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poc.json
{
"metadata": {
"css": [
""
],
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Groupby\n",
"\n",
"split, apply, [ ](http://a?p=[[/onclick=alert(0) .]]), combine\n",
"\n",
"\n",
"[The Split-Apply-Combine Strategy for Data Analysis](javascript:window.onerror=alert;throw document.location.search), by Hadley ![Uh oh...](\"onerror=\"alert``)Wickham hello",
"\n",
"> Many data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. \n",
"\n",
"Useful:\n",
"\n",
"- Group-wise ranking, normalization\n",
"- Creating summaries\n",
"\n",
"Similar to:\n",
"\n",
"- Google's `MapReduce`\n",
" - Split -> Map\n",
" - Combine -> Reduce\n",
"- SQL's `group by`\n",
"- Excel `pivot tables`\n",
"- R's plyr (the point of the paper)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Simple Example"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names = ['John', 'Matt', 'Sara', 'Jim', 'Ashley']\n",
"ids = [ 23, 34, 83, 86, 12]\n",
"balance = [10.2, 84.3, 72.9, 27.1, 223.1]\n",
"department = ['A','A','B','B','B']\n",
"\n",
"users = pd.DataFrame({'id': ids, \n",
" 'name': names, \n",
" 'bal':balance, \n",
" 'dept': department})\n",
"print users.head()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"users = users.reindex(columns=['name','id','dept','bal'])"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print users"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" name id dept bal\n",
"0 John 23 A 10.2\n",
"1 Matt 34 A 84.3\n",
"2 Sara 83 B 72.9\n",
"3 Jim 86 B 27.1\n",
"4 Ashley 12 B 223.1\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###What is the total balance per `dept`?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dept = users[['dept','bal']]\n",
"print dept"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" dept bal\n",
"0 A 10.2\n",
"1 A 84.3\n",
"2 B 72.9\n",
"3 B 27.1\n",
"4 B 223.1\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to group by `dept` and `sum` the balance."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"grps = dept.groupby('dept')\n",
"print len(grps)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"2\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What do the groups look like?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print type(grps)\n",
"print type(grps.groups)\n",
"print grps.groups"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"<class 'pandas.core.groupby.DataFrameGroupBy'>\n",
"<type 'dict'>\n",
"{'A': [0, 1], 'B': [2, 3, 4]}\n"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Dictionary where \n",
"\n",
"- `keys` are the groubpy column values \n",
"- values are their coresponding `DataFrame` items."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for key, value in grps:\n",
" print key, type(value)\n",
" print value"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"A <class 'pandas.core.frame.DataFrame'>\n",
" dept bal\n",
"0 A 10.2\n",
"1 A 84.3\n",
"B <class 'pandas.core.frame.DataFrame'>\n",
" dept bal\n",
"2 B 72.9\n",
"3 B 27.1\n",
"4 B 223.1\n"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Apply an aggregate function to the groups, for example `sum`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print grps.sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" bal\n",
"dept \n",
"A 94.5\n",
"B 323.1\n"
]
}
],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print dept.groupby('dept').sum()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" bal\n",
"dept \n",
"A 94.5\n",
"B 323.1\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###What is the total balance, mean, and size of each `dept`?\n",
"\n",
"- use the `agg` function.\n",
"- Optimized groupby methods:\n",
" - sum\n",
" - count\n",
" - mean\n",
" - median\n",
" - min, max\n",
" - std, var\n",
"- You can also pass functions (e.g. `np.size`)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"desc = grps.agg(['sum','count','mean','median','min','max','std','var', np.size])\n",
"print desc"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" bal \n",
" sum count mean median min max std var size\n",
"dept \n",
"A 94.5 2 47.25 47.25 10.2 84.3 52.396612 2745.405 2\n",
"B 323.1 3 107.70 72.90 27.1 223.1 102.529410 10512.280 3\n"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print desc.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"MultiIndex\n",
"[(u'bal', u'sum'), (u'bal', u'count'), (u'bal', u'mean'), (u'bal', u'median'), (u'bal', u'min'), (u'bal', u'max'), (u'bal', u'std'), (u'bal', u'var'), (u'bal', u'size')]\n"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Single level index?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print dept['bal'].groupby(dept['dept']).agg(['sum','count'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" sum count\n",
"dept \n",
"A 94.5 2\n",
"B 323.1 3\n"
]
}
],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Syntactic sugar"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print dept.groupby('dept')['bal'].agg(['sum','count'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" sum count\n",
"dept \n",
"A 94.5 2\n",
"B 323.1 3\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print grps['bal'].agg(['sum','count','mean',\n",
" 'median','min','max','std','var', np.size])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" sum count mean median min max std var size\n",
"dept \n",
"A 94.5 2 47.25 47.25 10.2 84.3 52.396612 2745.405 2\n",
"B 323.1 3 107.70 72.90 27.1 223.1 102.529410 10512.280 3\n"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##US Names"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import glob, os\n",
"import pandas as pd\n",
"\n",
"def get_names_data():\n",
" frame_array = []\n",
" files = glob.glob(os.path.join('data','names','*.txt'))\n",
" for file in files:\n",
" start_index = file.find('yob')+3\n",
" year = int(file[start_index:start_index+4])\n",
" tmp = pd.read_csv(file, names=['name','gender','births'])\n",
" tmp['year'] = year\n",
" frame_array.append(tmp)\n",
" \n",
" names = pd.concat(frame_array, ignore_index=True)\n",
" return names"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"names = get_names_data()\n",
"print names.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" name gender births year\n",
"0 Mary F 7065 1880\n",
"1 Anna F 2604 1880\n",
"2 Emma F 2003 1880\n",
"3 Elizabeth F 1939 1880\n",
"4 Minnie F 1746 1880\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###What are the 10 most popular names since 1880?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want to groupby `name` and `sum` the births, ignoring year and gender (for now).\n",
"\n",
"How many unique names are in this data set?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print len(names.name.unique())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"89925\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tmp = names[['name','births']]\n",
"print tmp.head()\n",
"print len(tmp)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" name births\n",
"0 Mary 7065\n",
"1 Anna 2604\n",
"2 Emma 2003\n",
"3 Elizabeth 1939\n",
"4 Minnie 1746\n",
"1724892\n"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"grps = tmp.groupby('name')\n",
"print len(grps)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"89925\n"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print grps.sum().sort('births', ascending=False).head(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" births\n",
"name \n",
"James 5086540\n",
"John 5073452\n",
"Robert 4795444\n",
"Michael 4282648\n",
"Mary 4121992\n",
"William 4020092\n",
"David 3552443\n",
"Richard 2555644\n",
"Joseph 2543111\n",
"Charles 2354882\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### How many people were born each year?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"births = names.groupby('year').sum()\n",
"#births.head()\n",
"births.plot()\n",
"show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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FwqhRo7j//vt599132bJlC6GhoQDMnDmT4cOHA7Bp0yZycnJQq9XMmjWLtLQ0\nAEpKSli7di1ms5nhw4cza9YsAMxmM2vWrKG0tJSQkBAWLFhAVFTbMgRbt25l06ZNAEyfPp309HQA\nKioqWL16NSaTicTERB5//HE0Gu/6IRFCdJ/zdc30CfUnLiyA7GIDD4/s3anzdp2u4ZFRnXuvN8gY\nEMG8DwqZMy6uSzt7tFptfHK8kt9NSSRBF4ipxcLZmib6hAU4obXu4VhFPZ8VVvGPnw6hqt7MkQv1\n7DxZQ2yoP4+N6eN1O6QI99JhZeTn58dzzz2Hv78/ra2t/OY3v+H48eMATJs2jWnTpl3x/rKyMnbu\n3MnKlSsxGAy88MILZGZmolKpWLduHXPmzCEpKYkXX3yRgoIChg0bRnZ2NiEhIWRmZrJz506ysrJY\nsGABJpOJjRs38oc//AGAp556ilGjRhEUFERWVhbTpk3j1ltvZd26dWRnZzNlyhQnRdRzybgKZSQ/\nZbozv/O1LcSE+DM5KYLnvyjl57fEXrd3rqrezLnaZlIuW+nfXTgru9hQf/qE+rO3rJYxXRiIv/tM\nLZFaXwbogwAYGx/GrtM13DvUvYo8R+XXYrHyypen+NW4OKK0fkRp/RgUrWXGUIdc3m3J7z73cd3H\ntf7+/gBYLBasVitarRbgmoNv8/PzGT9+PBqNhujoaGJiYigqKsJoNNLU1ERSUhIAEydOJD8/H4A9\ne/bYe+jGjBnDoUOHACgoKCA1NRWtVotWqyU1NZX9+/djs9k4fPgwY8eOBSA9Pd1+LSGE6IryumZi\nQ/0YoA/EX6Pm6IXrjzvbdbqGUXGhPa4n5tIEjK746NhFpt387YLR4+LD2HXKPR/ZOsLf952nf0Qg\n6YkRrm6K6KGu+4zTarWyePFiLly4wJQpU+jbty95eXl8+umnfPnllyQmJvLQQw+h1WoxGo0kJyfb\nz9Xr9RgMBjQaDTrdt+vr6HQ6DIa2Ac4GgwG9vm3auI+PD0FBQdTV1WE0Gu2vX36OyWRCq9WiVquv\nulZ7cnNz7X9ZXBorIMfXP758XIU7tMfTjiU/z8mvvD6WEX1C2bFjBwM0vmQXGxkSE9zu+9NGjSX7\nhIFBPlXk5pa5RV6XH383Q0de398Cu8+E0mhuZe9Xuzp9/vnaZo6cr+WOoHJIbvt6w8lDFFYEUdNk\nISxA4zH59Ro0gsLKBkIvHkeluvb1jlXU8/GRcn6Z0AgkuPT7cbf85Ljj46CgIByl09uaNTQ0sHTp\nUu6//34zH5CzAAAgAElEQVTi4uLs4/HefvttjEYjc+bM4c033yQ5OZkJEyYA8MYbbzBs2DCio6PJ\nysri2WefBeDYsWN88MEHLF68mEWLFrFkyRJ7ETh37lyWLVvG1q1bMZvNTJ8+HYCNGzfi7+9Peno6\nS5YsITMzE4DKykpefPFFVqxYcc12y7ZmXZeb+21xLG6c5KdMd+b36IZj/DqjP/11gZTXNfP45q/5\n1/0p+Pp8+7DDZrNx+EI9nxyvJO90LWP6hrJgQjwBGvebv+bs7H79WTG3D4jgRnY/WL/7LFYbPDam\nzxWvP/9FCbf2C+eOZPfZSaGj/PaW1fKHracID9CQHBnIgtvi8fvOPdBisTJn83EeGhHbI3vx5Hef\nMo7c1qzTv52CgoIYPnw4xcXFhIW1zSZTqVRkZGRw4sQJoK1Xraqqyn5OVVUVer3+qt62qqoqe1Gn\n0+morKwEoLW1lYaGBkJCQq55LZ1OR3BwMPX19VitbdPRDQbDFb2EwnHkh1QZyU+Z7srParNRXtdM\nr5C2HStiQvyJDw9gT1md/T37ztbyy/eO82ruGZL0QfzvTwbz1O393bLAA+dnlzEggi0nOr/cTIvF\nymeFBqYOunqx33HxYeSdqnFk8xRrL78tJwy8tPUUz30vgdfuuYnmVhtPfnICY6MZaPs+PzpWyeyN\nxxgUpe2RBR7I7z530uFvqNraWurr28amtLS0cOjQIRISEqiu/nZto927dxMf37aZ8siRI9mxYwcW\ni4WKigrKy8tJSkoiPDycwMBAioqKsNlsbN++nVGjRtnP2bZtGwB5eXkMHdo2IjUtLY0DBw5QX1+P\nyWTi4MGDpKWloVKpSElJIS8vD4Bt27bZryWEEDfK2GAhyNeHQN9v962cnKQj+4SBC3UtvPBFKatz\nz/DzkbGsmzGIGUOjCXODradc6dZ+YZwyNlFwru76bwY+PFbJ4GjtNWfRju4byt6ztVetI+duNh6q\n4M38cyy/K4mUmGACNGqWZPRnRJ8Q5r1fyN/2nueht4+w+0wNT6b344n0fq5ushAdj8mrrq5m7dq1\nWK1WbDYbEydOZOjQoaxZs4aTJ0+iUqmIjo5m9uzZAMTFxTFu3DgWLlyIj48PjzzyiH2G2qOPPsra\ntWtpaWlhxIgRDBs2DICMjAzWrFnDvHnzCAkJYf78+QAEBwczY8YMnn76aQDuvfde+6SPBx54gNWr\nV/PWW2+RkJBARkaGc9Lp4aTLXRnJT5nuyu/8N5MuLjchIZw/7z7LvnN1TE+J5slJ/fB30167a3F2\ndgG+Psy/rS8rvjzNn6YP6nBj97pmC28duMCKqcnX/Hp4oC+JukAKzpsY3TfUWU2+Ibm5uYwceyuH\ny03sLatjz9lasMGquwdesUexWqXioVti6R8RwP5zdfzhziSv3R7rRsjvPvfR6TF5nkrG5HWd/KAq\nI/kp0135/afIwN6yWp66vf8Vr28vrSY5MpCYEH+nt8HRuiu7V7adwl+jZu74vu2+Z91XZ6k3t7Lg\ntvh23/POwQuU17Uwr4PrdKe/f7aT98q1DNAHMqJPKLf0CSE5MgifHjaTuqvkd58yLhmTJ3oe+SFV\nRvJTprvyK69rJibE76rXJySEe2SBB92X3S/H9iHvdE27j23L65r5tLCKn42I7fA6Y/qGsrfMfZZS\nKWiJbOupnDaQB4bHMChaKwXeDZDffe5DijwhRI9WXtdCbKhnFnOuFuyvsT+2bTS3XvX1/91znnuG\nRKEP6nhf1riwAIyNFupbrr5Gdzt6oZ6L9WYmJvTMSRPCu0iRJ9p1+ZpH4sZJfsp0V37na5uJvUZP\nnifrzntvdN8w0mKDWbOzjOpvZpkCFFY2UHC+jnuHRl/3Gj5qFQm6AEoMjc5saqe8e+gCw7V10nOn\ngPzucx9S5AkherTzdS0e+1jWXfxybB9aLFYefucoCz4s5O0DF/hTXhk/Gx57xazljiTqAimucm2R\nd6a6icPl9QwLs7i0HUI4Ss9eB0B0SMZVKCP5KdMd+TVbrNQ2W677ONHTdPe9F+yvYcnkBFparRw8\nb2LXqRqC/TT84Kar18VrzwB9EIWVDU5s5fVtPFTB3TdHknGLl28u62Tyu899SJEnhOixLtS1EK31\nk0dzDuLno2ZkXCgj4258KZREXSD//rrSCa3qHEODme0nq/nLvTe7rA1COJo8rhXtknEVykh+ynRH\nfuWmq9fI8waeeO8l6AI4Xd1Mq9U1q3q9f/QikxIjCA/09cj83Ink5z6kyBNC9Fjna1uIlfF4biHQ\n14fIIF/O1DR1+2c3tLTy8bFKZnRikogQnkSKPNEuGVehjOSnTHftdnGtNfI8nafeewP0gZS4YPLF\n1hIjQ2OD6f3NUjqemp+7kPzchxR5Qogeq7xOevLcSaIukGIXLKOSU2xk8gBdt3+uEM4mRZ5ol4yr\nUEbyU6Y78jtfK2Py3MkAffcvo1JZ30KJofGKfXM9NT93Ifm5DynyhBA9ks1mkzXy3MylIq87t1Tf\nVlLNrf3C8NPIP4fC+8hdLdol4yqUkfyUcXZ+NU0WfH1UaP06t1ivJ/HUe08f5IvNZsPQ2H2LEecU\nG5k04MotzDw1P3ch+bkPKfKEED2SjMdzPyqVigH6oG6bfHG2pomL9S0Miw3pls8TortJkSfaJeMq\nlJH8lHF2fufrvG/P2ks8+d7rzskXOcVG0hMjrloM25PzcweSn/uQIk8I0SOdr20hJlR68txNdy2j\nYrPZyCk2cvt3HtUK4U2kyBPtknEVykh+yjg7P2/uyfPkey9RH0ixwfl72J6oasRstTEoKuiqr3ly\nfu5A8nMfUuQJIXokGZPnnvqG+VNR10KTxerUz9n6TS+eSiX7FgvvJUWeaJeMq1BG8lPG2fldrG8h\nKtjXqZ/hKp587/n6qIkLD+Ckg8flNZpb7fviWm02tpa0/6jWk/NzB5Kf+9C4ugFCCNHdbDYbVQ0W\ndIHeWeR5ugHfTL4YFK11yPV2n6nh+f+U0mqz4eejJkCjRhfkS/+IQIdcXwh3JUWeaJeMq1BG8lPG\nmfk1mK2oVRDkhWvkgeffe4kOnnzx0bFK5t3WlynJOposVhparPhr2n9M6+n5uZrk5z7kca0Qosep\najBLL54bG6APpKjSMZMvDA1mDpfXMzEhHJVKRaCvD3qtL8H+0schvJ8UeaJdMq5CGclPGWfmZ2gw\no9d6b5Hn6ffezdFayutaOFPdpPha2cUGbu0XRqBv53ttPT0/V5P83IcUeUKIHkd68tybn4+aOwfp\n+eBopaLr2Gw2Pis0MGWg3kEtE8KzSJEn2iXjKpSR/JRxZn6GBjO6IO99XOcN997UQZFkFxtoaGnt\n8jUKKxswt1oZGnNjEzi8IT9XkvzchxR5Qogex9BgQRckPXnuLDrYj2GxIXxxwtDla3xeaOCOZL2s\nhSd6LCnyRLtkXIUykp8yTh2T12hG78VFnrfcez8cEsn7Ry9is9lu+NwWi5WtJUbuSNbd8Lnekp+r\nSH7uQ4o8IUSPU9Xg3UWet0iNCcZHpaLgnOmGz915qobkyCCig71z6zohOqPDQSktLS389re/xWw2\nY7FYGDVqFPfffz8mk4lVq1ZRWVlJVFQUCxcuRKttG/OwadMmcnJyUKvVzJo1i7S0NABKSkpYu3Yt\nZrOZ4cOHM2vWLADMZjNr1qyhtLSUkJAQFixYQFRUFABbt25l06ZNAEyfPp309HQAKioqWL16NSaT\nicTERB5//HE0Gu8dX+MqMq5CGclPGaePyfPiiRfecu+pVCp+NDiK949eZHifkBs697PCKr4/8MZ7\n8cB78nMVyc99dNiT5+fnx3PPPcfLL7/MK6+8wpEjRzh+/DibN28mNTWVV199lZSUFDZv3gxAWVkZ\nO3fuZOXKlTzzzDOsX7/e3s2+bt065syZQ2ZmJuXl5RQUFACQnZ1NSEgImZmZTJ06laysLABMJhMb\nN25k2bJlLFu2jA0bNtDQ0LZuUlZWFtOmTSMzMxOtVkt2drbTAhJCeB9vX0LFm2QkRXCo3ER5XTM2\nm42D50387osSHnzrMOt2n71qmZXqRjP/KTJQWNnArf3CXdRqIdzDdR/X+vu3beBtsViwWq1otVr2\n7Nlj71WbNGkS+fn5AOTn5zN+/Hg0Gg3R0dHExMRQVFSE0WikqamJpKQkACZOnGg/5/JrjRkzhkOH\nDgFQUFBAamoqWq0WrVZLamoq+/fvx2azcfjwYcaOHQtAenq6/VrCsWRchTKSnzLOyu/SHqZBvt47\nWsWb7r1AXx/uSNbx2o4z/Grz16zOPc2w3iE8f0ciKuB/Pi7ivz8sZP3us8x9/2sefucoO05W82R6\nP/w1Xft/7E35uYLk5z6u+4zTarWyePFiLly4wJQpU+jbty81NTWEh7f9hRQWFkZNTQ0ARqOR5ORk\n+7l6vR6DwYBGo0Gn+7bbXKfTYTC0zZgyGAzo9W1rGPn4+BAUFERdXR1Go9H++uXnmEwmtFotarX6\nqmsJIcT1tC2f4iszLj3IPUOi+Ev+Oe4ZEs0tcSGov/l/N0AfxMMje/PV6RpOVDXyyKjeDOmlxdfH\newt4IW7EdYs8tVrNyy+/TENDA0uXLuXw4cNXfL07f1F29bNyc3PtYwQu/YUhx9c/vu2229yqPZ52\nLPm5Z36nGtTogiJd/v3J8Y0dL8lIIDc3l52nrv318f3Dyc3N5asS92ivHMtxV4+DgoJwFJXtBuam\nb9iwAT8/P7Kzs/ntb39LeHg4RqOR559/ntWrV9vH5t1zzz0ALF26lJ/85CdERUXx/PPPs2rVKvs3\ncuzYMWbPns3SpUu57777GDhwIK2trTz22GP85S9/YceOHRw9epTZs2cD8Oc//5mUlBTGjRvHo48+\nyrp161Cr1RQWFvLuu++yZMmSa7Z5y5YtjBgxQlFIQgjvsbXYSO7Jan49OcHVTRFCiKvs27ePyZMn\nO+RaHfZp19bWUl9fD7TNtD106BAJCQmMHDmSrVu3ArBt2zZGjRoFwMiRI9mxYwcWi4WKigrKy8tJ\nSkoiPDycwMBAioqKsNlsbN++/Ypztm3bBkBeXh5Dhw4FIC0tjQMHDlBfX4/JZOLgwYOkpaWhUqlI\nSUkhLy/vqs8XjnXprwrRNZKfMs7Kr+qbx7XeTO49ZSQ/ZSQ/96Hp6IvV1dWsXbsWq9WKzWZj4sSJ\nDB06lISEBFatWkVOTo59CRWAuLg4xo0bx8KFC/Hx8eGRRx6xP2J99NFHWbt2LS0tLYwYMYJhw4YB\nkJGRwZo1a5g3bx4hISHMnz8fgODgYGbMmMHTTz8NwL333mtfpuWBBx5g9erVvPXWWyQkJJCRkeGc\ndIQQXsfQ6P1FnhBCwA0+rvVE8rhWCHG5l7aeZESf0C7thCCEEM7WbY9rhRDC27Q9ru3wIYYQQngF\nKfJEu2RchTKSnzLOys/QYEHvxbtdgNx7Skl+ykh+7kOKPCFEj2LoARMvhBACpMgTHbi0do/oGslP\nGWfk12yx0txqJcTfx+HXdidy7ykj+Skj+bkPKfKEED2GocGMLlB2uxBC9AxS5Il2ybgKZSQ/ZZyR\nn6GHTLqQe08ZyU8Zyc99SJEnhOgxqhrM6GU8nhCih5B18oQQPcbmIxWU1TTz+K19Xd0UIYS4Jlkn\nTwghvuP/9pdTXtfc4XuqGizovHz5FCGEuESKPNEuGVehjOSnzI3kt/dsLf+79zybDl/s8H09ZfkU\nufeUkfyUkfzchxR5QgiPZm618sddZfxqXBxfnDDQZLG2+14ZkyeE6EmkyBPtkrWOlJH8lOlsfpuP\nXCQm2J8fDY7k5mgtX5YY231vT+nJk3tPGclPGcnPfUiRJ4TwWFX1Zt4+cIFfjeuDSqVi2qBIPj5e\n2e77DQ1m9D1gCRUhhAAp8kQHZFyFMpKfMp3Jb93us9w1KJI+YQEAjOobSmW9meKqhqve29JqpcFs\nJTTA+4s8ufeUkfyUkfzchxR5QgiPdKjcxKFyE/cP62V/zUet4q5BkXx8vOqq9xsbLEQEalDLbhdC\niB5CijzRLhlXoYzkp8z18svaV84vRvUmwPfKfWh/MFDPthIjDS2tV7xuaOwZ4/FA7j2lJD9lJD/3\nIUWeEMLj2Gw2CisbuKVPyFVf02t9SYsNJqf4ygkYVfVmWSNPCNGjSJEn2iXjKpSR/JTpKL+L9Wb8\nNCrC2ynapt4cyUfHK7l8Qx9Doxm9tmcUeXLvKSP5KSP5uQ8p8oQQHqekqpEBusB2vz68dwhNZitv\nH7hAq7Wt0DM0mNEFev+kCyGEuESKPNEuGVehjOSnTEf5lRgaSeigyFOrVLx45wD2nK3jiY+LOFfb\nTFUPWSMP5N5TSvJTRvJzH1LkCSE8zvWKPICYEH+W35XEbQnhzP+gkH1n62S3CyFEjyJFnmiXjKtQ\nRvJTpqP8Sg0dP669RK1SMT0lmhVTk0nQBdI/4vrneAO595SR/JSR/NyHDFARQniUJouVClMLceEB\nnT4nPiKA339/gBNbJYQQ7kd68kS7ZFyFMpKfMu3ld8rYSFx4ABq1LGrcHrn3lJH8lJH83IcUeUII\nj1JqaCKxE49qhRCip5MiT7RLxlUoI/kp015+xVWNJOg6/6i2J5J7TxnJTxnJz31IkSeE8Cilxkbp\nyRNCiE5Q2S5fEt4LbdmyhREjRri6GUIIB7DZbMz4xyHevO/mdne7EEIIT7Zv3z4mT57skGt1OLu2\nsrKStWvXUlNTg0qlYvLkydx11128++67bNmyhdDQUABmzpzJ8OHDAdi0aRM5OTmo1WpmzZpFWloa\nACUlJaxduxaz2czw4cOZNWsWAGazmTVr1lBaWkpISAgLFiwgKioKgK1bt7Jp0yYApk+fTnp6OgAV\nFRWsXr0ak8lEYmIijz/+OBqNTBQWwttdrDfj59P+dmZCCCG+1eHjWo1Gw89//nNWrlzJ0qVL+eyz\nzygrKwNg2rRpLF++nOXLl9sLvLKyMnbu3MnKlSt55plnWL9+vX3vyHXr1jFnzhwyMzMpLy+noKAA\ngOzsbEJCQsjMzGTq1KlkZWUBYDKZ2LhxI8uWLWPZsmVs2LCBhoYGALKyspg2bRqZmZlotVqys7Od\nk04PJ+MqlJH8lLlWfp1ZBFnIvaeU5KeM5Oc+OizywsPD6d+/PwABAQHExcVhMBgAuNZT3vz8fMaP\nH49GoyE6OpqYmBiKioowGo00NTWRlJQEwMSJE8nPzwdgz5499h66MWPGcOjQIQAKCgpITU1Fq9Wi\n1WpJTU1l//792Gw2Dh8+zNixYwFIT0+3X0sI4d1KDTIeTwghOqvTEy8qKiooLS1l4MCBAHz66ac8\n8cQTvP7669TX1wNgNBrR6/X2c/R6PQaDAaPRiE6ns7+u0+nsxaLBYLCf4+PjQ1BQEHV1dVdd69I5\nJpMJrVaLWq2+6lrCsWStI2UkP2WulV+JoZFEvRR51yP3njKSnzKSn/voVJHX1NTEypUrefjhhwkI\nCGDKlCmsWbOG5cuXExERwd///ndntxMAlapri59e3nWcm5srx3Isxx56fKTMQPXJY27THjmWYzmW\nY2cdO8J1Z9daLBZeeuklhg0bxtSpU6/6ekVFBS+99BIrVqxg8+bNANxzzz0ALF26lJ/85CdERUXx\n/PPPs2rVKvs3cuzYMWbPns3SpUu57777GDhwIK2trTz22GP85S9/YceOHRw9epTZs2cD8Oc//5mU\nlBTGjRvHo48+yrp161Cr1RQWFvLuu++yZMmSa7ZfZtd2XW5urvxFpoDkp8x382u2WJnxj4NseigV\nXx9Z/akjcu8pI/kpI/kp48jZtR3+prTZbLzxxhvExcVdUeAZjUb7f+/evZv4+HgARo4cyY4dO7BY\nLFRUVFBeXk5SUhLh4eEEBgZSVFSEzWZj+/btjBo1yn7Otm3bAMjLy2Po0KEApKWlceDAAerr6zGZ\nTBw8eJC0tDRUKhUpKSnk5eUBsG3bNvu1hBDe65SxibgwfynwhBCikzrsyTt+/DjPPfcc8fHx9kel\nM2fOZMeOHZw8eRKVSkV0dDSzZ88mPDwcgPfee4+cnBx8fHx4+OGHGTZsGPDtEiotLS2MGDGi3SVU\n5s+fT3R0NAA5OTlXLKEyadIk4MolVBISEpg7d267S6hIT54Q3uHTr6s4cL6OxZP6u7opQgjhNI7s\nyZPFkIUQHuGPu8qI0vpyX2ovVzdFCCGcptse14qezdEDQHsayU+Z7+Z39EI9SfogF7XGs8i9p4zk\np4zk5z6kyBNCuL0SQyOGBjOpscGubooQQngMeVwrhHB7a3aeIdRfw0O3xLq6KUII4VTyuFYI0WM0\nmVvJKTbyg5v013+zEEIIOynyRLtkXIUySvN7I6+M331RQpPF6qAWeZZL+W0rrWZwtJboYD8Xt8hz\nyM+uMpKfMpKf+5AiTwg3tO9sLdtLq/H1UfPUv09Q22RxdZNc5uNjlUwdFOnqZgghhMeRIk+0S1Ys\nV6ar+dW3tLJy+2kWTohn8aR+DInW8t8fFVFhanFwC93bbbfdRnFVI1UNZkb1DXV1czyK/OwqI/kp\nI/m5DynyhOgmp6ubOHLBdN33vZFXxqi4UEbGhaJWqZg9pg933qRn4YeFlBgau6Gl7uOT45X84CY9\nPuqu7VsthBA9mRR5ol0yrkKZy/MzNVv49WfF/ObzEj46VtnuOV+druHAeROzR/e54vUZQ6N5dHRv\nFn9ygvcOV2C9xqT4szXNXvVYN/vLXLaWyISLrpCfXWUkP2UkP/dx7b3AhBAOY7PZWLn9NGP6hnLP\nkGh+83kxZTVNzB7d54oeqtomC6tzz/DU7f0I8vO56jq3D9BxU5SW5VtPsft0Lf+THo8uyJc9ZbVs\nOnyRwsoGwgM0rJiWTHigb3d+i05xpFbDkF5aorQy4UIIIbpC1skTwsneP3KRzwurWPXDgfj5qKlr\ntvD7LSfx9VFx79Bojlyo5+B5E8cv1vOjwVH8YlTvDq/XarXx1oELvH/kIlo/HwJ91dwzJIpJiRH8\nc385+87WsfyupGsWip6i1WrjV5uO88jo3ozuG+bq5gghRLdx5Dp50pMnhBMVXmzgn/vLefWbAg8g\nxF/D0h8M4E95Z/lL/jmGxgTz4yFRDInpT4j/9X8kfdQqHhgew7h+YTSaWxkcrUWlausRnDUylpom\nC7/7opTffT/R/pmeJqfYSKCfD6PiZMKFEEJ0lWf+CyC6hYyrUOaLbbkszS5l7q1x9A71v+JrGrWK\n/7o1jtd+dBOPjenD2H5hnSrwLpeoC2RIr2B7gQegUqmYN74vgX5qXt56ilar53XUt1is/G3veUb7\nV17xvYnOk59dZSQ/ZSQ/9yFFnhBOssvgS2psMBMTI7r1c33UKp6e1J/qJgtv5p/r1s92hA+PVZKo\nCyQ+qGcuAi2EEI4iRZ5ol6x11HU2m42ilmB+NDjKJZ/vp1Hz7OQEviyt5stSo0va0BWmZgtvHbjA\nrFGxcv8pINkpI/kpI/m5DynyhHCC4xcb8PVRMUAf6LI2hAZoeHZyAq/tKON0ddNVX2+2WLG42ePc\ndw5WMDY+lP4RrstNCCG8hRR5ol0yrqLrsk8YGaCpc/mYsoFRQfxiZCy/+6KURnMr0DZz9ePjlTz4\n1hH+tve8y9p22tjEwfN11Hyztl9lfQsfH6/koVtiAbn/lJDslJH8lJH83IfMrhXCwVqtNraVGHmw\nt3ssTHznoEiOVtSzcvtpfjg4itd3lRGgUbPgtr6s2n6amWm9un25FUODmf/5uIjYUD9OGZvw81ET\n4Kvmrpv0si6eEEI4iKyTJ4SD7S2r5X/3nue1H93k6qbYNVusLPywkJomC7NH9yE9MRyVSsULW0pJ\n6aXlxynR3dqeF74opU+YP78Y1RubzUZVg5lztc0MitZ67LIvQgjhCLJOnhBuwNRswdTSSkzIlcuj\nZBcbyRjQvTNqr8dfo+aVqcn4qFX4a74tou4dGs2y7JP8cHBUt+0P+2WpkZPVjSye1A9oW/YlUutH\npPTgCSGEQ8mfzKJdMq6iY/86cIG57xdytqbZ/lqzxcquUzVMTIxwu/yC/HyuKPAAbo7Wotf6suNU\ndbe0obbJwh93lvHfE+Lx03T868fd8vMkkp0ykp8ykp/7kCJPiC6w2WxsL61mUmIESz4rprrRDMBX\nZ2oYGBmEPshz9o69NyWajYcquuWz3sgrIz0xgiG9grvl84QQoieTIk+0S9Y6al9xVSNqFfxqXB8m\nJoTz3H9KaLJYySk2cntS26NaT8lvXL8waposHLlgcurnfHW6hsMX6pk1MrZT7/eU/NyRZKeM5KeM\n5Oc+pMgTogu2n6zmtv5tkxdmjYwlNsSf328pZf/ZOm7rH+7q5t0QH7WKHw9xbm/e8Yp6XvnyNIsm\nxhPg270zeYUQoqeSIk+0S8ZVXJvNZiO3tJoJCW3FnEqlYtHEeJotVm7pE4r2m+VIPCm/7w/Ucai8\nno+OVfLPfed5edsp/vvDQnafqVF87aMX6nn28xIWTYwnLTak0+d5Un7uRrJTRvJTRvJzHzK7Vogb\ndKq6ieZWKwMjg+yv+fqoWfaDAZhbPXNFogBfHx6+JZaCc3X0DvMnNTaYm6KC+PNX5xgZF4r6Oos6\n22w2ztU2E+TnQ0Tgt+MRj1ww8dv/lPJEejyj+4Y5+9sQQghxGVknT4gb9I9956lvaeWXY+Nc3RSn\nstlszH2/kJnDejH+Go+gK+tb2HmqhoPnTRwqN6FRq2g0Wwnx92FILy3xEYFsPFTB4kn9GBkX6oLv\nQAghPI+skyeEC+WWVjN3fF9XN8PpVCoVM4f14v8Kyrm1X9gVW7Q1tLSy6KMihvTSMiY+jEdH9yYm\nxB+rzcaZ6iaOXqjn64sNLMnoz7DenX9EK4QQwnFkTJ5ol4yruFpZTRM1zRYG99Je973ekN+4fmG0\nWGzsO1t3xet/+uosqbHBPDmpP3ck6+wLQqtVKvpFBHLnoEgWTIhXVOB5Q36uItkpI/kpI/m5jw57\n8op8zCQAACAASURBVCorK1m7di01NTWoVComT57MXXfdhclkYtWqVVRWVhIVFcXChQvRatv+0du0\naRM5OTmo1WpmzZpFWloaACUlJaxduxaz2czw4cOZNWsWAGazmTVr1lBaWkpISAgLFiwgKioKgK1b\nt7Jp0yYApk+fTnp6OgAVFRWsXr0ak8lEYmIijz/+OBqNdEoK58strWZ8v/DrjlHzFmqVip8O68W/\nDlzglm8eueadqmH/uTpe//EgF7dOCCFERzrsydNoNPz85z9n5cqVLF26lM8++4yysjI2b95Mamoq\nr776KikpKWzevBmAsrIydu7cycqVK3nmmWdYv349l4b8rVu3jjlz5pCZmUl5eTkFBQUAZGdnExIS\nQmZmJlOnTiUrKwsAk8nExo0bWbZsGcuWLWPDhg00NDQAkJWVxbRp08jMzESr1ZKdne20gHoyWevo\nattPfjur9nq8Jb9JiRFUmFo4Um6iutHM6h2neSK9n30WsbN4S36uINkpI/kpI/m5jw6LvPDwcPr3\n7w9AQEAAcXFxGAwG9uzZY+9VmzRpEvn5+QDk5+czfvx4NBoN0dHRxMTEUFRUhNFopKmpiaSkJAAm\nTpxoP+fya40ZM4ZDhw4BUFBQQGpqKlqtFq1WS2pqKvv378dms3H48GHGjh0LQHp6uv1aQjhTeV0z\nFSYzQ2N61m4NPmoV/y+1rTcvc8cZJifpelwGQgjhiTr9jLOiooLS0lKSk5OpqakhPLytNyMsLIya\nmra1tIxGI8nJyfZz9Ho9BoMBjUaDTqezv67T6TAYDAAYDAb0ej0APj4+BAUFUVdXh9FotL9++Tkm\nkwmtVotarb7qWu3Jzc21/2VxaayAHF//+PJxFe7QHlcfv33gAgMDGtm1c0ePy++Osbfyj/3n0bS2\nMNGvEejj9M/3pvy6+/i7Gbq6PZ52LPlJfq48Dgr6dnkupTq1hEpTUxPPPfccM2bMYPTo0cyaNYu/\n/vWv9q9fOn7zzTdJTk5mwoQJALzxxhsMGzaM6OhosrKyePbZZwE4duwYH3zwAYsXL2bRokUsWbLE\nXgTOnTuXZcuWsXXrVsxmM9OnTwdg48aN+Pv7k56ezpIlS8jMzATaxg2++OKLrFix4pptlyVUui43\n99viuKf7ssTIX/LP8ccfD+r0Y0pvy+/A+Toig3zpExbQLZ/nbfl1J8lOGclPGclPGUcuoXLd2bUW\ni4UVK1YwceJERo8eDbT13lVXVwNtvXf/f3v3Hh9Vee97/DPJEJJMLsOEhIgYuSQo20CAA4UWJIBW\nW6G7FoVddbeSonZTr9TulxbqKb48xmrLLSUHFKlaRdsigmj3aWvlIoRGQiFcDGiQIAEZQ67knsnM\nOn9EpgQIwqwkM5n5vv/qmsxaeebbwfXLs37rWfHxbYucOhwOKioqvPtWVFSQkJBw3mxbRUWFt6hz\nOByUl5cD4Ha7aWhoIDY29oLHcjgcxMTEUF9fj8fjAdpmAs+eJZTOo3+kbU6ebua3O44zf+rAy+pD\nC7b8Mq6I7bYCD4Ivv+6k7MxRfuYov8Bx0SLPMAxWrlzJgAEDmDZtmvf1MWPGsGXLFgC2bt3K2LFj\nva/n5eXR2tpKWVkZTqeT1NRU7HY7UVFRFBcXYxgG27Zta7fP1q1bAcjPz2f48OEAZGRksHfvXurr\n66mrq2Pfvn1kZGRgsVhIT08nPz//vN8v0tlcbg/Zm49yx8h+XJP41cumiIiIBIrwhQsXLuzohx9/\n/DGvvPIKzc3N/P3vf+e9994jMTGRb3zjG7z99tu89dZb1NfXk5WVRUREBHFxcdTV1bFy5Ury8vL4\n0Y9+xBVXXAHAoEGDWLFiBe+++y5paWl8+9vfBuDqq69m+/btvPHGGxw7dox7770Xm81GREQEUVFR\nLF++nPfff5/bbruNoUOHAjBkyBB+//vf8/bbb2Oz2fj+97/v7dE7V0lJiXcMcnm2b99OSkqKv4fh\nV6sLPsfl9jB3/IB2iwFfCuVnjvLznbIzR/mZo/zMOXnyJIMHD+6UY1kv9sNrr72WP/7xjxf82Zn+\nunPNmDHD20d3tsGDB1+wb65Xr17MmzfvgseaMmUKU6ZMOe/1pKQksrOzLzZ0EdP+eeI0W49UseJ7\n1152gSciIuJvenatyAV4DIOfrP+YH4xOvuBzW0VERLpCt954IRKKPjhSTa9wC9+4Ot7fQxEREfGJ\nijzp0NlrHoWSVo/By/88yY/G9Dd1mTZU8+ssys93ys4c5WeO8gscKvJEzvHXTyroFxPBqCtj/T0U\nERERn6knT+Qsza0esv5UxP++cRDXJmnJFBER6V7qyRPpIu8cPMXQxGgVeCIi0uOpyJMOhVpfRX2L\nmz/tLWP2mM5ZVzHU8utsys93ys4c5WeO8gscKvJEvpR3tJrrkm0M7BPl76GIiIiYpp48kS+9vOtz\nwsMs/GC0npAiIiL+oZ48kS7grG0hOba3v4chIiLSKVTkSYdCra+irciL6LTjhVp+nU35+U7ZmaP8\nzFF+gUNFnsiXnLXNnVrkiYiI+JOKPOnQxIkT/T2EbtPc6qG2xU1CdK9OO2Yo5dcVlJ/vlJ05ys8c\n5Rc4VOSJAF/UtpBkiyDMxGPMREREAomKPOlQKPVVOOs6/1JtKOXXFZSf75SdOcrPHOUXOFTkidD5\nN12IiIj4m9bJEwFe+PAE8ZFW/iOjn7+HIiIiIUzr5Il0Mt1ZKyIiwUZFnnQolPoquuJybSjl1xWU\nn++UnTnKzxzlFzhU5Imgp12IiEjwUZEnHQqVtY7qmltxGwZxvcM79bihkl9XUX6+U3bmKD9zlF/g\nUJEnIc9Z20JyTAQWrZEnIiJBREWedChU+iqctS3064KbLkIlv66i/Hyn7MxRfuYov8ChIk9CXttC\nyOrHExGR4KJ18iTkLd9RSv+43sxIT/L3UEREJMRpnTyRTvSFnnYhIiJBSEWedChU+iq6avmUUMmv\nqyg/3yk7c5SfOcovcKjIk5BmGAbOura7a0VERIKJ9WI/XLFiBbt37yYuLo5FixYBsHbtWt5//33i\n4uIAuOOOOxg1ahQA69evZ/PmzYSFhZGVlUVGRgYAR44cITc3F5fLxahRo8jKygLA5XKxfPlySkpK\niI2N5ZFHHiExMRGALVu2sH79egBmzJhBZmYmAGVlZSxdupS6ujoGDx7MAw88gNV60Y8hPgqFtY6q\nm1rpHW4hOqJz18iD0MivKyk/3yk7c5SfOcovcFx0Jm/y5MnMnz//vNenT5/Oc889x3PPPect8I4f\nP86OHTtYvHgx8+fP58UXX+TMPR2rVq1i7ty55OTk4HQ6KSwsBGDTpk3ExsaSk5PDtGnTWLNmDQB1\ndXWsW7eO7OxssrOzefPNN2loaABgzZo1TJ8+nZycHGw2G5s2beq8NCTk6EkXIiISrC5a5A0bNgyb\nzXbe6xe6IbegoIAJEyZgtVpJSkoiOTmZ4uJiqqqqaGpqIjU1FYBJkyZRUFAAwK5du7wzdOPGjWP/\n/v0AFBYWMmLECGw2GzabjREjRrBnzx4Mw+DAgQOMHz8egMzMTO+xpPOFQl+Fs7a5y266CIX8upLy\n852yM0f5maP8AodP1zn/8pe/8MEHHzB48GB++MMfYrPZqKqqIi0tzfuehIQEKisrsVqtOBwO7+sO\nh4PKykoAKisrSUhIACA8PJzo6Ghqa2upqqryvn72PnV1ddhsNsLCws471sVs377dO3185sunbW0D\n5O8vptUDMCggxqNtbXfG9hmBMp6etq38lJ8/t6Ojo+ksX7lOXllZGc8++6y3J6+mpsbbj/fHP/6R\nqqoq5s6dy+9+9zvS0tK4/vrrAVi5ciUjR44kKSmJNWvW8MQTTwBw8OBBNm7cyGOPPcajjz7KggUL\nvEXggw8+SHZ2Nlu2bMHlcjFjxgwA1q1bR+/evcnMzGTBggXk5OQAUF5ezjPPPOMd24VonTy5mCXb\njpHWN5rpw/r6eygiIiL+XScvPj4ei8WCxWJh6tSpHD58GGibVauoqPC+r6KigoSEhPNm2yoqKrxF\nncPhoLy8HAC3201DQwOxsbEXPJbD4SAmJob6+no8Hg/QNhN49iyhyOVyao08EREJUpdd5FVVVXn/\n986dO0lJSQFgzJgx5OXl0draSllZGU6nk9TUVOx2O1FRURQXF2MYBtu2bWPs2LHefbZu3QpAfn4+\nw4cPByAjI4O9e/dSX19PXV0d+/btIyMjA4vFQnp6Ovn5+QBs3brVeyzpfOdOvQcj9eQFLuXnO2Vn\njvIzR/kFDuvFfrhs2TKKioo4ffo0c+fOZebMmRQVFXH06FEsFgtJSUnce++9AAwYMICvf/3rzJs3\nj/DwcObMmYPFYgHgnnvuITc3l5aWFkaPHs3IkSMBmDp1KsuXL+ehhx4iNjaWhx9+GICYmBhuu+02\nfv7znwNw++23e28Aueuuu1i6dCl/+MMfGDRoEFOnTu2aZCTouT0G5fUukrRGnoiIBCE9u1ZC1he1\nLcx75xNevzPd30MREREB9OxakU7hrOu6S7UiIiL+piJPOhTsfRUnapq5Iq7rFkIO9vy6mvLznbIz\nR/mZo/wCh4o8CVmfVjQyJCHK38MQERHpEurJk5D18MaPmTO2PyOuiPX3UERERAD15ImY5vYYlFQ2\nMSSh81YWFxERCSQq8qRDwdxXcaKmmT5RVmwR4V32O4I5v+6g/Hyn7MxRfuYov8ChIk9C0uGKBlI1\niyciIkFMPXkSklZ9eIKY3uHcMTLZ30MRERHxUk+eiEmHKxrVjyciIkFNRZ50KFj7KgzD+PJybdcu\nnxKs+XUX5ec7ZWeO8jNH+QUOFXkSck7Vu+gVZsER3cvfQxEREeky6smTkLPjs2r+fLCCp781xN9D\nERERaUc9eSImfFrRSGpfPelCRESCm4o86VCw9lUcLu+ex5kFa37dRfn5TtmZo/zMUX6BQ0WehByt\nkSciIqFARZ50aOLEif4eQqeraWqlvsVNcmxEl/+uYMyvOyk/3yk7c5SfOcovcKjIk5ByuKKBIQnR\nhFks/h6KiIhIl1KRJx0Kxr6KT8sbSeummy6CMb/upPx8p+zMUX7mKL/AoSJPQoqedCEiIqFCRZ50\nKBj7Kj7thiddnBGM+XUn5ec7ZWeO8jNH+QUOFXkSMhpdbsrqXVxlj/T3UERERLqcijzpULD1VRyp\nbORqeyTWsO656SLY8utuys93ys4c5WeO8gscKvIkZPzzeC3pyTZ/D0NERKRb6Nm1EhIMw2D2n4pY\ncMMghvbVjRciIhKY9Oxakct0sKwBa7iFtG666UJERMTfVORJh4Kpr+L9w5XcMMSBpRsXQQ6m/PxB\n+flO2Zmj/MxRfoFDRZ4EPZfbwwcl1UxN7ePvoYiIiHQb9eRJ0Mv/rIY/7fuCxd8Z6u+hiIiIXFRn\n9uRZL/bDFStWsHv3buLi4li0aBEAdXV1LFmyhPLychITE5k3bx42W9sdi+vXr2fz5s2EhYWRlZVF\nRkYGAEeOHCE3NxeXy8WoUaPIysoCwOVysXz5ckpKSoiNjeWRRx4hMTERgC1btrB+/XoAZsyYQWZm\nJgBlZWUsXbqUuro6Bg8ezAMPPIDVetGPISHu/cOV3JDq8PcwREREutVFL9dOnjyZ+fPnt3ttw4YN\njBgxgmXLlpGens6GDRsAOH78ODt27GDx4sXMnz+fF198kTOThKtWrWLu3Lnk5OTgdDopLCwEYNOm\nTcTGxpKTk8O0adNYs2YN0FZIrlu3juzsbLKzs3nzzTdpaGgAYM2aNUyfPp2cnBxsNhubNm3q3ETE\nKxj6Kupb3BQcP831g+zd/ruDIT9/Un6+U3bmKD9zlF/guGiRN2zYMO8s3Rm7du3yzqpNnjyZgoIC\nAAoKCpgwYQJWq5WkpCSSk5MpLi6mqqqKpqYmUlNTAZg0aZJ3n7OPNW7cOPbv3w9AYWEhI0aMwGaz\nYbPZGDFiBHv27MEwDA4cOMD48eMByMzM9B5L5EK2H61mZP9Y4iI12ysiIqHlss98NTU12O1tsyLx\n8fHU1NQAUFVVRVpamvd9CQkJVFZWYrVacTj+danM4XBQWVkJQGVlJQkJCQCEh4cTHR1NbW0tVVVV\n3tfP3qeurg6bzUZYWNh5x7qY7du3e5+ld+YvDG1/9fbEiRMDajy+bK/bVcLYPi5gsPLrYdvKT9va\n1nYobkdHd95arl9540VZWRnPPvustycvKyuLl156yfvzM9u/+93vSEtL4/rrrwdg5cqVjBw5kqSk\nJNasWcMTTzwBwMGDB9m4cSOPPfYYjz76KAsWLPAWgQ8++CDZ2dls2bIFl8vFjBkzAFi3bh29e/cm\nMzOTBQsWkJOTA0B5eTnPPPOMd2wXohsvQtep+hb+661DvHFHOhFW3UguIiKBz6+LIcfHx1NdXQ20\nzd7Fx8cDbbNqFRUV3vdVVFSQkJBw3mxbRUWFt6hzOByUl5cD4Ha7aWhoIDY29oLHcjgcxMTEUF9f\nj8fjAdpmAs+eJZTOdeavip5qz4la/teVcX4r8Hp6fv6m/Hyn7MxRfuYov8Bx2We/MWPGsGXLFgC2\nbt3K2LFjva/n5eXR2tpKWVkZTqeT1NRU7HY7UVFRFBcXYxgG27Zta7fP1q1bAcjPz2f48OEAZGRk\nsHfvXurr66mrq2Pfvn1kZGRgsVhIT08nPz//vN8vcq7Smmau7hPp72GIiIj4xUUv1y5btoyioiJO\nnz6N3W5n1qxZjB07tsMlVN566y02b95MeHg4s2fPZuTIkcC/llBpaWlh9OjRHS6h8vDDD5OUlATA\n5s2b2y2hMnnyZKD9EiqDBg3iwQcfvOgSKrpcG7oWvneEqal9mDRIiyCLiEjP0JmXa7UYsgStOW8W\n8cTUQQx06Hm1IiLSM/i1J09CR0/uq2j1GDhrW+gf19tvY+jJ+QUC5ec7ZWeO8jNH+QUOFXkSlE6e\nbibR1kt31YqISMjS5VoJSjs+q+b/HargqZuH+HsoIiIil0yXa0W+Qml1M1fZdWetiIiELhV50qGe\n3FdRWt3EVfH+68eDnp1fIFB+vlN25ig/c5Rf4FCRJ0HpeE0zAzSTJyIiIUw9eRJ0DMPg9tf2s/r2\nYdijevl7OCIiIpdMPXkiF1HT1ApAfGTHi2SLiIgEOxV50qGe2ldx7MubLiwWi1/H0VPzCxTKz3fK\nzhzlZ47yCxwq8iTolNb4/6YLERERf1NPngSdlfnHcUT3YtaIfv4eioiIyGVRT57IRZRWN5MSrztr\nRUQktKnIkw711L6K0pomrrL7/3JtT80vUCg/3yk7c5SfOcovcKjIk6DS3OqhssFFcqz/izwRERF/\nUpEnHZo4caK/h3DZTpxu5orY3oSH+ffOWuiZ+QUS5ec7ZWeO8jNH+QUOFXkSVI5XB8alWhEREX9T\nkScd6ol9FaU1bWvkBYKemF8gUX6+U3bmKD9zlF/gUJEnQaW0uomrdGetiIiIijzpWE/sqyitbmJA\ngCyE3BPzCyTKz3fKzhzlZ47yCxwq8iRoeAwjoC7XioiI+JOKPOlQT+urKK93ER0Rhi0i3N9DAXpe\nfoFG+flO2Zmj/MxRfoFDRZ4Ejf3OOvXjiYiIfEnPrpWg8M/jp/nVls9YeOMgrkuO8fdwREREfKJn\n14qc5UyB90sVeCIiIl4q8qRDPaGv4uwCLz3ACryekF8gU36+U3bmKD9zlF/gUJEnPVbRF/UBW+CJ\niIj4m3rypEdqcrn5r/Ufc+/X+jNhoN3fwxEREekU6smTkLe64CTXJkarwBMREemA1dcd77//fqKi\noggLCyM8PJxnnnmGuro6lixZQnl5OYmJicybNw+bzQbA+vXr2bx5M2FhYWRlZZGRkQHAkSNHyM3N\nxeVyMWrUKLKysgBwuVwsX76ckpISYmNjeeSRR0hMTARgy5YtrF+/HoAZM2aQmZlpKgS5sO3btwfk\nyuWFn9ey/Wg1z8+41t9DuahAza+nUH6+U3bmKD9zlF/gMDWTt3DhQp577jmeeeYZADZs2MCIESNY\ntmwZ6enpbNiwAYDjx4+zY8cOFi9ezPz583nxxRc5c5V41apVzJ07l5ycHJxOJ4WFhQBs2rSJ2NhY\ncnJymDZtGmvWrAGgrq6OdevWkZ2dTXZ2Nm+++Sb19fVmPob0IA0tbhZ9cIxHJl5FXKTPf6OIiIgE\nPVNF3rntfLt27fLOqk2ePJmCggIACgoKmDBhAlarlaSkJJKTkykuLqaqqoqmpiZSU1MBmDRpknef\ns481btw49u/fD0BhYSEjRozAZrNhs9kYMWKEtzCUzhWIf4mt2nmCkf1jGJcS7++hfKVAzK8nUX6+\nU3bmKD9zlF/g8HkqxGKx8NRTTxEWFsaNN97IjTfeSE1NDXZ7W49UfHw8NTU1AFRVVZGWlubdNyEh\ngcrKSqxWKw6Hw/u6w+GgsrISgMrKShISEgAIDw8nOjqa2tpaqqqqvK+fu48ErxM1TWz46BQ7S0/z\nwm3D/D0cERGRgOdzkffUU0/Rp08fTp8+zVNPPcWVV17Z7ucWi8X04DrL2f0BZ9bv0fZXb5+91pE/\nfr/HMPj9X/PZWWXllDuSb1+TwF3JNezZ+Y+AyCfQ8+vp28rP9+1zM/T3eHratvJTfv7cjo6OprN0\nyhIqa9euJTIykvfff5+FCxdit9upqqriySefZOnSpd7evFtvvRWAp59+mlmzZpGYmMiTTz7JkiVL\ngLYPePDgQe69916efvppZs6cydChQ3G73dx3332sXr2avLw8ioqKuPfeewF44YUXSE9P5xvf+MYF\nx6YlVHy3fbt/mmfrW9y8V1zB2x+VE9UrjO9el8jkwX3obe1ZN4P7K79gofx8p+zMUX7mKD9z/L6E\nSnNzM42NjQA0NTWxb98+UlJSGDNmDFu2bAFg69atjB07FoAxY8aQl5dHa2srZWVlOJ1OUlNTsdvt\nREVFUVxcjGEYbNu2rd0+W7duBSA/P5/hw4cDkJGRwd69e6mvr6euro59+/Z579SVztXd/0grG1zk\n7jjOD//4ER8563l0Ugq5t17DzUMTelyBB92fX7BRfr5TduYoP3OUX+Cw+rJTTU0Nv/71rwHweDxM\nnDiRjIwMhgwZwpIlS9i8ebN3CRWAAQMG8PWvf5158+YRHh7OnDlzvJdz77nnHnJzc2lpaWH06NGM\nHDkSgKlTp7J8+XIeeughYmNjefjhhwGIiYnhtttu4+c//zkAt99+u3eZFulcHsPg5OlmroyP7PLf\nta2kmuU7Spk6xMHKGdeSaIvo8t8pIiISzPTEC+nQij//g3fLonhl1r/Rt4uKrrrmVnL/cZyDZQ08\nNvlqhiUFT8GuSxbmKD/fKTtzlJ85ys+czrxc69NMngQ/j2GwvSKCtIRo3ij8ggcnXGX6mJUNLvZ8\nXkt1YyvVTa1UN7rYfaKW8SnxrPzeNUT2Cu+EkYuIiAhoJk86kHe0mtcLnTx98xDmvHmQ/3vrtfSL\nvfzZvFaPwYfHavjrJxUccNYzqn8sfW29iI+0Yo+yMtgRxbVBNHsnIiJihmbypEsZhsHrhU7uHJmM\nPaoX04f15fVCJ/OuT7nkYzhrm/nzoQr+9kkFA+J7c9PQBH4+ZSBRmq0TERHpFj3vlkXpNIZhUN/i\nPu/1f56opcVt4Ck9AMBt6UnkHa3m89PN7d73+elmVhd8ztp9X7DpcCX7TtaSf6yGJ/72KQ9s+BiX\n28Oi6Wksmj6Um4cmhFyBd/aaUXL5lJ/vlJ05ys8c5Rc4NJMXosrqWsjJK2X3iVruG9ef7/5boveO\n59f3OLkjox8WZwUAcZFWvntdImv2OPnvzKuBtsu5S7eXcmOqg4oGF5+UN1BR78IAbh6awIKpg4js\ngcueiIiIBAv15AWhRpebL+paKKtrobbZzcA+kaTYI+kVHobHMHj3YDmv7nZy63WJZA628/Smo1xt\nj+SR61P45FQDi7cdY/XtwwgP+9dTS+pb3Mz+UxG/viWVvxVXsq2kmgVTB6qfTkREpBOpJ0+8XG4P\nxeWN7HfWccBZx6FTDTS53CTFRtAvJgJbr3D+UPgFztpmUvpEggER1jAWTUtr2waWfGcoOduP8cjG\nj4m0hvP9jH7tCjwAW0Q4t6Un8uDbHzPiilhyb72GuEh9fURERAKVztIBrLy+hdd2O3FE92Jgn0gG\n9okiwdaL4vIG9jvr2Heyjk/KG7gyrjfpyTHcNDSBRyam4Ii2nvfs4CaXm08rGznd5GZcShxhZ/08\n0hrGf2dezcaicv5WXMENqX2A89c6ujU9iX6xEWQO7tNuf7kwrRVljvLznbIzR/mZo/wCh4q8AHW0\nqpFf/PVTMgf1wQA2H6niaOVJTtW3kNo3muHJMcwckcS/JdmI6f3V/zdG9grnun4xHf7cYrHw3esS\n+e51iR0fwxrGlCEOXz6OiIiIdDP15HWxVo+BNezCs14trR4+r21mQHxku/fsd9bx1N9L+PH4K7kh\ntX1RZRjGebN0IiIiEhzUk9dD5H9Ww//ZVEJsbyupfaNIS4jmyvjeHKls5CNnPZ9WNuKIsnK62U3G\nFTGMvjKWiPAwVhd8zuNTruZ/XRl33jFV4ImIiMil0BoXXWRbSTVLth9j0fQ0lnwnjZvTEnAbBv/4\nrIboXuHMHnMFa+9K55X/uI6XZg5j0iA7H59q4H8OlfPMt4ZcsMDrblrryBzlZ47y852yM0f5maP8\nAodm8rrApsOVvPDhCbK/NYQhCdEAJMf2ZuIg+wXfb4/qxdRUB1NT1e8mIiIinUM9eT5ocXso+qKe\nf56o5VBZPfYoKwPiIxkQ35uaplbW7ivjmW8NYaAjqlN/r4iIiAQ39eR1g+ZWD/9zqJx3DpbT6jGI\n7hVGVK9wwizwaUUjKfZIRl8Zy39k9ON0UysnTjezs/Q0NY2tPHtLKin2SH9/BBEREQlhIV3kGYZB\ncXkjAPYoK/GRVgzgzwfLWbv/C65NtPHfmVcTH2ml0eWh0eXG5TZI7RtF7CUsW9LTaa0jc5SfpSql\ncAAADANJREFUOcrPd8rOHOVnjvILHMFfqXTgUFk9LxZ8zqm6FmIiwqluaqW6sRUD+HpKPE/fnMqQ\nBF1uFRERkZ4pJHry/lLt4Oovn99qj7Ky/kAZH59q4Aejr+CbaQ7vI7wMw6DFbdDbqpuORUREpPup\nJ+8yfe2qOD6rbuLvxZU4a5u5IdXBY5MHnlfMWSwWelu1Dp2IiIj0fCExZXVjmoM5Y/vz5E2Def62\nYczK6KfZukugtY7MUX7mKD/fKTtzlJ85yi9wqNIRERERCUIh0ZPnz2fXioiIiFyqzuzJ00yeiIiI\nSBBSkScdUl+FOcrPHOXnO2VnjvIzR/kFDhV5IiIiIkFIPXkiIiIiAUI9eSIiIiJyUSrypEPqqzBH\n+Zmj/Hyn7MxRfuYov8ChIk9EREQkCPXYnrzCwkJefvllPB4PU6dO5dZbb73g+9STJyIiIj1FyPfk\neTweVq9ezfz581m8eDF5eXkcP37c38MSERERCRg9ssg7fPgwycnJJCUlYbVamTBhArt27fL3sIKO\n+irMUX7mKD/fKTtzlJ85yi9wWP09AF9UVlbSt29f77bD4eDw4cMdvn/37t3dMaygEx0drexMUH7m\nKD/fKTtzlJ85yi9w9Mgi73J01nVtERERkZ6kR16udTgclJeXe7crKipwOBx+HJGIiIhIYOmRRd6Q\nIUNwOp2UlZXR2trKjh07GDNmjL+HJSIiIhIweuwSKnv27Gm3hMr3vvc9fw9JREREJGD02CJPRERE\nRDrWIy/XioiIiMjF9bi7a1esWMHu3buJi4tj0aJFQNu6eatXr8btdhMeHs6cOXNITU2lpaWFFStW\nUFpaitvtJjMz0/tkjCNHjpCbm4vL5WLUqFFkZWX582N1mwvld/ToUVatWkVzczOJiYk89NBDREVF\nAbB+/Xo2b95MWFgYWVlZZGRkAMrvUvLbt28fr7/+Oq2trVitVv7zP/+T9PR0IDTzu9zvHkB5eTnz\n5s1j1qxZfOc73wFCMzu4/Pw+++wzXnjhBZqamrBYLPzqV7/CarUqv0vIT+eO9srLy8nNzaWmpgaL\nxcINN9zALbfcQl1dHUuWLKG8vJzExETmzZuHzWYDdO442+Xm16nnDqOHKSoqMo4cOWL89Kc/9b72\ny1/+0tizZ49hGIaxe/duY+HChYZhGMbmzZuNpUuXGoZhGM3NzcZPfvIT49SpU4ZhGMbjjz9uFBcX\nG4ZhGNnZ2d79g92F8nv88ceNoqIiwzAMY9OmTcYf/vAHwzAMo7S01PjZz35muFwu44svvjAeeOAB\nw+PxePdRfm06yq+kpMSoqqoyDMMwjh07Zvz4xz9ut0+o5Xc52Z3xm9/8xli8eLGxcePGdvuEWnaG\ncXn5tba2Gj/72c+Mzz77zDAMw6itrTXcbrd3H+XXpqP8dO5or6qqyigpKTEMwzAaGxuNhx56yCgt\nLTVeffVVY8OGDYZhGMb69euN1157zTAMnTvOdbn5dea5o8ddrh02bJj3L4Uz+vTpQ0NDAwD19fXe\n5VTsdjtNTU14PB6ampqwWq1ERUVRVVVFU1MTqampAEyaNImCgoLu/SB+cqH8Tp48ybBhwwAYPnw4\nH374IQAFBQVMmDABq9VKUlISycnJFBcXK79LzG/gwIHY7XYABgwYQEtLC62trSGb3+VkB7Bz5076\n9evHgAEDvK+FanZwefnt3buXlJQUUlJSAIiJiSEsLEz5XWJ+One0Z7fbGThwIACRkZEMGDCAyspK\ndu3aRWZmJgCTJ0/2ZqFzR3uXm19nnjt6XJF3IXfeeSevvvoqc+fO5bXXXuOOO+4AYOTIkURHR3Pf\nffdx//338+///u/YbDYqKyvbravncDiorKz01/D97qqrrvJ+UfLz86moqADaTqgJCQne9yUkJFBZ\nWUlVVZXyO0tH+Z3tww8/ZPDgwVitVn3/ztJRdk1NTWzcuJGZM2e2e7+ya6+j/E6ePInFYuHpp5/m\nscceY+PGjYDyO1dH+enc0bGysjJKSkpIS0ujpqbGW4zEx8dTU1MD6NxxMZeS39nMnjuCoshbuXIl\nWVlZrFixgrvvvpsVK1YA8MEHH9DS0sILL7xAbm4u77zzDmVlZX4ebeCZO3cuf/vb33j88ce9f7XK\npfuq/EpLS1mzZg333Xefn0YYuDrKbu3atUybNo3evXv7eYSBraP83G43hw4d4uGHH+app55i586d\nHDhwAIvF4ucRB5aO8tO548KamppYtGgRs2fPbtc7C+i7dQkuN7/OOHcExdn88OHDPPHEEwCMHz+e\nlStXAvDJJ58wduxYwsLCiIuL45prruHIkSNce+217arfUH9iRv/+/VmwYAEAn3/+ufeZgw6Ho92s\nVEVFBQkJCef99aD8LpwftGXzm9/8hgcffJCkpCTg/L++Qjm/c7Pbs2cP0PZvOj8/n9dee42GhgYs\nFgsRERGMGzdO2Z2lo+9e3759GTZsGDExMQCMGjWKI0eOMGnSJOV3lo6+fzp3nK+1tZVFixYxadIk\nvva1rwFts0/V1dXY7XaqqqqIj48HdO64kMvJDzrv3BEUM3nJyckUFRUBcODAAfr37w+0/QP+6KOP\ngLYKuri4mP79+2O324mKiqK4uBjDMNi2bRtjx4712/j97fTp0wB4PB7eeustbrrpJgDGjBlDXl4e\nra2tlJWV4XQ6SU1NVX7n6Ci/+vp6fvWrX3HXXXcxdOhQ7/v79Omj/L50bnbf/OY3AXjyySfJzc0l\nNzeXW265hRkzZnDzzTfru3eOjr57GRkZHDt2jJaWFtxuN0VFRVx11VXK7xwdff907mjPMAxWrlzJ\ngAEDmDZtmvf1MWPGsGXLFgC2bt3qzULnjvYuN7/OPHf0uMWQly1bRlFREadPn8ZutzNr1ixSUlJY\nvXo1LpeLiIgI7rnnHgYNGoTL5WLlypUcPXoUwzCYMmXKecswtLS0MHr06JC4jRvOz2/mzJk0NTXx\n17/+FWibCT3T0wjw1ltvsXnzZsLDw5k9ezYjR44ElN+l5Ldu3To2bNjAFVdc4d3/F7/4BXFxcSGZ\n3+V+985Yu3YtUVFRTJ8+HdB371Lz27ZtGxs2bABg9OjR3HXXXYDyu5T8dO5o79ChQ/zyl78kJSXF\ne1nxzjvvJDU1tcMlVHTu+JfLza8zzx09rsgTERERka8WFJdrRURERKQ9FXkiIiIiQUhFnoiIiEgQ\nUpEnItIN3G63v4cgIiEmKNbJExExY+PGjRQXF/Poo496X3vppZewWCzMmjWLV155hcLCQiwWC1Om\nTGHmzJmEhYXhdDp5/vnnOXbsGND2pIQ5c+YQHR0NwP33389NN93Etm3bOHnyJK+++iphYfrbWkS6\nh/5rIyIhb9KkSRQWFnqfge12u9mxYweZmZnk5uZitVr57W9/y3PPPcfevXvZtGmTd98ZM2bw/PPP\ne5dCWLt2bbtj79ixg/nz5/Pyyy+rwBORbqX/4ohIyLPb7QwbNox//OMfABQWFhIXF4fD4aCwsJC7\n776biIgI4uLiuOWWW8jLywPaFmIfPnw4VquVuLg4pk2b5l2Y/Yxvf/vbOBwOevXq1e2fS0RCmy7X\niogAmZmZvPfee9xwww1s27aN66+/nlOnTtHa2sqPf/xj7/s8Hg99+/YFoLq6mpdffplDhw7R2NiI\nYRjeR4mdcfaD2kVEupOKPBERYOzYsbz44oscO3aM3bt384Mf/ICwsDB69erF6tWrL3ip9Y033iAs\nLIxFixZhs9nYuXMnL730Urv36MHtIuIvulwrIgJEREQwfvx4cnJySEtLIyEhgT59+pCRkcHvf/97\nGhsb8Xg8OJ1O7yXZpqYmevfuTVRUFJWVlbzzzjt+/hQiIv+iIk9E5EuZmZmUlpZy/fXXe1+7//77\naW1t5ac//Sk/+tGPWLJkCdXV1QDMnDmTkpISZs+ezbPPPsu4ceP8NXQRkfPo2bUiIl8qLy9n3rx5\nrFq1isjISH8PR0TEFM3kiYjQdkPFu+++y4QJE1TgiUhQUJEnIiGvqamJu+++m/379zNr1ix/D0dE\npFPocq2IiIhIENJMnoiIiEgQUpEnIiIiEoRU5ImIiIgEIRV5IiIiIkFIRZ6IiIhIEFKRJyIiIhKE\n/j96sxH+w10vLAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x102161290>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mask1 = names['name'] == 'Jesus'\n",
"mask2 = names['gender'] == 'M'\n",
"names[(mask1) & (mask2)].groupby('year').sum().plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 34,
"text": [
"<matplotlib.axes.AxesSubplot at 0x107b1ee90>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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rFmaJYtqIHPY2dxII9cR7KINGhVmSSYQ+AStTfuYov+gpO3MSIb8DvgDjLXop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\n",
"text": [
"<matplotlib.figure.Figure at 0x107b1d950>"
]
}
],
"prompt_number": 34
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A special groupby function: `pivot_table`.\n",
"\n",
"The \"wide\" aggregate format."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print names.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
" name gender births year\n",
"0 Mary F 7065 1880\n",
"1 Anna F 2604 1880\n",
"2 Emma F 2003 1880\n",
"3 Elizabeth F 1939 1880\n",
"4 Minnie F 1746 1880\n"
]
}
],
"prompt_number": 35
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We want: \n",
"\n",
"- the row index to be year, \n",
"- gender as the column headings, \n",
"- and the value of the table to be the sum of births.\n",
"\n",
"We need the sum (or another aggregate mesure) because we are dropping the name group"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"births_by_gender = pd.pivot_table(names, values='births', \n",
" rows='year', \n",
" cols='gender', aggfunc=sum)\n",
"print births_by_gender.head()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"gender F M\n",
"year \n",
"1880 90994 110492\n",
"1881 91955 100747\n",
"1882 107851 113687\n",
"1883 112322 104631\n",
"1884 129022 114445\n"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"births_by_gender.plot()\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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dxthkm14tfWUD+7fnc8Otiai7mNwRMs6b8UlBJE4by9S5EUybF4FDNxN+BtLU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\nTGH9+vX4/X4qKiooKyujsLCQjIwMkpKSKC4uxjAM1q5d2+45a9asAWD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01ntoqPNw/b9M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\nIiIiIlHo/wGdjNATiZJLZAAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x10318f250>"
]
}
],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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