",
       "      <td>1ARY2XfYSt3AYdE5ZgGBC8DSCcYR4W5iap</td>\n",
       "      <td>6416.45685891</td>\n",
       "    </tr>\n",
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       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>Dec 23, 2015</td>\n",
       "      <td>aktia-savings-bank</td>\n",
       "      <td>Aktia Savings Bank</td>\n",
       "      <td>Finland</td>\n",
       "      <td>FIN</td>\n",
       "      <td>trasta-komercbanka-riga-latvia-lva</td>\n",
       "      <td>Trasta Komercbanka</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>15.0</td>\n",
       "      <td>447610.40</td>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>87364d1d26e89dc6ec66b889c26feaa333c8d24830227b...</td>\n",
       "      <td>36ygPHwbkuDDhZbcuj6QkRgGmCdmBg6CXJ</td>\n",
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       "      <td>Jan 25, 2016</td>\n",
       "      <td>alfabank</td>\n",
       "      <td>Alfabank</td>\n",
       "      <td>Russia</td>\n",
       "      <td>RUS</td>\n",
       "      <td>banco-de-sabadell-sa-maimi-fl-usa</td>\n",
       "      <td>Banco De Sabadell S.A.</td>\n",
       "      <td>United States</td>\n",
       "      <td>USA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.00</td>\n",
       "      <td>2016-01-25</td>\n",
       "      <td>80f6e17065551d71a45c9d0bd7fef8ce83a22cc926c556...</td>\n",
       "      <td>14ShStQGqiMeLroQiGgJXD6mzw4r5jT9kH</td>\n",
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       "      <td>2016-12-01</td>\n",
       "      <td>Dec 22, 2016</td>\n",
       "      <td>societe-generale-zurich</td>\n",
       "      <td>Societe Generale Zurich</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>ablv-bank-as-riga-latvia-lva</td>\n",
       "      <td>ABLV Bank, AS</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>Dec 28, 2016</td>\n",
       "      <td>gazprombank-switzerland-ltd</td>\n",
       "      <td>Gazprombank Switzerland Ltd</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>regionala-investiciju-banka-riga-latvia-lva</td>\n",
       "      <td>Regionala Investiciju Banka</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1148457.07</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>8e763da4ed14164f99c9a32c3afa2de8652d7efd9def37...</td>\n",
       "      <td>19MKPteXrJeZUR6QcWwSgGCXkQ1QpxmJF7</td>\n",
       "      <td>18yeoPYtZbr3bJqUx6xcxQAoSepHqHUJMQ</td>\n",
       "      <td>0.207008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146357</th>\n",
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       "      <td>the-bank-of-new-york-mellon-corp</td>\n",
       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>Dec 28, 2016</td>\n",
       "      <td>gazprombank-switzerland-ltd</td>\n",
       "      <td>Gazprombank Switzerland Ltd</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>regionala-investiciju-banka-riga-latvia-lva</td>\n",
       "      <td>Regionala Investiciju Banka</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1148457.07</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>75ad768eac099af9c0f4bf2867ba2a0d2e5ef72f0335a6...</td>\n",
       "      <td>1GFeeASVjq13QhdHv8gAqnN6fA6ZC6BWDd</td>\n",
       "      <td>1D2yXaP9Z6NSDadJQ8rAMZwtTzAaFYp24a</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134081</th>\n",
       "      <td>226824</td>\n",
       "      <td>3913</td>\n",
       "      <td>the-bank-of-new-york-mellon-corp</td>\n",
       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>Dec 22, 2016</td>\n",
       "      <td>societe-generale-zurich</td>\n",
       "      <td>Societe Generale Zurich</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>ablv-bank-as-riga-latvia-lva</td>\n",
       "      <td>ABLV Bank, AS</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5799925.00</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>cb5327855c39d7e5f3439a98349179a13b0705111d11a7...</td>\n",
       "      <td>1HZCMe7QfGoqstR4LME2gCLUHyNCkfV1TN</td>\n",
       "      <td>3EKn8nVWRPHAzHVpN3LZ3tNVKjQfQb3hA8</td>\n",
       "      <td>0.00131436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151514</th>\n",
       "      <td>234911</td>\n",
       "      <td>4055</td>\n",
       "      <td>the-bank-of-new-york-mellon-corp</td>\n",
       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>Dec 28, 2016</td>\n",
       "      <td>gazprombank-switzerland-ltd</td>\n",
       "      <td>Gazprombank Switzerland Ltd</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>CHE</td>\n",
       "      <td>regionala-investiciju-banka-riga-latvia-lva</td>\n",
       "      <td>Regionala Investiciju Banka</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1148457.07</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>56b7b4d1854d9bbb7a359f79e38dbbaa06787fccfdc4f8...</td>\n",
       "      <td>1HasheerTLWT4Di9cnArqECtGtmHVTrJmS</td>\n",
       "      <td>1NRu7jk5gken5LJ54smsVN3YMFcTXoFSfT</td>\n",
       "      <td>0.0016748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298770</th>\n",
       "      <td>234436</td>\n",
       "      <td>2494</td>\n",
       "      <td>the-bank-of-new-york-mellon-corp</td>\n",
       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>Sep 29, 2016</td>\n",
       "      <td>rietumu-banka</td>\n",
       "      <td>Rietumu Banka</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>LVA</td>\n",
       "      <td>lloyds-tsb-bank-plc-crawley-united-kingdom-gbr</td>\n",
       "      <td>Lloyds TSB Bank Plc</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GBR</td>\n",
       "      <td>19.0</td>\n",
       "      <td>8697026.81</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>82f695f6446f8eb506e8a87eedd8ec625a5ecd754c0bcd...</td>\n",
       "      <td>1Db5E3SsRd76dwUAtbstbDXTuegQXMkn4Z</td>\n",
       "      <td>17Yv7sdFzTuDMsrnvrzrX5sJkvQjTKaXYR</td>\n",
       "      <td>0.00183665</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id  icij_sar_id                 filer_org_name_id  \\\n",
       "13587   224725         4145  the-bank-of-new-york-mellon-corp   \n",
       "258421  230117         2280  the-bank-of-new-york-mellon-corp   \n",
       "35841   225079         2476  the-bank-of-new-york-mellon-corp   \n",
       "270595  230119         2280  the-bank-of-new-york-mellon-corp   \n",
       "136127  226824         3913  the-bank-of-new-york-mellon-corp   \n",
       "156874  234911         4055  the-bank-of-new-york-mellon-corp   \n",
       "146357  234911         4055  the-bank-of-new-york-mellon-corp   \n",
       "134081  226824         3913  the-bank-of-new-york-mellon-corp   \n",
       "151514  234911         4055  the-bank-of-new-york-mellon-corp   \n",
       "298770  234436         2494  the-bank-of-new-york-mellon-corp   \n",
       "\n",
       "                           filer_org_name begin_date      end_date  \\\n",
       "13587   The Bank of New York Mellon Corp. 2015-09-01  Sep 10, 2015   \n",
       "258421  The Bank of New York Mellon Corp. 2016-01-25  Jan 25, 2016   \n",
       "35841   The Bank of New York Mellon Corp. 2015-12-23  Dec 23, 2015   \n",
       "270595  The Bank of New York Mellon Corp. 2016-01-25  Jan 25, 2016   \n",
       "136127  The Bank of New York Mellon Corp. 2016-12-01  Dec 22, 2016   \n",
       "156874  The Bank of New York Mellon Corp. 2016-12-01  Dec 28, 2016   \n",
       "146357  The Bank of New York Mellon Corp. 2016-12-01  Dec 28, 2016   \n",
       "134081  The Bank of New York Mellon Corp. 2016-12-01  Dec 22, 2016   \n",
       "151514  The Bank of New York Mellon Corp. 2016-12-01  Dec 28, 2016   \n",
       "298770  The Bank of New York Mellon Corp. 2016-09-01  Sep 29, 2016   \n",
       "\n",
       "                 originator_bank_id              originator_bank  \\\n",
       "13587                      dbs-bank                     DBS Bank   \n",
       "258421                     alfabank                     Alfabank   \n",
       "35841            aktia-savings-bank           Aktia Savings Bank   \n",
       "270595                     alfabank                     Alfabank   \n",
       "136127      societe-generale-zurich      Societe Generale Zurich   \n",
       "156874  gazprombank-switzerland-ltd  Gazprombank Switzerland Ltd   \n",
       "146357  gazprombank-switzerland-ltd  Gazprombank Switzerland Ltd   \n",
       "134081      societe-generale-zurich      Societe Generale Zurich   \n",
       "151514  gazprombank-switzerland-ltd  Gazprombank Switzerland Ltd   \n",
       "298770                rietumu-banka                Rietumu Banka   \n",
       "\n",
       "       originator_bank_country originator_iso  \\\n",
       "13587                    China            CHN   \n",
       "258421                  Russia            RUS   \n",
       "35841                  Finland            FIN   \n",
       "270595                  Russia            RUS   \n",
       "136127             Switzerland            CHE   \n",
       "156874             Switzerland            CHE   \n",
       "146357             Switzerland            CHE   \n",
       "134081             Switzerland            CHE   \n",
       "151514             Switzerland            CHE   \n",
       "298770                  Latvia            LVA   \n",
       "\n",
       "                                   beneficiary_bank_id  \\\n",
       "13587              bank-of-east-asia-ltd-hong-kong-hkg   \n",
       "258421                wells-fargo-bank-na-miami-fl-usa   \n",
       "35841               trasta-komercbanka-riga-latvia-lva   \n",
       "270595               banco-de-sabadell-sa-maimi-fl-usa   \n",
       "136127                    ablv-bank-as-riga-latvia-lva   \n",
       "156874     regionala-investiciju-banka-riga-latvia-lva   \n",
       "146357     regionala-investiciju-banka-riga-latvia-lva   \n",
       "134081                    ablv-bank-as-riga-latvia-lva   \n",
       "151514     regionala-investiciju-banka-riga-latvia-lva   \n",
       "298770  lloyds-tsb-bank-plc-crawley-united-kingdom-gbr   \n",
       "\n",
       "                   beneficiary_bank beneficiary_bank_country beneficiary_iso  \\\n",
       "13587        Bank of East Asia, Ltd                Hong Kong             HKG   \n",
       "258421          Wells Fargo Bank Na            United States             USA   \n",
       "35841            Trasta Komercbanka                   Latvia             LVA   \n",
       "270595       Banco De Sabadell S.A.            United States             USA   \n",
       "136127                ABLV Bank, AS                   Latvia             LVA   \n",
       "156874  Regionala Investiciju Banka                   Latvia             LVA   \n",
       "146357  Regionala Investiciju Banka                   Latvia             LVA   \n",
       "134081                ABLV Bank, AS                   Latvia             LVA   \n",
       "151514  Regionala Investiciju Banka                   Latvia             LVA   \n",
       "298770          Lloyds TSB Bank Plc           United Kingdom             GBR   \n",
       "\n",
       "        number_transactions  amount_transactions       time  \\\n",
       "13587                   2.0           2814980.00 2015-09-01   \n",
       "258421                  1.0               180.00 2016-01-25   \n",
       "35841                  15.0            447610.40 2015-12-23   \n",
       "270595                  1.0               270.00 2016-01-25   \n",
       "136127                  5.0           5799925.00 2016-12-01   \n",
       "156874                  5.0           1148457.07 2016-12-01   \n",
       "146357                  5.0           1148457.07 2016-12-01   \n",
       "134081                  5.0           5799925.00 2016-12-01   \n",
       "151514                  5.0           1148457.07 2016-12-01   \n",
       "298770                 19.0           8697026.81 2016-09-01   \n",
       "\n",
       "                                                     Hash  \\\n",
       "13587   f41295870d93de44f3e0c0e965a85089fda4cee42a72b6...   \n",
       "258421  41eb90ea86882cdaac2da340704e6c2f19e5c75fd0068c...   \n",
       "35841   87364d1d26e89dc6ec66b889c26feaa333c8d24830227b...   \n",
       "270595  80f6e17065551d71a45c9d0bd7fef8ce83a22cc926c556...   \n",
       "136127  81d5b52b16affa61819436da3cd85373d7477202fec20d...   \n",
       "156874  8e763da4ed14164f99c9a32c3afa2de8652d7efd9def37...   \n",
       "146357  75ad768eac099af9c0f4bf2867ba2a0d2e5ef72f0335a6...   \n",
       "134081  cb5327855c39d7e5f3439a98349179a13b0705111d11a7...   \n",
       "151514  56b7b4d1854d9bbb7a359f79e38dbbaa06787fccfdc4f8...   \n",
       "298770  82f695f6446f8eb506e8a87eedd8ec625a5ecd754c0bcd...   \n",
       "\n",
       "                                                   Sender  \\\n",
       "13587   19r8tM8xh54gao3J63PpvTeQqQnPrkR9Ny 1PWmrXsaEFi...   \n",
       "258421                 13z8KW7TBTMdcq7JHZUZVN79P7rJu4fNA3   \n",
       "35841                  36ygPHwbkuDDhZbcuj6QkRgGmCdmBg6CXJ   \n",
       "270595                 14ShStQGqiMeLroQiGgJXD6mzw4r5jT9kH   \n",
       "136127                 13KWmYje52mjt4GdvbhAhrtXJFSvmfcpfE   \n",
       "156874                 19MKPteXrJeZUR6QcWwSgGCXkQ1QpxmJF7   \n",
       "146357                 1GFeeASVjq13QhdHv8gAqnN6fA6ZC6BWDd   \n",
       "134081                 1HZCMe7QfGoqstR4LME2gCLUHyNCkfV1TN   \n",
       "151514                 1HasheerTLWT4Di9cnArqECtGtmHVTrJmS   \n",
       "298770                 1Db5E3SsRd76dwUAtbstbDXTuegQXMkn4Z   \n",
       "\n",
       "                                  Receiver Transaction_amount_BTC  \n",
       "13587   1MmB5whtFNHLV6Wdp55cL9x1Mm5pWod24e                   0.95  \n",
       "258421  1ARY2XfYSt3AYdE5ZgGBC8DSCcYR4W5iap          6416.45685891  \n",
       "35841   3JHzpKn8qHiBuMcgnzFjP1nZ4GPBGUfxzR            4481.253224  \n",
       "270595  1P1kn2mHBVn4m2KPyzPMXQnrULjpUbCGub          3933.71812718  \n",
       "136127  1GJneduoqVJBQdL1R1vCuYDNg3r74zdHc9              0.0105201  \n",
       "156874  18yeoPYtZbr3bJqUx6xcxQAoSepHqHUJMQ               0.207008  \n",
       "146357  1D2yXaP9Z6NSDadJQ8rAMZwtTzAaFYp24a                   0.14  \n",
       "134081  3EKn8nVWRPHAzHVpN3LZ3tNVKjQfQb3hA8             0.00131436  \n",
       "151514  1NRu7jk5gken5LJ54smsVN3YMFcTXoFSfT              0.0016748  \n",
       "298770  17Yv7sdFzTuDMsrnvrzrX5sJkvQjTKaXYR             0.00183665  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suspicious_bank_and_blockchain_transactions.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations\n",
    "- Visualizing the originator bank country with more suspicious activities based on year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>icij_sar_id</th>\n",
       "      <th>filer_org_name_id</th>\n",
       "      <th>filer_org_name</th>\n",
       "      <th>begin_date</th>\n",
       "      <th>end_date</th>\n",
       "      <th>originator_bank_id</th>\n",
       "      <th>originator_bank</th>\n",
       "      <th>originator_bank_country</th>\n",
       "      <th>originator_iso</th>\n",
       "      <th>beneficiary_bank_id</th>\n",
       "      <th>beneficiary_bank</th>\n",
       "      <th>beneficiary_bank_country</th>\n",
       "      <th>beneficiary_iso</th>\n",
       "      <th>number_transactions</th>\n",
       "      <th>amount_transactions</th>\n",
       "      <th>time</th>\n",
       "      <th>Hash</th>\n",
       "      <th>Sender</th>\n",
       "      <th>Receiver</th>\n",
       "      <th>Transaction_amount_BTC</th>\n",
       "      <th>year_activitie</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>223254</td>\n",
       "      <td>3297</td>\n",
       "      <td>the-bank-of-new-york-mellon-corp</td>\n",
       "      <td>The Bank of New York Mellon Corp.</td>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>Sep 25, 2015</td>\n",
       "      <td>cimb-bank-berhad</td>\n",
       "      <td>CIMB Bank Berhad</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>SGP</td>\n",
       "      <td>barclays-bank-plc-london-england-gbr</td>\n",
       "      <td>Barclays Bank Plc</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>GBR</td>\n",
       "      <td>68.0</td>\n",
       "      <td>56898523.47</td>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>e27f7544ef6d370234e9df8fc5775077b8d7d0bc6f8898...</td>\n",
       "      <td>3KBUuGko4H5ke7EVsq9B7PLK1c5Askdd7y</td>\n",
       "      <td>3KgtbGgaX2ngstNpvyv7LwpHSweVeqGbpM</td>\n",
       "      <td>7000</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  icij_sar_id                 filer_org_name_id  \\\n",
       "0  223254         3297  the-bank-of-new-york-mellon-corp   \n",
       "\n",
       "                      filer_org_name begin_date      end_date  \\\n",
       "0  The Bank of New York Mellon Corp. 2015-03-25  Sep 25, 2015   \n",
       "\n",
       "  originator_bank_id   originator_bank originator_bank_country originator_iso  \\\n",
       "0   cimb-bank-berhad  CIMB Bank Berhad               Singapore            SGP   \n",
       "\n",
       "                    beneficiary_bank_id   beneficiary_bank  \\\n",
       "0  barclays-bank-plc-london-england-gbr  Barclays Bank Plc   \n",
       "\n",
       "  beneficiary_bank_country beneficiary_iso  number_transactions  \\\n",
       "0           United Kingdom             GBR                 68.0   \n",
       "\n",
       "   amount_transactions       time  \\\n",
       "0          56898523.47 2015-03-25   \n",
       "\n",
       "                                                Hash  \\\n",
       "0  e27f7544ef6d370234e9df8fc5775077b8d7d0bc6f8898...   \n",
       "\n",
       "                               Sender                            Receiver  \\\n",
       "0  3KBUuGko4H5ke7EVsq9B7PLK1c5Askdd7y  3KgtbGgaX2ngstNpvyv7LwpHSweVeqGbpM   \n",
       "\n",
       "  Transaction_amount_BTC  year_activitie  \n",
       "0                   7000            2015  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#engineering a new feature called 'year' from the 'begin_date' column\n",
    "suspicious_bank_and_blockchain_transactions['year_activitie'] = suspicious_bank_and_blockchain_transactions['begin_date'].dt.year\n",
    "suspicious_bank_and_blockchain_transactions.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>number_transactions</th>\n",
       "      <th>originator_bank_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>1544042.0</td>\n",
       "      <td>Latvia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>407649.0</td>\n",
       "      <td>Switzerland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>272731.0</td>\n",
       "      <td>Liechtenstein</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>132954.0</td>\n",
       "      <td>China</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>70387.0</td>\n",
       "      <td>Russia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    number_transactions originator_bank_country\n",
       "32            1544042.0                  Latvia\n",
       "58             407649.0             Switzerland\n",
       "33             272731.0           Liechtenstein\n",
       "14             132954.0                   China\n",
       "51              70387.0                  Russia"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "suspicious_bank_and_blockchain_transactions_year = suspicious_bank_and_blockchain_transactions.groupby('originator_bank_country', as_index=False).sum()\n",
    "suspicious_bank_and_blockchain_transactions_year = suspicious_bank_and_blockchain_transactions_year[['number_transactions', 'originator_bank_country']]\n",
    "suspicious_bank_and_blockchain_transactions_year = suspicious_bank_and_blockchain_transactions_year.sort_values(by='number_transactions', ascending=False)\n",
    "suspicious_bank_and_blockchain_transactions_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,15))\n",
    "\n",
    "plt.bar(suspicious_bank_and_blockchain_transactions_year['originator_bank_country'], suspicious_bank_and_blockchain_transactions_year['number_transactions'], width=0.85)\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel('Countries')\n",
    "plt.ylabel('Suspicious Transactions')\n",
    "plt.title('Countries With Higher Suspicious Transactions')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Countries With Higher Suspicious Transactions')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing countries with higher suspicious transactions\n",
    "colors = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9']\n",
    "suspicious_bank_and_blockchain_transactions_year.plot.bar(x='originator_bank_country', y='number_transactions',\n",
    "                   figsize=(25,15), color=colors,\n",
    "                   legend=False)\n",
    "plt.ylabel('Suspicious Transactions')\n",
    "plt.xlabel('Countries')\n",
    "plt.title('Countries With Higher Suspicious Transactions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}