{
 "metadata": {
  "Author": "Nikolay Koldunov",
  "Category": "Data processing",
  "Date": "2013-4-16",
  "Tags": "pandas, data analysis",
  "Title": "Climatology data access with ulmo",
  "name": "ulmo",
  "slug": "ulmo"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "**Task:**\n",
      "    \n",
      "    easy access to climatology data \n",
      "    \n",
      "**Solution:**\n",
      "    \n",
      "    ulmo\n",
      "    \n",
      "[**Notebook file**](http://nbviewer.ipython.org/urls/raw.github.com/koldunovn/earthpy.org/master/content/ulmo.ipynb)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "One of the main things that bothers me most at work is data conversion. World would be a much better place for somebody like me if everybody use netCDF file format for data distribution. While situation slightly changing, and more and more organisations switch to netCDF, there are still plenty of those who distribute their data in some crazy forms.\n",
      "\n",
      "Would it be nice if somebody once and for all create converters for all this formats and provide a way to directly search and access data from python? Imagine - instead of spending time writing regular expressions for another converter you could <s>watch cat videos on youtube</s> do more actual research stuff. [Project ulmo](http://ulmo.readthedocs.org/en/latest/index.html) tries to do something like this (AFAIU) and provide \"clean, simple and fast access to public hydrology and climatology data\".\n",
      "\n",
      "They certainly now do more hydrology than climatology data, but for me there is also something interesting - historical measurements from meteorological stations. In the following I will give you an example of how to access those, so you get an idea of what ulmo is doing."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Nessesary preparations, as usual:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.kernel.zmq.pylab.backend_inline].\n",
        "For more information, type 'help(pylab)'.\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Installation is very simple with pip, but if you need development version, go to [githib repository](https://github.com/twdb/ulmo)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "!pip install ulmo"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Import ulmo and pandas (you already use pandas for data analysis, right? if not, you have to, go and read [this post](http://earthpy.org/pandas-basics.html) first)."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import ulmo\n",
      "import pandas"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We are going to work with data from [National Climatic Data Center Global Historical Climate Network - Daily](http://www.ncdc.noaa.gov/oa/climate/ghcn-daily/) dataset. Say, we would like to find station in our home town. It's a good idea to start by getting information obout stations in the country of interest. Ulmo has a function **ulmo.ncdc.ghcn_daily.get_stations** that can obtain information about stations available in GHCN dataset and also let you define some conditions for the search, like country, time span, and specific variables. \n",
      "\n",
      "Country should be provided as a country code, and list of countries available [here](http://www1.ncdc.noaa.gov/pub/data/ghcn/daily/ghcnd-countries.txt). We will search for station in Germany (GM) and ask to return pandas Data Frame. Be patient it might take some time."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st = ulmo.ncdc.ghcn_daily.get_stations(country='GM', as_dataframe=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we have a nice table with information about all available German meteorological stations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>country</th>\n",
        "      <th>network</th>\n",
        "      <th>network_id</th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "      <th>elevation</th>\n",
        "      <th>state</th>\n",
        "      <th>name</th>\n",
        "      <th>gsn_flag</th>\n",
        "      <th>hcn_flag</th>\n",
        "      <th>wm_oid</th>\n",
        "      <th>id</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>GM000001153</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00001153</td>\n",
        "      <td> 51.9506</td>\n",
        "      <td> 7.5914</td>\n",
        "      <td>  62</td>\n",
        "      <td> NaN</td>\n",
        "      <td>       MUENSTER</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 10313</td>\n",
        "      <td> GM000001153</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GM000001474</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00001474</td>\n",
        "      <td> 53.0464</td>\n",
        "      <td> 8.7992</td>\n",
        "      <td>   4</td>\n",
        "      <td> NaN</td>\n",
        "      <td>         BREMEN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 10224</td>\n",
        "      <td> GM000001474</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GM000002277</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00002277</td>\n",
        "      <td> 49.7517</td>\n",
        "      <td> 6.6467</td>\n",
        "      <td> 144</td>\n",
        "      <td> NaN</td>\n",
        "      <td>          TRIER</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 10609</td>\n",
        "      <td> GM000002277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GM000002288</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00002288</td>\n",
        "      <td> 49.4253</td>\n",
        "      <td> 7.7367</td>\n",
        "      <td> 285</td>\n",
        "      <td> NaN</td>\n",
        "      <td> KAISERSLAUTERN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> GM000002288</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GM000002698</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00002698</td>\n",
        "      <td> 49.0392</td>\n",
        "      <td> 8.3650</td>\n",
        "      <td> 112</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      KARLSRUHE</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 10727</td>\n",
        "      <td> GM000002698</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "            country network network_id  latitude  longitude  elevation state  \\\n",
        "id                                                                             \n",
        "GM000001153      GM       0   00001153   51.9506     7.5914         62   NaN   \n",
        "GM000001474      GM       0   00001474   53.0464     8.7992          4   NaN   \n",
        "GM000002277      GM       0   00002277   49.7517     6.6467        144   NaN   \n",
        "GM000002288      GM       0   00002288   49.4253     7.7367        285   NaN   \n",
        "GM000002698      GM       0   00002698   49.0392     8.3650        112   NaN   \n",
        "\n",
        "                       name gsn_flag hcn_flag wm_oid           id  \n",
        "id                                                                 \n",
        "GM000001153        MUENSTER      NaN      NaN  10313  GM000001153  \n",
        "GM000001474          BREMEN      NaN      NaN  10224  GM000001474  \n",
        "GM000002277           TRIER      NaN      NaN  10609  GM000002277  \n",
        "GM000002288  KAISERSLAUTERN      NaN      NaN    NaN  GM000002288  \n",
        "GM000002698       KARLSRUHE      NaN      NaN  10727  GM000002698  "
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's search for Hamburg stations:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "st[st.name.str.contains('HAMBURG')]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>country</th>\n",
        "      <th>network</th>\n",
        "      <th>network_id</th>\n",
        "      <th>latitude</th>\n",
        "      <th>longitude</th>\n",
        "      <th>elevation</th>\n",
        "      <th>state</th>\n",
        "      <th>name</th>\n",
        "      <th>gsn_flag</th>\n",
        "      <th>hcn_flag</th>\n",
        "      <th>wm_oid</th>\n",
        "      <th>id</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>id</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>GM000003865</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00003865</td>\n",
        "      <td> 53.4806</td>\n",
        "      <td> 10.2428</td>\n",
        "      <td> 35</td>\n",
        "      <td> NaN</td>\n",
        "      <td>    HAMBURG BERGEDORF</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> GM000003865</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>GM000010147</th>\n",
        "      <td> GM</td>\n",
        "      <td> 0</td>\n",
        "      <td> 00010147</td>\n",
        "      <td> 53.6350</td>\n",
        "      <td>  9.9900</td>\n",
        "      <td> 11</td>\n",
        "      <td> NaN</td>\n",
        "      <td> HAMBURG FUHLSBUETTEL</td>\n",
        "      <td> GSN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> 10147</td>\n",
        "      <td> GM000010147</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 45,
       "text": [
        "            country network network_id  latitude  longitude  elevation state  \\\n",
        "id                                                                             \n",
        "GM000003865      GM       0   00003865   53.4806    10.2428         35   NaN   \n",
        "GM000010147      GM       0   00010147   53.6350     9.9900         11   NaN   \n",
        "\n",
        "                             name gsn_flag hcn_flag wm_oid           id  \n",
        "id                                                                       \n",
        "GM000003865     HAMBURG BERGEDORF      NaN      NaN    NaN  GM000003865  \n",
        "GM000010147  HAMBURG FUHLSBUETTEL      GSN      NaN  10147  GM000010147  "
       ]
      }
     ],
     "prompt_number": 45
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "There are only two stations, and we are interested only in data from HAMBURG FUHLSBUETTEL. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Getting the data is also very easy,. The only thing you need is the id of your station:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = ulmo.ncdc.ghcn_daily.get_data('GM000010147', as_dataframe=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "This function returns dictionary with names of the variables as keys and pandas data frames with measurements as values:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "{'PRCP': <class 'pandas.core.frame.DataFrame'>\n",
        "PeriodIndex: 44591 entries, 1891-01-01 to 2013-01-31\n",
        "Data columns:\n",
        "value    44591  non-null values\n",
        "mflag    0  non-null values\n",
        "qflag    0  non-null values\n",
        "sflag    44591  non-null values\n",
        "dtypes: object(4),\n",
        " 'SNWD': <class 'pandas.core.frame.DataFrame'>\n",
        "PeriodIndex: 28855 entries, 1934-02-01 to 2013-01-31\n",
        "Data columns:\n",
        "value    27829  non-null values\n",
        "mflag    0  non-null values\n",
        "qflag    0  non-null values\n",
        "sflag    27829  non-null values\n",
        "dtypes: object(4),\n",
        " 'TMAX': <class 'pandas.core.frame.DataFrame'>\n",
        "PeriodIndex: 44591 entries, 1891-01-01 to 2013-01-31\n",
        "Data columns:\n",
        "value    44591  non-null values\n",
        "mflag    0  non-null values\n",
        "qflag    195  non-null values\n",
        "sflag    44591  non-null values\n",
        "dtypes: object(4),\n",
        " 'TMIN': <class 'pandas.core.frame.DataFrame'>\n",
        "PeriodIndex: 44591 entries, 1891-01-01 to 2013-01-31\n",
        "Data columns:\n",
        "value    44591  non-null values\n",
        "mflag    0  non-null values\n",
        "qflag    195  non-null values\n",
        "sflag    44591  non-null values\n",
        "dtypes: object(4)}"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Let's get maximum daily temperatures:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tm = data['TMAX'].copy()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tm.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>value</th>\n",
        "      <th>mflag</th>\n",
        "      <th>qflag</th>\n",
        "      <th>sflag</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1891-01-01</th>\n",
        "      <td>-72</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1891-01-02</th>\n",
        "      <td>-43</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1891-01-03</th>\n",
        "      <td>-32</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1891-01-04</th>\n",
        "      <td> 12</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> E</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1891-01-05</th>\n",
        "      <td>-29</td>\n",
        "      <td> NaN</td>\n",
        "      <td> NaN</td>\n",
        "      <td> E</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "           value mflag qflag sflag\n",
        "1891-01-01   -72   NaN   NaN     E\n",
        "1891-01-02   -43   NaN   NaN     E\n",
        "1891-01-03   -32   NaN   NaN     E\n",
        "1891-01-04    12   NaN   NaN     E\n",
        "1891-01-05   -29   NaN   NaN     E"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Values has to be divided by 10 in order to get degrees Celsius:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tm.value=tm.value/10.0"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now you can plot the data, as you would usually do with pandas:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tm['value']['1980':'2010'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xbc0e46c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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z6RmS6qeILzf1nkQLdR38Gn3IEHrYoMJ5wYIMKZxO/N6HQmf2QuWf/1T/7urU\ndQ41FpVRNZX0juhMPcQqs8qgNs/ffiv/jZ9ZDRrkzAh4ZOXS6RPeOG6+Geje3SnrmjXA1KkZemSE\nvFGQCXVTVjNz5jj/o7BTv/RSWjhTffKZZ/KvUcpVEiN1HXSPaIsCPl5VGmF23ZkiSLw/+Um4NFVC\n3fvdfZkdfnju9U8/pZv+8fEAwJVX0u9TIfJv7sILdT+9L7W/+OHu32jY0PG0+NZbweMKikyoF/Ko\nSR5Zfao2xPmhI2POOEOeF69nSGqaVqjHwA9/mCaF0xHqH31k5kg2QG1RosLVqeu4NJWN1I87Tvy7\nVx3jCkevCq5nz/wFWGo+Djkk97egOnWdQw+ohBmpu5YnrmBt3jwdKA9hVCH//nf2M99fTQp1xug6\n9Sg2t+nImKlT9eKm2KlboW4orA58R9JJw09or1tHi8dPMPCjFFN10KgRPWwqlS9YXW65Jfe7Wxbv\nw6n7sPLhTQkYVT0HMdPbvRvY6/9Oyvbt/vG5+y+Ctq33PpE7gl/8AhgwIP96hw7Zz7zlkF+dR7UL\nWtZPVKqzqOSCN9/evFHNXK1Qj4EPPsgEuo83bQuDX6fnO5POA+Lq1E34NnEfcr69du7Mt3Y54gjn\nf5gR17x5uWfaegWMSo+p6k+qPPH3UevrjjuAjh3VYRYs8I/nzTed/0uXZmgJ+yAy7RWdKuQtp6v/\nBsw/l3GevxqVKqlVK5rvl6JZKDVV+ZTO43bSqHYumtr8432YTNWRbJRMxcSoStQxv/e9/FObKiud\n/14BqiMkvLtvde498ED5b6p6CCLEKDMxnWl4kA0rVCjlmzJF/luQPSAuOvVeSPVLy5Z68ejo1Cnp\ne0mcUNc5u5GHUuDHHqPFpfMg8OqHTp3S9BsVBPUH7deRg46aVHbqutYcunkI83D6Lcaq9LOtWsnj\nVfWP55/3zxclnjC0b5+OJmLotx9FpSPj4Ydzv6t06oUQ6jJ0relELwFKuah+2mMX6t7KP/NM+r38\nVmFVxX7/+87/IOdi+rmCb9Ik+9nUSD1oh4x6o5JsJ6NOmrrpek06dbbZe+tV5WBMRxh76/lHPxKH\n49UQKkQbrbzXgo7URfTrJ75OEdhRqTlF+wmOPx647LLs97i9kAZVv4Rxnx2kvmMX6t6DjVu0EG9t\nnj07/9rxx2c/X3klMGGCOA2RHlfGsGHAE09kv48bpw7Pv3WD6tRl5ny6qDqyzi7Z8eNzvwfx/SLL\niysQVXl/YhfsAAAgAElEQVTl/YiYHKmfe27uoR28HtOvnU3Qvbv4umhNpWFD4KCD/OPkzqap5+OP\nM8p7/vEP/3jPO88/DAWqcBWN4E8+OXfmrvLXozMwopywBOS71pXJj1TKOeBj7Fjx75Q6MGl/H7tQ\nP/nk3O+NGoktKkQVxlfWtGly1c3++zv/qW/atm1p4byoOtIbb+R+V+0MFe0YlHHUUbT0VafKePGO\n5EyOety4VPp9Xr9uUqjvv3+uiaT3JPeg/OUvwB//6B9u5szc7z/7mTo8xZcQbw8dhCVLsp8pi3Km\nR+p8+l68MyvGnPUH0aCvefPcPQ2qPkt1Biay8hHFn0oBJ54ofxGGPens9NP17oldqFPd4Mo6GWWK\n6+4qFHVI7y7JMGcsVlampeF+/OPc76+9lv28dWv2c4cO+S86VZ54dYQqnHfhb8MGeVgv3bqlpe3U\nq1d+2YD8lxjg1L+bR9VOz0MPdV4shx8un31R4Nu7Q4f8l/XvfpdW3i+zh/fWc1UVcMIJ+vnzg5+x\nyNQvoj7N90NvXr2Csl27rA8aiuWOrlB3rZhktG9PlwGMAYMHp4WOt+bNy/XNohoMdOki9p/jLdug\nQbn+eVQjdRUUmaJa39FxhAckQKj7jcTc47FEQv2442g+QlwBwsfhNuopp/jfr4JvMB07df4+7wYb\nyrRbhM6olmr7DgBduzqCVsQbbwBNm+Zfv/32/GupVDaPfvVTXg7U1NDy9/bb4ut8GosWObMDilBy\nz0nn3e/ymD6KT5anoLMUVV5Eu2pFZ9Camp3xB1R06yYOQ1U3unkSrY01a5arl1edO5BKOX3aj4YN\nHSusp5/OTd8Lb6MvQmcvh5dUSr5mIyN2oc5XlOhwWXdTikioU82j3NEJ//C4Li8p9r+qkQSf/w8/\nzNR/DmM26aqLXKij1agWkr75JiP9jR99+5FKZQ/KoLjW5UeqGzcCtbX5YaZOBU47jZY+4M1rRnjd\n9QNvwk+LCKqTsaALc4sXZ/KuuWo6kanmt9/mjkh5XCMDF2+duDuEZfB11aaN89/ra4Van064DCms\nzEVD8+bOf++Mxev7n8ddC5G9ZHnVsOggmDA6dX5vB5XYhTqPV5gBWf2Zt2AiQSvbLSYS6jJEDaA6\na5TXd/G+o0WjHx7XSb4XkVqC6qfdxI47Hf2drnBr2tTZ5k+FF0DvvguMGpUfRrVrXNTelNGve5+O\nR0WdujjnnOznpUvlA4vDDsvNU8eOwP/9nzxer8nb3/6WVSG5MiOVyj/rd/FioFMnsfrF+xLw1qnf\nKFIkLN2Rb1DCvFRl/n/cfREivIv7w4blzuL4ndp+Z7fy1jxUdFVesQt16i5H/gGrrAQaN86/X+Zw\n3hWUlMNy3eki1dfzo49mPy9cmFaG/elP/eNz88g/oH7TU3c3Yl2dWJctQlbX4hX8NC1SyIVmr17O\nNFSng7rt9vbb5nbz5eYvLQzjJ9TDql8GDcp+/s1v1GHdfp5KOSoklZdOdwbQoUMaQK4bYL7evYLa\nXVcSDYr8XoJ+5eadn4VdZHXSSpPq2sTuZ29c7gxj9GjglVfE94lkDJ8XmTGHyk696IQ69WQW2co8\npfHcjSR8hY8Z41gteHFNxHi9tin94pgx/mHccvLl5U0sRbhe+TZuzJ596IesTCL9uE5cgwaJ9xq4\nYfwWznjc9tIZ3fvhNWcTLYbqjNRdSyEd/XevXvSwrg9vikdHlemuqF+pcNPwbsLS3VMR9NmZMSNc\nXGGfWV4T4O4ZoKhqRTPtoHn57W+d/0Un1HlUp8HLTgDx61RNmgAvvuh85oX6smWOcyJZOqI3rv+D\nm/EL4Is7MvM7pJknjDtR0TpGPhnlr3y9zJiR67HPC6922rRJfogykNsG1Adj5ky1AMtV8WXw6187\nn156KXvVTwDyeXGn85SFN2/8MkQvE3e2prJacu/77LOMNE2V+wMet4zemYF3kZKvC9GAgLcu01mj\nkF/LkPqCzBMqtR/x6zn8wHPzZnX7+Y3UZYh06q58KmqhrlpACqrfLCvLCkqdKbxIoPAnpAwcSFd1\nULnuuqxe3m9azuMVQqtW+d/jCmLXCZSX//ovevr8rEbWAV1hylsCNGsG/P738nhVZo+AeLGVP8Xp\nySfV98vQGalT7O5ViPqvSPXnCvVp0/JtpL2fXYHkXdeZN8/Rp/fvT88Xr9cH8r1F8i90P/PioAvP\nbju4Lxjv4IqfnZaXO/+nT1fHKUKmUuGF+ve/r/aTrxLqfv3Zi1tfuiP9RAl1FTpCnV9Q4zuSajHE\ni6hx+FHSn/4kss9O0xMQ0K5d1qIniFmjq6ulHAPovpBkD9rLL/Pf0sq42rXLfpbFJxN6XkdePHwb\niF5UPXoos5W32SeftPBqkIVSHYLolvl1FZnwdON1z8rl/ee7ZnwHH+zYaZtCx7Wx7GXiNwN2f3f6\nbDrviED+JCF3MPKDH4jjUjnfkvn7oc5uAHEduP1FZSmkslPXnYnHKtRVD7QX2YYLfprkIjvMwX2L\ny+A38vipX3QeTG8nlBF0Icl7n8o8S6XiMoHswRa1kx/8wzR0qDpsGEHldVXhwtcrv/tUNlKmwt8j\nck/QokX+NV6oi9aCgPy652dFqp2bYax5+Hz51YXs5CxqWq7w9h5azj+rV13l/HdNF72o8ijrBzp2\n4u3b519jzHmZeF2diEx0eXTXQVxiFerUE+PnzZMLde+mmLPPluvf/SqHt3vnO4p7iIXsYXbVOzLd\n89FHZz97Pc/xFOLor8svz/1OUyFllL/yIxDZqUCPPEJJJxf+XFk/3npLvkFKTgZA/qhONFIfPNj5\n/+239H4bFNFmFUqabn7ds3L56b6OP6HaWuBXv6KFVQl1qrAWnX0rutcZVGWUjtp03NqGdUUtQmTd\nwpiztudVZfEvb5FOPeiMMFahTs10t250gffGG8Af/iD+zU+o87/zHeWMM5wFIplQ9zuLkkc2gqDk\nT4bOSKtv32BpqJDtvOShClxeoOnsxGvQwHno3XKPGEG/14tIqLu+eo47Tj6ii5ItW/zDBNXBern/\nfpoKD8gV6t5ndOdOWhx+M2g/dAZDvPVTUDffKkRqn1/+Ur65S4Wun3aXUEL9sssuQ1lZGTpzHqi2\nbNmC6upqVFRUoE+fPtjKOzYJQVQuP2V41S/ffitXv2QtKtLa8UaFWdejaeWvYdrGVLumUo75n9tG\n3g02YtLCq+7LRCQsKCpDnbNcqbgzBS/8LMmty8ceS2PXrtw+4N3Yp3ITodMmqkXyaJ7ZdF682Zly\nFtGi5GmnmV1PECHqM7JFW16lJ9KpB62/UEL90ksvxcSJE3OujRw5EtXV1ViyZAl69+6NkSNHhkmi\nHr6AQae/fqvPKl1pgwZ0XercufLf+Eb3ukDVFfgqVY4Mvgz8AlNceEdzYU+Fee65cPkBskKd8lCd\neGL2s6tm0/Gr44efXT/vd8TtW8uX5+fBqxfmZ4zXX5/7m44wueee/PSDxOPF655AhUiQFmrwFAaZ\nz6KwhBLqp512Gpp6jFPHjx+Pmr2emGpqajBW5mQYeqNJHd24DL+GVv3uFeoilwaujlYl1PkNDG++\nmRunbCFJhqv3DLrQRd+ynMm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LRx/px2mS5cvpYXWtcoLo1P3K6I1zwAB5WB2LJZlrYgom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\nLRaLpYT4f76tI84K/LkyAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbbac96c>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or do some statistical analysis:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pandas.rolling_mean(tm.value, window=365).plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xb544e6c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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PR1j9+998QA8wC5J4w4wCwN13B6epy6YFWcDE2gZ1dfxcr0JHhcqeDNjbsMms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\nH344t+nKJhE3RM8PynxNQY6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nsKur01PDjhVHgd2/f3+0kqavDRgwAFkHpEyfPn2w1s9U\nOAViIB0nSFB99hl/ePbuVduw7Zg0ydDuxBRRAP8MJIEtJ+cl4hXYVVXc9EL+qvv3c7OB3W9QdWI/\nNmzRJuvHJCIHnQqaIE0iLVrwGNdUroxon6RuvGQJ99oA1DFAxFmnItXV5rrRvfDygnnoIef9QGza\ncizneLXZyv2vupp/GVLsD9l7SYbGnET7tOq4eNKzyfVTEY2mpw1bjr3ilbg+hidMmIBSG4/0srIy\ndDgwk6Jly5YoLi6u//yIRCL41a+ANWuMdQCm/eI6EEF5OQCEsXw5X9+7dyFqa+2Pj0TM6zwiGF+f\nOZMff8stYTz+ODBiRASzZwPRaPjAyHvkwHlG+Vu2ADk56uvt3m09Xq7PV18B112nro/RaYz9/OvB\nvD87m5s9VO21cCFvj+xsa3lffmktHwBqa8OoqQF27TLai7/EIigoALp3D2PiRPf749b+4v7sbOCH\nHyJo2VK9v7YW2LjR/nxxfdSoMCorgU8+oZe33J6Q1vn+jz7i5dP9FOs7bZrRPmJ7zZoF1NUZ7RsK\n8farrQW2b3eub7NmkQOZTeT68fXWrSMHBhiN64n7Ve0LAFVVfP3MMyMHQhaoj49lXS6vtBTYuNG4\n/tdfG/tXrLD+fh69j/cvKj8ry1z/6mq+Tv0z1voCkQNjBub9QBhVVcCePd76UzLX+VhBGJFIBJMO\nDNCRvHSEuVBeXs569Ohh2X7fffexSy65RHmOh2LZMceQFVA+l7EdO6zb/vAH/v9f/8rXhwxh7O23\nVdc2l1lXZ2xTXU8+l8qnv717+b5wmLEvvlCfd9ZZ9uXTtv/+ly+bNOHL7dutx4tldO5svc611zL2\n3HP29W/blrF168zldexorI8da77Gd98xNnUqY+eeaxzz9dd8X6tWjP373/bXsmPgQMZuu81+/6WX\nMvbmm/b7n3iCsZtu8natBx5gbMwYXt+XX3Y/nn43/a4pU6z34Morrf0FYGz0aMaOPJKxn3/mx+Xn\nM7Z7N9/XvLnzdefNY6ykRF0XgLHCQsaKivj2aNS5n4rnffUV33bDDYw984z77/fDnj3mOhx1lPna\nVVXuz9QddzA2apSxPniw+ZydOxnLy4u/rgBjffuat73zDt/+7ruMnXlm/NcImoceUrebm+yMyUtk\n0qRJ+Oijj/AaBUKIASf7s+qznD55yP2JMfvPIHGE28/EnNatreYD8gxxMok4fXJRRnHyhqEkAqow\nqeLAmV+TCKC2p/IvE47o633EEWqTCJ2/Y0dsn6off2z4NqvwYsP2ahKh9ujZ096rQYRSo1GIWNX9\nFBNUiJ/zDz9sNn9kZwPvvcf/l6M+ypCLph2VlcaEkhdfdC5LhEwiiRg4btTI3JcuucS830vfaN3a\neM7PPReQhsNQWRlcveXnfMgQ7hFWVZWeNuxYZ3j7FtjTp0/HI488gvfeew+N43CgdBLYw4ZZtz37\nLF/SQMx770Vsw0iKjeEn+E1OjvEQ0MNNwsPpofjkE74U00MRH37IBTEJbCpfld1D7FhLl1r3f/WV\nvbudbMMGuGuZeB0a4X/tNR5fQeUlIsa7Vg08xUuQE2fEaH0qASKbRijIvjhxRkb0FhFdTkePNr9M\nsrO9Ty7KzuZeQnYBqkjgM+Z9TAcwXgJ+BLZXmy21LT1LoscUwMd/KGGCHeKM0I8/tgrVffuCC3sq\nJ1AAjJmY6WjDHjjQ7BnkFUeBXVpair59++LHH39E+/btMWHCBNx0003Ys2cPBgwYgJKSEvzud7+L\nqcK1tdy/NT/fCK5DmpfTTRQnA5Dnxbhx/OGiziVqcH7erjk5xgg8dQASYF4eivHjuY+3TOPGVoFN\nv1lk9mzn8hcvxgEbK2fVKiOQEWDVlg87zOyKePPNfHn55YamK3uJiLMGExFDOBHBn/xmPSc9Q/X7\n7HQQuhYJ7Kws765ZJKjE8AiNGvEZleL+rCwjfK6XsACxCGyvUFJjp3s1ZIhzGW5xwD/5JLh6y8mx\n6fpuAjtVlJTwKId+cWyuKVOmWLaNFNNFxEFNDRe4+/bxWWpiYH0vZozRo8P1I//33cc/2eizWNTg\nSAAffrj7ZIZ168zTcs87z+y/bde5rr8eeP55rrXKSXoBrkHR1HgS3CqTSHGxt0QAS5bwcJGLFxsz\nEsPhsOusLgrQIz6MTg97KjRsvwkMaLamSvjKPrZDh3J/aicN205gy7kDKRSrF+grhxIgALzvkw+x\nSGEh73dkOpO57Taee7Sw0GwS8dpmfvyOqX3tyn7pJR5J8oIL1PvloGgya9cGpxSo/N7pizkdY4nE\nSspmOorZIMRtgDd3pcaNrXZBEgRiuSQg3abbqhAjozm5H5EAsJsAMGOGEauCZnupPn3lOAt20ExM\n2dbrpp2GQrw9aPKHW7aWRAjsRCQw8Kph0ye8auIMYVcOhaUVTSJuGeIJMq+J/ZJelLNnc3MJ9YvK\nSufA3TQAABwQSURBVOeJJHR/mzZNrIYN2AtcOV2X3ctFTuxBUCCsoqLg6q26DmnYfmfOpjMpFdiy\nYKbOYecLLbJuXcRkY1y92ggHuWqV0ZlJw16xgi+9ZoYeOdIqsN38sL3M2KI6P/mkdV/btsYg4Xnn\n2ZexciW3ZYvtpLJhq6A6iiYR+Xf16mWua5Ck0oZN4x/Uz/xM0pU1bDH2hxuNGnF7pfjlSO3ety8P\n8EVCxU1gv/MOXx5yiDnEa9A2bMB+kFtua6fIjCqBn5tr9LGgNGyVEkCDvelow46VlAns2lprxmC6\nuaNGuZ+fl2cWKLNmmWOITJjAl5WV5jes3fRyGca8C2yaau5lDJY+o+2yJZNWq/rEI/t99+48vvLY\nseb9fkOTqmzYgPGbVQOf8RLkTEe/GnZZGV9S3BHqF3LgLppYI1JVZdawvb74CTFoE2DtT1Sem8Am\nxaNp08R6iQBmDdspmJidwCYNWw69On48L2///uDqrXquo9H0tWHHSko1bDkspvg2VplFKPpbNMon\ndYgmEdmeSCPv+/fHZg5hzBzK0stDYfegqUwddh2JNA6V5sEnX6jPO/30sC9hRwJbpZ2Rfd1rvHE/\nuA1kxWIS8WrDpiQBVD6dQ4KcOOkk67RwWcP2i5x4wK4/7dvn7UstVpOIXxu2GOSK8Hot0rDFyIeH\nHMKVkexsXv8gNOylS40BXJENG/iXqFN/0jZsj6gCtIsdWuW3Kk4pFj0vAKvApkhnlZXeksPK+NGw\n77qLL+0etPbtrdt27VJHmCMNW9WRVZ4lRF2dfSApFWSaUP2uW2/ly6FDvZXlh0SYRPx4iTBm2Iup\nz6hMP7Tv++/5UrZh+yVPiAbpFDTKTcMmmjblX3ZXX50cDfvwww3FR66fykNDPp+g35yTE5yG3bWr\nNRUa0aaN1rDjhjwb5IYUb65KYFPSAwBYvTqCqirDrU32uiDBt3+/fYdywo/ApgFEO2Gp8ibYtYsL\nRDnxqZOGTRMPZIHHGPDFFxFfnd/OrQ8wxgJi+TLxel07EmnDVl0LUOeipDoefzz3W6+qCk7Dpt+o\n6i9eNezsbD7HYMKExPhh0zWojfLygB9+4P/Tlx7ATTR2E6VUGX1EL5tEmytOPZXLB6eXrLZhe4BG\n1+UOqxLYdq5TZMMm4U/ZQQhK71RZGZvg6dfPu8CmjmwHTZgQ3bp27eJ2Nzm5qJOGTdtkrYW8Pfx0\nfjJNvP22tUMnclRdpWG/+ipw1VX8/0TasGXIRNKunXWf+FJp1coQ2PFo2F760v33exPY4vnJ0LAp\nqt7OnebcnZ0723uJZGdbB2dFL5tEz0LMzbVPsJCppERg29mtVO54diPgJ5zAbdgqc8f555s/d/0K\n7DlzgOuu4xrAH/5g1M3uoaCBIDsoyLr4u+fOxYFAVmZEDUSGZlLKAru6GujXLxyTwJ41y1r/7t3j\nT2Bgh0rDfuklIxmtXxt2TY29kHezT3btaq8QiGaSRo24cFGZnAYMcM96ApgHHd0ErJPAfustvty5\n09iWDBs2XaNFC+9255wcHAhyZkCulZ9/zk2JiRSmpNRpP+w4qasz4loceaTRQVU2bFF7EhuebNiq\njtq0qVEWxc72Az3EYowIp4dCNZVehH6fLGgV85IcbdCPP85fPpQMQaxbLBo2PYzyA5iVZcRACRo3\nG/bnn3uP80xaWk5OYrNi5+VZvY2I668H7rjDvYzaWsMTRNWXxLKdBDbFK5861diWDA07lkQDqv4o\nxsSO54vFC6Rhaz/sOBFv/uzZ5unfBGk4YmcUH/SlS7kNW3UzxI62f78x3dcpKJEICWxx4ojTZ/fd\nd3vzHaeXyKWXcgGjEthyHURCIZ6wVPY6qa7mISr9CGxR003EFHQv173lFuCNN8y/ddEiPmvUa1k/\n/WQ/my4e+yT1GcAQ2E5ePW5MmWLEw1EJWPGrw00wyn0jUTZsceJLLAJbVSc52bSfmPZ+yc21v29E\nptmwE/BedicaNYSh/NlF2Lkskb0sN9c+CHhurlHWXXcZ17r4Ym/1oxss+tw6zXRs0gTo1s29XJoU\nk5dnDGraYfepnptr7eQUXN+vhh1LAPx4ETXs8eO5wBYHsQDvAZCys729KGNBbONGjYxoesS8eUDv\n3t4F9s03G8fu3WtNsNutm/Hl5GWG6bBhhhkpURo2jeEsXRpbogG5TldfbbV3+wl25ReaZHTccYm7\nRrJJmoa9caPxEMQisGkiCWne/fqFba8lj06TYFIlARWZOJHbrGkqMWk9r70W7OcbdXynB9NOYKtc\npWpqeI5LPw9tVpbhWeMn1Vq8yDbsjh2t8VNoFpyXspyIxz4p1lF1nyjwmFch9uSTPAYIoDahiIGA\nvPQzcZD9hx8SE0uEbPdUN79mJ7lONLAsIob/TRTPPGO/T9uwbTjiCN5pGeOahBiARxTYlNpKzIZM\nPpuA8YA4fZ6JQi03lwf8OflkXgcnysqAJ54wOiYJzU2b+DJeO+m113J/USfXPTd27rTGX66u9hcA\nCOBCgabtJ/KzVHVd8Xo8cwnH70SdRCYPFuuoEthO8UjcoDC3ImKoUi/9TL7uypX+6+FGkyb8pR7r\nBCr5/hx1lPWYZPa9hkBSBPY99/Dlhg181lNZmVnDFl2HyMWKhAkJbPqUovx48+dHLNehiR6iSaRL\nF+71IAoGr7zyCl/u2mV1v4uFfv2476wqCayMnYb98cdWraS6Gpg9278Ne8MG/n+yNeyFC3l2ehn6\nqvKaV9Lt98ZjnxSFlEqAksD2GiDrhReA/v35/6pIjSKxfMl5jRzop01WreLzBGId45D7lViO11ji\niSbTbNhJEdiUxeORR4zPQnL3kU0icucggU1a7gkn8KWqU48dy6eoiiaRxYtj14zpvO7dg9HmqDz6\njbG4NF18sdW04+TaZkdWlqHpJVvDfvdd4z6KvPACdxu78EJvZSXSJUwU2CphSALbq/aZnw988w3/\n3y18cCz9tVMn/+e4sW4ddz/t0sX961SF/DvEl1sylITu3RN/jWSTdC+RjRvN6ypfTxF5Gz2kKttT\nfj5w9tlWO6/dtFU39uzhwjUIB//rr+c+u4A3gW2nMR1zjGE/JaqrgZ49w77sgRRiFUhMzBCn69px\nyy3+PBESacMWX2Kq/kP30KsL4t69xrFu5/jVsJs0MUyJbvhpkxEjePq1vXtj6yOipw1g9bpKNM89\n536MtmG7IAtk2YatEti5ucakEXpIxVRWxNFH8yWZRChrRyxT0wHjBbBvX/wa9nPPGVqKyq/cK3Ze\nIk8/7a+crCzjofGSrDkovIZ/9YJXYRkL7dsbwclUdaKXpioejIr+/flEHUAdiVHEb76/RHmJ0KDj\nXXcZX7h+oN9LiF/PydCwvUbmzCSSKrDPPdc6yYRs2KtW8U9hOw2b3vCkfTjZnkjDfvdd8zmxQD64\nQT4Q8WjYKoHN2y/iqw4TJ3KXuhNP9C/s48EtLZKfSU6LFzvvj8c+OW+e2s5OhEL8Hnlx5wT471q2\njGurc+ZYkwCIeBXYNTW8byfKDzsvjwvsWBPGEqQwiQI7GV919Nw7JWjWNmwJ0aOhutqc1w4wTCKr\nV/N1O4FNPqrifhp17tnTfA7ZsGXTQSzk5XENO0h7KX0axvISyM21dvbqavUXhxe+/db/hIh4cHs+\n3Kb5i9BsTDFGS1AceqjZc4NmGMYKvYgotoaTScCrgKTwAowlZjZfo0a8nqoEt37LAczPUDIENg3u\ninHyM52ET5wRzRGysAZ4R6NwkwAXSIWFPN8bYNUeSDiHw2GsWGF82otaJ9mdW7SI/2GeO9eI3xAU\nooeMHX4nzpx6qj8bdqoIcrIOtSN5IckEaZ986SV1kCivUF3F4Ed2eB3gi2Vw0k+byEkXYoVeVmJ9\nkyGw+/QBfvc7Z8VI27A9MmIEX4ZC5oHHt97iWhg9HLJ/sXjTc3P5vsaNzTOoyCQSjVqnwvolFtud\nGyqNQ95vl5HGTsP+v/9zz7ou8vDD3o8NErJdyllIYoGEYCJyT8o0b8599GOF+icJQJUgpIkldklt\nk01FhZHlKJ42ppm9IskQ2NnZzpNmMpGUCWwxqH92tjn0ZOPG3KXo66+N4E2yH7ST7Wn9eu5CWFUV\n/8N87bXxna/CTWDv3w/89rfqfSqthwvBiK86JNMzREUQg05kE7XTNNPJPtm4MTdbUQAoVfq1IUMS\nXw8/bSKOEdiFUPWCqp97jeuTaNKpj3ghZQJbjAYnu+HRJ9T69dwO1aSJPxsdBVWKRuO3zybCvuvF\nJOJ0bl2dYWoaPDg2FymagJRs6EVk5/tNfvpeoHacNy++OiWL7GxjQpiqX118cfwDfEEihiWO9QV/\nzjnA5Zdbt7dvb/Ui0bjjKAZHjhyJgoICFAnDrG+99Ra6d++O7OxszPdgzVelmcrLM3/yy9mVSWDX\n1RlxR7p3N3cgJ9sTCUIxZkms+A3N6gU3DdsJ2cMkN5fMNmFf5ZDgcIuvEjTXXceXdhp28+bey6J7\na3eP0s0+KQrsVCl2fm3YRKwvkunT7ccY0iGxQLr1ETccBfaIESMwnfJSHaCoqAjvvPMOTjvtNE8X\noM/V0083tskPmNiR27Qx9ldV8SD/eXk88pZTTkMRGmSYM8d/dmuZRGjYQQhs+uLIyeGTcvxC9yWW\nGWzxQNel+w0AV15p/O/nBUttEUvOzlSQk8O/GI86ise2CQIn98B4EZ0EEqH5N6Q41cnCscn69++P\nVtLT0LVrVxxLuZU8QK514oCNLASpIwNcw8rO5hMSWrQA/vQn/mA2aWLOZO1ke6KO8NRTwPvve66q\nkkTYeoPWsDkRX+XQ70rmpBnAuDfkBQSYP5n9xK0g4W/3lZBu9snsbO85G73i94Uba5sk4jm47TYe\ndjaVpFsfcSNhbn1lZWXo0KHDAV/Wlli5shjNm4exaxdQVxdBJGJ8jtTWRg7EG+YhQiORCNq1Ayor\n+f7//c98fCQSwcKFC03rgLH/zDMjmDiRl6fa72edp8qiwErxlwcAy5fz9exs/+dzgRY5YFIw1oGF\n+Pxz7+XR+bW18f8eP+slJUA4HDkQUpXvX7aM7wfCyMryX/7Kldb+IZLM3+e0np0dRmUlUF2trm8s\n63T/gypPXP/mm/AB06XRX4Isf/jwMIYPT5/7k4r1SCSCSZMmAQA6eNGemAvl5eWsR48elu3hcJh9\n9913ynPEYisrGQMY27qVsfx8/v+vfmU+/sgjGbvnHr7vwQf5tvPPZ2ziRL7NvZZmVqxg7OijYztX\nZtgwXsbEifGVIxKJ8DLXr/d/7gcf8HMLC/nymmti+51XXsnP+e9//dchXlauNOoMMPbZZ8b/b7zh\nr6wvvmCsqiox9Qyabt14P+rVK7gyw+H4+7gdVVXGfWnUKDHX0JhxE8kJtyKRPblxY2OgSTYF0Kfi\n9dcDY8bwbbm51sD2fq4ZVAQ6MRlrUJCNPgiTiCqRrxcuu4wvTzkltvPjQTZ7iG55fieDnHFGcvyw\ng4BMf0HWN5Ff9Hl5/Jnt2hXo2zdx19F4Jy6BzTyMRJBgadTI8B++4grzMdSRRdtebi5w4EtBifzZ\nK18zqOAy5Burck2KlXgENj3sZ5wBjBwJfPkl7Yn4KieV7mOywF63LjHXceojqYAUkyAF9i23+Dve\nb5tkZ/P0aJ9+6u86mUK69RE3HAV2aWkp+vbtix9//BHt27fHhAkT8O6776J9+/aYM2cOBg0ahPPO\nO8/xAhQkRxROd95pPiYvj3tziB05nsSw4szJeBk6lNc/SPe3eKP1Afxl9tJL3oP9y6RSYMtThRcu\nNP5PZPbzVEPeUEEOOibjPubkpIcLnsZl0HGKTVrvITFMyZKnlIvk5fGgOGKoSkqgaQcZ8FUEaRJJ\nBNT54xHYhJEIIOyrnHTSsEXXtCAFtlMfSQUksIPUsMWXnRfSrU1STaa1R0o8IWUNKyuLxxARO7Jo\nO/YTIwMI1iSSCILQsEePNq/7jSaXDgL7s8/4cuBAY19D1rBVpr94mTQJeOCB4MrTpDcpEdjyQ0m2\nbTsziGrAw82GTRq2OGEnXQhCwz7nHL6kl1xBQcRXOWLo0GRDL6xGjfiLo00bY1+QAjvd7JOJsGF3\n6MDnKngl3dok1WRae6TVXKOgnPNFk0g6RusKQmDTQy/PfPRKOAxs3uz/+kFAdVZNKW/IGnYiBLbm\n4CLpAlv1kJKg9mN3drI9ZWcbU9LTcdpyEAJbXnbtGvZd1uGH+79+ENDLRRWrIsjMPulmn0zEoKNf\n0q1NUk2mtUfSBbZKSNHDGlSOPlEQJCLXXbyQFhmLNknambzcsiX+eiUbyr5CvPwyz6HZUEmEH7bm\n4CLpAtvp053SgAFGp7bTup1sT6KQjsc9MB2xM4nMnx9JSX3iQdbwr7oq2OiI6WafzM7m0etmzEhd\nHdKtTVJNprVH0vVPlVap8liYM4ebSvzaZomCAh52NJ017FiwE9jdu8dXp2SzeXPqTDKpgr4ulyxJ\nbT00mUtaDDpS4tUzzjC2lZQAvXrZn+Nme6LwlekosONJDizbrklwH398OK46JZtkCOt0s0+mw+ST\ndGuTVJNp7ZEWApsIMtQiCep0NIk0axa7H7Sdhp2OLyaNmXQQ2JrMJukC20lQ+TEVuNme4vHESGfs\nBLYRolRDpJt9Mh1equnWJqkm09ojrTTsRNDQ/HrlF5EcvU+TvtA9ijephubgJWMFtpvtyW+MhUyB\nXkDky0uadklJOCX1SWfSzT5JAjsReUK9km5tkmoyrT3S4CONE/TnYqxxojOBmhqrhp0On9saZ6xp\n3TQaf6SNDduv+16m2Z6CRDR/kKBetCiSkrqkM+nWR9JhIDzd2iTVZFp7JFUve+gh+6BD2gYbG2Qi\nSWX0PY03tIatiZcQ85I2xm+hoZCnbDTE1KncFjtgQJB14MuGLsiiUW7P/vTThj2tuyFw443As88C\n8+ebY4BrNISb7EwLy+egQamuQebiNoVfkz5QKjStYWtiJWO9RNxsT126JKce6cLChZFUVyHtSFf7\nZCoHiNO1TVJFprVHxgpsN268MdU1SC56DCD9oa8grWFrYiUtbNiJYPx4nlG6oduwAeDrr4E+fWIP\nlKVJDuecA3zyCbBxIw9OptHIZIQNOxEElb0mEzjllFTXQOMFiveeyvRsmswmY3UyN9vTwSSwgcyz\nxSWDdGsTt9ylySDd2iTVZFp7ZKzAXugy9/y884ALL0xSZdIAt/Y4GEm3Ngkqo1I8pFubpJpMaw9H\ngT1y5EgUFBSgqKioftv27dsxYMAAHHvssRg4cCB27tyZ8EqqcLtut27Ae+8lqTJpQKruQzqTbm2S\nDgI73dok1WRaezgK7BEjRmD69OmmbQ8++CAGDBiA5cuX46yzzsKDDz4YVwVi/SSpqKhI2vVirWMy\nz0tmeyT7vIbSR1QCO9ntH0ub6D4S3PXiNcE4Cuz+/fujlZR2/P3338fw4cMBAMOHD8e7774bVwVi\n/QGxfso0VIGdzPZI9nkNpY906AC0bBn/teI5L5Y20X0kuOvFK7Bd3foqKiowePBgLFq0CADQqlUr\n7NixAwDAGMOhhx5av15faEMLQq3RaDRJImFufaFQSCmcU+2DrdFoNA0R314iBQUF2LhxIwBgw4YN\naNOmTeCV0mg0Go0V3wL7wgsvxMsvvwwAePnllzFkyJDAK6XRaDQaK4427NLSUsycORNbt25FQUEB\n7r33Xlx00UW47LLLsGbNGnTo0AFvvvkmWsojKRqNRqMJHpZGjBgxgrVp04b16NGjftvChQvZySef\nzIqKitjgwYPZrl276vf97W9/Y507d2ZdunRhH3/8cf32efPmsR49erDOnTuzm2++Oam/IUj8tMcn\nn3zCevXqxYqKilivXr3YF198UX/O6aefzrp06cKKi4tZcXEx27JlS9J/S1D4aZPy8nLWuHHj+t99\nww031J9zMPaRV199tb4tiouLWVZWFvv+++8ZYw2rj6xZs4aFw2HWrVs31r17dzZ+/HjGGGPbtm1j\nZ599NjvmmGPYgAED2I4dO+rPyRRZklYC+8svv2Tz5883db7evXuzL7/8kjHG2IQJE9i4ceMYY4wt\nWbKE9ezZk0WjUVZeXs46derE6urqGGOMnXjiiWzu3LmMMcbOO+88Nm3atCT/kmDw0x4LFixgGzZs\nYIwxtnjxYtauXbv6c8LhMPvuu++SWPPE4adNysvLTceJHIx9RGTRokWsc+fO9esNqY9s2LCBLViw\ngDHG2O7du9mxxx7L/ve//7Hbb7+dPfTQQ4wxxh588EE2ZswYxlhmyZK0EtiMWR+yFi1a1P+/Zs0a\n1q1bN8YYfyM++OCD9fvOOecc9vXXX7P169ezrl271m+fMmUKu/7665NQ88TgtT1E6urq2KGHHsqi\n0ShjjD+M8+bNS3xlk4TXNrET2LqPMDZ27Fh255131q83tD4ictFFF7FPP/2UdenShW3cuJExxoV6\nly5dGGOZJUvSPpZI9+7d8d6BOeZvvfUWfv75ZwDA+vXrUVhYWH9cYWEh1q1bZ9nerl07rKNUHw0A\nu/YQefvtt9GrVy/kClGGhg8fjpKSEtx3331Jq2uycGqT8vJylJSUIBwO46uvvgIArFu37qDvI2++\n+SZKS0tN2xpiH6moqMCCBQvQp08fbNq0CQUH4toWFBRg06ZNADJLlqS9wJ4wYQKeffZZ9O7dG3v2\n7EEe5cQ6SHFrjyVLluBPf/oTnn/++fptr732GhYvXoxZs2Zh1qxZmDx5crKrnVDs2uTII4/Ezz//\njAULFuCxxx7D0KFDsXv37hTXNvG49ZG5c+ciPz8f3bp1q9/WEPvInj17cOmll2L8+PFo1qyZaZ/d\nHJJ0J+3jYXfp0gUff/wxAGD58uWYOnUqAP62EzWHtWvXorCwEO3atcPatWtN29u1a5fcSicQu/YA\n+G+95JJLMHnyZHTs2LF++5FHHgkAOOSQQzB06FB88803GDZsWHIrnkDs2iQvL69eWJ1wwgno1KkT\nVqxYcVD3EQB4/fXXMXToUNO2htZHqqurcemll2LYsGH1rsc0h6Rt27amOSSZJEvSXsPesmULAKCu\nrg733XcfbrjhBgDcH/z1119HNBpFeXk5VqxYgZNOOglt27ZF8+bNMXfuXDDGMHny5AblK27XHjt3\n7sSgQYPw0EMP4RQho0FtbS22bt0KgHfiDz74wBR9sSFg1yZbt25F7YG8XKtXr8aKFStw9NFH44gj\njjgo+whte+utt3DFFVfUb2tofYQxhquvvhrdunXDLbfcUr/dbg5JRsmSlFrQJa644gp2xBFHsNzc\nXFZYWMheeuklNn78eHbssceyY489lo0dO9Z0/P333886derEunTpwqZPn16/nVxxOnXqxG666aZk\n/4zA8NMef/3rX1nTpk1Nbltbtmxhe/bsYb169WLHH3886969O7vlllvqR8AzET9t8vbbb7Pu3buz\n4uJidsIJJ7APP/ywft/B2EcYY2zGjBnslFNOMW3bu3dvg+ojs2bNYqFQiPXs2bP+WZg2bRrbtm0b\nO+uss5RufZkiSxKS01Gj0Wg0wZP2JhGNRqPRcLTA1mg0mgxBC2yNRqPJELTA1mg0mgxBC2yNRqPJ\nELTA1mg0mgzh/wHcUUhjt09WHQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcda5bec>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Unfortunately something like\n",
      "\n",
      "    tm.value.resample('A')\n",
      "    \n",
      "will not work, since *value* have a type that pandas can't process in this case. We first have to convert *value* variable to float:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tm.value = tm.value.astype('float')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now it's working:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tm['1950':'2012'].value.resample('A').plot()\n",
      "title('Annual mean daily maximum temperature in Hamburg')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.text.Text at 0xb3afcac>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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xc1YMwDx2vyIl7Hb2bOVg9bIWSsLudDpVR+xDhlgnQDHaYxfLYedQ8tmNjtYB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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xbafd5ec>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now you know how to easily find meteo station and get it's data with ulmo. Maybe if you want data only for one station ulmo is not that useful, but when you begin to collect statistics about many stations, then it became very handy. If you use hydrological data you certainly has to give ulmo a try ([see list of supported data sets](http://ulmo.readthedocs.org/en/latest/index.html#api-reference)). Hopefully authors will continue development and add more data sources in future:)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}