QuestDB.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a Database in QuestDB\n",
    "We would generate some random data and store that data into a test database named weather which we would create.\n",
    "The create statement in QuestDB pushes the data into the bottom of the table.\n",
    "Our data is comprised of:\n",
    "* `temp` is the temperature in Celcius.\n",
    "* `rain24H` is the amount of precipitation in last 24 hours.\n",
    "* `thunder` is a boolean returning True if thunder is present.\n",
    "* `timestamp` is the date and time.\n",
    "If the below code block prints `200`, it means the database was created successfully.\n",
    "If it prints `400` then it means that database already exists.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "400\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import urllib.parse as par\n",
    "\n",
    "q = 'create table weather'\\\n",
    "    '(temp int,'\\\n",
    "    'rain24H double,'\\\n",
    "    'thunder boolean,'\\\n",
    "    'timestamp timestamp)'\\\n",
    "    'timestamp(timestamp)'\n",
    "r = requests.get(\"http://localhost:9000/exec?query=\" + q)\n",
    "print(r.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding data to our Database\n",
    "In the next code cell we generate and add 1000 entries of data to our database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows inserted: 1000\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import random\n",
    "from datetime import datetime\n",
    "success = 0\n",
    "fail = 0\n",
    "random.seed()\n",
    "for x in range(1000):\n",
    "    temp = random.randint(-40, 55)\n",
    "    rain24H = round(random.uniform(10.45, 235.15), 2)\n",
    "    thunder = bool(random.getrandbits(1))\n",
    "    query = \"insert into weather values(\"\\\n",
    "        + str(temp) + \",\"\\\n",
    "        + str(rain24H) + \",\" \\\n",
    "        + str(thunder) +\",systimestamp())\"\n",
    "    r = requests.get(\"http://localhost:9000/exec?query=\" + query)\n",
    "    if r.status_code == 200:\n",
    "        success += 1\n",
    "    else:\n",
    "        fail += 1\n",
    "    \n",
    "print(\"Rows inserted: \" + str(success))\n",
    "if fail > 0:\n",
    "    print(\"Rows Failed: \" + str(fail))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now let's try querying some data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "-18,181.66,false,\"2020-08-24T11:50:51.387891Z\"\r\n",
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      "-40,219.07,true,\"2020-08-24T11:50:51.671369Z\"\r\n",
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      "-19,129.59,true,\"2020-08-24T11:50:51.710473Z\"\r\n",
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      "-14,228.87,false,\"2020-08-24T11:50:51.761552Z\"\r\n",
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      "-37,109.17,true,\"2020-08-24T11:50:51.776020Z\"\r\n",
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      "26,199.13,true,\"2020-08-24T11:58:31.898002Z\"\r\n",
      "11,95.91,false,\"2020-08-24T11:58:31.900331Z\"\r\n",
      "28,116.16,false,\"2020-08-24T11:58:31.902802Z\"\r\n",
      "-34,118.320000000000,true,\"2020-08-24T11:58:31.906199Z\"\r\n",
      "-9,128.4,false,\"2020-08-24T11:58:31.908532Z\"\r\n",
      "51,45.76,true,\"2020-08-24T11:58:31.912675Z\"\r\n",
      "20,152.71,false,\"2020-08-24T11:58:31.914936Z\"\r\n",
      "15,69.31,false,\"2020-08-24T11:58:31.919039Z\"\r\n",
      "-10,68.66,true,\"2020-08-24T11:58:31.923156Z\"\r\n",
      "26,157.96,true,\"2020-08-24T11:58:31.927199Z\"\r\n",
      "39,158.28,false,\"2020-08-24T11:58:31.931446Z\"\r\n",
      "49,31.77,true,\"2020-08-24T11:58:31.935511Z\"\r\n",
      "-19,137.79,false,\"2020-08-24T11:58:31.939764Z\"\r\n",
      "7,45.99,false,\"2020-08-24T11:58:31.942052Z\"\r\n",
      "3,126.91,false,\"2020-08-24T11:58:31.944263Z\"\r\n",
      "19,215.74,false,\"2020-08-24T11:58:31.948572Z\"\r\n",
      "-5,108.2,true,\"2020-08-24T11:58:31.950848Z\"\r\n",
      "33,160.35,false,\"2020-08-24T11:58:31.954940Z\"\r\n",
      "41,72.5,true,\"2020-08-24T11:58:31.957214Z\"\r\n",
      "-9,209.920000000000,false,\"2020-08-24T11:58:31.959475Z\"\r\n",
      "43,58.65,true,\"2020-08-24T11:58:31.963563Z\"\r\n",
      "16,170.57,false,\"2020-08-24T11:58:31.967766Z\"\r\n",
      "-26,181.700000000000,true,\"2020-08-24T11:58:31.970153Z\"\r\n",
      "26,128.51,true,\"2020-08-24T11:58:31.974295Z\"\r\n",
      "30,177.420000000000,true,\"2020-08-24T11:58:31.978350Z\"\r\n",
      "-6,42.480000000000,false,\"2020-08-24T11:58:31.982662Z\"\r\n",
      "23,102.53,true,\"2020-08-24T11:58:31.986906Z\"\r\n",
      "-26,37.45,false,\"2020-08-24T11:58:31.991252Z\"\r\n",
      "33,128.75,true,\"2020-08-24T11:58:31.995453Z\"\r\n",
      "-29,116.850000000000,false,\"2020-08-24T11:58:31.999670Z\"\r\n",
      "26,97.83,false,\"2020-08-24T11:58:32.003705Z\"\r\n",
      "4,156.33,true,\"2020-08-24T11:58:32.007929Z\"\r\n",
      "-24,71.44,true,\"2020-08-24T11:58:32.012141Z\"\r\n",
      "30,144.43,false,\"2020-08-24T11:58:32.014544Z\"\r\n",
      "-13,170.71,false,\"2020-08-24T11:58:32.016814Z\"\r\n",
      "-17,101.97,false,\"2020-08-24T11:58:32.019065Z\"\r\n",
      "28,20.23,false,\"2020-08-24T11:58:32.021356Z\"\r\n",
      "39,224.22,false,\"2020-08-24T11:58:32.023622Z\"\r\n",
      "-23,157.51,false,\"2020-08-24T11:58:32.027808Z\"\r\n",
      "0,198.36,true,\"2020-08-24T11:58:32.032013Z\"\r\n",
      "47,80.05,true,\"2020-08-24T11:58:32.036188Z\"\r\n",
      "48,152.890000000000,false,\"2020-08-24T11:58:32.038489Z\"\r\n",
      "-8,109.38,false,\"2020-08-24T11:58:32.042645Z\"\r\n",
      "23,195.27,false,\"2020-08-24T11:58:32.046752Z\"\r\n",
      "-18,121.740000000000,true,\"2020-08-24T11:58:32.049066Z\"\r\n",
      "7,56.65,true,\"2020-08-24T11:58:32.053290Z\"\r\n",
      "-33,194.05,true,\"2020-08-24T11:58:32.057524Z\"\r\n",
      "16,187.99,false,\"2020-08-24T11:58:32.061604Z\"\r\n",
      "-7,118.56,true,\"2020-08-24T11:58:32.065643Z\"\r\n",
      "-24,37.83,false,\"2020-08-24T11:58:32.067947Z\"\r\n",
      "30,117.350000000000,true,\"2020-08-24T11:58:32.070361Z\"\r\n",
      "-18,106.850000000000,true,\"2020-08-24T11:58:32.072671Z\"\r\n",
      "-24,78.55,true,\"2020-08-24T11:58:32.076776Z\"\r\n",
      "21,133.44,false,\"2020-08-24T11:58:32.080865Z\"\r\n",
      "8,109.59,false,\"2020-08-24T11:58:32.084893Z\"\r\n",
      "37,13.290000000000,false,\"2020-08-24T11:58:32.087104Z\"\r\n",
      "20,45.75,true,\"2020-08-24T11:58:32.089307Z\"\r\n",
      "43,49.17,false,\"2020-08-24T11:58:32.091469Z\"\r\n",
      "-14,96.45,true,\"2020-08-24T11:58:32.093743Z\"\r\n",
      "49,20.02,false,\"2020-08-24T11:58:32.095971Z\"\r\n",
      "-5,79.820000000000,false,\"2020-08-24T11:58:32.098258Z\"\r\n",
      "32,107.44,true,\"2020-08-24T11:58:32.100453Z\"\r\n",
      "-1,96.5,false,\"2020-08-24T11:58:32.102772Z\"\r\n",
      "26,22.53,false,\"2020-08-24T11:58:32.104954Z\"\r\n",
      "-23,117.04,false,\"2020-08-24T11:58:32.107183Z\"\r\n",
      "-36,47.53,true,\"2020-08-24T11:58:32.109553Z\"\r\n",
      "33,49.51,true,\"2020-08-24T11:58:32.113657Z\"\r\n",
      "-8,10.600000000000,true,\"2020-08-24T11:58:32.115892Z\"\r\n",
      "42,104.100000000000,true,\"2020-08-24T11:58:32.120164Z\"\r\n",
      "43,146.98,false,\"2020-08-24T11:58:32.124250Z\"\r\n",
      "17,39.59,true,\"2020-08-24T11:58:32.128416Z\"\r\n",
      "-15,151.38,false,\"2020-08-24T11:58:32.130665Z\"\r\n",
      "31,133.59,true,\"2020-08-24T11:58:32.134776Z\"\r\n",
      "27,191.65,true,\"2020-08-24T11:58:32.138817Z\"\r\n",
      "-34,161.47,false,\"2020-08-24T11:58:32.142943Z\"\r\n",
      "-10,93.49,false,\"2020-08-24T11:58:32.147029Z\"\r\n",
      "-2,218.91,true,\"2020-08-24T11:58:32.151155Z\"\r\n",
      "11,188.51,false,\"2020-08-24T11:58:32.153419Z\"\r\n",
      "44,170.18,false,\"2020-08-24T11:58:32.155667Z\"\r\n",
      "52,187.96,false,\"2020-08-24T11:58:32.157878Z\"\r\n",
      "7,200.85,true,\"2020-08-24T11:58:32.160098Z\"\r\n",
      "-24,183.22,false,\"2020-08-24T11:58:32.164254Z\"\r\n",
      "1,151.96,true,\"2020-08-24T11:58:32.166610Z\"\r\n",
      "-40,213.86,true,\"2020-08-24T11:58:32.170839Z\"\r\n",
      "-33,101.91,true,\"2020-08-24T11:58:32.173131Z\"\r\n",
      "-38,182.98,false,\"2020-08-24T11:58:32.177191Z\"\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import io\n",
    "\n",
    "r = requests.get(\"http://localhost:9000/exp?query=select * from weather\")\n",
    "rawData = r.text\n",
    "print(rawData)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Pandas to frame our data :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      tempF  rain24H  thunder                        timestamp\n",
      "0       -37   234.00    False 2020-08-24 11:41:37.191530+00:00\n",
      "1       -11   165.52    False 2020-08-24 11:41:37.195714+00:00\n",
      "2       -31   178.13     True 2020-08-24 11:41:37.202453+00:00\n",
      "3        14    31.47     True 2020-08-24 11:41:37.208365+00:00\n",
      "4        33    83.31     True 2020-08-24 11:41:37.214347+00:00\n",
      "...     ...      ...      ...                              ...\n",
      "3995    -24   183.22    False 2020-08-24 11:58:32.164254+00:00\n",
      "3996      1   151.96     True 2020-08-24 11:58:32.166610+00:00\n",
      "3997    -40   213.86     True 2020-08-24 11:58:32.170839+00:00\n",
      "3998    -33   101.91     True 2020-08-24 11:58:32.173131+00:00\n",
      "3999    -38   182.98    False 2020-08-24 11:58:32.177191+00:00\n",
      "\n",
      "[4000 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "pData = pd.read_csv(io.StringIO(rawData), parse_dates=['timestamp'])\n",
    "print(pData)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Note:** The query string _must_ be URL-encoded before it is sent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      tempF  rain24H                    timestamp\n",
      "0       -37   234.00  2020-08-24T11:41:37.191530Z\n",
      "1       -11   165.52  2020-08-24T11:41:37.195714Z\n",
      "2       -31   178.13  2020-08-24T11:41:37.202453Z\n",
      "3        14    31.47  2020-08-24T11:41:37.208365Z\n",
      "4        33    83.31  2020-08-24T11:41:37.214347Z\n",
      "...     ...      ...                          ...\n",
      "3995    -24   183.22  2020-08-24T11:58:32.164254Z\n",
      "3996      1   151.96  2020-08-24T11:58:32.166610Z\n",
      "3997    -40   213.86  2020-08-24T11:58:32.170839Z\n",
      "3998    -33   101.91  2020-08-24T11:58:32.173131Z\n",
      "3999    -38   182.98  2020-08-24T11:58:32.177191Z\n",
      "\n",
      "[4000 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "import urllib.parse\n",
    "\n",
    "q = \"select tempF,\"\\\n",
    "    \" rain24H,\"\\\n",
    "    \" timestamp\"\\\n",
    "    \" from weather\"\\\n",
    "\n",
    "query = urllib.parse.quote(q)\n",
    "r = requests.get(\"http://localhost:9000/exp?query=\" + query)\n",
    "queryData = r.content\n",
    "rawData = pd.read_csv(io.StringIO(queryData.decode('utf-8')))\n",
    "print(rawData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting our data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4000 artists>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdsAAAD4CAYAAACkNUwYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAANw0lEQVR4nO3df8ivd13H8dc7lxYaONlpjG10RA7VolpyMKH+sLSaCs0gRAMdYZygCQX9s/6yf4T9U4FQwkxxgmlCiiNHJSOQ/rA8ExF/4mFO3JjbyZVZQbX57o9zTW/Pzo/71/W9fj0ecHN/v9d93ff9+f76PD/XdX85p7o7AMB4fmDqAQDA2oktAIxMbAFgZGILACMTWwAY2TVTD2Currvuuj558uTUwwBYlAcffPBfu/vE1OOYG7G9jJMnT+bs2bNTDwNgUarqa1OPYY6cRgaAkYktAIxMbAFgZGILACMTWwAYmdgCwMjEFgBGJrYAMDKxBYCRiS0AjExsgUmdvOtjB9oOSyS2LJbJGPbP62VaYgsAIxNb2ChHOrA7YgsAIxNbZmPtR1rHdft2fT+t/XGBXRBbFk0IgCUQW4AJWTBug9gCHIMrRXPqoE79+xFbYA+T8m64n7dHbDfMCx7mxWtyvcQWAEYmtozKSp2tGvO573W1PGILACMTW5gBRyrTOex97zHjIMQWAEYmtgAwMrGFgdOCHJclP5eWPPY5E1sAGJnYAiyEo87lElsAGJnYzoDVKsC6iS3ASCykeYbYAsDIxJZDsWIH2D+xZacOGmlRB9ZgkbGtqpur6h+r6gtV9fmq+v1h+4uq6uNV9ZXh87XD9qqqd1TVuar6bFW9dNpbAMCWLDK2SZ5K8ofdfUuSlye5s6puSXJXkge6+1SSB4brSfLqJKeGjzNJ3rn7IQNr4GwLh7HI2Hb3Y9396eHyt5N8McmNSW5Pcu+w271JXjdcvj3J+/qCTyZ5YVXdsNtRw3IIChyvRcZ2r6o6meTnkvxzkuu7+7HhS99Icv1w+cYkX9/zbY8M2wBgdIuObVW9IMnfJPmD7v6PvV/r7k7SB/x5Z6rqbFWdPX/+/DGOFIAtW2xsq+oHcyG07+/uDw+bH3/m9PDw+Ylh+6NJbt7z7TcN275Pd9/T3ae7+/SJEyfGGzzASPwJYJ4WGduqqiTvTvLF7v7TPV+6L8kdw+U7knx0z/Y3D+9KfnmSb+053QxXZPICjmqRsU3yC0nelOSXq+ozw8drktyd5Feq6itJXjVcT5L7kzyU5FySdyX5vQnGvHMiATAPi4xtd/9Td1d3/0x33zp83N/d3+zuV3b3qe5+VXc/Oezf3X1nd7+ku3+6u89OfRu4usMuFna5yLja79rFWCyqtsXjvUyLjC3wbCZhmC+xZRaEAlgzsQWAkYktbNDeMwnOKsD4xBZgnyxMOCyxXZmxJ4OLf77JB+DqxBZghixk10VsV8CLEmDexJbvEm2AcYgtXMSiAzhuYsuziM349nsfeyxgHcQW9kH0gKMQW47MURpL4nnIFMQWAEYmtiOwcuaoPIdgXcQWAEYmtgBxNoFxiS2X5X+G4SCO8zky5fNtV7/7qL/Ha3JZxBY2xiS9Hh7L5RBbFsXksk4eV9ZObGfEhAOwTmLLaCweAC4QWxZP1IG5E1sWQVCBJRPbHRIMgG0SW5gZizJYH7EFgJGJLZfk6Arg+IjthggowDTEFg7AggU4DLEFZsWChjUSWwAYmdgCm3G5o2ZH04xNbAE24lKLCguN3RBb4PssZfJdyjghWWhsq+o9VfVEVX1uz7Y/rqpHq+ozw8dr9nztj6rqXFV9uap+bZpRMxcmaWDXFhnbJO9Nctsltv9Zd986fNyfJFV1S5I3JPmp4Xv+oqqes7ORArNgkcWUFhnb7v5Ekif3ufvtST7Y3f/T3V9Nci7Jy0YbHABcZJGxvYK3VtVnh9PM1w7bbkzy9T37PDJsA4CdWFNs35nkJUluTfJYkj856A+oqjNVdbaqzp4/f/6Yhwewe06fz8NqYtvdj3f30939nSTvyvdOFT+a5OY9u940bLvUz7inu0939+kTJ06MO2AANmM1sa2qG/Zc/Y0kz7xT+b4kb6iq51XVi5OcSvIvux4fANt1zdQDOIyq+kCSVyS5rqoeSfK2JK+oqluTdJKHk/xuknT356vqQ0m+kOSpJHd299MTDBuAjVpkbLv7jZfY/O4r7P/2JG8fb0Qc1Mm7PpaH737t1MMA2InVnEYGgLkS2x077ncGeqchHJzXDbsmthtkolkWjxcsn9gCwMjEFgBGJrYsjtOqwNKI7UxsOSBbvu1Htav7zmMERyO2G+Ad0LBMXmvrIbYAMDKxhQVxpAPLJLYAMDKxBYCRiS0AjExsYSL+/spx8VyaP7FlVUw6wByJLewxVqwtAmDbxBYYhQUGfI/YcqxMsADPJrYwUxYusB5iC/skfu4DOCyxZdXEAa7O62R8YgtwBVOESPzWR2wXyAsRYFnEFlbGYmwc7leOQmwBYGRiCwAjE1sAGJnYclX+VsXceE6yNGLLIpls2Yr9Pte9JuZNbGFCJkjYBrEFdsoCgy0SWwAYmdgCq+PombkRW2bPxHk83I8wHbFlsw4bH9ECDkpsF8IED7Bci4xtVb2nqp6oqs/t2faiqvp4VX1l+HztsL2q6h1Vda6qPltVL51u5PsnrizZVp6/W7mdHN0iY5vkvUluu2jbXUke6O5TSR4YrifJq5OcGj7OJHnnjsbIYAsT0hZu4xy531mKRca2uz+R5MmLNt+e5N7h8r1JXrdn+/v6gk8meWFV3bCTgR6RiWQ8c7xvj3tMc7yNsFWLjO1lXN/djw2Xv5Hk+uHyjUm+vme/R4Ztz1JVZ6rqbFWdPX/+/HgjBRbBgoXjsqbYfld3d5I+xPfd092nu/v0iRMnRhgZAFu0ptg+/szp4eHzE8P2R5PcvGe/m4Zti2F1zVp5brMVa4rtfUnuGC7fkeSje7a/eXhX8suTfGvP6WZWyiQOzMk1Uw/gMKrqA0lekeS6qnokyduS3J3kQ1X1liRfS/L6Yff7k7wmybkk/53kt3c+YHZCYIG5WmRsu/uNl/nSKy+xbye5c9wRcZxO3vWxPHz3a6ceBsCxWdNpZCZ2nEeWjlLXxePJ1oktwA5ZeGyT2MIGmOBhWmK7UiZXgPkQW2DWjmPhaPHJ1MQWAEYmtsAkHG2yJWILACMTWybjyAbYCrEFGNF+FpUWnusntivkhQswL2K7UYIMsDtiSxLxhbXxmp4XsYUVuXiCXeOEu8bbxPqJ7Y6YIJgDz0OYhtgCwMjEFrisqY+ED/L7px7rnLgv5kds2RSTEDAFsQU4Jkv7H4osPndHbAFgZGILACMTWwAYmdiyGf4+BUxFbAFgZGI7MkdT2+LxBi5FbAFgZGILMBFnQrZDbGGFtjyJb/m2M19iO7E1TgxrvE3sj8ceLk1s4ZgIDXA5YgsAIxNbuARHqft3pfvK/QgXiC1wKEJ6gfuB/RBbOCKT7Xx4LJgrsQWAkV0z9QCOW1U9nOTbSZ5O8lR3n66qFyX56yQnkzyc5PXd/W9TjRFYF0fUXM1aj2x/qbtv7e7Tw/W7kjzQ3aeSPDBcB9gcC4NprDW2F7s9yb3D5XuTvG66ocD87GcCNknD4a0xtp3kH6rqwao6M2y7vrsfGy5/I8n1l/rGqjpTVWer6uz58+d3MVaAWbCYGtfq/mab5Be7+9Gq+tEkH6+qL+39Ynd3VfWlvrG770lyT5KcPn36kvsAzJVgztfqjmy7+9Hh8xNJPpLkZUker6obkmT4/MR0IwRga1YV26p6flX9yDOXk/xqks8luS/JHcNudyT56DQjBObMkSFjWdtp5OuTfKSqkgu37a+6+++q6lNJPlRVb0nytSSvn3CMAGzMqmLb3Q8l+dlLbP9mklfufkRHt5aV9lpuB8BhrOo0MgDMkdjugKM6gG0TW4gFUTL+feA+ng+Pxe6JLXBku5q8RWJc7t/xiC3HwosU4PLEFgBGJrawQHM5kzCXccDciS0AjExsN8aRyPx4TGD9xBYARia2ADAysQV2zqlztkZsYWTCAogt+zZGNISIrfBc3zaxnZAXH4zH64s5EVsAGJnYAiyAI/VlE1sAGJnYslqOBIC5EFsAGJnYAsDIxHbjnGoFGJ/YAsDIxBYWylkJWA6xXbD9TLYHnZBN4PPkcYFlE9uFMvkCLIfYwkjWuiBytgQOTmwBYGRiC3AVjs45KrEFgJGJLQCMTGwBYGRiCwAjE1sAGJnYAsDINhPbqrqtqr5cVeeq6q6pxwPAdmwitlX1nCR/nuTVSW5J8saqumXaUQGwFZuIbZKXJTnX3Q919/8m+WCS2yceEwAbUd099RhGV1W/meS27v6d4fqbkvx8d7/1ov3OJDkzXP3xJF8+5K/8iSTPP+T3AkztySRfPeT3/lh3nzjOwazBNVMPYE66+54k9xz151TV08cwHICp/FB3n556EGuyldPIjya5ec/1m4ZtADC6rcT2U0lOVdWLq+q5Sd6Q5L6JxwTARmziNHJ3P1VVb03y90mek+Q93f35EX/lp3LhTVkAS/ThqQewNpt4gxQATGkrp5EBYDJiCwAju+rfbKvqL5O8ZQdjAYBF6u660tf38wapbyX59yTPG67/8BHHBABr8sjVdjjwG6SqyjuqAGBwtaPa5IB/s62q9x9+OACwOj+5n532Hduq+vUkv3Xo4QDAuvxtd39pPzvu6zRyVb04yUNHHRUArMT/dfdz97vzVWNbVZXkO0cdFQCsyAu6+7/2u/N+TiMf9r9ZAoC1+s+q6j0f115pZ/9cIwCMzL8gBQAjE1sAGJnYAsDIxBYARia2ADAysQWAkYktAIzs/wHqKWz0GkJQfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.bar(rawData['timestamp'], rawData['rain24H'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 4000 artists>"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.bar(rawData['timestamp'], rawData['tempF'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}