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Parse ofac
This commit is contained in:
ashpect
593
common/ofacdata/scripts/ofac.ipynb
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593
common/ofacdata/scripts/ofac.ipynb
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@@ -0,0 +1,593 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"SDN OFAC csv file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Index(['ent_num', 'SDN_name', 'SDN_type', 'Program', 'Title', 'Call_Sign',\n",
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" 'Vess_type', 'Tonnage', 'GRT', 'Vess_flag', 'Vess_owner', 'Remarks'],\n",
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" dtype='object')\n"
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]
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},
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{
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"data": {
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"text/html": [
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||||
"<div>\n",
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||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
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||||
" vertical-align: middle;\n",
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||||
" }\n",
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"\n",
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||||
" .dataframe tbody tr th {\n",
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||||
" vertical-align: top;\n",
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||||
" }\n",
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"\n",
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||||
" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>ent_num</th>\n",
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" <th>SDN_name</th>\n",
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" <th>SDN_type</th>\n",
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" <th>Program</th>\n",
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" <th>Title</th>\n",
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" <th>Call_Sign</th>\n",
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" <th>Vess_type</th>\n",
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" <th>Tonnage</th>\n",
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" <th>GRT</th>\n",
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" <th>Vess_flag</th>\n",
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" <th>Vess_owner</th>\n",
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" <th>Remarks</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>36</td>\n",
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" <td>AEROCARIBBEAN AIRLINES</td>\n",
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" <td>-0-</td>\n",
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" <td>CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>173</td>\n",
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" <td>ANGLO-CARIBBEAN CO., LTD.</td>\n",
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" <td>-0-</td>\n",
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" <td>CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>306</td>\n",
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" <td>BANCO NACIONAL DE CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>a.k.a. 'BNC'.</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>424</td>\n",
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" <td>BOUTIQUE LA MAISON</td>\n",
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" <td>-0-</td>\n",
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" <td>CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>475</td>\n",
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" <td>CASA DE CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>CUBA</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" <td>-0-</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" ent_num SDN_name SDN_type Program Title Call_Sign \\\n",
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"0 36 AEROCARIBBEAN AIRLINES -0- CUBA -0- -0- \n",
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"1 173 ANGLO-CARIBBEAN CO., LTD. -0- CUBA -0- -0- \n",
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"2 306 BANCO NACIONAL DE CUBA -0- CUBA -0- -0- \n",
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"3 424 BOUTIQUE LA MAISON -0- CUBA -0- -0- \n",
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"4 475 CASA DE CUBA -0- CUBA -0- -0- \n",
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"\n",
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" Vess_type Tonnage GRT Vess_flag Vess_owner Remarks \n",
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"0 -0- -0- -0- -0- -0- -0- \n",
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"1 -0- -0- -0- -0- -0- -0- \n",
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"2 -0- -0- -0- -0- -0- a.k.a. 'BNC'. \n",
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"3 -0- -0- -0- -0- -0- -0- \n",
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"4 -0- -0- -0- -0- -0- -0- "
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import json\n",
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"import re\n",
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"\n",
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"file_path = 'sdn.csv'\n",
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"df1 = pd.read_csv(file_path)\n",
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"print(df1.columns)\n",
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"df1.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['ent_num', 'SDN_name', 'SDN_type', 'Program', 'Title', 'Call_Sign',\n",
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" 'Vess_type', 'Tonnage', 'GRT', 'Vess_flag', 'Vess_owner', 'Remarks',\n",
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" 'country'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# COUNTRY\n",
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"\n",
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"# read add.csv which is the helper file to add the country column to sdn.csv\n",
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"add_file_path = 'add.csv'\n",
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"df2 = pd.read_csv(add_file_path)\n",
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"df2 = df2[['ent_num', 'country']]\n",
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"if 'country' in df1.columns:\n",
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" df1.drop(columns=['country'], inplace=True)\n",
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"\n",
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"# add a new column country in sdn.csv and add the values from add.csv when ent_num matches then concatanate the values\n",
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"merged_df = pd.merge(df1, df2[['ent_num', 'country']], on='ent_num', how='left')\n",
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"grouped_df = merged_df.groupby('ent_num')['country'].apply(lambda x: ';'.join(x.dropna())).reset_index()\n",
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"result_df = pd.merge(df1, grouped_df, on='ent_num', how='left')\n",
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"result_df.columns\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Index(['ent_num', 'SDN_name', 'SDN_type', 'Remarks', 'country'], dtype='object')"
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]
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},
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"col = ['Program', 'Title', 'Call_Sign',\n",
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" 'Vess_type', 'Tonnage', 'GRT', 'Vess_flag', 'Vess_owner']\n",
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"columns_to_drop = [col_name for col_name in col if col_name in result_df.columns]\n",
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"result_df.drop(columns=columns_to_drop, inplace=True)\n",
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"result_df.columns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"SDN_type\n",
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"-0- 7252\n",
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"individual 6915\n",
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"vessel 861\n",
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"aircraft 374\n",
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"Name: count, dtype: int64\n",
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"Cleaned SDN_type\n",
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"individual 6915\n",
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"Name: count, dtype: int64\n"
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]
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}
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],
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"source": [
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"print(result_df['SDN_type'].value_counts())\n",
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"result_df = result_df[result_df['SDN_type'] == 'individual']\n",
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"print(\"Cleaned\",result_df['SDN_type'].value_counts())\n",
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"result_df.drop(columns=\"SDN_type\", inplace=True)"
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]
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||||
},
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||||
{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Individual have proper names and some vessels are right name, but most of them are not. Aircrafts seem to be aircraft manufacturers and have codes in names, hence not of any use. -0- seems to be names of company or groups, hence disregarded."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"6915"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(result_df) #total individuals"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"6855\n",
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"5924\n"
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]
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}
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],
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"source": [
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"# DOB\n",
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"\n",
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"result_df['Remarks'] = result_df['Remarks'].str.lower() \n",
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"# for format dd mmm yyyy\n",
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"result_df['DOB'] = result_df['Remarks'].str.extract(r'(\\d{2} \\w{3} \\d{4})')\n",
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"result_df['day'] = result_df['DOB'].str.extract(r'(\\d{2})')\n",
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"result_df['month'] = result_df['DOB'].str.extract(r'(\\w{3})')\n",
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"result_df['year'] = result_df['DOB'].str.extract(r'(\\d{4})')\n",
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"# for yyyy only format\n",
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"result_df['year'] = result_df['Remarks'].str.extract(r'(\\d{4})')\n",
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"result_df.head()\n",
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"\n",
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"print(result_df['year'].count()) # total individuals with at least year in dob\n",
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"print(result_df['DOB'].count()) # total individuals with whole dob\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"4325"
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||||
]
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},
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"execution_count": 19,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# GENDER\n",
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"def extract_gender(text):\n",
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" pattern = r'gender (male|female)'\n",
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" match = re.search(pattern, text)\n",
|
||||
" if match:\n",
|
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" return match.group(1)\n",
|
||||
" else:\n",
|
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" return None\n",
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" \n",
|
||||
"# Apply the function to extract the gender\n",
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"result_df[\"Gender\"] = result_df['Remarks'].apply(extract_gender)\n",
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"result_df.head()\n",
|
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"result_df[\"Gender\"].count() \n",
|
||||
"# result_df[result_df['ent_num'] == \"12610\"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"metadata": {},
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"outputs": [
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{
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||||
"data": {
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||||
"text/html": [
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||||
"<div>\n",
|
||||
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
|
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" }\n",
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"\n",
|
||||
" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>ent_num</th>\n",
|
||||
" <th>SDN_name</th>\n",
|
||||
" <th>Remarks</th>\n",
|
||||
" <th>country</th>\n",
|
||||
" <th>DOB</th>\n",
|
||||
" <th>day</th>\n",
|
||||
" <th>month</th>\n",
|
||||
" <th>year</th>\n",
|
||||
" <th>Gender</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2096</th>\n",
|
||||
" <td>12599</td>\n",
|
||||
" <td>PATEK, Umar</td>\n",
|
||||
" <td>dob 20 jul 1966; pob central java, indonesia; ...</td>\n",
|
||||
" <td>-0-</td>\n",
|
||||
" <td>20 jul 1966</td>\n",
|
||||
" <td>20</td>\n",
|
||||
" <td>jul</td>\n",
|
||||
" <td>1966</td>\n",
|
||||
" <td>None</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
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"text/plain": [
|
||||
" ent_num SDN_name Remarks \\\n",
|
||||
"2096 12599 PATEK, Umar dob 20 jul 1966; pob central java, indonesia; ... \n",
|
||||
"\n",
|
||||
" country DOB day month year Gender \n",
|
||||
"2096 -0- 20 jul 1966 20 jul 1966 None "
|
||||
]
|
||||
},
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||||
"execution_count": 20,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
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||||
"source": [
|
||||
"result_df[result_df['ent_num'] == \"12599\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 21,
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||||
"metadata": {},
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||||
"outputs": [
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||||
{
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||||
"name": "stdout",
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||||
"output_type": "stream",
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||||
"text": [
|
||||
"20\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def extract_nationality(remark):\n",
|
||||
" pattern = r'nationality ([A-Za-z]+);'\n",
|
||||
" match = re.search(pattern, remark)\n",
|
||||
" if match:\n",
|
||||
" return match.group(1)\n",
|
||||
" else:\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"def extract_citizen(remark):\n",
|
||||
" pattern = r'citizen ([A-Za-z]+);'\n",
|
||||
" match = re.search(pattern, remark)\n",
|
||||
" if match:\n",
|
||||
" return match.group(1)\n",
|
||||
" else:\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Apply the extract_nationality function to the 'remarks' column\n",
|
||||
"result_df['Citizen'] = result_df['Remarks'].apply(extract_citizen)\n",
|
||||
"result_df['Nationality'] = result_df['Remarks'].apply(extract_nationality)\n",
|
||||
"result_df['Nationality'].count()\n",
|
||||
"\n",
|
||||
"filtered_df = result_df.dropna(subset=['Citizen', 'Nationality'])\n",
|
||||
"diff_values_df = filtered_df[filtered_df['Citizen'] != filtered_df['Nationality']]\n",
|
||||
"count_diff_values = diff_values_df.shape[0]\n",
|
||||
"print(count_diff_values) # 20 instances where in remark both citizen <country1> and nationality <country2> are mentioned, hence seperated\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1549"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pattern = r'passport ([^\\(]+) \\(([^)]+)\\)'\n",
|
||||
"\n",
|
||||
"def extract_passport_info(remark):\n",
|
||||
" match = re.search(pattern, remark)\n",
|
||||
" if match:\n",
|
||||
" return match.group(1), match.group(2)\n",
|
||||
" else:\n",
|
||||
" return None, None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"result_df[['Pass_No', 'Pass_Country']] = result_df['Remarks'].apply(lambda x: pd.Series(extract_passport_info(x)))\n",
|
||||
"result_df['Pass_No'].count() # total individuals with passport number"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"11"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pattern = r'eth (0x[0-9a-fA-F]+);'\n",
|
||||
"\n",
|
||||
"def extract_eth_address(remark):\n",
|
||||
" match = re.search(pattern, remark)\n",
|
||||
" if match:\n",
|
||||
" return match.group(1)\n",
|
||||
" else:\n",
|
||||
" return None\n",
|
||||
"\n",
|
||||
"result_df['Eth_address'] = result_df['Remarks'].apply(extract_eth_address)\n",
|
||||
"\n",
|
||||
"# Filter rows where 'eth_address' is not null\n",
|
||||
"filtered_df = result_df.dropna(subset=['Eth_address'])\n",
|
||||
"filtered_df['Eth_address'].count() # total individuals with eth address"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['ent_num', 'SDN_name', 'Remarks', 'country', 'DOB', 'day', 'month',\n",
|
||||
" 'year', 'Gender', 'Citizen', 'Nationality', 'Pass_No', 'Pass_Country',\n",
|
||||
" 'Eth_address'],\n",
|
||||
" dtype='object')"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"result_df.columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"result_df.to_csv('cleaned_sdn.csv', index=False)\n",
|
||||
"\n",
|
||||
"filtered_df = result_df.dropna(subset=['Pass_No', 'Pass_Country'], how='all')\n",
|
||||
"passport_df = filtered_df[['Pass_No', 'Pass_Country']]\n",
|
||||
"filtered_df = result_df.dropna(subset=['Eth_address'], how='all')\n",
|
||||
"eth_df = filtered_df[['Eth_address']]\n",
|
||||
"\n",
|
||||
"passport_list = passport_df.to_dict(orient='records')\n",
|
||||
"with open('passports.json', 'w') as json_file:\n",
|
||||
" json.dump(passport_list, json_file, indent=4)\n",
|
||||
"etherum_list = eth_df.to_dict(orient='records')\n",
|
||||
"with open('etherum_add.json','w') as json_file:\n",
|
||||
" json.dump(etherum_list, json_file, indent=4)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "base",
|
||||
"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": "3.11.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in New Issue
Block a user