demo_basic_jupyter.ipynb

demo_basic_jupyter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f8f8d0c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('BRL', 0.5218652823754089)\n",
      "('GBP', 0.5275451357990851)\n",
      "('EUR', 0.6203255959451334)\n",
      "('INR', 0.278229649849598)\n",
      "('KWD', 0.7747921260072678)\n",
      "('JPY', 0.6589074527071597)\n",
      "('CLP', 0.8100600481532131)\n",
      "('AUD', 0.6334735510882324)\n"
     ]
    }
   ],
   "source": [
    "import psycopg as pg\n",
    "import os\n",
    "\n",
    "# Fetch environment variables with defaults\n",
    "host = os.getenv('QDB_CLIENT_HOST', 'localhost')\n",
    "port = os.getenv('QDB_CLIENT_PORT', '8812')\n",
    "user = os.getenv('QDB_CLIENT_USER', 'admin')\n",
    "password = os.getenv('QDB_CLIENT_PASSWORD', 'quest')\n",
    "\n",
    "# Create the connection string using the environment variables or defaults\n",
    "conn_str = f'user={user} password={password} host={host} port={port} dbname=qdb'\n",
    "with pg.connect(conn_str, autocommit=True) as connection:\n",
    "\n",
    "    # Open a cursor to perform database operations\n",
    "\n",
    "    with connection.cursor() as cur:\n",
    "\n",
    "        #Query the database and obtain data as Python objects.\n",
    "\n",
    "        cur.execute(\"\"\"\\\n",
    "        SELECT \n",
    "            rnd_str('EUR','USD', 'GBP', 'BRL','CLP','AUD','EGP','INR', 'KWD', 'JPY') as currency, \n",
    "            avg(rnd_double()) as value \n",
    "        FROM \n",
    "            long_sequence(20,128349234,4327897);\n",
    "        \"\"\")\n",
    "        records = cur.fetchall()\n",
    "        for row in records:\n",
    "            print(row)\n",
    "\n",
    "# the connection is now closed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6d177c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Separating x and y data\n",
    "locotype = [record[0] for record in records]\n",
    "code = [record[1] for record in records]\n",
    "\n",
    "# Plotting the graph\n",
    "plt.bar(locotype, code)\n",
    "plt.xlabel('currency')\n",
    "plt.ylabel('value')\n",
    "plt.title('currencies')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c72457d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}

generate_as_pdf.bash

jupyter nbconvert --to webpdf demo_basic_jupyter.ipynb --allow-chromium-download

generate_chart.py

import psycopg as pg
import os
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages

# Fetch environment variables with defaults
host = os.getenv('QDB_CLIENT_HOST', '127.0.0.1')
port = os.getenv('QDB_CLIENT_PORT', '8812')
user = os.getenv('QDB_CLIENT_USER', 'admin')
password = os.getenv('QDB_CLIENT_PASSWORD', 'quest')

conn_str = f'user={user} password={password} host={host} port={port} dbname=qdb'
with pg.connect(conn_str, autocommit=True) as connection:
   with connection.cursor() as cur:
     cur.execute("""\
           SELECT
               rnd_str('EUR','USD', 'GBP', 'BRL','CLP','AUD','EGP','INR', 'KWD', 'JPY') as currency,
               avg(rnd_double()) as value
           FROM
               long_sequence(20,128349234,4327897);
           """)
     records = cur.fetchall()

plt.figure(figsize=(3, 3))
currency = [record[0] for record in records]
value = [record[1] for record in records]

plt.bar(currency, value)
plt.xlabel('currency')
plt.ylabel('value')
plt.title('currencies')
plt.xticks(rotation=45)
plt.plot()

pp = PdfPages('report.pdf')
pp.savefig()
pp.close()