3-In-Memory-Tables.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Querying tables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Objectives:\n",
    "> * Compare queries of tabular data for **in-memory** containers\n",
    "> * Compare sizes and times for those"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [1] used 0.043 MiB RAM in 0.001s, peaked 0.000 MiB above current, total RAM usage 30.984 MiB\n"
     ]
    }
   ],
   "source": [
    "from ipython_memwatcher import MemWatcher\n",
    "mw = MemWatcher()\n",
    "mw.start_watching_memory()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [2] used 0.023 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 31.008 MiB\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "dset = 'movielens-1m'\n",
    "fdata = os.path.join(dset, 'ratings.dat.gz')\n",
    "fitem = os.path.join(dset, 'movies.dat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [3] used 77.062 MiB RAM in 0.688s, peaked 2.234 MiB above current, total RAM usage 108.070 MiB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# pass in column names for each CSV\n",
    "r_cols = ['user_id', 'movie_id', 'rating', 'unix_timestamp']\n",
    "ratings = pd.read_csv(fdata, sep=';', names=r_cols, compression='gzip')\n",
    "\n",
    "m_cols = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_csv(fitem, sep=';', names=m_cols,\n",
    "                     dtype={'title': object, 'genres': object})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [4] used 54.027 MiB RAM in 0.179s, peaked 0.000 MiB above current, total RAM usage 162.098 MiB\n"
     ]
    }
   ],
   "source": [
    "lens = pd.merge(movies, ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   movie_id             title                       genres  user_id  rating  \\\n",
      "0         1  Toy Story (1995)  Animation|Children's|Comedy        1       5   \n",
      "1         1  Toy Story (1995)  Animation|Children's|Comedy        6       4   \n",
      "2         1  Toy Story (1995)  Animation|Children's|Comedy        8       4   \n",
      "3         1  Toy Story (1995)  Animation|Children's|Comedy        9       5   \n",
      "4         1  Toy Story (1995)  Animation|Children's|Comedy       10       5   \n",
      "5         1  Toy Story (1995)  Animation|Children's|Comedy       18       4   \n",
      "6         1  Toy Story (1995)  Animation|Children's|Comedy       19       5   \n",
      "7         1  Toy Story (1995)  Animation|Children's|Comedy       21       3   \n",
      "8         1  Toy Story (1995)  Animation|Children's|Comedy       23       4   \n",
      "9         1  Toy Story (1995)  Animation|Children's|Comedy       26       3   \n",
      "\n",
      "   unix_timestamp  \n",
      "0       978824268  \n",
      "1       978237008  \n",
      "2       978233496  \n",
      "3       978225952  \n",
      "4       978226474  \n",
      "5       978154768  \n",
      "6       978555994  \n",
      "7       978139347  \n",
      "8       978463614  \n",
      "9       978130703  \n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1000209 entries, 0 to 1000208\n",
      "Data columns (total 6 columns):\n",
      "movie_id          1000209 non-null int64\n",
      "title             1000209 non-null object\n",
      "genres            1000209 non-null object\n",
      "user_id           1000209 non-null int64\n",
      "rating            1000209 non-null int64\n",
      "unix_timestamp    1000209 non-null int64\n",
      "dtypes: int64(4), object(2)\n",
      "memory usage: 53.4+ MB\n",
      "In [5] used 24.797 MiB RAM in 0.250s, peaked 0.000 MiB above current, total RAM usage 186.895 MiB\n"
     ]
    }
   ],
   "source": [
    "print(lens[:10])\n",
    "lens.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.785934448242188"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [6] used 0.055 MiB RAM in 0.021s, peaked 0.000 MiB above current, total RAM usage 186.949 MiB\n"
     ]
    }
   ],
   "source": [
    "size_pandas = lens.memory_usage().sum(index=True) / 2**20.\n",
    "size_pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 39.6 ms per loop\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5121      75\n",
       "5164    3842\n",
       "5187    6031\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [7] used 0.234 MiB RAM in 2.011s, peaked 0.000 MiB above current, total RAM usage 187.184 MiB\n"
     ]
    }
   ],
   "source": [
    "result = lens.query(\"(title == 'Tom and Huck (1995)') & (rating == 5)\")['user_id']\n",
    "t = %timeit -o lens.query(\"(title == 'Tom and Huck (1995)') & (rating == 5)\")['user_id']\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [8] used 0.004 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 187.188 MiB\n"
     ]
    }
   ],
   "source": [
    "qtime_pandas = t.best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Use a compressed in-memory container via `bcolz`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n",
      "bcolz version:     0.9.0\n",
      "NumPy version:     1.9.2\n",
      "Blosc version:     1.4.1 ($Date:: 2014-07-08 #$)\n",
      "Blosc compressors: ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib']\n",
      "Numexpr version:   2.4.3\n",
      "Python version:    2.7.10 |Anaconda 2.1.0 (64-bit)| (default, May 28 2015, 17:02:03) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "Platform:          linux2-x86_64\n",
      "Byte-ordering:     little\n",
      "Detected cores:    4\n",
      "-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [9] used 5.895 MiB RAM in 0.085s, peaked 0.000 MiB above current, total RAM usage 193.082 MiB\n"
     ]
    }
   ],
   "source": [
    "import bcolz\n",
    "bcolz.print_versions()\n",
    "bcolz.defaults.cparams['cname'] = 'lz4'\n",
    "bcolz.defaults.cparams['clevel'] = 5\n",
    "bcolz.set_nthreads(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [10] used 0.426 MiB RAM in 0.329s, peaked 77.930 MiB above current, total RAM usage 193.508 MiB\n"
     ]
    }
   ],
   "source": [
    "zlens = bcolz.ctable.fromdataframe(lens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes the memory reported as used is too much biased (don't know why), so let's repeat the operation, but using a different container:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [11] used 0.867 MiB RAM in 0.321s, peaked 77.352 MiB above current, total RAM usage 194.375 MiB\n"
     ]
    }
   ],
   "source": [
    "zlens2 = bcolz.ctable.fromdataframe(lens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [12] used 0.000 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 194.375 MiB\n"
     ]
    }
   ],
   "source": [
    "size_bcolz = zlens.cbytes / 2**20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ctable((1000209,), [('movie_id', '<i8'), ('title', 'S82'), ('genres', 'S47'), ('user_id', '<i8'), ('rating', '<i8'), ('unix_timestamp', '<i8')])\n",
       "  nbytes: 153.57 MB; cbytes: 7.89 MB; ratio: 19.45\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='lz4')\n",
       "[(1, 'Toy Story (1995)', \"Animation|Children's|Comedy\", 1, 5, 978824268)\n",
       " (1, 'Toy Story (1995)', \"Animation|Children's|Comedy\", 6, 4, 978237008)\n",
       " (1, 'Toy Story (1995)', \"Animation|Children's|Comedy\", 8, 4, 978233496)\n",
       " ...,\n",
       " (3952, 'Contender, The (2000)', 'Drama|Thriller', 5837, 4, 1011902656)\n",
       " (3952, 'Contender, The (2000)', 'Drama|Thriller', 5927, 1, 979852537)\n",
       " (3952, 'Contender, The (2000)', 'Drama|Thriller', 5998, 4, 1001781044)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [13] used 0.008 MiB RAM in 0.013s, peaked 0.000 MiB above current, total RAM usage 194.383 MiB\n"
     ]
    }
   ],
   "source": [
    "zlens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.5408477783203125"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [14] used 0.000 MiB RAM in 0.005s, peaked 0.000 MiB above current, total RAM usage 194.383 MiB\n"
     ]
    }
   ],
   "source": [
    "size_pandas / size_bcolz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the space taken by a bcolz container is around 7x smaller (!) than a pandas one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excercise\n",
    "\n",
    "Why do you think that the number of uncompressed bytes (nbytes) that the ctable reports is 3x more than pandas (153 MB vs 54 MB)?\n",
    "\n",
    "*Hint:* Pandas stores the string columns in NumPy containers with 'object' dtype whereas bcolz uses the equivalent to NumPy's 'string' objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 25 ms per loop\n",
      "In [15] used 0.348 MiB RAM in 1.173s, peaked 0.000 MiB above current, total RAM usage 194.730 MiB\n"
     ]
    }
   ],
   "source": [
    "# Querying a bcolz dataset\n",
    "resultz = [(r.nrow__, r.user_id) for r in zlens.where(\"(title == b'Tom and Huck (1995)') & (rating == 5)\", outcols=['nrow__', 'user_id'])]\n",
    "t = %timeit -o [(r.nrow__, r.user_id) for r in zlens.where(\"(title == b'Tom and Huck (1995)') & (rating == 5)\", outcols=['nrow__', 'user_id'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [16] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 194.730 MiB\n"
     ]
    }
   ],
   "source": [
    "qtime_bcolz = t.best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5845635397851394"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [17] used 0.000 MiB RAM in 0.006s, peaked 0.000 MiB above current, total RAM usage 194.730 MiB\n"
     ]
    }
   ],
   "source": [
    "qtime_pandas / qtime_bcolz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that bcolz containers, besides being in compressed state, provide ~2x faster query times than pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('results with pandas Dataframe:', 5121      75\n",
      "5164    3842\n",
      "5187    6031\n",
      "Name: user_id, dtype: int64)\n",
      "('results with bcolz ctable:', [(5121, 75), (5164, 3842), (5187, 6031)])\n",
      "In [18] used 0.000 MiB RAM in 0.005s, peaked 0.000 MiB above current, total RAM usage 194.730 MiB\n"
     ]
    }
   ],
   "source": [
    "# Sanity check (always check for your results!)\n",
    "print(\"results with pandas Dataframe:\", result)\n",
    "print(\"results with bcolz ctable:\", resultz)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Using structured NumPy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [19] used 53.418 MiB RAM in 0.593s, peaked 0.000 MiB above current, total RAM usage 248.148 MiB\n"
     ]
    }
   ],
   "source": [
    "nalens = lens.to_records()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [20] used 0.000 MiB RAM in 0.003s, peaked 0.000 MiB above current, total RAM usage 248.148 MiB\n"
     ]
    }
   ],
   "source": [
    "size_numpy = (nalens.size * nalens.dtype.itemsize) / 2**20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 16.3 ms per loop\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rec.array([ (5121, 8, 'Tom and Huck (1995)', \"Adventure|Children's\", 75, 5, 977851520),\n",
       "       (5164, 8, 'Tom and Huck (1995)', \"Adventure|Children's\", 3842, 5, 967986151),\n",
       "       (5187, 8, 'Tom and Huck (1995)', \"Adventure|Children's\", 6031, 5, 956718223)], \n",
       "      dtype=[('index', '<i8'), ('movie_id', '<i8'), ('title', 'O'), ('genres', 'O'), ('user_id', '<i8'), ('rating', '<i8'), ('unix_timestamp', '<i8')])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [21] used 0.000 MiB RAM in 6.822s, peaked 0.000 MiB above current, total RAM usage 248.148 MiB\n"
     ]
    }
   ],
   "source": [
    "resultna = nalens[(nalens['title'] == 'Tom and Huck (1995)') & (nalens['rating'] == 5)]\n",
    "t = %timeit -o nalens[(nalens['title'] == 'Tom and Huck (1995)') & (nalens['rating'] == 5)]\n",
    "resultna"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, NumPy works the fastest for in-memory data containers, while memory consumption is close to pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [22] used 0.000 MiB RAM in 0.002s, peaked 0.000 MiB above current, total RAM usage 248.148 MiB\n"
     ]
    }
   ],
   "source": [
    "qtime_numpy = t.best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##Performance comparsion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [23] used 7.578 MiB RAM in 0.143s, peaked 0.000 MiB above current, total RAM usage 255.727 MiB\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f460dc406d0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/faltet/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1279: UserWarning: findfont: Font family [u'monospace'] not found. Falling back to Bitstream Vera Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAFKCAYAAAD4/gXmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n86RuFJ4qh4IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f460dc40050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [24] used 6.680 MiB RAM in 0.326s, peaked 0.000 MiB above current, total RAM usage 262.406 MiB\n"
     ]
    }
   ],
   "source": [
    "# Query times\n",
    "labels = [\"pandas\", \"bcolz\", \"numpy (recarray)\"]\n",
    "df = pd.DataFrame({'time (sec)': [qtime_pandas, qtime_bcolz, qtime_numpy]}, index=labels)\n",
    "pd.options.display.mpl_style = 'default'\n",
    "df.plot(kind='barh', figsize=(12,5), fontsize=16, title=\"Query times for MovieLens 1m (in-memory)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Size comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f460dc12e10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0YAAAFKCAYAAAA5eU9KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlYlPX+//HXICKyqYiCirjhiqLiUi5pReXRskIPaqVp\nm9+yzNNpzyz1aKV0WrTV0urY4lqalkcFV9BK5Ki4ZRhuSQiSCyiCcP/+KObnCKMEwnB3Px/X5XV1\n75+Z12C8vT/ve2yGYRgCAAAAAAtzc/UAAAAAAMDVKIwAAAAAWB6FEQAAAADLozACAAAAYHkURgAA\nAAAsj8IIAAAAgOVRGAEAKtTHH3+s6tWru3oYTh04cEBubm7atGmTS8excOFCtWjRQu7u7rr33ntd\nOpYrpapnDwAXojACABM7fvy4nnrqKbVp00Y1a9ZUYGCg+vbtq7lz56qgoOCKXis0NFSTJk3608cN\nGzZMR48evaJjuZJCQkL066+/qnv37i4bQ0FBge69914NGzZMhw8f1ptvvnnFr+Hm5iY3NzctW7as\n2LaoqCi5ubnpgQceuKLXLEv2VbWY2rVrl6Kjo9WqVStVq1btir9XAFzP3dUDAACUzeHDh9W7d295\neHho8uTJ6ty5s6pXr66EhAS9+uqr6tixo8LDw6/Y9Ww2W5mO8/T0lKen5xUbhzOGYaigoEDu7n/u\nf21ubm6qX79+BY2qdI4ePaqcnBz1799fDRo0KPN58vPzL1lUNGnSRB9++KEGDhzocO1vv/1WISEh\nZc7YmcrKvjKcPXtWTZs21W233abXXnvtir9XAFyPO0YAYFJjxoxRfn6+kpKSdMcdd6hNmzZq0aKF\n7r77biUlJSk0NFTS778sP/PMMwoODlaNGjUUFhamL774wuFcbm5uevfddzVixAj5+fmpcePGeuWV\nV+zbr732Wu3fv1+TJk2y33k4dOiQJOmBBx5QaGiovLy81KJFC40fP155eXn2Yy++A1C0vGnTJkVE\nRMjb21tdu3ZVYmKiw5hSUlI0ePBg1alTR/7+/urXr5927txZ7Dzr1q1T586d5enpqbi4uBLfq6VL\nl6pz587y9vZWnTp1dNVVV2nbtm2Sik+lGzVqlP01Xvjnwrtl8+bNU6dOnVSzZk01a9ZMjz/+uM6c\nOWPfHh8fr169esnPz09+fn7q1KmTVq1aVeLYPv74YzVp0kSS1KdPH7m5uWnDhg2SpG+//VZdunSR\np6enAgMD9fDDDztcZ9SoUbrxxhs1c+ZMNW3aVJ6enjp37lyJ15Gke+65R//9738d7uLMmTNH11xz\njZo1aybDMOzrL/e5ueuuu9SvX79i1+jfv79GjBhhf20XF2pbt27VTTfdJF9fX9WvX1+DBw+2f5ZK\nIz8/XxMnTlTz5s1Vs2ZNtW/fXrNmzXLY53KfZ+nSn4mSdO3aVTExMRo+fLhq1apV4j4TJ05Uy5Yt\ntXDhQoWGhsrb21uDBw9Wdna2Fi5cqNatW8vPz0/R0dE6depUqV8zgEpiAABM5/jx40a1atWMqVOn\nXnbfJ554wqhbt66xaNEi46effjJeeuklw83NzYiLi7PvY7PZjMDAQOPDDz80fv75Z+Ptt982bDab\nfZ+srCyjWbNmxpNPPmmkp6cb6enpRkFBgVFYWGiMHz/e+OGHH4yDBw8aX3/9tdGgQQPjxRdftJ/7\no48+Mtzd3R2W3dzcjL59+xrx8fHG3r17jf79+xvNmjUzzp8/bxiGYfz6669GYGCgMWbMGGPnzp3G\nvn37jLFjxxp169Y1MjIyHM5z1VVXGevWrTNSU1Pt2y6UlpZmVK9e3YiJiTEOHDhg7N271/jiiy+M\n5ORkwzAMIzU11bDZbEZCQoJhGIZx8uRJ+2tMT0833n//faN69erGmjVr7NetU6eO8emnnxqpqanG\nhg0bjPDwcGPEiBGGYRhGfn6+UadOHePxxx83UlJSjJSUFGPJkiXGxo0bS8zn7NmzxpYtWwybzWYs\nW7bMSE9PN/Ly8ozt27cb1apVM/75z38aP/74o7FixQojJCTEfh3DMIyRI0cafn5+xqBBg4wdO3YY\nO3fuNAoKCkq8js1mMz799FPjhhtuMKZMmWIYhmEUFBQYzZo1M+bPn29ce+21xgMPPFDqz82qVauM\natWqGUePHrUfc/ToUcPd3d1YvXp1idnv2rXL8PHxMSZOnGj8+OOPxs6dO43o6GijVatWRm5ubonH\nXGzkyJFGx44djdWrVxsHDhww5s+fb9SuXduYPXu2w2u91Of5cp+Jy7n4vSry4osvGt7e3sYtt9xi\nJCcnG+vXrzfq1atn3HjjjcaAAQOMHTt2GPHx8UZgYKDx9NNPl+paACoPhREAmND3339v2Gw246uv\nvrrkfjk5OUaNGjWMd99912F9VFSUcf3119uXbTabMW7cOId92rZtazz77LP25dDQUGPSpEmXHdtr\nr71mtGzZ0r5cUmFks9mM//3vf8Vez759+wzD+P0XzKuvvtrhvIWFhUaLFi2MN954w+E88fHxlxxP\nUlKSYbPZjAMHDpS4/eLC6EL/+9//DB8fH4f3r0mTJsb777/vsN/69esNm81mnDhxwsjKyjJsNpux\nbt26S47rcmMYPny4cdVVVznst3TpUsPNzc04dOiQYRi/Fwl16tQxcnJyLnsNm81mfPbZZ8aCBQuM\nZs2aGYZhGCtWrDDq169v5OXlGX379rX/sl+az01BQYHRqFEjIyYmxr49JibGaNy4sX354uxHjhxp\nDBs2zOGcubm5hpeXl7FkyZISj7nQzz//bLi5uRk//vijw/pJkyYZnTp1cnitl/o8X+4zcTmXKozc\n3d2N48eP29c9/PDDRrVq1YzMzEz7unHjxhldu3Yt07UBVBym0gGACRkXTHm6lJSUFOXl5alPnz4O\n6/v06aNdu3Y5rOvUqZPDcsOGDXXs2LHLXuODDz7QVVddpaCgIPn6+uq555677NQom82mjh072peL\n+mrS09MlSVu2bNHWrVvl6+tr/+Pn56eDBw8qJSXF4VzdunW75LU6duyofv36qX379ho0aJBmzJih\nI0eOXPZ1paWlaeDAgXrggQf04IMPSpIyMjJ06NAhPfbYYw5jGzBggGw2m1JSUlSnTh3df//96tev\nnwYMGKBp06Zp3759l73exXbv3l1iboZhaPfu3fZ1bdu2lZeXV6nPe/vttysnJ0erV6/WrFmzdPfd\ndxeb7laaz42bm5uGDx+uuXPn2rfPnTtXd911l9Nrb9myRV999ZXDexcQEKBz584Vy7UkiYmJMgxD\nXbp0cTjHyy+/XOz4S32ey/qZKI1GjRrJ39/fvhwYGKigoCDVrVvXYV1pfrYAVC4evgAAJtSyZUu5\nublp165duv3226/IOT08PByWbTabCgsLL3nMwoUL9cgjj2jatGnq27ev/Pz8tGDBAo0fP/6Sx7m5\nuTk0rxf9d9H1DMPQDTfcoLfeeqvYsRf2d1SrVq3YuEu61ooVK7RlyxbFxsZq8eLFeuaZZ7Rw4ULd\nfPPNJR5z5swZ3XrrrerSpYtee+01+/qi8c2YMUPXXXddseMaNWokSZo1a5bGjRunVatWafXq1Zow\nYYLeeustjR49+pJjvVhpCuA/UxRJUvXq1TVq1ChNmTJF3333nZKTkyWV7eEad999t6ZPn67t27fL\nMAwlJydr/vz5Tvc3DEN33323nnnmmWLbLiwmnCl6/zdv3lzsdV88/kt9nsvymSiti4tMm81W4rrL\n/WwBqHwURgBgQv7+/urfv7/eeustjR07Vn5+fg7b8/PzlZ+fr9DQUNWoUUPr169Xu3bt7NvXr1+v\nDh06/Klrenh4FHsE+IYNG9S5c2f94x//sK9LTU0twyty1LVrV3388cdq1KiRatSoUe7zSb/fWerW\nrZueffZZ9e/fXx999FGJvwQX/fJeWFhY7CEVgYGBaty4sfbu3av77rvvktcLCwtTWFiYHnvsMT30\n0EOaNWvWnyqMwsLC7A9hKLJ+/XrZbDaFhYWV+jwlGT16tF599VVdc801atWqVbHtpf3ctGvXTl26\ndNHcuXNVWFiorl27qk2bNk6v27VrV23fvl3Nmzcv07i7dOkiSTp48GC5Cxip9J8JANZAYQQAJvXO\nO++oV69e6tKliyZPnqyOHTvKw8ND3333nV599VX95z//UXh4uB599FFNmDBB9erVU3h4uBYtWqSv\nv/5asbGxlzy/8Xsfqn25WbNmio+P1+HDh1WzZk3VrVtXbdq00Zw5c/T1118rLCxMy5cv11dffVXu\n1/bII49o9uzZuu222/T8888rODhYR44c0YoVK3TLLbeoR48epT7X5s2bFRsbq379+ikoKEg//fST\nduzYofvvv7/E/SdNmqS1a9dq9erVOnnypE6ePClJ8vX1lbe3t6ZOnar77rtPderU0a233qrq1atr\nz549+u9//6v33ntPKSkp+uCDD3TrrbcqODhYR48e1caNG+2/1JfWk08+qYiICP3zn//U6NGjdeDA\nAY0dO1bDhw9XcHDwnzrXxVq0aKHjx487FJ0X5u3l5VXqz83dd9+tl156STab7bJ3Cp977jl1795d\nw4cP17hx4xQQEKADBw5o6dKlGjdunJo1a2bft+gu1IU6dOige++9Vw888ICmT5+uq6++Wjk5Odq6\ndasyMzP11FNPOb32ha9v06ZNiouLK/VnQvr9HxuKphGePn1ax48f17Zt2+Th4eFQPAIwLwojADCp\nxo0bKykpSdOmTdPEiRN16NAh+fn5qU2bNnrooYfsdxWmTp0qNzc3/eMf/1BGRoZatmypzz77rMSp\nYBey2WwO05MmTZqk0aNHq3Xr1jp37pxSU1P1f//3f0pOTtY999yj8+fPa+DAgZo4caIeffTRYue6\n1PLF6+rXr6/Nmzfrueee06BBg3Tq1CkFBQWpT58+atiw4SXPc7FatWrpu+++0zvvvKPffvtNQUFB\nGj58uCZMmFDiedavX68TJ06oa9euDueZOHGiXnjhBQ0fPly+vr6aNm2apk6dKnd3dzVv3lyDBw+W\nJPn4+CglJUXDhg1TRkaG6tatq1tuuUWvvvrqJcd58Wvp0KGDvv76a02YMEHvvPOO/THPF57n4oz+\njNq1axe7/oXnKu3n5s4779QTTzwhm82mO+6445Kvq02bNtq0aZOef/559evXT7m5uWrUqJEiIyPt\n47HZbCooKFDnzp2LnSctLU2zZs3Sv//9b02dOlU///yz/Pz81L59ez3yyCOXfL0Xvr7atWtf9jNx\nsV9++UURERH2cyUlJemrr75S06ZN9fPPP5f4Hv6ZdQBcz2aUtoMXAAAAAP6ieCodAAAAAMujMAIA\nAABgeRRGAAAAACyPwggAAACA5fFUOlS4uLg4Vw8BAAAAFhAZGVnmYymMUCmKHnEKAAAAVISkpKRy\nHc9UOgBOxcfHu3oIKAfyMzfyMy+yMzfysy4KIwAAAACWxxe8osLFxcUxlQ4AAAAVKikpqVw9Rtwx\nAgAAAGB5FEYAnGKetbmRn7mRn3mRnbmRn3VRGAEAAACwPHqMUOHoMQIAAEBFo8cIAAAAAMqJwgiA\nU8yzNjfyMzfyMy+yMzfysy4KIwAAAACWR48RKhw9RgAAAKho9BgBAAAAQDlRGAFwinnW5kZ+5kZ+\n5kV25kZ+1kVhBAAAAMDy6DFChaPHCAAAABWNHiMAAAAAKCcKIwBOMc/a3MjP3MjPvMjO3MjPuiiM\nAAAAAFgePUaocPQYAQAAoKLRYwQAAAAA5URhBMAp5lmbG/mZG/mZF9mZG/lZl7urBwBr2H70tKuH\ngDIorNuU7EyM/MyN/MyL7MytKuQX6OuhIN8aLh2DFVEYoVI8+W2Kq4eAMstw9QBQLuRnbuRnXmRn\nbq7NL2ZAKIWRCzCVDgAAAIDlURgBAAAAsDwKIwAAAACWR2EEAAAAwPIojAAAAABYHoURAAAAAMuj\nMAIAAABgeRRGAAAAACyPwggAAACA5VEYAQAAALA8CiMAAAAAlkdhBAAAAMDyKIwAAAAAWB6FEQAA\nAADLozC6hA8//FDTp0939TCqrFdffVUffPCBq4cBAAAAlBuFkRNHjhxRXFychg0b5uqhVFlDhw7V\nmjVrdPjwYVcPBQAAACgXCiMnli9frlatWikkJOSS++Xn51fSiC7P2VgMw9D58+ev+PUaN26sVq1a\nadmyZVf83AAAAEBlcr/cDgsWLNDixYv15ptv6qOPPtKePXvk6+ur6667ToMHD5bNZpMkrVu3Tu++\n+67efvttBQQEFDt+/vz59nVDhw5VVFSUvLy8tGLFCmVnZyssLExjxoxRQUGBZs+ereTkZHl7e6tf\nv3667bbb7McWXWfixIlatmyZdu7cqerVq6tnz54aMWKEPDw8lJ+frwcffFB9+vTRyJEjHV5P0fGv\nv/66GjZsWOJrzs3NVUJCQrFjd+3apcmTJ+vxxx9XUlKStmzZosLCQn300UeSpNjYWK1cuVJHjx6V\np6enunXrpuHDh8vHx8d+joKCAi1btkzr16/XsWPHVLNmTTVv3lyjRo1Sw4YNlZ+fr88++0zJycnK\nyMiQp6enWrRooREjRjiM98L3YcWKFUpOTlb9+vU1bdo0Pfzww2rTpo3CwsL09ddfKz09Xf/85z/V\nrVs3LViwQFu3btWvv/4qd3d3NWnSRHfccYdatmwpSTpx4oQeeughjRgxQgMGDCj2WVi2bJnef/99\neXl5SZJ69+6t//znP7r33nvl6el5mU8TAAAAUDVdtjAqEhMTo+uuu04DBw5UYmKiFi5cqICAAF17\n7bVluvCGDRsUEhKi0aNH68SJE/r44481c+ZMZWdnq1u3bvrb3/6mTZs26fPPP1dISIg6d+7scPzM\nmTPVs2dP/e1vf1NKSooWLVqkc+fOacyYMapevbquu+46rV27VnfeeaeqV69uP2716tVq166d06JI\nkvbs2aO8vDy1adOmxO1z5sxR586d9eijj9rv0nz22Wdavny5BgwYoBEjRigrK0vz5s3T4cOH9a9/\n/Utubr/fnHvjjTeUmJiom2++WR06dFBeXp727Nmj3377zV4Y5ebmKioqSv7+/srJydHKlSs1fvx4\nvf7666pdu7bDWGbMmKHevXvrpptuUkFBgX39rl27dPDgQQ0ZMkR+fn6qV6+eJCkrK0sDBgxQvXr1\nlJubqw0bNujFF1/UK6+8opCQENWuXVvdu3dXXFycQ2FUWFiotWvXqkePHvaiSJLatGmjvLw87d69\nWxEREZfMHAAAAKiqSl0YDRw40F4EtW/fXjt37lRCQkKZCyMPDw899dRT9oLh8OHD+uabbzR06FAN\nGjRIktSuXTtt2bJFmzdvLlYYRUREaPjw4ZKk8PBwSb/f0YiKilKDBg100003afny5dq8ebP69Okj\nSTp48KBSUlI0bty4S45t//79stlsCg4OLnF7aGio/u///s++fOzYMS1btkzR0dEaPHiwfX2DBg30\nwgsvaOvWrerWrZt27typH374Qffcc4/+9re/2ffr1q2b/b+9vLz04IMP2pcLCwsVHh6u0aNHKyEh\nQTfffLPDWHr06KG77rqr2BhzcnI0bdo01apVy2H9xefu2LGjHn/8ca1Zs0ajRo2SJPXr10+TJk3S\n3r177cVhUlKSsrKydNNNNzmcLzg4WG5ubtq/fz+FEQAAAEyr1IXRxb/0BgcH6+DBg2W+cHh4uL0o\nkmS/g9OpUyf7Ojc3NwUFBSkrK6vY8T169HBY7tmzp+bPn6/9+/erQYMGql+/vjp27KjY2Fh7YbR6\n9Wr5+fnpqquuuuTYTpw4IW9vb6fbu3fv7rC8Y8cOGYah3r17O9y1CQ0Nlaenp/bs2aNu3bpp+/bt\nkqTIyMhLXn/Tpk1avny50tLSdObMGfv6tLS0y46lSKtWrYoVRUVj/eqrr3To0CFlZ2fb1wcGBtr/\nu127dgoODtbq1avthdHq1avVpEkThYaGOpzPZrPJx8dHJ06cuORrAgAAAKqyUhdGF/bJSFL16tWV\nl5dX5gtfXHi4u7uXuL5atWolPlTg4l/6i6aYXVhE9evXT9OmTdORI0cUEBCgjRs3ql+/fqpWrVqZ\nxy1JderUcVg+deqUJOnRRx8tcf+iAuT06dPy8fFxmNp3scTERL355pvq27evhgwZIl9fX9lsNr38\n8sslvt8XT6271Pqff/5ZL7/8sjp37qyHHnpItWvXlpubm957771i577xxhs1d+5c3XPPPTp79qy2\nb9+u++67z+m4AQAAcOXEx8dL+r2fm+XSLV/Y7lEWpS6MLqfol/2Ln3524V2JK+nEiRMOU92K7lj4\n+/vb13Xu3FkBAQFatWqVmjRpotzcXN1www2XPXft2rWVk5MjwzDsD5e40MXriorG559/vsQ7Tb6+\nvpIkPz8/ZWdnKy8vTx4eHiVee9OmTQoKCtKYMWPs686fP6/Tp0+XuH9J43O2/vvvv5e7u7ueeOIJ\nh7t12dnZxcbdt29fff7551q3bp2ys7NVo0YNXXPNNcXOaRiGsrOznRZoAAAA+POKfuFnufTLSUlJ\nKo9yPa77wl++i5r7Dx06ZF9XUFBgnz52pW3evNlhedOmTbLZbA5TvWw2m2688UZt3LhRK1euVHh4\nuOrXr3/Zczdv3lyGYZT6+3k6duwom82mjIwMNW/evNifovemY8eOkqQ1a9Y4Pde5c+ccihbp9wdV\nGIZRqrFcSknn3rlzp44fP15s35o1a6p3795avXq11q5dq169epX41LkjR46osLBQLVq0KPf4AAAA\nAFcp1x2jC39ZDw0NVWBgoObOnavCwkK5u7tr1apVV+T7c0oqCrZt26ZPP/1U4eHh9qfS9e3bV0FB\nQQ77XX/99Vq4cKEOHjyoxx9/vFTXa9u2rTw8PLRnz57Lfo+R9Ht/zm233aY5c+bo6NGjateunapX\nr67jx49rx44dioyMVFhYmMLCwnTVVVfpk08+UWZmptq3b6/z589rz5496tKli9q1a6fOnTsrMTFR\nn3zyiSIiIrR//36tXLmy3LcGpd/voK1YsUJvv/22rr32WqWlpWnx4sUOd9ku1K9fP8XFxUlSsYcu\nFNm7d6/c3d3Vtm3bco8PAAAAcJXLFkaXmqp14TY3Nzc99dRTmj17tt555x35+vpqwIABatmypRYt\nWlTmAV58nSJjx47V119/rVWrVql69eq64YYbNGLEiGL7+fn5qW3btjpy5Ii6du1aqmvWrFlTPXr0\nUEJCgvr161eqY+644w41atRIK1eu1MqVK2Wz2VS3bl116NBBDRo0sO/3j3/8Q0uWLNH69ev17bff\nysvLS6GhofYpfpGRkcrMzNTatWsVGxur0NBQPf3004qJiSnVOC6lY8eOuueee7R8+XJ9//33CgkJ\n0dixY7V48eIS92/SpImCgoLk7e2tpk2blrhPfHy8evbsqZo1a5Z7fAAAAICr2IwrMUerEhV9semM\nGTMcnqTmTHZ2tsaMGaNbbrlFQ4YMKfV1jhw5oieffFKvvPKKmjRpUp4hm9bRo0f12GOP6cEHH9R1\n111XbHvRezRt2rRL3lmLi4vTM0klF9gAAABwFDMgVB0b+rp6GKaTlJR02ac/X0q5eoyqslOnTmnv\n3r2aNWuWJOdTwZwJDg5WZGSkFixYUBHDq9KysrK0a9cuvf/++/L39y/W3FZk/vz5uv7660s13RAA\nAACoyq7YU+mqmqSkJL377rsKCAjQww8/XKanpt1///0VMLKqLzY2VosXL1bDhg316KOPOn28eGl7\ntgAAAICqznRT6WA+TKUDAAAoPabSlQ1T6QAAAACgnCiMAAAAAFgehREAAAAAy6MwAgAAAGB5FEYA\nAAAALI/CCAAAAIDlURgBAAAAsDwKIwAAAACWR2EEAAAAwPIojAAAAABYHoURAAAAAMujMAIAAABg\neRRGAAAAACyPwggAAACA5VEYAQAAALA8d1cPANYQMyDU1UNAGZw8eVK1atVy9TBQRuRnbuRnXmRn\nblUhv0BfD5de36oojFApOjb0dfUQUAbxP29Xx7a9XT0MlBH5mRv5mRfZmRv5WZfNMAzD1YPAX1tc\nXJwiIiJcPQwAAAD8hSUlJSkyMrLMx9NjBAAAAMDyKIwAOBUfH+/qIaAcyM/cyM+8yM7cyM+6KIwA\nAAAAWB49Rqhw9BgBAACgotFjBAAAAADlRGEEwCnmWZsb+Zkb+ZkX2Zkb+VkXhREAAAAAy6PHCBWO\nHiMAAABUNHqMAAAAAKCcKIwAOMU8a3MjP3MjP/MiO3MjP+uiMAIAAABgefQYocLRYwQAAICKRo8R\nAAAAAJQThREAp5hnbW7kZ27kZ15kZ27kZ10URgAAAAAsjx4jVDh6jAAAAFDR6DECAAAAgHKiMALg\nFPOszY38zI38zIvszI38rIvCCAAAAIDl0WOECkePEQAAACoaPUYAAAAAUE4URgCcYp61uZGfuZGf\neZGduZGfdVEYAQAAALA8eoxQ4egxAgAAQEWjxwgAAAAAyonCCIBTzLM2N/IzN/IzL7IzN/KzLgoj\nAAAAAJZHjxEqHD1GAAAAqGj0GAEAAABAOVEYAXCKedbmRn7mRn7mRXbmRn7WRWEEAAAAwPLoMUKF\no8cIAAAAFY0eIwAAAAAoJwojAE4xz9rcyM/cyM+8yM7cyM+6KIwAAAAAWB49Rqhw9BgBAACgotFj\nBAAAAADlRGEEwCnmWZsb+Zkb+ZkX2Zkb+VkXhREAAAAAy6PHCBWOHiMAAABUNHqMAAAAAKCcKIwA\nOMU8a3MjP3MjP/MiO3MjP+uiMAIAAABgefQYocLFxcWpWlBLVw/DdAJ9PRTkW8PVwwAAADCF8vYY\nuV/BsQBOPfltiquHYDoxA0IpjAAAACoJU+kAOMU8a3MjP3MjP/MiO3MjP+uiMAIAAABgeRRGAJzq\n3bu3q4eAciA/cyM/8yI7cyM/66IwAgAAAGB5FEYAnGKetbmRn7mRn3mRnbmRn3VRGAEAAACwPAoj\nAE4xz9rcyM/cyM+8yM7cyM+6KIwAAAAAWB6FEQCnmGdtbuRnbuRnXmRnbuRnXRRGAAAAACyPwgiA\nU8yzNjdPtuVsAAAeiUlEQVTyMzfyMy+yMzfysy4KIwAAAACWR2EEwCnmWZsb+Zkb+ZkX2Zkb+VkX\nhREAAAAAy6MwMpEFCxZo6NChKiwsrJTrTZw4UZMmTaqUa6FqYp61uZGfuZGfeZGduZGfdVEYwSmb\nzebqIQAAAACVgsIIThmG4eohwMWYZ21u5Gdu5GdeZGdu5Gdd7q4eAP68I0eO6KOPPlJKSoq8vLwU\nGRmp6Oho+x2eU6dOacGCBdq6datOnTolPz8/hYWF6cEHH5S7+++Rb9u2TQsXLtTBgwfl7u6usLAw\n3XXXXWrYsKHT6w4dOrTE9QEBAXr77bev/AsFAAAAKgmFkQnFxMTo+uuvV1RUlLZt26bFixfLZrMp\nOjpa2dnZev7555WTk6PBgwcrJCREJ0+eVGJios6fPy93d3dt27ZNL7/8ssLDw/XYY4/p7NmzWrBg\ngV544QVNnz5d/v7+JV536tSpDstZWVmaOXOmgoODK+NlwwWYZ21u5Gdu5GdeZGdu5GddFEYmdMMN\nN+i2226TJIWHh+vs2bNavny5br75Zn3zzTc6duyYXnnlFTVt2tR+TK9evez/PW/ePAUFBenZZ5+V\nm9vvsylbtWqlcePGafny5br77rtLvG5oaKj9v/Py8jR79mz5+/tr7NixFfAqAQAAgMpDj5EJ9ejR\nw2G5Z8+eys3N1aFDh7Rjxw6FhoY6FEUXys3NVWpqqnr27GkviiSpfv36atOmjXbv3n3Z6xuGobfe\nekvHjh3Ts88+Kx8fn3K9HlRdzLM2N/IzN/IzL7IzN/KzLu4YmVDt2rUdlmvVqiXp96lt2dnZatKk\nidNjc3JyJEl16tQptq1WrVrKyMi47PXnz5+vxMREPf/88woKCvozQ0cZFP0FXXRrn2WWWWaZ5aq9\nXKSqjIdl8rPKspeXl8rDZvDoMdNYsGCBFi9erJkzZ6p+/fr29cnJyZoyZYomTZqkTz/9VJI0ZcqU\nEs+Rm5urkSNHavDgwRoyZIjDtokTJ+rcuXN6+eWX7cs2m00vvviifZ/4+HjNnDlTY8aMUd++fUs1\n7ri4OD2TxKO//6yYAaHq2NDX1cMAAAAwhaSkJEVGRpb5eKbSmdCmTZsclhMSEuTp6akmTZooPDxc\nKSkpOnjwYInHenp6qnnz5tq8ebPDF8VmZGToxx9/VLt27Zxed9++fXr33XcVFRVV6qIIAAAAMAMK\nIxNas2aNvvrqK+3YsUP/+c9/tHbtWg0cOFA1a9bULbfcosDAQP3rX//St99+q507d2rTpk2aMWOG\ncnNzJf3+2O20tDRNmzZNiYmJio+P15QpU+Tj46OBAweWeM0zZ84oJiZGjRo1UkREhPbt22f/k5qa\nWpkvH5Xo4mkFMBfyMzfyMy+yMzfysy53Vw8ApVf0PUVPPfWUZs+ercWLF8vb21uDBw/W3//+d0mS\nl5eX/vWvf2nevHlaunSpTp8+rVq1aqlDhw727zDq1KmTnnnmGS1atEhvvPGG/XuMhg8f7tC/VHQ9\nScrOztapU6d06tQpTZgwwWFc9erV01tvvVXRLx8AAACoMPQYocLRY1Q29BgBAACUHj1GAAAAAFBO\nFEYAnGKetbmRn7mRn3mRnbmRn3VRGAEAAACwPAojAE4VfWkazIn8zI38zIvszI38rIvCCAAAAIDl\nURgBcIp51uZGfuZGfuZFduZGftZFYQQAAADA8iiMADjFPGtzIz9zIz/zIjtzIz/rojACAAAAYHkU\nRgCcYp61uZGfuZGfeZGduZGfdVEYAQAAALA8CiMATjHP2tzIz9zIz7zIztzIz7oojAAAAABYHoUR\nAKeYZ21u5Gdu5GdeZGdu5GddFEYAAAAALI/CCIBTzLM2N/IzN/IzL7IzN/KzLgojAAAAAJZHYQTA\nKeZZmxv5mRv5mRfZmRv5WZe7qwcAa4gZEOrqIZhOoK+Hq4cAAABgGTbDMAxXDwJ/bXFxcYqIiHD1\nMAAAAPAXlpSUpMjIyDIfz1Q6AAAAAJZHYQTAKeZZmxv5mRv5mRfZmRv5WReFEQAAAADLo8cIFY4e\nIwAAAFQ0eowAAAAAoJwojAA4xTxrcyM/cyM/8yI7cyM/66IwAgAAAGB59BihwtFjBAAAgIpGjxEA\nAAAAlBOFEQCnmGdtbuRnbuRnXmRnbuRnXRRGAAAAACyPHiNUOHqMAAAAUNHoMQIAAACAcqIwAuAU\n86zNjfzMjfzMi+zMjfysi8IIAAAAgOXRY4QKR48RAAAAKho9RgAAAABQThRGAJxinrW5kZ+5kZ95\nkZ25kZ91URgBAAAAsDx6jFDh6DECAABARaPHCAAAAADKicIIgFPMszY38jM38jMvsjM38rMuCiMA\nAAAAlkePESocPUYAAACoaPQYAQAAAEA5URgBcIp51uZGfuZGfuZFduZGftZFYQQAAADA8ugxQoWj\nxwgAAAAVjR4jAAAAACgnCiMATjHP2tzIz9zIz7zIztzIz7oojAAAAABYHj1GqHD0GAEAAKCi0WME\nAAAAAOVEYQTAKeZZmxv5mRv5mRfZmRv5WReFEQAAAADLo8cIFY4eIwAAAFQ0eowAAAAAoJwojAA4\nxTxrcyM/cyM/8yI7cyM/66IwAgAAAGB59BihwtFjBAAAgIpGjxEAAAAAlBOFEQCnmGdtbuRnbuRn\nXmRnbuRnXRRGAAAAACyPHiNUOHqMAAAAUNHK22PkfgXHAji1/ehpVw8BAABUkkBfDwX51nD1MIA/\nhcIIleLJb1NcPQQAAFBJYgaEmrYwio+PV+/evV09DLgAPUYAAAAALI/CCAAAAPgDd4usi8IIAAAA\ngOVRGAEAAAB/4HuMrIvCCAAAAIDlURgBAAAAf6DHyLoojAAAAABYHoURAAAA8Ad6jKyLwggAAACA\n5VEYAQAAAH+gx8i6KIwAAAAAWB6FEQAAAPAHeoysi8IIAAAAgOVRGAEAAAB/oMfIuiiMLGjdunUa\nOnSoMjMzXT0UAAAAoEqgMAIAAAD+QI+RdVEYAQAAALA8d1cPwCoWLFigxYsXKyYmRh999JFSUlLk\n5eWlyMhIRUdHy2azKT8/X5999pmSk5OVkZEhT09PtWjRQiNGjFDDhg3t51q3bp3effddTZkyRStW\nrNDWrVvl6empq6++WsOHD1f16tXt+6anp2vOnDnavXu3atSood69e6tRo0bFxpeQkKDY2FgdPnxY\neXl5atCggQYMGKC+ffs67Pftt99q9erVysjIUPXq1RUUFKSoqCh179694t48AACASkKPkXVRGFWy\nmJgYXX/99YqKitK2bdu0ePFi2Ww2RUdHKz8/X7m5uYqKipK/v79ycnK0cuVKjR8/Xq+//rpq167t\ncK633npLvXr10pNPPqkff/xRCxculLe3t4YMGSJJOn/+vKZMmaL8/Hzdd9998vPzU2xsrL7//vti\n40pPT9dVV12l22+/XdWqVdPu3bv13nvvKS8vTzfeeKMkaePGjZo7d67+/ve/q23btsrLy9OBAweU\nk5NT8W8cAAAAUIEojCrZDTfcoNtuu02SFB4errNnz2r58uW6+eab5eXlpQcffNC+b2FhocLDwzV6\n9GglJCTo5ptvdjhX7969FR0dLUlq3769fvrpJyUkJNgLo3Xr1unYsWOaOnWqQkNDJUmdO3fWE088\noaysLIdzDRo0yOG6bdu2VVZWllatWmUvjPbt26eQkBANHjzYvm+nTp2u1FsDAADgcvHx8dw1sigK\no0rWo0cPh+WePXtqzZo1Onz4sFq3bq1NmzZp+fLlSktL05kzZ+z7paWlFTtXRESEw3JISIiSk5Pt\ny/v27VNAQIC9KJIkm82mq6++WosWLXI4Ni0tTfPnz9fevXt14sQJGYYhSQ7T8kJDQ7Vq1SrNmTNH\nXbt2VevWrVWjRo0yvAsAAABA1UJhVMkung5Xq1YtSVJWVpYSExP15ptvqm/fvhoyZIh8fX1ls9n0\n8ssvKy8vr9i5fHx8HJbd3d11/vx5+/KJEyfs5y/pmkVyc3M1ZcoUeXp66q677lJgYKDc3d21atUq\nrV271r5f3759lZ+frzVr1mjVqlWqVq2aOnfurJEjR6pevXp//s0AAAB/WUVPdyu6+8IyyxW97OXl\npfKwGUW3BlChih6+MHPmTNWvX9++Pjk5WVOmTNHkyZO1cuVK7d+/X2+++aZ9+/nz5zV8+HD16dNH\nY8aMkfT/H74wY8YMBQYGFrvG/PnzJUnvvPOOdu7cqXfeecdhLAsXLtSiRYv09ttvKyAgQDt27NDU\nqVM1efJktW7d2r7fW2+9pY0bN9rPd6EzZ85o27Ztmjt3rvz9/TV16lSnrz0uLk7PJNn+5DsGAADM\nKmZAqDo29HX1MGAxSUlJioyMLPPxPK67km3atMlhOSEhQZ6enmrcuLHOnTsnNzfHSDZs2KCy1q6t\nW7fW8ePH9dNPP9nXFRYWavPmzQ77nTt3TpJUrVo1+7rs7GwlJiY6PbeXl5d69uypq6++WocOHSrT\n+AAAAKoavsfIuphKV8nWrFkjwzDUokULbdu2TWvXrlV0dLS8vLzUuXNnJSYm6pNPPlFERIT279+v\nlStXlvm2YN++fbVkyRK9+uqruuOOO+Tn56fVq1fr7NmzDvu1bt1aNWvW1OzZsxUdHa3c3Fx9+eWX\n8vPzc9j3/fffV82aNdWqVSv5+fkpLS1NGzduVMeOHcv1ngAAAACuRmFUyZ566inNnj1bixcvlre3\ntwYPHmx/yltkZKQyMzO1du1axcbGKjQ0VE8//bRiYmJKdW6bzXG6mru7uyZMmKDZs2dr9uzZ9u8x\n6tKliz744AP7fn5+fnriiSc0d+5cvfbaa/L399eAAQN0+vRph4c0tGnTRuvWrdPGjRt15swZ1alT\nR3369LE/BQ8AAMDseCKdddFjVEmK+n+++OKLYtPl/uroMQIAwFroMYIr0GMEAAAAXCH0GFkXhVEl\nuXiaGwAAAICqgx6jShIdHa3o6GhXDwMAAACXQI+RdXHHCAAAAIDlURgBAAAAf6DHyLoojAAAAABY\nHoURAAAA8Ad6jKyLwggAAACA5VEYAQAAAH+gx8i6KIwAAAAAWB6FEQAAAPAHeoysi8IIAAAAgOW5\nu3oAAAAAQFURHx9/2btGeXl5yszMrKQR4UIeHh4KCAiokHNTGAEAAACllJeXp/T0dDVq1Ehubky+\nqmzHjx9Xdna2fHx8rvi5SRMAAAD4w+XuFmVmZlIUuZC/v79OnjxZIecmUQAAAOBPoChyHZvNJpvN\nViHnJlUAAADgD3yPkXVRGAEAAAB/ca+//rrGjRt3xc8bFxenESNGXPHzfvDBB5o0adIVP++l2AzD\nMCr1irCcuLg4VQtq6ephAACAShLo66Eg3xquHkaFOHr0qBo2bOjqYVQZ119/vWJiYtSlSxdJUt26\ndRUQEKDdu3erWrVqkqT8/HyFhYXp+PHjOn78uCRp4MCB2rp1q9zd3eXm5qb27dtr+vTpateunSTp\n3Llz6tKli9atW1fsKXTOMkhKSlJkZGSZXwtPpUOl6NjQ19VDAAAAqBC/nj6n9NN5FXb+qlpoJiUl\n6fTp0/aiqEjt2rUVGxurfv36SZJiY2NVp04dZWVl2fex2WyaPn26hg8frsLCQk2bNk0PPfSQ1q9f\nL0mqUaOGbrjhBs2bN0+PPPJIpbweCiMATpXmuxxQdZGfuZGfeZGduZUlv/TTeXry25QKGpEUMyC0\n1IXRm2++qVmzZun06dNq0KCBYmJi1KdPH73yyis6cOCA3nvvPT311FOaN2+e/Zjc3Fw9/vjjevrp\np5WWlqann35a3333nby9vfXQQw9p9OjRJV4rNjZWvXr1KrZ+6NChmjdvnr0wmjdvnoYOHaqpU6eW\neB43NzdFRUXpzTffdFjfu3dvzZ07t9IKI3qMAAAAgL+An376SR9++KHWrFmjQ4cOafHixQoJCZEk\nhye5TZ8+XYcOHdKhQ4f0zTffqHbt2rr55ptVWFioO++8U+Hh4dq9e7eWLFmi9957T2vWrCnxenv2\n7FFoaGix9f3799fmzZt16tQpnThxQt9//7369+9fbL+ijp68vDwtWrRI3bp1c9jesmVL7dy5s8zv\nx5/FHSMATvEvnuZGfuZGfuZFduZm5vyqVaumvLw87d27V/7+/goODrZvK+mxApmZmRo+fLimTZum\n9u3bKzExUcePH9cTTzwhSWrSpIlGjBihL7/8Utdff32x40+ePFniF616enqqX79++vLLL2UYhvr3\n7y9PT0+HfQzD0LPPPqsXXnhBubm58vT01CeffOKwj4+Pj06dOlWm96IsuGMEAAAA/AU0b95cL730\nkqZNm6bWrVvr/vvv16+//lrivvn5+Ro1apSGDBmiqKgoSdLhw4f166+/qlmzZvY/r7/+ujIzM0s8\nR+3atXX69Oli6202m4YNG6Z58+Zp/vz5Gjp0aLHCzGaz6ZVXXlFqaqrS0tL0xRdfaNSoUdq9e7d9\nn+zsbPn5+ZX17fjTKIwAOMV3OZgb+Zkb+ZkX2Zmb2fMbPHiwvv32W23fvl02m83pI6+ffvpp1apV\nS+PHj7evCw4OVpMmTZSammr/c+jQIYd+pAuFhYVp//79JW7r0aOHjh07pszMTF199dWXHffVV1+t\n5s2bO0zb27dvnzp06HDZY68UCiMAAADgLyAlJUUbNmzQuXPnVKNGDdWoUUNubsV/3f/444+1adMm\nvffeew7ru3TpIh8fH82YMUNnz55VQUGBdu/erf/9738lXu/GG2/Upk2bnI7n888/12effeZ0+4V3\nkX744Qf9+OOPatu2rX1dQkJCuR6//WdRGAFwyszzrEF+Zkd+5kV25mbm/PLy8jR58mS1bNlSbdu2\nVVZWll544QVJv09dK3oAw5dffqlDhw4pLCxMISEhCgkJ0RtvvCE3Nzd98cUXSk5OVkREhFq2bKnH\nHnusxOlykhQeHi4/Pz9t3brVvu7Chzy0adNGrVu3LnGb9Ptdq6LrjxkzRuPHj7cXQrm5uYqNjdUd\nd9xxZd6cUuALXlHh4uLiFBER4ephAAAAlFtJXy5q1e8xkqS1a9dqzpw5mjt37hU97wcffKCjR4/q\nxRdfLLaNL3gFUOn4Lg5zIz9zIz/zIjtzK0t+Qb41qmzhUtGuu+46XXfddVf8vA888MAVP+flMJUO\nAAAAgOVRGAFwin/xNDfyMzfyMy+yMzfysy4KIwAAAACWR2EEwCmzf5eD1ZGfuZGfeZGduZGfdVEY\nAQAAAH9CYWGhq4dgWYZhqKIeqs3julHheFw3AAD4q8jLy1N6eroaNWpU4penomIdP35cNWrUkI+P\nT7FtPK4bAAAAqCQeHh4KDAzUr7/+6uqhWJKHh0eJRdGVQGEEwCm+i8PcyM/cyM+8yM7cSpOfh4dH\niV8wCnPj/h8AAAAAy6PHCBWOHiMAAABUtPL2GHHHCAAAAIDlURgBcIrvcjA38jM38jMvsjM38rMu\nptKhwsXFxbl6CAAAALCA8kylozACAAAAYHlMpQMAAABgeRRGAAAAACyPwggAAACA5VEYAQAAALA8\nd1cPAH9NmZmZ+uSTT5ScnCzDMNShQweNGjVKAQEBrh4aLnD8+HEtXbpU+/fv18GDB5Wfn6+33367\nWE7Z2dn69NNPtWXLFuXl5alVq1YaOXKkQkJCXDRyfPfdd9qwYYNSU1N1+vRpBQQEqHv37ho0aJA8\nPT3t+5Fd1bRt2zYtXbpUv/zyi3JycuTn56dWrVopOjpawcHB9v3IzxymTp2qHTt2KCoqSsOGDbOv\nJ7+qZ9euXZo8eXKx9V5eXvroo4/sy2RXdSUlJWnp0qVKTU2VzWZTw4YNddddd6l9+/aSypcdT6XD\nFXfu3Dk9+eST8vDwsP8PYt68eTp37pxeffVV1ahRw8UjRJFdu3bpjTfeUIsWLVRQUKAdO3YUK4wM\nw9ALL7ygzMxMjRgxQl5eXlqyZIkOHz6smJgY+fv7u/AVWNf48eNVt25dde/eXf7+/jpw4IAWLlyo\nhg0basqUKbLZbGRXhSUkJCg1NVUtW7aUn5+fMjIytHTpUmVmZurf//63AgICyM8k4uPjNXfuXJ04\ncUKDBg3S0KFDJfF3Z1VVVBjdc889Cg0Nta93c3NT8+bNJZFdVbZ69WrNmTNH/fv3V+fOnVVYWKiD\nBw8qODhYERER5c/OAK6wb775xhg6dKjx66+/2telp6cbw4YNM5YtW+bCkeFihYWF9v+OjY01hgwZ\nYmRkZDjs88MPPxhDhgwxdu3aZV+Xk5Nj3HPPPcacOXMqbaxwdOrUqWLr1q9fbwwZMsRITk42DIPs\nzOaXX34xhgwZYixfvtwwDPIzg9OnTxsPPPCAkZCQYAwZMsSYN2+efRv5VU07d+50+HuyJGRXNaWn\npxt33nmn8c033zjdp7zZ0WOEKy4xMVGtWrVSYGCgfV39+vXVunVrJSYmunBkuJjNZrvsPomJifL3\n91e7du3s67y8vNSlSxfydCFfX99i64r+tfO3336TRHZm4+PjI+n//1ySX9X32WefKSQkRD179iy2\njfyqNuMSE6bIrmpau3atqlWrpptuusnpPuXNjsIIV9zhw4fVuHHjYuuDg4N15MgRF4wI5XHkyBGn\neWZmZurcuXMuGBVKsnv3bklSo0aNJJGdGRQWFur8+fNKS0vTrFmzVKtWLfXq1UsS+VV1e/fu1YYN\nG3TfffeVuJ38qrYZM2Zo2LBhuu+++zRjxgxlZmbat5Fd1bR37141bNhQ8fHxGjt2rO644w49+uij\nWrlypX2f8mbHwxdwxeXk5Mjb27vYeh8fH+Xk5LhgRCiP7Oxs1a9fv9j6on/dzsnJoW+sCsjKytKC\nBQsUHh5uv3NEdlXfc889p9TUVElSQECAJkyYoFq1akkiv6rs/PnzmjVrlm699VY1aNCgxH3Ir2ry\n9vbWwIED1a5dO9WsWVOpqan66quv9Pzzz2v69Ony8/Mjuyrqt99+02+//aZPP/1Ud955pwIDA7V5\n82bNmTNHBQUFGjBgQLmzozACAJPLzc3V9OnT5e7uroceesjVw8GfMHbsWJ09e1bp6elatmyZXnrp\nJU2ePFn16tVz9dBwCUuXLlV+fr4GDRrk6qHgT2ratKmaNm1qX27btq3atm2r5557TitWrLA/PANV\nj2EYys3N1cMPP6zu3btLksLCwpSRkaElS5ZowIAB5b4GU+lwxXl7e5d4Zyg7O9tescM8fHx8lJ2d\nXWx90bqS7g6i8uTl5WnatGnKyMjQ+PHjHZ64Q3ZVX6NGjRQaGqpevXrphRdeUG5urpYsWSLp93zI\nr+rJzMzUl19+qaFDhyovL085OTn2/+fl5+frzJkzKiws5OfPRJo1a6YGDRpo//79kvjZq6qKfocM\nDw93WN+hQwedPHlSJ06cKHd23DHCFde4cWMdPny42PojR444fD8HzCE4OFg7duwotv7IkSMKCAhg\nOoELnT9/Xv/+97/1888/a8KECcXmVZOduXh5eSkwMFDp6emSfs8vOTm52H7k51rp6ek6f/68Zs6c\nWWzbsmXLtGzZMk2bNo2fP5O58GEM/OxVTY0bN1ZKSsol9ylvdtwxwhXXtWtX/fTTTzp27Jh93bFj\nx/Tjjz+qS5cuLhwZyqJr167KysqyN/ZL0pkzZ7R161Z17drVhSOztsLCQs2YMUO7d+/Wk08+6fB9\nHEXIzlxOnDihX375xf5Ez27dupFfFdSsWTO9+OKLxf5I0jXXXKMXX3xRQUFB/PyZyP79+5WWlmb/\ne5SfvaqpaPrctm3bHNZv375ddevWVe3atcv9c8cdI1xxkZGR+u9//6vp06fbv+B1/vz5CggI0I03\n3uji0eFi3333nSTp559/lvT7N0r7+fnJz89P7dq1U9euXdWqVSvNnDnT4cvSbDabbrvtNlcO3dJm\nz56t77//XlFRUfLw8NC+ffvs2wICAuTv7092VVhMTIyaN2+ukJAQ1axZU2lpafrmm2/k7u6ugQMH\nShL5VVFeXl4OjwK+UL169ezbyK9qmjFjhoKCgtS0aVN5eXkpNTVVS5YsUd26de09KmRXNUVERCgs\nLEwffPCBTp8+rfr162vz5s3asWOHxowZI+n3orY82dmMSz3IHSijzMxMffLJJ/ZpBB06dNCoUaMU\nEBDg4pHhYs4aTdu1a2f/V9Ds7GzNnTtXW7ZsUX5+vlq1aqWRI0cqJCSkMoeKCzz88MMOj5e9UHR0\ntP7+979LIruqaunSpdq8ebN9WlbdunUVFhamqKgoh78nyc88hg4dqkGDBjn8nUp+Vc+SJUuUkJCg\njIwMnTt3TnXq1FGnTp00ZMgQ1a5d274f2VVNZ8+e1eeff67vvvtOOTk5atSokW6//Xb71xxI5cuO\nwggAAACA5dFjBAAAAMDyKIwAAAAAWB6FEQAAAADLozACAAAAYHkURgAAAAAsj8IIAAAAgOVRGAEA\nAACwPAojAAAAAJZHYQQAAADA8v4fN6sQMQgDh9gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f460dc1bc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In [25] used 2.699 MiB RAM in 0.280s, peaked 0.000 MiB above current, total RAM usage 265.105 MiB\n"
     ]
    }
   ],
   "source": [
    "# Container sizes\n",
    "df = pd.DataFrame({'size (MB)': [size_pandas, size_bcolz, size_numpy]}, index=labels)\n",
    "pd.options.display.mpl_style = 'default'\n",
    "df.plot(kind='barh', figsize=(12,5), fontsize=16, title=\"Container sizes for MovieLens 1m\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Rules of thumb for querying in-memory tabular datasets\n",
    "\n",
    "* Choose pure NumPy recarrays if you need the fastest speed\n",
    "* Choose bcolz ctables if you need to store lots of data in limited memory and not want to loose too much speed\n",
    "* Choose pandas if what you need is rich functionality on top of your containers (at the penalty of some speed)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}