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  1. {
  2.   "cells": [
  3.     {
  4.       "metadata": {
  5.         "trusted": true
  6.       },
  7.       "cell_type": "code",
  8.       "source": "# Kod 1\nimport sqlite3\nimport random\nimport time\nimport pandas as pd\nimport numpy as np",
  9.       "execution_count": 1,
  10.       "outputs": []
  11.     },
  12.     {
  13.       "metadata": {
  14.         "trusted": true
  15.       },
  16.       "cell_type": "code",
  17.       "source": "# Kod 2\nconn = sqlite3.connect('../output/example.db')\nc = conn.cursor()",
  18.       "execution_count": 2,
  19.       "outputs": []
  20.     },
  21.     {
  22.       "metadata": {
  23.         "trusted": true
  24.       },
  25.       "cell_type": "code",
  26.       "source": "# Kod 3\nc.execute('''CREATE TABLE IF NOT EXISTS readings (date text, PM25 real, PM10 real)''')",
  27.       "execution_count": 3,
  28.       "outputs": [
  29.         {
  30.           "output_type": "execute_result",
  31.           "execution_count": 3,
  32.           "data": {
  33.             "text/plain": "<sqlite3.Cursor at 0x7f78f7ba1ab0>"
  34.           },
  35.           "metadata": {}
  36.         }
  37.       ]
  38.     },
  39.     {
  40.       "metadata": {
  41.         "trusted": true
  42.       },
  43.       "cell_type": "code",
  44.       "source": "# Kod 4\ncounter = 0\nwhile True:\n    readings = {}\n    readings[\"PM25\"] = round(random.uniform(0, 121),1)\n    readings[\"PM10\"] = round(random.uniform(0, 201),1)\n    readings[\"datetime\"] = time.strftime('%Y-%m-%d %H:%M:%S')\n    \n    c.execute(\"INSERT INTO readings VALUES (?,?,?)\", (readings[\"datetime\"], readings[\"PM25\"], readings[\"PM10\"]))\n    \n    conn.commit()\n    \n    counter += 1\n    if counter == 10:\n        break",
  45.       "execution_count": 4,
  46.       "outputs": []
  47.     },
  48.     {
  49.       "metadata": {
  50.         "trusted": true
  51.       },
  52.       "cell_type": "code",
  53.       "source": "# Kod 5\nfor row in c.execute('SELECT * FROM readings ORDER BY PM25 DESC LIMIT 10'):\n        print(row)",
  54.       "execution_count": 5,
  55.       "outputs": [
  56.         {
  57.           "output_type": "stream",
  58.           "text": "('2018-12-16 11:18:08', 120.2, 53.4)\n('2018-12-16 11:18:07', 117.2, 14.6)\n('2018-12-16 09:22:54', 116.6, 186.9)\n('2018-12-16 12:25:49', 109.8, 189.4)\n('2018-12-16 09:22:54', 108.8, 77.6)\n('2018-12-16 11:18:07', 100.5, 160.5)\n('2018-12-16 12:25:50', 99.5, 10.6)\n('2018-12-16 09:24:15', 92.9, 27.4)\n('2018-12-16 09:22:53', 87.1, 180.8)\n('2018-12-16 11:18:08', 85.6, 40.4)\n",
  59.           "name": "stdout"
  60.         }
  61.       ]
  62.     },
  63.     {
  64.       "metadata": {
  65.         "trusted": true
  66.       },
  67.       "cell_type": "code",
  68.       "source": "# Kod 6\ndf = pd.read_sql_query(\"select * from readings\", conn)",
  69.       "execution_count": 6,
  70.       "outputs": []
  71.     },
  72.     {
  73.       "metadata": {
  74.         "trusted": true
  75.       },
  76.       "cell_type": "code",
  77.       "source": "# Kod 7\ndf[\"additional\"] = np.where(df[\"PM10\"] > 100, \"Alert!\", None)\ndf.to_sql(\"readings_modified\", conn, if_exists=\"replace\", index=False)\nfor row in c.execute('SELECT * FROM readings_modified ORDER BY PM25 DESC LIMIT 10'):\n        print(row)",
  78.       "execution_count": 7,
  79.       "outputs": [
  80.         {
  81.           "output_type": "stream",
  82.           "text": "('2018-12-16 11:18:08', 120.2, 53.4, None)\n('2018-12-16 11:18:07', 117.2, 14.6, None)\n('2018-12-16 09:22:54', 116.6, 186.9, 'Alert!')\n('2018-12-16 12:25:49', 109.8, 189.4, 'Alert!')\n('2018-12-16 09:22:54', 108.8, 77.6, None)\n('2018-12-16 11:18:07', 100.5, 160.5, 'Alert!')\n('2018-12-16 12:25:50', 99.5, 10.6, None)\n('2018-12-16 09:24:15', 92.9, 27.4, None)\n('2018-12-16 09:22:53', 87.1, 180.8, 'Alert!')\n('2018-12-16 11:18:08', 85.6, 40.4, None)\n",
  83.           "name": "stdout"
  84.         }
  85.       ]
  86.     },
  87.     {
  88.       "metadata": {
  89.         "trusted": true
  90.       },
  91.       "cell_type": "code",
  92.       "source": "# Kod 8\nconn.close()",
  93.       "execution_count": 8,
  94.       "outputs": []
  95.     }
  96.   ],
  97.   "metadata": {
  98.     "kernelspec": {
  99.       "name": "conda-env-jakbadacdane.pl-py",
  100.       "display_name": "Python [conda env:jakbadacdane.pl]",
  101.       "language": "python"
  102.     },
  103.     "language_info": {
  104.       "name": "python",
  105.       "version": "3.6.7",
  106.       "mimetype": "text/x-python",
  107.       "codemirror_mode": {
  108.         "name": "ipython",
  109.         "version": 3
  110.       },
  111.       "pygments_lexer": "ipython3",
  112.       "nbconvert_exporter": "python",
  113.       "file_extension": ".py"
  114.     },
  115.     "gist": {
  116.       "id": "",
  117.       "data": {
  118.         "description": " SQLite i Python – czy warto?",
  119.         "public": true
  120.       }
  121.     }
  122.   },
  123.   "nbformat": 4,
  124.   "nbformat_minor": 2
  125. }
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