PyDataStack2016.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# These examples are heavily influenced by the documentation of the packages and the work of Rob Story"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<dask.context.set_options at 0x107064908>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import dask as dask\n",
    "import pymc3 as pm\n",
    "dask.set_options(get=dask.async.get_sync)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", header=None)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>White</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Black</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Black</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0                  1       2           3   4                    5   \\\n",
       "0  39          State-gov   77516   Bachelors  13        Never-married   \n",
       "1  50   Self-emp-not-inc   83311   Bachelors  13   Married-civ-spouse   \n",
       "2  38            Private  215646     HS-grad   9             Divorced   \n",
       "3  53            Private  234721        11th   7   Married-civ-spouse   \n",
       "4  28            Private  338409   Bachelors  13   Married-civ-spouse   \n",
       "\n",
       "                   6               7       8        9     10  11  12  \\\n",
       "0        Adm-clerical   Not-in-family   White     Male  2174   0  40   \n",
       "1     Exec-managerial         Husband   White     Male     0   0  13   \n",
       "2   Handlers-cleaners   Not-in-family   White     Male     0   0  40   \n",
       "3   Handlers-cleaners         Husband   Black     Male     0   0  40   \n",
       "4      Prof-specialty            Wife   Black   Female     0   0  40   \n",
       "\n",
       "               13      14  \n",
       "0   United-States   <=50K  \n",
       "1   United-States   <=50K  \n",
       "2   United-States   <=50K  \n",
       "3   United-States   <=50K  \n",
       "4            Cuba   <=50K  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I only have the standard library, I'm stuck on a restricted machine\n",
    "* The Python standard library works kinda well. \n",
    "* We have to write our own converter though"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Altered version of Rob Story's example\n",
    "import csv\n",
    "\n",
    "conversion_map = {\n",
    "    'age': int,\n",
    "    'workclass': str,\n",
    "    'fnlwgt': int,\n",
    "    'education-categorical': str,\n",
    "    'educ': int,\n",
    "    'occupation': str,\n",
    "    'sex': str,\n",
    "    'capital-gain': float,\n",
    "    'capital-loss': float,\n",
    "    'hours': int,\n",
    "    'native-country': str,\n",
    "    'income': str\n",
    "}\n",
    "def converter(type_map, row):\n",
    "    \"\"\"Yep, we need to roll our own type conversions.\"\"\"\n",
    "    converted_row = {}\n",
    "    for col, val in row.items():\n",
    "        converter = type_map.get(col)\n",
    "        if converter:\n",
    "            converted_row[col] = converter(val)\n",
    "        else:\n",
    "            converted_row[col] = val\n",
    "    return converted_row\n",
    "\n",
    "with open('adult.csv', 'r') as f:\n",
    "    reader = csv.DictReader(f)\n",
    "    adult2 = [converter(conversion_map, r) for r in reader]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'': '0',\n",
       "  'age': 39,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '2174',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 77516,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Adm-clerical',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' State-gov'},\n",
       " {'': '1',\n",
       "  'age': 50,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 83311,\n",
       "  'hours': 13,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Self-emp-not-inc'}]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult2[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# defaultdict is awesome. defaultdict is awesome.\n",
    "from collections import defaultdict\n",
    "\n",
    "def grouper(grouping_col, seq):\n",
    "    \"\"\"People have definitely written a faster version than what I'm about to write\n",
    "    Thanks to Rob Story for this one\"\"\"\n",
    "    groups = defaultdict(lambda: defaultdict(list))\n",
    "    for row in seq:\n",
    "        group = groups[row[grouping_col]]\n",
    "        for k, v in row.items():\n",
    "            if k != grouping_col:\n",
    "                group[k].append(v)\n",
    "    return groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "groups = grouper('occupation', adult2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's say we wanted to group by occupation and get the mean hours worked. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ?': 31.90613130765057,\n",
       " ' Adm-clerical': 37.55835543766578,\n",
       " ' Armed-Forces': 40.666666666666664,\n",
       " ' Craft-repair': 42.30422054159551,\n",
       " ' Exec-managerial': 44.9877029021151,\n",
       " ' Farming-fishing': 46.989939637826964,\n",
       " ' Handlers-cleaners': 37.947445255474456,\n",
       " ' Machine-op-inspct': 40.755744255744254,\n",
       " ' Other-service': 34.70166919575114,\n",
       " ' Priv-house-serv': 32.88590604026846,\n",
       " ' Prof-specialty': 42.38671497584541,\n",
       " ' Protective-serv': 42.87057010785824,\n",
       " ' Sales': 40.78109589041096,\n",
       " ' Tech-support': 39.432112068965516,\n",
       " ' Transport-moving': 44.65623043206011}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary = {}\n",
    "for group, values in groups.items():\n",
    "    summary[group] = sum(values['hours']) / len(values['hours'])\n",
    "summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# We want the maximum age in the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_max_age():\n",
    "    max_age = 0\n",
    "    for row in adult2:\n",
    "        if row['age'] > 1 and row['age'] > max_age:\n",
    "            max_age = row['age']\n",
    "    return max_age\n",
    "get_max_age()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# More compact yet again: generator expression!\n",
    "max(row['age'] for row in adult2 if row['age'] > 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 7.93 ms per loop\n"
     ]
    }
   ],
   "source": [
    "# Which is faster? \n",
    "%timeit max(row['age'] for row in adult2 if row['age'] > 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 6.25 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit get_max_age()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# I wanna count stuff in a functional way \n",
    "* I care about composibility, purity, all that Functional stuff \n",
    "* PSA: It's got a really cool API well worth a read - docs are here [toolz](http://toolz.readthedocs.org/en/latest/index.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import toolz as tz\n",
    "accounts = [(1, 'Alice', 100, 'F'),(2, 'Bob', 200, 'M'),(3, 'Charlie', 150, 'M'),\n",
    "            (4, 'Dennis', 50, 'M'),(5, 'Edith', 300, 'F'), (6, 'Alice', 150, 'F'), (7, 'Charlie', 350, 'M')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* We can do things like compute frequencies. Let's look at the 2 column (python counts from 0) and the frequency of names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alice': 2, 'Bob': 1, 'Charlie': 2, 'Dennis': 1, 'Edith': 1}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tz.frequencies([r[1] for r in accounts])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to reproduce \n",
    "```sql \n",
    "SELECT name, balance\n",
    "FROM accounts\n",
    "WHERE balance > 200``` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Charlie': 350, 'Edith': 300}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Toolz has currying!\n",
    "import toolz.curried as tzc\n",
    "tzc.pipe(accounts, \n",
    "         tzc.filter(lambda r: r[2] > 200),\n",
    "         tzc.map(tzc.get([1,2])),dict)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'': '0',\n",
       "  'age': 39,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '2174',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 77516,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Adm-clerical',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' State-gov'},\n",
       " {'': '1',\n",
       "  'age': 50,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 83311,\n",
       "  'hours': 13,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Self-emp-not-inc'},\n",
       " {'': '2',\n",
       "  'age': 38,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 215646,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Handlers-cleaners',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '3',\n",
       "  'age': 53,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 7,\n",
       "  'education-categorical': ' 11th',\n",
       "  'fnlwgt': 234721,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Handlers-cleaners',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '4',\n",
       "  'age': 28,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 338409,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' Cuba',\n",
       "  'occupation': ' Prof-specialty',\n",
       "  'relationship': ' Wife',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '5',\n",
       "  'age': 37,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 14,\n",
       "  'education-categorical': ' Masters',\n",
       "  'fnlwgt': 284582,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Wife',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '6',\n",
       "  'age': 49,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 5,\n",
       "  'education-categorical': ' 9th',\n",
       "  'fnlwgt': 160187,\n",
       "  'hours': 16,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-spouse-absent',\n",
       "  'native-country': ' Jamaica',\n",
       "  'occupation': ' Other-service',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
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       "  'fnlwgt': 164966,\n",
       "  'hours': 40,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' India',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '969',\n",
       "  'age': 36,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 11,\n",
       "  'education-categorical': ' Assoc-voc',\n",
       "  'fnlwgt': 94954,\n",
       "  'hours': 20,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Widowed',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' ?',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' ?'},\n",
       " {'': '970',\n",
       "  'age': 34,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 202046,\n",
       "  'hours': 35,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Craft-repair',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '971',\n",
       "  'age': 28,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 161538,\n",
       "  'hours': 35,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Tech-support',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '972',\n",
       "  'age': 67,\n",
       "  'capital-loss': 2392.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 105252,\n",
       "  'hours': 40,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Widowed',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '973',\n",
       "  'age': 37,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 200153,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Craft-repair',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '974',\n",
       "  'age': 44,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 32185,\n",
       "  'hours': 70,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Transport-moving',\n",
       "  'relationship': ' Unmarried',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '975',\n",
       "  'age': 25,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 10,\n",
       "  'education-categorical': ' Some-college',\n",
       "  'fnlwgt': 178326,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Sales',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '976',\n",
       "  'age': 21,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '4101',\n",
       "  'educ': 10,\n",
       "  'education-categorical': ' Some-college',\n",
       "  'fnlwgt': 255957,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '977',\n",
       "  'age': 40,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 14,\n",
       "  'education-categorical': ' Masters',\n",
       "  'fnlwgt': 188693,\n",
       "  'hours': 35,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Prof-specialty',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' State-gov'},\n",
       " {'': '978',\n",
       "  'age': 78,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '2964',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 182977,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Widowed',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Other-service',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '979',\n",
       "  'age': 34,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 159929,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Handlers-cleaners',\n",
       "  'relationship': ' Own-child',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '980',\n",
       "  'age': 49,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 123207,\n",
       "  'hours': 44,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Adm-clerical',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '981',\n",
       "  'age': 22,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 12,\n",
       "  'education-categorical': ' Assoc-acdm',\n",
       "  'fnlwgt': 284317,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Adm-clerical',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '982',\n",
       "  'age': 23,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 184699,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' ?',\n",
       "  'relationship': ' Unmarried',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' ?'},\n",
       " {'': '983',\n",
       "  'age': 60,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 154474,\n",
       "  'hours': 42,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Farming-fishing',\n",
       "  'relationship': ' Unmarried',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Self-emp-not-inc'},\n",
       " {'': '984',\n",
       "  'age': 45,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 318280,\n",
       "  'hours': 40,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Widowed',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Protective-serv',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Local-gov'},\n",
       " {'': '985',\n",
       "  'age': 63,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 11,\n",
       "  'education-categorical': ' Assoc-voc',\n",
       "  'fnlwgt': 254907,\n",
       "  'hours': 20,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Other-service',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '986',\n",
       "  'age': 41,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 349221,\n",
       "  'hours': 35,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Craft-repair',\n",
       "  'relationship': ' Own-child',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '987',\n",
       "  'age': 47,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 335973,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Adm-clerical',\n",
       "  'relationship': ' Unmarried',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '988',\n",
       "  'age': 44,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 126701,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Craft-repair',\n",
       "  'relationship': ' Unmarried',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '989',\n",
       "  'age': 51,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '3325',\n",
       "  'educ': 10,\n",
       "  'education-categorical': ' Some-college',\n",
       "  'fnlwgt': 122159,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Widowed',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Prof-specialty',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Female',\n",
       "  'workclass': ' Private'},\n",
       " {'': '990',\n",
       "  'age': 46,\n",
       "  'capital-loss': 1504.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 187370,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Sales',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '991',\n",
       "  'age': 41,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 11,\n",
       "  'education-categorical': ' Assoc-voc',\n",
       "  'fnlwgt': 194636,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Machine-op-inspct',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '992',\n",
       "  'age': 50,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 124793,\n",
       "  'hours': 30,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Craft-repair',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Self-emp-not-inc'},\n",
       " {'': '993',\n",
       "  'age': 47,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 192835,\n",
       "  'hours': 50,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Adm-clerical',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '994',\n",
       "  'age': 35,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 290226,\n",
       "  'hours': 45,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Never-married',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '995',\n",
       "  'age': 56,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 9,\n",
       "  'education-categorical': ' HS-grad',\n",
       "  'fnlwgt': 112840,\n",
       "  'hours': 55,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '996',\n",
       "  'age': 45,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 14,\n",
       "  'education-categorical': ' Masters',\n",
       "  'fnlwgt': 89325,\n",
       "  'hours': 45,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Prof-specialty',\n",
       "  'relationship': ' Not-in-family',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '997',\n",
       "  'age': 48,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '0',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 33109,\n",
       "  'hours': 58,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Divorced',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Unmarried',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Federal-gov'},\n",
       " {'': '998',\n",
       "  'age': 40,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '2580',\n",
       "  'educ': 10,\n",
       "  'education-categorical': ' Some-college',\n",
       "  'fnlwgt': 82465,\n",
       "  'hours': 40,\n",
       "  'income': ' <=50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Machine-op-inspct',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Private'},\n",
       " {'': '999',\n",
       "  'age': 39,\n",
       "  'capital-loss': 0.0,\n",
       "  'captial-gain': '15024',\n",
       "  'educ': 13,\n",
       "  'education-categorical': ' Bachelors',\n",
       "  'fnlwgt': 329980,\n",
       "  'hours': 50,\n",
       "  'income': ' >50K',\n",
       "  'marital-status': ' Married-civ-spouse',\n",
       "  'native-country': ' United-States',\n",
       "  'occupation': ' Exec-managerial',\n",
       "  'relationship': ' Husband',\n",
       "  'sex': ' Male',\n",
       "  'workclass': ' Self-emp-inc'},\n",
       " ...]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
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       " 90: 43}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import toolz as tz\n",
    "tz.frequencies([r['age'] for r in adult2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ?': 1843,\n",
       " ' Adm-clerical': 3770,\n",
       " ' Armed-Forces': 9,\n",
       " ' Craft-repair': 4099,\n",
       " ' Exec-managerial': 4066,\n",
       " ' Farming-fishing': 994,\n",
       " ' Handlers-cleaners': 1370,\n",
       " ' Machine-op-inspct': 2002,\n",
       " ' Other-service': 3295,\n",
       " ' Priv-house-serv': 149,\n",
       " ' Prof-specialty': 4140,\n",
       " ' Protective-serv': 649,\n",
       " ' Sales': 3650,\n",
       " ' Tech-support': 928,\n",
       " ' Transport-moving': 1597}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# What are our occupation counts?\n",
    "tz.countby(lambda x: x['occupation'], adult2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What about our the max hours from above? Reduction!\n",
    "def comparo(accum, row):\n",
    "    hours = row['hours']\n",
    "    if hours > accum:\n",
    "        return hours\n",
    "    else:\n",
    "        return accum\n",
    "    \n",
    "tz.reduce(comparo, adult2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# We could have also threaded here\n",
    "tz.thread_last(adult2, \n",
    "               (tz.map, lambda x: x['hours']), \n",
    "               max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare the times a bit here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 8.24 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit tz.reduce(comparo, adult2, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100 loops, best of 3: 9.82 ms per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit tz.thread_last(adult2, (tz.map, lambda x: x['hours']),   max)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's say we wanted to find out a count of all the occupations who have education greater than 15. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ?': 15,\n",
       " ' Adm-clerical': 5,\n",
       " ' Craft-repair': 2,\n",
       " ' Exec-managerial': 55,\n",
       " ' Farming-fishing': 1,\n",
       " ' Machine-op-inspct': 1,\n",
       " ' Other-service': 1,\n",
       " ' Prof-specialty': 321,\n",
       " ' Sales': 8,\n",
       " ' Tech-support': 3,\n",
       " ' Transport-moving': 1}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Toolz has currying!\n",
    "\n",
    "import toolz.curried as tzc\n",
    "tzc.pipe(adult2, \n",
    "         tzc.filter(lambda r: r['educ'] > 15),\n",
    "         tzc.map(lambda r: (r['occupation'],)),\n",
    "         tzc.countby(lambda r: r[0]),\n",
    "         dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ?': 15,\n",
       " ' Adm-clerical': 5,\n",
       " ' Armed-Forces': 0,\n",
       " ' Craft-repair': 2,\n",
       " ' Exec-managerial': 55,\n",
       " ' Farming-fishing': 1,\n",
       " ' Handlers-cleaners': 0,\n",
       " ' Machine-op-inspct': 1,\n",
       " ' Other-service': 1,\n",
       " ' Priv-house-serv': 0,\n",
       " ' Prof-specialty': 321,\n",
       " ' Protective-serv': 0,\n",
       " ' Sales': 8,\n",
       " ' Tech-support': 3,\n",
       " ' Transport-moving': 1}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleanest/best way: reduceby: Groupby + reduce\n",
    "def increment(accum, row):\n",
    "    if row['educ'] > 15:\n",
    "        return accum + 1\n",
    "    else:\n",
    "        return accum\n",
    "\n",
    "tz.reduceby('occupation', \n",
    "            increment,\n",
    "            adult2, 0)       "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Xarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import xarray as xray\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3, y: 4)>\n",
       "array([[  1,   2,   3,   4],\n",
       "       [ 10,  20,  30,  40],\n",
       "       [100, 200, 300, 400]])\n",
       "Coordinates:\n",
       "  * x        (x) <U1 'a' 'b' 'c'\n",
       "  * y        (y) <U3 'foo' 'bar' 'baz' 'qux'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "arr = np.array([[1, 2, 3, 4], \n",
    "                [10, 20, 30, 40],\n",
    "                [100, 200, 300, 400]])\n",
    "dim0_coords = ['a', 'b', 'c']\n",
    "dim1_coords = ['foo', 'bar', 'baz', 'qux']\n",
    "da = xray.DataArray(arr, [('x', dim0_coords), ('y', dim1_coords)])\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1,   2,   3,   4],\n",
       "       [ 10,  20,  30,  40],\n",
       "       [100, 200, 300, 400]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3, y: 4)>\n",
       "array([[  1,   2,   3,   4],\n",
       "       [ 10,  20,  30,  40],\n",
       "       [100, 200, 300, 400]])\n",
       "Coordinates:\n",
       "  * x        (x) <U1 'a' 'b' 'c'\n",
       "  * y        (y) <U3 'foo' 'bar' 'baz' 'qux'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('x', 'y')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.dims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Coordinates:\n",
       "  * x        (x) <U1 'a' 'b' 'c'\n",
       "  * y        (y) <U3 'foo' 'bar' 'baz' 'qux'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (y: 4)>\n",
       "array([10, 20, 30, 40])\n",
       "Coordinates:\n",
       "    x        <U1 'b'\n",
       "  * y        (y) <U3 'foo' 'bar' 'baz' 'qux'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Indexing!\n",
    "da.loc['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.DataArray (x: 3)>\n",
       "array([   2.5,   25. ,  250. ])\n",
       "Coordinates:\n",
       "  * x        (x) <U1 'a' 'b' 'c'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a mean by label\n",
    "da.mean(dim='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Pandas \n",
    "* Pandas works very well for tabular data tasks. \n",
    "* The API is stabilizing\n",
    "* We should see Pandas increasingly become a core member of the PyData stack. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The pipe operator\n",
    "* One of the newest features in Pandas which is worth talking about is the pipe operator.\n",
    "* This is very similar in style to Unix pipes. \n",
    "* I include it because it simplifies some of the ETL like code we often do in Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "column_names =['age', 'workclass', 'fnlwgt', \n",
    "                'education-categorical', 'educ', \n",
    "                'marital-status', 'occupation',\n",
    "                'relationship', 'race', 'sex', \n",
    "                'captial-gain', 'capital-loss', \n",
    "                'hours', 'native-country', \n",
    "                'income']\n",
    "adult = (pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", header=None, names=column_names)\n",
    "         .drop(['race'], axis='columns'))\n",
    "\n",
    "adult.head()\n",
    "adult.to_csv('adult.csv')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>captial-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educ</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>42.764706</td>\n",
       "      <td>235889.372549</td>\n",
       "      <td>898.392157</td>\n",
       "      <td>66.490196</td>\n",
       "      <td>36.647059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46.142857</td>\n",
       "      <td>239303.000000</td>\n",
       "      <td>125.875000</td>\n",
       "      <td>48.327381</td>\n",
       "      <td>38.255952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42.885886</td>\n",
       "      <td>232448.333333</td>\n",
       "      <td>176.021021</td>\n",
       "      <td>68.252252</td>\n",
       "      <td>38.897898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>48.445820</td>\n",
       "      <td>188079.171827</td>\n",
       "      <td>233.939628</td>\n",
       "      <td>65.668731</td>\n",
       "      <td>39.366873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>41.060311</td>\n",
       "      <td>202485.066148</td>\n",
       "      <td>342.089494</td>\n",
       "      <td>28.998054</td>\n",
       "      <td>38.044747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>37.429796</td>\n",
       "      <td>196832.465166</td>\n",
       "      <td>404.574491</td>\n",
       "      <td>56.845659</td>\n",
       "      <td>37.052519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>32.355745</td>\n",
       "      <td>194928.077447</td>\n",
       "      <td>215.097872</td>\n",
       "      <td>50.079149</td>\n",
       "      <td>33.925957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>199097.508083</td>\n",
       "      <td>284.087760</td>\n",
       "      <td>32.337182</td>\n",
       "      <td>35.780600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>38.974479</td>\n",
       "      <td>189538.739739</td>\n",
       "      <td>576.800114</td>\n",
       "      <td>70.466622</td>\n",
       "      <td>40.575374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>35.756275</td>\n",
       "      <td>188742.922370</td>\n",
       "      <td>598.824167</td>\n",
       "      <td>71.637087</td>\n",
       "      <td>38.852284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>38.553546</td>\n",
       "      <td>181936.016643</td>\n",
       "      <td>715.051375</td>\n",
       "      <td>72.754703</td>\n",
       "      <td>41.610709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37.381443</td>\n",
       "      <td>193424.093721</td>\n",
       "      <td>640.399250</td>\n",
       "      <td>93.418932</td>\n",
       "      <td>40.504217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>38.904949</td>\n",
       "      <td>188055.914846</td>\n",
       "      <td>1756.299533</td>\n",
       "      <td>118.350327</td>\n",
       "      <td>42.614006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>44.049913</td>\n",
       "      <td>179852.362739</td>\n",
       "      <td>2562.563552</td>\n",
       "      <td>166.719675</td>\n",
       "      <td>43.836332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>44.746528</td>\n",
       "      <td>185663.706597</td>\n",
       "      <td>10414.416667</td>\n",
       "      <td>231.203125</td>\n",
       "      <td>47.425347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>47.702179</td>\n",
       "      <td>186698.760291</td>\n",
       "      <td>4770.145278</td>\n",
       "      <td>262.845036</td>\n",
       "      <td>46.973366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            age         fnlwgt  captial-gain  capital-loss      hours\n",
       "educ                                                                 \n",
       "1     42.764706  235889.372549    898.392157     66.490196  36.647059\n",
       "2     46.142857  239303.000000    125.875000     48.327381  38.255952\n",
       "3     42.885886  232448.333333    176.021021     68.252252  38.897898\n",
       "4     48.445820  188079.171827    233.939628     65.668731  39.366873\n",
       "5     41.060311  202485.066148    342.089494     28.998054  38.044747\n",
       "6     37.429796  196832.465166    404.574491     56.845659  37.052519\n",
       "7     32.355745  194928.077447    215.097872     50.079149  33.925957\n",
       "8     32.000000  199097.508083    284.087760     32.337182  35.780600\n",
       "9     38.974479  189538.739739    576.800114     70.466622  40.575374\n",
       "10    35.756275  188742.922370    598.824167     71.637087  38.852284\n",
       "11    38.553546  181936.016643    715.051375     72.754703  41.610709\n",
       "12    37.381443  193424.093721    640.399250     93.418932  40.504217\n",
       "13    38.904949  188055.914846   1756.299533    118.350327  42.614006\n",
       "14    44.049913  179852.362739   2562.563552    166.719675  43.836332\n",
       "15    44.746528  185663.706597  10414.416667    231.203125  47.425347\n",
       "16    47.702179  186698.760291   4770.145278    262.845036  46.973366"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One little example of the power of the Pandas API\n",
    "adult.groupby('educ').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* I want to create a new column, quickly and in a fast way without harming my data integrity.\n",
    "* One of the coolest new features in Pandas is the assign operator. It makes the API really easy to use, \n",
    "especially with the pipe operator mentioned elsewhere in the presentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-categorical</th>\n",
       "      <th>educ</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>captial-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "      <th>age2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>284582</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>160187</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-spouse-absent</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>Jamaica</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>209642</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>2704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>45781</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>14084</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>42</td>\n",
       "      <td>Private</td>\n",
       "      <td>159449</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>5178</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>280464</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>141297</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>India</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>23</td>\n",
       "      <td>Private</td>\n",
       "      <td>122272</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>205019</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>121772</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>?</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>245487</td>\n",
       "      <td>7th-8th</td>\n",
       "      <td>4</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>176756</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>186824</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>28887</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>292175</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>193524</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>302146</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>35</td>\n",
       "      <td>Federal-gov</td>\n",
       "      <td>76845</td>\n",
       "      <td>9th</td>\n",
       "      <td>5</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>117037</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>2042</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>59</td>\n",
       "      <td>Private</td>\n",
       "      <td>109015</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>3481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>216851</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>3136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>19</td>\n",
       "      <td>Private</td>\n",
       "      <td>168294</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>54</td>\n",
       "      <td>?</td>\n",
       "      <td>180211</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>South</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>2916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>39</td>\n",
       "      <td>Private</td>\n",
       "      <td>367260</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>49</td>\n",
       "      <td>Private</td>\n",
       "      <td>193366</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32531</th>\n",
       "      <td>30</td>\n",
       "      <td>?</td>\n",
       "      <td>33811</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32532</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>204461</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32533</th>\n",
       "      <td>54</td>\n",
       "      <td>Private</td>\n",
       "      <td>337992</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>Japan</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>2916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32534</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>179137</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32535</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>325033</td>\n",
       "      <td>12th</td>\n",
       "      <td>8</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32536</th>\n",
       "      <td>34</td>\n",
       "      <td>Private</td>\n",
       "      <td>160216</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>55</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32537</th>\n",
       "      <td>30</td>\n",
       "      <td>Private</td>\n",
       "      <td>345898</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32538</th>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>139180</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>15020</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32539</th>\n",
       "      <td>71</td>\n",
       "      <td>?</td>\n",
       "      <td>287372</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>16</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>5041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32540</th>\n",
       "      <td>45</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>252208</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32541</th>\n",
       "      <td>41</td>\n",
       "      <td>?</td>\n",
       "      <td>202822</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Separated</td>\n",
       "      <td>?</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32542</th>\n",
       "      <td>72</td>\n",
       "      <td>?</td>\n",
       "      <td>129912</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>?</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>5184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32543</th>\n",
       "      <td>45</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>119199</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>2025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32544</th>\n",
       "      <td>31</td>\n",
       "      <td>Private</td>\n",
       "      <td>199655</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Other-service</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32545</th>\n",
       "      <td>39</td>\n",
       "      <td>Local-gov</td>\n",
       "      <td>111499</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32546</th>\n",
       "      <td>37</td>\n",
       "      <td>Private</td>\n",
       "      <td>198216</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32547</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>260761</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32548</th>\n",
       "      <td>65</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>99359</td>\n",
       "      <td>Prof-school</td>\n",
       "      <td>15</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>4225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32549</th>\n",
       "      <td>43</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>255835</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Other-relative</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32550</th>\n",
       "      <td>43</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>27242</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32551</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>34066</td>\n",
       "      <td>10th</td>\n",
       "      <td>6</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32552</th>\n",
       "      <td>43</td>\n",
       "      <td>Private</td>\n",
       "      <td>84661</td>\n",
       "      <td>Assoc-voc</td>\n",
       "      <td>11</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Sales</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32553</th>\n",
       "      <td>32</td>\n",
       "      <td>Private</td>\n",
       "      <td>116138</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>Taiwan</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>1024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32554</th>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>321865</td>\n",
       "      <td>Masters</td>\n",
       "      <td>14</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>2809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32555</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>310152</td>\n",
       "      <td>Some-college</td>\n",
       "      <td>10</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32556</th>\n",
       "      <td>27</td>\n",
       "      <td>Private</td>\n",
       "      <td>257302</td>\n",
       "      <td>Assoc-acdm</td>\n",
       "      <td>12</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Tech-support</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32557</th>\n",
       "      <td>40</td>\n",
       "      <td>Private</td>\n",
       "      <td>154374</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32558</th>\n",
       "      <td>58</td>\n",
       "      <td>Private</td>\n",
       "      <td>151910</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Widowed</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>3364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32559</th>\n",
       "      <td>22</td>\n",
       "      <td>Private</td>\n",
       "      <td>201490</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Own-child</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32560</th>\n",
       "      <td>52</td>\n",
       "      <td>Self-emp-inc</td>\n",
       "      <td>287927</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>15024</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&gt;50K</td>\n",
       "      <td>2704</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32561 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       age          workclass  fnlwgt education-categorical  educ  \\\n",
       "0       39          State-gov   77516             Bachelors    13   \n",
       "1       50   Self-emp-not-inc   83311             Bachelors    13   \n",
       "2       38            Private  215646               HS-grad     9   \n",
       "3       53            Private  234721                  11th     7   \n",
       "4       28            Private  338409             Bachelors    13   \n",
       "5       37            Private  284582               Masters    14   \n",
       "6       49            Private  160187                   9th     5   \n",
       "7       52   Self-emp-not-inc  209642               HS-grad     9   \n",
       "8       31            Private   45781               Masters    14   \n",
       "9       42            Private  159449             Bachelors    13   \n",
       "10      37            Private  280464          Some-college    10   \n",
       "11      30          State-gov  141297             Bachelors    13   \n",
       "12      23            Private  122272             Bachelors    13   \n",
       "13      32            Private  205019            Assoc-acdm    12   \n",
       "14      40            Private  121772             Assoc-voc    11   \n",
       "15      34            Private  245487               7th-8th     4   \n",
       "16      25   Self-emp-not-inc  176756               HS-grad     9   \n",
       "17      32            Private  186824               HS-grad     9   \n",
       "18      38            Private   28887                  11th     7   \n",
       "19      43   Self-emp-not-inc  292175               Masters    14   \n",
       "20      40            Private  193524             Doctorate    16   \n",
       "21      54            Private  302146               HS-grad     9   \n",
       "22      35        Federal-gov   76845                   9th     5   \n",
       "23      43            Private  117037                  11th     7   \n",
       "24      59            Private  109015               HS-grad     9   \n",
       "25      56          Local-gov  216851             Bachelors    13   \n",
       "26      19            Private  168294               HS-grad     9   \n",
       "27      54                  ?  180211          Some-college    10   \n",
       "28      39            Private  367260               HS-grad     9   \n",
       "29      49            Private  193366               HS-grad     9   \n",
       "...    ...                ...     ...                   ...   ...   \n",
       "32531   30                  ?   33811             Bachelors    13   \n",
       "32532   34            Private  204461             Doctorate    16   \n",
       "32533   54            Private  337992             Bachelors    13   \n",
       "32534   37            Private  179137          Some-college    10   \n",
       "32535   22            Private  325033                  12th     8   \n",
       "32536   34            Private  160216             Bachelors    13   \n",
       "32537   30            Private  345898               HS-grad     9   \n",
       "32538   38            Private  139180             Bachelors    13   \n",
       "32539   71                  ?  287372             Doctorate    16   \n",
       "32540   45          State-gov  252208               HS-grad     9   \n",
       "32541   41                  ?  202822               HS-grad     9   \n",
       "32542   72                  ?  129912               HS-grad     9   \n",
       "32543   45          Local-gov  119199            Assoc-acdm    12   \n",
       "32544   31            Private  199655               Masters    14   \n",
       "32545   39          Local-gov  111499            Assoc-acdm    12   \n",
       "32546   37            Private  198216            Assoc-acdm    12   \n",
       "32547   43            Private  260761               HS-grad     9   \n",
       "32548   65   Self-emp-not-inc   99359           Prof-school    15   \n",
       "32549   43          State-gov  255835          Some-college    10   \n",
       "32550   43   Self-emp-not-inc   27242          Some-college    10   \n",
       "32551   32            Private   34066                  10th     6   \n",
       "32552   43            Private   84661             Assoc-voc    11   \n",
       "32553   32            Private  116138               Masters    14   \n",
       "32554   53            Private  321865               Masters    14   \n",
       "32555   22            Private  310152          Some-college    10   \n",
       "32556   27            Private  257302            Assoc-acdm    12   \n",
       "32557   40            Private  154374               HS-grad     9   \n",
       "32558   58            Private  151910               HS-grad     9   \n",
       "32559   22            Private  201490               HS-grad     9   \n",
       "32560   52       Self-emp-inc  287927               HS-grad     9   \n",
       "\n",
       "               marital-status          occupation     relationship      sex  \\\n",
       "0               Never-married        Adm-clerical    Not-in-family     Male   \n",
       "1          Married-civ-spouse     Exec-managerial          Husband     Male   \n",
       "2                    Divorced   Handlers-cleaners    Not-in-family     Male   \n",
       "3          Married-civ-spouse   Handlers-cleaners          Husband     Male   \n",
       "4          Married-civ-spouse      Prof-specialty             Wife   Female   \n",
       "5          Married-civ-spouse     Exec-managerial             Wife   Female   \n",
       "6       Married-spouse-absent       Other-service    Not-in-family   Female   \n",
       "7          Married-civ-spouse     Exec-managerial          Husband     Male   \n",
       "8               Never-married      Prof-specialty    Not-in-family   Female   \n",
       "9          Married-civ-spouse     Exec-managerial          Husband     Male   \n",
       "10         Married-civ-spouse     Exec-managerial          Husband     Male   \n",
       "11         Married-civ-spouse      Prof-specialty          Husband     Male   \n",
       "12              Never-married        Adm-clerical        Own-child   Female   \n",
       "13              Never-married               Sales    Not-in-family     Male   \n",
       "14         Married-civ-spouse        Craft-repair          Husband     Male   \n",
       "15         Married-civ-spouse    Transport-moving          Husband     Male   \n",
       "16              Never-married     Farming-fishing        Own-child     Male   \n",
       "17              Never-married   Machine-op-inspct        Unmarried     Male   \n",
       "18         Married-civ-spouse               Sales          Husband     Male   \n",
       "19                   Divorced     Exec-managerial        Unmarried   Female   \n",
       "20         Married-civ-spouse      Prof-specialty          Husband     Male   \n",
       "21                  Separated       Other-service        Unmarried   Female   \n",
       "22         Married-civ-spouse     Farming-fishing          Husband     Male   \n",
       "23         Married-civ-spouse    Transport-moving          Husband     Male   \n",
       "24                   Divorced        Tech-support        Unmarried   Female   \n",
       "25         Married-civ-spouse        Tech-support          Husband     Male   \n",
       "26              Never-married        Craft-repair        Own-child     Male   \n",
       "27         Married-civ-spouse                   ?          Husband     Male   \n",
       "28                   Divorced     Exec-managerial    Not-in-family     Male   \n",
       "29         Married-civ-spouse        Craft-repair          Husband     Male   \n",
       "...                       ...                 ...              ...      ...   \n",
       "32531           Never-married                   ?    Not-in-family   Female   \n",
       "32532      Married-civ-spouse      Prof-specialty          Husband     Male   \n",
       "32533      Married-civ-spouse     Exec-managerial          Husband     Male   \n",
       "32534                Divorced        Adm-clerical        Unmarried   Female   \n",
       "32535           Never-married     Protective-serv        Own-child     Male   \n",
       "32536           Never-married     Exec-managerial    Not-in-family   Female   \n",
       "32537           Never-married        Craft-repair    Not-in-family     Male   \n",
       "32538                Divorced      Prof-specialty        Unmarried   Female   \n",
       "32539      Married-civ-spouse                   ?          Husband     Male   \n",
       "32540               Separated        Adm-clerical        Own-child   Female   \n",
       "32541               Separated                   ?    Not-in-family   Female   \n",
       "32542      Married-civ-spouse                   ?          Husband     Male   \n",
       "32543                Divorced      Prof-specialty        Unmarried   Female   \n",
       "32544                Divorced       Other-service    Not-in-family   Female   \n",
       "32545      Married-civ-spouse        Adm-clerical             Wife   Female   \n",
       "32546                Divorced        Tech-support    Not-in-family   Female   \n",
       "32547      Married-civ-spouse   Machine-op-inspct          Husband     Male   \n",
       "32548           Never-married      Prof-specialty    Not-in-family     Male   \n",
       "32549                Divorced        Adm-clerical   Other-relative   Female   \n",
       "32550      Married-civ-spouse        Craft-repair          Husband     Male   \n",
       "32551      Married-civ-spouse   Handlers-cleaners          Husband     Male   \n",
       "32552      Married-civ-spouse               Sales          Husband     Male   \n",
       "32553           Never-married        Tech-support    Not-in-family     Male   \n",
       "32554      Married-civ-spouse     Exec-managerial          Husband     Male   \n",
       "32555           Never-married     Protective-serv    Not-in-family     Male   \n",
       "32556      Married-civ-spouse        Tech-support             Wife   Female   \n",
       "32557      Married-civ-spouse   Machine-op-inspct          Husband     Male   \n",
       "32558                 Widowed        Adm-clerical        Unmarried   Female   \n",
       "32559           Never-married        Adm-clerical        Own-child     Male   \n",
       "32560      Married-civ-spouse     Exec-managerial             Wife   Female   \n",
       "\n",
       "       captial-gain  capital-loss  hours  native-country  income  age2  \n",
       "0              2174             0     40   United-States   <=50K  1521  \n",
       "1                 0             0     13   United-States   <=50K  2500  \n",
       "2                 0             0     40   United-States   <=50K  1444  \n",
       "3                 0             0     40   United-States   <=50K  2809  \n",
       "4                 0             0     40            Cuba   <=50K   784  \n",
       "5                 0             0     40   United-States   <=50K  1369  \n",
       "6                 0             0     16         Jamaica   <=50K  2401  \n",
       "7                 0             0     45   United-States    >50K  2704  \n",
       "8             14084             0     50   United-States    >50K   961  \n",
       "9              5178             0     40   United-States    >50K  1764  \n",
       "10                0             0     80   United-States    >50K  1369  \n",
       "11                0             0     40           India    >50K   900  \n",
       "12                0             0     30   United-States   <=50K   529  \n",
       "13                0             0     50   United-States   <=50K  1024  \n",
       "14                0             0     40               ?    >50K  1600  \n",
       "15                0             0     45          Mexico   <=50K  1156  \n",
       "16                0             0     35   United-States   <=50K   625  \n",
       "17                0             0     40   United-States   <=50K  1024  \n",
       "18                0             0     50   United-States   <=50K  1444  \n",
       "19                0             0     45   United-States    >50K  1849  \n",
       "20                0             0     60   United-States    >50K  1600  \n",
       "21                0             0     20   United-States   <=50K  2916  \n",
       "22                0             0     40   United-States   <=50K  1225  \n",
       "23                0          2042     40   United-States   <=50K  1849  \n",
       "24                0             0     40   United-States   <=50K  3481  \n",
       "25                0             0     40   United-States    >50K  3136  \n",
       "26                0             0     40   United-States   <=50K   361  \n",
       "27                0             0     60           South    >50K  2916  \n",
       "28                0             0     80   United-States   <=50K  1521  \n",
       "29                0             0     40   United-States   <=50K  2401  \n",
       "...             ...           ...    ...             ...     ...   ...  \n",
       "32531             0             0     99   United-States   <=50K   900  \n",
       "32532             0             0     60   United-States    >50K  1156  \n",
       "32533             0             0     50           Japan    >50K  2916  \n",
       "32534             0             0     39   United-States   <=50K  1369  \n",
       "32535             0             0     35   United-States   <=50K   484  \n",
       "32536             0             0     55   United-States    >50K  1156  \n",
       "32537             0             0     46   United-States   <=50K   900  \n",
       "32538         15020             0     45   United-States    >50K  1444  \n",
       "32539             0             0     10   United-States    >50K  5041  \n",
       "32540             0             0     40   United-States   <=50K  2025  \n",
       "32541             0             0     32   United-States   <=50K  1681  \n",
       "32542             0             0     25   United-States   <=50K  5184  \n",
       "32543             0             0     48   United-States   <=50K  2025  \n",
       "32544             0             0     30   United-States   <=50K   961  \n",
       "32545             0             0     20   United-States    >50K  1521  \n",
       "32546             0             0     40   United-States   <=50K  1369  \n",
       "32547             0             0     40          Mexico   <=50K  1849  \n",
       "32548          1086             0     60   United-States   <=50K  4225  \n",
       "32549             0             0     40   United-States   <=50K  1849  \n",
       "32550             0             0     50   United-States   <=50K  1849  \n",
       "32551             0             0     40   United-States   <=50K  1024  \n",
       "32552             0             0     45   United-States   <=50K  1849  \n",
       "32553             0             0     11          Taiwan   <=50K  1024  \n",
       "32554             0             0     40   United-States    >50K  2809  \n",
       "32555             0             0     40   United-States   <=50K   484  \n",
       "32556             0             0     38   United-States   <=50K   729  \n",
       "32557             0             0     40   United-States    >50K  1600  \n",
       "32558             0             0     40   United-States   <=50K  3364  \n",
       "32559             0             0     20   United-States   <=50K   484  \n",
       "32560         15024             0     40   United-States    >50K  2704  \n",
       "\n",
       "[32561 rows x 15 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult.assign(age2 = lambda x: np.square(x.age))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Blaze\n",
    "What is Blaze and why should I care?\n",
    "\n",
    "The Blaze ecosystem is a set of libraries that help users store, describe, query and process data. It is composed of the following core projects:\n",
    "\n",
    "* Blaze: An interface to query data on different storage systems\n",
    "* Dask: Parallel computing through task scheduling and blocked algorithms\n",
    "* Datashape: A data description language\n",
    "* DyND: A C++ library for dynamic, multidimensional arrays\n",
    "* Odo: Data migration between different storage systems"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blaze.expr.expressions.Symbol"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import blaze as bz\n",
    "\n",
    "\n",
    "bz_adult = bz.symbol('adult2', bz.discover(adult))\n",
    "type(bz_adult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_age = bz.by(bz_adult.occupation, price=bz_adult.age.mean())\n",
    "hours_count = bz.by(bz_adult[bz_adult.hours > 35].educ,\n",
    "                      count=bz_adult.workclass.count())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>occupation</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>?</td>\n",
       "      <td>40.882800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>36.964456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Armed-Forces</td>\n",
       "      <td>30.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Craft-repair</td>\n",
       "      <td>39.031471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>42.169208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Farming-fishing</td>\n",
       "      <td>41.211268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>32.165693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Machine-op-inspct</td>\n",
       "      <td>37.715285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Other-service</td>\n",
       "      <td>34.949621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Priv-house-serv</td>\n",
       "      <td>41.724832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>40.517633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Protective-serv</td>\n",
       "      <td>38.953775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Sales</td>\n",
       "      <td>37.353973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tech-support</td>\n",
       "      <td>37.022629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Transport-moving</td>\n",
       "      <td>40.197871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            occupation      price\n",
       "0                    ?  40.882800\n",
       "1         Adm-clerical  36.964456\n",
       "2         Armed-Forces  30.222222\n",
       "3         Craft-repair  39.031471\n",
       "4      Exec-managerial  42.169208\n",
       "5      Farming-fishing  41.211268\n",
       "6    Handlers-cleaners  32.165693\n",
       "7    Machine-op-inspct  37.715285\n",
       "8        Other-service  34.949621\n",
       "9      Priv-house-serv  41.724832\n",
       "10      Prof-specialty  40.517633\n",
       "11     Protective-serv  38.953775\n",
       "12               Sales  37.353973\n",
       "13        Tech-support  37.022629\n",
       "14    Transport-moving  40.197871"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We haven't actually computed anything yet!\n",
    "# Let's make Pandas compute it. \n",
    "bz.compute(mean_age, adult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 18.5 ms per loop\n"
     ]
    }
   ],
   "source": [
    "# We have here the count of number of years of education by a certain filter of greater than 35 hours of work per week. \n",
    "%timeit bz.compute(hours_count, adult)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "adult = adult.rename(columns={\"education-categorical\":\"educationcategorical\", \n",
    "                              \"marital-status\":'maritalstatus', \"captial-gain\":'captialgain', \"capital-loss\":\"capitalloss\",\n",
    "                              \"native-country\":\"nativecountry\"})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000000 loops, best of 3: 38.7 ns per loop\n"
     ]
    }
   ],
   "source": [
    "# Blaze/Odo make it easy to move data between containers\n",
    "# Note that we have an empty table already created\n",
    "pg_datasource = bz.odo(adult, \"postgresql://peadarcoyle@localhost/pydata::adult2\")\n",
    "# Now we're going to use Postgres as our computation engine\n",
    "result = bz.compute(hours_count, pg_datasource)\n",
    "%timeit result\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 loops, best of 3: 78 ms per loop\n"
     ]
    }
   ],
   "source": [
    "# I don't want a selectable. I want a DataFrame\n",
    "# odo again\n",
    "%timeit bz.odo(bz.compute(hours_count, pg_datasource), pd.DataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Observation**, the compute time with Postgres is slightly quicker than the compute time with dataframes. You could also use other backends such as Spark to do this. Blaze is very powerful, the ability to seperate your compute engine and your user interface is very powerful. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use Bcolz as a backend for storage via Blaze.\n",
    "We'll use the ctable format, we'll come to Bcolz in more detail later on in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>age</th>\n",
       "      <th>workclass</th>\n",
       "      <th>fnlwgt</th>\n",
       "      <th>education-categorical</th>\n",
       "      <th>educ</th>\n",
       "      <th>marital-status</th>\n",
       "      <th>occupation</th>\n",
       "      <th>relationship</th>\n",
       "      <th>sex</th>\n",
       "      <th>captial-gain</th>\n",
       "      <th>capital-loss</th>\n",
       "      <th>hours</th>\n",
       "      <th>native-country</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>39</td>\n",
       "      <td>State-gov</td>\n",
       "      <td>77516</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Never-married</td>\n",
       "      <td>Adm-clerical</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>2174</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>Self-emp-not-inc</td>\n",
       "      <td>83311</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Exec-managerial</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>Private</td>\n",
       "      <td>215646</td>\n",
       "      <td>HS-grad</td>\n",
       "      <td>9</td>\n",
       "      <td>Divorced</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Not-in-family</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>53</td>\n",
       "      <td>Private</td>\n",
       "      <td>234721</td>\n",
       "      <td>11th</td>\n",
       "      <td>7</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Handlers-cleaners</td>\n",
       "      <td>Husband</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>United-States</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>Private</td>\n",
       "      <td>338409</td>\n",
       "      <td>Bachelors</td>\n",
       "      <td>13</td>\n",
       "      <td>Married-civ-spouse</td>\n",
       "      <td>Prof-specialty</td>\n",
       "      <td>Wife</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>&lt;=50K</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  age          workclass  fnlwgt education-categorical  educ  \\\n",
       "0           0   39          State-gov   77516             Bachelors    13   \n",
       "1           1   50   Self-emp-not-inc   83311             Bachelors    13   \n",
       "2           2   38            Private  215646               HS-grad     9   \n",
       "3           3   53            Private  234721                  11th     7   \n",
       "4           4   28            Private  338409             Bachelors    13   \n",
       "\n",
       "        marital-status          occupation    relationship      sex  \\\n",
       "0        Never-married        Adm-clerical   Not-in-family     Male   \n",
       "1   Married-civ-spouse     Exec-managerial         Husband     Male   \n",
       "2             Divorced   Handlers-cleaners   Not-in-family     Male   \n",
       "3   Married-civ-spouse   Handlers-cleaners         Husband     Male   \n",
       "4   Married-civ-spouse      Prof-specialty            Wife   Female   \n",
       "\n",
       "   captial-gain  capital-loss  hours  native-country  income  \n",
       "0          2174             0     40   United-States   <=50K  \n",
       "1             0             0     13   United-States   <=50K  \n",
       "2             0             0     40   United-States   <=50K  \n",
       "3             0             0     40   United-States   <=50K  \n",
       "4             0             0     40            Cuba   <=50K  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adult_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 10.3 s, sys: 18.1 s, total: 28.4 s\n",
      "Wall time: 28.8 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "ctable((32561,), [('age', '<i8'), ('workclass', 'O'), ('fnlwgt', '<i8'), ('educationcategorical', 'O'), ('educ', '<i8'), ('maritalstatus', 'O'), ('occupation', 'O'), ('relationship', 'O'), ('sex', 'O'), ('captialgain', '<i8'), ('capitalloss', '<i8'), ('hours', '<i8'), ('nativecountry', 'O'), ('income', 'O')])\n",
       "  nbytes: 7.76 MB; cbytes: 43.54 MB; ratio: 0.18\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "  rootdir := 'adult.bcolz'\n",
       "[ (39, ' State-gov', 77516, ' Bachelors', 13, ' Never-married', ' Adm-clerical', ' Not-in-family', ' Male', 2174, 0, 40, ' United-States', ' <=50K')\n",
       " (50, ' Self-emp-not-inc', 83311, ' Bachelors', 13, ' Married-civ-spouse', ' Exec-managerial', ' Husband', ' Male', 0, 0, 13, ' United-States', ' <=50K')\n",
       " (38, ' Private', 215646, ' HS-grad', 9, ' Divorced', ' Handlers-cleaners', ' Not-in-family', ' Male', 0, 0, 40, ' United-States', ' <=50K')\n",
       " ...,\n",
       " (58, ' Private', 151910, ' HS-grad', 9, ' Widowed', ' Adm-clerical', ' Unmarried', ' Female', 0, 0, 40, ' United-States', ' <=50K')\n",
       " (22, ' Private', 201490, ' HS-grad', 9, ' Never-married', ' Adm-clerical', ' Own-child', ' Male', 0, 0, 20, ' United-States', ' <=50K')\n",
       " (52, ' Self-emp-inc', 287927, ' HS-grad', 9, ' Married-civ-spouse', ' Exec-managerial', ' Wife', ' Female', 15024, 0, 40, ' United-States', ' >50K')]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bcolz\n",
    "\n",
    "%time bz.odo(adult, 'adult.bcolz')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "\n",
    "# Time to clean this up :) \n",
    "!rm -r adult.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Exploring Point of Interests with Dask and Pandas\n",
    "* Based on Jake Vanderplas and his excellent blog.\n",
    "\n",
    "Recently we've been looking at the PyData ecosystem, and there are many interesting tools such as BColz and Biggus/. \n",
    "Dask is a very versatile tool that allows out-of-core dataframes without learning a whole host of other techniques. Under the hood, Dask simply uses standard Python, NumPy, and Pandas commands on each chunk, and transparently executes operations and aggregates results so that you can work with datasets that are larger than your machine's memory.\n",
    "\n",
    "In this notebook, I'll take a look at how dask can be useful when looking at a large dataset: the full extracted points of interest from OpenStreetMap. We will use Dask to manipulate and explore the data, and also see the use of matplotlib's Basemap toolkit to visualize the results on a map.\n",
    "\n",
    "The data points that will be drawn are pub locations extracted from OpenStreetMap and provided by osm-x-tractor.\n",
    "When you download and extract the Points Of Interest (POI) dataset as a CSV file it has a file size of about 800 MB and more than 9 million entries for different types of locations. To filter out only the locations tagged as pubs you can use csvgrep, which is part of the csvkit toolkit.\n",
    "csvgrep -c amenity -m pub POIWorld.csv > POIWorld-pub.csv\n",
    "\n",
    "The resulting CSV file is much smaller with a size of 7.7 MB and contains a little more than 120,000 entries for pub locations all over the world. Since the coverage varies across countries and regions, I chose to limit the map to Britain and Ireland where POI coverage seems quite comprehensive and there are a lot of pubs. Who could have thought?\n",
    "Next we load the required libraries and define a function that checks whether a given location tuple is within the given bounding box.\n",
    "https://jakevdp.github.io/blog/2015/08/14/out-of-core-dataframes-in-python/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9140114"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask as dask\n",
    "dask.set_options(get=dask.async.get_sync)\n",
    "nrows = sum(1 for _ in open('POIWorld.csv'))\n",
    "nrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-11.8917670891\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# create an array of normally-distributed random numbers\n",
    "a = np.random.randn(1000)\n",
    "\n",
    "# multiply this array by a factor\n",
    "b = a * 4\n",
    "\n",
    "# find the minimum value\n",
    "b_min = b.min()\n",
    "print(b_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dask.array<p_reduc..., shape=(), dtype=float64, chunksize=()>\n"
     ]
    }
   ],
   "source": [
    "import dask.array as da\n",
    "\n",
    "# create a dask array from the above array\n",
    "a2 = da.from_array(a, chunks=200)\n",
    "\n",
    "# multiply this array by a factor\n",
    "b2 = a2 * 4\n",
    "\n",
    "# find the minimum value\n",
    "b2_min = b2.min()\n",
    "print(b2_min)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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evbffftvKyqpgzdzcXJ1Ot2rVqsaNG0+aNInWCwAoT/RdwLh88sknzZo18/Pz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\nuuCnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#With dask you can see how dask works :) \n",
    "from dask.dot import dot_graph\n",
    "dot_graph(b2_min.dask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/peadarcoyle/anaconda/envs/pydataamsterdam/lib/python3.4/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (11) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "place_of_worship             248985\n",
       "school                       199875\n",
       "bench                        132532\n",
       "restaurant                   120449\n",
       "grave_yard                   113132\n",
       "parking                      111135\n",
       "post_box                      93335\n",
       "fuel                          86591\n",
       "recycling                     51791\n",
       "bank                          48477\n",
       "fast_food                     46517\n",
       "pub                           44977\n",
       "telephone                     44622\n",
       "cafe                          43699\n",
       "post_office                   41622\n",
       "pharmacy                      41478\n",
       "hospital                      36130\n",
       "bicycle_parking               27100\n",
       "fire_station                  26170\n",
       "toilets                       23948\n",
       "atm                           23355\n",
       "drinking_water                21000\n",
       "police                        19655\n",
       "shelter                       17780\n",
       "library                       17515\n",
       "hunting_stand                 17243\n",
       "kindergarten                  16036\n",
       "waste_basket                  15811\n",
       "vending_machine               14640\n",
       "bar                           13555\n",
       "                              ...  \n",
       "car_workshop                      1\n",
       "Pond                              1\n",
       "cadet hut                         1\n",
       "Netaji Nagar Market               1\n",
       "CAI                               1\n",
       "boatslip                          1\n",
       "Purple Trail                      1\n",
       "Marienfigur                       1\n",
       "gas installation                  1\n",
       "cleaners                          1\n",
       "Center for kids                   1\n",
       "Salvacion Ferry Terminal          1\n",
       "Synagoga Wolfa Richtera           1\n",
       "imigration_center                 1\n",
       "closedpub                         1\n",
       "civic hall                        1\n",
       "GIS Software/Services             1\n",
       "Loan office                       1\n",
       "ferry_tourist                     1\n",
       "Residential care facility         1\n",
       "Aerial Services, Inc.             1\n",
       "poi                               1\n",
       "mini_ramp                         1\n",
       "Jugendhaus                        1\n",
       "Home                              1\n",
       "municipalidad                     1\n",
       "self service                      1\n",
       "civic_building                    1\n",
       "assembly_hall                     1\n",
       "kitchen                           1\n",
       "Name: amenity, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('POIWorld.csv', nrows=3000000)\n",
    "data.columns\n",
    "data.amenity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dd.DataFrame<read-csv-POIWorld.csv-805c83c59787385c1a1443421473c417, divisions=(None, None, None, ..., None, None)>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask import dataframe as dd\n",
    "columns = [\"name\", \"amenity\", \"Longitude\", \"Latitude\"]\n",
    "data = dd.read_csv('POIWorld.csv', usecols=columns)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with_name = data[data.name.notnull()]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with_amenity = data[data.amenity.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(342025,)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I want to tell if this is a School or not and then plot it on a graph\n",
    "is_school = with_amenity.amenity.str.contains('[Ss]chool')\n",
    "school = with_amenity[is_school]\n",
    "dd.compute(school.amenity.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lon, lat = dd.compute(school.Longitude, school.Latitude)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4jowO2RU13rgdUiPAAcJBwAMgl8/8HA1c7J4aHebJKw745uXTJOC5upknAdGXrzMZ/VdK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\nAQAAtUfgAQAAtfc/wDcSu4jepS0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1256bd1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "fig, ax = plt.subplots(figsize=(10, 15))\n",
    "m = Basemap(projection='mill',\n",
    "            lon_0=-5.23636, lat_0=53.866772,\n",
    "            llcrnrlon=-10.65073, llcrnrlat=49.16209,\n",
    "            urcrnrlon=1.76334, urcrnrlat=60.860699)\n",
    "m.drawmapboundary(fill_color='#ffffff', linewidth=.0)\n",
    "x, y = m(lon.values, lat.values)\n",
    "m.scatter(x, y, s=1, marker=',', color=\"steelblue\", alpha=0.6);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bcolz \n",
    "* What if I have medium data/ lots of data and databases aren't quite quick enough.\n",
    "* I want to compress my data and use it for in-memory computations. \n",
    "* This is very typical as an application for time series data. \n",
    "* An example below is copied from Jagged documentation and Jagged is built on top of Bcolz.\n",
    "* Castra is also built on bcolz. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import bcolz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "adult_df = adult[['age', 'workclass', 'educ', 'occupation', 'sex', 'hours']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ctable((32561,), [('age', '<i8'), ('workclass', 'S17'), ('educ', '<i8'), ('occupation', 'S18'), ('sex', 'S7'), ('hours', '<i8')])\n",
       "  nbytes: 2.05 MB; cbytes: 1.63 MB; ratio: 1.26\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[(39, b' State-gov', 13, b' Adm-clerical', b' Male', 40)\n",
       " (50, b' Self-emp-not-inc', 13, b' Exec-managerial', b' Male', 13)\n",
       " (38, b' Private', 9, b' Handlers-cleaners', b' Male', 40) ...,\n",
       " (58, b' Private', 9, b' Adm-clerical', b' Female', 40)\n",
       " (22, b' Private', 9, b' Adm-clerical', b' Male', 20)\n",
       " (52, b' Self-emp-inc', 9, b' Exec-managerial', b' Female', 40)]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "dc = bcolz.ctable.fromdataframe(adult_df)\n",
    "dc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age : carray((32561,), int64)\n",
       "  nbytes: 254.38 KB; cbytes: 256.00 KB; ratio: 0.99\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[39 50 38 ..., 58 22 52]\n",
       "workclass : carray((32561,), |S17)\n",
       "  nbytes: 540.56 KB; cbytes: 303.83 KB; ratio: 1.78\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[b' State-gov' b' Self-emp-not-inc' b' Private' ..., b' Private'\n",
       " b' Private' b' Self-emp-inc']\n",
       "educ : carray((32561,), int64)\n",
       "  nbytes: 254.38 KB; cbytes: 256.00 KB; ratio: 0.99\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[13 13  9 ...,  9  9  9]\n",
       "occupation : carray((32561,), |S18)\n",
       "  nbytes: 572.36 KB; cbytes: 338.49 KB; ratio: 1.69\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[b' Adm-clerical' b' Exec-managerial' b' Handlers-cleaners' ...,\n",
       " b' Adm-clerical' b' Adm-clerical' b' Exec-managerial']\n",
       "sex : carray((32561,), |S7)\n",
       "  nbytes: 222.58 KB; cbytes: 256.00 KB; ratio: 0.87\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[b' Male' b' Male' b' Male' ..., b' Female' b' Male' b' Female']\n",
       "hours : carray((32561,), int64)\n",
       "  nbytes: 254.38 KB; cbytes: 256.00 KB; ratio: 0.99\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[40 13 40 ..., 40 20 40]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc.cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total size for the ctable: 1.6272649765014648 MB\n",
      "Compression ratio the ctable: 1.2594573908273463\n"
     ]
    }
   ],
   "source": [
    "\n",
    "dsize = dc.cbytes / 2**20.\n",
    "print(\"Total size for the ctable: {} MB\".format(dsize))\n",
    "print(\"Compression ratio the ctable: {}\".format((dc.nbytes / float(dc.cbytes))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39, b' State-gov', 13, b' Adm-clerical', b' Male', 40)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dc.cols\n",
    "# You can do DataFrame-like stuff\n",
    "dc['workclass' == ' State-gov']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Let's check the in-memory Ctable functionality\n",
    "Since this is integers it has *high compression* \n",
    "The example dataset I produce is 1GB in size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100000000\n",
      "CPU times: user 58.4 s, sys: 1.15 s, total: 59.5 s\n",
      "Wall time: 58 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "N = 100000 * 1000\n",
    "print(N)\n",
    "import bcolz\n",
    "ct = bcolz.fromiter(((i, i ** 2) for i in range(N)),\n",
    "                    dtype=\"i4, i8\",\n",
    "                    count=N,\n",
    "                    cparams=bcolz.cparams(clevel=9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ctable((100000000,), [('f0', '<i4'), ('f1', '<i8')])\n",
       "  nbytes: 1.12 GB; cbytes: 151.84 MB; ratio: 7.54\n",
       "  cparams := cparams(clevel=9, shuffle=True, cname='blosclz')\n",
       "[(0, 0) (1, 1) (2, 4) ..., (99999997, 9999999400000009)\n",
       " (99999998, 9999999600000004) (99999999, 9999999800000001)]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.38 s, sys: 1.94 s, total: 6.31 s\n",
      "Wall time: 1.21 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "carray((100000000,), float64)\n",
       "  nbytes: 762.94 MB; cbytes: 347.33 MB; ratio: 2.20\n",
       "  cparams := cparams(clevel=5, shuffle=True, cname='blosclz')\n",
       "[  0.00000000e+00   2.00000000e+00   6.00000000e+00 ...,   1.37491943e+09\n",
       "   1.57491943e+09   1.77491942e+09]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time ct.eval('f0 ** 2 + sqrt(f1)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# There are some tools built on top of Bcolz.\n",
    "Like **Jagged** \n",
    "* What is Jagged for? \n",
    "* What are its attributes? \n",
    "* Jagged is an ongoing amateur project exploring the storage panorama for datasets containing (large amounts of) arrays with the same type and number of columns, but varying number of rows. Examples of such datasets for which jagged has been used are collections of multivariate timeseries (short animal behaviour snippets) and collections of molecules (represented as varying length strings).\n",
    "\n",
    "* Jagged aims to help analyzing data in the laptop and the cluster, in batch or interactively, providing a very lightweight store. Jagged provides fast retrieval of array subsets for many-GB datasets containing millions of rows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating a random dataset...\n",
      "Populating the jagged store...\n",
      "Number of arrays: 1000, number of rows: 1022477\n",
      "Jagged shape=(1022477, 100), dtype=dtype('float64'), order='C'\n",
      "Roundtrip checks pass\n",
      "JaggedByMemMap(autoviews=True,contiguity=None)\n",
      "Roundtrip checks for iteration pass\n",
      "Roundtrip checks for row retrieval pass\n",
      "Retrieving 100000 arrays...\n",
      "Making 100000 dataframes...\n",
      "Checking roundtrip...\n",
      "Roundtrip checks for lazy dataframes pass\n",
      "Saving compressed (although these data are not compressable)...\n",
      "JaggedByBlosc(compressor=BloscCompressor(cname='lz4hc',level=5,n_threads=1,shuffle=True))\n",
      "Roundtrip checks for compressed arrays pass\n"
     ]
    }
   ],
   "source": [
    "import os.path as op\n",
    "import shutil\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tempfile\n",
    "from jagged.mmap_backend import JaggedByMemMap\n",
    "from jagged.blosc_backend import JaggedByBlosc\n",
    "\n",
    "# A Jagged instance is all you need\n",
    "mmap_dir = tempfile.mkdtemp('mmap')\n",
    "jagged = JaggedByMemMap(op.expanduser(path=mmap_dir))\n",
    "# You can drop here any JaggedRawStore implementation you want to\n",
    "\n",
    "# Generate a random dataset\n",
    "print('Creating a random dataset...')\n",
    "rng = np.random.RandomState(0)\n",
    "num_arrays = 1000\n",
    "max_length = 2000\n",
    "num_columns = 100\n",
    "originals = [rng.randn(rng.randint(0, max_length), num_columns)\n",
    "             for _ in range(num_arrays)]\n",
    "\n",
    "# Add these to the store (\"with\" context is usually optional, but recommended)\n",
    "print('Populating the jagged store...')\n",
    "with jagged:\n",
    "    indices = list(map(jagged.append, originals))\n",
    "\n",
    "# Some jagged stores optimize queries retrieving arrays by their insertion order\n",
    "# Retrieval speed should not suffer much even with random queries\n",
    "shuffled_indices = rng.permutation(indices).tolist()\n",
    "shuffled_originals = [originals[i] for i in shuffled_indices]\n",
    "\n",
    "# What do we have in store?\n",
    "print('Number of arrays: %d, number of rows: %d' % (jagged.narrays, jagged.nrows))\n",
    "# Out: Number of arrays: 200, number of rows: 193732\n",
    "print('Jagged shape=%r, dtype=%r, order=%r' %\n",
    "      (jagged.shape, jagged.dtype, jagged.order))\n",
    "# Out: Jagged shape=(193732, 50), dtype=dtype('float64'), order='C'\n",
    "\n",
    "# Check roundtrip\n",
    "roundtrippeds = jagged.get(shuffled_indices)\n",
    "for original, roundtripped in zip(shuffled_originals, roundtrippeds):\n",
    "    assert np.array_equal(original, roundtripped)\n",
    "print('Roundtrip checks pass')\n",
    "\n",
    "# Jagged stores self-identified themselves (using whatami)\n",
    "print(jagged.what().id())\n",
    "# Out: JaggedByMemMap(autoviews=True,contiguity=None)\n",
    "\n",
    "# Jagged stores can be iterated in chunks (see iter)\n",
    "for original, roundtripped in zip(originals, jagged):\n",
    "    assert np.array_equal(original, roundtripped[0])\n",
    "print('Roundtrip checks for iteration pass')\n",
    "\n",
    "# Some jagged stores allow to retrieve arbitrary rows without penalty\n",
    "# (i.e. without retrieving the whole containing array).\n",
    "# These are marked as \"linear\" in the store feature matrix.\n",
    "# You do so by passing a list of (base, size) segments.\n",
    "some_rows = jagged.get([[3, 22], [45, 1000]])\n",
    "assert len(some_rows[1]) == 1000\n",
    "assert np.array_equal(some_rows[0], originals[0][3:25])\n",
    "print('Roundtrip checks for row retrieval pass')\n",
    "\n",
    "# Some jagged stores allow to be lazy retrieving the arrays.\n",
    "# On top of that, the MemMap implementation allow memmapped arrays.\n",
    "# Can be handy to have long lists of views in memory\n",
    "# while letting the OS managing memory fetching and eviction for us.\n",
    "jbmm = JaggedByMemMap(op.expanduser(path=mmap_dir),\n",
    "                      autoviews=True,\n",
    "                      contiguity='auto')\n",
    "print('Retrieving %d arrays...' % (len(shuffled_indices) * 100))\n",
    "many_arrays = jbmm.get(shuffled_indices * 100)\n",
    "# This will work also for pandas DataFrames as long as\n",
    "# \"copy=True\" is honored by the pandas constructor\n",
    "# that is, the dtype of the arrays is simple),\n",
    "print('Making %d dataframes...' % (len(shuffled_indices) * 100))\n",
    "columns = pd.Index(np.arange(num_columns))\n",
    "dfs = [pd.DataFrame(data=array, columns=columns, copy=False)\n",
    "       for array in many_arrays]\n",
    "print('Checking roundtrip...')\n",
    "for original, roundtripped in zip(shuffled_originals * 100, dfs):\n",
    "    assert np.array_equal(original, roundtripped)\n",
    "print('Roundtrip checks for lazy dataframes pass')\n",
    "\n",
    "# Jagged stores can be populated from other jagged stores\n",
    "blosc_dir = tempfile.mkdtemp('mmap')\n",
    "jbb = JaggedByBlosc(path=blosc_dir)\n",
    "print('Saving compressed (although these data are not compressable)...')\n",
    "jbb.append_from(jagged)\n",
    "for a_from_mmap, a_from_blosc in zip(jbb, jagged):\n",
    "    assert np.array_equal(a_from_mmap, a_from_blosc)\n",
    "print(jbb.what().id())\n",
    "print('Roundtrip checks for compressed arrays pass')\n",
    "# Out: JaggedByBlosc(compressor=BloscCompressor(cname='lz4hc',\n",
    "#                                               level=5,\n",
    "#                                               n_threads=1,\n",
    "#                                               shuffle=True))\n",
    "\n",
    "# We are done, cleanup\n",
    "shutil.rmtree(mmap_dir, ignore_errors=True)\n",
    "shutil.rmtree(blosc_dir, ignore_errors=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ibis\n",
    "* If you want to split your expression engine (pandas like syntax) from your compute engine (in memory, versus Impala/ Sqlite)\n",
    "* There are similarities with this project and Blaze. \n",
    "* Ibis is a new Python data analysis framework with the goal of enabling data scientists and data engineers to be as productive working with big data as they are working with small and medium data today. In doing so, we will enable Python to become a true first-class language for Apache Hadoop, without compromises in functionality, usability, or performance. Having spent much of the last decade improving the usability of the single-node Python experience (with pandas and other projects), we are looking to achieve:\n",
    "\n",
    "* 100% Python end-to-end user workflows\n",
    "* Native hardware speeds for a broad set of use cases\n",
    "* Full-fidelity data analysis without extractions or sampling\n",
    "* Scalability for big data\n",
    "* Integration with the existing Python data ecosystem (pandas, scikit-learn, NumPy, and so on)\n",
    "\n",
    "\n",
    "# Demo \n",
    "* For our demo we'll look at Pokemon data. \n",
    "[Pokemon source](https://github.com/danmane/PokemonSQLTutorial)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ibis.sql.sqlite.client.SQLiteClient at 0x147c2a278>"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ibis\n",
    "ibis.options.interactive = True\n",
    "\n",
    "con = ibis.sqlite.connect('data/veekun-pokedex.sqlite')\n",
    "con\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abilities',\n",
       " 'ability_changelog',\n",
       " 'ability_changelog_prose',\n",
       " 'ability_flavor_text',\n",
       " 'ability_names',\n",
       " 'ability_prose',\n",
       " 'berries',\n",
       " 'berry_firmness',\n",
       " 'berry_firmness_names',\n",
       " 'berry_flavors',\n",
       " 'conquest_episode_names',\n",
       " 'conquest_episode_warriors',\n",
       " 'conquest_episodes',\n",
       " 'conquest_kingdom_names',\n",
       " 'conquest_kingdoms',\n",
       " 'conquest_max_links',\n",
       " 'conquest_move_data',\n",
       " 'conquest_move_displacement_prose',\n",
       " 'conquest_move_displacements',\n",
       " 'conquest_move_effect_prose',\n",
       " 'conquest_move_effects',\n",
       " 'conquest_move_range_prose',\n",
       " 'conquest_move_ranges',\n",
       " 'conquest_pokemon_abilities',\n",
       " 'conquest_pokemon_evolution',\n",
       " 'conquest_pokemon_moves',\n",
       " 'conquest_pokemon_stats',\n",
       " 'conquest_stat_names',\n",
       " 'conquest_stats',\n",
       " 'conquest_transformation_pokemon',\n",
       " 'conquest_transformation_warriors',\n",
       " 'conquest_warrior_archetypes',\n",
       " 'conquest_warrior_names',\n",
       " 'conquest_warrior_rank_stat_map',\n",
       " 'conquest_warrior_ranks',\n",
       " 'conquest_warrior_skill_names',\n",
       " 'conquest_warrior_skills',\n",
       " 'conquest_warrior_specialties',\n",
       " 'conquest_warrior_stat_names',\n",
       " 'conquest_warrior_stats',\n",
       " 'conquest_warrior_transformation',\n",
       " 'conquest_warriors',\n",
       " 'contest_combos',\n",
       " 'contest_effect_prose',\n",
       " 'contest_effects',\n",
       " 'contest_type_names',\n",
       " 'contest_types',\n",
       " 'egg_group_prose',\n",
       " 'egg_groups',\n",
       " 'encounter_condition_prose',\n",
       " 'encounter_condition_value_map',\n",
       " 'encounter_condition_value_prose',\n",
       " 'encounter_condition_values',\n",
       " 'encounter_conditions',\n",
       " 'encounter_method_prose',\n",
       " 'encounter_methods',\n",
       " 'encounter_slots',\n",
       " 'encounters',\n",
       " 'evolution_chains',\n",
       " 'evolution_trigger_prose',\n",
       " 'evolution_triggers',\n",
       " 'experience',\n",
       " 'genders',\n",
       " 'generation_names',\n",
       " 'generations',\n",
       " 'growth_rate_prose',\n",
       " 'growth_rates',\n",
       " 'item_categories',\n",
       " 'item_category_prose',\n",
       " 'item_flag_map',\n",
       " 'item_flag_prose',\n",
       " 'item_flags',\n",
       " 'item_flavor_summaries',\n",
       " 'item_flavor_text',\n",
       " 'item_fling_effect_prose',\n",
       " 'item_fling_effects',\n",
       " 'item_game_indices',\n",
       " 'item_names',\n",
       " 'item_pocket_names',\n",
       " 'item_pockets',\n",
       " 'item_prose',\n",
       " 'items',\n",
       " 'language_names',\n",
       " 'languages',\n",
       " 'location_area_encounter_rates',\n",
       " 'location_area_prose',\n",
       " 'location_areas',\n",
       " 'location_game_indices',\n",
       " 'location_names',\n",
       " 'locations',\n",
       " 'machines',\n",
       " 'move_battle_style_prose',\n",
       " 'move_battle_styles',\n",
       " 'move_changelog',\n",
       " 'move_damage_class_prose',\n",
       " 'move_damage_classes',\n",
       " 'move_effect_changelog',\n",
       " 'move_effect_changelog_prose',\n",
       " 'move_effect_prose',\n",
       " 'move_effects',\n",
       " 'move_flag_map',\n",
       " 'move_flag_prose',\n",
       " 'move_flags',\n",
       " 'move_flavor_summaries',\n",
       " 'move_flavor_text',\n",
       " 'move_meta',\n",
       " 'move_meta_ailment_names',\n",
       " 'move_meta_ailments',\n",
       " 'move_meta_categories',\n",
       " 'move_meta_category_prose',\n",
       " 'move_meta_stat_changes',\n",
       " 'move_names',\n",
       " 'move_target_prose',\n",
       " 'move_targets',\n",
       " 'moves',\n",
       " 'nature_battle_style_preferences',\n",
       " 'nature_names',\n",
       " 'nature_pokeathlon_stats',\n",
       " 'natures',\n",
       " 'pal_park',\n",
       " 'pal_park_area_names',\n",
       " 'pal_park_areas',\n",
       " 'pokeathlon_stat_names',\n",
       " 'pokeathlon_stats',\n",
       " 'pokedex_prose',\n",
       " 'pokedex_version_groups',\n",
       " 'pokedexes',\n",
       " 'pokemon',\n",
       " 'pokemon_abilities',\n",
       " 'pokemon_color_names',\n",
       " 'pokemon_colors',\n",
       " 'pokemon_dex_numbers',\n",
       " 'pokemon_egg_groups',\n",
       " 'pokemon_evolution',\n",
       " 'pokemon_form_generations',\n",
       " 'pokemon_form_names',\n",
       " 'pokemon_form_pokeathlon_stats',\n",
       " 'pokemon_forms',\n",
       " 'pokemon_game_indices',\n",
       " 'pokemon_habitat_names',\n",
       " 'pokemon_habitats',\n",
       " 'pokemon_items',\n",
       " 'pokemon_move_method_prose',\n",
       " 'pokemon_move_methods',\n",
       " 'pokemon_moves',\n",
       " 'pokemon_shape_prose',\n",
       " 'pokemon_shapes',\n",
       " 'pokemon_species',\n",
       " 'pokemon_species_flavor_summaries',\n",
       " 'pokemon_species_flavor_text',\n",
       " 'pokemon_species_names',\n",
       " 'pokemon_species_prose',\n",
       " 'pokemon_stats',\n",
       " 'pokemon_types',\n",
       " 'region_names',\n",
       " 'regions',\n",
       " 'stat_hint_names',\n",
       " 'stat_hints',\n",
       " 'stat_names',\n",
       " 'stats',\n",
       " 'super_contest_combos',\n",
       " 'super_contest_effect_prose',\n",
       " 'super_contest_effects',\n",
       " 'type_efficacy',\n",
       " 'type_game_indices',\n",
       " 'type_names',\n",
       " 'types',\n",
       " 'version_group_pokemon_move_methods',\n",
       " 'version_group_regions',\n",
       " 'version_groups',\n",
       " 'version_names',\n",
       " 'versions']"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "con.list_tables()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table rows: 1179\n",
      "\n",
      "Column      Type                 Non-null #\n",
      "------      ----                 ----------\n",
      "pokemon_id  int64[non-nullable]  1179      \n",
      "type_id     int64[non-nullable]  1179      \n",
      "slot        int64[non-nullable]  1179      "
     ]
    },
    {
     "data": {
      "text/plain": [
       "     pokemon_id  count\n",
       "0             1      2\n",
       "1             2      2\n",
       "2             3      2\n",
       "3             4      1\n",
       "4             5      1\n",
       "5             6      2\n",
       "6             7      1\n",
       "7             8      1\n",
       "8             9      1\n",
       "9            10      1\n",
       "10           11      1\n",
       "11           12      2\n",
       "12           13      2\n",
       "13           14      2\n",
       "14           15      2\n",
       "15           16      2\n",
       "16           17      2\n",
       "17           18      2\n",
       "18           19      1\n",
       "19           20      1\n",
       "20           21      2\n",
       "21           22      2\n",
       "22           23      1\n",
       "23           24      1\n",
       "24           25      1\n",
       "25           26      1\n",
       "26           27      1\n",
       "27           28      1\n",
       "28           29      1\n",
       "29           30      1\n",
       "..          ...    ...\n",
       "754       10034      2\n",
       "755       10035      2\n",
       "756       10036      1\n",
       "757       10037      1\n",
       "758       10038      2\n",
       "759       10039      1\n",
       "760       10040      2\n",
       "761       10041      2\n",
       "762       10042      2\n",
       "763       10043      2\n",
       "764       10044      1\n",
       "765       10045      2\n",
       "766       10046      2\n",
       "767       10047      2\n",
       "768       10048      2\n",
       "769       10049      2\n",
       "770       10050      2\n",
       "771       10051      2\n",
       "772       10052      2\n",
       "773       10053      1\n",
       "774       10054      2\n",
       "775       10055      1\n",
       "776       10056      1\n",
       "777       10057      1\n",
       "778       10058      2\n",
       "779       10059      2\n",
       "780       10060      2\n",
       "781       10061      1\n",
       "782       10062      2\n",
       "783       10063      2\n",
       "\n",
       "[784 rows x 2 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let us do some pandas like value counts on SQLite (could also be on Impala)\n",
    "# Firstly we connect to the table pokemon_types\n",
    "types = con.table('pokemon_types')\n",
    "types.info()\n",
    "types.pokemon_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1250.70737913486"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "types.pokemon_id.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10063"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find the maximum pokemon id\n",
    "types.pokemon_id.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's find the minimum pokemon id\n",
    "types.pokemon_id.min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us connect to another table just to show the functionality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "moves = con.table('pokemon_moves')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "     move_id  count\n",
       "0          1    389\n",
       "1          2    111\n",
       "2          3    263\n",
       "3          4     68\n",
       "4          5    408\n",
       "5          6     64\n",
       "6          7    734\n",
       "7          8    768\n",
       "8          9    798\n",
       "9         10    701\n",
       "10        11    120\n",
       "11        12    109\n",
       "12        13    175\n",
       "13        14   1373\n",
       "14        15   1962\n",
       "15        16    339\n",
       "16        17    341\n",
       "17        18    342\n",
       "18        19    679\n",
       "19        20    178\n",
       "20        21    433\n",
       "21        22    168\n",
       "22        23    359\n",
       "23        24    237\n",
       "24        25    347\n",
       "25        26     42\n",
       "26        27     50\n",
       "27        28    770\n",
       "28        29   1249\n",
       "29        30    163\n",
       "..       ...    ...\n",
       "579      582      2\n",
       "580      583     30\n",
       "581      584     16\n",
       "582      585     20\n",
       "583      586      6\n",
       "584      587      1\n",
       "585      588      2\n",
       "586      589     19\n",
       "587      590    765\n",
       "588      591      1\n",
       "589      592      2\n",
       "590      593      2\n",
       "591      594      2\n",
       "592      595      2\n",
       "593      596      2\n",
       "594      597      1\n",
       "595      598     10\n",
       "596      599     12\n",
       "597      600      2\n",
       "598      601      1\n",
       "599      602      6\n",
       "600      604     12\n",
       "601      605    101\n",
       "602      608      9\n",
       "603      609      6\n",
       "604      611     98\n",
       "605      612    222\n",
       "606      613      1\n",
       "607      616      1\n",
       "608      617      1\n",
       "\n",
       "[609 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's look at the move_id and how they are distributed\n",
    "# Using a pandas like syntax\n",
    "moves.move_id.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bayesian Logistic Regression\n",
    "* It is outside of scope to explain in depth what all this means. But you can look at \n",
    "[Bayesian Logreg](https://pymc-devs.github.io/pymc3/Bayesian_LogReg/)\n",
    "* I want to do a Bayesian Logistic regression model in PyData, how do I do it?\n",
    "* Use **PyMC3** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\", header=None, names=['age', 'workclass', 'fnlwgt', \n",
    "                'education-categorical', 'educ', \n",
    "                'marital-status', 'occupation',\n",
    "                'relationship', 'race', 'sex', \n",
    "                'captial-gain', 'capital-loss', \n",
    "                'hours', 'native-country', \n",
    "                'income'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_traces(traces, retain=1000):\n",
    "    ''' \n",
    "    Convenience function:\n",
    "    Plot traces with overlaid means and values\n",
    "    '''\n",
    "\n",
    "    ax = pm.traceplot(traces[-retain:], figsize=(12,len(traces.varnames)*1.5),\n",
    "        lines={k: v['mean'] for k, v in pm.df_summary(traces[-retain:]).iterrows()})\n",
    "\n",
    "    for i, mn in enumerate(pm.df_summary(traces[-retain:])['mean']):\n",
    "        ax[i,0].annotate('{:.2f}'.format(mn), xy=(mn,0), xycoords='data'\n",
    "                    ,xytext=(5,10), textcoords='offset points', rotation=90\n",
    "                    ,va='bottom', fontsize='large', color='#AA0022')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = data[~pd.isnull(data['income'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[data['native-country']==\" United-States\"]\n",
    "income = 1 * (data['income'] == \" >50K\")\n",
    "age2 = np.square(data['age'])\n",
    "data = data[['age', 'educ', 'hours']]\n",
    "data['age2'] = age2\n",
    "data['income'] = income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.0\n"
     ]
    }
   ],
   "source": [
    "import pymc3 as pm\n",
    "print(pm.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applied logodds-transform to p and added transformed p_logodds to model.\n",
      " [--                7%                  ] 147 of 2000 complete in 34.3 sec"
     ]
    }
   ],
   "source": [
    "with pm.Model() as logistic_model:\n",
    "    pm.glm.glm('income ~ age + age2 + educ + hours', data, family=pm.glm.families.Binomial())\n",
    "    trace_logistic_model = pm.sample(2000, pm.NUTS(), progressbar=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "plot_traces(trace_logistic_model, retain=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# That's it, up and down the PyData stack for 2016. "
   ]
  }
 ],
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