{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark for querying the MovieLens dataset using pandas and bcolz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This benchmark has been run on a Xeon E3-1245 v5 @ 3.50GHz (Skylake, 4 physical cores) with Ubuntu Linux 16.04.  The virtual machine for bcolz is numexpr."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os.path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import bcolz\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n",
      "bcolz version:     1.1.1.dev16\n",
      "NumPy version:     1.11.1\n",
      "Blosc version:     1.9.3 ($Date:: 2016-07-06 #$)\n",
      "Blosc compressors: ['blosclz', 'lz4', 'lz4hc', 'snappy', 'zlib']\n",
      "Numexpr version:   2.6.1\n",
      "Dask version:      0.10.1\n",
      "Python version:    2.7.12 |Continuum Analytics, Inc.| (default, Jul  2 2016, 17:42:40) \n",
      "[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]\n",
      "Platform:          linux2-x86_64\n",
      "Byte-ordering:     little\n",
      "Detected cores:    8\n",
      "-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=\n"
     ]
    }
   ],
   "source": [
    "bcolz.print_versions()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the CSV files in a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Files where the data is (using the 10M dataset here)\n",
    "dset = 'ml-10m'\n",
    "ftags = os.path.join(dset, 'tags.dat')\n",
    "fdata = os.path.join(dset, 'ratings.dat.gz')\n",
    "fitem = os.path.join(dset, 'movies.dat')\n",
    "\n",
    "zdset = 'ml-10m.blz'\n",
    "pdset = 'ml-10m.h5'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Global settings for bcolz and pandas (for experimenting, modify some of the lines below)\n",
    "bcolz.defaults.cparams['cname'] = 'blosclz'\n",
    "bcolz.defaults.cparams['clevel'] = 1\n",
    "bcolz.defaults.eval_vm = \"numexpr\"\n",
    "#bcolz.blosc_set_nthreads(4)\n",
    "#bcolz.numexpr.set_num_threads(4)\n",
    "# from pandas.computation import expressions as expr\n",
    "# expr.set_use_numexpr(True)\n",
    "# expr.set_numexpr_threads(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for parsing the data: 2.94\n",
      "Time for dataframe merges: 0.68\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000054 entries, 0 to 10000053\n",
      "Data columns (total 6 columns):\n",
      "movie_id          int64\n",
      "title             object\n",
      "genres            object\n",
      "user_id           int64\n",
      "rating            float64\n",
      "unix_timestamp    int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 534.1+ MB\n",
      "('Info for movie_ratings:', None)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10000054 entries, 0 to 10000053\n",
      "Data columns (total 6 columns):\n",
      "movie_id          int64\n",
      "title             object\n",
      "genres            object\n",
      "user_id           int64\n",
      "rating            float64\n",
      "unix_timestamp    int64\n",
      "dtypes: float64(1), int64(3), object(2)\n",
      "memory usage: 534.1+ MB\n",
      "('Info for lens:', None)\n"
     ]
    }
   ],
   "source": [
    "# Load the ratings in a pandas dataframe\n",
    "t0 = time()\n",
    "# pass in column names for each CSV\n",
    "t_cols = ['user_id', 'movie_id', 'tag', 'unix_timestamp']\n",
    "tags = pd.read_csv(ftags, sep=';', names=t_cols)\n",
    "#print(\"Info for tags:\", tags.info())\n",
    "\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': \"S100\", 'genres': \"S100\"})\n",
    "print(\"Time for parsing the data: %.2f\" % (time()-t0,)) \n",
    "\n",
    "t0 = time()\n",
    "# create one merged DataFrame\n",
    "movie_ratings = pd.merge(movies, ratings)\n",
    "# You probably need more than 8 GB in RAM for the next merge\n",
    "#lens = pd.merge(movie_ratings, tags, on='user_id')\n",
    "lens = movie_ratings\n",
    "print(\"Time for dataframe merges: %.2f\" % (time()-t0,)) \n",
    "print(\"Info for movie_ratings:\", movie_ratings.info())\n",
    "print(\"Info for lens:\", lens.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute the size of the dataframe, taking in account the true object overhead"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size for regular values in lens dataframe: 457.8 MB\n"
     ]
    }
   ],
   "source": [
    "# Size of the dataframe in-memory\n",
    "rsize = lens.values.nbytes / 2**20.\n",
    "print(\"Size for regular values in lens dataframe: %.1f MB\" % (rsize,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overhead of objects: 1115.1 MB\n"
     ]
    }
   ],
   "source": [
    "# Compute the overhead of the objects in dataframe\n",
    "dfo = lens.blocks['object']\n",
    "import sys\n",
    "overhead = sum(sys.getsizeof(o) for o in dfo.values.flat) / 2**20.\n",
    "print(\"Overhead of objects: %.1f MB\" % (overhead ,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size for the dataframe: 1572.8 MB\n"
     ]
    }
   ],
   "source": [
    "# Total size:\n",
    "dfsize = rsize + overhead\n",
    "print(\"Total size for the dataframe: %.1f MB\" % (dfsize,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying with pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for simple query with pandas: 0.16 (899)\n"
     ]
    }
   ],
   "source": [
    "# Simple query\n",
    "t0 = time()\n",
    "result = lens[lens['title'] == b'Tom and Huck (1995)']\n",
    "#result = lens.query(\"title == 'Tom and Huck (1995)'\")\n",
    "dfsmplqtime = time() - t0\n",
    "print(\"time (and length) for simple query with pandas: %.2f (%d)\" %\n",
    "      (dfsmplqtime, len(result)))\n",
    "#print repr(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for complex query with pandas: 0.18 (74)\n"
     ]
    }
   ],
   "source": [
    "# Complex query\n",
    "t0 = time()\n",
    "result = lens[(lens['title'] == b'Tom and Huck (1995)') & (lens['rating'] == 5)]['user_id']\n",
    "#result = lens.query(\"(title == 'Tom and Huck (1995)') & (rating == 5)\")['user_id']\n",
    "dfcmplxqtime = time() - t0\n",
    "print(\"time (and length) for complex query with pandas: %.2f (%d)\" %\n",
    "      (dfcmplxqtime, len(result)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build a ctable out of the dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for ctable conversion: 0.94\n"
     ]
    }
   ],
   "source": [
    "# Get a bcolz ctable out of the lens dataframe (using compression by default)\n",
    "t0 = time()\n",
    "zlens = bcolz.ctable.fromdataframe(lens)\n",
    "print(\"time for ctable conversion: %.2f\" % (time()-t0))\n",
    "#print repr(zlens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get rid of the dataframe to cleanup memory\n",
    "del lens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size for the ctable: 92.7 MB\n",
      "Compression ratio the ctable: 19.8x\n",
      "Storage savings compared with a dataframe: 17.0x\n"
     ]
    }
   ],
   "source": [
    "# Size for the compressed ctable\n",
    "zsize = zlens.cbytes / 2**20.\n",
    "print(\"Total size for the ctable: %.1f MB\" % (zsize,))\n",
    "# Compression ratio\n",
    "print(\"Compression ratio the ctable: %.1fx\" % (zlens.nbytes / float(zlens.cbytes),))\n",
    "# Reduction in size compared with the dataframe\n",
    "print(\"Storage savings compared with a dataframe: %.1fx\" % (dfsize / zsize,))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query the ctable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for simple query with bcolz: 0.08 (899)\n"
     ]
    }
   ],
   "source": [
    "# Simple query\n",
    "t0 = time()\n",
    "result = zlens[\"title == 'Tom and Huck (1995)'\"]\n",
    "zsmplqtime = time() - t0\n",
    "print(\"time (and length) for simple query with bcolz: %.2f (%d)\" %\n",
    "      (zsmplqtime, len(result)))\n",
    "#print repr(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for complex query with bcolz: 0.09 (74)\n"
     ]
    }
   ],
   "source": [
    "# Complex query\n",
    "t0 = time()\n",
    "#result = zlens[\"(title == 'Tom and Huck (1995)') & (rating == 5)\"]['user_id']\n",
    "result = [r.user_id for r in zlens.where(\n",
    "    \"(title == 'Tom and Huck (1995)') & (rating == 5)\", outcols=['user_id'])]\n",
    "zcmplxqtime = time() - t0\n",
    "print(\"time (and length) for complex query with bcolz: %.2f (%d)\" %\n",
    "      (zcmplxqtime, len(result)))\n",
    "#print(repr(result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing an uncompressed ctable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size for the ctable: 1833.0 MB\n",
      "Compression ratio the ctable: 1.0x\n",
      "Storage savings compared with a dataframe: 0.9x\n"
     ]
    }
   ],
   "source": [
    "# Size for the uncompressed ctable\n",
    "nzlens = zlens.copy(cparams=bcolz.cparams(clevel=0))\n",
    "nzsize = nzlens.cbytes / 2**20.\n",
    "print(\"Total size for the ctable: %.1f MB\" % (nzsize,))\n",
    "# Compression ratio\n",
    "print(\"Compression ratio the ctable: %.1fx\" % (nzlens.nbytes / float(nzlens.cbytes),))\n",
    "# Reduction in size compared with the dataframe\n",
    "print(\"Storage savings compared with a dataframe: %.1fx\" % (dfsize / nzsize,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for simple query with bcolz: 0.14 (899)\n"
     ]
    }
   ],
   "source": [
    "# Time for a simple query with the uncompressed ctable\n",
    "t0 = time()\n",
    "result = nzlens[\"title == 'Tom and Huck (1995)'\"]\n",
    "nzsmplqtime = time() - t0\n",
    "print(\"time (and length) for simple query with bcolz: %.2f (%d)\" %\n",
    "      (nzsmplqtime, len(result)))\n",
    "#print repr(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for complex query with bcolz: 0.14 (74)\n"
     ]
    }
   ],
   "source": [
    "# Time for a complex query with the uncompressed ctable\n",
    "t0 = time()\n",
    "#result = zlens[\"(title == 'Tom and Huck (1995)') & (rating == 5)\"]['user_id']\n",
    "result = [r.user_id for r in nzlens.where(\n",
    "    \"(title == 'Tom and Huck (1995)') & (rating == 5)\", outcols=['user_id'])]\n",
    "nzcmplxqtime = time() - t0\n",
    "print(\"time (and length) for complex query with bcolz: %.2f (%d)\" %\n",
    "      (nzcmplxqtime, len(result)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Get rid of previous dataset to clean up memory\n",
    "del nzlens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing a disk-based ctable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size for the ctable: 92.7 MB\n",
      "Compression ratio for the ctable: 19.8x\n",
      "Storage savings compared with a dataframe: 17.0x\n"
     ]
    }
   ],
   "source": [
    "# Size for a disk-based ctable\n",
    "fname = \"ctable-10m.bcolz\"\n",
    "import shutil\n",
    "if os.path.exists(fname):\n",
    "    shutil.rmtree(fname)\n",
    "dblens = zlens.copy(rootdir=fname)\n",
    "# \"du -sh \"ctable-10m.bcolz\"\n",
    "dbsize = dblens.cbytes / 2**20.\n",
    "print(\"Total size for the ctable: %.1f MB\" % (dbsize,))\n",
    "# Compression ratio\n",
    "print(\"Compression ratio for the ctable: %.1fx\" % (dblens.nbytes / float(dblens.cbytes),))\n",
    "# Reduction in size compared with the dataframe\n",
    "print(\"Storage savings compared with a dataframe: %.1fx\" % (dfsize / dbsize,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95M\tctable-10m.bcolz\r\n"
     ]
    }
   ],
   "source": [
    "!du -sh \"ctable-10m.bcolz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for simple query with on-disk bcolz: 0.11 (899)\n"
     ]
    }
   ],
   "source": [
    "# Time for a simple query with the on-disk ctable\n",
    "t0 = time()\n",
    "result = dblens[\"title == 'Tom and Huck (1995)'\"]\n",
    "dbsmplqtime = time() - t0\n",
    "print(\"time (and length) for simple query with on-disk bcolz: %.2f (%d)\" %\n",
    "      (dbsmplqtime, len(result)))\n",
    "#print repr(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time (and length) for complex query with on-disk bcolz: 0.12 (74)\n"
     ]
    }
   ],
   "source": [
    "# Time for a complex query with the uncompressed ctable\n",
    "t0 = time()\n",
    "#result = dblens[\"(title == 'Tom and Huck (1995)') & (rating == 5)\"]['user_id']\n",
    "result = [r.user_id for r in dblens.where(\n",
    "    \"(title == 'Tom and Huck (1995)') & (rating == 5)\", outcols=['user_id'])]\n",
    "dbcmplxqtime = time() - t0\n",
    "print(\"time (and length) for complex query with on-disk bcolz: %.2f (%d)\" %\n",
    "      (dbcmplxqtime, len(result)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/francesc/miniconda3/envs/py27/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2869: FutureWarning: \n",
      "mpl_style had been deprecated and will be removed in a future version.\n",
      "Use `matplotlib.pyplot.style.use` instead.\n",
      "\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4464189a10>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/francesc/miniconda3/envs/py27/lib/python2.7/site-packages/matplotlib/font_manager.py:1288: 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": 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myt3gwYP11ltv6Z133lG3bt2UmZmplStXqlq1aqWeg+joaB04cECtWrVSzZo1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\n0tGjRzVt2jS5urpq+PDheuihh7Rv3z7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z507t2bNH4eHhqqys1IcffqiuXbvq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4464430f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Sizes\n",
    "index = [\"pandas\", \"bcolz (memory, nocompr)\", \"bcolz (memory, compr)\", \"bcolz (disk, compr)\"]\n",
    "#index = [\"bcolz (disk)\", \"bcolz (compr)\", \"bcolz (nocompr)\", \"pandas\"]\n",
    "df = pd.DataFrame({'size (MB)': [dfsize, nzsize, zsize, dbsize]}, index=index)\n",
    "pd.options.display.mpl_style = 'default'\n",
    "df.plot(kind='barh', figsize=(12,7), fontsize=16, title=\"Size of the datasets\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4461c208d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GOWkOFpMAfH194e/vDwBwd3fH06dPsWfPHvTo0QP6+voICwtDWloaFi9ejEaN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\nAMCy/wMdLvD14SxvigAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4461ba8850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Simple query times\n",
    "df = pd.DataFrame({'time (sec)': [dfsmplqtime, nzsmplqtime, zsmplqtime, dbsmplqtime]}, index=index)\n",
    "pd.options.display.mpl_style = 'default'\n",
    "df.plot(kind='barh', figsize=(12,5), fontsize=16, title=\"Simple query times for MovieLens 10M\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4461c74c10>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iEBAQgNDQUCgUCqSnp6OgoAAuLi4oLy/HihUrcPfuXUybNg0dOnTAzz//jHXr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\no5gEAAAAAFj2f0sgfKlPD1pMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4461b526d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Complex query times\n",
    "df = pd.DataFrame({'time (sec)': [dfcmplxqtime, nzcmplxqtime, zcmplxqtime, dbcmplxqtime]}, index=index)\n",
    "pd.options.display.mpl_style = 'default'\n",
    "df.plot(kind='barh', figsize=(12,5), fontsize=16, title=\"Complex query times for MovieLens 10M\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}