Data_Interchange_not_working_with_sas.df2sd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Interchange Examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "INSTALLED VERSIONS\n",
      "------------------\n",
      "commit: None\n",
      "python: 3.6.3.final.0\n",
      "python-bits: 64\n",
      "OS: Linux\n",
      "OS-release: 3.10.0-693.11.1.el7.x86_64\n",
      "machine: x86_64\n",
      "processor: x86_64\n",
      "byteorder: little\n",
      "LC_ALL: None\n",
      "LANG: en_US.UTF-8\n",
      "LOCALE: en_US.UTF-8\n",
      "\n",
      "pandas: 0.20.3\n",
      "pytest: 3.2.1\n",
      "pip: 9.0.1\n",
      "setuptools: 38.2.4\n",
      "Cython: 0.26.1\n",
      "numpy: 1.13.3\n",
      "scipy: 0.19.1\n",
      "xarray: None\n",
      "IPython: 6.2.1\n",
      "sphinx: 1.6.3\n",
      "patsy: 0.4.1\n",
      "dateutil: 2.6.1\n",
      "pytz: 2017.2\n",
      "blosc: None\n",
      "bottleneck: 1.2.1\n",
      "tables: 3.4.2\n",
      "numexpr: 2.6.2\n",
      "feather: 0.4.0\n",
      "matplotlib: 2.1.0\n",
      "openpyxl: 2.4.8\n",
      "xlrd: 1.1.0\n",
      "xlwt: 1.3.0\n",
      "xlsxwriter: 1.0.2\n",
      "lxml: 4.1.0\n",
      "bs4: 4.6.0\n",
      "html5lib: 0.999999999\n",
      "sqlalchemy: 1.1.13\n",
      "pymysql: None\n",
      "psycopg2: None\n",
      "jinja2: 2.9.6\n",
      "s3fs: None\n",
      "pandas_gbq: None\n",
      "pandas_datareader: None\n"
     ]
    }
   ],
   "source": [
    "import feather\n",
    "import pandas as pd\n",
    "import saspy\n",
    "pd.show_versions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using SAS Config named: default\n",
      "SAS Connection established. Subprocess id is 4942\n",
      "\n"
     ]
    }
   ],
   "source": [
    "sas = saspy.SASsession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example from:\n",
    "https://github.com/sassoftware/saspy/blob/master/saspy_example_github.ipynb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cars = sas.sasdata('cars', libref='sashelp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'saspy.sasbase.SASdata'>\n"
     ]
    }
   ],
   "source": [
    " print(type(cars))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SAS dataset SASHELP.CARS converted to panda dataframe car_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "car_df = cars.to_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    " print(type(car_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "panda dataframe car_df converted to SAS dataset WORK.CARS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "car_df2 = sas.df2sd(car_df, 'cars')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'saspy.sasbase.SASdata'>\n"
     ]
    }
   ],
   "source": [
    " print(type(car_df2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read a dataframe from disk with the feather library created previously from a R dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = '/home/sas/notebook/r_staff.feather'\n",
    "pd_staff = feather.read_dataframe(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "employee                  category\n",
       "salary                     float64\n",
       "startdate           datetime64[ns]\n",
       "stringsASFactors              bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_staff.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(pd_staff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             employee   salary  startdate  stringsASFactors\n",
      "0      Thomas Gunther  27500.0 2010-11-01             False\n",
      "1  Nicholas Harbinger  33900.0 2008-03-25             False\n",
      "2       Gisela Benito  28000.0 2007-03-14             False\n",
      "3    Herbert Rudelich  35000.0 2011-01-24             False\n",
      "4     Emily Sirignano  36350.0 2009-12-15             False\n",
      "5    Michael Morrison  40000.0 2009-10-10             False\n",
      "6   Jacqueline Onieda  36500.0 2015-01-02             False\n"
     ]
    }
   ],
   "source": [
    "print(pd_staff)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe pd_staff appears acceptable to Python, but is not acceptable to the sas.df2sd call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-7607062bf09b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msd_staff\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf2sd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd_staff\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'staff'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasbase.py\u001b[0m in \u001b[0;36mdf2sd\u001b[0;34m(self, df, table, libref, results)\u001b[0m\n\u001b[1;32m    618\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSASdata\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \"\"\"\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe2sasdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     def dataframe2sasdata(self, df: 'pd.DataFrame', table: str = '_df', libref: str = '',\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasbase.py\u001b[0m in \u001b[0;36mdataframe2sasdata\u001b[0;34m(self, df, table, libref, results)\u001b[0m\n\u001b[1;32m    637\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe2sasdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasiostdio.py\u001b[0m in \u001b[0;36mdataframe2sasdata\u001b[0;34m(self, df, table, libref)\u001b[0m\n\u001b[1;32m    894\u001b[0m          \u001b[0minput\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"'\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"'n \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m          \u001b[0;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'O'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'S'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'U'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'V'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 896\u001b[0;31m             \u001b[0mcol_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcol_l\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m                \u001b[0mcol_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   2127\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mna_action\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2129\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2130\u001b[0m             \u001b[0mmap_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "sd_staff = sas.df2sd(pd_staff, 'staff')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the pd_staff dataframe with default deep=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140465635419640"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_deep_true = pd_staff.copy()\n",
    "id(pd_staff)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "140464967919432"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(copy_deep_true))\n",
    "id(copy_deep_true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the sas.df2sd method.  The copied dataframe is not acceptable to this call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-9d457eae85df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf2sd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy_deep_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'staff_t'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasbase.py\u001b[0m in \u001b[0;36mdf2sd\u001b[0;34m(self, df, table, libref, results)\u001b[0m\n\u001b[1;32m    618\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSASdata\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \"\"\"\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe2sasdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     def dataframe2sasdata(self, df: 'pd.DataFrame', table: str = '_df', libref: str = '',\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasbase.py\u001b[0m in \u001b[0;36mdataframe2sasdata\u001b[0;34m(self, df, table, libref, results)\u001b[0m\n\u001b[1;32m    637\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe2sasdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasiostdio.py\u001b[0m in \u001b[0;36mdataframe2sasdata\u001b[0;34m(self, df, table, libref)\u001b[0m\n\u001b[1;32m    894\u001b[0m          \u001b[0minput\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"'\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"'n \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m          \u001b[0;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'O'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'S'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'U'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'V'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 896\u001b[0;31m             \u001b[0mcol_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcol_l\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m                \u001b[0mcol_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   2127\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mna_action\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2129\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2130\u001b[0m             \u001b[0mmap_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cdt = sas.df2sd(copy_deep_true, 'staff_t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy pd_staff operation with deep=false parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "140465635419640"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy_deep_false = pd_staff.copy(deep=False)\n",
    "id(pd_staff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "140464967921000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(type(copy_deep_false))\n",
    "id(copy_deep_false)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the sas.df2sd method.  copy_deep_false dataframe is not acceptable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotImplementedError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-7cbfd61b3812>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf2sd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy_deep_false\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'staff_f'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasbase.py\u001b[0m in \u001b[0;36mdf2sd\u001b[0;34m(self, df, table, libref, results)\u001b[0m\n\u001b[1;32m    618\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mSASdata\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \"\"\"\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe2sasdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m     def dataframe2sasdata(self, df: 'pd.DataFrame', table: str = '_df', libref: str = '',\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasbase.py\u001b[0m in \u001b[0;36mdataframe2sasdata\u001b[0;34m(self, df, table, libref, results)\u001b[0m\n\u001b[1;32m    637\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    638\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 639\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataframe2sasdata\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    640\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlibref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/saspy/sasiostdio.py\u001b[0m in \u001b[0;36mdataframe2sasdata\u001b[0;34m(self, df, table, libref)\u001b[0m\n\u001b[1;32m    894\u001b[0m          \u001b[0minput\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"'\"\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"'n \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    895\u001b[0m          \u001b[0;32mif\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtypes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'O'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'S'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'U'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'V'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 896\u001b[0;31m             \u001b[0mcol_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    897\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mcol_l\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    898\u001b[0m                \u001b[0mcol_l\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/root/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, arg, na_action)\u001b[0m\n\u001b[1;32m   2127\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2128\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mna_action\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2129\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2130\u001b[0m             \u001b[0mmap_f\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2131\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotImplementedError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cdf = sas.df2sd(copy_deep_false, 'staff_f')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create the medals dataframe with the read.csv method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_medals = pd.read_csv(\"http://winterolympicsmedals.com/medals.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>City</th>\n",
       "      <th>Sport</th>\n",
       "      <th>Discipline</th>\n",
       "      <th>NOC</th>\n",
       "      <th>Event</th>\n",
       "      <th>Event gender</th>\n",
       "      <th>Medal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1924</td>\n",
       "      <td>Chamonix</td>\n",
       "      <td>Skating</td>\n",
       "      <td>Figure skating</td>\n",
       "      <td>AUT</td>\n",
       "      <td>individual</td>\n",
       "      <td>M</td>\n",
       "      <td>Silver</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1924</td>\n",
       "      <td>Chamonix</td>\n",
       "      <td>Skating</td>\n",
       "      <td>Figure skating</td>\n",
       "      <td>AUT</td>\n",
       "      <td>individual</td>\n",
       "      <td>W</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1924</td>\n",
       "      <td>Chamonix</td>\n",
       "      <td>Skating</td>\n",
       "      <td>Figure skating</td>\n",
       "      <td>AUT</td>\n",
       "      <td>pairs</td>\n",
       "      <td>X</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1924</td>\n",
       "      <td>Chamonix</td>\n",
       "      <td>Bobsleigh</td>\n",
       "      <td>Bobsleigh</td>\n",
       "      <td>BEL</td>\n",
       "      <td>four-man</td>\n",
       "      <td>M</td>\n",
       "      <td>Bronze</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1924</td>\n",
       "      <td>Chamonix</td>\n",
       "      <td>Ice Hockey</td>\n",
       "      <td>Ice Hockey</td>\n",
       "      <td>CAN</td>\n",
       "      <td>ice hockey</td>\n",
       "      <td>M</td>\n",
       "      <td>Gold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year      City       Sport      Discipline  NOC       Event Event gender  \\\n",
       "0  1924  Chamonix     Skating  Figure skating  AUT  individual            M   \n",
       "1  1924  Chamonix     Skating  Figure skating  AUT  individual            W   \n",
       "2  1924  Chamonix     Skating  Figure skating  AUT       pairs            X   \n",
       "3  1924  Chamonix   Bobsleigh       Bobsleigh  BEL    four-man            M   \n",
       "4  1924  Chamonix  Ice Hockey      Ice Hockey  CAN  ice hockey            M   \n",
       "\n",
       "    Medal  \n",
       "0  Silver  \n",
       "1    Gold  \n",
       "2    Gold  \n",
       "3  Bronze  \n",
       "4    Gold  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_medals.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Call the sas.df2sd method to create the SAs dataset WORK.medals2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sd_medals = sas.df2sd(df_medals, 'medals2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Attributes':          Member               Label1  \\\n",
       " 0  WORK.MEDALS2        Data Set Name   \n",
       " 1  WORK.MEDALS2          Member Type   \n",
       " 2  WORK.MEDALS2               Engine   \n",
       " 3  WORK.MEDALS2              Created   \n",
       " 4  WORK.MEDALS2        Last Modified   \n",
       " 5  WORK.MEDALS2           Protection   \n",
       " 6  WORK.MEDALS2        Data Set Type   \n",
       " 7  WORK.MEDALS2                Label   \n",
       " 8  WORK.MEDALS2  Data Representation   \n",
       " 9  WORK.MEDALS2             Encoding   \n",
       " \n",
       "                                              cValue1       nValue1  \\\n",
       " 0                                       WORK.MEDALS2           NaN   \n",
       " 1                                               DATA           NaN   \n",
       " 2                                                 V9           NaN   \n",
       " 3                                01/14/2018 15:12:11  1.831562e+09   \n",
       " 4                                01/14/2018 15:12:11  1.831562e+09   \n",
       " 5                                                              NaN   \n",
       " 6                                                              NaN   \n",
       " 7                                                              NaN   \n",
       " 8  SOLARIS_X86_64, LINUX_X86_64, ALPHA_TRU64, LIN...           NaN   \n",
       " 9                              latin1  Western (ISO)           NaN   \n",
       " \n",
       "                  Label2 cValue2  nValue2  \n",
       " 0          Observations    2311   2311.0  \n",
       " 1             Variables       8      8.0  \n",
       " 2               Indexes       0      0.0  \n",
       " 3    Observation Length      96     96.0  \n",
       " 4  Deleted Observations       0      0.0  \n",
       " 5            Compressed      NO      NaN  \n",
       " 6                Sorted      NO      NaN  \n",
       " 7                                    0.0  \n",
       " 8                                    0.0  \n",
       " 9                                    0.0  ,\n",
       " 'Enginehost':           Member                      Label1  \\\n",
       " 0   WORK.MEDALS2          Data Set Page Size   \n",
       " 1   WORK.MEDALS2    Number of Data Set Pages   \n",
       " 2   WORK.MEDALS2             First Data Page   \n",
       " 3   WORK.MEDALS2            Max Obs per Page   \n",
       " 4   WORK.MEDALS2      Obs in First Data Page   \n",
       " 5   WORK.MEDALS2  Number of Data Set Repairs   \n",
       " 6   WORK.MEDALS2                    Filename   \n",
       " 7   WORK.MEDALS2             Release Created   \n",
       " 8   WORK.MEDALS2                Host Created   \n",
       " 9   WORK.MEDALS2                Inode Number   \n",
       " 10  WORK.MEDALS2           Access Permission   \n",
       " 11  WORK.MEDALS2                  Owner Name   \n",
       " 12  WORK.MEDALS2                   File Size   \n",
       " 13  WORK.MEDALS2           File Size (bytes)   \n",
       " \n",
       "                                               cValue1      nValue1  \n",
       " 0                                               65536      65536.0  \n",
       " 1                                                   4          4.0  \n",
       " 2                                                   1          1.0  \n",
       " 3                                                 681        681.0  \n",
       " 4                                                 652        652.0  \n",
       " 5                                                   0          0.0  \n",
       " 6   /home/sas/tmp/SAS_work41FB0000134E_localhost.l...          NaN  \n",
       " 7                                            9.0401M4          NaN  \n",
       " 8                                               Linux          NaN  \n",
       " 9                                           149271914  149271914.0  \n",
       " 10                                          rw-rw-r--          NaN  \n",
       " 11                                                sas          NaN  \n",
       " 12                                              320KB          NaN  \n",
       " 13                                             327680     327680.0  ,\n",
       " 'Variables':          Member  Num      Variable  Type  Len  Pos\n",
       " 0  WORK.MEDALS2    2          City  Char   22    8\n",
       " 1  WORK.MEDALS2    4    Discipline  Char   15   40\n",
       " 2  WORK.MEDALS2    6         Event  Char   31   58\n",
       " 3  WORK.MEDALS2    7  Event gender  Char    1   89\n",
       " 4  WORK.MEDALS2    8         Medal  Char    6   90\n",
       " 5  WORK.MEDALS2    5           NOC  Char    3   55\n",
       " 6  WORK.MEDALS2    3         Sport  Char   10   30\n",
       " 7  WORK.MEDALS2    1          Year   Num    8    0}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " sd_medals.contents()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Access Method         = STDIO\n",
       "SAS Config name       = default\n",
       "WORK Path             = /home/sas/tmp/SAS_work41FB0000134E_localhost.localdomain/\n",
       "SAS Version           = 9.04.01M4P11092016\n",
       "SASPy Version         = 2.2.1\n",
       "Teach me SAS          = False\n",
       "Batch                 = False\n",
       "Results               = Pandas\n",
       "SAS Session Encoding  = LATIN1\n",
       "Python Encoding value = utf-8"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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