visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Script for MongoDB Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pymongo\n",
    "import pprint as p\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = pymongo.MongoClient('localhost', 27017)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "db = client.tanken\n",
    "preise = db.preise\n",
    "stationen = db.stationen\n",
    "res = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average price of E5, E10 and Diesel in 2020 total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - takes a while\n",
    "res = preise.aggregate([\n",
    "    {\n",
    "        \"$group\" : {\"_id\": \"null\" , \"e10\": {\"$avg\": \"$e10\"},\"e5\": {\"$avg\": \"$e5\"},\"diesel\": {\"$avg\": \"$diesel\"}}\n",
    "    }\n",
    "])\n",
    "result = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durchschnittliche Preise in 2020 nach Kategorie\n",
      "E10: 1.2092071354722296\n",
      "E05: 1.27504803260726\n",
      "Diesel: 1.1118849282747298\n"
     ]
    }
   ],
   "source": [
    "prices = result[0]\n",
    "print(\"Durchschnittliche Preise in 2020 nach Kategorie\")\n",
    "print(\"E10: \"+ str(prices[\"e10\"]))\n",
    "print(\"E05: \"+ str(prices[\"e5\"]))\n",
    "print(\"Diesel: \"+ str(prices[\"diesel\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average E5, E10 and Diesel price per month in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - Avg. E10/E05 prices - takes a while\n",
    "res = preise.aggregate([\n",
    "    {\n",
    "        \"$group\" : {\"_id\": \"$date\" , \"e5\": {\"$avg\": \"$e5\"}, \"e10\": {\"$avg\": \"$e10\"}}\n",
    "    }\n",
    "])\n",
    "pricesEClass = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - Avg. Diesel prices - takes a while\n",
    "res = preise.aggregate([\n",
    "    {\n",
    "        \"$group\" : {\"_id\": \"$date\" , \"diesel\": {\"$avg\": \"$diesel\"}}\n",
    "    }\n",
    "])\n",
    "pricesDiesel = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getMonths(l):\n",
    "    month = []\n",
    "    for element in l:\n",
    "        month.append(element[\"_id\"][5:7])\n",
    "    a = list(set(month))\n",
    "    a.sort()\n",
    "    return a\n",
    "\n",
    "def getAvgPriceOfMonth(month, l, cat):\n",
    "    price = 0\n",
    "    count = 0\n",
    "    for element in l:\n",
    "        if element[\"_id\"][5:7] == month:\n",
    "            price = price + element[cat]\n",
    "            count = count + 1\n",
    "    price = price / count\n",
    "    return price\n",
    "\n",
    "def getPriceOfMonths(l, cat):\n",
    "    print(\"Durchschnittliche Preise pro Monat von: \" + cat)\n",
    "    months = getMonths(l)\n",
    "    pricesDict = []\n",
    "    prices = []\n",
    "    for month in months:\n",
    "        price = getAvgPriceOfMonth(month, l, cat)\n",
    "        prices.append(price)\n",
    "        pricesDict.append({\"Monat\": month, \"Preis\": price})\n",
    "        print(\"Monat: \" + month+\" | Durchschnittlicher Preis: \"+ str(price))\n",
    "    return pricesDict, prices, months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durchschnittliche Preise pro Monat von: e5\n",
      "Monat: 01 | Durchschnittlicher Preis: 1.3809709460691029\n",
      "Monat: 02 | Durchschnittlicher Preis: 1.369651922929607\n",
      "Monat: 03 | Durchschnittlicher Preis: 1.2740411152446394\n",
      "Monat: 04 | Durchschnittlicher Preis: 1.1639490629888167\n",
      "Monat: 05 | Durchschnittlicher Preis: 1.1596067276434443\n",
      "Monat: 06 | Durchschnittlicher Preis: 1.2282043897184967\n",
      "Monat: 07 | Durchschnittlicher Preis: 1.2535407977442299\n",
      "Monat: 08 | Durchschnittlicher Preis: 1.2446060539947743\n",
      "Monat: 09 | Durchschnittlicher Preis: 1.24132803012062\n",
      "Monat: 10 | Durchschnittlicher Preis: 1.2306790167741468\n",
      "Monat: 11 | Durchschnittlicher Preis: 1.1879558184113654\n"
     ]
    }
   ],
   "source": [
    "monthDictE5, pricesE5, monthsE5 = getPriceOfMonths(pricesEClass, \"e5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durchschnittliche Preise pro Monat von: e10\n",
      "Monat: 01 | Durchschnittlicher Preis: 1.30897259178415\n",
      "Monat: 02 | Durchschnittlicher Preis: 1.285275932764745\n",
      "Monat: 03 | Durchschnittlicher Preis: 1.1897528740154848\n",
      "Monat: 04 | Durchschnittlicher Preis: 1.0884780636680933\n",
      "Monat: 05 | Durchschnittlicher Preis: 1.0761478887482752\n",
      "Monat: 06 | Durchschnittlicher Preis: 1.1426070264700603\n",
      "Monat: 07 | Durchschnittlicher Preis: 1.1601250803569079\n",
      "Monat: 08 | Durchschnittlicher Preis: 1.1514669124804522\n",
      "Monat: 09 | Durchschnittlicher Preis: 1.1492240240170315\n",
      "Monat: 10 | Durchschnittlicher Preis: 1.13293027554719\n",
      "Monat: 11 | Durchschnittlicher Preis: 1.0906952574675215\n"
     ]
    }
   ],
   "source": [
    "monthDictE10, pricesE10, monthsE10 = getPriceOfMonths(pricesEClass, \"e10\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durchschnittliche Preise pro Monat von: diesel\n",
      "Monat: 01 | Durchschnittlicher Preis: 1.2934095407590105\n",
      "Monat: 02 | Durchschnittlicher Preis: 1.2321420778009649\n",
      "Monat: 03 | Durchschnittlicher Preis: 1.1439764428697003\n",
      "Monat: 04 | Durchschnittlicher Preis: 1.0726133613955449\n",
      "Monat: 05 | Durchschnittlicher Preis: 1.0419652863005462\n",
      "Monat: 06 | Durchschnittlicher Preis: 1.0793916271860222\n",
      "Monat: 07 | Durchschnittlicher Preis: 1.0831937992088532\n",
      "Monat: 08 | Durchschnittlicher Preis: 1.0741834315383334\n",
      "Monat: 09 | Durchschnittlicher Preis: 1.0422071083021294\n",
      "Monat: 10 | Durchschnittlicher Preis: 1.0364162958660041\n",
      "Monat: 11 | Durchschnittlicher Preis: 1.0233096240470723\n"
     ]
    }
   ],
   "source": [
    "monthDictDiesel, pricesD, monthsDiesel = getPriceOfMonths(pricesDiesel, \"diesel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monatlicher Preisverlauf von E10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(monthsE10, pricesE10)\n",
    "plt.xlabel('Monat')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monatlicher Preisverlauf von E5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(monthsE5, pricesE5)\n",
    "plt.xlabel('Monat')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Monatlicher Preisverlauf von Diesel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(monthsDiesel, pricesD)\n",
    "plt.xlabel('Monat')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E10, E5 und Diesel im Vergleich"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(monthsDiesel, pricesD)\n",
    "plt.plot(monthsE5, pricesE5)\n",
    "plt.plot(monthsE10, pricesE10)\n",
    "plt.legend(['Diesel','E5','E10'])\n",
    "plt.xlabel('Monat')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average price per day during year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getPricesPerDayCat(l,date,cat):\n",
    "    dayPrices = []\n",
    "    price = 0\n",
    "    count = 0\n",
    "    for element in l:\n",
    "        if element[\"_id\"][0:10] == date:\n",
    "            dayPrices.append(element[cat])\n",
    "            price = price + (element[cat])\n",
    "            count = count + 1\n",
    "    return dayPrices, (price/count)\n",
    "\n",
    "\n",
    "def getDates(l):\n",
    "    dates = []\n",
    "    for element in l:\n",
    "        dates.append(element[\"_id\"][0:10])\n",
    "    a = list(set(dates))\n",
    "    a.sort()\n",
    "    return a\n",
    "\n",
    "def getAvgPricePerDay(l, cat):\n",
    "    dates = getDates(l)\n",
    "    priceDict = []\n",
    "    prices = []\n",
    "    for date in dates:\n",
    "        dayPrices, avgPrice = getPricesPerDayCat(l, date, cat)\n",
    "        priceDict.append({\"Tag\":date,\"Preis\":avgPrice })\n",
    "        prices.append(avgPrice)\n",
    "    return priceDict, prices, dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "dieselyear, prices, dates = getAvgPricePerDay(pricesDiesel,\"diesel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preisverlauf von Diesel im Jahr 2020 (Jeder Tag)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(dates, prices)\n",
    "plt.xlabel('Durchschnittlicher Preis pro Tag in 2020')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average price during day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getHours(l):\n",
    "    hours = []\n",
    "    for element in l:\n",
    "        hours.append(element[\"_id\"][11:13])\n",
    "    a = list(set(hours))\n",
    "    a.sort()\n",
    "    return a\n",
    "\n",
    "def getAvgPriceAtHour(l, date, time, cat):\n",
    "    price = 0\n",
    "    count = 0\n",
    "    for element in l:\n",
    "        if(element[\"_id\"][0:10] == date):\n",
    "            if (element[\"_id\"][11:13] == time):\n",
    "                price = price + element[cat]\n",
    "                count = count + 1\n",
    "    if count == 0:\n",
    "        count = 1\n",
    "    return (price/count)\n",
    "\n",
    "def getAvgPriceEveryHour(l,date,cat):\n",
    "    print(\"Tag: \" + date + \", Kategorie: \" +cat)\n",
    "    hours = getHours(l)\n",
    "    priceDict = []\n",
    "    prices = []\n",
    "    for hour in hours:\n",
    "        hourPrice = getAvgPriceAtHour(l, date, hour, cat)\n",
    "        priceDict.append({\"Stunde\": hour, \"Preis\": hourPrice})\n",
    "        prices.append(hourPrice)\n",
    "       # print(hour+\":00 - \"+hour+\":59 Uhr | Durchschnittspreis: \" + str(hourPrice))\n",
    "    return priceDict, prices, hours\n",
    "\n",
    "def getAvgPriceEveryHourTotal(l,cat):\n",
    "    print(\"Durchschnittlicher Tagesverlauf in 2020, Kategorie: \" + cat)\n",
    "    dates = getDates(l)\n",
    "    pricesTotal = []\n",
    "    count = 0\n",
    "    for i in range(0,24):\n",
    "        pricesTotal.append(0)\n",
    "    for date in dates:\n",
    "        priceDict, dayPrices, dayHours = getAvgPriceEveryHour(l,date,cat)\n",
    "        i = 0\n",
    "        while i < len(dayPrices):\n",
    "            if count > 0:\n",
    "                if dayPrices[i] != 0:\n",
    "                    pricesTotal[i] = (pricesTotal[i] + dayPrices[i])/2\n",
    "            else:\n",
    "                pricesTotal[i] = (pricesTotal[i] + dayPrices[i])\n",
    "            i = i + 1\n",
    "            count = count + 1\n",
    "    return pricesTotal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Durchschnittlicher Tagesverlauf in 2020, Kategorie: e5\n",
      "Tag: 2020-01-01, Kategorie: e5\n",
      "Tag: 2020-01-02, Kategorie: e5\n",
      "Tag: 2020-01-03, Kategorie: e5\n",
      "Tag: 2020-01-04, Kategorie: e5\n",
      "Tag: 2020-01-05, Kategorie: e5\n",
      "Tag: 2020-01-06, Kategorie: e5\n",
      "Tag: 2020-01-07, Kategorie: e5\n",
      "Tag: 2020-01-08, Kategorie: e5\n",
      "Tag: 2020-01-09, Kategorie: e5\n",
      "Tag: 2020-01-10, Kategorie: e5\n",
      "Tag: 2020-01-11, Kategorie: e5\n",
      "Tag: 2020-01-12, Kategorie: e5\n",
      "Tag: 2020-01-13, Kategorie: e5\n",
      "Tag: 2020-01-14, Kategorie: e5\n",
      "Tag: 2020-01-15, Kategorie: e5\n",
      "Tag: 2020-01-16, Kategorie: e5\n",
      "Tag: 2020-01-17, Kategorie: e5\n",
      "Tag: 2020-01-18, Kategorie: e5\n",
      "Tag: 2020-01-19, Kategorie: e5\n",
      "Tag: 2020-01-20, Kategorie: e5\n",
      "Tag: 2020-01-21, Kategorie: e5\n",
      "Tag: 2020-01-22, Kategorie: e5\n",
      "Tag: 2020-01-23, Kategorie: e5\n",
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      "Tag: 2020-07-01, Kategorie: e5\n",
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      "Tag: 2020-08-01, Kategorie: e5\n",
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      "Tag: 2020-09-11, Kategorie: e5\n",
      "Tag: 2020-09-12, Kategorie: e5\n",
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      "Tag: 2020-09-15, Kategorie: e5\n",
      "Tag: 2020-09-16, Kategorie: e5\n",
      "Tag: 2020-09-17, Kategorie: e5\n",
      "Tag: 2020-09-18, Kategorie: e5\n",
      "Tag: 2020-09-19, Kategorie: e5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tag: 2020-09-20, Kategorie: e5\n",
      "Tag: 2020-09-21, Kategorie: e5\n",
      "Tag: 2020-09-22, Kategorie: e5\n",
      "Tag: 2020-09-23, Kategorie: e5\n",
      "Tag: 2020-09-24, Kategorie: e5\n",
      "Tag: 2020-09-25, Kategorie: e5\n",
      "Tag: 2020-09-26, Kategorie: e5\n",
      "Tag: 2020-09-27, Kategorie: e5\n",
      "Tag: 2020-09-28, Kategorie: e5\n",
      "Tag: 2020-09-29, Kategorie: e5\n",
      "Tag: 2020-09-30, Kategorie: e5\n",
      "Tag: 2020-10-01, Kategorie: e5\n",
      "Tag: 2020-10-02, Kategorie: e5\n",
      "Tag: 2020-10-03, Kategorie: e5\n",
      "Tag: 2020-10-04, Kategorie: e5\n",
      "Tag: 2020-10-05, Kategorie: e5\n",
      "Tag: 2020-10-06, Kategorie: e5\n",
      "Tag: 2020-10-07, Kategorie: e5\n",
      "Tag: 2020-10-08, Kategorie: e5\n",
      "Tag: 2020-10-09, Kategorie: e5\n",
      "Tag: 2020-10-10, Kategorie: e5\n",
      "Tag: 2020-10-11, Kategorie: e5\n",
      "Tag: 2020-10-12, Kategorie: e5\n",
      "Tag: 2020-10-13, Kategorie: e5\n",
      "Tag: 2020-10-14, Kategorie: e5\n",
      "Tag: 2020-10-15, Kategorie: e5\n",
      "Tag: 2020-10-16, Kategorie: e5\n",
      "Tag: 2020-10-17, Kategorie: e5\n",
      "Tag: 2020-10-18, Kategorie: e5\n",
      "Tag: 2020-10-19, Kategorie: e5\n",
      "Tag: 2020-10-20, Kategorie: e5\n",
      "Tag: 2020-10-21, Kategorie: e5\n",
      "Tag: 2020-10-22, Kategorie: e5\n",
      "Tag: 2020-10-23, Kategorie: e5\n",
      "Tag: 2020-10-24, Kategorie: e5\n",
      "Tag: 2020-10-25, Kategorie: e5\n",
      "Tag: 2020-10-26, Kategorie: e5\n",
      "Tag: 2020-10-27, Kategorie: e5\n",
      "Tag: 2020-10-28, Kategorie: e5\n",
      "Tag: 2020-10-29, Kategorie: e5\n",
      "Tag: 2020-10-30, Kategorie: e5\n",
      "Tag: 2020-10-31, Kategorie: e5\n",
      "Tag: 2020-11-01, Kategorie: e5\n",
      "Tag: 2020-11-02, Kategorie: e5\n",
      "Tag: 2020-11-03, Kategorie: e5\n",
      "Tag: 2020-11-04, Kategorie: e5\n",
      "Tag: 2020-11-05, Kategorie: e5\n",
      "Tag: 2020-11-06, Kategorie: e5\n",
      "Tag: 2020-11-07, Kategorie: e5\n"
     ]
    }
   ],
   "source": [
    "pricesHourTotal = getAvgPriceEveryHourTotal(pricesEClass, \"e5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tagesverlauf von E5 in 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dayHoursDiesel' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-41-9cd26877256e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mSize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_size_inches\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mSize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSize\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m1.5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdayHoursDiesel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpricesHourTotal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Uhrzeit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Preis pro Liter in Euro'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dayHoursDiesel' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 648x432 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(dayHoursDiesel, pricesHourTotal)\n",
    "plt.xlabel('Uhrzeit')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.title(\"Tagesverlauf von E5 im Jahr 2020\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tagesverlauf von Diesel an einem bestimmten Tag \n",
    "## 28.08.2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dayDictDiesel, dayPricesDiesel, dayHoursDiesel = getAvgPriceEveryHour(pricesDiesel, \"2020-08-28\", \"diesel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*1.5, Size[1]*1.5, forward=True)\n",
    "plt.plot(dayHoursDiesel, dayPricesDiesel)\n",
    "plt.xlabel('Uhrzeit')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.title(\"Tagesverlauf von Diesel am 28.08.2020\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pricing cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = stationen.aggregate([\n",
    "    { \n",
    "        \"$match\": \n",
    "        {\n",
    "            \"$or\": [ { \"city\": \"München\" }, { \"city\": \"Freiburg\" }, { \"city\": \"Mannheim\" },{ \"city\": \"Frankfurt\"}, { \"city\": \"Hamburg\"}, { \"city\": \"Berlin\"},{ \"city\": \"Köln\"}]\n",
    "        }\n",
    "    }\n",
    "\n",
    "])\n",
    "cityStations = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def getCities(l):\n",
    "    cities = []\n",
    "    for element in l:\n",
    "        cities.append(element[\"city\"])\n",
    "    return list(set(cities))\n",
    "\n",
    "def splitIDforCity(l, cities):\n",
    "    cityIDs = []\n",
    "    for city in cities:\n",
    "        for element in l:\n",
    "            if element[\"city\"] == city:\n",
    "                cityIDs.append({\"Stadt\": city, \"ID\": element[\"uuid\"]})\n",
    "    return cityIDs\n",
    "\n",
    "def getIDsByCity(ids, city):\n",
    "    cityIDs = []\n",
    "    for id in ids:\n",
    "        if id['Stadt'] == city:\n",
    "            cityIDs.append({\"station_uuid\": id['ID']})\n",
    "    return cityIDs\n",
    "\n",
    "def getCheapestID(ids):\n",
    "    a = ids[0]['e5']\n",
    "    c = 0 \n",
    "    idNumber = 0\n",
    "    for id in ids:\n",
    "        c = c + 1\n",
    "        if a > id['e5']:\n",
    "            a = id['e5']\n",
    "            idNumber = c\n",
    "    return ids[idNumber-1]\n",
    "\n",
    "def getCheapestStationName(ids):\n",
    "    station = getCheapestID(ids)\n",
    "    \n",
    "    res = stationen.aggregate([\n",
    "    { \n",
    "        \"$match\": \n",
    "        {\n",
    "             \"uuid\": station['_id'] \n",
    "        }\n",
    "    }\n",
    "\n",
    "    ])\n",
    "    station = list(res)\n",
    "    print('Adresse: ',station[0]['name'],' Name: ',station[0]['brand'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "ids = splitIDforCity(cityStations, getCities(cityStations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "munichIds = getIDsByCity(ids, \"München\")\n",
    "ffIds = getIDsByCity(ids, \"Frankfurt\")\n",
    "hamburgIds = getIDsByCity(ids, \"Hamburg\")\n",
    "mannheimIds = getIDsByCity(ids, \"Mannheim\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - Avg. E5 prices for Munich - takes forever\n",
    "res = preise.aggregate([\n",
    "    \n",
    "      \n",
    "        {\n",
    "            \"$match\":   {\"$or\":munichIds }\n",
    "        },\n",
    "        {\n",
    "            \"$group\" : {\"_id\": \"$date\" , \"e5\": {\"$avg\": \"$e5\"}}\n",
    "        }\n",
    "        \n",
    "    \n",
    "])\n",
    "e5Munich = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - Avg. E5 prices for Frankfurt - takes forever too\n",
    "res = preise.aggregate([\n",
    "    \n",
    "      \n",
    "        {\n",
    "            \"$match\":   {\"$or\":ffIds }\n",
    "        },\n",
    "        {\n",
    "            \"$group\" : {\"_id\": \"$date\" , \"e5\": {\"$avg\": \"$e5\"}}\n",
    "        }\n",
    "        \n",
    "    \n",
    "])\n",
    "e5Frankfurt = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - Avg. E5 prices for Hamburg - takes forever too\n",
    "res = preise.aggregate([\n",
    "    \n",
    "      \n",
    "        {\n",
    "            \"$match\":   {\"$or\":hamburgIds }\n",
    "        },\n",
    "        {\n",
    "            \"$group\" : {\"_id\": \"$date\" , \"e5\": {\"$avg\": \"$e5\"}}\n",
    "        }\n",
    "        \n",
    "    \n",
    "])\n",
    "e5Hamburg = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthDictE5Munich, pricesE5Munich, monthsE5Munich = getPriceOfMonths(e5Munich, \"e5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthDictE5ff, pricesE5ff, monthsE5ff = getPriceOfMonths(e5Frankfurt, \"e5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthDictE5hh, pricesE5hh, monthsE5hh = getPriceOfMonths(e5Hamburg, \"e5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preisverlauf pro Monat\n",
    "### Vergleich München, Frankfurt und Durchschnitt Deutschland 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F = plt.gcf()\n",
    "Size = F.get_size_inches()\n",
    "F.set_size_inches(Size[0]*2.5, Size[1]*2, forward=True)\n",
    "plt.plot(monthsE5Munich, pricesE5Munich)\n",
    "plt.plot(monthsE5ff, pricesE5ff)\n",
    "plt.plot(monthsE5hh, pricesE5hh)\n",
    "plt.plot(monthsE5, pricesE5)\n",
    "plt.legend(['München','Frankfurt', 'Hamburg', 'Durchschnitt Deutschland'])\n",
    "plt.xlabel('Monat')\n",
    "plt.ylabel('Preis pro Liter in Euro')\n",
    "plt.title(\"Vergleich München, Frankfurt und Durchschnitt Deutschland 2020\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Billigiste Tankstelle in Mannheim "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MongoDB Query - Avg. E5 prices for each station in Mannheim\n",
    "res = preise.aggregate([\n",
    "    \n",
    "      \n",
    "        {\n",
    "            \"$match\":   {\"$or\":mannheimIds }\n",
    "        },\n",
    "        {\n",
    "            \"$group\" : {\"_id\": \"$station_uuid\" , \"e5\": {\"$avg\": \"$e5\"}}\n",
    "        }\n",
    "        \n",
    "    \n",
    "])\n",
    "e5MannheimStations = list(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adresse:  Mannheim, Waldstr. 172  Name:  HEM\n"
     ]
    }
   ],
   "source": [
    "getCheapestStationName(e5MannheimStations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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