Weather forecast.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weather Forecast with PixieDust\n",
    "\n",
    "This notebook shows how to:\n",
    "1. use the Weather Company Data API to get weather forecast json data based on latitude and longitude\n",
    "2. convert this json data into a [pandas](http://pandas.pydata.org/pandas-docs/stable/) DataFrame\n",
    "4. create a weather chart and map with [matplotlib](http://matplotlib.org/)\n",
    "3. create a weather chart and map with [PixieDust](https://ibm-cds-labs.github.io/pixiedust/index.html)\n",
    "\n",
    "Before running the notebook:\n",
    "* Sign up for a free 30-day trial [Bluemix account](https://console.ng.bluemix.net/)\n",
    "* Launch the Weather Data service in Bluemix and fill in the credentials below. Learn more [here](https://console.ng.bluemix.net/docs/services/Weather/index.html)\n",
    "* Run this notebook locally or in the Cloud using the [IBM Data Science Experience](https://datascience.ibm.com/)\n",
    "\n",
    "## 1. Load and install packages\n",
    "\n",
    "First, uncomment the lines in the below cell and upgrade the pixiedust and bokeh packages. When this is done restart the kernel. You have to do this only once, or when there is an update available.\n",
    "\n",
    "Then import the package needed to run this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#!pip install --upgrade pixiedust\n",
    "#!pip install --upgrade bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pixiedust database opened successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <div style=\"margin:10px\">\n",
       "            <a href=\"https://github.com/ibm-cds-labs/pixiedust\" target=\"_new\">\n",
       "                <img src=\"https://github.com/ibm-cds-labs/pixiedust/raw/master/docs/_static/pd_icon32.png\" style=\"float:left;margin-right:10px\"/>\n",
       "            </a>\n",
       "            <span>Pixiedust version 1.0.4</span>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import datetime\n",
    "import time\n",
    "import pixiedust"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Get weather data\n",
    "\n",
    "Find the latitude and longitude of your current location by running this magic javascript cell. Then fill in your Weather Company API credentials to load the weather forecast for where you are. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "navigator.geolocation.getCurrentPosition(function(position) {\n",
       "  console.log(position.coords.latitude, position.coords.longitude);\n",
       "setTimeout(function() {\n",
       "IPython.notebook.kernel.execute('lat=\"' + position.coords.latitude + '\";')\n",
       "IPython.notebook.kernel.execute('lon=\"' + position.coords.longitude + '\";')\n",
       "},5000)});"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%javascript\n",
    "navigator.geolocation.getCurrentPosition(function(position) {\n",
    "  console.log(position.coords.latitude, position.coords.longitude);\n",
    "setTimeout(function() {\n",
    "IPython.notebook.kernel.execute('lat=\"' + position.coords.latitude + '\";')\n",
    "IPython.notebook.kernel.execute('lon=\"' + position.coords.longitude + '\";')\n",
    "},5000)});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait a few seconds to run the second cell to allow the above geolocation function to run. Or provide the lat and lon of the location you want to get a weather forecast for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Barcelona\n",
    "lat = str(41.3850639)\n",
    "lon = str(2.1734035)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('41.3850639', '2.1734035')\n"
     ]
    }
   ],
   "source": [
    "print(lat, lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# @hidden_cell\n",
    "# Weather company data API credentials\n",
    "username='xxxx'\n",
    "password='xxxx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "line='https://'+username+':'+password+\\\n",
    "    '@twcservice.mybluemix.net/api/weather/v1/geocode/'+\\\n",
    "    lat+'/'+lon+'/forecast/intraday/10day.json?&units=m'\n",
    "r=requests.get(line)\n",
    "weather = json.loads(r.text)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Uncomment and run the next cell to have a look what the json data file looks like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#print json.dumps(weather, indent=4, sort_keys=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Convert json data to pandas DataFrame\n",
    "\n",
    "Convert the data into a DataFrame with each timestep on a new row. Convert the timestamp into a datetime format and drop the columns that are not needed. See this [Cheat sheet](http://www.saltycrane.com/blog/2008/11/python-datetime-time-conversions/) for date format conversions. Finally, convert the data type into numeric. \n",
    "\n",
    "You may have to change to format of the time to your time zone '%Y-%m-%dT%H:%M:%S+0200' for Europe or '%Y-%m-%dT%H:%M:%S+0100' for the UK during summer time for example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop                    object\n",
       "wspd                   object\n",
       "rh                     object\n",
       "icon_code              object\n",
       "clds                   object\n",
       "wdir                   object\n",
       "temp                   object\n",
       "precip_type            object\n",
       "dow                    object\n",
       "date           datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(weather['forecasts'][0],orient='index').transpose()\n",
    "for forecast in weather['forecasts'][1:]:\n",
    "    df = pd.concat([df, pd.DataFrame.from_dict(forecast,orient='index').transpose()])\n",
    "\n",
    "df['date'] = df['fcst_valid_local'].apply(lambda x: datetime.strptime(x, '%Y-%m-%dT%H:%M:%S+0200'))\n",
    "\n",
    "df = df.drop(['expire_time_gmt','num','qualifier','qualifier_code'],1)   \n",
    "df = df.drop(['fcst_valid','fcst_valid_local','icon_extd','wdir_cardinal'],1)   \n",
    "df = df.drop(['subphrase_pt1','subphrase_pt2','subphrase_pt3','class'],1)   \n",
    "df = df.drop(['daypart_name','phrase_12char','phrase_22char','phrase_32char'],1)   \n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop                     int64\n",
       "wspd                    int64\n",
       "rh                      int64\n",
       "icon_code              object\n",
       "clds                    int64\n",
       "wdir                    int64\n",
       "temp                    int64\n",
       "precip_type            object\n",
       "dow                    object\n",
       "date           datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['pop','wspd','rh','clds','wdir','temp']] = df[['pop','wspd','rh','clds','wdir','temp']].apply(pd.to_numeric)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there seems to be an issue with the `pop` column (percentage of precipitation), create a new column rain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df['rain'] = df['pop'].as_matrix()\n",
    "df=df.drop('pop',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wspd</th>\n",
       "      <th>rh</th>\n",
       "      <th>icon_code</th>\n",
       "      <th>clds</th>\n",
       "      <th>wdir</th>\n",
       "      <th>temp</th>\n",
       "      <th>precip_type</th>\n",
       "      <th>dow</th>\n",
       "      <th>date</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>125</td>\n",
       "      <td>21</td>\n",
       "      <td>rain</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2017-05-17 19:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>79</td>\n",
       "      <td>33</td>\n",
       "      <td>22</td>\n",
       "      <td>357</td>\n",
       "      <td>17</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 01:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>75</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "      <td>19</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 07:00:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17</td>\n",
       "      <td>67</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>155</td>\n",
       "      <td>22</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 13:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>87</td>\n",
       "      <td>260</td>\n",
       "      <td>19</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 19:00:00</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   wspd  rh icon_code  clds  wdir  temp precip_type        dow  \\\n",
       "0    12  66        29    33   125    21        rain  Wednesday   \n",
       "0    10  79        33    22   357    17        rain   Thursday   \n",
       "0    16  75        32    11    59    19        rain   Thursday   \n",
       "0    17  67        34    20   155    22        rain   Thursday   \n",
       "0    16  74        12    87   260    19        rain   Thursday   \n",
       "\n",
       "                 date  rain  \n",
       "0 2017-05-17 19:00:00     5  \n",
       "0 2017-05-18 01:00:00     5  \n",
       "0 2017-05-18 07:00:00     3  \n",
       "0 2017-05-18 13:00:00     2  \n",
       "0 2017-05-18 19:00:00    84  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Plot data with matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>wspd</th>\n",
       "      <th>rh</th>\n",
       "      <th>icon_code</th>\n",
       "      <th>clds</th>\n",
       "      <th>wdir</th>\n",
       "      <th>temp</th>\n",
       "      <th>precip_type</th>\n",
       "      <th>dow</th>\n",
       "      <th>date</th>\n",
       "      <th>rain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <th>2017-05-17 19:00:00</th>\n",
       "      <td>12</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>33</td>\n",
       "      <td>125</td>\n",
       "      <td>21</td>\n",
       "      <td>rain</td>\n",
       "      <td>Wednesday</td>\n",
       "      <td>2017-05-17 19:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-18 01:00:00</th>\n",
       "      <td>10</td>\n",
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       "      <td>33</td>\n",
       "      <td>22</td>\n",
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       "      <td>17</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 01:00:00</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-18 07:00:00</th>\n",
       "      <td>16</td>\n",
       "      <td>75</td>\n",
       "      <td>32</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "      <td>19</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 07:00:00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-18 13:00:00</th>\n",
       "      <td>17</td>\n",
       "      <td>67</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>155</td>\n",
       "      <td>22</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 13:00:00</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-18 19:00:00</th>\n",
       "      <td>16</td>\n",
       "      <td>74</td>\n",
       "      <td>12</td>\n",
       "      <td>87</td>\n",
       "      <td>260</td>\n",
       "      <td>19</td>\n",
       "      <td>rain</td>\n",
       "      <td>Thursday</td>\n",
       "      <td>2017-05-18 19:00:00</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     wspd  rh icon_code  clds  wdir  temp precip_type  \\\n",
       "date                                                                    \n",
       "2017-05-17 19:00:00    12  66        29    33   125    21        rain   \n",
       "2017-05-18 01:00:00    10  79        33    22   357    17        rain   \n",
       "2017-05-18 07:00:00    16  75        32    11    59    19        rain   \n",
       "2017-05-18 13:00:00    17  67        34    20   155    22        rain   \n",
       "2017-05-18 19:00:00    16  74        12    87   260    19        rain   \n",
       "\n",
       "                           dow                date  rain  \n",
       "date                                                      \n",
       "2017-05-17 19:00:00  Wednesday 2017-05-17 19:00:00     5  \n",
       "2017-05-18 01:00:00   Thursday 2017-05-18 01:00:00     5  \n",
       "2017-05-18 07:00:00   Thursday 2017-05-18 07:00:00     3  \n",
       "2017-05-18 13:00:00   Thursday 2017-05-18 13:00:00     2  \n",
       "2017-05-18 19:00:00   Thursday 2017-05-18 19:00:00    84  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.set_index('date',drop=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f696ec6de50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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kH5jZ77v7V8N9tylIv/uQpMOS/jHsvyTdq3hmGm57haS/lvRDBeduuaRc+HO/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W4uYX6TSgAXUUlbHwXwnwGYAAoA/gUzvxDXGHWkxrwNAPhlVORN32Lm345tgJpCRA8C2A/gFVTm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\nwCIIgiAIgiAIgrb8/x8uCOJuo64iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f696ece7790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "%matplotlib inline\n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(14, 8))\n",
    "\n",
    "df['temp'].plot(ax=axes[1], color='#6EEDD8',lw=3.0,sharex=True)\n",
    "axes[1].set_title('Temperature',loc='left',fontsize=20)\n",
    "\n",
    "df['rain'].plot(ax=axes[0], kind='bar', color='#C93D79',lw=2.0,sharex=True)\n",
    "axes[0].set_title('Chance of rain',loc='left',fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Create a temperature map for the UK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cities = [\n",
    "    ('Exeter',50.7184,-3.5339),\n",
    "    ('Truro',50.2632,-5.051),\n",
    "    ('Carmarthen',51.8576,-4.3121),\n",
    "    ('Norwich',52.6309,1.2974),\n",
    "    ('Brighton And Hove',50.8225,-0.1372),\n",
    "    ('Bristol',51.44999778,-2.583315472),\n",
    "    ('Durham',54.7753,-1.5849),\n",
    "    ('Llanidloes',52.4135,-3.5883),\n",
    "    ('Penrith',54.6641,-2.7527),\n",
    "    ('Jedburgh',55.4777,-2.5549),\n",
    "    ('Coventry',52.42040367,-1.499996583),\n",
    "    ('Edinburgh',55.94832786,-3.219090618),\n",
    "    ('Cambridge',52.2053,0.1218),\n",
    "    ('Glasgow',55.87440472,-4.250707236),\n",
    "    ('Kingston upon Hull',53.7457,-0.3367),\n",
    "    ('Leeds',53.83000755,-1.580017539),\n",
    "    ('London',51.49999473,-0.116721844),\n",
    "    ('Manchester',53.50041526,-2.247987103),\n",
    "    ('Nottingham',52.97034426,-1.170016725),\n",
    "    ('Aberdeen',57.1497,-2.0943),\n",
    "    ('Fort Augustus',57.1448,-4.6805),\n",
    "    ('Lairg',58.197,-4.6173),\n",
    "    ('Oxford',51.7517,-1.2553),\n",
    "    ('Inverey',56.9855,-3.5055),\n",
    "    ('Shrewsbury',52.7069,-2.7527),\n",
    "    ('Colwyn Bay',53.2932,-3.7276),\n",
    "    ('Newton Stewart',54.9186,-4.5918),    \n",
    "    ('Portsmouth',50.80034751,-1.080022218)]   \n",
    "\n",
    "icons=[]\n",
    "temps=[]\n",
    "for city in cities:\n",
    "    lat = city[1]\n",
    "    lon = city[2]\n",
    "    line='https://'+username+':'+password+'@twcservice.mybluemix.net/api/weather/v1/geocode/'+str(lat)+'/'+str(lon)+'/observations.json?&units=m'\n",
    "    r=requests.get(line)\n",
    "    weather = json.loads(r.text)    \n",
    "    icons=np.append(icons,weather['observation']['wx_icon'])    \n",
    "    temps=np.append(temps,weather['observation']['temp']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>temp</th>\n",
       "      <th>icon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Exeter</td>\n",
       "      <td>50.718400</td>\n",
       "      <td>-3.533900</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Truro</td>\n",
       "      <td>50.263200</td>\n",
       "      <td>-5.051000</td>\n",
       "      <td>12</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carmarthen</td>\n",
       "      <td>51.857600</td>\n",
       "      <td>-4.312100</td>\n",
       "      <td>13</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Norwich</td>\n",
       "      <td>52.630900</td>\n",
       "      <td>1.297400</td>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brighton And Hove</td>\n",
       "      <td>50.822500</td>\n",
       "      <td>-0.137200</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Bristol</td>\n",
       "      <td>51.449998</td>\n",
       "      <td>-2.583315</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Durham</td>\n",
       "      <td>54.775300</td>\n",
       "      <td>-1.584900</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Llanidloes</td>\n",
       "      <td>52.413500</td>\n",
       "      <td>-3.588300</td>\n",
       "      <td>12</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Penrith</td>\n",
       "      <td>54.664100</td>\n",
       "      <td>-2.752700</td>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jedburgh</td>\n",
       "      <td>55.477700</td>\n",
       "      <td>-2.554900</td>\n",
       "      <td>14</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Coventry</td>\n",
       "      <td>52.420404</td>\n",
       "      <td>-1.499997</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Edinburgh</td>\n",
       "      <td>55.948328</td>\n",
       "      <td>-3.219091</td>\n",
       "      <td>17</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Cambridge</td>\n",
       "      <td>52.205300</td>\n",
       "      <td>0.121800</td>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Glasgow</td>\n",
       "      <td>55.874405</td>\n",
       "      <td>-4.250707</td>\n",
       "      <td>16</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Kingston upon Hull</td>\n",
       "      <td>53.745700</td>\n",
       "      <td>-0.336700</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Leeds</td>\n",
       "      <td>53.830008</td>\n",
       "      <td>-1.580018</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>London</td>\n",
       "      <td>51.499995</td>\n",
       "      <td>-0.116722</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Manchester</td>\n",
       "      <td>53.500415</td>\n",
       "      <td>-2.247987</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nottingham</td>\n",
       "      <td>52.970344</td>\n",
       "      <td>-1.170017</td>\n",
       "      <td>12</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Aberdeen</td>\n",
       "      <td>57.149700</td>\n",
       "      <td>-2.094300</td>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Fort Augustus</td>\n",
       "      <td>57.144800</td>\n",
       "      <td>-4.680500</td>\n",
       "      <td>17</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Lairg</td>\n",
       "      <td>58.197000</td>\n",
       "      <td>-4.617300</td>\n",
       "      <td>13</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Oxford</td>\n",
       "      <td>51.751700</td>\n",
       "      <td>-1.255300</td>\n",
       "      <td>13</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Inverey</td>\n",
       "      <td>56.985500</td>\n",
       "      <td>-3.505500</td>\n",
       "      <td>10</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shrewsbury</td>\n",
       "      <td>52.706900</td>\n",
       "      <td>-2.752700</td>\n",
       "      <td>12</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  city        lat       lon  temp  icon\n",
       "0               Exeter  50.718400 -3.533900     9     9\n",
       "1                Truro  50.263200 -5.051000    12    26\n",
       "2           Carmarthen  51.857600 -4.312100    13    28\n",
       "3              Norwich  52.630900  1.297400    19    30\n",
       "4    Brighton And Hove  50.822500 -0.137200    17  None\n",
       "5              Bristol  51.449998 -2.583315    12    11\n",
       "6               Durham  54.775300 -1.584900    14  None\n",
       "7           Llanidloes  52.413500 -3.588300    12  None\n",
       "8              Penrith  54.664100 -2.752700    13  None\n",
       "9             Jedburgh  55.477700 -2.554900    14  None\n",
       "10            Coventry  52.420404 -1.499997    11    12\n",
       "11           Edinburgh  55.948328 -3.219091    17    30\n",
       "12           Cambridge  52.205300  0.121800    15    12\n",
       "13             Glasgow  55.874405 -4.250707    16    34\n",
       "14  Kingston upon Hull  53.745700 -0.336700    14    11\n",
       "15               Leeds  53.830008 -1.580018    11    11\n",
       "16              London  51.499995 -0.116722    18    26\n",
       "17          Manchester  53.500415 -2.247987    12    11\n",
       "18          Nottingham  52.970344 -1.170017    12    40\n",
       "19            Aberdeen  57.149700 -2.094300    15    34\n",
       "20       Fort Augustus  57.144800 -4.680500    17  None\n",
       "21               Lairg  58.197000 -4.617300    13  None\n",
       "22              Oxford  51.751700 -1.255300    13    12\n",
       "23             Inverey  56.985500 -3.505500    10  None\n",
       "24          Shrewsbury  52.706900 -2.752700    12    11"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmap = pd.DataFrame(cities, columns=['city','lat','lon'])    \n",
    "dfmap['temp']=temps\n",
    "dfmap['icon']=icons\n",
    "dfmap.head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Trybh9hR0XqOyChGR2PhyfRyffNzuMEyV2dbGgnfeYeaOHQw6bv7X6nM42FFS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\ngAJkEREREZEACpBFRERERAL8f7XP/xnoA1HeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f696e9ec090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.basemap import Basemap\n",
    "from matplotlib.offsetbox import AnnotationBbox, OffsetImage\n",
    "from matplotlib._png import read_png\n",
    "from itertools import izip\n",
    "import urllib\n",
    "\n",
    "matplotlib.style.use('bmh')\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(10, 12))\n",
    "\n",
    "# background maps\n",
    "m1 = Basemap(projection='mill',resolution=None,llcrnrlon=-7.5,llcrnrlat=49.84,urcrnrlon=2.5,urcrnrlat=59,ax=axes[0])\n",
    "m1.drawlsmask(land_color='dimgrey',ocean_color='dodgerBlue',lakes=True)\n",
    "\n",
    "m2 = Basemap(projection='mill',resolution=None,llcrnrlon=-7.5,llcrnrlat=49.84,urcrnrlon=2.5,urcrnrlat=59,ax=axes[1])\n",
    "m2.drawlsmask(land_color='dimgrey',ocean_color='dodgerBlue',lakes=True)\n",
    "\n",
    "# weather icons map\n",
    "for [icon,city] in izip(icons,cities):\n",
    "    lat = city[1]\n",
    "    lon = city[2]\n",
    "    try:\n",
    "        pngfile=urllib.urlopen('https://github.com/ibm-cds-labs/python-notebooks/blob/master/weathericons/icon'+str(int(icon))+'.png?raw=true')\n",
    "        icon_hand = read_png(pngfile)\n",
    "        imagebox = OffsetImage(icon_hand, zoom=.15)\n",
    "        ab = AnnotationBbox(imagebox,m1(lon,lat),frameon=False) \n",
    "        axes[0].add_artist(ab)\n",
    "    except:\n",
    "        pass\n",
    "        \n",
    "# temperature map    \n",
    "for [temp,city] in izip(temps,cities):\n",
    "    lat = city[1]\n",
    "    lon = city[2]\n",
    "    if temp>16: \n",
    "        col='indigo'\n",
    "    elif temp>14:\n",
    "        col='darkmagenta'\n",
    "    elif temp>12:\n",
    "        col='red'\n",
    "    elif temp>10:\n",
    "        col='tomato'\n",
    "    elif temp>0:\n",
    "        col='turquoise'\n",
    "            \n",
    "    x1, y1 = m2(lon,lat)\n",
    "    bbox_props = dict(boxstyle=\"round,pad=0.3\", fc=col, ec=col, lw=2)\n",
    "    axes[1].text(x1, y1, temp, ha=\"center\", va=\"center\",\n",
    "                size=11,bbox=bbox_props)\n",
    "    \n",
    "plt.tight_layout()  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "## 6. Plot data with PixieDust\n",
    "\n",
    "https://ibm-cds-labs.github.io/pixiedust/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "pixiedust": {
     "displayParams": {
      "aggregation": "SUM",
      "handlerId": "lineChart",
      "keyFields": "date",
      "lineChartType": "subplots",
      "rendererId": "matplotlib",
      "rowCount": "100",
      "valueFields": "temp,rain"
     }
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">.pd_warning{display:none;}</style><div class=\"pd_warning\"><em>Hey, there's something awesome here! To see it, open this notebook outside GitHub, in a viewer like Jupyter</em></div>\n",
       "        <div class=\"pd_save is-viewer-good\" style=\"padding-right:10px;text-align: center;line-height:initial !important;font-size: xx-large;font-weight: 500;color: coral;\">\n",
       "            \n",
       "        </div>\n",
       "    <div id=\"chartFigure8816b7b3\" class=\"pd_save is-viewer-good\" style=\"overflow-x:auto\">\n",
       "            \n",
       "                    \n",
       "                            <center><img style=\"max-width:initial !important\" 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\" class=\"pd_save\"></center>\n",
       "                        \n",
       "                    \n",
       "                \n",
       "        </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#display(dfmap)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2 with Spark 2.0",
   "language": "python",
   "name": "python2-spark20"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}