01_intro_numpy_pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IPython Notebooks\n",
    "\n",
    "* You can run a cell by pressing ``[shift] + [Enter]`` or by pressing the \"play\" button in the menu.\n",
    "* You can get help on a function or object by pressing ``[shift] + [tab]`` after the opening parenthesis ``function(``\n",
    "* You can also get help by executing: ``function?``\n",
    "\n",
    "We'll use the following standard imports.  Execute this cell first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy refresher\n",
    "\n",
    "NumPy arrays form the underlying data-structure for most computational work in Python, and is used extensively by Pandas underneath the hood.  Here, we give only a very brief overview.  See http://scipy-lectures.github.io/ for a more detailed lesson."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.85638745  0.20377704  0.85337137  0.93617308  0.68708019]\n",
      " [ 0.69351693  0.26113142  0.72097638  0.5146434   0.6605884 ]\n",
      " [ 0.57871646  0.29711588  0.93694272  0.36485267  0.03489225]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Generating a random array\n",
    "X = np.random.random((3, 5))  # a 3 x 5 array\n",
    "\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pure Python arrays are slow: let's square a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "vector_py = [random.random() for i in range(50000)]\n",
    "vector_np = np.random.random(50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 8.04 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit [e**2 for e in vector_py]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 6.52 times longer than the fastest. This could mean that an intermediate result is being cached \n",
      "10000 loops, best of 3: 35.1 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit vector_np**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229.05982905982907"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8.04e-3 / 35.1e-6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85638745342108125"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a single element\n",
    "X[0, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.69351693,  0.26113142,  0.72097638,  0.5146434 ,  0.6605884 ])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a row\n",
    "X[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.20377704,  0.26113142,  0.29711588])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get a column\n",
    "X[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.85638745,  0.69351693,  0.57871646],\n",
       "       [ 0.20377704,  0.26113142,  0.29711588],\n",
       "       [ 0.85337137,  0.72097638,  0.93694272],\n",
       "       [ 0.93617308,  0.5146434 ,  0.36485267],\n",
       "       [ 0.68708019,  0.6605884 ,  0.03489225]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transposing an array\n",
    "X.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,   3.,   6.,   9.,  12.])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Turning a row vector into a column vector\n",
    "y = np.linspace(0, 12, 5)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.],\n",
       "       [  3.],\n",
       "       [  6.],\n",
       "       [  9.],\n",
       "       [ 12.]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make into a column vector\n",
    "y[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 5)\n",
      "[[ 0.85638745  0.20377704  0.85337137]\n",
      " [ 0.93617308  0.68708019  0.69351693]\n",
      " [ 0.26113142  0.72097638  0.5146434 ]\n",
      " [ 0.6605884   0.57871646  0.29711588]\n",
      " [ 0.93694272  0.36485267  0.03489225]]\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X.reshape(5, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 1 0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[ 0.93617308,  0.20377704,  0.85638745],\n",
       "       [ 0.5146434 ,  0.26113142,  0.69351693],\n",
       "       [ 0.36485267,  0.29711588,  0.57871646]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# indexing by an array of integers (fancy indexing)\n",
    "indices = np.array([3, 1, 0])\n",
    "print(indices)\n",
    "X[:, indices]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operations along an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.85638745,  0.20377704,  0.85337137,  0.93617308,  0.68708019],\n",
       "       [ 0.69351693,  0.26113142,  0.72097638,  0.5146434 ,  0.6605884 ],\n",
       "       [ 0.57871646,  0.29711588,  0.93694272,  0.36485267,  0.03489225]])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 5)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.53678914,  2.85085652,  2.21251998])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(X, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.85638745,  0.29711588,  0.93694272,  0.93617308,  0.68708019])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(X, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A quick-ish introduction to Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Credit: this is a notebook cribbed from [the official \"10 Minutes to Pandas\"](http://pandas.pydata.org/pandas-docs/stable/10min.html).\n",
    "\n",
    "<a href=\"http://pandas.pydata.org\" target=\"_blank\"> \n",
    "<img src=\"figures/pandas-book.jpg\" style=\"float: left; width: 25%; margin-right: 2em;\"></a>\n",
    "\n",
    "* emphasis on tabular data (csv and the like)\n",
    "* database/spreadsheet-like functionality\n",
    "* rich support for mixed data (numpy is for homogeneous arrays)\n",
    "* integrates cleanly with numpy and matplotlib\n",
    "\n",
    "There is also a wonderful [set of free tutorials](https://bitbucket.org/hrojas/learn-pandas) on Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unlike NumPy, pandas is a *columnar data store*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "# Let's also load the plotting/numerical tools\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# make figures a bit larger by default\n",
    "plt.rcParams['figure.figsize'] = (10,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A quick taste of Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider a standard stocks file you downloaded from [Yahoo Finance](http://finance.yahoo.com/q/hp?s=AAPL+Historical+Prices) (note that Pandas actually [ships with a utility to get that data for you](http://pandas.pydata.org/pandas-docs/stable/remote_data.html#remote-data-yahoo)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,Open,High,Low,Close,Volume,Adj Close\r\n",
      "2013-08-23,503.27,503.35,499.35,501.02,7877000,501.02\r\n",
      "2013-08-22,504.98,505.59,498.20,502.96,8721700,502.96\r\n",
      "2013-08-21,503.59,507.15,501.20,502.36,11995700,502.36\r\n",
      "2013-08-20,509.71,510.57,500.82,501.07,12810300,501.07\r\n",
      "2013-08-19,504.34,513.74,504.00,507.74,18232800,507.74\r\n",
      "2013-08-16,500.15,502.94,498.86,502.33,12939500,502.33\r\n",
      "2013-08-15,496.42,502.40,489.08,497.91,17510500,497.91\r\n",
      "2013-08-14,497.88,504.25,493.40,498.50,27013300,498.50\r\n",
      "2013-08-13,470.94,494.66,468.05,489.57,31497900,489.57\r\n"
     ]
    }
   ],
   "source": [
    "!head data/AAPL.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-08-23</td>\n",
       "      <td>503.27</td>\n",
       "      <td>503.35</td>\n",
       "      <td>499.35</td>\n",
       "      <td>501.02</td>\n",
       "      <td>7877000</td>\n",
       "      <td>501.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-08-22</td>\n",
       "      <td>504.98</td>\n",
       "      <td>505.59</td>\n",
       "      <td>498.20</td>\n",
       "      <td>502.96</td>\n",
       "      <td>8721700</td>\n",
       "      <td>502.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-08-21</td>\n",
       "      <td>503.59</td>\n",
       "      <td>507.15</td>\n",
       "      <td>501.20</td>\n",
       "      <td>502.36</td>\n",
       "      <td>11995700</td>\n",
       "      <td>502.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-08-20</td>\n",
       "      <td>509.71</td>\n",
       "      <td>510.57</td>\n",
       "      <td>500.82</td>\n",
       "      <td>501.07</td>\n",
       "      <td>12810300</td>\n",
       "      <td>501.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-08-19</td>\n",
       "      <td>504.34</td>\n",
       "      <td>513.74</td>\n",
       "      <td>504.00</td>\n",
       "      <td>507.74</td>\n",
       "      <td>18232800</td>\n",
       "      <td>507.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date    Open    High     Low   Close    Volume  Adj Close\n",
       "0  2013-08-23  503.27  503.35  499.35  501.02   7877000     501.02\n",
       "1  2013-08-22  504.98  505.59  498.20  502.96   8721700     502.96\n",
       "2  2013-08-21  503.59  507.15  501.20  502.36  11995700     502.36\n",
       "3  2013-08-20  509.71  510.57  500.82  501.07  12810300     501.07\n",
       "4  2013-08-19  504.34  513.74  504.00  507.74  18232800     507.74"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock = pd.read_csv('data/AAPL.csv')\n",
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2013-08-23</th>\n",
       "      <td>503.27</td>\n",
       "      <td>503.35</td>\n",
       "      <td>499.35</td>\n",
       "      <td>501.02</td>\n",
       "      <td>7877000</td>\n",
       "      <td>501.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-22</th>\n",
       "      <td>504.98</td>\n",
       "      <td>505.59</td>\n",
       "      <td>498.20</td>\n",
       "      <td>502.96</td>\n",
       "      <td>8721700</td>\n",
       "      <td>502.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-21</th>\n",
       "      <td>503.59</td>\n",
       "      <td>507.15</td>\n",
       "      <td>501.20</td>\n",
       "      <td>502.36</td>\n",
       "      <td>11995700</td>\n",
       "      <td>502.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-20</th>\n",
       "      <td>509.71</td>\n",
       "      <td>510.57</td>\n",
       "      <td>500.82</td>\n",
       "      <td>501.07</td>\n",
       "      <td>12810300</td>\n",
       "      <td>501.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-08-19</th>\n",
       "      <td>504.34</td>\n",
       "      <td>513.74</td>\n",
       "      <td>504.00</td>\n",
       "      <td>507.74</td>\n",
       "      <td>18232800</td>\n",
       "      <td>507.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open    High     Low   Close    Volume  Adj Close\n",
       "Date                                                           \n",
       "2013-08-23  503.27  503.35  499.35  501.02   7877000     501.02\n",
       "2013-08-22  504.98  505.59  498.20  502.96   8721700     502.96\n",
       "2013-08-21  503.59  507.15  501.20  502.36  11995700     502.36\n",
       "2013-08-20  509.71  510.57  500.82  501.07  12810300     501.07\n",
       "2013-08-19  504.34  513.74  504.00  507.74  18232800     507.74"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.index = stock.pop('Date')\n",
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>1996-01-02</th>\n",
       "      <td>32.25</td>\n",
       "      <td>32.25</td>\n",
       "      <td>31.75</td>\n",
       "      <td>32.13</td>\n",
       "      <td>4983200</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-03</th>\n",
       "      <td>32.00</td>\n",
       "      <td>32.88</td>\n",
       "      <td>31.87</td>\n",
       "      <td>32.13</td>\n",
       "      <td>15368800</td>\n",
       "      <td>7.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-04</th>\n",
       "      <td>32.38</td>\n",
       "      <td>32.38</td>\n",
       "      <td>31.37</td>\n",
       "      <td>31.56</td>\n",
       "      <td>10721200</td>\n",
       "      <td>7.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-05</th>\n",
       "      <td>31.62</td>\n",
       "      <td>34.25</td>\n",
       "      <td>31.37</td>\n",
       "      <td>34.25</td>\n",
       "      <td>15929200</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-01-08</th>\n",
       "      <td>34.50</td>\n",
       "      <td>35.50</td>\n",
       "      <td>34.00</td>\n",
       "      <td>34.63</td>\n",
       "      <td>4341200</td>\n",
       "      <td>8.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close    Volume  Adj Close\n",
       "Date                                                       \n",
       "1996-01-02  32.25  32.25  31.75  32.13   4983200       7.81\n",
       "1996-01-03  32.00  32.88  31.87  32.13  15368800       7.81\n",
       "1996-01-04  32.38  32.38  31.37  31.56  10721200       7.67\n",
       "1996-01-05  31.62  34.25  31.37  34.25  15929200       8.33\n",
       "1996-01-08  34.50  35.50  34.00  34.63   4341200       8.42"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.sort(inplace=True)\n",
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAGCCAYAAADT6dY2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XmYXFWd//F3VfW+d3pLp7NvNyGQkAABDCBhU3AERkd0\nVIQJzriOoM444Og4o+MAMz8FdXQUAUVHh0VRQEAQg4AsCXtIOjlkXzrpLd3pfant90dVV1d1VXVX\nVVdXVXd9Xs/Dw7237r11bgfoD+ec+z02r9eLiIiIiCSXPd0NEBEREZmJFLJEREREpoBCloiIiMgU\nUMgSERERmQIKWSIiIiJTQCFLREREZArkxHOyZVnLgfsAL2ADFgNfBX7uP74AOABcZYzp8l9zE7AJ\ncAHXG2OeTFbjRURERDKVLdE6WZZl2YEjwJnAZ4Hjxpj/tCzrn4BKY8yNlmWdBPwCOAOYCzwFLDPG\nqDiXiIiIzGiTGS68CNhrjDkMXAHc4z9+D3Clf/ty4F5jjMsYcwDYDayfxHeKiIiITAuTCVkfBH7p\n364zxrQAGGOagVr/8QbgcNA1Tf5jIiIiIjNaQiHLsqxcfL1UD/gPjR3+03CgiIiIZLW4Jr4HuRR4\n1RjT7t9vsSyrzhjTYlnWbKDVf7wJmBd03Vz/sai8Xq/XZrMl2CwRERGRlIoaWhINWX8N/F/Q/sPA\ntcCtwDXAQ0HHf2FZ1m34hgmXAlvHbanNRltbT4LNmt5qakr17FlIz559z56tzw16dj37zFNTUxr1\ns7iHCy3LKsI36f3BoMO3AhdblmWAC4FbAIwxjcD9QCPwGPBpvVkoIiIi2SDunixjTD9QM+ZYB77g\nFen8m4GbE2qdiIiIyDSliu8iIiIiU0AhS0RERGYEjyezZiQpZImIiMi0d9ejjXzxB8/jcnvS3ZQA\nhSwRERGZ9p5/q5mu3mG27T2e7qYEKGSJiIjIjLHvaHe6mxCgkCUiIiLTmtc7Oherobo4jS0JpZAl\nIiIi01rwPCyn5mSJiIiIJIfT5Ym4nW4KWSIiIjKtBQcrt3qyRERERJIjOGRlUqkshSwRERGZ1oaD\ne7I86skSERERSYrgnqzWzoE0tiSUQpaIiIhMa799bl9g+7ltx9LYklAKWSIiIjKtvZlBVd6DKWSJ\niIjItPXWvswMWKCQJSIiItPYbfe/me4mRKWQJSIiIjNKd/9wupsAKGSJiIjIDPPb5/anuwmAQpaI\niIhMUx3dgxGP96onS0RERCRx0WpiudyZUfZdIUtERESmpd8E1ccKZrOluCFRKGSJiIjItDTsjLyE\njjczOrIUskRERGR68kZJU4X5jhS3JDKFLBEREZmWPEEZ6+4bL+CqjUsBqCovSFOLQilkiYiIyLS0\nbG45APm5jpB9tya+i4iIiCRuTnUxAH9z2QoAHA7fjHe3RyFLREREJGEef5hy2O0hf3/y5cNRa2il\nkkKWiIiITEvuQMiyhfwd4IcP7UhLm4IpZImIiMi05Pb4SjjYR0KWYzRkHTvel5Y2BVPIEhERkWlp\nZIJ7rj9cOYKqkDpdkWtopZJCloiIiExLLn9PlsNhD/k7hJZ3SBeFLBEREZmWRtYoHBkmDJ6TBelP\nWQpZIiIiMi253L6erBz/W4U5QXOyMmFpHYUsERERmZZGerJGwlVBfk7gs0yolaWQJSIiIhnB4/Xy\npf95gV/+4e2Yzu8dcAKQn+er+G632cY7PeUUskRERCQj9PQ7ae8a5KlXj8R0/iu7WgEoyMuZ4Mz0\nUMgSERGRjNA/6EzouqIChSwRERGRqI60xV5A1Ouf2b58bnnIMOGi+tKktytRClkiIiKSEX7y2M6Y\nzx2Z9J6bExplvvjBtUlt02TE3b9mWVY5cCdwMuABNgFvA/cBC4ADwFXGmC7/+Tf5z3EB1xtjnkxK\ny0VERGRGGRp2B7Y9Xu+4E9mdLt+5uTmOkOMF+aP7E91jqiXSk/Ud4DFjzEpgDbALuBF4yhhjAZuB\nmwAsyzoJuApYCVwK/MCyrMya+i8iIiIZYdm8isD2wJBr3HMbD3QCUJgfGrKCQ5UnzWUc4gpZlmWV\nAecaY34CYIxx+XusrgDu8Z92D3Clf/ty4F7/eQeA3cD6ZDRcREREZpZVi2YFticKWT/47XYADrf2\nRj0n3SEr3uHCRUC7ZVk/wdeL9QpwA1BnjGkBMMY0W5ZV6z+/AXgx6Pom/zERERGREMeOj058jzUf\nrVwwK+pnR9r6WDynbLLNSli8w4U5wDrg+8aYdUAfvqHCsT+K9JdZFRERkWnlpR0tge2JeqHWLqsG\n4D1nL4h6zv5j3clpWILi7ck6Ahw2xrzi3/81vpDVYllWnTGmxbKs2UCr//MmYF7Q9XP9x8ZVU5M5\nr1+mmp49O+nZs0+2Pjfo2bPVRM8+NlSVlxeGXeP1ern/qbf5/UsHaT8xAMDchgryc0PnZY1Y0FCe\n1p95XCHLH6IOW5a13BjzNnAhsMP/17XArcA1wEP+Sx4GfmFZ1m34hgmXAlsn+p62tp54mjVj1NSU\n6tmzkJ49+549W58b9Ox69uiC3ywEaD/eR6Ej9F25pvY+/vf3u0KOdXX2YYvyBmFvz9CU/8zHC3GJ\nvF34OXzB6Q1887L+A1+4utiyLIMveN0CYIxpBO4HGoHHgE8bYzSUKCIiIiGGXaEh62t3bw2b/N7a\n0R92XaSAVVKYC8CQ0x32WSrFXSfLGPMmcEaEjy6Kcv7NwM3xfo+IiIhkD6fLE3bs0RcP8lfnLwns\nP/jcvpju9dcXLuPHv2tkcHj8NxSnmiq+i4iISNr98bXwRaG7eodC9l0Rglgk+Xm+OVqDw+ntyVLI\nEhERkbR7/KVDYcfGhqQNp9SH7K+YX0Ekef6ldiL1jqWSQpaIiIhkpKEx87TyxqxTWDerKOJ1I+sZ\nutwKWSIiIiJhvGPKOjjHhKZotbRyHL5486ppm5qGxUghS0RERDLS2Aw1dvgvWumGYf95Te19ET9P\nFYUsERERyUheb2jKGg4LWZGvW+JfSqe+qohv/vwV/u0nL4fdKxUUskRERCTt6qt886sqS/MDx8b2\nZA2PqXtlj5Ky7Hbf8YI8B3ubujnY0kNzhBpbU00hS0RERNLu2PF+8nMdFBWMlvCcqCfLYR8/ZLnd\no9dv39eRrKbGTCFLRERE0urYcd/cqSGnG0dQ75QnKGT19A9zdMwcK4cjSsjy36N30Bk4lo7q73FX\nfBcRERFJphO9w4Fte1Dv1N6m7sD2v9y9la6g88aeG0lH92gx0/LivMk2M27qyRIREZG0Ksof7fOJ\n9sbg2IAF0YcLI3FFKfcwlRSyREREJK1GctUZK2qjvjEYicMee4yJdUmeZFLIEhERkbRy+3uZKkvz\nA4VER/QNOvnKnVsiXjfRcGEwl0chS0RERLLMyAR3u93GtZeuCPns7kd3hk14HxHPcGEaymQpZImI\niEh6ef2dTA67jdlj1iM0h05EvS5anSyANUuqQr9DxUhFREQk24z0ZEWa9D7sCi+9cO7qegCWNpRH\nvWdpUejbhGmY964SDiIiIpJeIws9Rxr9c7nD09HV77J495nzqa8qjnrPsXPi1ZMlIiIiWcftD0Cx\nzrHKcdjHDVgQPpSYjjlZ6skSERGRtPJ6Rie+j2fF/Iqwtw+jsdnHhqzUpyyFLBEREUkrd4wh60sf\nXhfzPR1jerLSMSdLw4UiIiKSViNzsuIpLjqRsYFNc7JEREQk67g98c3JikUmzMlSyBIREZG0Gjtc\neOFpc8PO+bdN6+O6p23s24WoJ0tERESyjNu/5M1IT9ZHLl7OnV/aGHLOvNqSuO45tldMPVkiIiKS\ndSINFwbPqTpjRW3c9wwfLvTy5NZD/On1pgRbGT+9XSgiIiJp5Zng7cJ4e7EAegecIftPvXIkEObO\nX9sQ9/0SoZ4sERERSauRqu7RamC53J6477n5tdAeK3caajgoZImIiEhaDTt96xPm54bGkhyHr2fL\nmUDIygQKWSIiIpI2LreHB/60F4C8HEfIZyM9W05X/CGrsjR/8o2bJIUsERERSZsXtjcHtvPzQkPW\nyER4bwIdWQVj7hUsVYVJFbJEREQkbVo6+wPbpUW5IZ/Z/G8IehKocXXq0uqon6WqnINCloiIiKSN\n2z2aePJzQ3ufRl42TCQU/eV5i6N/pyc1c7wUskRERCRtgt/6G/t24UhPViLDe9HeVBz7nVNJIUtE\nRETSxuMNDlmhdbJsgZ6s5IYihSwRERGZ8YLzkyNKT1ayM1HwEOVUUsgSERGRtAnuuxq7FE55cR4A\n3kmmrMvOWhCy3zfojHJmcilkiYiISNqMFBqtryoK++ySM+aR47Bx3qlzErr3xnUNLKovJW9MkdP/\n+r/XE7pfvLR2oYiIiKTNyJI5X7jq1LDPzlo1m9NX1I47iX08V19iAfDwn/eHHD/RO5zQ/eKlniwR\nERFJm6Fh/5I6UYqHJhqwgqV+1UKfuHuyLMs6AHQBHsBpjFlvWVYlcB+wADgAXGWM6fKffxOwCXAB\n1xtjnkxKy0VERGTa6x1wYrNBUcHUDa5NRYV3j8fL/7v3df7r+ndGPSeReOgBzjfGrDXGrPcfuxF4\nyhhjAZuBmwAsyzoJuApYCVwK/MCyLFuEe4qIiEgWGhhyU5iXEzbpPZkilWwY6UFL1IneIXYdOjHu\nOYmELFuE664A7vFv3wNc6d++HLjXGOMyxhwAdgPrEREREQGGnK6oQ4XJ4okQsv7r3slNfnfF8MZj\nIiHLC/zBsqyXLcv6uP9YnTGmBcAY0wzU+o83AIeDrm3yHxMRERGh7cQgPf1TOxE9Uk/WvqPdk7pn\nLD1hiQyAbjDGHLMsqwZ40rIsQ/icsnTNMRMREZFpYiTouKa4OGik8hCT1dE9OOE5cYcsY8wx/9/b\nLMv6Lb7hvxbLsuqMMS2WZc0GWv2nNwHzgi6f6z82rpqa0nibNWPo2bOTnj37ZOtzg549W0V69s4B\n17ifJ8tfXmgxu7aU1o4BfvK7HUn5zlcf3zXhOXGFLMuyigC7MabXsqxi4BLg34CHgWuBW4FrgIf8\nlzwM/MKyrNvwDRMuBbZO9D1tbT3xNGvGqKkp1bNnIT179j17tj436Nn17KG+c+9rgK9o6FT/bKw5\nZVhzykJC1mS+s7o0f8Jz4p2TVQf82bKs14GXgEf8JRluBS72Dx1eCNwCYIxpBO4HGoHHgE8bYzSU\nKCIiIhxq6QXgRM9QmlsSv1jCTFw9WcaY/UBYSVZjTAdwUZRrbgZujud7REREJHvk507t24VTYaRS\n/XhU8V1ERETSym5PfQnN8pK8SV3vdClkiYiISIYbGHJNfFKSLZpdNqnrI5WFGEshS0RERNJq6dzy\nlH/nZAvMe2JYqkchS0RERNJq1cJZKf/OyQ5RemPoyZq61RhFRERExlFSmIvb42V+Xerrh9km2ZWl\nniwRERHJSE6Xm94BJzUVBWn5/slOtfdMPO9dIUtERERSz+ny9QTNKk1PyJps0U71ZImIiEhGGgkp\njhSXb7juPSsBaOsc4DfP7oup3lUksYQszckSERGRlHP7w02qa2SdsqQKgIMtPRxs6aGqvIDz1syJ\n+z4elXAQERGRTDRSZyrVPVljv20wwRpdMWQshSwRERFJPU+6QtaYtwpzcxKLQrGEM4UsERERSTm3\nf05TOpbUCeZwxB+FGg90sH1/x4TnKWSJiIhIyqWvJyt0P5Hv37qzJabzFLJEREQk5UYWWE6kJ2ky\nkhHpch2OmM5TyBIREZGUGwlZeQnOiUpcaMzaf6w77jvUzSqM6TyFLBEREUm5YX/ISnTieaLGDhf2\nDjgBuP/pPdy3eXfg+Nfu3sqdv2uMeI+RgDgRhSwRERFJOafLDUBebmxDb8kyNmQNDvva8fsth3hi\n62EA3B4Ph1t7eWF7c8R7DDndMX2XipGKiIhIyo2Em4K8FIesMcOFbScGcActROhyeyac2D7S9omo\nJ0tERERSbsBfZ6owL8X9PRF6soadoyHL6fKQmzMa/Lr6hsNusfvICQBu+MCacb9KIUtERERSbmDI\n35OVn+qerFAut4fHtxwK2Q/uXbvrd41s33885Jr9x3oAWFRfOu53KWSJiIhIyvUP+SacFxfkpvR7\nx87Jcrm9PP7SwcC+0+UJWTR6+/4Ovn3fmxHvVVqUN+53KWSJiIhIyu082AlAYX6qp4eHpqyBIRcN\n1cWBfafbg9sdvjDhQNAyOiWFudRXFU34TQpZIiIiknJ7m3z1qVI+8T1CNdJDrb2BbZfLE7FEQ3DI\nGna6Y3orUiFLREREUir4bb7K0vyUfnekkBXM5fbSH2Hx55FjHq+XYZeH/BjqeylkiYiISEqNlEBY\nu6yanBQvq2OfIGU53Z6QXqsR2/f5FoR2OmNfDkghS0RERFJqKE01sgBsE4Usl4cHn90Xdvz+p/cA\nsPm1I8DonLLxKGSJiIhISh1o9pVAiLWoZyoFD2VG0jvojPleClkiIiKSUnc8sgOA13e3p7Ud569t\nCJujFenNwmAL6ny1sa7auHTC+ytkiYiISEpddNo8AK48Z1Fa2/Gxd1l4x2Sqnzy2k6qygqjX9A36\n5mvFMtSptQtFREQkpUoKfQVI59eNXzF9qnznc+dEnZvV3e8EIg8J7jzQwc+fMADY7RO8poh6skRE\nRCTFvP7uo4nKKUyV0qK8QNCbSEPNaKHSgy2j9bQmeksRFLJEREQkxTz+kBVLb1A6za8t4br3rOR9\n5y0G4IXtzXFdr5AlIiIiKeXxz4NKV09WLGaV5fOvm9azcHZZoGDqkbbRniyVcBAREZGM4/WnrFiG\n3FLFmlcRsu8I6mXLjVDdvahg4mntClkiIiKSUo9vOQRkVsj6xBWrQvYd9tGIFClkuT3jl3oAhSwR\nERFJod4BJ0NOXxHSiWNK6pQU5rJyQWVgP8cxGgDzIywGXVY08cR5hSwRERFJmeDFl9tODKSxJaEc\ndhsfe5cVtD8akcaGrCvPXcSlZy6Y8J6qkyUiIiJJ9eybRykrzuPUpdVhn/37Pa8EtjNhuPDWT55N\nV98wNpuNullFgeOOoJ6s4jHlHi7fEFsRVfVkiYiISNJ09w/z08d38d1fbYv4ee/AaKFP79hy62lQ\nU1HI0obywH5Rvq//KccxGpGCJ7mvXRYeHKNJqCfLsiw78ApwxBhzuWVZlcB9wALgAHCVMabLf+5N\nwCbABVxvjHkyke8UERGRzOd0Rl9g2TNmsvjyMW/0ZYKRGl7Bk91zgwLXsrmxtznRnqzrgcag/RuB\np4wxFrAZuAnAsqyTgKuAlcClwA8sy0p/36CIiIhMCfc4vVMDw775WHabjf/36XeEDM9lipG3BoOD\nVU7IduwxJu6QZVnWXOAy4M6gw1cA9/i37wGu9G9fDtxrjHEZYw4Au4H18X6niIiIZDaP18u2ve04\nXdF7sgb8iyuftaqOWeMswpxObrcvZOUE9WQFz88KDlwTSaQn6zbgHwl987LOGNMCYIxpBmr9xxuA\nw0HnNfmPiYiIyAzyx1eOcPsD23jg6T1Rz+nzh6yReU+ZaGS4sKdvOHAseIK+I46lgOJ6Ssuy3gO0\nGGPesCzr/HFOndRMtpqa9KzKnQn07NlJz559svW5Qc8+U+051g3Atr3HA8eCn7emppT9rX0AzJ1d\nlvE/iwGnO2IbKyuLYm57vFFyA3C5ZVmXAYVAqWVZPweaLcuqM8a0WJY1G2j1n98EzAu6fq7/2Lja\n2nribNbMUFNTqmfPQnr27Hv2bH1u0LPP5GcfDqp/NaK11Re8amvLaGvr4cixLgA8LnfG/ywuPm1u\nxDb29w2FHB8vcMU1XGiM+bIxZr4xZjHwIWCzMeZq4BHgWv9p1wAP+bcfBj5kWVaeZVmLgKXA1ni+\nU0RERDKfLULNq+tufZrbHxgt5fCTx3cBkSuoZ5poQ5ojc7Zikaw6WbcAF1uWZYAL/fsYYxqB+/G9\nifgY8GljTPqLYoiIiEjChobd3PVoI0daeyc89619x8OO5eVmfpnOSOsVAjjd0Sf2j5XwzDNjzDPA\nM/7tDuCiKOfdDNyc6PeIiIhIZnnq1cM8/1YzjQc6+dZnNgAw7HLHfP2yDKyPNdbYkFVWnEd33zCV\npfkx3yNzp/eLiIhIRhqp2t43OFq9vWqCkgxuj68HqDA/h7KivKlrXJLkjRnS/MrVp7F9fwcnL5oV\n8z0UskRERCQuI7WwhoOqu080jDZSvmHlgsqpa1gSjZ1iVl1RyPlr46tClfmDoiIiIpJRXEGTv0d6\nqMZbTgdg9+ETANRUZGYR0hHzaksAKC7IneDMiaknS0REROLiCuq16htwUVacx1CUOVmF+Tn8/PGd\n3P/U28DEw4rpduNH1tHeNUhNReGk76WQJSIiInHJC5oU/tSrR6guL+BwhDcNZ5Xl09PvDAQsiG9Z\nmnQozM8J9GZNVmY/qYiIiGQce9DSMtv3Heenj++iq3c45JyvX7eeYacnbC3DeJalme4UskRERCQu\nnT1Dge3ZVUURz3HYbYG3EEOOOxSyRERERCLq7h/ttXprb3ixUYjeY+WwZ0/0yJ4nFRERkaRwuUbf\nLhwpzTCWPWrIUk+WiIiISEQuz8RLy9gjrGUIUFESe8X06U4hS0REROLick0cshx2G2etqgs7Hm0O\n10ykkCUiIiJxGY4SshbMLg1s2+w2PnqxFfL5l68+jZLCyRf5nC4UskRERCRmXq+Xnv7wtwYBLt+w\nMLDtsNsoKggtx7m0oXwqm5ZxFLJEREQkZm6PF5fbE1ZU9N1nzmduzWgRz7FzsgryQhdczgaq+C4i\nIiIxGykumptjI3glnas2Lg05b+y894tPnzfVTcs4ClkiIiISM6d/3cJch50Bwtcr/OKHTuXAsW4K\n8kIjhjfszJlPIUtERERi5gr0ZEWecbRq4SxWLZwV2M/NseN0eWJ6I3Gm0ZwsERGRLOVye/jqXVt4\n9MUDMV8z8mZhTk5sc6xGJruXFmXPW4Uj1JMlIiKSpVo7B2hq6+PXz+yjqqyAs1bNnvCaY8f7AMiP\n0pM11icuX8Wb+zs406qZVFunI/VkiYiIZKmXGlsC23c80kj/YOTSDMG+9+u3AF8drFiUFefxvo3L\nyMvNvrcLFbJERESy1O9eOBCy39k7HPnECDq7B5PcmplHIUtEREQAeOzFA4Hto+19eDzR3wk8ZXFV\nYHvtsuopbNX0pZAlIiIiALy4o4UHnt7DG7vb+cqdW/jNc/uinnvmSb51CStK8vjs+05JVROnFU18\nFxERyTJOlxuvF3Icdlzu0NIKj285xO6mLgAeffEgDdXFgQnxA0MuAFbMr+DkxVXc+JF11FcVYRtb\neVQAhSwREZGs88Xvv0DvQPRJ7s3H+wPbdzzSyJbGFs47dQ71VcUA1FQUArB8XsXUNnSa03ChiIhI\nlhkvYAH0D7pC9t/ce5zv/fothoZ9Fd7zs3AdwkQoZImIiGSRiQIWgMcbecL7kNMfsrKwHEMiFLJE\nRESyyDNvNMV87qL6spD9kZCVjTWvEqGQJSIikkXGLtw8vtAerZ5+Xx2tWKu9Zzv9lERERLJYZWl+\n1M88Y9Z07u7zDTXmqicrJgpZIiIiWeRgS0/I/tevW89/33BexHPtY1LCg8/uBcA9puyDRKaQJSIi\nkkW27WkPbBcX5FBckEtRQeQhxOrywpB9l9s3fFhdURjpdBlDIUtERCSLBK+UM1FYuvpdVsTja5ZU\nRTwuoRSyREREsojTNTrU53aPJq6ifF9v1t8HLZFTUpjLnV/ayBc/eGrIPVThPTaq+C4iIpIljrb3\nBcowAJQUjsaA715/LoPDLooKckOusdttlBSGHpPYKGSJiIhkia/cuSWw/c5T5/CesxcE9u12WyBg\n3fKJs8hxjA521VZqDlYiFLJERESywM+fMCH717x7RdRzayuLQvYL8xUXEqGfmoiIyAzVO+BkcMhF\n64kBnn59tNL7V685Pe571VYW0to5wPzakmQ2cUZTyBIREZlhntx6iKdePUJ712DYZ9/4+Jk0VBfH\nfc+R4cMaDR3GLK6QZVlWPvAskOf/6yFjzJcty6oE7gMWAAeAq4wxXf5rbgI2AS7gemPMk8lrvoiI\niIx17+Y9EY9/+sqTEwpYADkO3xuFLpcKkcYqrhIOxpghYKMxZi2wGrjAsqwNwI3AU8YYC9gM3ARg\nWdZJwFXASuBS4AeWZem9TxERkTQ4zapJ+NoPblyKzQbvecfC5DVohou7TpYxpt+/me+/vhO4ArjH\nf/we4Er/9uXAvcYYlzHmALAbWD+ZBouIiEhiJlPfauXCWdz1TxewtKE8iS2a2eIOWZZl2S3Leh1o\nBv5kjGkE6owxLQDGmGag1n96A3A46PIm/zERERGRGS3uie/GGA+w1rKsMuAJy7LOB7xjThu7H5ea\nmtLJXD6t6dmzk549+2Trc4OePV3ed/7StH5/Nv65J/x2oTGm27Ksx4DTgRbLsuqMMS2WZc0GWv2n\nNQHzgi6b6z82rra2nolOmZFqakr17FlIz559z56tzw169lQ9e31VEceO94ccu3hdQ9p+9jP5z328\n8BjXcKFlWdWWZZX7twuBi4HXgYeBa/2nXQM85N9+GPiQZVl5lmUtApYCW+P5ThEREYmP2xM+oJSf\n50hDS7JbvHOy6oGn/XOyXgIeNsb8EbgVuNiyLANcCNwC4J+vdT/QCDwGfNoYM6mhRBERERlf/6Ar\n3U0Q4hwuNMa8BayLcLwDuCjKNTcDNyfUOhEREYlL/6CT3gEnc6qLWbOkCpfbyyReKpRJUMV3ERGR\nGaSjZwgAa34FH9i4NM2tyW5xl3AQERGRzNXR7VtKp6I4L80tEfVkiYiIzBC/33KI+5/2LalTU6E1\nBtNNPVkiIiIzxEjAAijMVz9KuilkiYiIzEC1lerJSjeFLBERkRmovqo43U3IegpZIiIiM4DT5U53\nE2QMhSwREZEZ4Hj3ULqbIGMoZImIiMwAw071ZGUahSwREZEZ4HjXYGC7oUbzsTKBQpaIiMg04PV6\nuef3u3hjT3vEz594+TAANuDfNq1PYcskGoUsERGRaWD7/g6eeeMo3/3Vtoifr1laBcBHL1mOXYsV\nZgSFLBHHJEC9AAAgAElEQVQRkQzjcnv44vef5+dPmsCx9hMD41/j8gBQO6toStsmsVPIEhERyTAn\neobo7Bni6deaONHre2vQ4Rj9lX2krTfsmp5+JwDFBar0nikUskRERDLMwPDom4K//tNeIPTtwUee\nPxB2zZG2XmxAXaV6sjKFQpaIiEiGGQoKVM9vb+bF7c388qndgWOnLK7iF394m2/+/BV6B3w9WJ29\nwxQX5mrNwgyikCUiIpJhxta8+vHvGkP2PV4vf3z1CHubuvncd56js2eI1o5+yorzUtlMmYBCloiI\nSIYZ9k9ij+anj+8K2W/u6McLLG0om8JWSbwUskRERDKMa4KQNdaDz/rmbb1q2qaiOZIghSwREZEM\n4/LEF7JqK3yT3S85Y95UNEcSpJAlIiKSYdxub0znjZRreHFHMwCL6jVcmEkUskRERDKMxxNbyOob\ndIXsF+TpzcJMopAlIiKSYR554UBC11WW5ie3ITIpClkiIiIZpr1rMKbzPnzRspD9gnzHVDRHEqSQ\nJSIiMo1cvmFhYHvd8hrmVBcH9vNy9Gs9k2jwVkREJIN09Q2P+/mV5y7m3NVzaGrvZVZZAYvqSzna\n3gdAjkMhK5MoZImIiGSQHz20HYB3r5/P77ceChy/6PS5bDi5HoCq8gKqygsAeP6t5sA5NpsthS2V\niSjyioiIZBBz+AQAZ6ysDTm+cW0DC2aXhp3/ufevDnwumUU9WSIiIhnC6XLj9VdvGFvzqqqsIOI1\npy6r5u4bL5jqpkkCFLJEREQyxM6DnSH7G06ZTUf3EJ+/ao3mW01DClkiIiIZYtjpW07nPWcvAOC6\n95yUzubIJCkWi4iIZIjHtxwEYPEcLY8zEyhkiYiIZICWjn72H+vx7cS2qo5kOIUsERGRDHDTHS8F\ntufUFI9zpkwXClkiIiJp5nS5Q/brKovS1BJJJoUsERGRNOvqHa3y3qBerBlDbxeKiIik2dHjvmVx\nTl1azd++V28UzhTqyRIREUkDr3d0dnvvgBOA+XUlFOar/2OmiOtP0rKsucDPgDrAA/zYGPNdy7Iq\ngfuABcAB4CpjTJf/mpuATYALuN4Y82Tymi8iIjK9vH34BLc98CZDw24++75TWLe8BqfLVx9Lc7Fm\nlnh7slzAF4wxq4Czgc9YlrUCuBF4yhhjAZuBmwAsyzoJuApYCVwK/MCyLK1eKSIiWeuWX7zG0LBv\novtvn9sPwLA/ZOXmaIBpJonrT9MY02yMecO/3QvsBOYCVwD3+E+7B7jSv305cK8xxmWMOQDsBtYn\nod0iIiLTTlNbb8j+wJBvmPB41yCgkDXTJPynaVnWQuBU4CWgzhjTAr4gBowsHd4AHA66rMl/TERE\nJCsMOd3sPNABQE+/M+Sz491DeLxetu/3fT57loYLZ5KEZtdZllUC/ArfHKtey7LG1qZVrVoREclq\nu4+coLvPyUuNzbxq2thq2lm3rCrsvP3Humk7MQBAnULWjBJ3yLIsKwdfwPq5MeYh/+EWy7LqjDEt\nlmXNBlr9x5uAeUGXz/UfG1dNTWm8zZox9OzZSc+efbL1uSF7nn3TLZtD9p95/Qhnr64H4OQlVWzf\nexyAXz2zD6fLw7J5FTP6ZzOTny2aRHqy7gYajTHfCTr2MHAtcCtwDfBQ0PFfWJZ1G75hwqXA1om+\noK2tJ4FmTX81NaV69iykZ8++Z8/W54bsfnaApmNdAJy8oDIQsszBTsBX0mGm/mxm8p/7eOEx3hIO\nG4CPAG9ZlvU6vmHBL+MLV/dblrUJOIjvjUKMMY2WZd0PNAJO4NPGGA0liohksS2NLfzo4R3c8IE1\nrF4SPnw2EwTXwAq20x+oaioLwz4beeNQZo64QpYx5nnAEeXji6JcczNwc5ztEhGRGepHD+8A4Jd/\neJvVS85Oc2umhsvtiXj8FdMGQH1V+NI5qm808+hdURERSZnXTWtgu2/QOc6Z09tIcdERKxdUhuxX\nlOSFXfPhi5dPaZsk9RSyRERkSnX1DnG8a5D+QRf/cseLgePFBblpbNXUGhuyNpwyO2S/IC+HM1bU\nBvY/deXJLJ9XkZK2SeooZImIyJTxeL18/r+f5x//5wV6B4ZDPrPbYxsgO9E7xO9eOIDL7aF/0DUV\nzUwqj8f3zMEW1ZeFnWfNHw1VwYFLZg6tQikiIlPmDy+P1qMeGAqd2O32RJ63NNYX/IHlwWf3AfCt\nz2ygsjQ/SS1Mvu37j4cdqygZbe/XN/kWPtm4toHSojwWzc6+0gbZQiFLRESmhNfr5b7NewL7Pf2h\nPVluz8Qvm3ePuQZgT1NXxvb8DDvd3P7AtrDjhfk5XHf5Krp7BplbWwKAzWbL2OeQ5FDIEhGRKdE9\nZgmZ3/55f8h+LCHrsRcPhh37n99u54wbL5hc46bIJ7/1TGD72ktXcO7qemw237Dole9cOmNrRUlk\nmpMlIiJTwjVm8ve+o90h+253Yj1ZEL0OVTqNbdOCutJAwJLspJAlIiJTwhVlztXIIsi9A04Ghsaf\nyB5t7tVX7tzCm3vaAejoHuRPrzdFrU2VKoNjiokWFmiwKNspZImIyJRwRemp+sD5SwLbn7nt2XHv\ncbxrMOLxY8f7+c6vfHOf7np0Jz97wvDE1kMJtjQ5hp2+kDWvtoRPXrGK2orwqu6SXRSyRERkSrij\n9Cyduqw65nvs2N8x7uftXQOBpWp+/cw+BofTV+JhyD88Or+uhPUr69LWDskcClkiIjIlDjSHT/I+\n+5T6uOYp9fnrYt322Q1882/PDPv8iS2HQ/bv/eOesHNS5VV/NfuZXGRV4qOQJSIiU+Knj+8KOxZp\nYeRoPEFvH5aX5Edc7++Prx0JmbdlDp+Is5XJ4fV6eeDpvQCUFilkiY9CloiIJF20t/8aanw1oj58\n0TIA8nKj/xpq7ugH4PSgWlL//LHTws4LnnC+MA2FPfce7eK6W58O7J+7ek7K2yCZSSFLRESS7qUd\nLYHtWWWjPU1F+b437i46fR4lhblUlRVEvcdX7twCQEGuI3BsyZzysPMGhlwsqPOFqy2NLWGfTyVz\nqJNv/uzVkGNlxeGLP0t2UsgSEZGke3lXa2B7VulokFq1eHTSe26OPaayC4PO0NIIX7hqTdg5Dkd6\n6lHd+svXQ/aDA6WIQpaIiCTdsGs0GAXPUSouHK0dleuwx7Tgc3BPFsDJi6u4e0zF931Huyn216VK\nVaHSvkFn2LFr3r0iJd8t04NCloiIJF1JoS9YVZTkhbxNWBT05l3riQH6Bl109gyNe6+iGIp6nnVS\nHYvqy4DwoqBTZeuYocmvX7eeUxZXpeS7ZXpQyBIRkaTbutM3XPhPH1nHSMZqqA5/OxCgxT/BPZqz\nV82OeHx+XUlge9nccuoqfZXkmye4X7K8YtpC9ksL9VahhFLIEhGRpBoOmkNVUpgb6MnyjBnGO3d1\nve98V/i8rG17fUvm5ObYWRDljcFrLx0dmusbdFFV7pv7dWKCnrFkae8aAODWT57NDR9YQ3mJ5mNJ\nKC2sJCIiSfXa7tEensL8HOz+nqyxU6Xm1fp6ooad4cN7tz+wbcLvyQ+aqzUw7KKm3FeDayjC/aZC\n24lBFtSVUlNRSI2W0JEI1JMlIiJJdcfDjYCvF8tus3HWSb7hvgtPmxtynsPh+xXk9oSmL2fQpHln\nhF6uEXX+haYB5taUBCa8//qZfZNofWwO+qvZH2wJr2ovMkI9WSIiklRLGsrY29TNTR9dB/jWKvzv\nG84Lm8Du8HdxecaErIMtvTF9j91m40f/8E7M4ROsWjiLt/3V3o93R15UOpn2N3dP+XfI9KeQJSIi\nSVWUP/JmYVAR0ghvCNr9c7VcntDeqrse3RnYHq8iPEBujoOTF/ne6LPmVwKhE+KnjD8X/s1lKtkg\n0Wm4UEREEnbn7xr5/oNvBfaHht30Dzmx22wU5DnGuTJ6T5YraLjwynMWx9WeksLccYcYJ6OprTcw\n2X3kDcZazcWScagnS0REEnK4tZcXtjcDvgKgj285xK/+5FskOfitwmjsUULW8W7f24GbLlvJOf43\nEGOVm2OfkpDldHn46l1bAfi7957Eky8fBhh3WSAR9WSJiEhCnth6KLDdO+DkN8/uC9mfyEhPVvDE\n9+Bq7cvnV8TdJrfHS3vX4IQFTuPVETTP645HGgPbwcVVRcZSyBIRkYSM9GIB9A+6wupgTSTScGFP\n/2g4q06gl6i7bxiAOx7eEfe149nT1BXxeCzV6CV7KWSJiEjcxgaq9u7BkDpY7zx1zoT3yPPXuQpe\nAHokJC2qLwsMJyaiqb0v4WsjaTsxkNT7SXZQyBIRkbgNDIUu7Pyte98I2b9q49IJ7zGyvuFvn9sf\nGI4bKSS6IoGhwmDJXiS6ozs1VeRlZlHIEhGRuP3yD7vH/bwwf+JhtML80bcPn/cPPe7Y3wGEV4eP\nV9+ga+KTYvDctqPc9Wgjh1t7yXGE/sq8+RNnJeU7ZObSYLKIiMTtxR3NUT+rLo9tLlVB3uivoDf3\ntDOrNJ/f/nk/AFt2tnDVBRP3hk21nzy2K7BdN6uIb1y3noPNPSxpKE9jq2S6UE+WiIgk7BvXrQ87\n9tVrTo/p2uBJ4/uOdocUIV29pGrSbXO5k1vKobQwlxyHXQFLYqaQJSIicWnp6KeyNJ+qsnwaasKr\nq5cW5cV0nxyHnS9ffVrEz95z9oKE2mbNG53LFWnh6Xi0j5nsHu0NQ5FoFLJERCQmg8MuNt2ymZvu\neInOnqGQ4b4RH3u3Fdc9oxXznJVgkc9//Ou1gW3PJOZ1eb1evvTDFxO/gQgKWSIiEqPfbzkUsj9S\nJuH6v1odOPaOVbPjumd+hLUJ59YUB9Y1jJfdbuP0FbVAeCX5eLy8qzXha0VGKGSJiEhMHn7+QMTj\ntZWj6/fl5sT3a6UgwluIR9omV+NqpLxWvMVRg/3wofBipjd+ZF3C95PspJAlIiIJ+eIHTwVG610B\nE65XOJbdZuPbn90QEtRu+MCaSbUr2pqIsTrY3BPY3nDKbArzHayYX8HyeZOr3SXZRyUcREQkLjUV\nBfzr36wP1MIaCVm1FYXjXRZVRUk+N//dWTS19dFQUxx3UBtrZKgx0ZD16tujQ4XvO28J173npEm1\nR7KXQpaIiExo684WwDdf6uvXnRnymc1m47vXn0uuI/HBEZvNxtza8DcVE7HniO8twKPH+6hOIPgF\nP0dlaX5S2iTZKa6QZVnWXcBfAC3GmNX+Y5XAfcAC4ABwlTGmy//ZTcAmwAVcb4x5MnlNFxGRVBmZ\noxRtvlTwkGG6tfpLL9z+wDb+7r0ncccjjdz8ibOoqyyK6fp+/5JBX7hqcsOWIvH+b8dPgHeNOXYj\n8JQxxgI2AzcBWJZ1EnAVsBK4FPiBZVmT6wMWEZGUa+noD2y//52L09iS+N3xSCMAX//pKzGd390/\nzBNbDwOx1/sSiSaukGWM+TPQOebwFcA9/u17gCv925cD9xpjXMaYA8BuILw0sIiIZLT7Nu8JbF92\nVmJFQtNt7ILW0dz3x9E1GYsLNaNGJicZbxfWGmNaAIwxzUCt/3gDcDjovCb/MRERmUaG/JXTr36X\nNelJ6ekyUjtrIodbewPbmTQEKtPTVJRwmOTa6SIikkma2nqpLi9g49rp8f/JV20MX1i6PMrQn8fj\n5RdPvo055BukqS73TZTPcdjJz3VMXSMlKySjL7TFsqw6Y0yLZVmzgZF3X5uAeUHnzfUfm1BNTWkS\nmjU96dmzk549+0yX535zdxvd/U5WLixNWpun+tnXn1LP/U/vCTnmtdsoKingN3/aw7vOWkiNvy7X\nG2+38sfXjvDH147wyLeu4K19xwH48ZcvSujNxIlMlz/3qZCNz55IyLL5/xrxMHAtcCtwDfBQ0PFf\nWJZ1G75hwqXA1li+oK2tZ+KTZqCamlI9exbSs2ffs2fKc/cOOOkbcFI3K/pbd3f8ZhsA82uLk9Lm\nVDz7QN9Q2LHW4338/NEdPPriQe576m3uvvECAI61jLZl38HjeLxeqssL8DpdSW9npvy5p8NMfvbx\nwmO8JRx+CZwPVFmWdQj4GnAL8IBlWZuAg/jeKMQY02hZ1v1AI+AEPm2M0VCiiEiG+Nx3ngPgm397\nJvVVxYHjR9p6eXF7M5eetYD+Qd+E8Q+cHz4El6nKS8JrW23f38H2/R1hx51uT2D79d3teL0w7PKE\nnSeSiLhCljHmw1E+uijK+TcDN8fbKBERmVpuz2iQOHCsJyRk/ctdvkGHx/0LQhfm5wSWqpkOYpmw\nvumWzWxc28DTr4/OYvnT6zHNaBGJmdYuFBHJEr974QCbbtnMK7taGRhyB447HKMBajKLKk83T48J\nVQf8axZefcnydDRHZiAVARERyRIPPrsPgB/8dntINfOhYTedPUP87oUDYcEDmDZvFSbLrLKCdDdB\nZgj1ZImIZIGxPVTfvv/NwPbLu1p57KWDIQHrby5bwdmr6sjLtfOu9fOYrv756tN47zsWjnvOwtmh\nE5eLCtT/IMmhkCUikiZOl5tv3/8Gz791bMq/68XtzVE/276/gz++eiTk2Lmr5/C3713FD794/rRc\nXub2vz+Hz/zlKSxpKOeKcxcFjl/zbivs3Ov/anXIfmGeQpYkh0KWiEia7DjQyfZ9Hdz16M4p/Z5D\nLT2B7/jL8yZee/A7nztnStuTCmXFeZxm1QBgD6pSv2rhrLBziwtz+e715wb2C/JUhFSSQyFLRCRN\n2joHpvw77nq0kX/9ycuB/UvPnB8yH+vOL23k8g0LAVi9pIq/f/8p07LnKlYF+Tnc+aWNIaEqx2Gn\npDCXjWsbWFBXSm6OfjVKcqhPVEQkTXoHnIFtr9eb1HUBB4ddOOx2XtrREjj2nrMXkOOws3JhJWXF\necyrLcFut3HluYu58tyJe7ims8rSfDp7higuyMFms0Us83D1u8KHEkUmQyFLRCRNRpZwAXj+rWbO\nWV2flPt6PF4+/e1nw46vWVoNgMNu57bPbpi2iz0n4t8/fiZuT3KDrMhEFLJERFJo2Onm3+/ewpYd\noRPR735sJ6uXVFFWPLmhOq/XG6jkHmxsVfdsCxuF+eG/7r6dZUFTUk8DzyIiKfS9X28LC1gj3tp3\nnB1BS7/0D7oYGnaz72g3dz+2M2R4MZqfPr6L/iFXyLG5NcUhAUt8KkryKZ9kqBUZj3qyRERSxOv1\nsuNAZ8ixs1bVBeZNjbwB+Nn3ncK65TV89vZnKSnMDYSrHIedj0WYN9TRPcix4/2sWjSL57aNloO4\n8pxFuDwe/nKGz7cSyVQKWSIiKdJ4cDRgnbO6nnk1JZy/dg6rl1Rxx8ONgc/++8G3+NZnNgChk+Nf\n2tEcMWT9ww9eCDt21z9t1FCYSJopZImIpMibe9oD25suWxnYrp8VPpT3xe8/H3Zs0L/8TWVpfuBY\n32D4EOLaZdUKWCIZQHOyRERSwOX28NQrvqrqP/jSBSGfza8rmfD6kcj0xe8/z5BzdHHnnWOGHwE+\ndOGyxBsqIkmjkCUiMsWcLg9f+O/Rnql5daFr5dlsNj595cmct2YOf3X+krDrb/3k2XzwgqWB/W/c\n80pge/v+0TIQuTl27r7xAmoqCpPZfBFJkIYLRUSm2MHmnsDcqo9esjziOaevqOX0FbWAr8zDw88f\nAOCSM+ZRU1HIJevn89Dz+xkYcnO0vY9Nt2zm7FV1HGrtBeA/P3U2VWUFU/8wIhIz9WSJiEwhr9fL\nf/zvq4H9809tmPCa95y9MLD9gY2jPVv//vGzQs57cUcLTW191FcVUV1eqHlYIhlGPVkiIlPA4/Vi\nDp3gVdMaOPbZ952C3T5xEMrNsfOfnzwbl8eLwz76/8KVpfl84vJV/OjhHSHnF0dYIkZE0k8hS0Qk\nyQaGXHzmtvBlbZY0lMd8j+oo86rOPKmO1UuqMIdO8N1fbwPAoR4skYyk4UIRkSTyeLwRA9YXrlqT\ntOrihfk5nLqsmooS3/2UsUQyk0KWiEiStHT2s/NQeEkFgJMXVyX9+672Fya9UhXdRTKShgtFRJLg\nVdPK93+zPbCfn+vgezecy+0PvMnlGxZNyXeuXVbDnV/aGNM8LxFJPYUsERG/b9zzMvuP9fCu9fM4\ndWk1ZcV5NLX1sc6qwR5lTK5v0Mm/3r2V491Dofe6bj05Djv/8KG1U9pmBSyRzKWQJSKCb7L6/mM9\nADyx9TBPbD0c+Oyi0+fy4Ysi17f66p1bONE7HNhfMLuUa9+9IurEdRHJHpqTJSJZz+uNPFl9xKum\nDYC9TV1sumUzm27ZzLDTTe+AMyRgVZTk8bVrz2DB7NJotxKRLKKeLBHJetv3dwS2LzptLk+9egSH\n3cbqJVW8vrudzp4hHnl+P49vORQ475PfeiawvXxuOTd+9LSUtllEMp9ClmS8V3a18oun3uabHz+T\nogIVXZTkOtDczW33vwlAaVEuH7xwKWesrKW+qpjighyuu/VpAH7z3P6o93jXmfNT0lYRmV4UsiRj\nuT0euvuc/OC3vje2Pnv7c9z+uXMoK0pOrSHJTt/79TZe393OqUurWbe8hrsf2wlA3awivr5pPQ67\nnWVzK2K+3/vfuZi1y2qmqrkiMo0pZEnG+tHDjbyyqzXk2A3f/TMA3/j4mTRUF497fUf3IP/0wxe5\n5Ix5XLBuLlXlWjw327k9Hl7f3Q7AG3vaeWNPe+Czr3zsNHJzwqepfu+Gc2k/MciQ0819m3fz6StP\n0T9LIhITTXyXtHpi6yEe+vPoMEzbiQFaOvvZdMvmsIAV7Kf+3odgn//en7nxRy/icnsA+PUze3F7\nvDy+5RD/+D8vJL/xMu00HohcKHTTZSspjjIUXVyQy4LZpSyfV8FXrzlDAUtEYqaeLEmbE71D3Ld5\nDwDFRXn88kkT9dzTrJrAG14Ae4928+KOZs5eNZufP2l4+rUm3wd98NPHd/Hhi5azpTE0pO0+ciKu\nYSCZObxeL//+s1cCJRre/87FXHbWArxe1ZkSkamjnixJi6a2Xg74f+EBUQPWhpNnc8MH1vCpK07m\njBW1IZ/9+JFGDjb3jAYsvxe2N/PZ25/F4/Vy3pr6wPGb//e1JD6BTCcP/Xl/IGBVlubzjpPrsdls\nClgiMqXUkyUpt6WxhR89vGPccwrzc/j+588LOfapK0/mU8Cug5385/+9DsC//fTlce9z8enzsOZX\n8uNHGifVZpm+XtzRzMPPHwDgnz92GkvmlKe3QSKSNdSTJVOuo3uQ412Dgf0Hn90b2LYBG9c18Mm/\nPCVw7Jt/eybf/syGqPdbsaCSj/kXxh2xdlk1d994AV+79ozAsQ9dsJSGmpJAD9jSufrlmm26+oYD\nAfucU+oVsEQkpdSTJVPqn3/8EseO9wPwH393Fl++46XAZ1dfspzzTp2Dw26npqaUedVFHG7tpb5q\n/LcGAc4+eTY/e8I3xPifnzqb6nLfEiYLZpdy940XhJzr8A8JRVt7TmaWvkEn//vk22xpbAk5/jeX\nrUhTi0QkWylkyZRwuT08/XpTIGABIQHr0rPms3Hd3JBr6quKYwpYAPm5jrAwFY3NZsNus+H2eGI6\nXzJfV98w//D953F7vCycXUpLZz9gY+HsUnYeDH+D8K8vWoZNIVtEUkwhS5Lm/s17+P3WQxTl59A/\n5AocLynMpXfAGdj/m8tWcO7qOSltm8Nhw+32Tvo+A0Mumtr7WNqQ2mEnj8fLkNNNYX52/Cv7qmnl\nmTePsnxuBQ8+uw+AM1bUUlKUS+P+Dlo6BwLnHmgefYEiOGBdfPo8rjhnEUUF2fEzE5HMo//6zGBe\nrzeuV9Sb2noxh0/wjpNnU5AX+R8Np8vDjT96kc6eIS49cz4dPUNsaWyhsjSfzp4hgJCAtaCulK9e\nczr/9MMXcHm8/Ms1Z1BZmj/5h4uT0+XhQHMPHd2D2Gw2bDaoKAlvh9frpWfAGagq7/F62Xmwk6df\na8Lj8YYUr3z/OxezcW0Df3jlSKDW16lLq/ncX62OqU3PvnmUipJ8bAc7eX1XK06Xm+ffag45p7gg\nh75BV8Tr3/uOhVx+zkIc9syYWun2eHDY7bjcHt7Y3c62fcex22DZ3Ap+89w+evudvGv9fJbNK2fJ\nnHLy8xx4vV4eeHoPOw50MMdfXLaje4i3D58AYPu+0TUFX45QN+36v1rNr/60l6b2Puqrijh2vJ+l\nc8v5/AfWZE0gFZHMZfN6J/9/98k0OOzyHmvupqQw+9aoq6kppa2tZ+ITx2EOdTLk9PDYSwcDv6jq\nq4rIzbEzv7YUh8PGM28c5YwVtdTNKmJuTTHL51Vwx8M72HXoRNj9PnnFKpY2lNPeNcihlh7+vO0Y\nh1p7J2zHlecu4rKzFpDjiC0AJOPZx7Ppls0Rj9/00XWB2lk7D3TwX/e+MenvWr2kilml+Rw73s8Z\nK2vZuLaBgSE3927ezbY97RQV5NLc0T/xjWL0oQuWcrCll/e/czGzysYvlOn1evGS+Py0tw+fwBzq\npKw4D4/Hy85DJ9i2p51h19QMxVaV5VNZVsDi+jJ2HuykpqIQuw0uP2cRc2tKpuQ7U2Gq/3nPZHp2\nPftMU1NTGvU/qCkJWZZlvRu4Hd/bjHcZY26Ndu57v/hQoEFnrKjFZgNrXgVrllZT6u9dcLk9FOQ5\nePvwCTp7hlg2tyKuKsw9/cMUF+bSP+iiIM9BjsOO1+tN6pyN1s5+jrb3c+qy6pivifYP4Yle3//Z\n5+U46B1wcqyjD6fTgzl8gjNW1FJfVUTfoIv/fdLgSsKQWDzycuxcsG4ur5hW1iyt5sMJzn2Z6n8B\n9x7t4ps/e3XS96mvKuLGj6zjxR0tPPjsXoadyQ0XH71kOfWzivive9/ginMWcckZ82ju6GdOdTHH\nuwbZvr+DWaX5FBfkRAyEDruNvFwHs2cVUVyYQ0VxPiWFudjtNl59u43jXQO43F4cdhuVpfmsmF/J\nrLJ8hp0e+odc5PpD8bGOPhbOLmNo2E1pcS4ej5cTvcM8++bRmJ6jrDiPsqI8jrT5AvnFp8/D7fFw\ntKxUA8oAABB2SURBVL0vLMxXlxewoK6UipJ8hl2+IdE1S6tZuaBykj/NzDWTf+FMRM+uZ59p0hqy\nLMuyA28DFwJHgZeBDxljdkU6PzhkxaMgz0FJYS5rllTjcNg43NpLT7+TYZeb/kEXdhu4Pd6oQy8A\n+XkOTl9ew19sWMis0nwcdjs9/cOUFOViw8beo120dw1SWZJPbWUhNpuN3Bw7BXkOHHZbIFwMO918\n8lvPBO67qL4Um83GrNJ8hl0eBodcvHfDIrr7h6kpL2R2VRElhbmUlhXyu2f3BIbq2rsG2PxaU2AY\nLl43fmQd3//NW/T0OzlndT3b9x3nRO8w4FsM1+ly09E9eu+vfOx0FtaX0tk9xGNbDrL3SBfHuwcD\nP7N5tSWUFOZy1kl1nLsmuXOqUvEvoMvtYd/Rbnr6ndRUFPiHqcInSX//8+fx8q5WWjr6WTynjLrK\nIupm+d5ezM1xBM7zeL3YINAz5PV62bb3OP2DLlpPDPDctqMhP1+A8uI81iyt4pzVc1g8pwy7zZbw\ns7vcHu54eAevBFXCT4U1S6po6RygqCCHuTUlnG7VsHxeBXm5Drr7h8nLsUcdbh6rorKYE519U9zi\nzDOTf+FMRM+uZ59p0h2yzgK+Zoy51L9/I+AdpzfLe/BwB8+8cZT2rkFmleXT3eekpbOfbXuPA77e\nBJvNxrDTTU1FYcS3icYzMoRVXpxHUUEOfYNOOnuGmMyPwmG3UV1eEDIhNx5LGsrY29Qd9fPaikKK\nCnLIcdgpLcpl5YJKCvNz2LqzlYqSPMqK88jPdXDhaXOjzkXxer24Pd6Yh/BSKV3/Au4/1o05dIK+\nQSfvPnN+1PXrJqOrbzjwz2okyXh2t8fDc9uOMTTspr6qmJLCXF/vZ66dwrwcjrT1MqusgNOsGnIc\ndtweLwebu2k+3k9uroPCfAdvH+6itqIQh8P3NmZ5cR79Qy46ugc50NxDQ3Ux65bXsCSJk/5n8n94\nx5Otzw16dj37zDNeyErFzNAG4HDQ/hFg/XgXFBXkculZC+L+om17j2MOd7L/aDcnL65i3fIaqsry\nsdtt7G3qpr6qiPxcB3m5jrDhQbfHQ0f3ED9/wmCz2RgYdnG8axCP10t5UR5uj5f27kHWr6jF6fYw\nOOTG6/W98eXx+gputvsLbtqANUur+Yt3LKS+qojBYTd9A066+4cpKczl5V2tPPnyYZY2lFOQ56Cp\nrS8kYL33HQvpHXQyqzSfhbPLOGlhZdQhuA2n1Ec8HonNZiPHodfYgy2qL2NRfdmUfkd5cd6U3h/A\nYbdz/qkNIccWzxn/uVYvqWb1ktH9s06aPRVNExHJWjPq9ZvVS6pYvaQq4mfL54UuDDw2tDjsdmoq\nCvnCB09N+PuHht0MDLsoL84LuX9hfk7IG3Xz60p5/zuXhFw7OOzCkZeLze3OyJ4mERERiU8qQlYT\nMD9of67/WDS2mprSqW2RZKRs/nPXs2efbH1u0LNnq2x89lSErJeBpZZlLQCOAR8C/joF3ysiIiKS\nNlM+LmWMcQOfBZ4EdgD3GmN2TvX3ioiIiKRTxhUjFREREZkJNMNaREREZAooZImIiIhMAYUsERER\nkSkw4duFlmXdBfwF0GKMWe0/thr4IVAMHAA+YozptSwrF/gRcDrgBm4wxjzjvyYX+G/gfP9n/2yM\n+U2E71sH/BQoAB4zxtzgP34uvvUPVwMfNMY8GKW9ecDPgNOAdv+5hyzLWvP/2zvXoLumM47/cqNV\n0aj7VNyTB3WLKBPFhLqWwVBxLRrVS4yaph3TkFa1wUSr1Ac+1CCUToIiZVwr1F0qLi31j3QiJCQ0\nmkyTuESkH551ku3k7PPuc9nn6Ov5zbwz5+y9bv93rfWcZ6+9LsDVwMCU/8WSppSt3czWBR7FT1/p\ng29hcaOksTXymwCcCgyStF7m+o+A7wDLgXeA0ZLeqBG/ZrhuaU9xjgfOwx36uySNy8mvVe257cPM\nBgPXAIOBj4FvSHo9pxyb4+1nkxT295KuNLP1gcnAlkn7KEmLU5xxwGjgI+AcSffX09RJ7en+QOBl\n4HZJP6xTjnZqvwfYFBgAPAV8X9IaZ1qVXO+nAefjfe8iSTd0SPs0YDPgvZT3wZL+XYL2mrYu3VsB\nvIDbnDmSji5be5tsXVE7/z3gLNzevIe3rxfTvW7Ve6u2rqj2uu2jSH9vVLeZfQm4FfgqcF023TbY\nuaK6a4Yzs5HA5axud9un+1PzytJJioxkXQccUnXtGuBcSbsCtwPnputn4kfm7AIcDFyWiXM+/oNt\nknYEHqE2VwNnSBoKDDWzSt5zgNOAm3oo7xnAu5KG4BVyabq+DPiWpJ2Bw4ArzKynrb5b1i5piaRh\nknaXNCzpuC0nv6l4I65mBjBc0m4p7q9z4ueF64r21DEvBfZPeW9qZvvn5Neq9nrt4wZgYmp3ewJv\n56QBbjjHSvoKMAI4y8y2B34KPCjJgIeAcUnjjsAoYAf8f3uVmVV2os3TVE2Z2gF+RX5/y9JO7cel\ndr8TMAg4PifPUrSnH4ufp7T3Ai4ws3rnAbVTO8CJmX6/hoPVJu15tg5gacXm1HOw2qm9TbauqJ2/\nSdIuKZ9LgN+msnWl3ttk64pq76l9FOnvDekG3gfGAz9uQE/RcEV11wwn6eFKuwMOAJbiuxl8KujR\nyZL0GFB9OOCQdB3gQeCY9HlHvGKQ9A6wyMz2SPdG452hku671XmZ2abAQEnT06UbgKNT+Ncl/QP3\nVutxFDApfb4VP5gaSa9K+lf6/Bb+Q7tRvYTaqL2ibyiwkaTHc/J7RtKCGtcfkfR++voUflRRrfg1\nw3VR+zbAzExd/wU4Nie/VrXXbB9mtgPQT1KlfMsy6dVKZ76k59PnJcA/8SfybLuaRGqXwJH4tiQf\nSXoNeJV0bFSepk5pBzCz4cDGFDA6bda+JOU/AFgLWJiTZ1naDwHul7RY0iJc/6Gd0J4oYltb0k6O\nrUsUPj+rBO2t2LpCdr7SvhLr4iN50L16b4etK6o9t30U7e+N6k528wnggxpptWrniuouEu6bwD31\nbHynaXZO1ktmdmT6PAp/DQM+PH2kmfUzs63xYezBmSeJCWb2rJlNNrNaP/Jfxs82rDCXfAOTx6qz\nEuV7dC1KTxmrMLM9gQEVx6NBGtJeFfd4fCi2Fc4A7mk2XIe1z/IsbQsz64932Or/SSMU1Z5lKLDY\nzG5LbW9i1YhDLma2FbAbbsg2qRgISfNxQwZrns05j8bbbBEa1p50/gb4CQ386Ka4W9GidjO7F5gP\nvCfp3kbyr6KZem+6XtpU79eb2QwzG99guaupp72erVvbzP5mZk+Y2VFFM2tjm2+HreuprGPMbBY+\ncl4ZcelWvbfb1hVlVftotr8X1P3/wgnAH7tdiCzNOlmj8eHF6fj8nA/T9WvxRjcdH759HH9n3h/3\nkh+TNByvzMuqEy2JTzQ2M9sMHyE7vcn0GtWepaUGYGan4A5M3uuDuuE6rT09Sf4AmIIPX89mzf9J\nIYpqr0F/YB9gLD5UvS0F9Kf5Jbficy6WsObTU8c2mGtB+xjgbklvpu9Fncu2aJd0KD43aW0zO7VY\nkdcoS7Pam6JN2k+SvzLaF9g3aWimLI1qz9bvlpL2AE7GpwdsXSC/drb50n/sJF0laTu8b1/bSlqt\nam+nrStKjfbRcH//NNm5VklvwnYC7ut2WbI0dayOpJmk+TpmNgQ4PF1fgTd40r3H8SHUhWa2VKsn\nut8CjE6e9wy8Iqfik6qz3n9P5xxWJtMdjs8J2j2FHwy8aWb9gPUqQ7hpQuBdwDitfiVZqvbM913w\n11bPpe99gWcr2iX9ogedB+JPa/tJWp6jvWa4bmqXdDdwd7p+JrCiLO05zAWelzQnxbsDn6txXZ38\n+uOG50ZJd6bLC8xsE0kLUmeuzOuqtLcKddtsh7WPAPYxszH4oocBZvZfSefVya+t2iV9aGa3AXua\n2R86qH0evsgmW7ZpPeTXFu3yV/JIWmpmNyftN5egPdfWZcow28weBobhP/ylak9ptWTrcspXr94n\n478dlbKNrCpbp+q9ZVuXU76idr6h/t6g7kbLXHad12IUPtm/VOe2UYo6WX3IeMVmtpGkd9I/cjyp\ngZvZ54E+kpaZ2UHAckmvpGh/NrP9JU0DDgRelrQS7/xk0l6cXmlNx1ciXJlTHgAkjU9lqDAVnxz3\nNHAcaa6Q+dyQO4BJqrGqsWTt4Oc1rnqyk/RxtfZa+lLaw1I+h0haNbelWnteuG5qz8RZH3/SOq4M\n7XXSmA4MMrMNUvwD0rV6XIu3z99lrk3FR8Am4u3rzsz1m8zscvy1wXbAM3nl6aR2SatGUMxXXA2v\n52AlWtZuZl/A51bOT4b8cOCBDtf7fcBF5lMV+gIH4ZN669EO7f3w1VMLU787gvK059m6QcCy5OBu\nCOydyl+q9ky8pm1d3r0atm47SbPS1yOAv6fPXan3VKaWbF3evaJ2von+3ojuIuVuys7VSaNoX69w\nIj3Xdcfp8Vid9AQ2EtgAWABcgHvJZ+Fe6p8qFWl+CPR9+DDpPHyV4Bvp3hbAjcAX8WWn35Y0lyrM\nJ+5dz+otHM5J1/fAV7QNwlc6zJcPyVfHXzvlMwyfbHuCpNfM7GS8Ub2EV9BK4HSlZb9lak/3Z+Fb\nB8wkBzObCJyEv2J5E7hG0i/N7AF8GPQt6izJzgvXTe0pnV1TnAsl3VKS9tz2YWZfJ60+wp+uvqsa\n2wmksF8D/oob7ZXp7zzckE7Bn2Ln4EubF6U44/C5Ecv55JLumpo6qT0TpmJ0623h0BbtZrYxPmq6\nVir3/fiq1FoT88us99NZvZR/guov5W+X9nVSOv2BfvgCkbElac+zdSPwLVVW4I7G5ZKuL1t7Jr1W\nbF1RO38F/rD+IT7pfUzF6epGvafrrdq6otp7bB899fcmdc/GfwPWAhbhW5O80gY7V1R3vb6+JT4d\nqRPz4Boizi4MgiAIgiAogdjxPQiCIAiCoATCyQqCIAiCICiBcLKCIAiCIAhKIJysIAiCIAiCEggn\nKwiCIAiCoATCyQqCIAiCICiBpnZ8D4Ig+LSQ9siZyep94D4HvAicLSl3x2oze0jSAZ0pZRAEn0XC\nyQqCoDcwL3v0hpldjB8Zsl+dOCPLLlQQBJ9twskKgqA3cgEw38x2Bs7Gd8jeGBBwLOmYGTN7UtII\nMzsUuBC3ibOBMyX9pyslD4Kg1xBzsoIg6HXID82dBRwFfCBpb2AIsA5wWOW4ruRgbQhcgh8TMhw/\nBujS7pQ8CILeRIxkBUHQW1kJPAfMNrMxwPb4gb7rZu4D7AVsAUwzsz74w+dCgiAIWiScrCAIeh1m\nNgAwYFtgAnAFfkj6hvjk+Cz9gEcrh+ymg5cHdq60QRD0VuJ1YRAEvYFVjlMajboQeBLYBpgsaRLw\nNj4Rvl8KusLM+gJPAyPMbEi6/jPidWEQBG0gRrKCIOgNbGZmM3Bnqy/+mvAkYHPgZjM7BpgP3Als\nneJMBV4AhgOjgSnJ6ZoLnNLZ4gdB0Bvps3Llyp5DBUEQBEEQBA0RrwuDIAiCIAhKIJysIAiCIAiC\nEggnKwiCIAiCoATCyQqCIAiCICiBcLKCIAiCIAhKIJysIAiCIAiCEggnKwiCIAiCoATCyQqCIAiC\nICiB/wG3atAcrCKAqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9da4c3a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock['Adj Close'].plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2000-01-01</th>\n",
       "      <td>0.322330</td>\n",
       "      <td>0.768993</td>\n",
       "      <td>2.249961</td>\n",
       "      <td>-0.262111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-02</th>\n",
       "      <td>-0.102684</td>\n",
       "      <td>0.190375</td>\n",
       "      <td>-1.471382</td>\n",
       "      <td>0.597893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>1.989923</td>\n",
       "      <td>0.273032</td>\n",
       "      <td>-0.188176</td>\n",
       "      <td>-1.976469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>-0.901770</td>\n",
       "      <td>0.396484</td>\n",
       "      <td>-1.144343</td>\n",
       "      <td>0.539260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>0.406112</td>\n",
       "      <td>-0.182979</td>\n",
       "      <td>-1.087188</td>\n",
       "      <td>-0.557239</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   A         B         C         D\n",
       "2000-01-01  0.322330  0.768993  2.249961 -0.262111\n",
       "2000-01-02 -0.102684  0.190375 -1.471382  0.597893\n",
       "2000-01-03  1.989923  0.273032 -0.188176 -1.976469\n",
       "2000-01-04 -0.901770  0.396484 -1.144343  0.539260\n",
       "2000-01-05  0.406112 -0.182979 -1.087188 -0.557239"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts = pd.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))  # just more than a year\n",
    "\n",
    "rdf = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\n",
    "                   columns=['A', 'B', 'C', 'D'])\n",
    "rdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAF+CAYAAABee2RqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuEwBloiIiIjL/j+aAcLmmhybfQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9da4130828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rdf.cumsum().plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Object creation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See the [Data Structure Intro](http://pandas.pydata.org/pandas-docs/stable/dsintro.html#dsintro) section for more details.\n",
    "\n",
    "Creating a Series by passing a list of values, letting pandas create a default integer index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     1\n",
       "1     3\n",
       "2     5\n",
       "3   NaN\n",
       "4     6\n",
       "5     8\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series([1, 3, 5, np.nan, 6, 8])\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a DataFrame by passing a numpy array, with a datetime index and labeled columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D', tz=None)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.date_range('20130101', periods=6)\n",
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng = np.random.RandomState()\n",
    "\n",
    "df = pd.DataFrame(rng.normal(size=(6, 4)), index=dates, columns=('rainfall', 'temp', 'humidity', 'windchill'))\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Viewing Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the index,columns, and the underlying numpy data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',\n",
       "               '2013-01-05', '2013-01-06'],\n",
       "              dtype='datetime64[ns]', freq='D', tz=None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['rainfall', 'temp', 'humidity', 'windchill'], dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.30748348,  0.04260112,  0.27170732, -1.62037702],\n",
       "       [ 0.42379999, -0.66542967,  0.08179128, -0.35733301],\n",
       "       [ 1.12048335,  1.67051232, -1.0916368 ,  0.19466055],\n",
       "       [-2.41632692,  1.91494805, -1.61258502,  0.11331722],\n",
       "       [-0.45476237,  0.36889338, -0.31450615,  1.45540068],\n",
       "       [ 0.29570838,  0.22351487, -0.15666216, -0.8978446 ]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Describe shows a quick statistic summary of your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.120602</td>\n",
       "      <td>0.592507</td>\n",
       "      <td>-0.470315</td>\n",
       "      <td>-0.185363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.230874</td>\n",
       "      <td>0.998189</td>\n",
       "      <td>0.730602</td>\n",
       "      <td>1.050924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.267145</td>\n",
       "      <td>0.087830</td>\n",
       "      <td>-0.897354</td>\n",
       "      <td>-0.762717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.301596</td>\n",
       "      <td>0.296204</td>\n",
       "      <td>-0.235584</td>\n",
       "      <td>-0.122008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.394721</td>\n",
       "      <td>1.345108</td>\n",
       "      <td>0.022178</td>\n",
       "      <td>0.174325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rainfall      temp  humidity  windchill\n",
       "count  6.000000  6.000000  6.000000   6.000000\n",
       "mean  -0.120602  0.592507 -0.470315  -0.185363\n",
       "std    1.230874  0.998189  0.730602   1.050924\n",
       "min   -2.416327 -0.665430 -1.612585  -1.620377\n",
       "25%   -0.267145  0.087830 -0.897354  -0.762717\n",
       "50%    0.301596  0.296204 -0.235584  -0.122008\n",
       "75%    0.394721  1.345108  0.022178   0.174325\n",
       "max    1.120483  1.914948  0.271707   1.455401"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Transposing your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>2013-01-01 00:00:00</th>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rainfall</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.423800</td>\n",
       "      <td>1.120483</td>\n",
       "      <td>-2.416327</td>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.295708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>0.042601</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>0.223515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>0.271707</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>-0.156662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windchill</th>\n",
       "      <td>-1.620377</td>\n",
       "      <td>-0.357333</td>\n",
       "      <td>0.194661</td>\n",
       "      <td>0.113317</td>\n",
       "      <td>1.455401</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           2013-01-01  2013-01-02  2013-01-03  2013-01-04  2013-01-05  \\\n",
       "rainfall     0.307483    0.423800    1.120483   -2.416327   -0.454762   \n",
       "temp         0.042601   -0.665430    1.670512    1.914948    0.368893   \n",
       "humidity     0.271707    0.081791   -1.091637   -1.612585   -0.314506   \n",
       "windchill   -1.620377   -0.357333    0.194661    0.113317    1.455401   \n",
       "\n",
       "           2013-01-06  \n",
       "rainfall     0.295708  \n",
       "temp         0.223515  \n",
       "humidity    -0.156662  \n",
       "windchill   -0.897845  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by an axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sorting by values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort(columns='temp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note** While many of the NumPy access methods work on DataFrames, use the pandas-specific data access methods, `.at`, `.iat`, `.loc`, `.iloc` and `.ix`.\n",
    "\n",
    "See the [Indexing section](http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing) and below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting a single column, which yields a `Series`, equivalent to `df.rainfall`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01    0.307483\n",
       "2013-01-02    0.423800\n",
       "2013-01-03    1.120483\n",
       "2013-01-04   -2.416327\n",
       "2013-01-05   -0.454762\n",
       "2013-01-06    0.295708\n",
       "Freq: D, Name: rainfall, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['rainfall']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting via `[]`, which slices the rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['20130101':'20130103']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See more in [Selection by Label](http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-label).\n",
    "\n",
    "For getting a cross section using a label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rainfall     0.307483\n",
       "temp         0.042601\n",
       "humidity     0.271707\n",
       "windchill   -1.620377\n",
       "Name: 2013-01-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[dates[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Selecting on a multi-axis by label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp\n",
       "2013-01-01  0.307483  0.042601\n",
       "2013-01-02  0.423800 -0.665430\n",
       "2013-01-03  1.120483  1.670512\n",
       "2013-01-04 -2.416327  1.914948\n",
       "2013-01-05 -0.454762  0.368893\n",
       "2013-01-06  0.295708  0.223515"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['rainfall', 'temp']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection by Position"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select via the position of the passed integers—esentially equivalent to NumPy indexing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rainfall    -2.416327\n",
       "temp         1.914948\n",
       "humidity    -1.612585\n",
       "windchill    0.113317\n",
       "Name: 2013-01-04 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By integer slices, acting similar to numpy/python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp\n",
       "2013-01-04 -2.416327  1.914948\n",
       "2013-01-05 -0.454762  0.368893"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:5, 0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By lists of integer position locations, similar to the numpy/python style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>rainfall</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>0.081791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>-1.091637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>-0.314506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall  humidity\n",
       "2013-01-02  0.423800  0.081791\n",
       "2013-01-03  1.120483 -1.091637\n",
       "2013-01-05 -0.454762 -0.314506"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[1,2,4], [0,2]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For slicing rows or columns explicitly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:3, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boolean Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.rainfall > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What happens if I select on the whole table?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707        NaN\n",
       "2013-01-02  0.423800       NaN  0.081791        NaN\n",
       "2013-01-03  1.120483  1.670512       NaN   0.194661\n",
       "2013-01-04       NaN  1.914948       NaN   0.113317\n",
       "2013-01-05       NaN  0.368893       NaN   1.455401\n",
       "2013-01-06  0.295708  0.223515       NaN        NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df > 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simple statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rainfall    -0.120602\n",
       "temp         0.592507\n",
       "humidity    -0.470315\n",
       "windchill   -0.185363\n",
       "dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And the same along a different axis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2013-01-01   -0.249646\n",
       "2013-01-02   -0.129293\n",
       "2013-01-03    0.473505\n",
       "2013-01-04   -0.500162\n",
       "2013-01-05    0.263756\n",
       "2013-01-06   -0.133821\n",
       "Freq: D, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying a function to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            rainfall      temp  humidity  windchill\n",
      "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
      "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
      "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
      "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
      "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
      "2013-01-06  0.295708  0.223515 -0.156662  -0.897845\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.731283</td>\n",
       "      <td>-0.622829</td>\n",
       "      <td>0.353499</td>\n",
       "      <td>-1.977710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.851767</td>\n",
       "      <td>1.047684</td>\n",
       "      <td>-0.738138</td>\n",
       "      <td>-1.783049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-0.564560</td>\n",
       "      <td>2.962632</td>\n",
       "      <td>-2.350723</td>\n",
       "      <td>-1.669732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-1.019322</td>\n",
       "      <td>3.331525</td>\n",
       "      <td>-2.665229</td>\n",
       "      <td>-0.214332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>-0.723614</td>\n",
       "      <td>3.555040</td>\n",
       "      <td>-2.821892</td>\n",
       "      <td>-1.112176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.731283 -0.622829  0.353499  -1.977710\n",
       "2013-01-03  1.851767  1.047684 -0.738138  -1.783049\n",
       "2013-01-04 -0.564560  2.962632 -2.350723  -1.669732\n",
       "2013-01-05 -1.019322  3.331525 -2.665229  -0.214332\n",
       "2013-01-06 -0.723614  3.555040 -2.821892  -1.112176"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df)\n",
    "df.apply(np.cumsum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rainfall     3.536810\n",
       "temp         2.580378\n",
       "humidity     1.884292\n",
       "windchill    3.075778\n",
       "dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.apply(lambda column: column.max() - column.min())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SQL-style joins (merging data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'subject': ['history', 'literature'], 'papers': [10, 20]})\n",
    "right = pd.DataFrame({'subject': ['history', 'literature', 'science'], 'books': [4, 5, 9]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>papers</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>history</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>literature</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   papers     subject\n",
       "0      10     history\n",
       "1      20  literature"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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>books</th>\n",
       "      <th>subject</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>history</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>literature</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>science</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   books     subject\n",
       "0      4     history\n",
       "1      5  literature\n",
       "2      9     science"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>papers</th>\n",
       "      <th>subject</th>\n",
       "      <th>books</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>history</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>literature</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>science</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   papers     subject  books\n",
       "0      10     history      4\n",
       "1      20  literature      5\n",
       "2     NaN     science      9"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='subject', how='outer')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, `how` can be:\n",
    "\n",
    "  * left: use only keys from left frame (SQL: left outer join)\n",
    "  * right: use only keys from right frame (SQL: right outer join)\n",
    "  * outer: use union of keys from both frames (SQL: full outer join)\n",
    "  * inner: use intersection of keys from both frames (SQL: inner join)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append rows to a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            rainfall      temp  humidity  windchill\n",
       "2013-01-01  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-04 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-05 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-06  0.295708  0.223515 -0.156662  -0.897845"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.DataFrame(np.arange(4 * 3).reshape((4, 3)), columns=['A','B','C'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#s = df.iloc[2]\n",
    "#s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rainfall      temp  humidity  windchill\n",
       "0  0.307483  0.042601  0.271707  -1.620377\n",
       "1  0.423800 -0.665430  0.081791  -0.357333\n",
       "2  1.120483  1.670512 -1.091637   0.194661\n",
       "3 -2.416327  1.914948 -1.612585   0.113317\n",
       "4 -0.454762  0.368893 -0.314506   1.455401\n",
       "5  0.295708  0.223515 -0.156662  -0.897845\n",
       "6  0.307483  0.042601  0.271707  -1.620377"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.append(df.iloc[0], ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "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>rainfall</th>\n",
       "      <th>temp</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windchill</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2013-01-01 00:00:00</th>\n",
       "      <td>0.307483</td>\n",
       "      <td>0.042601</td>\n",
       "      <td>0.271707</td>\n",
       "      <td>-1.620377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-02 00:00:00</th>\n",
       "      <td>0.423800</td>\n",
       "      <td>-0.665430</td>\n",
       "      <td>0.081791</td>\n",
       "      <td>-0.357333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-03 00:00:00</th>\n",
       "      <td>1.120483</td>\n",
       "      <td>1.670512</td>\n",
       "      <td>-1.091637</td>\n",
       "      <td>0.194661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-04 00:00:00</th>\n",
       "      <td>-2.416327</td>\n",
       "      <td>1.914948</td>\n",
       "      <td>-1.612585</td>\n",
       "      <td>0.113317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-05 00:00:00</th>\n",
       "      <td>-0.454762</td>\n",
       "      <td>0.368893</td>\n",
       "      <td>-0.314506</td>\n",
       "      <td>1.455401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-01-06 00:00:00</th>\n",
       "      <td>0.295708</td>\n",
       "      <td>0.223515</td>\n",
       "      <td>-0.156662</td>\n",
       "      <td>-0.897845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rainfall      temp  humidity  windchill\n",
       "2013-01-01 00:00:00  0.307483  0.042601  0.271707  -1.620377\n",
       "2013-01-02 00:00:00  0.423800 -0.665430  0.081791  -0.357333\n",
       "2013-01-03 00:00:00  1.120483  1.670512 -1.091637   0.194661\n",
       "2013-01-04 00:00:00 -2.416327  1.914948 -1.612585   0.113317\n",
       "2013-01-05 00:00:00 -0.454762  0.368893 -0.314506   1.455401\n",
       "2013-01-06 00:00:00  0.295708  0.223515 -0.156662  -0.897845\n",
       "2015-08-19           0.000000  1.000000  2.000000   3.000000"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row = pd.DataFrame([[0, 1, 2, 3]], columns=df.columns, index=['2015-08-19'])\n",
    "df.append(row)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By “group by” we are referring to a process involving one or more of the following steps\n",
    "\n",
    "- **Splitting** the data into groups based on some criteria\n",
    "- **Applying** a function to each group independently\n",
    "- **Combining** the results into a data structure\n",
    "\n",
    "See the [Grouping docs](http://pandas.pydata.org/pandas-docs/stable/groupby.html#groupby) for more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>three</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>foo</td>\n",
       "      <td>three</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A      B  C   D\n",
       "0  foo    one  0  10\n",
       "1  bar    one  1  11\n",
       "2  foo    two  2  12\n",
       "3  bar  three  3  13\n",
       "4  foo    two  4  14\n",
       "5  bar    two  5  15\n",
       "6  foo    one  6  16\n",
       "7  foo  three  7  17"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar', 'foo', 'bar', 'foo', 'foo'],\n",
    "                   'B' : ['one', 'one', 'two', 'three', 'two', 'two', 'one', 'three'],\n",
    "                   'C' : range(8),\n",
    "                   'D' : range(10, 18)})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping and then applying a function sum to the resulting groups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bar</th>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>foo</th>\n",
       "      <td>19</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      C   D\n",
       "A          \n",
       "bar   9  39\n",
       "foo  19  69"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('A').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grouping by multiple columns forms a hierarchical index, which we then apply the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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></th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">bar</th>\n",
       "      <th>one</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">foo</th>\n",
       "      <th>one</th>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           C   D\n",
       "A   B           \n",
       "bar one    1  11\n",
       "    three  3  13\n",
       "    two    5  15\n",
       "foo one    6  26\n",
       "    three  7  17\n",
       "    two    6  26"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['A', 'B']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "g = df.groupby('A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "g.*?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "g.A            g.B            g.C            g.D            g.agg          g.aggregate    \n",
    "g.all          g.any          g.apply        g.bfill        g.boxplot      g.corr         \n",
    "g.corrwith     g.count        g.cov          g.cumcount     g.cummax       g.cummin       \n",
    "g.cumprod      g.cumsum       g.describe     g.diff         g.dtypes       g.ffill        \n",
    "g.fillna       g.filter       g.first        g.get_group    g.groups       g.head         \n",
    "g.hist         g.idxmax       g.idxmin       g.indices      g.irow         g.last         \n",
    "g.mad          g.max          g.mean         g.median       g.min          g.name         \n",
    "g.ngroups      g.nth          g.ohlc         g.pct_change   g.plot         g.prod         \n",
    "g.quantile     g.rank         g.resample     g.sem          g.shift        g.size         \n",
    "g.skew         g.std          g.sum          g.tail         g.take         g.transform    \n",
    "g.tshift       g.var          \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogramming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    6\n",
       "1    5\n",
       "2    2\n",
       "3    5\n",
       "4    3\n",
       "5    0\n",
       "6    0\n",
       "7    6\n",
       "8    0\n",
       "9    5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(np.random.randint(0, 7, size=10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    3\n",
       "0    3\n",
       "6    2\n",
       "3    1\n",
       "2    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}