{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Linking banking transactions\n",
"\n",
"This example shows how to perform a one-to-one link on banking transactions. \n",
"\n",
"The data is fake data, and was generated has the following features:\n",
"\n",
"- Money shows up in the destination account with some time delay\n",
"- The amount sent and the amount received are not always the same - there are hidden fees and foreign exchange effects\n",
"- The memo is sometimes truncated and content is sometimes missing\n",
"\n",
"Since each origin payment should end up in the destination account, the `probability_two_random_records_match` of the model is known."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'There are 1,000 records to match'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Use arrow to read in data to ensure date types are correct\n",
"from pyarrow import parquet as pq\n",
"from splink.duckdb.duckdb_linker import DuckDBLinker\n",
"import altair as alt\n",
"alt.renderers.enable('mimetype')\n",
"\n",
"df_origin = pq.read_table(\"./data/transactions_left.parquet\")\n",
"df_origin = df_origin.slice(length=1_000)\n",
"df_destination = pq.read_table(\"./data/transactions_right.parquet\")\n",
"df_destination = df_destination.slice(length=1_000)\n",
"f\"There are {df_origin.num_rows:,.0f} records to match\""
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" ground_truth | \n",
" memo | \n",
" transaction_date | \n",
" amount | \n",
" unique_id | \n",
"
\n",
" \n",
" \n",
" \n",
" | 0 | \n",
" 0 | \n",
" MATTHIAS C paym | \n",
" 2022-03-28 | \n",
" 36.36 | \n",
" 0 | \n",
"
\n",
" \n",
" | 1 | \n",
" 1 | \n",
" M CORVINUS dona | \n",
" 2022-02-14 | \n",
" 221.91 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"