427 lines
15 KiB
Plaintext
427 lines
15 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"# PostGIS Table Loader Notebook\n",
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"\n",
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"Use this notebook to load additional tabular files into the same PostgreSQL/PostGIS database used by the scripts in this folder.\n",
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"\n",
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"Expected environment variables (same pattern as your .py files):\n",
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"- `PGWEB_HOST`, `PGWEB_PORT`, `PGWEB_USER`, `PGWEB_PASSWORD`\n",
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"- Optional override: `PGDATABASE` (defaults to `data_centers`)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from pathlib import Path\n",
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"from typing import List, Tuple\n",
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"\n",
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"import pandas as pd\n",
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"import psycopg2\n",
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"from psycopg2 import sql\n",
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"from psycopg2.extras import execute_values\n",
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"\n",
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"print('pandas:', pd.__version__)\n",
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"print('psycopg2 loaded successfully')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Load DB env vars for notebook kernels (which often do not inherit shell exports).\n",
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"def load_env_file(env_path: str = '.env') -> None:\n",
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" p = Path(env_path)\n",
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" if not p.exists():\n",
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" print(f'No {env_path} file found in {Path.cwd()}')\n",
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" return\n",
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"\n",
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" loaded = 0\n",
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" for raw_line in p.read_text(encoding='utf-8').splitlines():\n",
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" line = raw_line.strip()\n",
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" if not line or line.startswith('#') or '=' not in line:\n",
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" continue\n",
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" key, value = line.split('=', 1)\n",
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" key = key.strip()\n",
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" value = value.strip().strip('\"').strip(\"'\")\n",
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" if key and key not in os.environ:\n",
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" os.environ[key] = value\n",
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" loaded += 1\n",
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" print(f'Loaded {loaded} env var(s) from {env_path}')\n",
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"\n",
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"\n",
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"def require_env(keys):\n",
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" missing = [k for k in keys if not os.getenv(k)]\n",
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" if missing:\n",
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" raise EnvironmentError(\n",
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" 'Missing required env vars in notebook kernel: ' + ', '.join(missing) +\n",
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" '.\\nSet them in this notebook, or add them to a .env file in this folder.'\n",
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" )\n",
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"\n",
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"\n",
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"load_env_file('.env')\n",
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"print('PGWEB_HOST:', os.getenv('PGWEB_HOST', '<not set>'))\n",
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"print('PGWEB_PORT:', os.getenv('PGWEB_PORT', '<not set>'))\n",
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"print('PGWEB_USER:', os.getenv('PGWEB_USER', '<not set>'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Connection setup: mirrors the existing scripts in this repository.\n",
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"required_keys = ['PGWEB_HOST', 'PGWEB_PORT', 'PGWEB_USER', 'PGWEB_PASSWORD']\n",
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"require_env(required_keys)\n",
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"\n",
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"DB_NAME = os.getenv('PGDATABASE', 'data_centers')\n",
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"\n",
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"def get_conn():\n",
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" return psycopg2.connect(\n",
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" host=os.environ['PGWEB_HOST'],\n",
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" port=os.environ['PGWEB_PORT'],\n",
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" user=os.environ['PGWEB_USER'],\n",
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" password=os.environ['PGWEB_PASSWORD'],\n",
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" dbname=\"data_centers\",\n",
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" )\n",
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"\n",
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"with get_conn() as conn:\n",
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" with conn.cursor() as cur:\n",
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" cur.execute('select current_database(), current_user, version()')\n",
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" db, usr, ver = cur.fetchone()\n",
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" print('Connected to DB:', db)\n",
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" print('As user:', usr)\n",
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" print('Postgres:', ver.split(',')[0])\n",
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" cur.execute('create extension if not exists postgis')\n",
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" print('PostGIS extension is available')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def parse_table_name(table_fqn: str) -> Tuple[str, str]:\n",
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" table_fqn = table_fqn.strip()\n",
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" if '.' in table_fqn:\n",
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" schema, table = table_fqn.split('.', 1)\n",
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" else:\n",
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" schema, table = 'public', table_fqn\n",
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" return schema, table\n",
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"\n",
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"\n",
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"def clean_column_name(name: str) -> str:\n",
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" s = str(name).strip().lower()\n",
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" out = []\n",
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" last_was_us = False\n",
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" for ch in s:\n",
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" keep = ch.isalnum() or ch == '_'\n",
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" c = ch if keep else '_'\n",
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" if c == '_':\n",
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" if last_was_us:\n",
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" continue\n",
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" last_was_us = True\n",
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" else:\n",
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" last_was_us = False\n",
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" out.append(c)\n",
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" cleaned = ''.join(out).strip('_')\n",
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" if not cleaned:\n",
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" cleaned = 'col'\n",
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" if cleaned[0].isdigit():\n",
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" cleaned = 'c_' + cleaned\n",
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" return cleaned\n",
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"\n",
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"\n",
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"def uniquify_columns(cols: List[str]) -> List[str]:\n",
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" seen = {}\n",
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" result = []\n",
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" for c in cols:\n",
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" base = clean_column_name(c)\n",
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" idx = seen.get(base, 0)\n",
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" if idx == 0:\n",
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" result.append(base)\n",
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" else:\n",
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" result.append(f'{base}_{idx}')\n",
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" seen[base] = idx + 1\n",
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" return result\n",
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"\n",
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"\n",
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"def postgres_type_for_series(s: pd.Series) -> str:\n",
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" dt = s.dtype\n",
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" if pd.api.types.is_integer_dtype(dt):\n",
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" return 'bigint'\n",
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" if pd.api.types.is_float_dtype(dt):\n",
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" return 'double precision'\n",
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" if pd.api.types.is_bool_dtype(dt):\n",
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" return 'boolean'\n",
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" if pd.api.types.is_datetime64_any_dtype(dt):\n",
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" return 'timestamp'\n",
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" return 'text'\n",
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"\n",
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"\n",
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"def read_tabular(path: str, sheet_name=0) -> pd.DataFrame:\n",
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" p = Path(path)\n",
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" suffix = p.suffix.lower()\n",
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" if suffix == '.csv':\n",
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" return pd.read_csv(p)\n",
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" if suffix in ['.xlsx', '.xls']:\n",
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" return pd.read_excel(p, sheet_name=sheet_name)\n",
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" if suffix == '.jsonl':\n",
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" return pd.read_json(p, lines=True)\n",
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" if suffix == '.json':\n",
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" return pd.read_json(p)\n",
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" if suffix == '.parquet':\n",
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" return pd.read_parquet(p)\n",
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" raise ValueError(f'Unsupported file type: {suffix}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5",
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"metadata": {},
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"outputs": [],
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"source": [
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"def load_dataframe_to_postgis(\n",
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" df: pd.DataFrame,\n",
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" table_fqn: str,\n",
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" if_exists: str = 'replace', # replace | append | fail\n",
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" batch_size: int = 5000,\n",
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" analyze: bool = True,\n",
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") -> None:\n",
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" if if_exists not in {'replace', 'append', 'fail'}:\n",
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" raise ValueError(\"if_exists must be one of: replace, append, fail\")\n",
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"\n",
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" schema, table = parse_table_name(table_fqn)\n",
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"\n",
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" work = df.copy()\n",
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" work.columns = uniquify_columns([str(c) for c in work.columns])\n",
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"\n",
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" # Convert pandas NaN/NaT to Python None for psycopg2.\n",
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" work = work.astype(object).where(pd.notna(work), None)\n",
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"\n",
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" col_defs = []\n",
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" for col in work.columns:\n",
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" pg_type = postgres_type_for_series(df[col]) if col in df.columns else 'text'\n",
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" col_defs.append((col, pg_type))\n",
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"\n",
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" rows = [tuple(row) for row in work.itertuples(index=False, name=None)]\n",
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"\n",
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" with get_conn() as conn:\n",
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" with conn.cursor() as cur:\n",
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" cur.execute('create extension if not exists postgis')\n",
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"\n",
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" cur.execute(\n",
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" 'select to_regclass(%s)',\n",
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" (f'{schema}.{table}',),\n",
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" )\n",
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" exists = cur.fetchone()[0] is not None\n",
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"\n",
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" if exists and if_exists == 'fail':\n",
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" raise RuntimeError(f'Table {schema}.{table} already exists')\n",
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"\n",
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" if exists and if_exists == 'replace':\n",
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" cur.execute(\n",
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" sql.SQL('drop table {}.{}').format(\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" )\n",
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" )\n",
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" exists = False\n",
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"\n",
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" if not exists:\n",
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" ddl_cols = [\n",
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" sql.SQL('{} {}').format(sql.Identifier(c), sql.SQL(t))\n",
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" for c, t in col_defs\n",
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" ]\n",
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" create_sql = sql.SQL('create table {}.{} ({})').format(\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" sql.SQL(', ').join(ddl_cols),\n",
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" )\n",
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" cur.execute(create_sql)\n",
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"\n",
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" if rows:\n",
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" insert_sql = sql.SQL('insert into {}.{} ({}) values %s').format(\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" sql.SQL(', ').join(sql.Identifier(c) for c in work.columns),\n",
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" )\n",
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" execute_values(cur, insert_sql.as_string(cur), rows, page_size=batch_size)\n",
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"\n",
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" if analyze:\n",
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" cur.execute(\n",
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" sql.SQL('analyze {}.{}').format(\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" )\n",
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" )\n",
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"\n",
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" conn.commit()\n",
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"\n",
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" print(f'Loaded {len(rows)} rows into {schema}.{table}')\n",
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" print('Columns:', ', '.join(work.columns))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6",
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"metadata": {},
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"outputs": [],
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"source": [
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"def add_point_geometry(\n",
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" table_fqn: str,\n",
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" lon_col: str = 'longitude',\n",
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" lat_col: str = 'latitude',\n",
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" geom_col: str = 'geom',\n",
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" srid: int = 4326,\n",
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") -> None:\n",
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" schema, table = parse_table_name(table_fqn)\n",
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"\n",
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" with get_conn() as conn:\n",
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" with conn.cursor() as cur:\n",
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" cur.execute(\n",
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" sql.SQL('alter table {}.{} add column if not exists {} geometry(Point, %s)').format(\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" sql.Identifier(geom_col),\n",
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" ),\n",
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" (srid,),\n",
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" )\n",
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"\n",
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" cur.execute(\n",
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" sql.SQL(\n",
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" 'update {}.{} set {} = case '\n",
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" 'when {} is not null and {} is not null '\n",
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" 'then ST_SetSRID(ST_MakePoint({}::double precision, {}::double precision), %s) '\n",
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" 'else null end'\n",
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" ).format(\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" sql.Identifier(geom_col),\n",
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" sql.Identifier(lon_col),\n",
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" sql.Identifier(lat_col),\n",
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" sql.Identifier(lon_col),\n",
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" sql.Identifier(lat_col),\n",
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" ),\n",
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" (srid,),\n",
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" )\n",
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"\n",
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" idx_name = f'{table}_{geom_col}_gix'\n",
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" cur.execute(\n",
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" sql.SQL('create index if not exists {} on {}.{} using gist ({})').format(\n",
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" sql.Identifier(idx_name),\n",
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" sql.Identifier(schema),\n",
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" sql.Identifier(table),\n",
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" sql.Identifier(geom_col),\n",
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" )\n",
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" )\n",
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" cur.execute(\n",
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" sql.SQL('analyze {}.{}').format(sql.Identifier(schema), sql.Identifier(table))\n",
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" )\n",
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" conn.commit()\n",
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"\n",
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" print(f'Geometry built in {schema}.{table}.{geom_col} and GiST indexed')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Example for the opposition cases table.\n",
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"FILE_PATH = 'new/Opposition_Cases_Geocoded.csv'\n",
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"TARGET_TABLE = 'public.opposition_cases_geocoded'\n",
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"IF_EXISTS = 'replace' # replace | append | fail\n",
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"\n",
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"# This file uses state abbreviations in the state column and includes lon/lat.\n",
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"df = read_tabular(FILE_PATH)\n",
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"print('Rows:', len(df), 'Cols:', len(df.columns))\n",
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"display(df[['id', 'state', 'state_id', 'lon', 'lat']].head(5))\n",
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"\n",
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"load_dataframe_to_postgis(df, TARGET_TABLE, if_exists=IF_EXISTS)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Optional: this table has lon/lat columns, so build geometry with those names.\n",
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"add_point_geometry(TARGET_TABLE, lon_col='lon', lat_col='lat', geom_col='geom', srid=4326)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Quick sanity check: show row count and latest tables in public schema.\n",
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"with get_conn() as conn:\n",
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" with conn.cursor() as cur:\n",
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" schema, table = parse_table_name(TARGET_TABLE)\n",
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" cur.execute(\n",
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" sql.SQL('select count(*) from {}.{}').format(sql.Identifier(schema), sql.Identifier(table))\n",
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" )\n",
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" print('Target row count:', cur.fetchone()[0])\n",
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"\n",
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" cur.execute(\n",
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" \"\"\"\n",
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" select schemaname, tablename\n",
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" from pg_tables\n",
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" where schemaname = 'public'\n",
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" order by tablename desc\n",
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" limit 25\n",
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" \"\"\"\n",
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" )\n",
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" for row in cur.fetchall():\n",
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" print(f'{row[0]}.{row[1]}')"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.5"
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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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