{
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d393e6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import os\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "\n",
    "# 1. Resolve repository pathing and connect to SQLite\n",
    "BASE_DIR = os.path.dirname(os.getcwd())\n",
    "DB_PATH = os.path.join(BASE_DIR, \"data\", \"met_office_weather.db\")\n",
    "conn = sqlite3.connect(DB_PATH)\n",
    "\n",
    "# 2. Extract dataset profile\n",
    "df = pd.read_sql_query(\"SELECT * FROM historic_weather\", conn)\n",
    "print(\"--- Dataset Shape ---\")\n",
    "print(f\"Total Rows: ${df.shape[0]}, Total column: ${df.shape[1]}\\n\")\n",
    "\n",
    "print (\"--- Column Data Types & Counts ---\")\n",
    "print(df.info())\n",
    "\n",
    "print(\"\\n--- Missing Values (NaN) Per Feature Column ---\")\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print(\"\\n --- Total Row Logs Collected Per Unique Station ---\")\n",
    "print(df[\"station_name\"].value_counts())\n",
    "\n",
    "conn.close()"
   ]
  }
 ],
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  "language_info": {
   "name": "python"
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