Working with pandas DataFrames.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Working with pandas DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Pandas (http://pandas.pydata.org) is great for data analysis, again we met it briefly in the software carpentry course, but it's worth revisiting. \n",
    "\n",
    "Note the book on that website - 'Python for data analysis', this is a useful text which much of this section was drawn from. \n",
    "\n",
    "We're also going to look at how we might use pandas to work with data read in with CIS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "A `Series` is essentially a container for series data (think time-series, but more general)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's create a time-series from the data we used for the last numpy example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-11-16 15:41:23     0.13\n",
      "2015-11-16 15:42:22     0.21\n",
      "2015-11-16 15:43:25     0.15\n",
      "2015-11-16 15:44:20      NaN\n",
      "2015-11-16 15:45:22     0.29\n",
      "2015-11-16 15:46:23     0.09\n",
      "2015-11-16 15:47:26     0.24\n",
      "2015-11-16 15:48:21   -10.00\n",
      "dtype: float32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "-1.2700001"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "s = pd.Series([0.13, 0.21, 0.15, 'NaN', 0.29, 0.09, 0.24, -10], dtype='f',\n",
    "                 index = [datetime(2015,11,16,15,41,23), datetime(2015,11,16,15,42,22), datetime(2015,11,16,15,43,25), datetime(2015,11,16,15,44,20), datetime(2015,11,16,15,45,22),\n",
    "                          datetime(2015,11,16,15,46,23), datetime(2015,11,16,15,47,26), datetime(2015,11,16,15,48,21)])\n",
    "print s\n",
    "s.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As you can see, it's dealt with our missing value much more nicely - this is one of the nice things about Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can get rid of the negative value easily as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2015-11-16 15:41:23    0.13\n",
      "2015-11-16 15:42:22    0.21\n",
      "2015-11-16 15:43:25    0.15\n",
      "2015-11-16 15:45:22    0.29\n",
      "2015-11-16 15:46:23    0.09\n",
      "2015-11-16 15:47:26    0.24\n",
      "dtype: float32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.185"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = s[s>0]\n",
    "print s\n",
    "s.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Note this also got rid of our NaN (as NaN comparisons are always negative)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now, as you probably noticed, I added a lot of datetimes to this data which represent the timings of the measurements. Pandas uses these times as an `index` on the data, and gives us access to some very powerful tools.\n",
    "\n",
    "For example, resampling our data to a minutely average is easy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015-11-16 15:40:00    0.21\n",
       "2015-11-16 15:45:00    0.29\n",
       "Freq: 5T, dtype: float32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.resample('5min').max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Another way of creating series is using dictionaries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Blue      42\n",
       "Green     12\n",
       "Yellow    37\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours = pd.Series({'Blue': 42, 'Green': 12, 'Yellow': 37})\n",
    "colours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can index Series just like numpy arrays, or using the named index:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n",
      "Blue     42\n",
      "Green    12\n",
      "dtype: int64\n",
      "42\n"
     ]
    }
   ],
   "source": [
    "print colours[1]\n",
    "print colours[:-1]\n",
    "print colours['Blue']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Or both:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12\n"
     ]
    }
   ],
   "source": [
    "print colours[1:]['Green'] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Another nice benefit of the indices is in data allignment. So for example when performing operations on two series, Pandas will line up the indices first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Blue      58.0\n",
       "Green     37.0\n",
       "Purple     NaN\n",
       "Red        NaN\n",
       "Yellow     NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_colours = pd.Series({'Blue': 16, 'Red': 22, 'Purple': 34, 'Green': 25,})\n",
    "\n",
    "more_colours + colours"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "As you can see, if not both of the indices are present then Pandas will return NaNs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Pandas uses numpy heavily underneath, so many of the numpy array operations work on Series as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30.333333333333332, 42)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colours.mean(), colours.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Data frames are essentially collections of Series, with a shared index. Each `column` of data is labelled and the whole frame can be pictured as a table, or spreadsheet of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        First  Second\n",
      "Blue     42.0    16.0\n",
      "Green    12.0    25.0\n",
      "Purple    NaN    34.0\n",
      "Red       NaN    22.0\n",
      "Yellow   37.0     NaN\n"
     ]
    }
   ],
   "source": [
    "df = pd.DataFrame({'First': colours, 'Second': more_colours})\n",
    "print df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "And can be indexed by row, or index via the ix attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column by index:  Blue      42.0\n",
      "Green     12.0\n",
      "Purple     NaN\n",
      "Red        NaN\n",
      "Yellow    37.0\n",
      "Name: First, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print 'Column by index: ', df['First']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column as attribute:  <bound method DataFrame.first of         First  Second\n",
      "Blue     42.0    16.0\n",
      "Green    12.0    25.0\n",
      "Purple    NaN    34.0\n",
      "Red       NaN    22.0\n",
      "Yellow   37.0     NaN>\n"
     ]
    }
   ],
   "source": [
    "print 'Column as attribute: ', df.first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Row via ix: First     42.0\n",
      "Second    16.0\n",
      "Name: Blue, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print 'Row via ix:', df.ix['Blue']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can then apply many of the same numpy functions on this data, on a per column basis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First     42.0\n",
       "Second    34.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "First     91.0\n",
       "Second    97.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using Pandas with CIS data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We can easily convert CIS data into pandas data to take advantage of this time-series functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Identified 12 point(s) which were missing values for some or all coordinates - these points have been removed from the data.\n"
     ]
    },
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BC_Mass</th>\n",
       "      <th>Lat</th>\n",
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       "      <th>2008-05-06 09:13:50.000000</th>\n",
       "      <td>170.9000</td>\n",
       "      <td>47.957699</td>\n",
       "      <td>11.005400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:13:56.000005</th>\n",
       "      <td>188.1840</td>\n",
       "      <td>47.956001</td>\n",
       "      <td>10.994500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:14:02.000012</th>\n",
       "      <td>220.0030</td>\n",
       "      <td>47.955200</td>\n",
       "      <td>10.983400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:14:08.000000</th>\n",
       "      <td>218.1180</td>\n",
       "      <td>47.955002</td>\n",
       "      <td>10.972000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:14:14.000000</th>\n",
       "      <td>228.1930</td>\n",
       "      <td>47.955002</td>\n",
       "      <td>10.960500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:14:20.000000</th>\n",
       "      <td>206.9730</td>\n",
       "      <td>47.955200</td>\n",
       "      <td>10.948900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:14:26.000003</th>\n",
       "      <td>208.6280</td>\n",
       "      <td>47.955299</td>\n",
       "      <td>10.937300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-06 09:14:32.000010</th>\n",
       "      <td>226.1960</td>\n",
       "      <td>47.955399</td>\n",
       "      <td>10.925600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:09:48.000009</th>\n",
       "      <td>31.7568</td>\n",
       "      <td>52.225700</td>\n",
       "      <td>-0.820907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:09:54.000016</th>\n",
       "      <td>23.9183</td>\n",
       "      <td>52.226200</td>\n",
       "      <td>-0.808870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:00.000000</th>\n",
       "      <td>39.2030</td>\n",
       "      <td>52.226101</td>\n",
       "      <td>-0.796926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:06.000000</th>\n",
       "      <td>29.8282</td>\n",
       "      <td>52.224602</td>\n",
       "      <td>-0.785400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:12.000000</th>\n",
       "      <td>40.5798</td>\n",
       "      <td>52.221600</td>\n",
       "      <td>-0.774858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:18.000007</th>\n",
       "      <td>26.0588</td>\n",
       "      <td>52.217400</td>\n",
       "      <td>-0.765715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:24.000014</th>\n",
       "      <td>29.0014</td>\n",
       "      <td>52.212200</td>\n",
       "      <td>-0.758168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:30.000000</th>\n",
       "      <td>35.8803</td>\n",
       "      <td>52.206501</td>\n",
       "      <td>-0.751883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:36.000000</th>\n",
       "      <td>27.6358</td>\n",
       "      <td>52.200802</td>\n",
       "      <td>-0.745414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:42.000000</th>\n",
       "      <td>37.0731</td>\n",
       "      <td>52.195702</td>\n",
       "      <td>-0.737763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:48.000005</th>\n",
       "      <td>44.1955</td>\n",
       "      <td>52.191101</td>\n",
       "      <td>-0.729222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:10:54.000013</th>\n",
       "      <td>36.7343</td>\n",
       "      <td>52.186798</td>\n",
       "      <td>-0.720336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:00.000000</th>\n",
       "      <td>36.6205</td>\n",
       "      <td>52.182701</td>\n",
       "      <td>-0.711381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:06.000000</th>\n",
       "      <td>36.1217</td>\n",
       "      <td>52.178501</td>\n",
       "      <td>-0.702407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:12.000000</th>\n",
       "      <td>37.4727</td>\n",
       "      <td>52.174500</td>\n",
       "      <td>-0.693422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:18.000003</th>\n",
       "      <td>36.8654</td>\n",
       "      <td>52.170502</td>\n",
       "      <td>-0.684442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:24.000011</th>\n",
       "      <td>44.7990</td>\n",
       "      <td>52.166500</td>\n",
       "      <td>-0.675497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:30.000000</th>\n",
       "      <td>38.1811</td>\n",
       "      <td>52.162601</td>\n",
       "      <td>-0.666593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:36.000000</th>\n",
       "      <td>40.5909</td>\n",
       "      <td>52.158798</td>\n",
       "      <td>-0.657709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:42.000000</th>\n",
       "      <td>43.9407</td>\n",
       "      <td>52.154900</td>\n",
       "      <td>-0.648849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:48.000001</th>\n",
       "      <td>46.3236</td>\n",
       "      <td>52.151001</td>\n",
       "      <td>-0.640025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:11:54.000009</th>\n",
       "      <td>39.3391</td>\n",
       "      <td>52.147099</td>\n",
       "      <td>-0.631228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:00.000000</th>\n",
       "      <td>55.6050</td>\n",
       "      <td>52.143299</td>\n",
       "      <td>-0.622282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:06.000000</th>\n",
       "      <td>47.1951</td>\n",
       "      <td>52.139900</td>\n",
       "      <td>-0.613028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:12.000000</th>\n",
       "      <td>43.5564</td>\n",
       "      <td>52.137001</td>\n",
       "      <td>-0.603329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:18.000000</th>\n",
       "      <td>52.5288</td>\n",
       "      <td>52.134800</td>\n",
       "      <td>-0.593106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:24.000007</th>\n",
       "      <td>58.3320</td>\n",
       "      <td>52.133400</td>\n",
       "      <td>-0.582495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:30.000000</th>\n",
       "      <td>63.8618</td>\n",
       "      <td>52.132599</td>\n",
       "      <td>-0.571736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:36.000000</th>\n",
       "      <td>51.2281</td>\n",
       "      <td>52.132000</td>\n",
       "      <td>-0.560973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-05-22 13:12:42.000000</th>\n",
       "      <td>NaN</td>\n",
       "      <td>52.131802</td>\n",
       "      <td>-0.550255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>33040 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             BC_Mass        Lat        Lon\n",
       "2008-05-06 09:11:38.000000  144.1440  48.031101  11.212400\n",
       "2008-05-06 09:11:44.000000  114.5720  48.026798  11.205300\n",
       "2008-05-06 09:11:50.000004  124.8760  48.022598  11.197500\n",
       "2008-05-06 09:11:56.000012  147.8370  48.018398  11.189200\n",
       "2008-05-06 09:12:02.000000  112.0370  48.014500  11.180400\n",
       "2008-05-06 09:12:08.000000  149.7000  48.010899  11.171200\n",
       "2008-05-06 09:12:14.000000  148.4620  48.007401  11.161600\n",
       "2008-05-06 09:12:20.000002  118.6980  48.004200  11.152000\n",
       "2008-05-06 09:12:26.000010  113.8810  48.001099  11.142200\n",
       "2008-05-06 09:12:32.000018  140.1160  47.998100  11.132500\n",
       "2008-05-06 09:12:38.000000  165.0810  47.995098  11.122700\n",
       "2008-05-06 09:12:44.000000  268.9960  47.992100  11.113100\n",
       "2008-05-06 09:12:50.000000  255.1670  47.989101  11.103600\n",
       "2008-05-06 09:12:56.000008  260.2810  47.986099  11.094000\n",
       "2008-05-06 09:13:02.000016  322.3670  47.983101  11.084500\n",
       "2008-05-06 09:13:08.000000  275.7970  47.980000  11.074800\n",
       "2008-05-06 09:13:14.000000  175.9630  47.976700  11.065100\n",
       "2008-05-06 09:13:20.000000  265.8300  47.973400  11.055400\n",
       "2008-05-06 09:13:26.000006  210.1380  47.970100  11.045600\n",
       "2008-05-06 09:13:32.000014  235.6680  47.966900  11.035700\n",
       "2008-05-06 09:13:38.000000  220.9130  47.963600  11.025800\n",
       "2008-05-06 09:13:44.000000  239.3960  47.960400  11.015800\n",
       "2008-05-06 09:13:50.000000  170.9000  47.957699  11.005400\n",
       "2008-05-06 09:13:56.000005  188.1840  47.956001  10.994500\n",
       "2008-05-06 09:14:02.000012  220.0030  47.955200  10.983400\n",
       "2008-05-06 09:14:08.000000  218.1180  47.955002  10.972000\n",
       "2008-05-06 09:14:14.000000  228.1930  47.955002  10.960500\n",
       "2008-05-06 09:14:20.000000  206.9730  47.955200  10.948900\n",
       "2008-05-06 09:14:26.000003  208.6280  47.955299  10.937300\n",
       "2008-05-06 09:14:32.000010  226.1960  47.955399  10.925600\n",
       "...                              ...        ...        ...\n",
       "2008-05-22 13:09:48.000009   31.7568  52.225700  -0.820907\n",
       "2008-05-22 13:09:54.000016   23.9183  52.226200  -0.808870\n",
       "2008-05-22 13:10:00.000000   39.2030  52.226101  -0.796926\n",
       "2008-05-22 13:10:06.000000   29.8282  52.224602  -0.785400\n",
       "2008-05-22 13:10:12.000000   40.5798  52.221600  -0.774858\n",
       "2008-05-22 13:10:18.000007   26.0588  52.217400  -0.765715\n",
       "2008-05-22 13:10:24.000014   29.0014  52.212200  -0.758168\n",
       "2008-05-22 13:10:30.000000   35.8803  52.206501  -0.751883\n",
       "2008-05-22 13:10:36.000000   27.6358  52.200802  -0.745414\n",
       "2008-05-22 13:10:42.000000   37.0731  52.195702  -0.737763\n",
       "2008-05-22 13:10:48.000005   44.1955  52.191101  -0.729222\n",
       "2008-05-22 13:10:54.000013   36.7343  52.186798  -0.720336\n",
       "2008-05-22 13:11:00.000000   36.6205  52.182701  -0.711381\n",
       "2008-05-22 13:11:06.000000   36.1217  52.178501  -0.702407\n",
       "2008-05-22 13:11:12.000000   37.4727  52.174500  -0.693422\n",
       "2008-05-22 13:11:18.000003   36.8654  52.170502  -0.684442\n",
       "2008-05-22 13:11:24.000011   44.7990  52.166500  -0.675497\n",
       "2008-05-22 13:11:30.000000   38.1811  52.162601  -0.666593\n",
       "2008-05-22 13:11:36.000000   40.5909  52.158798  -0.657709\n",
       "2008-05-22 13:11:42.000000   43.9407  52.154900  -0.648849\n",
       "2008-05-22 13:11:48.000001   46.3236  52.151001  -0.640025\n",
       "2008-05-22 13:11:54.000009   39.3391  52.147099  -0.631228\n",
       "2008-05-22 13:12:00.000000   55.6050  52.143299  -0.622282\n",
       "2008-05-22 13:12:06.000000   47.1951  52.139900  -0.613028\n",
       "2008-05-22 13:12:12.000000   43.5564  52.137001  -0.603329\n",
       "2008-05-22 13:12:18.000000   52.5288  52.134800  -0.593106\n",
       "2008-05-22 13:12:24.000007   58.3320  52.133400  -0.582495\n",
       "2008-05-22 13:12:30.000000   63.8618  52.132599  -0.571736\n",
       "2008-05-22 13:12:36.000000   51.2281  52.132000  -0.560973\n",
       "2008-05-22 13:12:42.000000       NaN  52.131802  -0.550255\n",
       "\n",
       "[33040 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from cis import read_data\n",
    "from cis.time_util import cis_standard_time_unit\n",
    "\n",
    "BC_MASS = read_data(\"/Users/watson-parris/Local Data/gassp_data/Level_2/EUCAARI/SP2_EUCAARI_B*200805*.nc\", \"BC_MASS\")\n",
    "\n",
    "time = cis_standard_time_unit.num2date(BC_MASS.coord(standard_name='time').data)\n",
    "\n",
    "cis_df = pd.DataFrame({'BC_Mass': BC_MASS.data, 'Lat': BC_MASS.lat.data, 'Lon': BC_MASS.lon.data}, index=time)\n",
    "cis_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BC_Mass     0.023154\n",
       "Lat        57.583900\n",
       "Lon        15.691700\n",
       "Name: 2008-05-10 10:03:22.000010, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now we can do cool Pandas stuff!\n",
    "cis_df.ix[cis_df['BC_Mass'].argmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Exercise\n",
    "\n",
    "In pairs, plot probability distributions (use kde) of the raw, minutely and hourly averaged black carbon mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/watson-parris/anaconda/envs/cis_test/lib/python2.7/site-packages/ipykernel/__main__.py:4: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n",
      "/Users/watson-parris/anaconda/envs/cis_test/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11198d910>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BAg03FpEPgTbAVOBtVd2b5bMfVfVs/xWx6Pw53BhcH0utWrBhA0RHw77kfbQZ14at92yl\nemR1v+VrjDH+VFLDjV9X1Xaq+s/MoCIiFQBKa1ApCaGhbrXjRYvc+7qV6xLbKtY68Y0x5VpBA8uT\nOZz73suClFXZm8P+0u0vTPhpgu3TYowpt/IMLCJST0S6ARVFpIuIdPUdMUClEilhKZc9sFzY9EJU\nlYU7rBPfGFM+5dnHIiJDcB32ZwM/ZvnoKK6v5QO/lq6Y/N3HApCa6vpZ9uyBqlXduZcWv8QPu35g\n2jXT/Jq3Mcb4Q3H7WAraeX+Nqr5f1EwCpSQCC0BMDIweDbGx7n1SShLNX2zOhpEbqFu5rt/zN8YY\nL/m1815EBvleNhOR+7MfRc002PTqBV9/ffp99cjqXH3m1bz+0+uBK5QxxgRIfp33lX0/qwBRORwG\nuPZamDIFTp48fe6+nvcxbuk4UtJTAlcwY4wJAFs23yOXXgo33ghDh54+1/+d/lzd9mpu63pbiZTB\nGGO8UCLzWETkXyJSVUTCRWSeiOzP0kxmgFGj4N//howsS4WNOm8U//7u37Z+mDGmXCnoPJZLVfUI\n8AfcWmGtgFH+KlRZ1KcPhIfDF1+cPhfTLIaoClF8uuHTwBXMGGNKWEEDS5jv5+XAe6p62E/lKbNE\n4K9/hWefzXpOGHXeKJ797tncLzTGmCBT0MDymYisB7oB80SkDmC90tlcdx1s3QpLl54+d3Xbq9l7\nbK+temyMKTcK3HkvIjWBw6p6SkQqAVVVNcGvpSumkuy8z/Sf/8D338PMmafPTV4xmUnLJ/HN0G8Q\nKXJ/mDHGlIgSmSDpy+g8oBmnm8VQ1VK90XsgAsvRo9C8OSxZAi1auHOnMk7R8dWOPHfpc/Rr3a9E\ny2OMMYVVUjPvpwItgRXAKd9pVdW7i5pxSQhEYAF4+GEXYMaNO33ug/gPePKbJ/nx9h8JsR2ejTGl\nWEkFlnigXUD+ShdDoALL3r3Qrh1s2gS1a7tzqkr3Sd0Zdd4ormt/XYmXyRhjCqqk9mNZA9Qraibl\nTf36cPXVMH786XMiwtOXPM2j8x/l5KmTuV9sjDFlXEFrLAuAzsASIDXzvKr+0X9FK75A1VgANm6E\nCy6Ajz+Gnj3dOVXl8mmXc3Gzixl1vk0DMsaUTiXVFHZRTudV9euczpcWgQws4CZL3nILfPMNnHGG\nO7f54GZ6TOrBir+soFHVRgErmzHG5KYkR4U1BVqr6lzfcONQVT1a1IxLQqADC8Abb8DTT7shyHV9\nK+g/vuBx4g/EM/NPM/O+2BhjAqCk1gr7MzALeM13qiHwUVEzLU+GDYNBg+APf4DkZHdu9AWj+Wnv\nT8z5eU5gC2eMMX5Q0M77O4HzgSMAqroJsB2sCmjMGGjfHgYOhPR0qBhekZf7vcyI/43g2MljgS6e\nMcZ4qqCBJVVVfx3KJCJhQJkaehxIIjBxotuvZcQItwJy/9b9ubDphTzw5QOBLp4xxniqoIHlaxF5\nBKgoIn2B9wBbsrcQwsNh1iyIj3f7tqSkwAuxLzBnyxw+2/hZoItnjDGeKeiosBBgGHApIMCXwKSA\n94znozR03meXkgJDhrhJlB99BGuPLuT6Wdez8i8rqVO5TqCLZ4wxJToqrA6Aqu4vamYlrTQGFnBN\nYQ89BJ9+6oYkv/bzaFYmruSzGz4jNCQ00MUzxpRzfg0s4pbifRwYyelms1PAy6r6f0XNtKSU1sCS\nadw4NxT5g4/SGL2uL72a9OKJc0fDli2wbRvs3g1HjriFx1QhNBQiI906MXXquNUuW7WCypUD/VWM\nMUHE34HlfqAfcLuqbvWdawG8CsxW1f8UNeOSUNoDC6rMe3UjXz44jzu7fkX6+s9oejSEsBatoGlT\naNgQqleHqCgICXFDyk6cgAMHYN8+F4C2bIHoaDj7bOje3W1l2bmzS2+MMUXg78CyHOirqgeyna8D\nzFHVLkXNuCSUysCi6tbUf+89eP99SE8nsWNvnloUQ8cHw3k87F7mDfuGtnXaFux+GRmwebPbXeyH\nH2DOHDh8GGJj3eSZP/zB1XKMMaaA/B1Y1qhqh8J+li1dLPACrintDVUdm0Oal3A1o2TgFlVd7jv/\nBvAHIFFVz8qSvgYwA2gKbAOuy2m75FIVWA4ehClT3Ljj9HS4/nr405+gY0cQYeVK6NcPYh+azALG\n8N2t31E/qn7R8tqyBWbPhg8+gOXLXT6DB7tFy2yjMWNMPvw98z6vZXjzXaLXN5psHHAZ0B64QUTO\nzJamH9BSVVsDw3HNbJne8l2b3Whgrqq2AeYDD+dXloBJSIAHH4TWrV2tYsIE2LABnngCzjrr1z/0\nnTrB11/DgheG0Cb5NvpP68+R1CNFy7NFCzdhZu5cWLHCNavdeqvL7+23ITU131sYY0xR5RdYOonI\nkRyOo0DHAty/O7BJVberahowHRiQLc0AYAqAqi4GqolItO/9IuBQDvcdAEz2vZ4MXFmAspSsvXth\n5Ei3McuJE67m8M47cOGFudYaWreGhQth29RHkN09uWbGNaSmFzMING7sdh6Lj4fnn4d333Wd/k8/\n7ZrMjDHGY3kGFlUNVdWqORxRqhpegPs3BHZmeb/Ldy6vNLtzSJNdXVVN9JUxgdK0vExqKowd65q4\nKlRwf9BffhmaNCnQ5Y0awaKFgnz+Mts2VOfamdcWP7iAC2Z9+8KXX7pmsvh4F8n+/W8X+IwxxiPB\nMnSodHSkfPyxq6F8/73rSH/uOTdiq5Bq14Z5c0OpPm8a61ZX4Bqvgkums86CqVNhwQL47jsXYCZO\nhFOn8r/WGGPyEebn++8Gsv5XvZHvXPY0jfNJk12iiESraqKI1AP25ZZwzJgxv76OiYkhJiYm/1IX\n1oEDcOedrj9jwgRXMyim6tVh7pfhXNb/XdbpQK7mGmb96T0qhlf0oMA+7du7Dv6lS+Gvf4VXX3WT\na84/37s8jDGlXlxcHHFxcZ7dr8Az74t0c5FQYAPQG9iL24HyBlWNz5KmP3Cnql4uIj2AF1S1R5bP\nmwGfqmrHLOfGAgdVdayIPATUUNXROeTv/1FhH3zggspNN7kO+Yoe/uHHzY2MvTyNxJ5DqddmO5/c\n8Ak1K9b0NA/ADYOePh1GjYKLL4Z//cvtsWyMKXdKas/7IlHVU7hZ+3OAtcB0VY0XkeEicrsvzefA\nVhHZjNvvZUTm9SIyDfgOOENEdojILb6PxgJ9RSQzaD3jz++Ro2PH4OabYfRot7rkv//teVABNzfy\ny8/DafDDVJLWnEuvN3ux8/DO/C8sLBG44QZYvx4aNHDNZW++6QKOMcYUgl9rLIHmtxrLmjVubsh5\n57mO+UqVvM8jm+RkuOIKSGr3HPta/If3/vQePRv39F+GK1a4Icq1a7v+l2bN/JeXMaZUKdU1lqD0\n1luuqWj0aLfvcAkEFXDLgf3vf1B/6wM0Wv4aA6YPYNKySf7LsHNnWLwYLrnELRczcaLVXowxBWI1\nloJKS4O774a4ONf01b69N/ctpJMn3VbHO09s4Je+A+jV9Hxe6vcSlSP8uBDlunVuE5nmzeH1110t\nxhgTtKzGUhIOHnRrb+3c6f4XH6CgAhAR4eY4to9uQ433lpKckka3id1YkbDCf5m2a+e+d8uWriYz\nd67/8jLGlHkWWPKzYQP06AFdurh5KlWrBrpEhIa6isN5Z0ex/p9TuLvzo/Sd2pdnFj1Deka6fzKt\nUMENUHj7bRg61A1PtqVhjDE5sMCSl4UL3RIsDz3k/qiGlp5NuETcCi1XXgnPDx3E9EuWMm/rPHpM\n6sGqxFX+y7hPH9exv2mTezY7dvgvL2NMmWSBJTcffQTXXOPW9xo2LNClyZEIjBkDjz4KN/ZvxoP1\n5nDH2XfQe0pvHvjygaIvYpmf2rXd87n2WrcHzOzZ/snHGFMmWWDJyeuvu9WBv/jC/Q+9lBs61G3v\ncvPNQvrSYawdsZaklCTOHHcmU1ZOIUMzvM9UxE2mnDnTBd7HH7clYYwxgI0K+72nn3bDiGfPdmto\nlSGbN7t9vfr1cy13P+5dzMgvRhIeEs64/uPoWr+rfzJOSHCTK8PDYdo0GzVmTBlno8K8ogqPPeb+\nMC5aVOaCCkCrVm79y1Wr3GTKVhXPZfFti7m1y630f6c/wz8dzoHjB/K/UWHVqwdffeVGjHXvDqtX\ne5+HMabMsMACLqiMHg2ffOJW/C3Da2TVqOEqW+3aQbdusHRJCLd1vY34O+OJDIuk7StteXnxy96P\nHgsLc+uLPfmkm1T5/vve3t8YU2ZYU5gq3H8/fPON2y++Vq2SKVwJ+OgjuP12eOQRuOce1y2yZt8a\n7p19LwnHEngx9kV6t+jtfcY//QRXXQW33OL6XkLs/y/GlCV+3fO+rMs3sGRkwF13uT+Es2e7teqD\nzJYtcN11bp+xN990X1FV+XD9hzww5wG61e/GC7Ev0KhqI28zTkx0o+pq13Z7v0RFeXt/Y4zfWB9L\nUame3kNlzpygDCoALVrAt99Cw4a+prGl7pfm6rZXs27EOjrU7UCX17rwypJXvB09Fh0N8+ZBnTrQ\ns6eLcMaYcqF81lhU4b773DIlc+aUm/9Nv/8+3HEHPPiga/3LbKFat38df/70zwBMuXIKLWu29C5T\nVXjlFXjqKbfGmm0iZkypZzWWwlJ1nQ7ffOPmqZSToAKuZWrJErc32eWXwz7fvpvt6rRj4S0Lua7d\ndfR4oweTlk3Cs/9wiMDIkW5V6KuuchNOjTFBrfzVWJ580u2UGBdXbudbpKW5Gftvvw2TJ/92Duja\nfWsZ9OEgmldvzpsD3qR6pIdNhGvWuIk2Q4e6Tn0p8n+IjDF+ZDWWwnjuOZgyxa3OW06DCrh5jE89\n5R7FkCGuApeW5j5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Yc5oqJCS41fx3rztMxqLvqLIyjoY75tIqaR3rKtdiSXRFljdP4YeW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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10af62fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "cis_df['BC_Mass'].plot(kind='kde', xlim=[0,200])\n",
    "cis_df['BC_Mass'].resample('1min', how='mean').plot(kind='kde')\n",
    "cis_df['BC_Mass'].resample('60min', how='mean').plot(kind='kde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vvQc8+SQHTEVJVbKyqKWLcFK9YgX77bZtyW5ZYogk93t/pER2hbIyFepK8hk/nhpDOtVv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TMpic4U9erxb2Iid/a1anGXHegAS02l/b66OYwV0UdaZeBoUZH714313avittu441corMz8+fR5\nMjv8uEsXhpoePGhuu+GkOv+iY0KIa4QQsZWPawAcq/IqP5BSlkopC6WUFQC+ARBUoZ6aCowYwfji\nzEwWzfjvf4FZs4Dly4Hp06meF4LCOCODE6unybUqkpJUjLzCf157TT8WgovDX3/Vz3mrPjhgAFXu\nN92kn7vjDobgHT1qfl8VCjP44APPxYuqQ2wso6AWLTK/7XBRneCtCaBN/WUAEsBSAONN6NPfCCFS\npJRaKYsBAF5z977Jkycbng2pfPhGTAy94JOSWODl+utpP1+1ihXddu3iJFlRQVV73br6teXleqWf\nrl3tFxahADIzM5GZmRnubvjF9dcDjz+uV2z7809gyBCOv/PO856WNi+Pf99+m853Tz/N54cOAfXr\nMxb/ww+D2n2Fwi/WrGE9g6FDg9P+0KFMvmTMCWFnTC3oIoS4x53duxrtjQLwNIAiAIuklJPcvKda\nNvUePbj73rePQvv667nrOXaMiUaaNWNyj/h44LTTWDBDQ0raxQHaMM206yvCg53sn999B9x5p+d4\ndHe8+ipw113686Ii3gNbtujnkpOpqfKU+17hH3YaU1Zk3Dh6vf/zn8Fpf/t2YPBgxsHbRXMasipt\nQoi9UsoWpjXo22cGLNSbNqUqMzWVO5XSUub1rl+fasoTJ2hzaduWO3UhaEdXwjtysesEPGkS8Prr\n7nPBG7njDr7PmfnzgbFj9epyQOSpJcOFXceUFVi2jONy506GIAeLTp2o4jfTsz6YhFKo75NSNq/6\nneYRqFBPTGSIxA03cFd+8iR3KytXUmi3aEH1eps29A6uWZO79ZYt6TSniEzsPgHn5/tWSnLECOD7\n7113JqNHMyOdhhCsZnjRReb2M5qw+5gKF8eOcYc+ZYr3qohm8NBDdID+17+C+zlmoXbqHoiPp+qx\nVy+u1IqLqdLMy6MjXF4eX7/4Ynq7FxXROS452fSvobAIdp+AjbXcExNdE9a441//Ah55RH++fTvv\nCc3+DnDcX3st75NOnYCGDYHFiznx7t8P9O4NXHCB6V8nIrD7mAoVu3dzRy4lnTaffJIJk55/Pvif\nvWIFcM01DG2zgybWVKEuhMgDHeNcXgKQLKUMaeb06gr1pk1pOz//fIYKtW/P3U5iIpPPlJdzAjP7\nhy4v52SYlsYFg8Ia2HkCPu00x3C28nJ9J+7v+BXC/8Q0L70E3Huvf9dEA3YeU6FASmDiRGDGDKbt\n1pJ6XX89Ve+hELJScu7/6COgT5/gf151MTVNrJTSQ7FHexEXR2EeF8cSlPPm0Rt440YK+/R02nDq\n1+ffNJOLyubmMj4yL08JdUX1uflmR4Gem+uoWj91iikxNcfOqghEBh0zNaBVES289hq1Ptu3h28u\nFEKP/LCDUPeGqer3cODrTr1WLdrH69WjMF+7ljXVW7fmRNe5M3c6LVsyNCgujk4TJSX0gDfbK7Ki\ngs55KSnmLxgUgWPXXZVx/M+b51kVvm0bdyRm0rQpd1XPPGNuu5GCXcdUKDh+nJFDy5aFP7Pmrl2M\n+Ni/33M5Y6sQMpt6ONCE+uOP6zG37hg+nE5Aw4dTwOflUcXTrBkFd1ycijNX2HcCNgp1X7ofF+c9\nR/yLL7KUa9++1e9btGPXMRUKJk+mEH3nnXD3hAwbBvzjH8AVV4S7J96JCqFuht0lNZVORlJSwNet\nyx+3Tx9WCNq/n0lmunfn+7dtYyauiy6qemVXVAT89RfbbNiw+n1VBAe7TsC+CPUePfyvm37zzXoB\nGUVg2HVMBZuKCmpKv/ySY9MKzJ7NXA5WD+UMSunVSMTo7Qtw0K1Zw9j0DRtoAz92jF7ySUks9NK+\nPfMGN2yoXx8XB+Tk0C5//Djj3cvL+XpxMW32depQ0J86xfcXFythrzCHNm2YTKmkhM/r1GEtan8F\nOgDs2GFu3xQKjaVLqS3VNklWYMwY4J57WLCra9dw9yYwIkao16jBcqa+EhvLR1ISBW6HDvwRy8ro\n+R4TQ1t3jx5UQw4fDrz/PkMuioqAbt04IA8fpuBv1oyCfPlyIDubIRmxsbThFxayre7dKcB37WJY\n3J499LTPz2dbKSmBl3JVRC+jRzs+d84wd/y464K1KurW5bifM6d6fVMoPDFjBsPIrBRCFh9P9fu0\naaz7YUciRqivW+fd0SIujiryevVoJ2zZkp7t9erRUSMpie/r1o3vBbizKS+nij0jA1i9mjvrNm2o\nNsrPZ9KavDw63yUm0l6fk0MVqPa8SRNOkq1bs62KCn5e3boU/I0a8XOCmTFJEZn07s1x6Y7YWKBB\nAxZvGTOmalv7tdcCM2fy3vjrL/P7qlBoFBdT1b1mTbh74spNNzEXw/PP2zMyyfI2dSHESwB6A1gt\npXSJgtVs6nPnMkmMO2JiKExjYqjivuAC7sq1XfRpp1FFCeh/Ab5WXq5n6Dp5kir41q3ZVl4e6/tm\nZFCAAxT0QlD1eeKE7jmvnPDsgZ3sn02aOJaMTEzkglIbiwprYKcxFSrmzKHt2qq1lK66igvmiRPD\n3RP32NamLoToAaCmlHKQEOJNIUQvKaXbfcnvv7tvIyaGlasuuYQObzVqsChLTAx3zHl5VI27UwE5\nZ46rVctx5Zaa6hoepKnPa9Sw5ypPYQ+cx2vLlv7VUVcowsmMGdQMWZW776aT9N13229DZvWaNH0B\n/FR5/DOAfp7e6KmGdJs2jDc/dIiqc23HDvBverq1bDoKRVU4J3kZOFAJdIV9OHGCpU6Dnc+9OvTp\nQ63u/Pnh7on/WF2o1wKQW3mcU/ncLe7KT8bFcQA98wyrWI0ZAzz4IJ3YFAq78vbbjs+tHn6jUBj5\n5BNg1CjrazLvuovZ7uyGpdXvoCDX8q2lATjp7k2TJ0/GoUPasyEAhqBZM9rCjx/XQ3sOHmR5veRk\nllndu5f2xxtuoLq8Xj2uzpTDWvSQmZmJTKsa9jxw4IB+rLRMCrvx3nv2qIY2bhxw//32C2+ztKNc\npU39H1LKW4UQ0wC8J6Vc5fQeKaXE/Pms6KPRqxcFdloa1e779wNZWXr51F27GJqWnEzv9wYNgMGD\ngcsvp8peEZ3Ywanpllscw2187e7hw1zADhoEPPBAcPqmcMUOYypUrFvH4ll79tjDVv2vf9G0NX16\nuHviiDdHOUur36WUawEUCyF+A1DmLNCNOMd3r17N5Aa7djHcLCZGT4156BB3OFqcekoKPd2PH2d4\nmUJhZbyld/XGY4+xVvqDDwJ33mlunxQKX/j3vzn27CDQAcasf/GFvUy2lt6p+4K2U1+9miEIziQk\n0NtdSlaukhJo0YI7+aQkCvGePRmK1q6dvdQsCvOxw67KqHKPi6MpyReWLgUGDOBxbKxed10RXOww\npkLBunXAiBHMUminIlb/93+UDZMmhbsnOrYNafOH7dvdny8pocAeORLo2JEC/pxz9N17djZt6KkR\nUVBWEcl06cLSwEbuu08/fvFFZj3MzWUiptdf55jXuPlm/bh166B2VaFwIDeXIWxTpthLoAN0mLvg\nAt5rnqKsrETECHVjEg5nDh0CfviBNsXp0+kQp6HyrSusTmkp/UDcjfF+/dwL+717mRVLIy2NE6vG\n1q3B6atC4cySJfTlGDECmDAh3L3xnx49qN394QfXlMxWxNI2dX9o1cr76zVrUlWpZYdTKOzAlCk0\nIXlatI4Z4yrQ3WEU6LNnm9M3hcIbJSXc3V56KcOKX3/dvtEal19un/smYmzqWmY4Z84/nw5C2dlU\nRSrPdoU3rGb/NE6CMTHA2rXA3LnAE084vi82lpPmrbfyeU4OU1xu385deXY2z7durSqvhRqrjalQ\nICVw9dV0Pv74Y9a5sDNZWfS3OnSo6jLboSAq6qkDtHt8/bX+2jnnMNGBMZ+7QuENq03Anuqkl5Qw\n6UyNGlTBK6yL1cZUKPj4Y+CFF4Bly1zTbduVAQOARx5h2vFwY9uQNn9x582blRX6figUZuOcECkh\nATj7bCXQFdajrIzC7803I0egAywYZoe0sREl1LXMcRppaYw/j7JFskKhUISNOXMYZdS/f7h7Yi7n\nnktnOavLk4gR6kVFek10jcJCYNs24NdfeaxQ2JWCgnD3QKHwjenTgdtvD3cvzOf007lxtLpPSsQI\n9cREOmUYOXKEKWITE4Hi4vD0S6Ewi+HDw90DhcI7x44By5fTvynSEIJ+Wt9/H+6eeCdihHpmJgeT\nkUaNqALq2tX6FYEUCncYMxwuWMCJ5d13w9cfRfA4ccL+Wf6++orx6M5puyOFkSOBH38Mdy+8Y2mh\nLoS4XgixRQjxixDiOW/vdVb3DB/ODEadO0fuAFNEPuvWAc8+63juhhuA9HTgpNuahQq7ISXDD5s3\nBzIymM7Xrnz9NXMnRCpDhjDqJND6C6HA0iFtQojrAcRKKT3uTbSQtlq1GJursWULU2XGWHrZorAa\nVg4/GjiQ2bmMnH++YxinwnpUNaZmzwYef5zhX0uWAOPH89huqXwrKoD69VmqtGnTcPcmeHToAMya\nBXTvHr4+2D2k7V4hRKYQYpi3N112mePzL75gFq1Dh4C8PJ4rKeHA87UAhkJhJRYvZrUoo+bpm29Y\nx0BhTyoqgEcfBd54g9qX884DHn4YuO46vmYn1q9nCu5IFugASxf/9lu4e+EZqwv1OVLKLgDGAZgq\nhOckg8895xjLu369vgK+4ALm7z33XODGG4GnnvJcAEahsDJ169L586WX9HMnTzpqqRT2YcECOvIO\nHaqfu+subjw++SR8/QqEhQsdv0ekYnWhbomCLkKIhgBmAdB0VALAQSnlVQAgpTwqhNgGoCGAQ87X\nT548GQCrUK1bNwT16g3B+PHc2cyfz926RkoK0K2bPRLzK4JPZmYmMjMzw90Nv7n3Xi5UtUl0xAhg\nxYrw9knhP59+SnW7czrgl14CrroKuOQS11Bdq7JwIVPDRjqDBgH//Cd9IayYy97qNvVUKWWeECIZ\nwCIAfaSU5U7vkcbv8McftHcUFPAfPnOmY4H7OnWAYcOo8mrf3j43jCI0WNmm7g5PaWQV1sHTmCor\nAxo3BlaupIOcM+edxyxmN90U/D5Wl/Jyqt63bImOypcZGUxE0759eD7fzvXU7xVCnAvu3Kc4C3Rn\n8vJYU3rGDD5PS+NuZvNmCvkWLeiZ2bMn1VunTimhrlAowsOKFbQ/uxPoAPDQQ4x0mDCBBXuszNq1\nQJMm0SHQAV0FHy6h7g1LC3Up5VMAnvL1/fHxjpm3cnM5yJ54gl6ZTZvSyUgILgCUg5HC7jRsCBw+\nzGOrqgMV7lm8GBg82PPrZ53Feeurr4CxY0PXr0CIFnu6xqBBzI1iRS2K1R3l/CIvz9Wzfds2qoY6\nd6Z3aXw8H3XqqAlQYX+ef14/tuIEo/DM0qXe86MLAdx2G/D226HrU6BEm1AfPJg7dSuavCxtU/cF\no029qAi48ELgp5/015s0off7DTcwRKRWLWuqTBTWwG42dUBfnCYnqxzxVsTdmJKSWpY1a4BmzTxf\nW1jIpDSrVnlW04eb0lLa03fu5N9oQEpqfhcvBlq1Cv3n2z1O3Wfi46lyN3L4MNP6ff01HVJ27rR2\nNiCFIlBU0SL7sGMH/Xm8CXSAC7WrrgL+97/Q9CsQVq/mgiNaBDrAhfSQIVTBW42IEuqxscCVV3IF\nXLcub4jycmDXLuC115gze+VK4MMPgT17wt1bhUIRrVSlejcyYQKdf62qQFqwADj77HD3IvQMHswK\noFYjooQ6wMQNixbRDtWhg37+5EmGW/zyC3ft8+a51l9XKOzIiBH6cVZW+Pqh8J2lS4EBA3x7b7du\nQEICNyRW5JdfGCYcbaideogQgjnfW7emaqt+ff21wkI+Sksp0J1V9QqFHXnzTf34jjvC1w+F7yxZ\n4vtOXQimwf7ss+D2KRAKC4Hff6c3eLTRrh1Leu/eHe6eOBJxQl0jIQFo0MDVG37PHsanp6fzr0Jh\nd9q00Y+/+y58/VD4xsmTnIeMZXWr4vLLKdStlg9+2TKgSxfmBIk2rGpXj0ihnm0xloMAABX3SURB\nVJ1Nx5JvvnEsT1mnDr3hmzalx6JVbVQKRaAok5L1Wb4cOOMMOvb6yumnM8X1778Hr1+B8O23wDnn\nhLsX4WPwYCXUQ8Iff9D+5Fxvuk0bOp08/DDQp49riEh5uXvPeOfVsZTUALg7769nvVpYKKzA1q1q\nLIYKf1TvGkIAl17KIlVWQUpgzhzgoovC3ZPwMXQofQqsdO9YOqNcIGRlsSBCSorra4cOAampwL59\nTNFYXk6bUHY2veUTEuhBX7MmPecbNAD++gs4cADo1Yur5caNgU2bgI0bGe/eo4fe/tatbK9TJ1Ze\nqopjx2iPad6cn6VQBErbtoFXHhw5kmGfAJ1JVR6H4LJ0KQuC+Mu4ccy5MXWqNRJnbdzIOTScdcXD\nTfv2QFwcsGGDf+aUYGIJoV6Z3/1lAEeklIMqz8UCeA9ABoCvpZT/9rW9lBTazJ130ocPA08/DeTn\nU01ZqxbPJyZSNd+5MwVsaSljSPPyuEjIy2Nim+Jivl9blVX3xiorc/yrUATKbbexchsAfPQRcO21\nvl9rLNvaoQN3kUuWmNs/BSkroxaxXz//r+3cmRuP1auB3r3N75u/zJzJhYYVFhjhQggutObPt45Q\nt0RGOSFEOoACAAsMQv1iAB2klFOEEPMB3CClzHZz7d8Z5b74AnjlFQrphQtdhXpCAoV3cTHf07Il\n0LEjV1qtWtGDs3174MQJCvHUVD5KS+kIkpRELYCmfo+Pd62SVVHhX/EFrS8Ka2DHjHIa2lg84wzv\nZVidc8SXlfFeOHDA9b0vvwzcc4+5/Yw2jGNqzRouuDZtCqythx/m7zdliokdDIDiYhbIWrSIXuDR\nzE8/AY89Rl+JUOEto5wlhLqGEOI3g1B/HsDnUspVQoiJALZJKb92c42UUiI93XuIWloa7eiXXAI0\nakR1d7duQI0awfo2CjsSCUJdCPde0gUFXMQePsySnp98or82bRpVwpo2ykh2tmNoqMI/jGPqjTeA\ndeuAd94JrK3Vq4ErrmBNi3DukN96i/b0H34IXx+sQkkJ5cnmzTTPhgI7pYk1drIWAE1M51Q+90hV\nMee5uVxRPfkk7dhdutBurlBEGlJyAfvbb/q50aPpK7J3LwX3qlX6a4sXA48/7l6gA+o+MRN/ks64\no2dPalY2bDCvT/6ydy8rXxqLCUUzCQlcJM+cGe6ekJDa1IUQDQHMAqBthQSAg1LKqyqfG7dIOQC0\n6Mc0AB7dgCZPnmx4NqTy4Z6DB4GnnqJ6/aKLlFNQtJOZmYlMq8WkBEjTpsD+/TxescJ7Wc8dO3zf\n6Q0aRLWxovoMG1a97GtCUNs4e3ZobbiHD3MhsWEDU24/9FB0O8g5M348cPvtwMSJ1dOgnDzJjKdS\nsjhZerr/bVhN/b5ISnlW5fHFANpJKZ8XQswDcJOU8rCba6SU0q9/ZMuWrNp22WVKqCscsbP6vbSU\nu8DqphM1Lg4A+pw4J3FS+I7ZY2rZMuDGGwO3y/vD/PnAI4/QYbhrVzrrjR4NnHtu8D/bTlRUMALl\n44+Bvn0Da2P+fJZP7tuXC4MVK3iuZ0/X93pTv0NKGfYHgF4AfgJwHMCPABJALcIMAL8BeMjLtZKe\ncq6P2Fgp09OlbNVKyjZtpBw8WMqpU6Vct07KoiIpKyqkQuGANp7sSl6elK1bu78fnB/p6bwn3ntP\nytJSXn/qlJQXXOD4vosuCuc3sj9mj6nycimbNpXyzz9NbdaFF1+UskULKb//np+p8M6rr0o5Zkxg\n1777rpSNGkm5dKl+7rPPpGzSRMr9+13fXzmm3MpES+3UA8HbTv2ee4D776cTw6lT9FZPSvLPO10R\nXdh1p15eDpx1FndxRtq1A9av505i4UKGq9Vy8k45fJhJmb7/3r2DXU5OdKYBNYtgjKm77uK89uij\npjb7N19+yRDJJUuqLg+rIIWFrDny1VeMQPGVF18EXn+dTofOmuPHHqNG5ssvHc/byVHOVJYupZNP\nRQUFenw8PYA1VWJZmbUyASkU/nLwIFV1cXGOAr1OHdrNt25lyGRyMnDeeY4CfexYhmg2asR0n84C\nvWNHTlRKoFuPceOAWbOCM39t3QrccgtDhJVA953kZDoP3nCDb+mapQQmTWJK80WL3JuCH3mEQv3b\nb33vR0Tv1AGmViwrY+rYmBju1IcOZaxoTg4TywwezIxyCoXddurO4z4mho42o0Z5viYnh+PdOaVx\nTAyTorz3Hu2DCnMIxpiqqOCi6513zK2QVlLCMXDTTRTsCv+QkjInMRH44AMutt1x9Cj9Ig4d4v1a\nr57nNufOZXTK2rW6ltk2ceqBEIijHKA7fHTtyko7oYovVFgbuwv1xERGdngiM5OLWiOXXELhULu2\n6d1TIHhj6vXXucMzsyTrAw9wpz53bnRniqsOhYW8p06eBJ55Bhg4kFri8nKmYf70U5ZLHj8eePZZ\nhsR5Q0pqZS69VF8kKKHugTZtgAcfZFnD1FTz+6awH3YT6s8842pX9dT9xx9nmmSNVq2AnTuD1zcF\nCdaYys2lDXfRIqb3rS4//EDfij/+UMmGqktFBdXq06axJkNqKjVkjRoxreydd1ZPG6aEuhf69AHG\njAFGjOBqqn17qugV0YndhDrguKP68EP3ed/793e0uV9zDXPEK4JPMMfUCy/QmW3u3Oq1s2kTNTiz\nZ5urzlew1khuLn1TatY0p82oFurNm7PAy+bN7l+vWZM79TZt6Ck8cqT7Cm+K6MDuQt2560VFNC0Z\nyxB/8AFw3XWh6ZsiuGOqqIhJYB57DLj66sDaWL6cGdFefBG46qqq368IP96EuiWqtAWTggI6JXii\nXj0K9qIiOs0VFyuhrrAPe/Z4fm3JEtrzNGJjmeKzSZPg90sRGpKSaKMdPpyLN3+y1e3YAbz6Km3y\n06dTLaywPxEd0hYbS8/3wkLP7ykqol3x1Ck6LGS71IFTKKzLyy87Pu/Th38feshRoLdowXtBCfTI\no1s3CuYrrwTuvps2cS1sV0racrdsAebNo7p+wgRqJfv3ZxjWhg1KoEcSEal+796dgnzECArs1as9\nC+sOHWhTP/98quDT01XltmjGbur3tWtd00gK4aiGv+IKx4psitASqjGVnQ1MncrkJ7t300eotJSb\nlYYNKcjbt+ejf39GAMVE9LYucrF8mtjqPOCQJnahdM7IWFAg5ZYtUs6dK+XEiVI++KCUL78s5Ucf\nSTljBlMtlpW5puFbuHCh68kAsFo7ZrZltXbMaAsWTBNb1XcaOdJzKtgvvghNH91h5u8aSszutxlj\nyt8+lZQwZXBhYfXaMas/qh1z24GXNLERs05jCEamS2Wq5GSuTMeMoSPIc88xfew119CxpGNH92lj\nzarcZbV2zGzLau2Y3ZZVqOo7ff89RfiiRXrMa+PG9LgdOzb4/fOEXX8LK/bb3z7Fx9M3yDmSx2r3\nrGrH/HYiRqhnZ7PGrwXvR4UiJAwcSEdPKYEDB1TuBYUiGokYoa5QKBQKRbQTEY5y4e6DQqFQKBSh\nREZq8hmFQqFQKBREqd8VCoVCoYgQlFBXKBQKhSJCUEJdoVAoFIoIQQl1hUKhUCgiBFsXdBFC9ALQ\nD0AtACcBLJdSrgpvrxQKhcJ6CCEEgIYAjkgpy/281tS5VgjRGUBnADullCsDbUfhim2934UQLwNI\nBPAzgBwAaQCGAyiTUt7tZ1spAG4BB206KgctgP9KKfPs2o7JfWoC4GEAp4MannIAfwJ4TkqZ5Wef\nTGnLzD5ZBSFELICL4DSBApgrpSwLZ9+8IYSoJaU8WXk8GpUTNoDZ0sKTjJX/30KIBgDONPRrpZTy\nsJ9tPCelfEgIMQzAVADbALQBMEVK+YWPbZgy1wohvpdSniuEuAfA2QC+ATAAQJaUcpIf7Zjym5k1\nZq3Wn7Dnbg/0AeA3f85X0dY8AJcCqAMgFkDtyufz7dyOyX1aAOAMp3NnAlgQQJ9MacvMPlnlAeAj\nAPcD6AmgNYAelc9nhLtvVfT7l8q/UwD8B8C5AJ4G8F64+2bH/zeABwDMBzARwA0A7q28lx8K8HfJ\nBFCv8jgZwDI/2jBlrjX05VcAMYbzi8Pxm5k1Zq3WHzur31cJIf4L4CcAueDq8WwAawJoqy6AL6SU\nFZXPTwghvgBwj83bMbOtZACbnM5tqjzvL2a1ZWafrEKGlPJap3NrhRCLwtIb/+kvpdQqMHwvhMgM\nZ2d8wKr/79FSykFO514WQvwG4Dk/2mkihJgAoK6U8igASCkL/UzaZdZc20kI8SEo+BIBaEWxkzxf\n4hazf7PqjllL9ce2Ql1KOVEI0QNAXwBtQbXQ21LKtQE0Nw1AphBiPTho00GV7ps2b8fMth4B8LUQ\nogBAHnhjJwF4LIA+mdWWmX2yCvOEEF+DOyttAh0M7tqsTM9KgdNJUyMKIWIAWD0D/VcW/X/vFkJM\ngqsg3etnO1Mq/04VQqRJKXOFEKkAvve1ARPn2j6Vfx8DUAb8bR7093719JvN87OdnpWCt2M1x6yn\nezaQ/lT7HrKtTd1shBBx4IBNBwftdhmATc1q7QShreTKdnKllAWBtGF2W2b2yQoIIeoD6A2gF2hT\n2yFt5ExkcILaD/4m68LcJY8IIRqDAuYMcAydBgrOWYHeIyb1KxbAhaAgrQXet8tAs1nA/RJCzJRS\nXmVOL8OH4R7R/jcrwR1zte4TIUQNAJ2llCv8vG4QgE6gPT23sj+tpJS/+9lOZwDlUsrNhv50lVIu\n97kNJdRNdXSwVDsm9ykFwM1u2gnUea/abZnZJ6vg5Ew0HMDXCMCZKNQY+n032O+AnKBCjRDiFynl\nMCHEawDyASwE0B1AbynlZeHtnStCiAQpZUk1rl8opRxqZp9CTeXu1R0/SClHVLMdAeB7P9t5EUAD\ncHFYD8AEKeURbWyFuh3bqt9N5n0AGwDMhKN35/sArrFxO2a2NRPADADvObUzE8AFfvbJrLbM7JNV\nqKyIjosBDK30hXhLCLE4jH3yBa3fY2Gvfmu+Jp2klMMrj38UQiwMV4eq4GsA51Tj+kjYxZ0CF+9G\nBICuAbYjoP9fAmnnDM3/QQjRFcDnQoj7/GzDtHaUUCdmOTpYrR0z26oLhlaY5bxnRltm9skqmOVM\nFGrs2u8PhBDTAewTQswAPbO7AghrvotK2+rfT0GhI0B/mGhnM4CLpZQ5xpNCiJ/C1E6spkGRUq4X\nQlwMbjb8/a1MaUcJdWKWo4NzO+kABsF/pxsznXfMcrwKhfPef8LYJ6tgljNRqLFlv6WUHwkhFgAY\nCSZmiQMw3QJ+APVBW2qp8WQAAscZt+U6bcZo6ItGI6PC1M69oPkvGwCklCeEEBeCocMhb0fZ1Csx\n0dFBc+DQnNJ6Symf9rMN05x3hBAJAK4A0ALADlBNmgHgNVmZ6OD/27v/kDvLOo7j7w822sqckZCF\n0i/EYTFDIxJJF4XNosLEIEst5x9DEKUMw/kjjKiQTFn2RwUbLJpMc0qkpAmy8tdsOjQVS9IWtFgo\nlRqW1qc/ruu409l5tvs+z/085zzn+bxg7Oy6z/k+19kG3/u+7u/9vVrEmovivV6hy+9mWVA4qzgR\nk0TSasqz5INXkcfb3jGLuG92ywY2sbAkqdNpocOv+P97M1BOFB4b8szp/uJ0VrwjaSuwndJ05nhK\nAdOzwJm2P9oiTpfFewI+RukAd0dv+VzSp2zf2ibWkNhX2b5iNjEiJs20VK3H3Mvye9FVocPNwLHA\nRtt313i32267nNNl8c6htr9Z5/Ko7Wvq6y+0jLOR7or3NgHPAC8D6ySdZ/tJ4EKgcVKXtIuygtH7\n+xLwbkmr2pxERSwAbxn3BGJhSFIvOilQsP3duty9RtJaSgIcRZfFOy9Kugx4PfCcpC8DzwH/ahmn\ny+K9I2x/HkDSD4GNkr43QpyLgNMpDTp+bPuVEU+iIiZdllSjkSy/A5LeDzxje0/f2EHAGbZvGDHm\na4CzgKNtf3WEz7+VvcU7fwfuHaV4pzZmWU1pYvJ74BzKFe1PBu/XHSDOxcAq9i2422b76pZz2gZ8\nvPcseT0R+gGlEnV5m1j186cCZ1Oac3yyb3UjZknS87YbdbSSdDLwb9v3zfG0Fp22twJj8UpSj8a6\n6uIk6e3A3wYL9SSdZHvb0A81i/shSiez+xdSB7ZJJukftg9p+N4rgRdsf2eOp7XoTEPTmJgfSerR\nSFddnLqM1VVHqJjZsKSusi3kZcASStHl54DXUQonXwH+Clxg+555nu7UStV6NJWkHo2obJoytIuT\n7TeNGGufTk5tYg3Egb0NOlrPKYabIakv7926kbQGWGH7K/VK/fleMWZEzL8UykVTXXVf6jJWl3OK\n5o6UtIVSkb0EeHrM84mIaqZl0IhBXXVf6jJWl3OK5tZTmhetBNYy+a1gIxaNXKlHI7Z3zzDeuntb\nV7G6nFPMaFhb0UOAP9fX5/SN9/a0j4gxyZV6ROzPMkm7JP2p/n4R8DXgJkkPUorien4GnCbpIUkn\njmOyMdkkLcgtkReSFMpFRMS8aPOIZIwmV+oRETE2kt4m6S5JOyXdKemIOr5B0nWS7pH0lKRPj3uu\nC0GSekREjNN6YIPt91Jaa6/vO3a47ROBTwDfHsfkFpok9Qkk6T/1vuROSb+R9IG+Y0dJ+rmkJ+ux\nG2qnt2FxTpb0X0nn9o0dW8e+NB/fJSLiAE4ANtfXm4D+eoxbAGw/QdlJMw4gSX0yvWj7uHrmeinw\nLQBJr6VsnXq97aNtvw/4PjA0qVe/Bfq3a/0ssHNuph0R0dr+Crv6N54a9iRGDEhSn0z9/3mXU3ZV\nAziTsrHLbb2DtrfZfnw/sf4ILO27ml8N3P7qD5LOk7Rd0sOSbpS0tI6fIenROn53HTtG0gN9qwjv\nmvU3jYjFZFhivpdysQFlG+eZdn5MUm8gz6lPpmWSHgKWAYcDvd2Z3gPsGCHeTcBnJD1cP99/9vtT\n2z8CkPR1YA1wPXA5cIrt3ZJ61aprgWttb6670B00wlwiYvFaJmkXe1tEXwNcQNl++WLKI5JfrO8d\nvILPo1oNJKlPpn/aPg6g3k/fREnoozCwpf5aQbl31X/PamVN5odS9lz/RR3/NWVf9y3AzXXsPmBd\nrU7davupEecUEYuQ7ZlyzoeHvPfcgT/nUbgGsvw+4WzfDxwm6TDgMcrWp21j7AFeBj4C3DVweANw\nfm35eRW15aft84F1wJHADklvtL2ZUoX6EnCbpFUjfamIiJgTSeqT6dV7R5JWUP6dnqU87nGCpFP7\njn9Q0jENYl4OXOJ9uw0dDPxF0hLKFpq9uO+0/aDtK4E9lE083mH7advrgVuBlSN+v4iImANZfp9M\nS+s99V5yP7sm45fqXtbXSbqWcvX9CHDhgQLWK/5hrgC2UxL3A8Ab6vjVko6qr39p+xFJl0g6q/7c\n3cA3RvhuERExR9ImNiIiYkpk+T0iImJKZPl9Ckg6hdJCsbfsIuAPtk8f36wiImK+Zfk9IiJiSmT5\nPSIiYkokqUdEREyJJPWIiIgpkaQeERExJZLUIyIipsT/AJt5RBnXf98XAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118675d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.tools.plotting import scatter_matrix\n",
    "m = scatter_matrix(cis_df, alpha=0.2, figsize=(8, 8), diagonal='kde', edgecolors='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "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"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}