part4 - slicing, string interpolation, list comprehension, JSON weather API.ipynb

{
 "metadata": {
  "name": "part4 - slicing, string interpolation, list comprehension, JSON weather API"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import print_function\n",
      "from __future__ import division"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "4 / 3"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "1.3333333333333333"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# The Zen of Python"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import this"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The Zen of Python, by Tim Peters\n",
        "\n",
        "Beautiful is better than ugly.\n",
        "Explicit is better than implicit.\n",
        "Simple is better than complex.\n",
        "Complex is better than complicated.\n",
        "Flat is better than nested.\n",
        "Sparse is better than dense.\n",
        "Readability counts.\n",
        "Special cases aren't special enough to break the rules.\n",
        "Although practicality beats purity.\n",
        "Errors should never pass silently.\n",
        "Unless explicitly silenced.\n",
        "In the face of ambiguity, refuse the temptation to guess.\n",
        "There should be one-- and preferably only one --obvious way to do it.\n",
        "Although that way may not be obvious at first unless you're Dutch.\n",
        "Now is better than never.\n",
        "Although never is often better than *right* now.\n",
        "If the implementation is hard to explain, it's a bad idea.\n",
        "If the implementation is easy to explain, it may be a good idea.\n",
        "Namespaces are one honking great idea -- let's do more of those!\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Slicing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = range(10)\n",
      "print(a)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[2])\n",
      "print(a[-1])\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2\n",
        "9\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[2:4])\n",
      "print(a[2:5:2])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2, 3]\n",
        "[2, 4]\n"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print(a[2:])\n",
      "# reverse the whole list\n",
      "print(a[::-1])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[2, 3, 4, 5, 6, 7, 8, 9]\n",
        "[9, 8, 7, 6, 5, 4, 3, 2, 1, 0]\n"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# replace a subset of the list in one go\n",
      "b = range(11,15)\n",
      "a[1:5] = b\n",
      "print(a)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[0, 11, 12, 13, 14, 5, 6, 7, 8, 9]\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# a string works like a list of characters\n",
      "s = \"The quick brown fox jumps over the lazy dog.\"\n",
      "s[4:-5]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "'quick brown fox jumps over the lazy'"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# List comprehension"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# turn the sentence into a list of words\n",
      "# by default this splits on space, but you could use any other delimiter\n",
      "words = s.split()\n",
      "# print the length of all words that are not in the stop list\n",
      "print([len(w) for w in words if w.lower() not in ['the', 'a']])\n",
      "# upper case only the words containing O\n",
      "[w.upper() if 'o' in w else w for w in words]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[5, 5, 3, 5, 4, 4, 4]\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "['The', 'quick', 'BROWN', 'FOX', 'jumps', 'OVER', 'the', 'lazy', 'DOG.']"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# String interpolation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = \"The quick brown %s jumps over the lazy %s.\" % ('dog', 'fox')\n",
      "print(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The quick brown dog jumps over the lazy fox.\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = \"The quick brown %(quick_animal)s jumps over the lazy %(lazy_animal)s.\" % {'quick_animal': 'fox', 'lazy_animal': 'dog'}\n",
      "print(s)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "The quick brown fox jumps over the lazy dog.\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Using a JSON weather forecasting API to plot nice graphs"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import urllib2\n",
      "\n",
      "json_fileobject = urllib2.urlopen('http://api.openweathermap.org/data/2.5/forecast/daily?q=Delft,nl&mode=json&units=metric&cnt=7')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdict = json.load(json_fileobject)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# wdict['list'] contains a list with a dictionary for each day\n",
      "print(wdict['list'][0]['rain'])\n",
      "print(wdict['list'][0]['speed'])\n",
      "# this will display the per-day dictionary\n",
      "wdict['list'][0]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "3\n",
        "3.4\n"
       ]
      },
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "{u'clouds': 56,\n",
        " u'deg': 193,\n",
        " u'dt': 1372762800,\n",
        " u'humidity': 77,\n",
        " u'pressure': 1023.03,\n",
        " u'rain': 3,\n",
        " u'speed': 3.4,\n",
        " u'temp': {u'day': 19,\n",
        "  u'eve': 19,\n",
        "  u'max': 19,\n",
        "  u'min': 16.11,\n",
        "  u'morn': 19,\n",
        "  u'night': 16.11},\n",
        " u'weather': [{u'description': u'light rain',\n",
        "   u'icon': u'10d',\n",
        "   u'id': 500,\n",
        "   u'main': u'Rain'}]}"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "[d['temp']['day'] for d in wdict['list']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "[19, 15.84, 17.07, 15.17, 17.37, 20.63, 18.27]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdict['main']['temp']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 58,
       "text": [
        "20"
       ]
      }
     ],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "import json\n",
      "import urllib2\n",
      "\n",
      "def plot_weather_forecast(city, country_code, num_days):\n",
      "    url = 'http://api.openweathermap.org/data/2.5/forecast/daily?q=%s,%s&mode=json&units=metric&cnt=%d' % (city, country_code, num_days)\n",
      "    json_fileobject = urllib2.urlopen(url)\n",
      "    wdict = json.load(json_fileobject)\n",
      "    \n",
      "    tdr = [(d['temp']['min'], d['temp']['max'], d.get('rain',0), d['speed']) for d in wdict['list']]\n",
      "    \n",
      "    days = range(num_days)\n",
      "    \n",
      "    plot(days, tdr)\n",
      "\n",
      "    \n",
      "    day_words = ['sun', 'mon', 'tue', 'wed', 'thu', 'fri', 'sat']\n",
      "    # which day is it today? [0 is Sunday]\n",
      "    day = datetime.date.today().weekday\n",
      "    # slice the day_words array in two, split at today, then stick\n",
      "    # the first part after the second part. this gives us a new\n",
      "    # array which starts at today!\n",
      "    day_words_starting_today = day_words[day:] + day_words[:day]\n",
      "    \n",
      "    # we can now use this for the correct day labels on the x-axis\n",
      "    xticks(days, day_words_starting_today)\n",
      "    \n",
      "    legend(['temp min', 'temp max', 'rain', 'wind speed'], loc='best')\n",
      "    \n",
      "    title(\"%d day forecast for %s\" % (num_days, city))\n",
      "    grid()\n",
      "    \n",
      "plot_weather_forecast('Delft','nl',7)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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UkWmRmOc2D295vAV7I3ultEedFBYCO3dKR7gYGwPLlgEBAYC2Gk/fVFEB3LtX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\n8mONjdXuRD6iHKo33JEx4NEjaaKvLN8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3bkVJSQmCg4NhYGAAX19fWTyVlQF18Wpbq67z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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "t = datetime.date.today()\n",
      "t.weekday"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "2"
       ]
      }
     ],
     "prompt_number": 123
    }
   ],
   "metadata": {}
  }
 ]
}