{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 07 -- Pandas, Part2\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topics Covered\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#SAS-Sort-Merge-with-by-group\">SAS Sort Merge with by-group </a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Inner-Join\">Inner Join </a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Right-Outer-Join\">Right Outer Join </a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Left-Outer-Join\">Left Outer Join </a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Full-Outer-Join\">Full Outer Join </a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Outer-Join-no-Matched-Keys\">Outer Join no Matched Keys </a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Outer-Join-no-Matched-Keys-in-Right\">Outer Join no Matched Keys in Right</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Outer-Join-no-Matched-Keys-in-Left\">Outer Join no Matched Keys in Left</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Many-to-Many-Join\">Many-to-Many Join</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Group-by:-split-apply-combine-Introduction\">Group by: split-apply-combine Introduction</a>\n",
    "   \n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Replace-Missing-Values-with-Group-Means\">Replace Missing Values with Group Means</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb?flush_cache=true/#FIRST.variable-and-LAST.variable\">FIRST.variable and LAST.variable Processing</a>\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Resources\">Resources</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The usual pre-amble to get packages loaded into the namespace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from numpy.random import randn\n",
    "from pandas import Series, DataFrame, Index\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The display method() defined below is from the Python Data Science Handbook, by Jake VanderPlas, available <a href=\"http://shop.oreilly.com/product/0636920034919.do\"> here </a>.  It is used to render DataFrames side-by-side for comparisons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class display(object):\n",
    "    \"\"\"Display HTML representation of multiple objects\"\"\"\n",
    "    template = \"\"\"<div style=\"float: left; padding: 10px;\">\n",
    "    <p style='font-family:\"Courier New\", Courier, monospace'>{0}</p>{1}\n",
    "    </div>\"\"\"\n",
    "    def __init__(self, *args):\n",
    "        self.args = args\n",
    "\n",
    "    def _repr_html_(self):\n",
    "        return '\\n'.join(self.template.format(a, eval(a)._repr_html_())\n",
    "                         for a in self.args)\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '\\n\\n'.join(a + '\\n' + repr(eval(a))\n",
    "                           for a in self.args)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SAS Sort Merge with by-group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Start with the example data from SAS' Step-by-Step Programming with Base SAS software doc located <a href=\"https://support.sas.com/documentation/cdl/en/basess/58133/HTML/default/viewer.htm#a001304305.htm\"> here</a> .  \n",
    "\n",
    "Create the SAS data sets 'left' and 'right'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_default_sort_merge.sas                         */\n",
    "    /******************************************************/\n",
    "    data left;\n",
    "       length name $ 32;\n",
    "       input name $ 1-25 age 27-28 gender $ 30;\n",
    "       datalines;\n",
    "    Gunter, Thomas            27 M\n",
    "    Harbinger, Nicholas       36 M\n",
    "    Benito, Gisela            32 F\n",
    "    Rudelich, Herbert         39 M\n",
    "    Sirignano, Emily          12 F\n",
    "    Morrison, Michael         32 M\n",
    "    Morrison, Michael         32 M\n",
    "    Onieda, Jacqueline        31 F\n",
    "    ;;;;\n",
    "\n",
    "    data right;\n",
    "       length name $ 32;\n",
    "       input idnumber $ 1-11 name $ 13-40 salary;\n",
    "       datalines;\n",
    "    929-75-0218 Gunter, Thomas               27500\n",
    "    446-93-2122 Harbinger, Nicholas          33900\n",
    "    228-88-9649 Benito, Gisela               28000\n",
    "    029-46-9261 Rudelich, Herbert            35000\n",
    "    442-21-8075 Sirignano, Emily             5000\n",
    "    321-82-5771 Valpolicella, Vino           88000\n",
    "    ;;;;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS log below shows both data sets sorted by 'name' and subsequently merged on the sort key.  If the keys from both tables match then the observation is merged into a single observation in the output dataset.   \n",
    "\n",
    "Observations not matching in both input data sets are included and their values are set to missing as illustrated in the SAS output below.  This is the equivalent of a full outer join shown <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Full-Outer-Join\"> below</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_default_sort_merge.sas                         */\n",
    "    /******************************************************/\n",
    "    34       proc sort data=left;\n",
    "    35          by name;\n",
    "    36       \n",
    "    37       proc sort data=right;\n",
    "    38          by name;\n",
    "    39       \n",
    "    40       data merge_both;\n",
    "    41       merge left\n",
    "    42             right;\n",
    "    43          by name;\n",
    "\n",
    "    NOTE: 8 observations were read from \"WORK.left\"\n",
    "    NOTE: 6 observations were read from \"WORK.right\"\n",
    "    NOTE: Data set \"WORK.merge_both\" has 9 observation(s) and 5 variable(s)\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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PhB4i8Oa5a6pca3rUzXBuo4Y2jWRcGLLFw2NwJPH8R619B678ELG7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\nnIIIz6jjnGMmvIE8GaP4E/ag8HaP4fh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S3f8AVmv1MVK2y/q6f6Hz/wCK0t7fTvDl/wCK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\nWdtp8/2i3gthFGiPh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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\default_match_merge.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct the DataFrames 'left' containing demographic information for employees and 'right' containing salary information.  They are replicates of the SAS data sets 'left' and 'right' used with the SAS program above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'name': ['Gunter, Thomas', 'Harbinger, Nicholas', 'Benito, Gisela', 'Rudelich, Herbert', \\\n",
    "                              'Sirignano, Emily', 'Morrison, Michael', 'Morrison, Michael', 'Onieda, Jacqueline'],\n",
    "                     'age':          [27, 36, 32, 39, 22, 32, 32, 31],\n",
    "                     'gender':       ['M', 'M', 'F', 'M', 'F', 'M', 'M', 'F']})\n",
    "\n",
    "right = pd.DataFrame({'name': ['Gunter, Thomas', 'Harbinger, Nicholas', \\\n",
    "                               'Benito, Gisela','Rudelich, Herbert', 'Sirignano, Emily', 'Valpolicella, Vino'],\n",
    "                      'id':           ['929-75-0218', '446-93-2122', \\\n",
    "                                       '228-88-9649', '029-46-9261', '442-21-8075', '321-82-5771'], \n",
    "                      'salary':      [27500, 33900, 28000, 35000, 5000, 80000]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>left</p><div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>right</p><div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>33900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "left\n",
       "   age gender                 name\n",
       "0   27      M       Gunter, Thomas\n",
       "1   36      M  Harbinger, Nicholas\n",
       "2   32      F       Benito, Gisela\n",
       "3   39      M    Rudelich, Herbert\n",
       "4   22      F     Sirignano, Emily\n",
       "5   32      M    Morrison, Michael\n",
       "6   32      M    Morrison, Michael\n",
       "7   31      F   Onieda, Jacqueline\n",
       "\n",
       "right\n",
       "            id                 name  salary\n",
       "0  929-75-0218       Gunter, Thomas   27500\n",
       "1  446-93-2122  Harbinger, Nicholas   33900\n",
       "2  228-88-9649       Benito, Gisela   28000\n",
       "3  029-46-9261    Rudelich, Herbert   35000\n",
       "4  442-21-8075     Sirignano, Emily    5000\n",
       "5  321-82-5771   Valpolicella, Vino   80000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(\"left\", \"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "panda uses two main constructs for joing and merging dataframes.  There is the <a href=\"http://pandas.pydata.org/pandas-docs/stable/merging.html#merge-join-and-concatenate\"> concat </a> function and the Database-style <a href=\"http://pandas.pydata.org/pandas-docs/stable/merging.html#database-style-dataframe-joining-merging\"> join/merge </a> operations with syntax similar to SQL.\n",
    "\n",
    "The examples below use the Database-style join/merge operations.  An example of the pd.concat() method is found <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Concatenating-DataFrames-(Join)\"> here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the SAS log below.  It shows 7 different output data sets created in a single Data Step.  Each are produced below with panda.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_create_all_data_sets.sas                       */\n",
    "    /******************************************************/\n",
    "    26      data both\n",
    "    27           right\n",
    "    28           left\n",
    "    29           allrows\n",
    "    30           nomatchl\n",
    "    31           nomatchr\n",
    "    32           nomatch;\n",
    "    33      \n",
    "    34            merge left(in=l)\n",
    "    35                   right(in=r);\n",
    "    36            by name;\n",
    "    37      \n",
    "    38      if (l=l and r=1) then output both; *Inner Join;\n",
    "    39      \n",
    "    40      if r = 1 then output right; * Right Outer Join;\n",
    "    41      \n",
    "    42      if l = 1 then output left;  * Left Outer Join;\n",
    "    43      \n",
    "    44      if (l=1 or r=1) then output allrows; *Full Outer Join;\n",
    "    45      \n",
    "    46      if (l=0 and r=1) then output nomatchl;\n",
    "    47      \n",
    "    48      if (l=1 and r=0) then output nomatchr;\n",
    "    49      \n",
    "    50      if (l=0 or r=0) then output nomatch;\n",
    "\n",
    "    NOTE: 8 observations were read from \"WORK.left\"\n",
    "    NOTE: 6 observations were read from \"WORK.right\"\n",
    "    NOTE: Data set \"WORK.both\" has 6 observation(s) and 5 variable(s)\n",
    "    NOTE: Data set \"WORK.right\" has 6 observation(s) and 5 variable(s)\n",
    "    NOTE: Data set \"WORK.left\" has 8 observation(s) and 5 variable(s)\n",
    "    NOTE: Data set \"WORK.allrows\" has 9 observation(s) and 5 variable(s)\n",
    "    NOTE: Data set \"WORK.nomatchl\" has 1 observation(s) and 5 variable(s)\n",
    "    NOTE: Data set \"WORK.nomatchr\" has 3 observation(s) and 5 variable(s)\n",
    "    NOTE: Data set \"WORK.nomatch\" has 4 observation(s) and 5 variable(s)\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inner Join\n",
    "### If (L=1 and R=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An INNER JOIN selects only those rows whose key values are found in both tables.  Another way to say this is the intersection of key values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_inner_join.sas                                 */\n",
    "    /******************************************************/\n",
    "    37       proc sql;\n",
    "    38          select monotonic() as row_num\n",
    "    39                ,*\n",
    "    40       \n",
    "    41        from left, right\n",
    "    42        where left.name = right.name;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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C96a5ubXa/wCGv/AE+W2nb8dP+CdIv/Hw/wDuL/M1JUY/4+H/ANxf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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\inner_join.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the panda merge() method, how='inner' is the analog to the SAS example above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The panda 'how' argument for the merge() method specifies which keys are to be included in the output table. If a key combination does not appear in either the left or right table then values in the joined table will be NaN for missing.  This is the same as the SAS sort/merge example above. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the how='inner' argument for an INNER JOIN.  This retrieves the intersection of key values from the 'left' and 'right' DataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "both = pd.merge(left, right, on='name', how='inner', sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender                 name           id  salary\n",
       "0   32      F       Benito, Gisela  228-88-9649   28000\n",
       "1   27      M       Gunter, Thomas  929-75-0218   27500\n",
       "2   36      M  Harbinger, Nicholas  446-93-2122   33900\n",
       "3   39      M    Rudelich, Herbert  029-46-9261   35000\n",
       "4   22      F     Sirignano, Emily  442-21-8075    5000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS Data Step eqivalent of an Inner Join.\n",
    "    \n",
    "    data both;\n",
    "    merge left(in=l)\n",
    "          right(in=r);\n",
    "    by name;\n",
    "    \n",
    "    if (l=1 and r=1) then output both;\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Right Outer Join\n",
    "### if R = 1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PROC SQL Right Outer Join example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_right_outer_join.sas                           */\n",
    "    /******************************************************/\n",
    "    38       proc sql;\n",
    "    39       create table r_outer(drop=old_name) as\n",
    "    40          select monotonic() as row_num\n",
    "    41                ,coalesce(left.old_name, right.old_name) as name\n",
    "    42                ,*\n",
    "    43          from left (rename=(name=old_name))\n",
    "    44          right join\n",
    "    45          right (rename=(name=old_name))\n",
    "    46        on left.old_name = right.old_name;\n",
    "    47       \n",
    "    48        select *\n",
    "    49        from r_outer;\n",
    "    50        quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The COALESCE function coerces the 'name' variable into a single column.  The 'name' column is renamed 'old_name' to provide a join key and is then dropped.  \n",
    "\n",
    "To produce the output above a second SELECT statement is used to display the single 'name' column in the output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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5R7jUX2OD8d2Pi/UtTax0izF3o13YtC22/Fr5E5J/eOR87rgj5Bwe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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\right_outer.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For panda use the how='right' argument for a Right Outer Join.  This returns only those key values found in the 'right' DataFrame with corresponding matches found the 'left' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r_outer = pd.merge(left, right, how='right', sort=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name           id  salary\n",
       "0  32.0      F       Benito, Gisela  228-88-9649   28000\n",
       "1  27.0      M       Gunter, Thomas  929-75-0218   27500\n",
       "2  36.0      M  Harbinger, Nicholas  446-93-2122   33900\n",
       "3  39.0      M    Rudelich, Herbert  029-46-9261   35000\n",
       "4  22.0      F     Sirignano, Emily  442-21-8075    5000\n",
       "5   NaN    NaN   Valpolicella, Vino  321-82-5771   80000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_outer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS Data Step equivalent of a RIGHT Outer Join.\n",
    "\n",
    "````\n",
    "    78       data r_outer;\n",
    "    79           merge left(in=l)\n",
    "    80                 right(in=r);\n",
    "    81       \n",
    "    82       if r = 1;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Left Outer Join\n",
    "### If L = 1;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PROC SQL Left Outer Join example.  See the SAS SQL Right Outer Join example <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#Right-Outer-Join\"> above</a> to use the COALESCE function to coerce the name column contributed by both both tables into a single column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_left_outer_join.sas                            */\n",
    "    /******************************************************/\n",
    "    29       proc sql;\n",
    "    30          select monotonic() as row_num\n",
    "    31                ,*\n",
    "    32          from\n",
    "    33             left\n",
    "    34          left join\n",
    "    35             right\n",
    "    36        on left.name = right.name;\n",
    "    37       \n",
    "    38        quit;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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An1uf+/D/AOFPmj3Fyy7DaKd5Fx/z63P/AH4f/CjyLj/n1uf+/D/4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\nFFFABRRRQB//2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\left_outer.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For panda use the how='left' argument for a LEFT OUTER JOIN.  This returns only those key values found in the 'left' DataFrame with corresponding matches found the 'right' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "l_outer = pd.merge(left, right, how='left', sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender                 name           id   salary\n",
       "0   32      F       Benito, Gisela  228-88-9649  28000.0\n",
       "1   27      M       Gunter, Thomas  929-75-0218  27500.0\n",
       "2   36      M  Harbinger, Nicholas  446-93-2122  33900.0\n",
       "3   32      M    Morrison, Michael          NaN      NaN\n",
       "4   32      M    Morrison, Michael          NaN      NaN\n",
       "5   31      F   Onieda, Jacqueline          NaN      NaN\n",
       "6   39      M    Rudelich, Herbert  029-46-9261  35000.0\n",
       "7   22      F     Sirignano, Emily  442-21-8075   5000.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l_outer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS Data Step equivalent of a Left Outer Join."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````    \n",
    "    73      data l_outer;\n",
    "    74          merge left(in=l)\n",
    "    75                right(in=r);\n",
    "    76      \n",
    "    77      if l = 1;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full Outer Join\n",
    "### If (L=1 or R=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PROC SQL Full Outer Join exanple.  This is the default behavior for the SORT/MERGE example located at the <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2007%20--%20Pandas%2C%20Part%202.ipynb#SAS-Sort-Merge-with-by-group\"> beginning</a> of this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_full_outer_join.sas                            */\n",
    "    /******************************************************/\n",
    "    6       proc sql;\n",
    "    7       create table sas_merge(drop=old_name) as\n",
    "    8       select monotonic() as obs\n",
    "    9             ,coalesce(left.old_name, right.old_name) as name\n",
    "    10             ,*\n",
    "    11          from left (rename=(name=old_name))\n",
    "    12       full join right (rename=(name=old_name))\n",
    "    13           on left.old_name = right.old_name;\n",
    "    14       \n",
    "    15       select * from sas_merge;   \n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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a6roFvpVrpNhc2BuobtJC++HYJNo5AJA45Oc5xXr3kXH/AD63P/fh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\nzX8l5BJPG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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\full_outer_join.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "merge_both = pd.merge(left, right, on='name', how='outer', sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": 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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name           id   salary\n",
       "0  32.0      F       Benito, Gisela  228-88-9649  28000.0\n",
       "1  27.0      M       Gunter, Thomas  929-75-0218  27500.0\n",
       "2  36.0      M  Harbinger, Nicholas  446-93-2122  33900.0\n",
       "3  32.0      M    Morrison, Michael          NaN      NaN\n",
       "4  32.0      M    Morrison, Michael          NaN      NaN\n",
       "5  31.0      F   Onieda, Jacqueline          NaN      NaN\n",
       "6  39.0      M    Rudelich, Herbert  029-46-9261  35000.0\n",
       "7  22.0      F     Sirignano, Emily  442-21-8075   5000.0\n",
       "8   NaN    NaN   Valpolicella, Vino  321-82-5771  80000.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_both"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A Full Outer Join is the default behavior for a SAS 'Sort-Merge' with a by-group except in the case of a many-to-many join (see below).  It combines all observations from both data sets.  Said another way, the UNION of all values from both datasets.  \n",
    "\n",
    "This is also the equivalent to setting the MERGE IN= flag as:\n",
    "\n",
    "    if (L=1 or R=1) then output merge_both;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    34       proc sort data=left;\n",
    "    35          by name;\n",
    "    36       \n",
    "    37       proc sort data=right;\n",
    "    38          by name;\n",
    "    39       \n",
    "    40       data merge_both;\n",
    "    41       merge left\n",
    "    42             right;\n",
    "    43          by name;\n",
    "\n",
    "    NOTE: 8 observations were read from \"WORK.left\"\n",
    "    NOTE: 6 observations were read from \"WORK.right\"\n",
    "    NOTE: Data set \"WORK.merge_both\" has 9 observation(s) and 5 variable(s)\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outer Join no Matched Keys\n",
    "### if (L=0 or R=0); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The examples above illustrate joining strategies based on matched key values in the data to be joined.  The next three examples illustrate joining data where keys are not matched.  \n",
    "\n",
    "Every SQL join is either a Cartesian product join or a sub-set of the Cartesian product join.  Thus requiring a different approach to cases involving non-matched key values.   Some form of WHERE processing is required.  This is where the SAS Data Step with its IN= processing logic is a common pattern.  \n",
    "\n",
    "PROC SQL can be used as well.\n",
    "\n",
    "The next three examples use panda's indicator= argument to the pd.merge() method as an analog to the SAS IN= flag.  \n",
    "\n",
    "For panda the WHERE processing filters utilize <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2005%20--%20Understanding%20Indexes.ipynb#Mixing-.loc-Indexer-with-Boolean-Operators\"> boolean comparisons</a>.\n",
    "\n",
    "Start with the SAS Data Step for no matched keys in either the 'left' or 'right' data sets. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_nomatch_left_or_right.sas                      */\n",
    "    /******************************************************/\n",
    "    45      data nomatch;\n",
    "    46            merge left(in=l)\n",
    "    47                   right(in=r);\n",
    "    48            by name;\n",
    "    49      \n",
    "    50      if (l=0 or r=0);\n",
    "    51      \n",
    "    52      title1 \"if (L=0 or R=0)\";\n",
    "````        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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j/wDiaPYyF7WJ2NFcd/a+p/8AP8//AH7j/wDiaP7X1P8A5/n/AO/c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\n3zF/8RR/Z8v/AEEbr/vmL/4inza3FbSxy03gid7qe1i1ONNBur8ahPY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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Image(filename='Anaconda3\\\\output\\\\nomatch.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "panda provisions the indicator= argument to the pd.merge() method.  The indicator argument can be a column name which takes on 1 of 3 possible values:\n",
    "\n",
    "    left_only\n",
    "    right_only\n",
    "    both\n",
    "    \n",
    "By applying a boolean filter in conjunction with these values, we can replicate the behaviors for the SAS IN= flag for MERGEs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct the 'nomatch' DataFrame with an Outer Join using the ['name'] column as the join key for both."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nomatch = pd.merge(left, right, on='name', how='outer', sort=True, indicator='in=')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 'nomatch' DataFrame displays the nomatch['in= '] column values.  These values are tested with boolean comparisons for the WHERE processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name           id   salary         in=\n",
       "0  32.0      F       Benito, Gisela  228-88-9649  28000.0        both\n",
       "1  27.0      M       Gunter, Thomas  929-75-0218  27500.0        both\n",
       "2  36.0      M  Harbinger, Nicholas  446-93-2122  33900.0        both\n",
       "3  32.0      M    Morrison, Michael          NaN      NaN   left_only\n",
       "4  32.0      M    Morrison, Michael          NaN      NaN   left_only\n",
       "5  31.0      F   Onieda, Jacqueline          NaN      NaN   left_only\n",
       "6  39.0      M    Rudelich, Herbert  029-46-9261  35000.0        both\n",
       "7  22.0      F     Sirignano, Emily  442-21-8075   5000.0        both\n",
       "8   NaN    NaN   Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2005%20--%20Understanding%20Indexes.ipynb#Mixing-.loc-Indexer-with-Boolean-Operators\"> boolean logic</a> and the logical 'or' (|) comparison, select those rows with the value 'left_only' and 'right_only'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                name           id   salary         in=\n",
       "3  32.0      M   Morrison, Michael          NaN      NaN   left_only\n",
       "4  32.0      M   Morrison, Michael          NaN      NaN   left_only\n",
       "5  31.0      F  Onieda, Jacqueline          NaN      NaN   left_only\n",
       "8   NaN    NaN  Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch[(nomatch['in='] == 'left_only') | (nomatch['in='] == 'right_only')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Brievity allows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                name           id   salary         in=\n",
       "3  32.0      M   Morrison, Michael          NaN      NaN   left_only\n",
       "4  32.0      M   Morrison, Michael          NaN      NaN   left_only\n",
       "5  31.0      F  Onieda, Jacqueline          NaN      NaN   left_only\n",
       "8   NaN    NaN  Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch[nomatch[\"in=\"] != 'both']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outer Join no Matched Keys in Right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locate the key value rows in the 'right' data set which have no corresponding match in the 'left' data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````  \n",
    "    /******************************************************/\n",
    "    /* c07_nomatch_right.sas                              */\n",
    "    /******************************************************/\n",
    "    12      data nomatchr;\n",
    "    13            merge left(in=l)\n",
    "    14                   right(in=r);\n",
    "    15            by name;\n",
    "    16      \n",
    "    17      if (l=0 and r=1);\n",
    "    18      \n",
    "     \n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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025u7rWrSAJo+kubnWbhzDE0iPvfbkF3OB8uVLHHzV1fg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\nAUUUUAf/2Q==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " Image(filename='Anaconda3\\\\output\\\\nomatch_in_right.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locate the rows in the 'right' DataFrame which have no corresonding key value matches in the 'left' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nomatch_r = pd.merge(left, right, on='name', how='outer', sort=True, indicator='in=')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display 'nonatch_r' DataFrame as the result of an Outer Join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name           id   salary         in=\n",
       "0  32.0      F       Benito, Gisela  228-88-9649  28000.0        both\n",
       "1  27.0      M       Gunter, Thomas  929-75-0218  27500.0        both\n",
       "2  36.0      M  Harbinger, Nicholas  446-93-2122  33900.0        both\n",
       "3  32.0      M    Morrison, Michael          NaN      NaN   left_only\n",
       "4  32.0      M    Morrison, Michael          NaN      NaN   left_only\n",
       "5  31.0      F   Onieda, Jacqueline          NaN      NaN   left_only\n",
       "6  39.0      M    Rudelich, Herbert  029-46-9261  35000.0        both\n",
       "7  22.0      F     Sirignano, Emily  442-21-8075   5000.0        both\n",
       "8   NaN    NaN   Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch_r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a boolean comparison to find key value rows contributed by the 'right' DataFrame.  In other words, key values in the 'right' DataFrame not found in the 'left' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender                name           id   salary         in=\n",
       "8  NaN    NaN  Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch_r[nomatch_r['in='] == 'right_only']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The non-matching key value rows from the 'right' DataFrame can also be sub-setted from the 'nomatch' DataFrame created above with:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender                name           id   salary         in=\n",
       "8  NaN    NaN  Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch[nomatch['in='] == 'right_only']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outer Join no Matched Keys in Left"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locate the key value rows in the 'left' data set having no corresonding match in the 'right' data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_nomatch_left.sas                               */\n",
    "    /******************************************************/\n",
    "    96      data nomatchl;\n",
    "    97            merge left(in=l)\n",
    "    98                   right(in=r);\n",
    "    99            by name;\n",
    "    100      \n",
    "    101      if (l=1 and r=0);\n",
    "    102      \n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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Vo+DvFsfjCyvruCzktYra8a3j8xwTKgRWWTA+7uDg454/\nKuhp+0kL2cTjfIuP+fW5/wC/D/4UeRcf8+tz/wB+H/wrsqKPaSH7OJxvkXH/AD63P/fh/wDCo44Z\nzJNi2uDh+cQtx8o9q7aqtp/x9X3/AF3H/otKXtJB7NHL+Rcf8+tz/wB+H/wo8i4/59bn/vw/+Fdl\nRT9pIPZxON8i4/59bn/vw/8AhR5Fx/z63P8A34f/AArsqKPaSD2cTjfIuP8An1uf+/D/AOFHkXH/\nAD63P/fh/wDCuyoo9pIPZxON8i4/59bn/vw/+FHkXH/Prc/9+H/wrsqKPaSD2cTiYIZzGcW1wfnb\npCx/iPtUnkXH/Prc/wDfh/8ACuo07/j1f/rvN/6MarVJVJWD2aON8i4/59bn/vw/+FHkXH/Prc/9\n+H/wrsqKftJB7OJw95paajaPa6hpb3VvJjfDPaM6Ng5GVK4PIzT4bN7eFIYLGaKKNQqIlswVQOAA\nAOBXa0Ue0kHs0VfDwI0SIMCpDyZBGCP3jdq0qp6V/wAeTf8AXeb/ANGtVyud7my2CiiikMKKKKAC\niiigAooooAx/+Yhff9dl/wDRaVJVaVrganfeRFE6+auS8pU58tOwU0b73/n3t/8Av+3/AMRXTF+6\nc8tyzXKfEfRb/XvCIstJg+0XH221l2b1X5UmRmOWIHABNdFvvf8An3t/+/7f/EUb73/n3t/+/wC3\n/wARTJtoLd/6qP8A67xf+jFrC8S+HdR1XxR9rs0CImjzRQ3DMuI7kTRyRfLnJwUz0xxWtdPeeXHu\nggH76LGJiefMXH8FaPmah/z7W3/gS3/xFZy+JNf1o1+prHa39dGea3ngbXtaRJrq3jtby/0m9N2X\nmVkhuZpoJEhYryyhY9m4AjCe4zZ8S6L4h8Xxz3n9gyaZJBpjWkdtcXELSTySTQuxUo5UIoh4LEEl\nvujHPoPmah/z7W3/AIEt/wDEUeZqH/Ptbf8AgS3/AMRS7W6X/G/+Y7Xv5/8AA/yOfvNGv5fGesX0\ncGba50OO0ifevzSiSYlcZyOHXk8c9a5QeGfEVjY6YmjaXqem61FYWsJ1G01GE2jOsWxluoHbDBee\nUR2IC4YdB6X5mof8+1t/4Et/8RR5mof8+1t/4Et/8RSj7u39at/qxy953/rZL9DhZdA11NNv/C8e\nlGaC+1R7xdWM0fkxxPP5zbkLeZ5g5UAKVPyncMnElv4Z1dNCsbdrTEsXiiTUHXzU4gNzI4fOf7rA\n4689K7bzNQ/59rb/AMCW/wDiKPM1D/n2tv8AwJb/AOIoWlvK34Wa/IJaprvf8b/5iadfrqlgbiKK\nSIF5I9sm3OUcoT8pIwSuRz0rgNA8I67Zaf4Z0+4tVjs/s0P9roZlJiktzvjAwTu3MQCQcYQV6B5m\nof8APtbf+BLf/EUeZqH/AD7W3/gS3/xFK2tw6WPLovAfiB9KsImtFjmtNLQbHmTa80d6s4iJBP3l\nTG7oM/hXUaiNd8R3OkyN4en02Cx1WCdhdXULSsgSQO22N2UKMqB8xY5PygDJ6nzNQ/59rb/wJb/4\nijzNQ/59rb/wJb/4ihKyS7O/43B638/8rHBX2i+JrbUtYe0t9ROn6hrJnnTSrm3juZ4PssSDa8jD\nYu9GBwySDgqR1rGsPA+sWU8GoXmkatKkN3fK1lY66yXJjnaN0kE/nIZMGPayu4POecDPq3mah/z7\nW3/gS3/xFHmah/z7W3/gS3/xFCVnfyt+S/T8WN6/ff8AP/M4/QvDV1Y+JtFvodJlsLSK0vvPSe/N\n1Ikk0sTLvdmLM7BWJwWAORuPBPdVV8zUP+fa2/8AAlv/AIijzNQ/59rb/wACW/8AiKq+iQgj/wCQ\nxcf9cIv/AEKSrVZiPff2pPi3t93kx5HntgDc+Odn1qz5mof8+1t/4Et/8RSA47WtN1vV9WhifQoo\ntQtr5HtvEULRKsNqJA5TlzNuZQY2QLsYknIBxSWLa7qPjJb/AMR+GNUEdtO0enJHPaG2tkOV89/3\n+95CpPO35QSqjOWbsvM1D/n2tv8AwJb/AOIo8zUP+fa2/wDAlv8A4ihaWB63CP8A5DFx/wBcIv8A\n0KSuCuvC/iBF1KTTYFjnvtXuI3YzKD9iuFRWlBz95SgYDr8pHeu0R77+1J8W9vu8mPI89sAbnxzs\n+tWfM1D/AJ9rb/wJb/4ilZXv8vxX+QP+vua/UxvCWjXGjTa6s1uIIbjU2ltQGBDReVGoPB45UjB5\n4ro6q+ZqH/Ptbf8AgS3/AMRR5mof8+1t/wCBLf8AxFVfRfJfcJKxaoqr5mof8+1t/wCBLf8AxFHm\nah/z7W3/AIEt/wDEUhlqqtp/x9X3/Xcf+i0o8zUP+fa2/wDAlv8A4iq1q999ovNtvbk+cN2Z2GD5\naf7HPGKANOiqvmah/wA+1t/4Et/8RR5mof8APtbf+BLf/EUAWqKq+ZqH/Ptbf+BLf/EUeZqH/Ptb\nf+BLf/EUAWqKq+ZqH/Ptbf8AgS3/AMRR5mof8+1t/wCBLf8AxFAFqiqvmah/z7W3/gS3/wARR5mo\nf8+1t/4Et/8AEUAGnf8AHq//AF3m/wDRjVarMsHvhbtst7cjzpes7DnzGz/B61Z8zUP+fa2/8CW/\n+IoAtUVV8zUP+fa2/wDAlv8A4ijzNQ/59rb/AMCW/wDiKALVRRzO9zLE1vLGke3bKxXbJnrtwSeO\n+QPbNReZqH/Ptbf+BLf/ABFHmah/z7W3/gS3/wARQBNpX/Hk3/Xeb/0a1XKpaQWOn/OAG86bIByA\nfNbvV2sywooooAKKKKACiiigAooooAx/+Yhff9dl/wDRaVJUf/MQvv8Arsv/AKLSpK6Y/Cc8twrz\nn4m6j4v09Z7rRdYt/D2j2Fg1w99JBFO13c7sJbhGJK545AzkgDPSvRq4Lxv4B1zxV4k03VNN8WjS\nY9NXdb2z6Yl0izc5lw7bS2CACV+XHB5NEk3a39f1/WgRaW502mXV9e+GNKutWtxa30620lxAAR5c\nhZCy4PIwe1Utd8Q6jpnxF0qzjlX+y5bNnuojGCcmaONHDdRhnGecYLcZwRpwQXdro1lBqN59uu43\ngWa68oR+c4dctsHC5POBTNZ8KRa1rEl5cXBWKXS5tOaFU5xIytvDZ4I29Me+aVR/vE+mv5P9bDgv\nct6fmv0Ob1Dx5d6b8Qr5bp/+KfsdMuH8mKIGSeeJoMkMfebywMgbg2e2LuoePdQ0G2uf+Eh0BILt\nLP7bBDaX3nLMgkRHUuyJtdTInGCpzw3XB/wrWC4tbWDUtSkuNmmXNlcypEEeeWeWOV7gHJCtvQsF\nwR83tyt/4D1HXbe4PiDXorm8az+x28tvYmGOJDIjuzIZGLOxjTJ3AADhRznPol5P79bfp8tDRvd/\n10/4JLN4r11NVm0mXRLOO5t7Bb65lj1NmSONnkXCEwZZ/wB2DgqBycngbqOk/EO5vrzTdOtbG2nu\nJbK1uJI7vU0hu5hIgZnhi8sLKFGcnMYyGAAxXR3Hhvz/ABFqGqfatv23TEsPK8vOza0jb855/wBZ\n0x2681gX/wAOrvUdBstBvNXs7jSbe3giKT6WHniaNdpkgl3jymYdyrlSTg9gR8/61l+lglvp/Wi/\nW5bm8cXKSXF9DpCy6Ba3psZ777ViYOH8tnWHZho1c4JLhuGIU4GYbTx7fT3VvLNocUWlz6tLpIuR\nfbpRIsjor+V5YGwlAPv5BPQgZM0vge5Z57GHWBFoF1e/bZ7L7LmYuX8xkWbfhY2cZIKFuWAYZGJo\nvBPlaZbWf9oZ+z62+rb/ACfvbpnl8vG7j7+N3tnHahX0v5X/AAv+tgls7fL8bfodSxwpx6V574V8\nZ6rc/DT7fqbxXOs+VF5BKBVnaYAREquMDeSpxj7hPFdxpzX0lj/xNFjS48yQYjXA2byEONzc7dpP\nPX06VzGl/D5NN/4RzOpPIui23kSoIQq3hUHy2bk7dhZiBzyaWuo9LeZiaH8QLy10XTp9ckmv7qTS\nRMwjEca3E7XIhRQoTKksyjO7aAeR3rU17xF4ssIdNVtHtrW7m1WG3VIL4TQXMbo5KmRo1ZMFRuOz\ngY2ljkUyL4YxCwt7abVGY2+ni1ikSDaVkW4E6Sj5j91lHy98da1JPDOs6jPZT67r0Fw9nfRXUcdr\np/kRAIrgjBkdtzb+WLEfKMKOSRXaV97/AIc3+X4CfW3bT/wH/Mrp4z1J9Kv5Do1nDe6ZePbXxn1Q\nR2dsFjWTzGnKbtpR16R5BOCAAWrIj+L9jLpVpJ/xJ7e9uZ7iIG71lIrMiFgrOtzsO8Hcu0BMnJyA\nATWhqHw9kudVm1K2v7M3DaqdSijv9P8AtMKN5CRAFPMUlgYwyuGGMkY70ll4B1HTLhdQsNfiOrLd\nXMv2m5sN8ckc5VnjeNZFz86KwYMuMAEHnJrfXsvv0/W/6dAdunf8Nf8AgCaH42fxJ4k0P7DNGtld\nWV6bmGGRJozNDJEmUlA+ZRubBGMggkA8Dua5+28OXY17TNX1HVftlzZ2txBJi2EayGV42+UA/Kq+\nXgA7jgjLEjJ6Cq6L+urAqx/8hi4/64Rf+hSVaqrH/wAhi4/64Rf+hSVapAcNqmpa/pHiexe71NvL\nvNREKWSWwFklqTtDPcNGCJycELv5YhQp60+5uvEGleIdL+260tzdalfvGNGgijMKWo3ZkVtgl3Iu\nxmYsV3HaB8y4t3nhDUL+4a1u9cM2hveretaSW7PcblcSCMTmTAjDqDjZkD5QwFRaR4V8QaZ4hutT\nn1zTL17ybM0kukyeeIc5WFH+0bUVR0wmM5YgkklR6X/rbT+vPyCXW39b6/15HTR/8hi4/wCuEX/o\nUlcp4e8VXja34kh1y4RrO0uZ2tH2BTHFFt8xSR1A3oQTz8x9K6uP/kMXH/XCL/0KSuYvfh8l7b3E\nbak8fn6u2oOUhHzROoWS3OTyGUEE+4OOKNeb5P77r8tf6YPb5/o/+AO+Het6xrmn6rN4gZPtEWos\nkcSRhRBGYo3WP3K7yCTyT+VdfWVomif2PcatL9o87+0b5rvGzb5eURdvU5+5nPHXpWrVdF6L8tRK\n+t+7/MKKKKBhVW0/4+r7/ruP/RaVaqraf8fV9/13H/otKQFqiiimAUUUUAFFFFABRRRQBV07/j1f\n/rvN/wCjGq1VXTv+PV/+u83/AKMarVIAooopgFFFRRrcC5laWWNoDt8pFjIZP725txDZ7YAx70AL\npX/Hk3/Xeb/0a1XKp6V/x5N/13m/9GtVysiwooooAKKKKACiiigAooooAwpYXk1O+KXMsQ81RtQL\nj/VpzyDR9ll/5/rj/vmP/wCJqX/mIX3/AF2X/wBFpUldMV7pzy3K32WX/n+uP++Y/wD4mj7LL/z/\nAFx/3zH/APE1Zryb4r6LoHiG8vvtF9M+t6RpTXUFrJcvDHaRbiTdRgKBJICoG0uFOFBK9aUnyq4R\nXM7HpV1bSiOPN7Of30Q5VP8AnovP3a0fsk3/AEEbn/vmP/4isPRr6LUvCOj3tvLPNFcR2siSXAxI\n4JQ5Yep6mqmt2dtr/wAQ7TRdciW50uPTHu0speYbmUSqpLp0fYCuAcgF84yAQqitPl/rS7/QqDvD\nm/rodP8AZJv+gjc/98x//EUfZJv+gjc/98x//EV5z4ill8F3t/Y+DZI9Ogkgs5fs0catDaSSXiRZ\nWLookVnyBgEoSMEk0ur6nqunXl7o97qTaslrqmjvDcXtrAZAs1wFZSFjVONmVYKGBPXIBqI2lbzd\nvxsW9L+X+Vz0X7JN/wBBG5/75j/+Io+yTf8AQRuf++Y//iK870XxJ4v1WS11q30/VpbOe5bzbdxY\nrZpbhmUlSJPtG9QAcnOSGG1QRtrQT6rqNz4A8S6rrnmx6hPJdNZvBGsVqXspmAjKgPhVJB3sxPBy\nvQi1Vx21sem/ZJv+gjc/98x//EUfZJv+gjc/98x//EV57oHiPW5vGeiRnUNWv9K1SO43TX+nwWsE\nrKgdWt0AE6qOR+93Ag5DNwx9IuLmC0gaa6mjgiXG6SRgqjJwMk+5pk3Ifsk3/QRuf++Y/wD4ij7J\nN/0Ebn/vmP8A+IritW1E6b4j8TSLqMunvIunxLLb2Ru5iT5nyxxgHLkZwSrAdSpANZOneKfEj3k2\nhXV1qFrNcatBaQ3eoQ2v2q2ie3aZsiEGEsfLIXI43cg4xUp30/rp/mPp9/4f8Mel/ZJv+gjc/wDf\nMf8A8RR9km/6CNz/AN8x/wDxFcjcL4gXxNYeGf8AhJ7lFe2uL5tQS0txcyKrxqkRyhjODISWVFOA\ng45Jq+EPEuuahrej2mq30dx51lqUlx5UKqjyQ3aRoy8ZACkgDPOecnmqWtvO/wCF7/kJ6f1/Xc7j\n7JN/0Ebn/vmP/wCIo+yTf9BG5/75j/8AiK8t03xd4p8QQ2tlaf2w80WmpdTXOkxWG+SSSSVV3i5K\nrtAi6IMknkjvHf3+qLrOoXWq2kNtqUlvoC3MDxxyqjteShsA7lzycEEkHBByM0R1Kel/67f5nq32\nSb/oI3P/AHzH/wDEUfZJv+gjc/8AfMf/AMRXntpr3iFFtNYn1qWeGTxFNpjaf9mhERg86WNTkJv3\njavO7BAAIJyS7wn4g8X65NpGsfYtVax1Bt9xFOLFbOGFgSGiKP5+5Tt+/nd83yrkbVHX+vn+ope6\n2u3/AAV+h26Ws39qTj7dcZEMZ3bY8n5n4+7/AJzVn7JN/wBBG5/75j/+Ioj/AOQxcf8AXCL/ANCk\nq1QBV+yTf9BG5/75j/8AiKPsk3/QRuf++Y//AIiuA17S7HTdVtPEtiYLpBq6G91hZxLeR7pfJ+zI\nAoHkgsFZd/ygN8rNzWhLYT2vxm0u4uNSurv7Rpt6UhlKiK3USQYVFUD15Y5Y8c4AAI62+f4K42rN\nnTJazf2pOPt1xkQxndtjyfmfj7v+c1Z+yTf9BG5/75j/APiKI/8AkMXH/XCL/wBCkrziy8Mpq3ib\nxDcTeC/DGsRnV2V7zU5cTgeXHkBfs75A7fOPwpLV2/rdL9RPRX/rZ/5Ho/2Sb/oI3P8A3zH/APEU\nfZJv+gjc/wDfMf8A8RVlEWNFSNQqqMKqjAA9KWmBV+yTf9BG5/75j/8AiKPsk3/QRuf++Y//AIir\nVFAFX7JN/wBBG5/75j/+Iqta2sxuLwC+uBiYAkLHz+7Tn7v+cVp1VtP+Pq+/67j/ANFpQAfZJv8A\noI3P/fMf/wARR9km/wCgjc/98x//ABFWqKAKv2Sb/oI3P/fMf/xFH2Sb/oI3P/fMf/xFWqKAKv2S\nb/oI3P8A3zH/APEUfZJv+gjc/wDfMf8A8RVqigCr9km/6CNz/wB8x/8AxFH2Sb/oI3P/AHzH/wDE\nVaooAzLC1ma3Yi+uF/fSjAWP/no3PK1Z+yTf9BG5/wC+Y/8A4ijTv+PV/wDrvN/6MarVAFX7JN/0\nEbn/AL5j/wDiKPsk3/QRuf8AvmP/AOIq1RQBV+yTf9BG5/75j/8AiKPsk3/QRuf++Y//AIirVFAE\nWkArp+CxYiaYFjjJ/etzxV2qelf8eTf9d5v/AEa1XKzLCiiigAooooAKKKKACiiigDH/AOYhff8A\nXZf/AEWlSVcl0+ynkMk9nBI7dWeJST+JFM/snTv+fC1/78r/AIVqqllYzcLu5WrnPE3gHwx4xntp\n/EmkRXstr/qnLujAZzglSNw9jkdfWus/snTv+fC1/wC/K/4Uf2Tp3/Pha/8Aflf8KPaJ7oXI11My\naKOC0ghgjWOKOWFURFwqgSKAAB0FS6xoGna9FEmpQM7QPvhmileGaFuhKSIQ6ZHBwRkcHir39k6d\n/wA+Fr/35X/Cj+ydO/58LX/vyv8AhUynzO7KjHlVjHj8H6HFpstj9jaSKeaOeZ5riSSWaSNgyM8r\nMXbBVcZY8ADpxU134Y0i+vJrq6tPMmmkt5JG81xloH3xHAOPlbn375rS/snTv+fC1/78r/hR/ZOn\nf8+Fr/35X/ClzDsY0Xg3Q4NWOoR2sol883IhN1KbdZT1kEBbyw+cncFzkk9TmoovAfhyHVIdQWwZ\np7eSSSBXuZXjgMisrhIyxRFYOcqoAPBxkDG9/ZOnf8+Fr/35X/Cj+ydO/wCfC1/78r/hRdDsc9ae\nAfD9hc291Z21wtzaKyWsst9cSm3VkKFE3SHamD9wYHAOMgEan9jwT+HF0fVC1/A1sLeZpmJaYbcE\nkk5yeuc5z3zV3+ydO/58LX/vyv8AhR/ZOnf8+Fr/AN+V/wAKOYXLrcwx4J0IabNYm3uHSaZJ3nkv\np3uDImNjCcv5gIxgYbgZA4JpsXgTw7DbXcEdlIBePHJPIbuYyvJH9yTzC+4SD++CG4GTxW9/ZOnf\n8+Fr/wB+V/wo/snTv+fC1/78r/hRcLGC3gTQDZpALe6UrM032lNQuFuWdlCsWnD+Y2VABBYjCqOi\njGdoXw8sLTw5p9hqsX7/AE+W4NvJY3UsBSOSVm2bkKsV2lQVPBI74Fdf/ZOnf8+Fr/35X/Cj+ydO\n/wCfC1/78r/hRzBYwH8CeHmtbO3htJ7RLKJoIDZXs9s6xsclN0bqxXPO0kgVO3g3QGzjTY0UpbJs\njdkULbuXhAUEABWJPHXvkVsf2Tp3/Pha/wDflf8ACj+ydO/58LX/AL8r/hQpW2Cxmp4Z0hLWK3W0\nxFFenUEXzX4nLly+c/3mJx056VBaeDdDsdTF9bWsqyLK88cTXUrQRSNnc6QljGjHc3zKoPzN6mtn\n+ydO/wCfC1/78r/hR/ZOnf8APha/9+V/wovb+v67Dtf+v67kEf8AyGLj/rhF/wChSVapn9k6d/z4\nWv8A35X/AAo/snTv+fC1/wC/K/4UcwrGO3g/RH1n+1HtZDP5wuPLNzL5BlAwJPJ3eXv/ANrbnIzn\nNaEumWc2rW+pyQ5vLaKSGKTcflRypYYzg5KL1HarH9k6d/z4Wv8A35X/AAo/snTv+fC1/wC/K/4U\nKVtgsQR/8hi4/wCuEX/oUlLZ6fa2DXLWkXlm6mM83zE7nIAJ5PHCjgcVN/ZOnf8APha/9+V/wo/s\nnTv+fC1/78r/AIUcwWH0Uz+ydO/58LX/AL8r/hR/ZOnf8+Fr/wB+V/wo5gsPopn9k6d/z4Wv/flf\n8KP7J07/AJ8LX/vyv+FHMFh9VbT/AI+r7/ruP/RaVP8A2Tp3/Pha/wDflf8ACj+ydO/58LX/AL8r\n/hRzBYfRTP7J07/nwtf+/K/4Uf2Tp3/Pha/9+V/wo5gsPopn9k6d/wA+Fr/35X/Cj+ydO/58LX/v\nyv8AhRzBYfRTP7J07/nwtf8Avyv+FH9k6d/z4Wv/AH5X/CjmCw+imf2Tp3/Pha/9+V/wo/snTv8A\nnwtf+/K/4UcwWINO/wCPV/8ArvN/6MarVM/snTv+fC1/78r/AIUf2Tp3/Pha/wDflf8ACjmCw+im\nf2Tp3/Pha/8Aflf8KP7J07/nwtf+/K/4UcwWH1FHbJHcyzq0pebbuDSsyjHTapOF98AZ75p39k6d\n/wA+Fr/35X/Cj+ydO/58LX/vyv8AhRzBYTSv+PJv+u83/o1quUyKKOCMRwRrGi9FRQAPwFPqSgoo\nooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiii\ngAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKA\nCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAK\nKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAoo\nooA//9k=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\nomatch_in_left.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Locate the key value rows in the 'left' DataFrame which have no corresonding matches in the 'right' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nomatch_l = pd.merge(left, right, on='name', how='outer', sort=True, indicator='in=')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display 'nonatch_r' DataFrame as the result of an Outer Join."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name           id   salary         in=\n",
       "0  32.0      F       Benito, Gisela  228-88-9649  28000.0        both\n",
       "1  27.0      M       Gunter, Thomas  929-75-0218  27500.0        both\n",
       "2  36.0      M  Harbinger, Nicholas  446-93-2122  33900.0        both\n",
       "3  32.0      M    Morrison, Michael          NaN      NaN   left_only\n",
       "4  32.0      M    Morrison, Michael          NaN      NaN   left_only\n",
       "5  31.0      F   Onieda, Jacqueline          NaN      NaN   left_only\n",
       "6  39.0      M    Rudelich, Herbert  029-46-9261  35000.0        both\n",
       "7  22.0      F     Sirignano, Emily  442-21-8075   5000.0        both\n",
       "8   NaN    NaN   Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch_l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a boolean comparison to find key values rows contributed by the 'left' DataFrame only.  In other words key values in the 'left' DataFrame not found in the 'right' DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                name   id  salary        in=\n",
       "3  32.0      M   Morrison, Michael  NaN     NaN  left_only\n",
       "4  32.0      M   Morrison, Michael  NaN     NaN  left_only\n",
       "5  31.0      F  Onieda, Jacqueline  NaN     NaN  left_only"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nomatch_l[nomatch_l['in='] == 'left_only']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Many-to-Many Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct the DataFrames with duplicate ['name'] column values in each. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'name': ['Gunter, Thomas', 'Harbinger, Nicholas', 'Benito, Gisela', 'Rudelich, Herbert', \\\n",
    "                              'Sirignano, Emily', 'Morrison, Michael', 'Morrison, Michael', 'Onieda, Jacqueline'],\n",
    "                     'age':          [27, 36, 32, 39, 22, 32, 32, 31],\n",
    "                     'gender':       ['M', 'M', 'F', 'M', 'F', 'M', 'M', 'F']})\n",
    "\n",
    "right = pd.DataFrame({'name': ['Gunter, Thomas', 'Harbinger, Nicholas', \\\n",
    "                               'Benito, Gisela','Rudelich, Herbert', 'Sirignano, Emily', 'Valpolicella, Vino', \\\n",
    "                                'Morrison, Michael', 'Morrison, Michael'],\n",
    "                      'id':           ['929-75-0218', '446-93-2122', \\\n",
    "                                       '228-88-9649', '029-46-9261', '442-21-8075', '321-82-5771', \\\n",
    "                                       '222-33-4444', '222-33-4444'], \n",
    "                      'salary':      [27500, 33900, 28000, 35000, 5000, 80000, 75000, 75000]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>left</p><div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>\n",
       "<div style=\"float: left; padding: 10px;\">\n",
       "    <p style='font-family:\"Courier New\", Courier, monospace'>right</p><div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>27500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>33900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>28000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>35000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>80000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>222-33-4444</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>222-33-4444</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>75000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    </div>"
      ],
      "text/plain": [
       "left\n",
       "   age gender                 name\n",
       "0   27      M       Gunter, Thomas\n",
       "1   36      M  Harbinger, Nicholas\n",
       "2   32      F       Benito, Gisela\n",
       "3   39      M    Rudelich, Herbert\n",
       "4   22      F     Sirignano, Emily\n",
       "5   32      M    Morrison, Michael\n",
       "6   32      M    Morrison, Michael\n",
       "7   31      F   Onieda, Jacqueline\n",
       "\n",
       "right\n",
       "            id                 name  salary\n",
       "0  929-75-0218       Gunter, Thomas   27500\n",
       "1  446-93-2122  Harbinger, Nicholas   33900\n",
       "2  228-88-9649       Benito, Gisela   28000\n",
       "3  029-46-9261    Rudelich, Herbert   35000\n",
       "4  442-21-8075     Sirignano, Emily    5000\n",
       "5  321-82-5771   Valpolicella, Vino   80000\n",
       "6  222-33-4444    Morrison, Michael   75000\n",
       "7  222-33-4444    Morrison, Michael   75000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(\"left\", \"right\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construct the 'm2m' DataFrame with an Outer Join using the 'left' and 'right' DataFrames on the ['name']' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m2m = pd.merge(left, right, on='name', how='outer', sort=True, indicator='in=')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the 'm2m' DataFrame."
   ]
  },
  {
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>222-33-4444</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>222-33-4444</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>222-33-4444</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>222-33-4444</td>\n",
       "      <td>75000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age gender                 name           id   salary         in=\n",
       "0   32.0      F       Benito, Gisela  228-88-9649  28000.0        both\n",
       "1   27.0      M       Gunter, Thomas  929-75-0218  27500.0        both\n",
       "2   36.0      M  Harbinger, Nicholas  446-93-2122  33900.0        both\n",
       "3   32.0      M    Morrison, Michael  222-33-4444  75000.0        both\n",
       "4   32.0      M    Morrison, Michael  222-33-4444  75000.0        both\n",
       "5   32.0      M    Morrison, Michael  222-33-4444  75000.0        both\n",
       "6   32.0      M    Morrison, Michael  222-33-4444  75000.0        both\n",
       "7   31.0      F   Onieda, Jacqueline          NaN      NaN   left_only\n",
       "8   39.0      M    Rudelich, Herbert  029-46-9261  35000.0        both\n",
       "9   22.0      F     Sirignano, Emily  442-21-8075   5000.0        both\n",
       "10   NaN    NaN   Valpolicella, Vino  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The PROC SQL illustrating a many-to-many Outer Join. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_many_2_many_join.sas                           */\n",
    "    /******************************************************/\n",
    "    6      proc sql;\n",
    "    7      create table m2m(drop=old_name) as\n",
    "    8      select monotonic() as obs\n",
    "    9            ,coalesce(left.old_name, right.old_name) as name\n",
    "    10            ,*\n",
    "    11         from left (rename=(name=old_name))\n",
    "    12      full join right (rename=(name=old_name))\n",
    "    13          on left.old_name = right.old_name;\n",
    "    14      \n",
    "    15      select * from m2m;\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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yv+FeQ63d3VxLq9x4W8VN4ngOmwreX4EUxs0W5Usim2WMHdE0zFQQ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JOB4Q123OGlt729SaaMjyppCS7tGMfKu5iNvJUggsxBYz/EXyf7J0z+0vL/ALG/tSH+0/Ox5fkf\nNjfnjZ5nl5zxjrxWpLLo9l4emsdMext4I7d0ht7coiqNpwFUcD6Crz6jpskbJJeWrowIZWlUgj0I\nzR28rfh/mPv53/E5zxcbceIPDZtCo1YXL+WY/v8A2bym87OP+Wf3OvG7y++K5HQpr6y0bwjqA1jV\nLi41jSLg3hub6SVXZbcOrBWJVGBHVQCcnOSc16BpWn+E9CEw0O00bTROAJRZxRQ+ZjON20DOMnr6\nmp1Tw8kNvEi6Ysdqhjt0AjAhUjaVUfwgjjA7cVMo+7JJ7gnqn2PLtOuvEviG1Yfagh0/SLKWG4n8\nQz2Jj324drh0SJxMC+4Eykr+7Ix94l3iXzdZ8H+L7/xFrU8N3p/lQQR2l48dqVMMTDEeQsgld3wz\nAnkAEFRj0a60vwhfG0N7Y6JcmxULa+dDC/2cDGAmR8oGB0x0FPv7Dwpql6t5qdpo15dJGYlnuI4p\nHVDnKhjkgcnj3NaXXM3/AFv/AEiUmkl/T0/pnnvinVbxbyXVdJn1OJLfWba0N5da0YYmbzkR4IbR\nMpKuN2TKFbkkFscevVgTaV4Pub+a+uLDQ5bu4ULNcSQwtJIBjAZiMkfKvX0HpVqxfSNPa6a3vbYG\n7uGuJf3qDLkAHpjso9+5yaiOis/62/4f+rj6/wBf1/XyOc1zXtPudW0DVrW7Y2i2l/P5sS/OqJGA\nzBWHUHjBHXANcTd63rug35uNMa/tzcaDeXccGoaw2oTybPL2TvB80cR5YgRMVIyMDGB6paWvhjT7\n66vbCDSLW6vCTczwpEkk5Jyd7Dlueeai0/S/CGkuj6VY6JYsjM6tbQwxlWYYYjaBgkAA+opJat/1\n1/4H9bPT+v6/r8+Z122g0LS7W30vxHqe3U57SK6M2qPO6QPKFadGcl4924JuQhRuBABwazbu7n0j\nX9TsbHXb6a0tdY0iILNevKbdZH+eIuzFiGyCdxJIYA8AV3FnpXg/TrW6tdPsNEtbe8XbcxQQwok4\nwRhwBhhgnr6ms/TfDXhfTbnV0i/sj+zNTSFDpiwxLCgQNkbc7TuLZ6Dn1qr2d/O/5afh+PyIavp8\nvwevrr/W5yut63rE3i7U9K0+5M1tca7DaMH1KS1WMfYVfylmRWaLdIP4QCTxkbqbMuq2Oo6Vb6je\nW7yWt/eiCODUpL2S1H2Fm2STOquWBJYbhkKy8niu9Gm+Ehpcumiy0UWEwUSWoii8pwuNuUxg4wMc\ncYFSW9p4XtLS3tbW30iC3td5ghjSJUi3AhtoHC5DHOOuT61Lj7rV+n+X9fP77vqn/X9f16eZRTah\nqPhnWdQn1rVln0zwvZXtsYtQljCzmCRzIwVgHJKDIbIPcVcvLvxLruo65PHcW9odKWFreefX57BL\nZTAknnPCkTJKhcvkyEghSuBgk+iLF4cSCWBI9LWKaFbeWMLGFkiUELGw7qASADwATUN3p/hPULy2\nu7+00a6ubQAW800UTvDg5GxiMrg8jFXJpzbXX8NyYq0Uu3/A/wCCbMBdraNpSjOUBYx/dJxzj2qH\nS/8AkD2f/XBP/QRR/amn/wDP9bf9/l/xqtp2pWKaXaI95bqywoCplUEHaOOtDeoJWQzxKNHfRSni\nMZsGnhVwQ+3f5i7N23+Hftzn5cdeM1zWk6ab3xR4q0fV7l5prkWlzcXWmu9ooBBVYsK5ZG2xAsd5\nLK46DArrbi80m7tpLe6ubOaCZCkkUjoyupGCCDwQR2qvpieHtFs/smjLpmn224v5NoI4k3Hqdq4G\naWnUrXoY/wAL4IrXwBa29uixwxXN0kaKMBVFzIAB+FN8YBz8KpRCypIbe32M67gDvTBIyMj2yK6G\n2utHsoBDZz2NvEGLCOJ0VQSSScDuSST7mq1rc6XcaHbW19NZyp5MYeKZ1IyAOoPoRSl7yJSsS6TB\nrsLynXdR068UgeWLPT3tip75LTSZ/StOqv8Aamn/APP9bf8Af5f8aP7U0/8A5/rb/v8AL/jTGWqK\nq/2pp/8Az/W3/f5f8aP7U0//AJ/rb/v8v+NAFqquo/8AHqn/AF3h/wDRi0f2pp//AD/W3/f5f8ar\nX+pWL26hLy3Y+dEcCVTwJFJPX0oA06Kq/wBqaf8A8/1t/wB/l/xo/tTT/wDn+tv+/wAv+NAFqiqv\n9qaf/wA/1t/3+X/Gj+1NP/5/rb/v8v8AjQBaoqr/AGpp/wDz/W3/AH+X/Gj+1NP/AOf62/7/AC/4\n0AWqKq/2pp//AD/W3/f5f8aP7U0//n+tv+/y/wCNAFqiqv8Aamn/APP9bf8Af5f8aP7U0/8A5/rb\n/v8AL/jQAXf/AB9WP/Xc/wDot6tVmXWpWLXFmVvLchZiWIlXgeW4z19SKs/2pp//AD/W3/f5f8aA\nLVFVf7U0/wD5/rb/AL/L/jR/amn/APP9bf8Af5f8aALVFVf7U0//AJ/rb/v8v+NRXN1o97bPb3k9\njcQPw8Uroyt35B4NAE9x/wAfth/13P8A6KetCshr21uNQsEt7mGVhMxKpIGOPKfniteoe5SCiiik\nMp6r/wAeS/8AXeH/ANGrUlR6r/x5L/13h/8ARq1JVxJYVk674gh0P7LH9kur+8vZTFbWdoqmSUgb\nmOXZVUBQSSzAdupAOtXNeM4IDBYXcv8AbMEttcExXujW4nlttykEtHtcujfdI2PgkHAxuDYi4mrQ\n6x4bvp4Y5YXjSWGe3mAEkEiqdyNgkZHqCQQQQSCDU2v64mgWEdy9nc3rzTx28UFrs3u7thR87Ko5\n9SK53wlp1xZeEdbu7tbxH1K4nu1F8MTlSgQNIuBtZgm7aANu7bhcYGh45sLvUtL0+Cwe5hk/tS1c\nz2sau8KiQEvhlZeOuSCPWh7x87X+drivpJ9r/hexJZ+L4n1M2GsaXfaFMbd7mM6g0GySNCBIQ8Uj\nqNu5SQxBwcjIBxsjUbIyxxC8tzJLCZ4081cvGMZcDPKjcvPTketed6hoGtReIdTtdUn1HxB9v0i4\nt9Hv5Yo1W0Zk/eQyCJFVS5VCJGGCBt4P3sjVbTUfFMFjFpujamot/Dc1rOt5ZSWweTzbVmg/eAZL\nKjDP3Tk4JwcCd/6/xf5L7/Qpr+vu/wA393qei6d410DV9Ums9K1G3vUgtftUl3bzxyQKu4qQXVjg\njGSPSibxnoQ0YapYX8GqWn2uK0MmnzJMFkkdUAJDYGC6k85x2rz/AMT2tz4o1XUb3QNH1W1iGnWn\nmzPpzQSXKxXQd4kSZPmYIDhWUhs45FWL3S5NQtbq/sZPE2qTyXmmJJLqempa7ljulc7Y1hic7VJJ\ncqVAPDcEAjra/f8AXcXV/wBdEd8/ivRrXTTf6pqNnplt9pkthJd3kKqzozKQGDlc/KTtzuHcAggV\np/HPh6z1yXS9Q1O2spEggmjmuriOOOcSlwojJb5j+7PbuOtcboEF34e12PV9Y03UHsvO1SFBBYyz\nvC8l4ZFfy0UttdBwwGOBk8jMz6QbqbxbNaeHp7K2uvDNvb2ls9oFPS5zEqrkZ5TKDkZXIzScrQ5v\n6/plct5OPn+qPTao2OrQahqGpWcKSLJp0ywylwAGZo1kG3nphx1xzmq/h+7RtNtrCTzVvLWzgM6S\nRMpXcnHJGDyrA4zggg1z9n4Th1Lxd4nu9R/te3El5F5LW2pXVokqi2iBIEUiq3zAjPPTGeKtq0mv\n63Ig+aN2bEPjTQTocWr6hqFvpVpNPJAj6jPHBudHZCAS2D9wkc9KuX/iLR9MMa32qWcEk23yY5Lh\nFaYtnaEBPzFtpxjrg15To+ialok2n3V7P4i02zFrdWsb6fpwvZo2+2SORIrwyuBIhQ7gMHZ8x+7X\nSeHtAfTtcTyLLUBbR+HPIhkvok8xSZnbyyYxsBwR8i4wMDAxWbk+W68/wT/O34lJK+vl+LX5X/A6\nnw34v0TxXZQzaPqFvLLJbx3EloJkaeBXUECRFY7TzVi08TaDfpdvY63p1ytiM3Rhu43Fv1+/g/L0\nPXHQ15T/AGLqWu+B/DuieH9IvtI1PS9InjuZLm0e2WNpLUx+UsjABy8jKx2k42ZbB4qze6U+qeGN\nUa0Piu9vIdAubWO3vtKitY4t6DEICQRmU5XgJvUbe2VzpKyk0v63/wAv66qHvJX6/hsd/Z+O/Deo\n+Jl0HTtYsru9aF5dtvdRyAFSAU4bO7qcY6AntXQ1ykUT2fjzSGa0uFgk0d7ZXjt3aONw6NtZlGE4\nU43YBxgc8V1dK2i+f5tEptv7vyQVV0v/AJA9n/1wT/0EVaqrpf8AyB7P/rgn/oIpFEetaxa6DpMu\noX28xRlVCRrueR2YKqKO7MxAHuaxm8d2UVncNd6dqFvf29xHbHS3SNrh5JBmMLscoQwyd2/Aw2SM\nHGxrV9c6bpcl5Z2D37RFS8ETYcx7hvKgA7mC5IUcsRgcmvP49OhlOs3ltZeIYtH8+1uLe5FvKdQS\n7UkSTKs4MroqeWu1lYEBgqkZBX9fl/V9r/MfT+v6/U73Rtaj1dJ1NrcWN3auEuLO6CeZESNy5KMy\nkEEEFWI7dQQKt5rlv4c8FxapexzSwwQQhkgUM53bVGASM8sKyfAdhcC/1zV7htSkjv5Yo4ZtUi8m\n4nWJNvmNFsTywSSAu1chQ2PmyX+LLW4vfhkILOCW4mZbQiOJCzECWMngegBP4U5XEjR17xlpHh7w\n5Brd3JLNa3LRrbi3Tc8xcZGAcfw5Y5xgA1vV5T4m0bV72y1rTRpt09ro6E6eUiLC5M8isNgHJMSB\n0+jV6tRe+outgooopjCquo/8eqf9d4f/AEYtWqq6j/x6p/13h/8ARi0gKut69Footoxa3N/d3khj\ntrO1CeZKQCzEF2VQAoJJZgO3UgHL/wCE6tJYbNLDTNRvtQujMP7NhWJZ4fJbbLvLuqAKxVc7zksN\nu4c0eJ/NsPEWh64bW4ubSz8+G5FrbvPLGJVXa4jQFmG5ADtBI3Z6Akcy+tato+nzLZ6bqVnL4g1C\n4uxdrpM9x/Z8HyqrtFGjHzWChlRgMEnd93aUnrb+umn6/Ifb+u+p058dWcyWK6Tpuo6pd3kTzCzt\nkjWSFEbY5kMjoi4f5cbsk5wDgkbum6jbatpsN9YuXgmXKkqVI5wQQeQQQQQeQQRXB3NrpWmabo91\no/8AwlmnrFbSwpeWGmPJNKNwLJPDJC7bnbLh2jAzu+YbsNoeFdE8T6N4XsYLW50+2URyTPYXlvJL\nKskjvJsNwJcDG5QTsY8E5Oaeyf8AXf8AT+rWJ6m1L4w0iDxDe6PLOyzafZG9u5SP3UMYxkM397BD\nY9CD3FGh+JW1qZVbQ9W06OWHz4JryFAkycd0dth5B2ybW56cNjzq78LeLLu+1DTbjSbFWvPD1zBJ\neRX0kqyzySbiSWgVdzN/D0C+wxW9oo1OXxEB4al1q3haxk/tB/EMFxJElzuTy9iSMmT/AK3IgIjx\nj/Zojsr+f5v/ACX9PQe7X9bL/N/0jp9W8UR6bq0el2umX+q3zQfaJIbJY/3MW7aHZpHReSCAASxw\ncDg1SuPH2njDaVY6hrEa2i3txJYxpi3hbO1mEjqSSFY7FDN8p+XpnM1+G5068t7/AFG61v8AtRrV\n4nu/DellorpQ+5YXjYTlCMnDkqPmb5xnFYvm32leHdK8FXlpq1jarYq2pXtnp11eEhyS1tE8cbDd\nyQ0hOQOmWOVm7t5/8P8A13+9FaX8v+G/rt9zOyvPGttFd+RpemajrWy2S6nk06ONlgjcZQne6liw\nBIVAzYHTkZn1Dxlo2na9puizXO/UdRkCR20Yy8YKlg0g6ouFOM8k9AcHHD6lpY07UNauLaHxHEdQ\nihn0RdJiuI40dbdIwsioAFYMiZW4+QAjj/WV0uuWOoTnwdJPamS7j1SKW/a3jyqMLaVWYkdF3EDJ\n9QKrr81+L/Tr/wAHRdPk/vSv/X/A16a7/wCPqx/67n/0W9Uta8RRaPdWtnFY3epX92HaGzsxHvZE\nxvcmRkQKNyjlhksAM1du/wDj6sf+u5/9FvWV4n1AWixW1/YalNpl0jJNdaX9oaaFxgqNsA8wKw3f\nOp4IAP3qTGiNvGltNZaZPpGm3+rPqUTzRW9t5SSIiEBy3myIBtZlUjOcnpS33i8WTw266Jqd1ftb\nfap7G38hpbWLONzkyhOSCAFZicHAODXHW2lWEOkaMvjbwdNf2UMNzHZkWL3bWsZlzFFJbRq2HMQX\n95g4KkEgnmbQBqXhG8F/rlhqd1He6XHDCILaS7mhaOaZkhkKBiD5cqDeflyjZboS3t/Xnp/W/TdC\n0/r1X9fn1Onu/HdhEsbabZX2sRmyW/lewRD5Fu2SkjB3UncAxCqGY7Tx0yl/480+zkkNvZ3uoWtt\nBHc3l5aKhitInG5Xfc6s3ygsQgYheSORnltAtb/wLbPFqWk3V1JeaNbRRLp9o84+0RiQPCxQEIMu\nuGYhTzyMGoNO0zUPB/h3V9AuNMur261LTreKze0s3liklFoluyO6grGAyA5cqNrZBODgd1e3Tbz3\n0/r9RpJtX/rbX8/6TPTpmD3WnspBVpyQR3HlPWjWLaWpsbbRbRmDNblYiwHXbC4z+lbVKdlLQINu\nKbCiiioKKWrkrp+QpYiaEhRjJ/erxzUX2ub/AKB1z/31H/8AF1Nqv/Hkv/XeH/0atSVUSWVftc3/\nAEDrn/vqP/4uj7XN/wBA65/76j/+LpmrX1xp+nPPZadPqU+QsdtAyKzE+rOQAB1Jz06AniuUsfF+\nsat4b8OGzhs7fVtcaTc8kbywWyxhmZtoZWfoFA3Ly2e2C7iOl1G6mbS7sGxuFBhcFi0eB8p54arP\n2ub/AKB1z/31H/8AF1i6bq1zqnh3V4tSWJb/AE95rS4MCssbsqbg6hiSAyspxk4zjJxmp/GOvS+H\nPD5voBEpM8ULXE6lobVXYKZpACCUXOSMj3KjkPbXvb8dg/r7jT+1zf8AQOuf++o//i6Ptc3/AEDr\nn/vqP/4uuPj8Yajb6FqOpS6ho+s6dBGrw6to8YaPfvCtC0PnklgCDuEgHPIGPmt6h48jijvzaWF6\nkVhex2U17JBG8QlM0aFAvmq5JEgO4DaM9yNpOtv67AdL9rm/6B1z/wB9R/8AxdH2ub/oHXP/AH1H\n/wDF1zsvxBsob6OM6VqjWc2oDTYdRWKPyJJ9+wqPn3gBgw3FQp2nBPGV0/4gWWoXlpGNL1O3tb25\nltLe9mjjEUk0e/cmA5f/AJZvgldpx1o/r+vvX3hs7f1/WjOh+1zf9A65/wC+o/8A4uj7XN/0Drn/\nAL6j/wDi6woPHETapbWl5omr6el4srWlxdwxqk/lruI2hzIhKgkCRUPB6Hiq+m/EjS76G3uLux1D\nS7K7sXv7a7vkjWOaJFVnI2uzKQGBwwGRnGaP6/r7g8jollZJXkTSplkkxvYeUC2OBk7+af8Aa5v+\ngdc/99R//F1laR4sh1TUY7G40zUNLnuIDcWq3yIv2mMEAsux2wRuXKttYbhx1xN4v1ifQPB+para\nGATWsBkQ3Ckxg+rAEHH4j60PRXAv/a5v+gdc/wDfUf8A8XR9rm/6B1z/AN9R/wDxdclpHi8T3E8k\nnjbwvrkVtbyTyWekW+J2VVySD9qfgf7v4ipoviNDcTQQ23hzXJZru1F5ZxCKFTcwcbnUtKAu3cuQ\n5VvmGAaV1/Xz/wAmH9f1950/2ub/AKB1z/31H/8AF0fa5v8AoHXP/fUf/wAXXGXnxE8xo5dG2Swz\nHSnRZ7YriO7nMZO4ScttB42jaR1bOBv6f4vsNSi0praC5L6nJLGsTKu6AxbhJ5nzYG1l2nGeSPXN\nVZr77f194Gp9rm/6B1z/AN9R/wDxdH2ub/oHXP8A31H/APF1aopAVftc3/QOuf8AvqP/AOLqtp11\nMul2gFjcMBCgDBo8H5Rzy1adVdL/AOQPZ/8AXBP/AEEUAH2ub/oHXP8A31H/APF0fa5v+gdc/wDf\nUf8A8XTr66ezsZbiK1nvHQZWC3C75D6DcQv5kD1Ncro/iTxJrvheS5tdLtYNT/tOayaN5N8VoiSl\nDI/zAyFQvRSNx6EDkHW39dg6X/rv+h1H2ub/AKB1z/31H/8AF1W066mXS7QCxuGAhQBg0eD8o55a\nqnhjV76/l1Wx1Vraa50y6Fu1zaRNHFNmNX4VmYqRv2kbm6ZzzgQa7rE/h/4dPqlq9vHLb2sRV7lS\nY0ztBZgGXgA56j60m7K4eRt/a5v+gdc/99R//F0fa5v+gdc/99R//F1z/hXXJtZvpgni/wANa9DE\nmXi0i3KvGSeCzfaZMDg8befXiuqqgKv2ub/oHXP/AH1H/wDF0fa5v+gdc/8AfUf/AMXVqikBV+1z\nf9A65/76j/8Ai6rX91M1uoNjcL++iOS0f/PReOGrTqrqP/Hqn/XeH/0YtAB9rm/6B1z/AN9R/wDx\ndH2ub/oHXP8A31H/APF0zV9TTSNMkunRpWBVIoU+9LIxCog9yxAz0HU8Vz2m+Mbqb4b2Wu3drC+p\n3ke2K0gJCSTEsAoJyQvBJPYAntik5JJsaV3Y6T7XN/0Drn/vqP8A+Lo+1zf9A65/76j/APi65fTt\na8U6/wCG9BvtNTTbEXumJe3d7dQtNEjsqnykiEqN3J3FiAFxyTxt+H9al1vwhZ6wLXEtzbCYQI3D\nnHG1mxwexOOCDVSTjdPp/X6ErW1i79rm/wCgdc/99R//ABdH2ub/AKB1z/31H/8AF1zen654htde\nsbHxGlg76jaS3K2tjE4lszHtJRmLsJR84XeAnzAcfNxY8K+JdR13Vtat9S0s6WLGSJYYJHV5drpu\nzIVJUH2BOPU0gNz7XN/0Drn/AL6j/wDi6Ptc3/QOuf8AvqP/AOLrmPGXim/0TVrWzt7zS9Jt5baS\nX7fqsLyRSyggLbptdBvIJb7xJA4U84i/4SbxJq7smjWdrp09npkN7d2+owSSM8soYi3UqybCNhBc\nhvvD5eDSvpzdP+H/AMn/AE0Prb+v61/qzOs+1zf9A65/76j/APi6Ptc3/QOuf++o/wD4uuN1vxN4\nhbw7Ya54avNL2askA0/TrrTZJJZZZVBCmQToAAMsTs4VSecU3UPGV/beJptMfVtPsI7IW8VzNLot\nzcRmV1DEmZJVSBTuUAOT16mqs07PvYV9L/M6u6upjcWZNjcDExIBaPn92/H3v84qz9rm/wCgdc/9\n9R//ABdF3/x9WP8A13P/AKLerVIZV+1zf9A65/76j/8Ai6Ptc3/QOuf++o//AIuub8ZeKbrRdU07\nTtPnjt5bqKad5G0q41EhYygwIoGVhkv948DGO4qJfEOtavdxWHhq+0meSLTY76e/ktJHguPMZljS\nNFlBQHy3JJZsccHnCvpf+v60/wAh2Op+1zf9A65/76j/APi6Ptc3/QOuf++o/wD4uuOtfGOt+JYh\nL4Xhs7UW+mQ306X8LyGWSUMRApV12Y2EFyG+8MLwajHjfV9ZsL3V/DUVmun6ZZw3U0N3A7S3ZeET\nmNGDqIyI2Ubir5ZugA5e179N/wCvkCTe39f1f+tTsGnkl1CwD2s0I85jucoQf3T8cMa16yluY7w6\nVcwkmOaTzEJGODC5H861amSadmEWmroKKKKkop6r/wAeS/8AXeH/ANGrUlRavuOn/IQG86HBIyAf\nNXtUXl6h/wA/Nt/4DN/8XVRJZaPSuIs/COr6V4e8O/YpLObVdEaTMUkzpBcJIGVl3hCy9VYHaeVx\njnI63y9Q/wCfm2/8B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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\many_2_many.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The SAS Data Step using the equivalent Full Outer Join with (L=1 or R=1) for the IN= option for MERGE does not produce the same results as PROC SQL in the case of a many-to-many join.  The SAS NOTE in the log 'At least one BY group was repeated in multiple datasets while merging' is an indication you may not be producing the desired results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_many_2_many_join.sas                           */\n",
    "    /******************************************************/\n",
    "    17      proc sort data = left;\n",
    "    18         by name;\n",
    "    19      \n",
    "    20      proc sort data = right;\n",
    "    21         by name;\n",
    "    22      \n",
    "    23      data m2m;\n",
    "    24         merge left(in=l)\n",
    "    25               right(in=r);\n",
    "    26         by name;\n",
    "    27      if (l=1 or r=1);\n",
    "    NOTE: At least one BY group was repeated in multiple datasets while merging\n",
    "    28      \n",
    "    29      proc print data=m2m;\n",
    "````"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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HSJBJpujpBIBIN6zSFiHXawJLZIx2PA6jB5rFwk428mvvv/VzXmjz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\nnmr9hcQ3GqXT28qSqIYgWRgwzuk44rSopX0sFtbhRRRSGf/Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='Anaconda3\\\\output\\\\m2m_datastep_merge.JPG')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by: split-apply-combine Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the capabilities of panda group add the additional column in the 'left' DataFrame left['status'] as an additional categorical column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'name': ['Gunter, Thomas', 'Harbinger, Nicholas', 'Benito, Gisela', 'Rudelich, Herbert', \\\n",
    "                              'Sirignano, Emily', 'Morrison, Michael', 'Morrison, Michael', 'Onieda, Jacqueline'],\n",
    "                     'age':          [27, 36, 32, 39, 22, 32, 32, 31],\n",
    "                     'gender':       ['M', 'M', 'F', 'M', 'F', 'M', 'M', 'F'],\n",
    "                     'status':       ['Pro', 'Amature', 'Amature', 'Pro', 'Amature', 'Amature', 'Pro', 'Pro']})\n",
    "\n",
    "right = pd.DataFrame({'name': ['Gunter, Thomas', 'Harbinger, Nicholas', \\\n",
    "                               'Benito, Gisela','Rudelich, Herbert', 'Sirignano, Emily', 'Valpolicella, Vino'],\n",
    "                      'id':           ['929-75-0218', '446-93-2122', \\\n",
    "                                       '228-88-9649', '029-46-9261', '442-21-8075', '321-82-5771'], \n",
    "                      'salary':      [27500, 33900, 28000, 35000, 5000, 80000]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data if often collected at one level with analysis needed at a different level.  For example, retailers collect data from invidiauls and need to aggregate to the household level.  Group by involves data splitting to create sub-populations and applying functions or transformation.  Then re-combining the grouped results into a single set of data. \n",
    "\n",
    "GroupBy is analogous to SAS BY group processing.  More details on panda's GroupBy are located in <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2010%20--%20Groupby.ipynb\"> Chapter 10--GroupBy</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replace Missing Values with Group Means"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The steps for replacing values with a group mean below are:\n",
    "    1. Construct the 'df' DataFrame\n",
    "    2. Drop duplicates values for df['name'] column\n",
    "    3. Create the grouper 'gb1' grouping df['gender'] column values\n",
    "    4. Define the function 'func' to call the .fillna missing method & replace missing values with the group mean\n",
    "    5. Extract the transformed trans['salary'] column as the new df['salary'] column."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    1. Construct the 'df DataFrame\n",
    "\n",
    "Outer Join to contruct the 'df' DataFrame on the ['name'] key value column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(left, right, on='name', how='outer', sort=True, indicator='in=')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    2. Drop duplicate rows using the 'df['name'] column values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.drop_duplicates('name')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the 'df' DataFrame containing missing (NaN) values for the df['salary'] column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>status</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>Amature</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>Pro</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>Amature</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>Amature</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>Pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>Pro</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>Amature</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name   status           id   salary         in=\n",
       "0  32.0      F       Benito, Gisela  Amature  228-88-9649  28000.0        both\n",
       "1  27.0      M       Gunter, Thomas      Pro  929-75-0218  27500.0        both\n",
       "2  36.0      M  Harbinger, Nicholas  Amature  446-93-2122  33900.0        both\n",
       "3  32.0      M    Morrison, Michael  Amature          NaN      NaN   left_only\n",
       "5  31.0      F   Onieda, Jacqueline      Pro          NaN      NaN   left_only\n",
       "6  39.0      M    Rudelich, Herbert      Pro  029-46-9261  35000.0        both\n",
       "7  22.0      F     Sirignano, Emily  Amature  442-21-8075   5000.0        both\n",
       "8   NaN    NaN   Valpolicella, Vino      NaN  321-82-5771  80000.0  right_only"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Missing value replacement can be approached with a range a methods described <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2004%20--%20Pandas%2C%20Part%201.ipynb#Missing-Value-Replacement\"> here</a> in detail.  Create the 'gb1' Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gb1 = df.groupby('gender')['salary'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the Series df['salary'] values grouped by df['gender'] values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender\n",
       "F    16500.000000\n",
       "M    32133.333333\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PROC SQL illustrating the aggregation method (mean) grouped by the gender column.  The \\_null\\_ data step is used to print values to the SAS log."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "````\n",
    "    /******************************************************/\n",
    "    /* c07_merge_both_groupby_mean_salary.sas             */\n",
    "    /******************************************************/\n",
    "    30       proc sql;\n",
    "    31          create table sal_by_gender as\n",
    "    32          select gender, mean(salary) as mean_sal\n",
    "    33          from left as l,\n",
    "    34               right as r\n",
    "    35          where l.name = r.name\n",
    "    36          group by gender;\n",
    "    NOTE: Data set \"WORK.sal_by_gender\" has 2 observation(s) and 2 variable(s)\n",
    "    37       quit;\n",
    "    38       \n",
    "    39       data _null_;\n",
    "    40          set sal_by_gender;\n",
    "    41          put _all_;\n",
    "\n",
    "    _N_=1 _ERROR_=0 gender=F mean_sal=16500\n",
    "    _N_=2 _ERROR_=0 gender=M mean_sal=32133.333333\n",
    "````"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    3. Create the DataFrameGroupBy (grouper) object from the df['gender'] column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gb2 = df.groupby('gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".count() attribute used to return the number of unique levels from the group."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>name</th>\n",
       "      <th>status</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        age  name  status  id  salary  in=\n",
       "gender                                    \n",
       "F         3     3       3   2       2    3\n",
       "M         4     4       4   3       3    4"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gb2.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    4. Define the 'func' function.\n",
    "    \n",
    "Lambda x is a short-hand for defining a function using the local variable x.  The argument to .fillna method defines a call replacing the missing values with the .mean() attribute. The function is defined and is called in step 5 below.\n",
    "\n",
    "More examples on handling and replacing missing data are located <a  href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2004%20--%20Pandas%2C%20Part%201.ipynb#Missing-Value-Replacement\"> here. </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "function"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "func = lambda x: x.fillna(x.mean())\n",
    "type(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply (or call) the defined function using the groupby.transform() attribute creating the new DataFrame 'trans'.  The .transform() attribute is applied to ints and floats to replace missing values with their respective column group mean. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trans = gb2.transform(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "     5. Extract the transformed trans['salary'] column as the new df['salary'] column\n",
    "We just need the transformed trans['salary'] values to replace the original df['salary'] column values.  More details on DROP, KEEP, and RENAME columns is described <a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/blob/master/Chapter%2012%20--%20Additional%20Data%20Handling%20.ipynb#Drop/Keep-Columns\"> here</a>. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df[\"salary\"] = trans[\"salary\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display the 'df' DataFrame with df['salary'] column values replaced with their group mean value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>status</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>Amature</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.000000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>Pro</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Harbinger, Nicholas</td>\n",
       "      <td>Amature</td>\n",
       "      <td>446-93-2122</td>\n",
       "      <td>33900.000000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Morrison, Michael</td>\n",
       "      <td>Amature</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32133.333333</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Onieda, Jacqueline</td>\n",
       "      <td>Pro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16500.000000</td>\n",
       "      <td>left_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>Pro</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>Amature</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Valpolicella, Vino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>321-82-5771</td>\n",
       "      <td>80000.000000</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age gender                 name   status           id        salary  \\\n",
       "0  32.0      F       Benito, Gisela  Amature  228-88-9649  28000.000000   \n",
       "1  27.0      M       Gunter, Thomas      Pro  929-75-0218  27500.000000   \n",
       "2  36.0      M  Harbinger, Nicholas  Amature  446-93-2122  33900.000000   \n",
       "3  32.0      M    Morrison, Michael  Amature          NaN  32133.333333   \n",
       "5  31.0      F   Onieda, Jacqueline      Pro          NaN  16500.000000   \n",
       "6  39.0      M    Rudelich, Herbert      Pro  029-46-9261  35000.000000   \n",
       "7  22.0      F     Sirignano, Emily  Amature  442-21-8075   5000.000000   \n",
       "8   NaN    NaN   Valpolicella, Vino      NaN  321-82-5771  80000.000000   \n",
       "\n",
       "          in=  \n",
       "0        both  \n",
       "1        both  \n",
       "2        both  \n",
       "3   left_only  \n",
       "5   left_only  \n",
       "6        both  \n",
       "7        both  \n",
       "8  right_only  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## FIRST.variable and LAST.variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The .first() attribute chained to a GroupBy for the df['status'] column as an analog to SAS' By Group processing using FIRST.variable and LAST.variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amature</th>\n",
       "      <td>32.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Benito, Gisela</td>\n",
       "      <td>228-88-9649</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>27.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Gunter, Thomas</td>\n",
       "      <td>929-75-0218</td>\n",
       "      <td>27500.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age gender            name           id   salary   in=\n",
       "status                                                          \n",
       "Amature  32.0      F  Benito, Gisela  228-88-9649  28000.0  both\n",
       "Pro      27.0      M  Gunter, Thomas  929-75-0218  27500.0  both"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('status').first()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>gender</th>\n",
       "      <th>name</th>\n",
       "      <th>id</th>\n",
       "      <th>salary</th>\n",
       "      <th>in=</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>status</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amature</th>\n",
       "      <td>22.0</td>\n",
       "      <td>F</td>\n",
       "      <td>Sirignano, Emily</td>\n",
       "      <td>442-21-8075</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pro</th>\n",
       "      <td>39.0</td>\n",
       "      <td>M</td>\n",
       "      <td>Rudelich, Herbert</td>\n",
       "      <td>029-46-9261</td>\n",
       "      <td>35000.0</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          age gender               name           id   salary   in=\n",
       "status                                                             \n",
       "Amature  22.0      F   Sirignano, Emily  442-21-8075   5000.0  both\n",
       "Pro      39.0      M  Rudelich, Herbert  029-46-9261  35000.0  both"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('status').last()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resources"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MERGING vs. JOINING: Comparing the DATA Step with SQL, by Malachy J. Foley, University of North Carolina at Chapel Hill, located <a href=\"http://www2.sas.com/proceedings/sugi30/249-30.pdf\"> here</a>.\n",
    "\n",
    "pandas.DataFrame.merge() method documumented <a href=\"<http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.merge.html\"> here</a>.\n",
    "\n",
    "pandas Group By: split-apply-combine located <a href=\"http://pandas.pydata.org/pandas-docs/stable/groupby.html\"> here</a>.\n",
    "\n",
    "SAS 9.2 SQL Procedure User's Guide located <a href=\"http://support.sas.com/documentation/cdl/en/sqlproc/62086/HTML/default/viewer.htm\"> here</a>.\n",
    "\n",
    "Combining SAS Data Sets: Methods: in the SAS Language Reference: Concepts Manual, Second Edition, found <a href=\"https://support.sas.com/documentation/cdl/en/lrcon/65287/HTML/default/viewer.htm#n1tgk0uanvisvon1r26lc036k0w7.htm\"> here</a>."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Navigation\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/RandyBetancourt/PythonForSASUsers/tree/master/\"> Return to Chapter List </a>    "
   ]
  }
 ],
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