Sartaj Assesment

Assessment_sartaj.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "fixed_width_parser"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "\n",
+    "class FixedWidthParser:\n",
+    "    def __init__(self, spec_file, input_file, output_file, encoding=\"utf-8\"):\n",
+    "        self.spec_file = spec_file\n",
+    "        self.input_file = input_file\n",
+    "        self.output_file = output_file\n",
+    "        self.encoding = encoding\n",
+    "        self.column_specs = []\n",
+    "\n",
+    "    def parse_spec(self):\n",
+    "        \"\"\"Reads the spec file and extracts column names and widths.\"\"\"\n",
+    "        with open(self.spec_file, \"r\", encoding=self.encoding) as file:\n",
+    "            for line in file:\n",
+    "                parts = line.strip().split()\n",
+    "                if len(parts) == 2:\n",
+    "                    column_name, width = parts\n",
+    "                    self.column_specs.append((column_name, int(width)))\n",
+    "\n",
+    "    def parse_fixed_width_file(self):\n",
+    "        \"\"\"Reads fixed-width data and writes to CSV.\"\"\"\n",
+    "        self.parse_spec()\n",
+    "        \n",
+    "        with open(self.input_file, \"r\", encoding=self.encoding) as infile, \\\n",
+    "             open(self.output_file, \"w\", newline=\"\", encoding=self.encoding) as outfile:\n",
+    "            writer = csv.writer(outfile)\n",
+    "            writer.writerow([col[0] for col in self.column_specs])  # Write header\n",
+    "\n",
+    "            for line in infile:\n",
+    "                values = []\n",
+    "                start = 0\n",
+    "                for _, width in self.column_specs:\n",
+    "                    values.append(line[start:start+width].strip())\n",
+    "                    start += width\n",
+    "                writer.writerow(values)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    parser = FixedWidthParser(\"spec.txt\", \"input_data/fixed_width.txt\", \"output_data/output.csv\")\n",
+    "    parser.parse_fixed_width_file()\n",
+    "    print(\"✅ Fixed-width file successfully parsed to CSV!\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "data_anonymizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "import hashlib\n",
+    "import random\n",
+    "import string\n",
+    "\n",
+    "class DataAnonymizer:\n",
+    "    def __init__(self, input_file, output_file, chunk_size=10000):\n",
+    "        self.input_file = input_file\n",
+    "        self.output_file = output_file\n",
+    "        self.chunk_size = chunk_size\n",
+    "        self.anonymized_map = {}\n",
+    "\n",
+    "    def anonymize_text(self, text):\n",
+    "        \"\"\"Generate a consistent hash-based anonymized string.\"\"\"\n",
+    "        if text in self.anonymized_map:\n",
+    "            return self.anonymized_map[text]\n",
+    "        hashed = hashlib.sha256(text.encode()).hexdigest()[:10]\n",
+    "        fake_value = \"\".join(random.choices(string.ascii_letters, k=10))\n",
+    "        self.anonymized_map[text] = fake_value\n",
+    "        return fake_value\n",
+    "\n",
+    "    def anonymize_csv(self):\n",
+    "        \"\"\"Reads and anonymizes data in chunks.\"\"\"\n",
+    "        with open(self.input_file, \"r\", encoding=\"utf-8\") as infile, \\\n",
+    "             open(self.output_file, \"w\", newline=\"\", encoding=\"utf-8\") as outfile:\n",
+    "            reader = csv.reader(infile)\n",
+    "            writer = csv.writer(outfile)\n",
+    "\n",
+    "            # Read header\n",
+    "            headers = next(reader)\n",
+    "            writer.writerow(headers)\n",
+    "\n",
+    "            for row in reader:\n",
+    "                row[0] = self.anonymize_text(row[0])  # first_name\n",
+    "                row[1] = self.anonymize_text(row[1])  # last_name\n",
+    "                row[2] = self.anonymize_text(row[2])  # address\n",
+    "                writer.writerow(row)\n",
+    "\n",
+    "if __name__ == \"__main__\":\n",
+    "    anonymizer = DataAnonymizer(\"input_data/data.csv\", \"output_data/anonymized_data.csv\")\n",
+    "    anonymizer.anonymize_csv()\n",
+    "    print(\"✅ Data successfully anonymized!\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}