From 5da6e49382bdc057a843adcca43079937ead8860 Mon Sep 17 00:00:00 2001
From: Lee Stott <leestott@microsoft.com>
Date: Fri, 3 Oct 2025 14:57:37 +0100
Subject: [PATCH] Update 2-Working-With-Data/08-data-preparation/notebook.ipynb

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
---
 .../08-data-preparation/notebook.ipynb        | 79 ++++++++++---------
 1 file changed, 41 insertions(+), 38 deletions(-)

diff --git a/2-Working-With-Data/08-data-preparation/notebook.ipynb b/2-Working-With-Data/08-data-preparation/notebook.ipynb
index fe10a4f6..294f278c 100644
--- a/2-Working-With-Data/08-data-preparation/notebook.ipynb
+++ b/2-Working-With-Data/08-data-preparation/notebook.ipynb
@@ -3997,49 +3997,52 @@
       "metadata": {},
       "outputs": [],
       "source": [
-        "from rapidfuzz import process, fuzz\n",
+        "try:\n",
+        "    from rapidfuzz import process, fuzz\n",
         "\n",
-        "# Function to find potential duplicates\n",
-        "def find_near_duplicates(df, column, threshold=90):\n",
-        "    \"\"\"\n",
-        "    Find near-duplicate entries in a column using fuzzy matching.\n",
-        "    \n",
-        "    Parameters:\n",
-        "    - df: DataFrame\n",
-        "    - column: Column name to check for duplicates\n",
-        "    - threshold: Similarity threshold (0-100)\n",
-        "    \n",
-        "    Returns: List of potential duplicate groups\n",
-        "    \"\"\"\n",
-        "    values = df[column].unique()\n",
-        "    duplicate_groups = []\n",
-        "    checked = set()\n",
-        "    \n",
-        "    for value in values:\n",
-        "        if value in checked:\n",
-        "            continue\n",
+        "    # Function to find potential duplicates\n",
+        "    def find_near_duplicates(df, column, threshold=90):\n",
+        "        \"\"\"\n",
+        "        Find near-duplicate entries in a column using fuzzy matching.\n",
+        "        \n",
+        "        Parameters:\n",
+        "        - df: DataFrame\n",
+        "        - column: Column name to check for duplicates\n",
+        "        - threshold: Similarity threshold (0-100)\n",
+        "        \n",
+        "        Returns: List of potential duplicate groups\n",
+        "        \"\"\"\n",
+        "        values = df[column].unique()\n",
+        "        duplicate_groups = []\n",
+        "        checked = set()\n",
+        "        \n",
+        "        for value in values:\n",
+        "            if value in checked:\n",
+        "                continue\n",
+        "                \n",
+        "            # Find similar values\n",
+        "            matches = process.extract(value, values, scorer=fuzz.ratio, limit=len(values))\n",
+        "            similar = [m[0] for m in matches if m[1] >= threshold]\n",
         "            \n",
-        "        # Find similar values\n",
-        "        matches = process.extract(value, values, scorer=fuzz.ratio, limit=len(values))\n",
-        "        similar = [m[0] for m in matches if m[1] >= threshold]\n",
+        "            if len(similar) > 1:\n",
+        "                duplicate_groups.append(similar)\n",
+        "                checked.update(similar)\n",
         "        \n",
-        "        if len(similar) > 1:\n",
-        "            duplicate_groups.append(similar)\n",
-        "            checked.update(similar)\n",
-        "    \n",
-        "    return duplicate_groups\n",
+        "        return duplicate_groups\n",
         "\n",
-        "# Find near-duplicate names\n",
-        "duplicate_groups = find_near_duplicates(dirty_data, 'name', threshold=90)\n",
+        "    # Find near-duplicate names\n",
+        "    duplicate_groups = find_near_duplicates(dirty_data, 'name', threshold=90)\n",
         "\n",
-        "print(\"Potential duplicate groups:\")\n",
-        "for i, group in enumerate(duplicate_groups, 1):\n",
-        "    print(f\"\\nGroup {i}:\")\n",
-        "    for name in group:\n",
-        "        matching_rows = dirty_data[dirty_data['name'] == name]\n",
-        "        print(f\"  '{name}': {len(matching_rows)} occurrence(s)\")\n",
-        "        for _, row in matching_rows.iterrows():\n",
-        "            print(f\"    - Customer {row['customer_id']}: age={row['age']}, country={row['country']}\")"
+        "    print(\"Potential duplicate groups:\")\n",
+        "    for i, group in enumerate(duplicate_groups, 1):\n",
+        "        print(f\"\\nGroup {i}:\")\n",
+        "        for name in group:\n",
+        "            matching_rows = dirty_data[dirty_data['name'] == name]\n",
+        "            print(f\"  '{name}': {len(matching_rows)} occurrence(s)\")\n",
+        "            for _, row in matching_rows.iterrows():\n",
+        "                print(f\"    - Customer {row['customer_id']}: age={row['age']}, country={row['country']}\")\n",
+        "except ImportError:\n",
+        "    print(\"rapidfuzz is not installed. Skipping fuzzy matching for near-duplicates.\")"
       ]
     },
     {