Basel Weekend Weather Analysis IPython NB version

Basel Weekend Weather Analysis.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Do we have more precipitations on Weekends in Basel?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "\n",
      "from bs4 import BeautifulSoup\n",
      "import pandas as pd\n",
      "import requests\n",
      "import scipy.stats as stats"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def getData(year=2014, month=1, proxies={}):\n",
      " \n",
      "    url = 'http://en.tutiempo.net/climate/%02d-%d/ws-66010.html' % (month, year)\n",
      " \n",
      "    r = requests.get(url, proxies=proxies)\n",
      " \n",
      "    soup = BeautifulSoup( r.text )\n",
      "    table = soup.find('table', 'medias mensuales')\n",
      "    rows = table.findAll('tr')\n",
      " \n",
      "    # Getting labels for cols\n",
      "    index = list()\n",
      "    for th in rows[0].findAll('th'):\n",
      "        index.append( th.text )\n",
      "        #th.contents[0].attrs['title']\n",
      " \n",
      "    # Parsing the table\n",
      "    temp = dict()\n",
      "    for rowind, tr in enumerate(rows[1:-1]): # first and last contain no data\n",
      "        row = dict()\n",
      "        for coln, td in enumerate(tr.findAll('td')):\n",
      "            try:\n",
      "                row[index[coln]] = float(td.text)\n",
      "            except:\n",
      "                row[index[coln]] = td.text\n",
      "            if index[coln] == 'Day':\n",
      "                day = int(td.text)\n",
      "                weekday = datetime.datetime(year=year, month=month, day=day).weekday()\n",
      "                row['WD'] = weekday\n",
      "        temp[rowind] = row\n",
      " \n",
      "    return pd.DataFrame(temp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def sortByWeekday(data, years, months, label='RA'):\n",
      "    # sort by weekday\n",
      "    week = [[],[],[],[],[],[],[]]\n",
      "    for year in years:\n",
      "        for month in months:\n",
      "            for n in range(7):\n",
      "                weekday = (data[(year,month)].T['WD'] == n).values\n",
      "                if label=='PP':\n",
      "                    sublist = data[(year,month)].loc['PP'][weekday].tolist()\n",
      "                    week[n] += [ele for ele in sublist if type(ele) == float]\n",
      "                elif label=='RA':  # RA (rain or not)\n",
      "                    sublist = data[(year,month)].loc[label][weekday].tolist()\n",
      "                    week[n] += [1 if ele == 'o' else 0 for ele in sublist]\n",
      "    return pd.DataFrame(week)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = range(2011, 2015) # past 5 years till 2014\n",
      "months = range(1,13)      # all months"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# proxies = {\"http\": \"http://proxy.of.yours:port\"} # if needed"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = dict()\n",
      "print 'Processing: ', \n",
      "for year in years:\n",
      "    for month in months:\n",
      "        print (year, month),\n",
      "        data[(year,month)] = getData(month=month, year=year, proxies=proxies)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Processing  (2011, 1) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 2) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 3) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 4) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 5) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 6) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 7) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 8) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 9) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 10) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 11) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2011, 12) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 1) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 2) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 3) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 4) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 5) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 6) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 7) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 8) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 9) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 10) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 11) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2012, 12) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 1) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 2) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 3) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 4) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 5) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 6) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 7) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 8) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 9) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 10) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 11) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2013, 12) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 1) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 2) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 3) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 4) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 5) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 6) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 7) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 8) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 9) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 10) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 11) "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(2014, 12)\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def myplot(x,y,ax,_title):\n",
      "    bar(x, y, facecolor='none')\n",
      "    xticks(x+0.4, ['Mon', 'Tue', 'Wed', 'Thr', 'Fri', 'Sat', 'Sun'])\n",
      "    ax.spines['right'].set_visible(False)\n",
      "    ax.spines['top'].set_visible(False)\n",
      "    tick_params(direction='out', right='off', top='off')\n",
      "    title(_title)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(facecolor='w')\n",
      "x = np.arange(7)+0.4\n",
      "\n",
      "ax = subplot(221)\n",
      "Data2014 = sortByWeekday(data, years=[2014], months=months, label='RA')\n",
      "myplot(x, Data2014.T.sum(), ax, '2014')\n",
      "\n",
      "ax = subplot(222)\n",
      "Data2013 = sortByWeekday(data, years=[2013], months=months, label='RA')\n",
      "myplot(x, Data2013.T.sum(), ax, '2013')\n",
      "\n",
      "ax = subplot(223)\n",
      "Data2012 = sortByWeekday(data, years=[2012], months=months, label='RA')\n",
      "myplot(x, Data2012.T.sum(), ax, '2012')\n",
      "\n",
      "ax = subplot(224)\n",
      "Data2011 = sortByWeekday(data, years=[2011], months=months, label='RA')\n",
      "myplot(x, Data2011.T.sum(), ax, '2011')\n",
      "\n",
      "tight_layout()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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48SKOHj2KRx55BDqdDqWlpXLkReTyWEtExpl9kkRnZyfq6+tx4sQJfPXVV5g3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\noIiISJH+Pz9B6Bo5erWUAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x12dbee50>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In 2013, we tend to have bad weather toward the end of week. And, what just happend to Saturdays in 2014!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Data2014.T.sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "0    19\n",
        "1    18\n",
        "2    19\n",
        "3    15\n",
        "4    15\n",
        "5    21\n",
        "6    11\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#[Mon=0 to Sun=6], [# of rainy days in 2014]\n",
      "#0    19\n",
      "#1    18\n",
      "#2    19\n",
      "#3    15\n",
      "#4    15\n",
      "#5    21 (10 more days to be significant at 5%)\n",
      "#6    11\n",
      "chi, p = stats.mstats.chisquare( Data2014.T.sum() )\n",
      "# p = 0.665\n",
      "# Chill, it's not statistically significant at all. it's all by chance."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "years = [2015]\n",
      "months = range(1,4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for year in years:\n",
      "    for month in months:\n",
      "        print 'Processing ', year, month\n",
      "        data[(year,month)] = getData(month=month, year=year, proxies=proxies)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Processing  2015 1\n",
        "Processing "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2015 2\n",
        "Processing "
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 2015 3\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(facecolor='w')\n",
      "ax = subplot(111)\n",
      "Data2015 = sortByWeekday(data, years=years, months=[1,2,3], label='RA')\n",
      "myplot(x, Data2015.T.sum(), ax, '2015')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x930fc70>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In 2015 Jun-Mar, so far we have again rainy Saturdays."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(facecolor='w')\n",
      "ax = subplot(111)\n",
      "Data2014_2015 = Data2014.T.sum() + Data2015.T.sum()\n",
      "myplot(x, Data2014_2015, ax, '2014-2015Mar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x12ec41f0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "Data2014_2015"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "0    21\n",
        "1    19\n",
        "2    20\n",
        "3    19\n",
        "4    17\n",
        "5    27\n",
        "6    15\n",
        "dtype: float64"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "chi, p = stats.mstats.chisquare( Data2014_2015 )\n",
      "print p"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.631664737015\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Still not significant. But p value decreased."
     ]
    }
   ],
   "metadata": {}
  }
 ]
}