46 lines
1.2 KiB
Plaintext
46 lines
1.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8d393e6d",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"import sqlite3\n",
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"import pandas as pd\n",
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"\n",
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"# 1. Resolve repository pathing and connect to SQLite\n",
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"BASE_DIR = os.path.dirname(os.getcwd())\n",
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"DB_PATH = os.path.join(BASE_DIR, \"data\", \"met_office_weather.db\")\n",
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"conn = sqlite3.connect(DB_PATH)\n",
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"\n",
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"# 2. Extract dataset profile\n",
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"df = pd.read_sql_query(\"SELECT * FROM historic_weather\", conn)\n",
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"print(\"--- Dataset Shape ---\")\n",
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"print(f\"Total Rows: ${df.shape[0]}, Total column: ${df.shape[1]}\\n\")\n",
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"\n",
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"print (\"--- Column Data Types & Counts ---\")\n",
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"print(df.info())\n",
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"\n",
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"print(\"\\n--- Missing Values (NaN) Per Feature Column ---\")\n",
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"print(df.isnull().sum())\n",
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"\n",
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"print(\"\\n --- Total Row Logs Collected Per Unique Station ---\")\n",
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"print(df[\"station_name\"].value_counts())\n",
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"\n",
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"conn.close()"
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]
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}
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],
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"metadata": {
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"language_info": {
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"name": "python"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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