dataviz.hu/parser.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('2013_2017_monthly_BUD_traffic.csv',encoding='latin-1',sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['LF']=df[' PAX']/df['Capacity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2006dea07f0>"
      ]
     },
     "execution_count": 27,
     "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": [
    "df.plot(kind='scatter',x='ATM',y='LF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.tools as tls\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
    "init_notebook_mode(connected=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dz=df.groupby(['Year','City']).sum()\n",
    "dz['LF']=dz[' PAX']/dz['Capacity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = {\n",
    "    'data': [\n",
    "  \t\t{\n",
    "  \t\t\t'x': dz.loc[2013]['ATM'], \n",
    "        \t'y': dz.loc[2013]['LF'], \n",
    "        \t'text': dz.loc[2013].index, \n",
    "        \t'mode': 'markers', \n",
    "        \t'name': '2013'},\n",
    "        {\n",
    "  \t\t\t'x': dz.loc[2017]['ATM'], \n",
    "        \t'y': dz.loc[2017]['LF'], \n",
    "        \t'text': dz.loc[2017].index, \n",
    "        \t'mode': 'markers', \n",
    "        \t'name': '2017'},\n",
    "        {\n",
    "  \t\t\t'x': dz.loc[2015]['ATM'], \n",
    "        \t'y': dz.loc[2015]['LF'], \n",
    "        \t'text': dz.loc[2015].index, \n",
    "        \t'mode': 'markers', \n",
    "        \t'name': '2015'}\n",
    "    ],\n",
    "    'layout': {\n",
    "        'xaxis': {'title': 'ATM','type':'log'},\n",
    "        'yaxis': {'title': \"LF\"}\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "mode": "markers",
         "name": "2013",
         "text": [
          "Alicante",
          "Amsterdam",
          "Angers",
          "Ankara",
          "Antalya",
          "Aqaba",
          "Argostólion",
          "Assiut",
          "Athens",
          "Atyrau",
          "Baku",
          "Barcelona",
          "Bari",
          "Basel",
          "Beirut",
          "Belgrade",
          "Berlin",
          "Bern",
          "Bilbao",
          "Billund",
          "Birmingham",
          "Bodrum",
          "Bologna",
          "Bordeaux",
          "Bourgas",
          "Bratislava",
          "Brescia",
          "Bristol",
          "Brussels",
          "Bucharest",
          "Cairo",
          "Casablanca",
          "Catania",
          "Clermont ",
          "Cluj",
          "Cold Bay",
          "Cologne",
          "Copenhagen",
          "Corfu",
          "Debrecen",
          "Delhi",
          "Dijon",
          "Djerba",
          "Doha",
          "Dortmund",
          "Dresden",
          "Dubai",
          "Dublin",
          "Dubrovnik",
          "Dusseldorf",
          "ENFIDHA",
          "East Midlands",
          "Edinburgh",
          "Egilsstadir",
          "Eindhoven",
          "Ekaterinburg",
          "Eskisehir",
          "Frankfurt",
          "Friedrichshafen",
          "Gdansk",
          "Geneva",
          "Glasgow",
          "Gothenburg",
          "Hahn",
          "Hamburg",
          "Harstad Narvik",
          "Helsinki",
          "Heraklion",
          "Hurghada",
          "INVERNESS",
          "Innsbruck",
          "Islamabad",
          "Istanbul",
          "Izmir",
          "Karlsruhe",
          "Katowice",
          "Kiev",
          "Kos",
          "Kosice",
          "Krakow",
          "Lamezia",
          "Larnaca",
          "Lemwerder",
          "Lisbon",
          "London",
          "Lvov",
          "Lyon",
          "Madrid",
          "Malaga",
          "Malmo",
          "Malta",
          "Manchester",
          "Marseille",
          "Memmingen",
          "Milan",
          "Minsk",
          "Monastir",
          "Moscow",
          "Mulhouse",
          "Munich",
          "Muscat",
          "Nantes",
          "Naples",
          "Norrkoping",
          "Oslo",
          "Paderborn",
          "Palermo",
          "Palma De Mallorca",
          "Paphos",
          "Paris",
          "Pisa",
          "Podgorica",
          "Poznan",
          "Prague",
          "Pristina",
          "Reykjavik",
          "Rhodos",
          "Riga",
          "Rome",
          "Rostov",
          "Rotterdam",
          "Rovaniemi",
          "Sandefjord",
          "Santiago de Compostela",
          "Sharm el Sheikh",
          "Skopje",
          "Sliac",
          "Sochi",
          "Sofia",
          "St.Petersburg",
          "Stockholm",
          "Strasbourg",
          "Stuttgart",
          "TROMSO",
          "Tallin",
          "Tampere",
          "Tel Aviv",
          "Tenerife",
          "Thessaloniki",
          "Tirgu Mures",
          "Tokio",
          "Torino",
          "Toulouse",
          "Treviso",
          "Trondheim",
          "Tunis",
          "Turku",
          "Valencia",
          "Valladolid",
          "Varna",
          "Venezia",
          "Vienna",
          "Warsaw",
          "Zadar",
          "Zagreb",
          "Zakinthos",
          "Zaporozhye",
          "Zilina",
          "Zurich"
         ],
         "type": "scatter",
         "uid": "575393f6-dba2-11e8-b9ef-d89ef31168fe",
         "x": [
          138,
          2579,
          1,
          1,
          424,
          55,
          30,
          1,
          260,
          24,
          114,
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          2,
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         ],
         "y": [
          0.9033049612759758,
          0.8372919936095913,
          0.3010752688172043,
          0.5181818181818182,
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   "source": [
    "iplot(fig, filename='plot1')"
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  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
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  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
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     "metadata": {},
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   ],
   "source": [
    "fig = {\n",
    "    'data': [\n",
    "  \t\t{\n",
    "  \t\t\t'x': dz.loc[2013]['ATM']/365*(1+np.random.randn(len(dz.loc[2013]))), \n",
    "        \t'y': dz.loc[2013]['LF'], \n",
    "        \t'text': dz.loc[2013].index, \n",
    "        \t'mode': 'markers', \n",
    "        \t'name': '2013'},\n",
    "        {\n",
    "  \t\t\t'x': dz.loc[2017]['ATM']/365*(1+np.random.randn(len(dz.loc[2017]))),\n",
    "        \t'y': dz.loc[2017]['LF'], \n",
    "        \t'text': dz.loc[2017].index, \n",
    "        \t'mode': 'markers', \n",
    "        \t'name': '2017'},\n",
    "        {\n",
    "  \t\t\t'x': dz.loc[2015]['ATM']/365*(1+np.random.randn(len(dz.loc[2015]))),\n",
    "        \t'y': dz.loc[2015]['LF'], \n",
    "        \t'text': dz.loc[2015].index, \n",
    "        \t'mode': 'markers', \n",
    "        \t'name': '2015'}\n",
    "    ],\n",
    "    'layout': {\n",
    "        'xaxis': {'title': 'ATM','type':'log'},\n",
    "        'yaxis': {'title': \"LF\"}\n",
    "    }\n",
    "}\n",
    "iplot(fig, filename='plot1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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